\n \n\n \n \n Araki, R.; Branger, F.; Wiekenkamp, I.; and McMillan, H.\n\n\n \n \n \n \n \n A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(4). April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{araki_signaturebased_2022,\n\ttitle = {A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14553},\n\tdoi = {10.1002/hyp.14553},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Hydrological Processes},\n\tauthor = {Araki, Ryoko and Branger, Flora and Wiekenkamp, Inge and McMillan, Hilary},\n\tmonth = apr,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Bahrami, B.; Hildebrandt, A.; Thober, S.; Rebmann, C.; Fischer, R.; Samaniego, L.; Rakovec, O.; and Kumar, R.\n\n\n \n \n \n \n \n Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(18): 6957–6984. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Developing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bahrami_developing_2022,\n\ttitle = {Developing a parsimonious canopy model ({PCM} v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest},\n\tvolume = {15},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/15/6957/2022/},\n\tdoi = {10.5194/gmd-15-6957-2022},\n\tabstract = {Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 \\%). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Bahrami, Bahar and Hildebrandt, Anke and Thober, Stephan and Rebmann, Corinna and Fischer, Rico and Samaniego, Luis and Rakovec, Oldrich and Kumar, Rohini},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {6957--6984},\n}\n\n

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\n Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.\n

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\n \n\n \n \n Bai, Y.; Bhattarai, N.; Mallick, K.; Zhang, S.; Hu, T.; and Zhang, J.\n\n\n \n \n \n \n \n Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 271: 112901. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Thermally$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bai_thermally_2022,\n\ttitle = {Thermally derived evapotranspiration from the {Surface} {Temperature} {Initiated} {Closure} ({STIC}) model improves cropland {GPP} estimates under dry conditions},\n\tvolume = {271},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000153},\n\tdoi = {10.1016/j.rse.2022.112901},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Bai, Yun and Bhattarai, Nishan and Mallick, Kaniska and Zhang, Sha and Hu, Tian and Zhang, Jiahua},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {112901},\n}\n\n

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\n \n\n \n \n Baldocchi, D. D.; Keeney, N.; Rey-Sanchez, C.; and Fisher, J. B.\n\n\n \n \n \n \n \n Atmospheric humidity deficits tell us how soil moisture deficits down-regulate ecosystem evaporation.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 159: 104100. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Atmospheric$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{baldocchi_atmospheric_2022,\n\ttitle = {Atmospheric humidity deficits tell us how soil moisture deficits down-regulate ecosystem evaporation},\n\tvolume = {159},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170821002517},\n\tdoi = {10.1016/j.advwatres.2021.104100},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Baldocchi, Dennis D. and Keeney, Nicole and Rey-Sanchez, Camilo and Fisher, Joshua B.},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {104100},\n}\n\n

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\n \n\n \n \n Bao, S.; Ibrom, A.; Wohlfahrt, G.; Koirala, S.; Migliavacca, M.; Zhang, Q.; and Carvalhais, N.\n\n\n \n \n \n \n \n Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 326: 109185. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Narrow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bao_narrow_2022,\n\ttitle = {Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level},\n\tvolume = {326},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322003720},\n\tdoi = {10.1016/j.agrformet.2022.109185},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Bao, Shanning and Ibrom, Andreas and Wohlfahrt, Georg and Koirala, Sujan and Migliavacca, Mirco and Zhang, Qian and Carvalhais, Nuno},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {109185},\n}\n\n

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\n \n\n \n \n Bauer, F. M.; Lärm, L.; Morandage, S.; Lobet, G.; Vanderborght, J.; Vereecken, H.; and Schnepf, A.\n\n\n \n \n \n \n \n Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline.\n \n \n \n \n\n\n \n\n\n\n Plant Phenomics, 2022: 1–14. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bauer_development_2022,\n\ttitle = {Development and {Validation} of a {Deep} {Learning} {Based} {Automated} {Minirhizotron} {Image} {Analysis} {Pipeline}},\n\tvolume = {2022},\n\tissn = {2643-6515},\n\turl = {https://spj.sciencemag.org/journals/plantphenomics/2022/9758532/},\n\tdoi = {10.34133/2022/9758532},\n\tabstract = {Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation ( \n               \n                r \n                = \n                0.9 \n               \n              ) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6\\%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Plant Phenomics},\n\tauthor = {Bauer, Felix Maximilian and Lärm, Lena and Morandage, Shehan and Lobet, Guillaume and Vanderborght, Jan and Vereecken, Harry and Schnepf, Andrea},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n

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\n \n\n \n \n Beck, H. E.; van Dijk, A. I. J. M.; Larraondo, P. R.; McVicar, T. R.; Pan, M.; Dutra, E.; and Miralles, D. G.\n\n\n \n \n \n \n \n MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 103(3): E710–E732. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"MSWX:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{beck_mswx_2022,\n\ttitle = {{MSWX}: {Global} 3-{Hourly} 0.1° {Bias}-{Corrected} {Meteorological} {Data} {Including} {Near}-{Real}-{Time} {Updates} and {Forecast} {Ensembles}},\n\tvolume = {103},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {{MSWX}},\n\turl = {https://journals.ametsoc.org/view/journals/bams/103/3/BAMS-D-21-0145.1.xml},\n\tdoi = {10.1175/BAMS-D-21-0145.1},\n\tabstract = {Abstract \n             \n              We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at \n              www.gloh2o.org/mswx \n              .},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Beck, Hylke E. and van Dijk, Albert I. J. M. and Larraondo, Pablo R. and McVicar, Tim R. and Pan, Ming and Dutra, Emanuel and Miralles, Diego G.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {E710--E732},\n}\n\n

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\n \n\n \n \n Bi, W.; He, W.; Zhou, Y.; Ju, W.; Liu, Y.; Liu, Y.; Zhang, X.; Wei, X.; and Cheng, N.\n\n\n \n \n \n \n \n A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 9(1): 213. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bi_global_2022,\n\ttitle = {A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020},\n\tvolume = {9},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-022-01309-2},\n\tdoi = {10.1038/s41597-022-01309-2},\n\tabstract = {Abstract \n             \n              Distinguishing gross primary production of sunlit and shaded leaves (GPP \n              sun \n              and GPP \n              shade \n              ) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP \n              shade \n              and GPP \n              sun \n              over 1992–2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP \n              sun \n              , and GPP \n              shade \n              over 1992–2020 at 125.0 ± 3.8 (mean ± std) Pg C a \n              −1 \n              , 50.5 ± 1.2 Pg C a \n              −1 \n              , and 74.5 ± 2.6 Pg C a \n              −1 \n              , respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP \n              sun \n              and GPP \n              shade \n              ) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP \n              shade \n              overwhelmingly outweighs GPP \n              sun \n              . This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Data},\n\tauthor = {Bi, Wenjun and He, Wei and Zhou, Yanlian and Ju, Weimin and Liu, Yibo and Liu, Yang and Zhang, Xiaoyu and Wei, Xiaonan and Cheng, Nuo},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {213},\n}\n\n

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\n \n\n \n \n Blume, T.; Schneider, L.; and Güntner, A.\n\n\n \n \n \n \n \n Comparative analysis of throughfall observations in six different forest stands: Influence of seasons, rainfall‐ and stand characteristics.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{blume_comparative_2022,\n\ttitle = {Comparative analysis of throughfall observations in six different forest stands: {Influence} of seasons, rainfall‐ and stand characteristics},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Comparative analysis of throughfall observations in six different forest stands},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14461},\n\tdoi = {10.1002/hyp.14461},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Hydrological Processes},\n\tauthor = {Blume, Theresa and Schneider, Lisa and Güntner, Andreas},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Boeing, F.; Rakovec, O.; Kumar, R.; Samaniego, L.; Schrön, M.; Hildebrandt, A.; Rebmann, C.; Thober, S.; Müller, S.; Zacharias, S.; Bogena, H.; Schneider, K.; Kiese, R.; Attinger, S.; and Marx, A.\n\n\n \n \n \n \n \n High-resolution drought simulations and comparison to soil moisture observations in Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(19): 5137–5161. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{boeing_high-resolution_2022,\n\ttitle = {High-resolution drought simulations and comparison to soil moisture observations in {Germany}},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/5137/2022/},\n\tdoi = {10.5194/hess-26-5137-2022},\n\tabstract = {Abstract. Germany's 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy\nproduction, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new\nsetup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological\nmodel (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor\nnetworks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed\nSM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in\nwinter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but\nsignificant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to\nobserved SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to\nobservations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher\nresolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial\nfor local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of\nthe water cycle and a broad, high-quality, observational soil moisture database.},\n\tlanguage = {en},\n\tnumber = {19},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Boeing, Friedrich and Rakovec, Oldrich and Kumar, Rohini and Samaniego, Luis and Schrön, Martin and Hildebrandt, Anke and Rebmann, Corinna and Thober, Stephan and Müller, Sebastian and Zacharias, Steffen and Bogena, Heye and Schneider, Katrin and Kiese, Ralf and Attinger, Sabine and Marx, Andreas},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {5137--5161},\n}\n\n

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\n \n\n \n \n Bogena, H. R.; Schrön, M.; Jakobi, J.; Ney, P.; Zacharias, S.; Andreasen, M.; Baatz, R.; Boorman, D.; Duygu, M. B.; Eguibar-Galán, M. A.; Fersch, B.; Franke, T.; Geris, J.; González Sanchis, M.; Kerr, Y.; Korf, T.; Mengistu, Z.; Mialon, A.; Nasta, P.; Nitychoruk, J.; Pisinaras, V.; Rasche, D.; Rosolem, R.; Said, H.; Schattan, P.; Zreda, M.; Achleitner, S.; Albentosa-Hernández, E.; Akyürek, Z.; Blume, T.; del Campo, A.; Canone, D.; Dimitrova-Petrova, K.; Evans, J. G.; Ferraris, S.; Frances, F.; Gisolo, D.; Güntner, A.; Herrmann, F.; Iwema, J.; Jensen, K. H.; Kunstmann, H.; Lidón, A.; Looms, M. C.; Oswald, S.; Panagopoulos, A.; Patil, A.; Power, D.; Rebmann, C.; Romano, N.; Scheiffele, L.; Seneviratne, S.; Weltin, G.; and Vereecken, H.\n\n\n \n \n \n \n \n COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(3): 1125–1151. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"COSMOS-Europe:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{bogena_cosmos-europe_2022,\n\ttitle = {{COSMOS}-{Europe}: a {European} network of cosmic-ray neutron soil moisture sensors},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {{COSMOS}-{Europe}},\n\turl = {https://essd.copernicus.org/articles/14/1125/2022/},\n\tdoi = {10.5194/essd-14-1125-2022},\n\tabstract = {Abstract. Climate change increases the occurrence and severity of\ndroughts due to increasing temperatures, altered circulation patterns, and\nreduced snow occurrence. While Europe has suffered from drought events in\nthe last decade unlike ever seen since the beginning of weather recordings,\nharmonized long-term datasets across the continent are needed to monitor\nchange and support predictions. Here we present soil moisture data from 66\ncosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short)\ncovering recent drought events. The CRNS sites are distributed across Europe\nand cover all major land use types and climate zones in Europe. The raw\nneutron count data from the CRNS stations were provided by 24 research\ninstitutions and processed using state-of-the-art methods. The harmonized\nprocessing included correction of the raw neutron counts and a harmonized\nmethodology for the conversion into soil moisture based on available in situ\ninformation. In addition, the uncertainty estimate is provided with the\ndataset, information that is particularly useful for remote sensing and\nmodeling applications. This paper presents the current spatiotemporal\ncoverage of CRNS stations in Europe and describes the protocols for data\nprocessing from raw measurements to consistent soil moisture products. The\ndata of the presented COSMOS-Europe network open up a manifold of potential\napplications for environmental research, such as remote sensing data\nvalidation, trend analysis, or model assimilation. The dataset could be of\nparticular importance for the analysis of extreme climatic events at the\ncontinental scale. Due its timely relevance in the scope of climate change\nin the recent years, we demonstrate this potential application with a brief\nanalysis on the spatiotemporal soil moisture variability. The dataset,\nentitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray\nNeutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich:\nhttps://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Bogena, Heye Reemt and Schrön, Martin and Jakobi, Jannis and Ney, Patrizia and Zacharias, Steffen and Andreasen, Mie and Baatz, Roland and Boorman, David and Duygu, Mustafa Berk and Eguibar-Galán, Miguel Angel and Fersch, Benjamin and Franke, Till and Geris, Josie and González Sanchis, María and Kerr, Yann and Korf, Tobias and Mengistu, Zalalem and Mialon, Arnaud and Nasta, Paolo and Nitychoruk, Jerzy and Pisinaras, Vassilios and Rasche, Daniel and Rosolem, Rafael and Said, Hami and Schattan, Paul and Zreda, Marek and Achleitner, Stefan and Albentosa-Hernández, Eduardo and Akyürek, Zuhal and Blume, Theresa and del Campo, Antonio and Canone, Davide and Dimitrova-Petrova, Katya and Evans, John G. and Ferraris, Stefano and Frances, Félix and Gisolo, Davide and Güntner, Andreas and Herrmann, Frank and Iwema, Joost and Jensen, Karsten H. and Kunstmann, Harald and Lidón, Antonio and Looms, Majken Caroline and Oswald, Sascha and Panagopoulos, Andreas and Patil, Amol and Power, Daniel and Rebmann, Corinna and Romano, Nunzio and Scheiffele, Lena and Seneviratne, Sonia and Weltin, Georg and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1125--1151},\n}\n\n

\n

\n\n\n

\n Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).\n

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\n \n\n \n \n Chen, H.; Huang, J. J.; Dash, S. S.; Wei, Y.; and Li, H.\n\n\n \n \n \n \n \n A hybrid deep learning framework with physical process description for simulation of evapotranspiration.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 606: 127422. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{chen_hybrid_2022,\n\ttitle = {A hybrid deep learning framework with physical process description for simulation of evapotranspiration},\n\tvolume = {606},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421014724},\n\tdoi = {10.1016/j.jhydrol.2021.127422},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Chen, Han and Huang, Jinhui Jeanne and Dash, Sonam Sandeep and Wei, Yizhao and Li, Han},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {127422},\n}\n\n

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\n \n\n \n \n Chen, X.; Huang, Y.; Nie, C.; Zhang, S.; Wang, G.; Chen, S.; and Chen, Z.\n\n\n \n \n \n \n \n A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 9(1): 427. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{chen_long-term_2022,\n\ttitle = {A long-term reconstructed {TROPOMI} solar-induced fluorescence dataset using machine learning algorithms},\n\tvolume = {9},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-022-01520-1},\n\tdoi = {10.1038/s41597-022-01520-1},\n\tabstract = {Abstract \n             \n              Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001–2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R \n              2 \n               = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Data},\n\tauthor = {Chen, Xingan and Huang, Yuefei and Nie, Chong and Zhang, Shuo and Wang, Guangqian and Chen, Shiliu and Chen, Zhichao},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {427},\n}\n\n

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\n \n\n \n \n De Cannière, S.; Vereecken, H.; Defourny, P.; and Jonard, F.\n\n\n \n \n \n \n \n Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(11): 2642. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{de_canniere_remote_2022,\n\ttitle = {Remote {Sensing} of {Instantaneous} {Drought} {Stress} at {Canopy} {Level} {Using} {Sun}-{Induced} {Chlorophyll} {Fluorescence} and {Canopy} {Reflectance}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/11/2642},\n\tdoi = {10.3390/rs14112642},\n\tabstract = {Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {De Cannière, Simon and Vereecken, Harry and Defourny, Pierre and Jonard, François},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2642},\n}\n\n

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\n \n\n \n \n De Pue, J.; Barrios, J. M.; Liu, L.; Ciais, P.; Arboleda, A.; Hamdi, R.; Balzarolo, M.; Maignan, F.; and Gellens-Meulenberghs, F.\n\n\n \n \n \n \n \n Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(17): 4361–4386. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Local-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{de_pue_local-scale_2022,\n\ttitle = {Local-scale evaluation of the simulated interactions between energy, water and vegetation in {ISBA}, {ORCHIDEE} and a diagnostic model},\n\tvolume = {19},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/19/4361/2022/},\n\tdoi = {10.5194/bg-19-4361-2022},\n\tabstract = {Abstract. The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined.\nTo accurately represent the terrestrial biosphere in land surface models (LSMs), the intrinsic coupling between these processes is required.\nSoil moisture and leaf area index (LAI) are two key variables at the nexus of water, energy and vegetation.\nHere, we evaluated two prognostic LSMs (ISBA and ORCHIDEE) and a diagnostic model (based on the LSA SAF, Satellite Application Facility for Land Surface Analysis, algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently and their interactions through LAI and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote-sensing-based LAI.\nIt was found that the diagnostic model performed consistently well, regardless of land cover, whereas important shortcomings of the prognostic models were revealed for herbaceous and dry sites.\nDespite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses.\nIn both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes.\nInstead, these errors were strongly correlated to errors in LAI.\nThe simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations and failed to capture the observed seasonal variability.\nThe feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSMs.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {De Pue, Jan and Barrios, José Miguel and Liu, Liyang and Ciais, Philippe and Arboleda, Alirio and Hamdi, Rafiq and Balzarolo, Manuela and Maignan, Fabienne and Gellens-Meulenberghs, Françoise},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {4361--4386},\n}\n\n

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\n \n\n \n \n Ding, J.; Zhu, Q.; Li, H.; Zhou, X.; Liu, W.; and Peng, C.\n\n\n \n \n \n \n \n Contribution of Incorporating the Phosphorus Cycle into TRIPLEX-CNP to Improve the Quantification of Land Carbon Cycle.\n \n \n \n \n\n\n \n\n\n\n Land, 11(6): 778. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Contribution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{ding_contribution_2022,\n\ttitle = {Contribution of {Incorporating} the {Phosphorus} {Cycle} into {TRIPLEX}-{CNP} to {Improve} the {Quantification} of {Land} {Carbon} {Cycle}},\n\tvolume = {11},\n\tissn = {2073-445X},\n\turl = {https://www.mdpi.com/2073-445X/11/6/778},\n\tdoi = {10.3390/land11060778},\n\tabstract = {Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p {\\textless} 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p {\\textless} 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Land},\n\tauthor = {Ding, Juhua and Zhu, Qiuan and Li, Hanwei and Zhou, Xiaolu and Liu, Weiguo and Peng, Changhui},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {778},\n}\n\n

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\n \n\n \n \n Dombrowski, O.; Brogi, C.; Hendricks Franssen, H.; Zanotelli, D.; and Bogena, H.\n\n\n \n \n \n \n \n CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5).\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(13): 5167–5193. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CLM5-FruitTree:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{dombrowski_clm5-fruittree_2022,\n\ttitle = {{CLM5}-{FruitTree}: a new sub-model for deciduous fruit trees in the {Community} {Land} {Model} ({CLM5})},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{CLM5}-{FruitTree}},\n\turl = {https://gmd.copernicus.org/articles/15/5167/2022/},\n\tdoi = {10.5194/gmd-15-5167-2022},\n\tabstract = {Abstract. The inclusion of perennial, woody crops in land surface\nmodels (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these\nagroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land\nModel version 5 (CLM5). The model development included (1) a new perennial\ncrop phenology description, (2) an adapted C and nitrogen allocation scheme,\nconsidering both storage and photosynthetic growth of annual and perennial\nplant organs, (3) typical management practices associated with fruit\norchards, and (4) the parameterization of an apple plant functional type.\nCLM5-FruitTree was tested using extensive field measurements from an apple\norchard in South Tyrol, Italy. Growth and partitioning of biomass to the\nindividual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 \\% of the observed values despite\nlow simulated inter-annual variability compared to observations. The\nsimulated seasonal course of C, energy, and water fluxes was in good\nagreement with the eddy covariance (EC) measurements owing to the accurate\nrepresentation of the prolonged growing season and typical leaf area\ndevelopment of the orchard. We found that gross primary production, net\nradiation, and latent heat flux were highly correlated (r{\\textgreater}0.94)\nwith EC measurements and showed little bias ({\\textless}±5 \\%).\nSimulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in\nthe orchard structure and to the presence of additional management practices\nthat are not yet represented in CLM5-FruitTree. Finally, the results\nsuggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires\nfurther attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land\nsurface processes in fruit orchards at the local, regional, or larger scale.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Dombrowski, Olga and Brogi, Cosimo and Hendricks Franssen, Harrie-Jan and Zanotelli, Damiano and Bogena, Heye},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {5167--5193},\n}\n\n

\n

\n\n\n

\n Abstract. The inclusion of perennial, woody crops in land surface models (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these agroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM5-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the eddy covariance (EC) measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r\\textgreater0.94) with EC measurements and showed little bias (\\textless±5 %). Simulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM5-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires further attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional, or larger scale.\n

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\n \n\n \n \n Dunkl, I.; and Ließ, M.\n\n\n \n \n \n \n \n On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization.\n \n \n \n \n\n\n \n\n\n\n SOIL, 8(2): 541–558. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dunkl_benefits_2022,\n\ttitle = {On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization},\n\tvolume = {8},\n\tissn = {2199-398X},\n\tshorttitle = {On the benefits of clustering approaches in digital soil mapping},\n\turl = {https://soil.copernicus.org/articles/8/541/2022/},\n\tdoi = {10.5194/soil-8-541-2022},\n\tabstract = {Abstract. High-resolution soil maps are urgently needed by land managers and researchers for a variety of applications. Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. In this study, a legacy soil dataset was used to train a machine learning algorithm in order to predict the particle size distribution within the catchment of the Bode River in Saxony-Anhalt (Germany). The random forest ensemble learning method was used to predict soil texture based on environmental covariates originating from a digital elevation model, land cover data and geologic maps. We studied the usefulness of clustering applications in addressing various aspects of the DSM procedure. To improve areal representativity of the legacy soil data in terms of spatial variability, the environmental covariates were used to cluster the landscape of the study area into spatial units for stratified random sampling. Different sampling strategies were used to create balanced training data and were evaluated on their ability to improve model performance. Clustering applications were also involved in feature selection and stratified cross-validation. Under the best-performing sampling strategy, the resulting models achieved an R2 of 0.29 to 0.50 in topsoils and 0.16–0.32 in deeper soil layers. Overall, clustering applications appear to be a versatile tool to be employed at various steps of the DSM procedure. Beyond their successful application, further application fields in DSM were identified. One of them is to find adequate means to include expert knowledge.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {SOIL},\n\tauthor = {Dunkl, István and Ließ, Mareike},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {541--558},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Döpper, V.; Jagdhuber, T.; Holtgrave, A.; Heistermann, M.; Francke, T.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: Exploring the potential of optical and SAR remote sensing.\n \n \n \n \n\n\n \n\n\n\n Science of Remote Sensing, 5: 100056. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Following$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dopper_following_2022,\n\ttitle = {Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: {Exploring} the potential of optical and {SAR} remote sensing},\n\tvolume = {5},\n\tissn = {26660172},\n\tshorttitle = {Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2666017222000189},\n\tdoi = {10.1016/j.srs.2022.100056},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of Remote Sensing},\n\tauthor = {Döpper, Veronika and Jagdhuber, Thomas and Holtgrave, Ann-Kathrin and Heistermann, Maik and Francke, Till and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {100056},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Döpper, V.; Rocha, A. D.; Berger, K.; Gränzig, T.; Verrelst, J.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 110: 102817. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dopper_estimating_2022,\n\ttitle = {Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning},\n\tvolume = {110},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S156984322200019X},\n\tdoi = {10.1016/j.jag.2022.102817},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Döpper, Veronika and Rocha, Alby Duarte and Berger, Katja and Gränzig, Tobias and Verrelst, Jochem and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {102817},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Eingrüber, N.; and Korres, W.\n\n\n \n \n \n \n \n Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model).\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 838: 155775. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{eingruber_climate_2022,\n\ttitle = {Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale {Rur} catchment in western {Germany} until 2099 using {Statistical} {Downscaling} {Model} ({SDSM}) and the {Soil} \\& {Water} {Assessment} {Tool} ({SWAT} model)},\n\tvolume = {838},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722028728},\n\tdoi = {10.1016/j.scitotenv.2022.155775},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Eingrüber, Nils and Korres, Wolfgang},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {155775},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Francke, T.; Heistermann, M.; Köhli, M.; Budach, C.; Schrön, M.; and Oswald, S. E.\n\n\n \n \n \n \n \n Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Instrumentation, Methods and Data Systems, 11(1): 75–92. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{francke_assessing_2022,\n\ttitle = {Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture},\n\tvolume = {11},\n\tissn = {2193-0864},\n\turl = {https://gi.copernicus.org/articles/11/75/2022/},\n\tdoi = {10.5194/gi-11-75-2022},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use. This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution. Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time. The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Instrumentation, Methods and Data Systems},\n\tauthor = {Francke, Till and Heistermann, Maik and Köhli, Markus and Budach, Christian and Schrön, Martin and Oswald, Sascha E.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {75--92},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Fu, Z.; Ciais, P.; Makowski, D.; Bastos, A.; Stoy, P. C.; Ibrom, A.; Knohl, A.; Migliavacca, M.; Cuntz, M.; Šigut, L.; Peichl, M.; Loustau, D.; El‐Madany, T. S.; Buchmann, N.; Gharun, M.; Janssens, I.; Markwitz, C.; Grünwald, T.; Rebmann, C.; Mölder, M.; Varlagin, A.; Mammarella, I.; Kolari, P.; Bernhofer, C.; Heliasz, M.; Vincke, C.; Pitacco, A.; Cremonese, E.; Foltýnová, L.; and Wigneron, J.\n\n\n \n \n \n \n \n Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(6): 2111–2123. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncovering$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{fu_uncovering_2022,\n\ttitle = {Uncovering the critical soil moisture thresholds of plant water stress for {European} ecosystems},\n\tvolume = {28},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16050},\n\tdoi = {10.1111/gcb.16050},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Global Change Biology},\n\tauthor = {Fu, Zheng and Ciais, Philippe and Makowski, David and Bastos, Ana and Stoy, Paul C. and Ibrom, Andreas and Knohl, Alexander and Migliavacca, Mirco and Cuntz, Matthias and Šigut, Ladislav and Peichl, Matthias and Loustau, Denis and El‐Madany, Tarek S. and Buchmann, Nina and Gharun, Mana and Janssens, Ivan and Markwitz, Christian and Grünwald, Thomas and Rebmann, Corinna and Mölder, Meelis and Varlagin, Andrej and Mammarella, Ivan and Kolari, Pasi and Bernhofer, Christian and Heliasz, Michal and Vincke, Caroline and Pitacco, Andrea and Cremonese, Edoardo and Foltýnová, Lenka and Wigneron, Jean‐Pierre},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2111--2123},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Groh, J.; Diamantopoulos, E.; Duan, X.; Ewert, F.; Heinlein, F.; Herbst, M.; Holbak, M.; Kamali, B.; Kersebaum, K.; Kuhnert, M.; Nendel, C.; Priesack, E.; Steidl, J.; Sommer, M.; Pütz, T.; Vanderborght, J.; Vereecken, H.; Wallor, E.; Weber, T. K. D.; Wegehenkel, M.; Weihermüller, L.; and Gerke, H. H.\n\n\n \n \n \n \n \n Same soil, different climate: Crop model intercomparison on translocated lysimeters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 21(4). July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Same$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{groh_same_2022,\n\ttitle = {Same soil, different climate: {Crop} model intercomparison on translocated lysimeters},\n\tvolume = {21},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {Same soil, different climate},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20202},\n\tdoi = {10.1002/vzj2.20202},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Diamantopoulos, Efstathios and Duan, Xiaohong and Ewert, Frank and Heinlein, Florian and Herbst, Michael and Holbak, Maja and Kamali, Bahareh and Kersebaum, Kurt‐Christian and Kuhnert, Matthias and Nendel, Claas and Priesack, Eckart and Steidl, Jörg and Sommer, Michael and Pütz, Thomas and Vanderborght, Jan and Vereecken, Harry and Wallor, Evelyn and Weber, Tobias K. D. and Wegehenkel, Martin and Weihermüller, Lutz and Gerke, Horst H.},\n\tmonth = jul,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Hari, M.; and Tyagi, B.\n\n\n \n \n \n \n \n Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems.\n \n \n \n \n\n\n \n\n\n\n Environmental Science: Atmospheres, 2(5): 867–890. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Terrestrial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hari_terrestrial_2022,\n\ttitle = {Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems},\n\tvolume = {2},\n\tissn = {2634-3606},\n\tshorttitle = {Terrestrial carbon cycle},\n\turl = {http://xlink.rsc.org/?DOI=D1EA00102G},\n\tdoi = {10.1039/D1EA00102G},\n\tabstract = {Being a climate change nexus, the study on the carbon cycle depicts the existence of its mechanistic link with the atmospheric and biospheric environment. \n          ,  \n             \n              Owing to its tendency to couple with multiple elements, carbon forms complex molecules, which is the basic chemistry of life. Given that the climate system is inextricably coupled with the biosphere, understanding the terrestrial mechanistic pathway of carbon is critical in the transformation of the augmenting atmospheric carbon dioxide (CO \n              2 \n              ) in future. Although the global terrestrial carbon sink reduces the accumulation of atmospheric CO \n              2 \n              , which is contingent on the climate and ecosystem, the underlying key biophysical function that controls the ecosystem-carbon-climate responses and their feedback is uncertain. Accordingly, numerous unprecedented multi-scale studies have highlighted the dynamics of terrestrial carbon by strategically employing \n              in situ \n              , earth observation and process-based models; however, to date, the driving force for its dynamics remains unclassified. Besides, the significant variability in carbon is related to the large uncertainties from changes in land use, unambiguously increasing the regional carbon source from the seasonal to interannual scale but without long-term positive or negative feedback. Accordingly, in this review, we attempt to present a holistic understanding of the terrestrial carbon cycle by addressing its nature and different key drivers. The heterogenetic data platforms that reliably address the terrestrial carbon sink and its source dynamics are discussed in detail to demonstrate the potential of systematic quantification. Moreover, we summarize the complexity of carbon-climate feedbacks and their associates, extending the pathway for understanding the recent terrestrial carbon allocation, where India's environment is highlighted. This comprehensive review can be valuable to the research community in understanding the importance of the present and future carbon-climate feedback.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Science: Atmospheres},\n\tauthor = {Hari, Manoj and Tyagi, Bhishma},\n\tyear = {2022},\n\tpages = {867--890},\n}\n\n

\n

\n\n\n

\n Being a climate change nexus, the study on the carbon cycle depicts the existence of its mechanistic link with the atmospheric and biospheric environment. , Owing to its tendency to couple with multiple elements, carbon forms complex molecules, which is the basic chemistry of life. Given that the climate system is inextricably coupled with the biosphere, understanding the terrestrial mechanistic pathway of carbon is critical in the transformation of the augmenting atmospheric carbon dioxide (CO 2 ) in future. Although the global terrestrial carbon sink reduces the accumulation of atmospheric CO 2 , which is contingent on the climate and ecosystem, the underlying key biophysical function that controls the ecosystem-carbon-climate responses and their feedback is uncertain. Accordingly, numerous unprecedented multi-scale studies have highlighted the dynamics of terrestrial carbon by strategically employing in situ , earth observation and process-based models; however, to date, the driving force for its dynamics remains unclassified. Besides, the significant variability in carbon is related to the large uncertainties from changes in land use, unambiguously increasing the regional carbon source from the seasonal to interannual scale but without long-term positive or negative feedback. Accordingly, in this review, we attempt to present a holistic understanding of the terrestrial carbon cycle by addressing its nature and different key drivers. The heterogenetic data platforms that reliably address the terrestrial carbon sink and its source dynamics are discussed in detail to demonstrate the potential of systematic quantification. Moreover, we summarize the complexity of carbon-climate feedbacks and their associates, extending the pathway for understanding the recent terrestrial carbon allocation, where India's environment is highlighted. This comprehensive review can be valuable to the research community in understanding the importance of the present and future carbon-climate feedback.\n

\n\n\n

\n \n\n \n \n Haruzi, P.; Schmäck, J.; Zhou, Z.; van der Kruk, J.; Vereecken, H.; Vanderborght, J.; and Klotzsche, A.\n\n\n \n \n \n \n \n Detection of Tracer Plumes Using Full‐Waveform Inversion of Time‐Lapse Ground Penetrating Radar Data: A Numerical Study in a High‐Resolution Aquifer Model.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(5). May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{haruzi_detection_2022,\n\ttitle = {Detection of {Tracer} {Plumes} {Using} {Full}‐{Waveform} {Inversion} of {Time}‐{Lapse} {Ground} {Penetrating} {Radar} {Data}: {A} {Numerical} {Study} in a {High}‐{Resolution} {Aquifer} {Model}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Detection of {Tracer} {Plumes} {Using} {Full}‐{Waveform} {Inversion} of {Time}‐{Lapse} {Ground} {Penetrating} {Radar} {Data}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030110},\n\tdoi = {10.1029/2021WR030110},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Haruzi, P. and Schmäck, J. and Zhou, Z. and van der Kruk, J. and Vereecken, H. and Vanderborght, J. and Klotzsche, A.},\n\tmonth = may,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n He, M.; Chen, S.; Lian, X.; Wang, X.; Peñuelas, J.; and Piao, S.\n\n\n \n \n \n \n \n Global Spectrum of Vegetation Light‐Use Efficiency.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(16). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{he_global_2022,\n\ttitle = {Global {Spectrum} of {Vegetation} {Light}‐{Use} {Efficiency}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022GL099550},\n\tdoi = {10.1029/2022GL099550},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {He, Mingzhu and Chen, Shaoyuan and Lian, Xu and Wang, Xuhui and Peñuelas, Josep and Piao, Shilong},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Heistermann, M.; Bogena, H.; Francke, T.; Güntner, A.; Jakobi, J.; Rasche, D.; Schrön, M.; Döpper, V.; Fersch, B.; Groh, J.; Patil, A.; Pütz, T.; Reich, M.; Zacharias, S.; Zengerle, C.; and Oswald, S.\n\n\n \n \n \n \n \n Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(5): 2501–2519. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{heistermann_soil_2022,\n\ttitle = {Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the {TERENO} site {Wüstebach}},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {Soil moisture observation in a forested headwater catchment},\n\turl = {https://essd.copernicus.org/articles/14/2501/2022/},\n\tdoi = {10.5194/essd-14-2501-2022},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Heistermann, Maik and Bogena, Heye and Francke, Till and Güntner, Andreas and Jakobi, Jannis and Rasche, Daniel and Schrön, Martin and Döpper, Veronika and Fersch, Benjamin and Groh, Jannis and Patil, Amol and Pütz, Thomas and Reich, Marvin and Zacharias, Steffen and Zengerle, Carmen and Oswald, Sascha},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2501--2519},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Helle, G.; Pauly, M.; Heinrich, I.; Schollän, K.; Balanzategui, D.; and Schürheck, L.\n\n\n \n \n \n \n \n Stable Isotope Signatures of Wood, its Constituents and Methods of Cellulose Extraction.\n \n \n \n \n\n\n \n\n\n\n In Siegwolf, R. T. W.; Brooks, J. R.; Roden, J.; and Saurer, M., editor(s), Stable Isotopes in Tree Rings, volume 8, pages 135–190. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@incollection{siegwolf_stable_2022,\n\taddress = {Cham},\n\ttitle = {Stable {Isotope} {Signatures} of {Wood}, its {Constituents} and {Methods} of {Cellulose} {Extraction}},\n\tvolume = {8},\n\tisbn = {9783030926977 9783030926984},\n\turl = {https://link.springer.com/10.1007/978-3-030-92698-4_5},\n\tabstract = {Abstract \n            In this chapter, we give some basic information on the chemical and isotopic properties of wood constituents and describe their relative contribution to the isotopic signature of wood. Based on these considerations we review studies that have compared stable isotope signals of wood with those of corresponding cellulose. We exemplify how relationships of wood-based tree-ring stable isotope sequences with climate can be affected by varying proportions of wood constituents like cellulose, lignin and extractives. A majority of benchmarking studies suggests that cellulose extraction may not be necessary. However, based upon existing research, a general statement cannot be made on the necessity of cellulose extraction. Changes in wood composition can particularly influence environmental signal strength during periods of low isotope variability. Cellulose extraction removes any effects from changing wood composition. We present the three established chemical approaches of extraction, outline how to test the purity of isolated cellulose and present user-friendly efficient experimental setups allowing to simultaneously process hundreds of samples in one batch. Further, we briefly address the analysis of stable isotopes of lignin methoxyl groups because of easy sample preparation and its potential additional value for studies on fossil wood.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Stable {Isotopes} in {Tree} {Rings}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Helle, Gerhard and Pauly, Maren and Heinrich, Ingo and Schollän, Karina and Balanzategui, Daniel and Schürheck, Lucas},\n\teditor = {Siegwolf, Rolf T. W. and Brooks, J. Renée and Roden, John and Saurer, Matthias},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-92698-4_5},\n\tpages = {135--190},\n}\n\n

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\n \n\n \n \n Hong, F.; Zhan, W.; Göttsche, F.; Liu, Z.; Dong, P.; Fu, H.; Huang, F.; and Zhang, X.\n\n\n \n \n \n \n \n A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(7): 3091–3113. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{hong_global_2022,\n\ttitle = {A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {A global dataset of spatiotemporally seamless daily mean land surface temperatures},\n\turl = {https://essd.copernicus.org/articles/14/3091/2022/},\n\tdoi = {10.5194/essd-14-3091-2022},\n\tabstract = {Abstract. Daily mean land surface temperatures (LSTs) acquired from\npolar orbiters are crucial for various applications such as global and\nregional climate change analysis. However, thermal sensors from\npolar orbiters can only sample the surface effectively with very limited\ntimes per day under cloud-free conditions. These limitations have produced a\nsystematic sampling bias (ΔTsb) on the daily mean LST\n(Tdm) estimated with the traditional method, which uses the averages of\nclear-sky LST observations directly as the Tdm. Several methods have\nbeen proposed for the estimation of the Tdm, yet they are becoming less\ncapable of generating spatiotemporally seamless Tdm across the globe.\nBased on MODIS and reanalysis data, here we propose an improved annual and\ndiurnal temperature cycle-based framework (termed the IADTC framework) to\ngenerate global spatiotemporally seamless Tdm products ranging from 2003\nto 2019 (named the GADTC products). The validations show that the IADTC\nframework reduces the systematic ΔTsb significantly. When\nvalidated only with in situ data, the assessments show that the mean absolute\nerrors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and\nFLUXNET data, respectively, and the mean biases are both close to zero.\nDirect comparisons between the GADTC products and in situ measurements indicate\nthat the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets,\nrespectively, and the mean biases are −1.6 and −1.5 K for these two\ndatasets, respectively. By taking the GADTC products as references, further\nanalysis reveals that the Tdm estimated with the traditional averaging\nmethod yields a positive systematic ΔTsb of greater than 2.0 K\nin low-latitude and midlatitude regions while of a relatively small value in\nhigh-latitude regions. Although the global-mean LST trend (2003 to 2019)\ncalculated with the traditional method and the IADTC framework is relatively\nclose (both between 0.025 to 0.029 K yr−1), regional discrepancies in LST\ntrend do occur – the pixel-based MAE in LST trend between these two\nmethods reaches 0.012 K yr−1. We consider the IADTC framework can guide the\nfurther optimization of Tdm estimation across the globe, and the\ngenerated GADTC products should be valuable in various applications such as\nglobal and regional warming analysis. The GADTC products are freely\navailable at https://doi.org/10.5281/zenodo.6287052 (Hong et\nal., 2022).},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Hong, Falu and Zhan, Wenfeng and Göttsche, Frank-M. and Liu, Zihan and Dong, Pan and Fu, Huyan and Huang, Fan and Zhang, Xiaodong},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {3091--3113},\n}\n\n

\n

\n\n\n

\n Abstract. Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔTsb) on the daily mean LST (Tdm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the Tdm. Several methods have been proposed for the estimation of the Tdm, yet they are becoming less capable of generating spatiotemporally seamless Tdm across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless Tdm products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔTsb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the Tdm estimated with the traditional averaging method yields a positive systematic ΔTsb of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr−1), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr−1. We consider the IADTC framework can guide the further optimization of Tdm estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).\n

\n\n\n

\n \n\n \n \n Huang, F.; Shangguan, W.; Li, Q.; Li, L.; and Zhang, Y.\n\n\n \n \n \n \n \n Beyond Prediction: An Integrated Post–Hoc Approach to Interpret Complex Model in Hydrometeorology.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Beyond$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{huang_beyond_2022,\n\ttitle = {Beyond {Prediction}: {An} {Integrated} {Post}–{Hoc} {Approach} to {Interpret} {Complex} {Model} in {Hydrometeorology}},\n\tissn = {1556-5068},\n\tshorttitle = {Beyond {Prediction}},\n\turl = {https://www.ssrn.com/abstract=4167751},\n\tdoi = {10.2139/ssrn.4167751},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Huang, Feini and Shangguan, Wei and Li, Qingliang and Li, Lu and Zhang, Ye},\n\tyear = {2022},\n}\n\n

\n

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\n \n\n \n \n Huang, L.; Lin, X.; Jiang, S.; Liu, M.; Jiang, Y.; Li, Z.; and Tang, R.\n\n\n \n \n \n \n \n A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(10): 104021. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{huang_two-stage_2022,\n\ttitle = {A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac8b98},\n\tdoi = {10.1088/1748-9326/ac8b98},\n\tabstract = {Abstract \n             \n              Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              , and (b) separately re-parameterizing \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with \n              in-situ \n              measurements and was found to outperform the three \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m \n              −2 \n              d \n              −1 \n              ) by 24.2\\% and 35.4\\% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7\\% and 20\\% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR \n              v \n              ) were used to re-parameterize the \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Huang, Lingxiao and Lin, Xiaofeng and Jiang, Shouzheng and Liu, Meng and Jiang, Yazhen and Li, Zhao-Liang and Tang, Ronglin},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {104021},\n}\n\n

\n

\n\n\n

\n Abstract Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( ε max ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of ε max , and (b) separately re-parameterizing ε max in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art ε max –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three ε max –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three ε max –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m −2 d −1 ) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR v ) were used to re-parameterize the ε max , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of ε max and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.\n

\n\n\n

\n \n\n \n \n Huber, R.; Le’Clec’h, S.; Buchmann, N.; and Finger, R.\n\n\n \n \n \n \n \n Economic value of three grassland ecosystem services when managed at the regional and farm scale.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 12(1): 4194. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Economic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{huber_economic_2022,\n\ttitle = {Economic value of three grassland ecosystem services when managed at the regional and farm scale},\n\tvolume = {12},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-022-08198-w},\n\tdoi = {10.1038/s41598-022-08198-w},\n\tabstract = {Abstract \n             \n              Grasslands cover a major share of the world’s agricultural land and their management influences ecosystem services. Spatially targeted policy instruments can increase the provision of ecosystem services by exploiting how they respond to spatial differences in environmental characteristics such as altitude, slope, or soil quality. However, most policy instruments focus on individual farms, where spatial differences are small. Here we assess the economic value of three grassland ecosystem services (i.e., forage provision, carbon sequestration, and habitat maintenance) and its variability in a Swiss region of 791 km \n              2 \n              that consists of 19,000 farmland parcels when managed at the regional and farm scale, respectively. Our spatially explicit bio-economic simulation approach combines biophysical information on grassland ecosystem services and their economic values. We find that in our case study region, spatial targeting on a regional scale management increases the economic value of ecosystem services by 45\\% compared to targeting at farm scale. We also find that the heterogeneity of economic values coming from prices and willingness to pay estimates is higher than the economic gains from spatial targeting that make use of the spatial difference in environmental characteristics. This implies that heterogeneity in prices and/or societal demand of these three ecosystem services is more important for grassland management than spatial heterogeneity in our case study region. The here applied framework allows for an ex-ante assessment of economic gains from spatial targeting and thus provides basic information for the implementation of incentive mechanisms addressing the nexus of food production and ecosystem service provision in grasslands.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Reports},\n\tauthor = {Huber, Robert and Le’Clec’h, Solen and Buchmann, Nina and Finger, Robert},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {4194},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Hurley, A. G.; Peters, R. L.; Pappas, C.; Steger, D. N.; and Heinrich, I.\n\n\n \n \n \n \n \n Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 17(5): e0268426. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Addressing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hurley_addressing_2022,\n\ttitle = {Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr {R} package},\n\tvolume = {17},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0268426},\n\tdoi = {10.1371/journal.pone.0268426},\n\tabstract = {Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {PLOS ONE},\n\tauthor = {Hurley, Alexander G. and Peters, Richard L. and Pappas, Christoforos and Steger, David N. and Heinrich, Ingo},\n\teditor = {Krug, Rainer M.},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {e0268426},\n}\n\n

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\n \n\n \n \n Jagdhuber, T.; Jonard, F.; Fluhrer, A.; Chaparro, D.; Baur, M. J.; Meyer, T.; and Piles, M.\n\n\n \n \n \n \n \n Toward estimation of seasonal water dynamics of winter wheat from ground-based L-band radiometry: a concept study.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(8): 2273–2294. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{jagdhuber_toward_2022,\n\ttitle = {Toward estimation of seasonal water dynamics of winter wheat from ground-based {L}-band radiometry: a concept study},\n\tvolume = {19},\n\tissn = {1726-4189},\n\tshorttitle = {Toward estimation of seasonal water dynamics of winter wheat from ground-based {L}-band radiometry},\n\turl = {https://bg.copernicus.org/articles/19/2273/2022/},\n\tdoi = {10.5194/bg-19-2273-2022},\n\tabstract = {Abstract. The vegetation optical depth (VOD) variable\ncontains information on plant water content and biomass. It can be estimated\nalongside soil moisture from currently operating satellite radiometer\nmissions, such as SMOS (ESA) and SMAP (NASA). The estimation of water\nfluxes, such as plant water uptake (PWU) and transpiration rate (TR),\nfrom these earth system parameters (VOD, soil moisture) requires assessing\nwater potential gradients and flow resistances in the soil, the vegetation\nand the atmosphere. Yet water flux estimation remains an elusive challenge\nespecially on a global scale. In this concept study, we conduct a\nfield-scale experiment to test mechanistic models for the estimation of\nseasonal water fluxes (PWU and TR) of a winter wheat stand using\nmeasurements of soil moisture, VOD, and relative air humidity (RH) in a\ncontrolled environment. We utilize microwave L-band observations from a\ntower-based radiometer to estimate VOD of a wheat stand during the 2017\ngrowing season at the Selhausen test site in Germany. From VOD,\nwe first extract the gravimetric moisture of vegetation and then determine\nthe relative water content (RWC) and vegetation water potential (VWP) of\nthe wheat field. Although the relative water content could be directly\nestimated from VOD, our results indicate this may be challenging for the\nphenological phases, when rapid biomass and plant structure development take\nplace within the wheat canopy. We estimate water uptake from the soil to the\nwheat plants from the difference between the soil and vegetation potentials\ndivided by the flow resistance from soil into wheat plants. The\nTR from the wheat plants into the atmosphere was obtained\nfrom the difference between the vegetation and atmosphere water potentials\ndivided by the flow resistances from plants to the atmosphere. For this, the\nrequired soil matric potential (SMP), the vapor pressure deficit (VPD),\nand the flow resistances were obtained from on-site observations of soil,\nplant, and atmosphere together with simple mechanistic models. This\npathfinder study shows that the L-band microwave radiation contains valuable\ninformation on vegetation water status that enables the estimation of water\ndynamics (up to fluxes) from the soil via wheat plants into the atmosphere,\nwhen combined with additional information of soil and atmosphere water\ncontent. Still, assumptions have to be made when estimating the vegetation\nwater potential from relative water content as well as the water flow\nresistances between soil, wheat plants, and atmosphere. Moreover, direct\nvalidation of water flux estimates for the assessment of their absolute\naccuracy could not be performed due to a lack of in situ PWU and TR\nmeasurements. Nonetheless, our estimates of water status, potentials, and\nfluxes show the expected temporal dynamics, known from the literature, and\nintercompare reasonably well in absolute terms with independent TR\nestimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor\ntype of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics\nnext to traditionally measured stand-scale or plot-scale techniques. They\nmight shed light on future capabilities of monitoring water dynamics in the\nsoil–plant–atmosphere system including wide-area, remote-sensing-based earth\nobservation data.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Jagdhuber, Thomas and Jonard, François and Fluhrer, Anke and Chaparro, David and Baur, Martin J. and Meyer, Thomas and Piles, María},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {2273--2294},\n}\n\n

\n

\n\n\n

\n Abstract. The vegetation optical depth (VOD) variable contains information on plant water content and biomass. It can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these earth system parameters (VOD, soil moisture) requires assessing water potential gradients and flow resistances in the soil, the vegetation and the atmosphere. Yet water flux estimation remains an elusive challenge especially on a global scale. In this concept study, we conduct a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWU and TR) of a winter wheat stand using measurements of soil moisture, VOD, and relative air humidity (RH) in a controlled environment. We utilize microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen test site in Germany. From VOD, we first extract the gravimetric moisture of vegetation and then determine the relative water content (RWC) and vegetation water potential (VWP) of the wheat field. Although the relative water content could be directly estimated from VOD, our results indicate this may be challenging for the phenological phases, when rapid biomass and plant structure development take place within the wheat canopy. We estimate water uptake from the soil to the wheat plants from the difference between the soil and vegetation potentials divided by the flow resistance from soil into wheat plants. The TR from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere water potentials divided by the flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit (VPD), and the flow resistances were obtained from on-site observations of soil, plant, and atmosphere together with simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions have to be made when estimating the vegetation water potential from relative water content as well as the water flow resistances between soil, wheat plants, and atmosphere. Moreover, direct validation of water flux estimates for the assessment of their absolute accuracy could not be performed due to a lack of in situ PWU and TR measurements. Nonetheless, our estimates of water status, potentials, and fluxes show the expected temporal dynamics, known from the literature, and intercompare reasonably well in absolute terms with independent TR estimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor type of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. They might shed light on future capabilities of monitoring water dynamics in the soil–plant–atmosphere system including wide-area, remote-sensing-based earth observation data.\n

\n\n\n

\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Fuchs, H.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Potential of Thermal Neutrons to Correct Cosmic‐Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(8). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jakobi_potential_2022,\n\ttitle = {Potential of {Thermal} {Neutrons} to {Correct} {Cosmic}‐{Ray} {Neutron} {Soil} {Moisture} {Content} {Measurements} for {Dynamic} {Biomass} {Effects}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022WR031972},\n\tdoi = {10.1029/2022WR031972},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Jakobi, J. and Huisman, J. A. and Fuchs, H. and Vereecken, H. and Bogena, H. R.},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Jarvis, N.; Groh, J.; Lewan, E.; Meurer, K. H. E.; Durka, W.; Baessler, C.; Pütz, T.; Rufullayev, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Coupled modelling of hydrological processes and grassland production in two contrasting climates.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(8): 2277–2299. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Coupled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jarvis_coupled_2022,\n\ttitle = {Coupled modelling of hydrological processes and grassland production in two contrasting climates},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/2277/2022/},\n\tdoi = {10.5194/hess-26-2277-2022},\n\tabstract = {Abstract. Projections of global climate models suggest that ongoing human-induced\nclimate change will lead to an increase in the frequency of severe droughts\nin many important agricultural regions of the world. Eco-hydrological models\nthat integrate current understanding of the interacting processes governing\nsoil water balance and plant growth may be useful tools to predict the\nimpacts of climate change on crop production. However, the validation status\nof these models for making predictions under climate change is still\nunclear, since few suitable datasets are available for model testing. One\npromising approach is to test models using data obtained in\n“space-for-time” substitution experiments, in which samples are\ntransferred among locations with contrasting current climates in order to\nmimic future climatic conditions. An important advantage of this approach is\nthat the soil type is the same, so that differences in soil properties are\nnot confounded with the influence of climate on water balance and crop\ngrowth. In this study, we evaluate the capability of a relatively simple\neco-hydrological model to reproduce 6 years (2013–2018) of measurements of\nsoil water contents, water balance components and grass production made in\nweighing lysimeters located at two sites within the TERENO-SoilCan network\nin Germany. Three lysimeters are located at an upland site at Rollesbroich\nwith a cool, wet climate, while three others had been moved from\nRollesbroich to a warmer and drier climate on the lower Rhine valley\nfloodplain at Selhausen. Four of the most sensitive parameters in the model\nwere treated as uncertain within the framework of the GLUE (generalized\nlikelihood uncertainty estimation) methodology, while the remaining\nparameters in the model were set according to site measurements or data in\nthe literature. The model satisfactorily reproduced the measurements at both sites, and some\nsignificant differences in the posterior ranges of the four uncertain\nparameters were found. In particular, the results indicated greater stomatal\nconductance as well an increase in dry-matter allocation below ground and a\nsignificantly larger maximum root depth for the three lysimeters that had\nbeen moved to Selhausen. As a consequence, the apparent water use efficiency\n(above-ground harvest divided by evapotranspiration) was significantly\nsmaller at Selhausen than Rollesbroich. Data on species abundance on the\nlysimeters provide one possible explanation for the differences in the plant\ntraits at the two sites derived from model calibration. These observations\nshowed that the plant community at Selhausen had changed significantly in\nresponse to the drier climate, with a significant decrease in the abundance\nof herbs and an increase in the proportion of grass species. The differences\nin root depth and leaf conductance may also be a consequence of plasticity\nor acclimation at the species level. Regardless of the reason, we may\nconclude that such adaptations introduce significant additional\nuncertainties into model predictions of water balance and plant growth in\nresponse to climate change.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Jarvis, Nicholas and Groh, Jannis and Lewan, Elisabet and Meurer, Katharina H. E. and Durka, Walter and Baessler, Cornelia and Pütz, Thomas and Rufullayev, Elvin and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2277--2299},\n}\n\n

\n

\n\n\n

\n Abstract. Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (generalized likelihood uncertainty estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model satisfactorily reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry-matter allocation below ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.\n

\n\n\n

\n \n\n \n \n Jiang, Y.; Tang, R.; and Li, Z.\n\n\n \n \n \n \n \n A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109087. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jiang_physical_2022,\n\ttitle = {A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration},\n\tvolume = {323},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002751},\n\tdoi = {10.1016/j.agrformet.2022.109087},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Jiang, Yazhen and Tang, Ronglin and Li, Zhao-Liang},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109087},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Jähkel, A.; Graeber, D.; Fleckenstein, J. H.; and Schmidt, C.\n\n\n \n \n \n \n \n Hydrologic Turnover Matters — Gross Gains and Losses of Six First‐Order Streams Across Contrasting Landscapes and Flow Regimes.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(7). July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydrologic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jahkel_hydrologic_2022,\n\ttitle = {Hydrologic {Turnover} {Matters} — {Gross} {Gains} and {Losses} of {Six} {First}‐{Order} {Streams} {Across} {Contrasting} {Landscapes} and {Flow} {Regimes}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022WR032129},\n\tdoi = {10.1029/2022WR032129},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Jähkel, A. and Graeber, D. and Fleckenstein, J. H. and Schmidt, C.},\n\tmonth = jul,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kamali, B.; Stella, T.; Berg-Mohnicke, M.; Pickert, J.; Groh, J.; and Nendel, C.\n\n\n \n \n \n \n \n Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics.\n \n \n \n \n\n\n \n\n\n\n European Journal of Agronomy, 134: 126464. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kamali_improving_2022,\n\ttitle = {Improving the simulation of permanent grasslands across {Germany} by using multi-objective uncertainty-based calibration of plant-water dynamics},\n\tvolume = {134},\n\tissn = {11610301},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1161030122000120},\n\tdoi = {10.1016/j.eja.2022.126464},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {European Journal of Agronomy},\n\tauthor = {Kamali, Bahareh and Stella, Tommaso and Berg-Mohnicke, Michael and Pickert, Jürgen and Groh, Jannis and Nendel, Claas},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {126464},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kamjunke, N.; Beckers, L.; Herzsprung, P.; von Tümpling, W.; Lechtenfeld, O.; Tittel, J.; Risse-Buhl, U.; Rode, M.; Wachholz, A.; Kallies, R.; Schulze, T.; Krauss, M.; Brack, W.; Comero, S.; Gawlik, B. M.; Skejo, H.; Tavazzi, S.; Mariani, G.; Borchardt, D.; and Weitere, M.\n\n\n \n \n \n \n \n Lagrangian profiles of riverine autotrophy, organic matter transformation, and micropollutants at extreme drought.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 828: 154243. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Lagrangian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kamjunke_lagrangian_2022,\n\ttitle = {Lagrangian profiles of riverine autotrophy, organic matter transformation, and micropollutants at extreme drought},\n\tvolume = {828},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722013353},\n\tdoi = {10.1016/j.scitotenv.2022.154243},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Kamjunke, Norbert and Beckers, Liza-Marie and Herzsprung, Peter and von Tümpling, Wolf and Lechtenfeld, Oliver and Tittel, Jörg and Risse-Buhl, Ute and Rode, Michael and Wachholz, Alexander and Kallies, Rene and Schulze, Tobias and Krauss, Martin and Brack, Werner and Comero, Sara and Gawlik, Bernd Manfred and Skejo, Hello and Tavazzi, Simona and Mariani, Giulio and Borchardt, Dietrich and Weitere, Markus},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {154243},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Khordakova, D.; Rolf, C.; Grooß, J.; Müller, R.; Konopka, P.; Wieser, A.; Krämer, M.; and Riese, M.\n\n\n \n \n \n \n \n A case study on the impact of severe convective storms on the water vapor mixing ratio in the lower mid-latitude stratosphere observed in 2019 over Europe.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 22(2): 1059–1079. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{khordakova_case_2022,\n\ttitle = {A case study on the impact of severe convective storms on the water vapor mixing ratio in the lower mid-latitude stratosphere observed in 2019 over {Europe}},\n\tvolume = {22},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/22/1059/2022/},\n\tdoi = {10.5194/acp-22-1059-2022},\n\tabstract = {Abstract. Extreme convective events in the troposphere not only have immediate impacts on the surface, but they can also influence the dynamics and composition of the lower stratosphere (LS). One major impact is the moistening of the LS by overshooting convection. This effect plays a crucial role in climate feedback, as small changes of water vapor in the upper troposphere and lower stratosphere (UTLS) have a large impact on the radiative budget of the atmosphere. In this case study, we investigate water vapor injections into the LS by two consecutive convective events in the European mid-latitudes within the framework of the MOSES (Modular Observation Solutions for Earth Systems) measurement campaign during the early summer of 2019. Using balloon-borne instruments, measurements of convective water vapor injection into the stratosphere were performed. Such measurements with a high vertical resolution are rare. The magnitude of the stratospheric water vapor reached up to 12.1 ppmv (parts per million by volume), with an estimated background value of 5 ppmv. Hence, the water vapor enhancement reported here is of the same order of magnitude as earlier reports of water vapor injection by convective overshooting over North America. However, the overshooting took place in the extratropical stratosphere over Europe and has a stronger impact on long-term water vapor mixing ratios in the stratosphere compared to the monsoon-influenced region in North America. At the altitude of the measured injection, a sharp drop in a local ozone enhancement peak makes the observed composition of air very unique with high ozone up to 650 ppbv (parts per billion by volume) and high water vapor up to 12.1 ppmv. ERA-Interim does not show any signal of the convective overshoot, the water vapor values measured by the Microwave Limb Sounder (MLS) in the LS are lower than the in situ observations, and the ERA5 overestimated water vapor mixing ratios. Backward trajectories of the measured injected air masses reveal that the moistening of the LS took place several hours before the balloon launch. This is in good agreement with the reanalyses, which shows a strong change in the structure of isotherms and a sudden and short-lived increase in potential vorticity at the altitude and location of the trajectory. Similarly, satellite data show low cloud-top brightness temperatures during the overshooting event, which indicates an elevated cloud top height.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {Khordakova, Dina and Rolf, Christian and Grooß, Jens-Uwe and Müller, Rolf and Konopka, Paul and Wieser, Andreas and Krämer, Martina and Riese, Martin},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {1059--1079},\n}\n\n

\n

\n\n\n

\n Abstract. Extreme convective events in the troposphere not only have immediate impacts on the surface, but they can also influence the dynamics and composition of the lower stratosphere (LS). One major impact is the moistening of the LS by overshooting convection. This effect plays a crucial role in climate feedback, as small changes of water vapor in the upper troposphere and lower stratosphere (UTLS) have a large impact on the radiative budget of the atmosphere. In this case study, we investigate water vapor injections into the LS by two consecutive convective events in the European mid-latitudes within the framework of the MOSES (Modular Observation Solutions for Earth Systems) measurement campaign during the early summer of 2019. Using balloon-borne instruments, measurements of convective water vapor injection into the stratosphere were performed. Such measurements with a high vertical resolution are rare. The magnitude of the stratospheric water vapor reached up to 12.1 ppmv (parts per million by volume), with an estimated background value of 5 ppmv. Hence, the water vapor enhancement reported here is of the same order of magnitude as earlier reports of water vapor injection by convective overshooting over North America. However, the overshooting took place in the extratropical stratosphere over Europe and has a stronger impact on long-term water vapor mixing ratios in the stratosphere compared to the monsoon-influenced region in North America. At the altitude of the measured injection, a sharp drop in a local ozone enhancement peak makes the observed composition of air very unique with high ozone up to 650 ppbv (parts per billion by volume) and high water vapor up to 12.1 ppmv. ERA-Interim does not show any signal of the convective overshoot, the water vapor values measured by the Microwave Limb Sounder (MLS) in the LS are lower than the in situ observations, and the ERA5 overestimated water vapor mixing ratios. Backward trajectories of the measured injected air masses reveal that the moistening of the LS took place several hours before the balloon launch. This is in good agreement with the reanalyses, which shows a strong change in the structure of isotherms and a sudden and short-lived increase in potential vorticity at the altitude and location of the trajectory. Similarly, satellite data show low cloud-top brightness temperatures during the overshooting event, which indicates an elevated cloud top height.\n

\n\n\n

\n \n\n \n \n Koedel, U.; Dietrich, P.; Fischer, P.; Greinert, J.; Bundke, U.; Burwicz-Galerne, E.; Haas, A.; Herrarte, I.; Haroon, A.; Jegen, M.; Kalbacher, T.; Kennert, M.; Korf, T.; Kunkel, R.; Kwok, C. Y.; Mahnke, C.; Nixdorf, E.; Paasche, H.; González Ávalos, E.; Petzold, A.; Rohs, S.; Wagner, R.; and Walter, A.\n\n\n \n \n \n \n \n The Digital Earth Smart Monitoring Concept and Tools.\n \n \n \n \n\n\n \n\n\n\n In Bouwer, L. M.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; and Greinert, J., editor(s), Integrating Data Science and Earth Science, pages 85–120. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{bouwer_digital_2022,\n\taddress = {Cham},\n\ttitle = {The {Digital} {Earth} {Smart} {Monitoring} {Concept} and {Tools}},\n\tisbn = {9783030995454 9783030995461},\n\turl = {https://link.springer.com/10.1007/978-3-030-99546-1_6},\n\tabstract = {Abstract \n            Reliable data are the base of all scientific analyses, interpretations and conclusions. Evaluating data in a smart way speeds up the process of interpretation and conclusion and highlights where, when and how additionally acquired data in the field will support knowledge gain. An extended SMART monitoring concept is introduced which includes SMART sensors, DataFlows, MetaData and Sampling approaches and tools. In the course of the Digital Earth project, the meaning of SMART monitoring has significantly evolved. It stands for a combination of hard- and software tools enhancing the traditional monitoring approach where a SMART monitoring DataFlow is processed and analyzed sequentially on the way from the sensor to a repository into an integrated analysis approach. The measured values itself, its metadata, and the status of the sensor, and additional auxiliary data can be made available in real time and analyzed to enhance the sensor output concerning accuracy and precision. Although several parts of the four tools are known, technically feasible and sometimes applied in Earth science studies, there is a large discrepancy between knowledge and our derived ambitions and what is feasible and commonly done in the reality and in the field.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Integrating {Data} {Science} and {Earth} {Science}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Koedel, Uta and Dietrich, Peter and Fischer, Philipp and Greinert, Jens and Bundke, Ulrich and Burwicz-Galerne, Ewa and Haas, Antonie and Herrarte, Isabel and Haroon, Amir and Jegen, Marion and Kalbacher, Thomas and Kennert, Marcel and Korf, Tobias and Kunkel, Ralf and Kwok, Ching Yin and Mahnke, Christoph and Nixdorf, Erik and Paasche, Hendrik and González Ávalos, Everardo and Petzold, Andreas and Rohs, Susanne and Wagner, Robert and Walter, Andreas},\n\teditor = {Bouwer, Laurens M. and Dransch, Doris and Ruhnke, Roland and Rechid, Diana and Frickenhaus, Stephan and Greinert, Jens},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-99546-1_6},\n\tpages = {85--120},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Kong, X.; Ghaffar, S.; Determann, M.; Friese, K.; Jomaa, S.; Mi, C.; Shatwell, T.; Rinke, K.; and Rode, M.\n\n\n \n \n \n \n \n Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change.\n \n \n \n \n\n\n \n\n\n\n Water Research, 221: 118721. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Reservoir$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kong_reservoir_2022,\n\ttitle = {Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change},\n\tvolume = {221},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135422006741},\n\tdoi = {10.1016/j.watres.2022.118721},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Water Research},\n\tauthor = {Kong, Xiangzhen and Ghaffar, Salman and Determann, Maria and Friese, Kurt and Jomaa, Seifeddine and Mi, Chenxi and Shatwell, Tom and Rinke, Karsten and Rode, Michael},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {118721},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Koppa, A.; Rains, D.; Hulsman, P.; Poyatos, R.; and Miralles, D. G.\n\n\n \n \n \n \n \n A deep learning-based hybrid model of global terrestrial evaporation.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 13(1): 1912. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{koppa_deep_2022,\n\ttitle = {A deep learning-based hybrid model of global terrestrial evaporation},\n\tvolume = {13},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-022-29543-7},\n\tdoi = {10.1038/s41467-022-29543-7},\n\tabstract = {Abstract \n             \n              Terrestrial evaporation ( \n              E \n              ) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, \n              E \n               \n                t \n               \n              ) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress ( \n              S \n               \n                t \n               \n              ), i.e., the reduction of \n              E \n               \n                t \n               \n              from its theoretical maximum. Then, we embed the new \n              S \n               \n                t \n               \n              formulation within a process-based model of \n              E \n              to yield a global hybrid \n              E \n              model. In this hybrid model, the \n              S \n               \n                t \n               \n              formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate \n              S \n               \n                t \n               \n              and \n              E \n              globally. The proposed framework may be extended to improve the estimation of \n              E \n              in Earth System Models and enhance our understanding of this crucial climatic variable.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Nature Communications},\n\tauthor = {Koppa, Akash and Rains, Dominik and Hulsman, Petra and Poyatos, Rafael and Miralles, Diego G.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1912},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Kunz, M.; Abbas, S. S.; Bauckholt, M.; Böhmländer, A.; Feuerle, T.; Gasch, P.; Glaser, C.; Groß, J.; Hajnsek, I.; Handwerker, J.; Hase, F.; Khordakova, D.; Knippertz, P.; Kohler, M.; Lange, D.; Latt, M.; Laube, J.; Martin, L.; Mauder, M.; Möhler, O.; Mohr, S.; Reitter, R. W.; Rettenmeier, A.; Rolf, C.; Saathoff, H.; Schrön, M.; Schütze, C.; Spahr, S.; Späth, F.; Vogel, F.; Völksch, I.; Weber, U.; Wieser, A.; Wilhelm, J.; Zhang, H.; and Dietrich, P.\n\n\n \n \n \n \n \n Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Earth Science, 10: 999593. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Swabian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kunz_swabian_2022,\n\ttitle = {Swabian {MOSES} 2021: {An} interdisciplinary field campaign for investigating convective storms and their event chains},\n\tvolume = {10},\n\tissn = {2296-6463},\n\tshorttitle = {Swabian {MOSES} 2021},\n\turl = {https://www.frontiersin.org/articles/10.3389/feart.2022.999593/full},\n\tdoi = {10.3389/feart.2022.999593},\n\tabstract = {The Neckar Valley and the Swabian Jura in southwest Germany comprise a hotspot for severe convective storms, causing tens of millions of euros in damage each year. Possible reasons for the high frequency of thunderstorms and the associated event chain across compartments were investigated in detail during the hydro-meteorological field campaign Swabian MOSES carried out between May and September 2021. Researchers from various disciplines established more than 25 temporary ground-based stations equipped with state-of-the-art \n              in situ \n              and remote sensing observation systems, such as lidars, dual-polarization X- and C-band Doppler weather radars, radiosondes including stratospheric balloons, an aerosol cloud chamber, masts to measure vertical fluxes, autosamplers for water probes in rivers, and networks of disdrometers, soil moisture, and hail sensors. These fixed-site observations were supplemented by mobile observation systems, such as a research aircraft with scanning Doppler lidar, a cosmic ray neutron sensing rover, and a storm chasing team launching swarmsondes in the vicinity of hailstorms. Seven Intensive Observation Periods (IOPs) were conducted on a total of 21 operating days. An exceptionally high number of convective events, including both unorganized and organized thunderstorms such as multicells or supercells, occurred during the study period. This paper gives an overview of the Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign, briefly describes the observation strategy, and presents observational highlights for two IOPs.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Earth Science},\n\tauthor = {Kunz, Michael and Abbas, Syed S. and Bauckholt, Matteo and Böhmländer, Alexander and Feuerle, Thomas and Gasch, Philipp and Glaser, Clarissa and Groß, Jochen and Hajnsek, Irena and Handwerker, Jan and Hase, Frank and Khordakova, Dina and Knippertz, Peter and Kohler, Martin and Lange, Diego and Latt, Melissa and Laube, Johannes and Martin, Lioba and Mauder, Matthias and Möhler, Ottmar and Mohr, Susanna and Reitter, René W. and Rettenmeier, Andreas and Rolf, Christian and Saathoff, Harald and Schrön, Martin and Schütze, Claudia and Spahr, Stephanie and Späth, Florian and Vogel, Franziska and Völksch, Ingo and Weber, Ute and Wieser, Andreas and Wilhelm, Jannik and Zhang, Hengheng and Dietrich, Peter},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {999593},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Kwiecien, O.; Braun, T.; Brunello, C. F.; Faulkner, P.; Hausmann, N.; Helle, G.; Hoggarth, J. A.; Ionita, M.; Jazwa, C. S.; Kelmelis, S.; Marwan, N.; Nava-Fernandez, C.; Nehme, C.; Opel, T.; Oster, J. L.; Perşoiu, A.; Petrie, C.; Prufer, K.; Saarni, S. M.; Wolf, A.; and Breitenbach, S. F.\n\n\n \n \n \n \n \n What we talk about when we talk about seasonality – A transdisciplinary review.\n \n \n \n \n\n\n \n\n\n\n Earth-Science Reviews, 225: 103843. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"What$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kwiecien_what_2022,\n\ttitle = {What we talk about when we talk about seasonality – {A} transdisciplinary review},\n\tvolume = {225},\n\tissn = {00128252},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0012825221003445},\n\tdoi = {10.1016/j.earscirev.2021.103843},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Earth-Science Reviews},\n\tauthor = {Kwiecien, Ola and Braun, Tobias and Brunello, Camilla Francesca and Faulkner, Patrick and Hausmann, Niklas and Helle, Gerd and Hoggarth, Julie A. and Ionita, Monica and Jazwa, Christopher S. and Kelmelis, Saige and Marwan, Norbert and Nava-Fernandez, Cinthya and Nehme, Carole and Opel, Thomas and Oster, Jessica L. and Perşoiu, Aurel and Petrie, Cameron and Prufer, Keith and Saarni, Saija M. and Wolf, Annabel and Breitenbach, Sebastian F.M.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {103843},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Lange, M.; Feilhauer, H.; Kühn, I.; and Doktor, D.\n\n\n \n \n \n \n \n Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 277: 112888. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{lange_mapping_2022,\n\ttitle = {Mapping land-use intensity of grasslands in {Germany} with machine learning and {Sentinel}-2 time series},\n\tvolume = {277},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000025},\n\tdoi = {10.1016/j.rse.2022.112888},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Lange, Maximilian and Feilhauer, Hannes and Kühn, Ingolf and Doktor, Daniel},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {112888},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Lausch, A.; Schaepman, M. E.; Skidmore, A. K.; Catana, E.; Bannehr, L.; Bastian, O.; Borg, E.; Bumberger, J.; Dietrich, P.; Glässer, C.; Hacker, J. M.; Höfer, R.; Jagdhuber, T.; Jany, S.; Jung, A.; Karnieli, A.; Klenke, R.; Kirsten, T.; Ködel, U.; Kresse, W.; Mallast, U.; Montzka, C.; Möller, M.; Mollenhauer, H.; Pause, M.; Rahman, M.; Schrodt, F.; Schmullius, C.; Schütze, C.; Selsam, P.; Syrbe, R.; Truckenbrodt, S.; Vohland, M.; Volk, M.; Wellmann, T.; Zacharias, S.; and Baatz, R.\n\n\n \n \n \n \n \n Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(9): 2279. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{lausch_remote_2022,\n\ttitle = {Remote {Sensing} of {Geomorphodiversity} {Linked} to {Biodiversity}—{Part} {III}: {Traits}, {Processes} and {Remote} {Sensing} {Characteristics}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\tshorttitle = {Remote {Sensing} of {Geomorphodiversity} {Linked} to {Biodiversity}—{Part} {III}},\n\turl = {https://www.mdpi.com/2072-4292/14/9/2279},\n\tdoi = {10.3390/rs14092279},\n\tabstract = {Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Schaepman, Michael E. and Skidmore, Andrew K. and Catana, Eusebiu and Bannehr, Lutz and Bastian, Olaf and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Glässer, Cornelia and Hacker, Jorg M. and Höfer, Rene and Jagdhuber, Thomas and Jany, Sven and Jung, András and Karnieli, Arnon and Klenke, Reinhard and Kirsten, Toralf and Ködel, Uta and Kresse, Wolfgang and Mallast, Ulf and Montzka, Carsten and Möller, Markus and Mollenhauer, Hannes and Pause, Marion and Rahman, Minhaz and Schrodt, Franziska and Schmullius, Christiane and Schütze, Claudia and Selsam, Peter and Syrbe, Ralf-Uwe and Truckenbrodt, Sina and Vohland, Michael and Volk, Martin and Wellmann, Thilo and Zacharias, Steffen and Baatz, Roland},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2279},\n}\n\n

\n

\n\n\n

\n Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.\n

\n\n\n

\n \n\n \n \n Lembrechts, J. J.; van den Hoogen, J.; Aalto, J.; Ashcroft, M. B.; De Frenne, P.; Kemppinen, J.; Kopecký, M.; Luoto, M.; Maclean, I. M. D.; Crowther, T. W.; Bailey, J. J.; Haesen, S.; Klinges, D. H.; Niittynen, P.; Scheffers, B. R.; Van Meerbeek, K.; Aartsma, P.; Abdalaze, O.; Abedi, M.; Aerts, R.; Ahmadian, N.; Ahrends, A.; Alatalo, J. M.; Alexander, J. M.; Allonsius, C. N.; Altman, J.; Ammann, C.; Andres, C.; Andrews, C.; Ardö, J.; Arriga, N.; Arzac, A.; Aschero, V.; Assis, R. L.; Assmann, J. J.; Bader, M. Y.; Bahalkeh, K.; Barančok, P.; Barrio, I. C.; Barros, A.; Barthel, M.; Basham, E. W.; Bauters, M.; Bazzichetto, M.; Marchesini, L. B.; Bell, M. C.; Benavides, J. C.; Benito Alonso, J. L.; Berauer, B. J.; Bjerke, J. W.; Björk, R. G.; Björkman, M. P.; Björnsdóttir, K.; Blonder, B.; Boeckx, P.; Boike, J.; Bokhorst, S.; Brum, B. N. S.; Brůna, J.; Buchmann, N.; Buysse, P.; Camargo, J. L.; Campoe, O. C.; Candan, O.; Canessa, R.; Cannone, N.; Carbognani, M.; Carnicer, J.; Casanova-Katny, A.; Cesarz, S.; Chojnicki, B.; Choler, P.; Chown, S. L.; Cifuentes, E. F.; Čiliak, M.; Contador, T.; Convey, P.; Cooper, E. J.; Cremonese, E.; Curasi, S. R.; Curtis, R.; Cutini, M.; Dahlberg, C. J.; Daskalova, G. N.; de Pablo, M. A.; Della Chiesa, S.; Dengler, J.; Deronde, B.; Descombes, P.; Di Cecco, V.; Di Musciano, M.; Dick, J.; Dimarco, R. D.; Dolezal, J.; Dorrepaal, E.; Dušek, J.; Eisenhauer, N.; Eklundh, L.; Erickson, T. E.; Erschbamer, B.; Eugster, W.; Ewers, R. M.; Exton, D. A.; Fanin, N.; Fazlioglu, F.; Feigenwinter, I.; Fenu, G.; Ferlian, O.; Fernández Calzado, M. R.; Fernández-Pascual, E.; Finckh, M.; Higgens, R. F.; Forte, T. G. W.; Freeman, E. C.; Frei, E. R.; Fuentes-Lillo, E.; García, R. A.; García, M. B.; Géron, C.; Gharun, M.; Ghosn, D.; Gigauri, K.; Gobin, A.; Goded, I.; Goeckede, M.; Gottschall, F.; Goulding, K.; Govaert, S.; Graae, B. J.; Greenwood, S.; Greiser, C.; Grelle, A.; Guénard, B.; Guglielmin, M.; Guillemot, J.; Haase, P.; Haider, S.; Halbritter, A. H.; Hamid, M.; Hammerle, A.; Hampe, A.; Haugum, S. V.; Hederová, L.; Heinesch, B.; Helfter, C.; Hepenstrick, D.; Herberich, M.; Herbst, M.; Hermanutz, L.; Hik, D. S.; Hoffrén, R.; Homeier, J.; Hörtnagl, L.; Høye, T. T.; Hrbacek, F.; Hylander, K.; Iwata, H.; Jackowicz-Korczynski, M. A.; Jactel, H.; Järveoja, J.; Jastrzębowski, S.; Jentsch, A.; Jiménez, J. J.; Jónsdóttir, I. S.; Jucker, T.; Jump, A. S.; Juszczak, R.; Kanka, R.; Kašpar, V.; Kazakis, G.; Kelly, J.; Khuroo, A. A.; Klemedtsson, L.; Klisz, M.; Kljun, N.; Knohl, A.; Kobler, J.; Kollár, J.; Kotowska, M. M.; Kovács, B.; Kreyling, J.; Lamprecht, A.; Lang, S. I.; Larson, C.; Larson, K.; Laska, K.; le Maire, G.; Leihy, R. I.; Lens, L.; Liljebladh, B.; Lohila, A.; Lorite, J.; Loubet, B.; Lynn, J.; Macek, M.; Mackenzie, R.; Magliulo, E.; Maier, R.; Malfasi, F.; Máliš, F.; Man, M.; Manca, G.; Manco, A.; Manise, T.; Manolaki, P.; Marciniak, F.; Matula, R.; Mazzolari, A. C.; Medinets, S.; Medinets, V.; Meeussen, C.; Merinero, S.; Mesquita, R. d. C. G.; Meusburger, K.; Meysman, F. J. R.; Michaletz, S. T.; Milbau, A.; Moiseev, D.; Moiseev, P.; Mondoni, A.; Monfries, R.; Montagnani, L.; Moriana-Armendariz, M.; Morra di Cella, U.; Mörsdorf, M.; Mosedale, J. R.; Muffler, L.; Muñoz-Rojas, M.; Myers, J. A.; Myers-Smith, I. H.; Nagy, L.; Nardino, M.; Naujokaitis-Lewis, I.; Newling, E.; Nicklas, L.; Niedrist, G.; Niessner, A.; Nilsson, M. B.; Normand, S.; Nosetto, M. D.; Nouvellon, Y.; Nuñez, M. A.; Ogaya, R.; Ogée, J.; Okello, J.; Olejnik, J.; Olesen, J. E.; Opedal, Ø. H.; Orsenigo, S.; Palaj, A.; Pampuch, T.; Panov, A. V.; Pärtel, M.; Pastor, A.; Pauchard, A.; Pauli, H.; Pavelka, M.; Pearse, W. D.; Peichl, M.; Pellissier, L.; Penczykowski, R. M.; Penuelas, J.; Petit Bon, M.; Petraglia, A.; Phartyal, S. S.; Phoenix, G. K.; Pio, C.; Pitacco, A.; Pitteloud, C.; Plichta, R.; Porro, F.; Portillo-Estrada, M.; Poulenard, J.; Poyatos, R.; Prokushkin, A. S.; Puchalka, R.; Pușcaș, M.; Radujković, D.; Randall, K.; Ratier Backes, A.; Remmele, S.; Remmers, W.; Renault, D.; Risch, A. C.; Rixen, C.; Robinson, S. A.; Robroek, B. J. M.; Rocha, A. V.; Rossi, C.; Rossi, G.; Roupsard, O.; Rubtsov, A. V.; Saccone, P.; Sagot, C.; Sallo Bravo, J.; Santos, C. C.; Sarneel, J. M.; Scharnweber, T.; Schmeddes, J.; Schmidt, M.; Scholten, T.; Schuchardt, M.; Schwartz, N.; Scott, T.; Seeber, J.; Segalin de Andrade, A. C.; Seipel, T.; Semenchuk, P.; Senior, R. A.; Serra-Diaz, J. M.; Sewerniak, P.; Shekhar, A.; Sidenko, N. V.; Siebicke, L.; Siegwart Collier, L.; Simpson, E.; Siqueira, D. P.; Sitková, Z.; Six, J.; Smiljanic, M.; Smith, S. W.; Smith-Tripp, S.; Somers, B.; Sørensen, M. V.; Souza, J. J. L. L.; Souza, B. I.; Souza Dias, A.; Spasojevic, M. J.; Speed, J. D. M.; Spicher, F.; Stanisci, A.; Steinbauer, K.; Steinbrecher, R.; Steinwandter, M.; Stemkovski, M.; Stephan, J. G.; Stiegler, C.; Stoll, S.; Svátek, M.; Svoboda, M.; Tagesson, T.; Tanentzap, A. J.; Tanneberger, F.; Theurillat, J.; Thomas, H. J. D.; Thomas, A. D.; Tielbörger, K.; Tomaselli, M.; Treier, U. A.; Trouillier, M.; Turtureanu, P. D.; Tutton, R.; Tyystjärvi, V. A.; Ueyama, M.; Ujházy, K.; Ujházyová, M.; Uogintas, D.; Urban, A. V.; Urban, J.; Urbaniak, M.; Ursu, T.; Vaccari, F. P.; Van de Vondel, S.; van den Brink, L.; Van Geel, M.; Vandvik, V.; Vangansbeke, P.; Varlagin, A.; Veen, G. F.; Veenendaal, E.; Venn, S. E.; Verbeeck, H.; Verbrugggen, E.; Verheijen, F. G. A.; Villar, L.; Vitale, L.; Vittoz, P.; Vives-Ingla, M.; von Oppen, J.; Walz, J.; Wang, R.; Wang, Y.; Way, R. G.; Wedegärtner, R. E. M.; Weigel, R.; Wild, J.; Wilkinson, M.; Wilmking, M.; Wingate, L.; Winkler, M.; Wipf, S.; Wohlfahrt, G.; Xenakis, G.; Yang, Y.; Yu, Z.; Yu, K.; Zellweger, F.; Zhang, J.; Zhang, Z.; Zhao, P.; Ziemblińska, K.; Zimmermann, R.; Zong, S.; Zyryanov, V. I.; Nijs, I.; and Lenoir, J.\n\n\n \n \n \n \n \n Global maps of soil temperature.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(9): 3110–3144. 2022.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16060\n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{lembrechts_global_2022,\n\ttitle = {Global maps of soil temperature},\n\tvolume = {28},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16060},\n\tdoi = {https://doi.org/10.1111/gcb.16060},\n\tabstract = {Abstract Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.},\n\tnumber = {9},\n\tjournal = {Global Change Biology},\n\tauthor = {Lembrechts, Jonas J. and van den Hoogen, Johan and Aalto, Juha and Ashcroft, Michael B. and De Frenne, Pieter and Kemppinen, Julia and Kopecký, Martin and Luoto, Miska and Maclean, Ilya M. D. and Crowther, Thomas W. and Bailey, Joseph J. and Haesen, Stef and Klinges, David H. and Niittynen, Pekka and Scheffers, Brett R. and Van Meerbeek, Koenraad and Aartsma, Peter and Abdalaze, Otar and Abedi, Mehdi and Aerts, Rien and Ahmadian, Negar and Ahrends, Antje and Alatalo, Juha M. and Alexander, Jake M. and Allonsius, Camille Nina and Altman, Jan and Ammann, Christof and Andres, Christian and Andrews, Christopher and Ardö, Jonas and Arriga, Nicola and Arzac, Alberto and Aschero, Valeria and Assis, Rafael L. and Assmann, Jakob Johann and Bader, Maaike Y. and Bahalkeh, Khadijeh and Barančok, Peter and Barrio, Isabel C. and Barros, Agustina and Barthel, Matti and Basham, Edmund W. and Bauters, Marijn and Bazzichetto, Manuele and Marchesini, Luca Belelli and Bell, Michael C. and Benavides, Juan C. and Benito Alonso, José Luis and Berauer, Bernd J. and Bjerke, Jarle W. and Björk, Robert G. and Björkman, Mats P. and Björnsdóttir, Katrin and Blonder, Benjamin and Boeckx, Pascal and Boike, Julia and Bokhorst, Stef and Brum, Bárbara N. S. and Brůna, Josef and Buchmann, Nina and Buysse, Pauline and Camargo, José Luís and Campoe, Otávio C. and Candan, Onur and Canessa, Rafaella and Cannone, Nicoletta and Carbognani, Michele and Carnicer, Jofre and Casanova-Katny, Angélica and Cesarz, Simone and Chojnicki, Bogdan and Choler, Philippe and Chown, Steven L. and Cifuentes, Edgar F. and Čiliak, Marek and Contador, Tamara and Convey, Peter and Cooper, Elisabeth J. and Cremonese, Edoardo and Curasi, Salvatore R. and Curtis, Robin and Cutini, Maurizio and Dahlberg, C. Johan and Daskalova, Gergana N. and de Pablo, Miguel Angel and Della Chiesa, Stefano and Dengler, Jürgen and Deronde, Bart and Descombes, Patrice and Di Cecco, Valter and Di Musciano, Michele and Dick, Jan and Dimarco, Romina D. and Dolezal, Jiri and Dorrepaal, Ellen and Dušek, Jiří and Eisenhauer, Nico and Eklundh, Lars and Erickson, Todd E. and Erschbamer, Brigitta and Eugster, Werner and Ewers, Robert M. and Exton, Dan A. and Fanin, Nicolas and Fazlioglu, Fatih and Feigenwinter, Iris and Fenu, Giuseppe and Ferlian, Olga and Fernández Calzado, M. Rosa and Fernández-Pascual, Eduardo and Finckh, Manfred and Higgens, Rebecca Finger and Forte, T'ai G. W. and Freeman, Erika C. and Frei, Esther R. and Fuentes-Lillo, Eduardo and García, Rafael A. and García, María B. and Géron, Charly and Gharun, Mana and Ghosn, Dany and Gigauri, Khatuna and Gobin, Anne and Goded, Ignacio and Goeckede, Mathias and Gottschall, Felix and Goulding, Keith and Govaert, Sanne and Graae, Bente Jessen and Greenwood, Sarah and Greiser, Caroline and Grelle, Achim and Guénard, Benoit and Guglielmin, Mauro and Guillemot, Joannès and Haase, Peter and Haider, Sylvia and Halbritter, Aud H. and Hamid, Maroof and Hammerle, Albin and Hampe, Arndt and Haugum, Siri V. and Hederová, Lucia and Heinesch, Bernard and Helfter, Carole and Hepenstrick, Daniel and Herberich, Maximiliane and Herbst, Mathias and Hermanutz, Luise and Hik, David S. and Hoffrén, Raúl and Homeier, Jürgen and Hörtnagl, Lukas and Høye, Toke T. and Hrbacek, Filip and Hylander, Kristoffer and Iwata, Hiroki and Jackowicz-Korczynski, Marcin Antoni and Jactel, Hervé and Järveoja, Järvi and Jastrzębowski, Szymon and Jentsch, Anke and Jiménez, Juan J. and Jónsdóttir, Ingibjörg S. and Jucker, Tommaso and Jump, Alistair S. and Juszczak, Radoslaw and Kanka, Róbert and Kašpar, Vít and Kazakis, George and Kelly, Julia and Khuroo, Anzar A. and Klemedtsson, Leif and Klisz, Marcin and Kljun, Natascha and Knohl, Alexander and Kobler, Johannes and Kollár, Jozef and Kotowska, Martyna M. and Kovács, Bence and Kreyling, Juergen and Lamprecht, Andrea and Lang, Simone I. and Larson, Christian and Larson, Keith and Laska, Kamil and le Maire, Guerric and Leihy, Rachel I. and Lens, Luc and Liljebladh, Bengt and Lohila, Annalea and Lorite, Juan and Loubet, Benjamin and Lynn, Joshua and Macek, Martin and Mackenzie, Roy and Magliulo, Enzo and Maier, Regine and Malfasi, Francesco and Máliš, František and Man, Matěj and Manca, Giovanni and Manco, Antonio and Manise, Tanguy and Manolaki, Paraskevi and Marciniak, Felipe and Matula, Radim and Mazzolari, Ana Clara and Medinets, Sergiy and Medinets, Volodymyr and Meeussen, Camille and Merinero, Sonia and Mesquita, Rita de Cássia Guimarães and Meusburger, Katrin and Meysman, Filip J. R. and Michaletz, Sean T. and Milbau, Ann and Moiseev, Dmitry and Moiseev, Pavel and Mondoni, Andrea and Monfries, Ruth and Montagnani, Leonardo and Moriana-Armendariz, Mikel and Morra di Cella, Umberto and Mörsdorf, Martin and Mosedale, Jonathan R. and Muffler, Lena and Muñoz-Rojas, Miriam and Myers, Jonathan A. and Myers-Smith, Isla H. and Nagy, Laszlo and Nardino, Marianna and Naujokaitis-Lewis, Ilona and Newling, Emily and Nicklas, Lena and Niedrist, Georg and Niessner, Armin and Nilsson, Mats B. and Normand, Signe and Nosetto, Marcelo D. and Nouvellon, Yann and Nuñez, Martin A. and Ogaya, Romà and Ogée, Jérôme and Okello, Joseph and Olejnik, Janusz and Olesen, Jørgen Eivind and Opedal, Øystein H. and Orsenigo, Simone and Palaj, Andrej and Pampuch, Timo and Panov, Alexey V. and Pärtel, Meelis and Pastor, Ada and Pauchard, Aníbal and Pauli, Harald and Pavelka, Marian and Pearse, William D. and Peichl, Matthias and Pellissier, Loïc and Penczykowski, Rachel M. and Penuelas, Josep and Petit Bon, Matteo and Petraglia, Alessandro and Phartyal, Shyam S. and Phoenix, Gareth K. and Pio, Casimiro and Pitacco, Andrea and Pitteloud, Camille and Plichta, Roman and Porro, Francesco and Portillo-Estrada, Miguel and Poulenard, Jérôme and Poyatos, Rafael and Prokushkin, Anatoly S. and Puchalka, Radoslaw and Pușcaș, Mihai and Radujković, Dajana and Randall, Krystal and Ratier Backes, Amanda and Remmele, Sabine and Remmers, Wolfram and Renault, David and Risch, Anita C. and Rixen, Christian and Robinson, Sharon A. and Robroek, Bjorn J. M. and Rocha, Adrian V. and Rossi, Christian and Rossi, Graziano and Roupsard, Olivier and Rubtsov, Alexey V. and Saccone, Patrick and Sagot, Clotilde and Sallo Bravo, Jhonatan and Santos, Cinthya C. and Sarneel, Judith M. and Scharnweber, Tobias and Schmeddes, Jonas and Schmidt, Marius and Scholten, Thomas and Schuchardt, Max and Schwartz, Naomi and Scott, Tony and Seeber, Julia and Segalin de Andrade, Ana Cristina and Seipel, Tim and Semenchuk, Philipp and Senior, Rebecca A. and Serra-Diaz, Josep M. and Sewerniak, Piotr and Shekhar, Ankit and Sidenko, Nikita V. and Siebicke, Lukas and Siegwart Collier, Laura and Simpson, Elizabeth and Siqueira, David P. and Sitková, Zuzana and Six, Johan and Smiljanic, Marko and Smith, Stuart W. and Smith-Tripp, Sarah and Somers, Ben and Sørensen, Mia Vedel and Souza, José João L. L. and Souza, Bartolomeu Israel and Souza Dias, Arildo and Spasojevic, Marko J. and Speed, James D. M. and Spicher, Fabien and Stanisci, Angela and Steinbauer, Klaus and Steinbrecher, Rainer and Steinwandter, Michael and Stemkovski, Michael and Stephan, Jörg G. and Stiegler, Christian and Stoll, Stefan and Svátek, Martin and Svoboda, Miroslav and Tagesson, Torbern and Tanentzap, Andrew J. and Tanneberger, Franziska and Theurillat, Jean-Paul and Thomas, Haydn J. D. and Thomas, Andrew D. and Tielbörger, Katja and Tomaselli, Marcello and Treier, Urs Albert and Trouillier, Mario and Turtureanu, Pavel Dan and Tutton, Rosamond and Tyystjärvi, Vilna A. and Ueyama, Masahito and Ujházy, Karol and Ujházyová, Mariana and Uogintas, Domas and Urban, Anastasiya V. and Urban, Josef and Urbaniak, Marek and Ursu, Tudor-Mihai and Vaccari, Francesco Primo and Van de Vondel, Stijn and van den Brink, Liesbeth and Van Geel, Maarten and Vandvik, Vigdis and Vangansbeke, Pieter and Varlagin, Andrej and Veen, G. F. and Veenendaal, Elmar and Venn, Susanna E. and Verbeeck, Hans and Verbrugggen, Erik and Verheijen, Frank G. A. and Villar, Luis and Vitale, Luca and Vittoz, Pascal and Vives-Ingla, Maria and von Oppen, Jonathan and Walz, Josefine and Wang, Runxi and Wang, Yifeng and Way, Robert G. and Wedegärtner, Ronja E. M. and Weigel, Robert and Wild, Jan and Wilkinson, Matthew and Wilmking, Martin and Wingate, Lisa and Winkler, Manuela and Wipf, Sonja and Wohlfahrt, Georg and Xenakis, Georgios and Yang, Yan and Yu, Zicheng and Yu, Kailiang and Zellweger, Florian and Zhang, Jian and Zhang, Zhaochen and Zhao, Peng and Ziemblińska, Klaudia and Zimmermann, Reiner and Zong, Shengwei and Zyryanov, Viacheslav I. and Nijs, Ivan and Lenoir, Jonathan},\n\tyear = {2022},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16060},\n\tkeywords = {bioclimatic variables, global maps, microclimate, near-surface temperatures, soil temperature, soil-dwelling organisms, temperature offset, weather stations},\n\tpages = {3110--3144},\n}\n\n

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\n \n\n \n \n Li, H.; Wei, M.; Dong, L.; Hu, W.; Xiong, J.; Sun, Y.; Sun, Y.; Yao, S.; Gong, H.; Zhang, Y.; Hou, Q.; Wang, X.; Xie, S.; Zhang, L.; Akram, M. A.; Rao, Z.; Degen, A. A.; Niklas, K. J.; Ran, J.; Ye, J.; and Deng, J.\n\n\n \n \n \n \n \n Leaf and ecosystem water use efficiencies differ in their global-scale patterns and drivers.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 319: 108919. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Leaf$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{li_leaf_2022,\n\ttitle = {Leaf and ecosystem water use efficiencies differ in their global-scale patterns and drivers},\n\tvolume = {319},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322001125},\n\tdoi = {10.1016/j.agrformet.2022.108919},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Li, Hailing and Wei, Maohong and Dong, Longwei and Hu, Weigang and Xiong, Junlan and Sun, Ying and Sun, Yuan and Yao, Shuran and Gong, Haiyang and Zhang, Yahui and Hou, Qingqing and Wang, Xiaoting and Xie, Shubin and Zhang, Liang and Akram, Muhammad Adnan and Rao, Zhiguo and Degen, A. Allan and Niklas, Karl J. and Ran, Jinzhi and Ye, Jian-sheng and Deng, Jianming},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {108919},\n}\n\n

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\n \n\n \n \n Li, L.; Dai, Y.; Shangguan, W.; Wei, Z.; Wei, N.; and Li, Q.\n\n\n \n \n \n \n \n Causality-Structured Deep Learning for Soil Moisture Predictions.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 23(8): 1315–1331. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Causality-Structured$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{li_causality-structured_2022,\n\ttitle = {Causality-{Structured} {Deep} {Learning} for {Soil} {Moisture} {Predictions}},\n\tvolume = {23},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/23/8/JHM-D-21-0206.1.xml},\n\tdoi = {10.1175/JHM-D-21-0206.1},\n\tabstract = {Abstract \n            The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as the long short-term memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we present a new causality-structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for forecasting SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash–Sutcliffe efficiency (NSE) suggested that more than 67\\% of sites witnessed an improvement of SM simulation larger than 10\\%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables.},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Li, Lu and Dai, Yongjiu and Shangguan, Wei and Wei, Zhongwang and Wei, Nan and Li, Qingliang},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {1315--1331},\n}\n\n

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\n \n\n \n \n Li, X.; Wigneron, J.; Fan, L.; Frappart, F.; Yueh, S. H.; Colliander, A.; Ebtehaj, A.; Gao, L.; Fernandez-Moran, R.; Liu, X.; Wang, M.; Ma, H.; Moisy, C.; and Ciais, P.\n\n\n \n \n \n \n \n A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 271: 112921. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{li_new_2022,\n\ttitle = {A new {SMAP} soil moisture and vegetation optical depth product ({SMAP}-{IB}): {Algorithm}, assessment and inter-comparison},\n\tvolume = {271},\n\tissn = {00344257},\n\tshorttitle = {A new {SMAP} soil moisture and vegetation optical depth product ({SMAP}-{IB})},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000359},\n\tdoi = {10.1016/j.rse.2022.112921},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Li, Xiaojun and Wigneron, Jean-Pierre and Fan, Lei and Frappart, Frédéric and Yueh, Simon H. and Colliander, Andreas and Ebtehaj, Ardeshir and Gao, Lun and Fernandez-Moran, Roberto and Liu, Xiangzhuo and Wang, Mengjia and Ma, Hongliang and Moisy, Christophe and Ciais, Philippe},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {112921},\n}\n\n

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\n \n\n \n \n Liebmann, L.; Vormeier, P.; Weisner, O.; and Liess, M.\n\n\n \n \n \n \n \n Balancing effort and benefit – How taxonomic and quantitative resolution influence the pesticide indicator system SPEARpesticides.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 848: 157642. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Balancing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{liebmann_balancing_2022,\n\ttitle = {Balancing effort and benefit – {How} taxonomic and quantitative resolution influence the pesticide indicator system {SPEARpesticides}},\n\tvolume = {848},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722047404},\n\tdoi = {10.1016/j.scitotenv.2022.157642},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Liebmann, Liana and Vormeier, Philipp and Weisner, Oliver and Liess, Matthias},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {157642},\n}\n\n

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\n \n\n \n \n Liu, Y.; Flournoy, O.; Zhang, Q.; Novick, K. A.; Koster, R. D.; and Konings, A. G.\n\n\n \n \n \n \n \n Canopy Height and Climate Dryness Parsimoniously Explain Spatial Variation of Unstressed Stomatal Conductance.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(15). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Canopy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{liu_canopy_2022,\n\ttitle = {Canopy {Height} and {Climate} {Dryness} {Parsimoniously} {Explain} {Spatial} {Variation} of {Unstressed} {Stomatal} {Conductance}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022GL099339},\n\tdoi = {10.1029/2022GL099339},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Liu, Yanlan and Flournoy, Olivia and Zhang, Quan and Novick, Kimberly A. and Koster, Randal D. and Konings, Alexandra G.},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Liu, Z.\n\n\n \n \n \n \n \n Estimating land evapotranspiration from potential evapotranspiration constrained by soil water at daily scale.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 834: 155327. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{liu_estimating_2022,\n\ttitle = {Estimating land evapotranspiration from potential evapotranspiration constrained by soil water at daily scale},\n\tvolume = {834},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722024202},\n\tdoi = {10.1016/j.scitotenv.2022.155327},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Liu, Zhaofei},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {155327},\n}\n\n

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\n \n\n \n \n Lopes, F. M.; Dutra, E.; and Trigo, I. F.\n\n\n \n \n \n \n \n Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(7): 1704. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{lopes_integrating_2022,\n\ttitle = {Integrating {Reanalysis} and {Satellite} {Cloud} {Information} to {Estimate} {Surface} {Downward} {Long}-{Wave} {Radiation}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/7/1704},\n\tdoi = {10.3390/rs14071704},\n\tabstract = {The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17\\% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Lopes, Francis M. and Dutra, Emanuel and Trigo, Isabel F.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1704},\n}\n\n

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\n The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.\n

\n\n\n

\n \n\n \n \n Luo, P.; Song, Y.; Huang, X.; Ma, H.; Liu, J.; Yao, Y.; and Meng, L.\n\n\n \n \n \n \n \n Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 185: 111–128. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{luo_identifying_2022,\n\ttitle = {Identifying determinants of spatio-temporal disparities in soil moisture of the {Northern} {Hemisphere} using a geographically optimal zones-based heterogeneity model},\n\tvolume = {185},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271622000132},\n\tdoi = {10.1016/j.isprsjprs.2022.01.009},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Luo, Peng and Song, Yongze and Huang, Xin and Ma, Hongliang and Liu, Jin and Yao, Yao and Meng, Liqiu},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {111--128},\n}\n\n

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\n \n\n \n \n Marino, B. D.; and Bautista, N.\n\n\n \n \n \n \n \n Commercial forest carbon protocol over-credit bias delimited by zero-threshold carbon accounting.\n \n \n \n \n\n\n \n\n\n\n Trees, Forests and People, 7: 100171. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Commercial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{marino_commercial_2022,\n\ttitle = {Commercial forest carbon protocol over-credit bias delimited by zero-threshold carbon accounting},\n\tvolume = {7},\n\tissn = {26667193},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2666719321001102},\n\tdoi = {10.1016/j.tfp.2021.100171},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Trees, Forests and People},\n\tauthor = {Marino, Bruno D.V. and Bautista, Nahuel},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {100171},\n}\n\n

\n

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\n \n\n \n \n Mengis, N.; Kalhori, A.; Simon, S.; Harpprecht, C.; Baetcke, L.; Prats‐Salvado, E.; Schmidt‐Hattenberger, C.; Stevenson, A.; Dold, C.; Zohbi, J.; Borchers, M.; Thrän, D.; Korte, K.; Gawel, E.; Dolch, T.; Heß, D.; Yeates, C.; Thoni, T.; Markus, T.; Schill, E.; Xiao, M.; Köhnke, F.; Oschlies, A.; Förster, J.; Görl, K.; Dornheim, M.; Brinkmann, T.; Beck, S.; Bruhn, D.; Li, Z.; Steuri, B.; Herbst, M.; Sachs, T.; Monnerie, N.; Pregger, T.; Jacob, D.; and Dittmeyer, R.\n\n\n \n \n \n \n \n Net‐Zero CO $_{\\textrm{2}}$ Germany—A Retrospect From the Year 2050.\n \n \n \n \n\n\n \n\n\n\n Earth's Future, 10(2). February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Net‐Zero$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{mengis_netzero_2022,\n\ttitle = {Net‐{Zero} {CO} $_{\\textrm{2}}$ {Germany}—{A} {Retrospect} {From} the {Year} 2050},\n\tvolume = {10},\n\tissn = {2328-4277, 2328-4277},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021EF002324},\n\tdoi = {10.1029/2021EF002324},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Earth's Future},\n\tauthor = {Mengis, Nadine and Kalhori, Aram and Simon, Sonja and Harpprecht, Carina and Baetcke, Lars and Prats‐Salvado, Enric and Schmidt‐Hattenberger, Cornelia and Stevenson, Angela and Dold, Christian and Zohbi, Juliane and Borchers, Malgorzata and Thrän, Daniela and Korte, Klaas and Gawel, Erik and Dolch, Tobias and Heß, Dominik and Yeates, Christopher and Thoni, Terese and Markus, Till and Schill, Eva and Xiao, Mengzhu and Köhnke, Fiona and Oschlies, Andreas and Förster, Johannes and Görl, Knut and Dornheim, Martin and Brinkmann, Torsten and Beck, Silke and Bruhn, David and Li, Zhan and Steuri, Bettina and Herbst, Michael and Sachs, Torsten and Monnerie, Nathalie and Pregger, Thomas and Jacob, Daniela and Dittmeyer, Roland},\n\tmonth = feb,\n\tyear = {2022},\n}\n\n

\n

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\n \n\n \n \n Mi, C.; Hamilton, D. P.; Frassl, M. A.; Shatwell, T.; Kong, X.; Boehrer, B.; Li, Y.; Donner, J.; and Rinke, K.\n\n\n \n \n \n \n \n Controlling blooms of Planktothrix rubescens by optimized metalimnetic water withdrawal: a modelling study on adaptive reservoir operation.\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Controlling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{mi_controlling_2022,\n\ttype = {preprint},\n\ttitle = {Controlling blooms of {Planktothrix} rubescens by optimized metalimnetic water withdrawal: a modelling study on adaptive reservoir operation},\n\tshorttitle = {Controlling blooms of {Planktothrix} rubescens by optimized metalimnetic water withdrawal},\n\turl = {https://www.researchsquare.com/article/rs-1752651/v1},\n\tabstract = {Abstract \n \n Background: \n Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as \n Planktothrix rubescens, \n are involved. Metalimnetic blooms of \n P. rubescens \n have apparently increased in frequency and severity in recent years, so there is a strong need to identify reservoir management options to control it. We hypothesized that \n P. rubescens \n blooms in reservoirs can be suppressed using selective withdrawal to maximize its export from the reservoir. We also expect that altering the light climate can affect the dynamics of this species. We tested our hypothesis in Rappbode Reservoir (the largest drinking water reservoir in Germany) by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2). \n Results: \n The novel withdrawal strategy, in which water is withdrawn from a certain depth below the surface within the metalimnion instead of at a fixed elevation relative to the dam wall, significantly reduced \n P. rubescens \n biomass in the reservoir. According to the simulation results, we defined an optimal withdrawal volume to control \n P. rubescens \n blooms in the reservoir as approximately 10 million m \n 3 \n (10\\% of the reservoir volume) during its bloom phase. The results also illustrated that \n P. rubescens \n growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth, i.e., before the bloom occurs. Additionally, our study showed that \n P. rubescens \n biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m \n -1 \n . \n Conclusion \n : Our study indicates the rise in \n P. rubescens \n biomass can be effectively offset by selective withdrawal strategy and controlling light intensity beneath the water surface. Considering the widespread occurrence of \n P. rubescens \n in stratified lakes and reservoirs worldwide, we believe the results will be helpful for scientists and water managers working on other water bodies to minimize the negative impacts of this harmful algae.},\n\turldate = {2022-11-21},\n\tinstitution = {In Review},\n\tauthor = {Mi, Chenxi and Hamilton, David P. and Frassl, Marieke A. and Shatwell, Tom and Kong, Xiangzhen and Boehrer, Bertram and Li, Yiping and Donner, Jan and Rinke, Karsten},\n\tmonth = jun,\n\tyear = {2022},\n\tdoi = {10.21203/rs.3.rs-1752651/v1},\n}\n\n

\n

\n\n\n

\n Abstract Background: Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as Planktothrix rubescens, are involved. Metalimnetic blooms of P. rubescens have apparently increased in frequency and severity in recent years, so there is a strong need to identify reservoir management options to control it. We hypothesized that P. rubescens blooms in reservoirs can be suppressed using selective withdrawal to maximize its export from the reservoir. We also expect that altering the light climate can affect the dynamics of this species. We tested our hypothesis in Rappbode Reservoir (the largest drinking water reservoir in Germany) by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2). Results: The novel withdrawal strategy, in which water is withdrawn from a certain depth below the surface within the metalimnion instead of at a fixed elevation relative to the dam wall, significantly reduced P. rubescens biomass in the reservoir. According to the simulation results, we defined an optimal withdrawal volume to control P. rubescens blooms in the reservoir as approximately 10 million m 3 (10% of the reservoir volume) during its bloom phase. The results also illustrated that P. rubescens growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth, i.e., before the bloom occurs. Additionally, our study showed that P. rubescens biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m -1 . Conclusion : Our study indicates the rise in P. rubescens biomass can be effectively offset by selective withdrawal strategy and controlling light intensity beneath the water surface. Considering the widespread occurrence of P. rubescens in stratified lakes and reservoirs worldwide, we believe the results will be helpful for scientists and water managers working on other water bodies to minimize the negative impacts of this harmful algae.\n

\n\n\n

\n \n\n \n \n Min, X.; Shangguan, Y.; Huang, J.; Wang, H.; and Shi, Z.\n\n\n \n \n \n \n \n Relative Strengths Recognition of Nine Mainstream Satellite-Based Soil Moisture Products at the Global Scale.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(12): 2739. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Relative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{min_relative_2022,\n\ttitle = {Relative {Strengths} {Recognition} of {Nine} {Mainstream} {Satellite}-{Based} {Soil} {Moisture} {Products} at the {Global} {Scale}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/12/2739},\n\tdoi = {10.3390/rs14122739},\n\tabstract = {Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Min, Xiaoxiao and Shangguan, Yulin and Huang, Jingyi and Wang, Hongquan and Shi, Zhou},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2739},\n}\n\n

\n

\n\n\n

\n Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.\n

\n\n\n

\n \n\n \n \n Molnar, P.\n\n\n \n \n \n \n \n Differences between soil and air temperatures: Implications for geological reconstructions of past climate.\n \n \n \n \n\n\n \n\n\n\n Geosphere, 18(2): 800–824. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Differences$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{molnar_differences_2022,\n\ttitle = {Differences between soil and air temperatures: {Implications} for geological reconstructions of past climate},\n\tvolume = {18},\n\tissn = {1553-040X},\n\tshorttitle = {Differences between soil and air temperatures},\n\turl = {https://pubs.geoscienceworld.org/geosphere/article/18/2/800/611659/Differences-between-soil-and-air-temperatures},\n\tdoi = {10.1130/GES02448.1},\n\tabstract = {Abstract \n            Among quantities of interest in paleoclimate, the mean annual air temperature, Ta, directly over the surface looms prominently. Most geologic estimates of past temperatures from continental regions, however, quantify temperatures of the soil or other material below the surface, Ts, and in general Ta \\&lt; Ts. Both theory and data from the FLUXNET2015 data set of surface energy balance indicate systematic dependences of temperature differences ΔT = Ts − Ta and also of Bowen ratios—ratios of sensible to latent heat fluxes from surface to the atmosphere—on the nature of the land-surface cover. In cold regions, with mean annual temperatures ≲5 °C, latent heat flux tends to be small, and values of ΔT can be large, 3–5 °C or larger. Over wet surfaces, latent heat fluxes dominate sensible heat fluxes, and values of both ΔT and Bowen ratios commonly are small. By contrast, over arid surfaces that provide only limited moisture to the overlying atmosphere, the opposite holds. Both theory and observation suggest the following, albeit approximate, mean annual values of ΔT: for wetlands, 1 °C; forests, 1 ± 1 °C; shrublands, 3–4 °C; savannas, 3.5 °C \\&lt; ΔT \\&lt; 5.5 °C; grasslands, 1 °C where wet to 3 °C where arid; and deserts, 4–6 °C. As geological tools for inferring past land-surface conditions improve, these approximate values of ΔT will allow geologic estimates of past mean annual surface temperatures, Ts, to be translated into estimates of past mean annual air temperatures, Ta.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Geosphere},\n\tauthor = {Molnar, Peter},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {800--824},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Moya, M. R.; López‐Ballesteros, A.; Sánchez‐Cañete, E. P.; Serrano‐Ortiz, P.; Oyonarte, C.; Domingo, F.; and Kowalski, A.\n\n\n \n \n \n \n \n Ecosystem CO $_{\\textrm{2}}$ release driven by wind occurs in drylands at global scale.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(17): 5320–5333. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecosystem$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{moya_ecosystem_2022,\n\ttitle = {Ecosystem {CO} $_{\\textrm{2}}$ release driven by wind occurs in drylands at global scale},\n\tvolume = {28},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16277},\n\tdoi = {10.1111/gcb.16277},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-21},\n\tjournal = {Global Change Biology},\n\tauthor = {Moya, María Rosario and López‐Ballesteros, Ana and Sánchez‐Cañete, Enrique P. and Serrano‐Ortiz, Penélope and Oyonarte, Cecilio and Domingo, Francisco and Kowalski, Andrew S.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {5320--5333},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Mwanake, R. M.; Gettel, G. M.; Ishimwe, C.; Wangari, E. G.; Butterbach‐Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Basin‐scale estimates of greenhouse gas emissions from the Mara River, Kenya: Importance of discharge, stream size, and land use/land cover.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 67(8): 1776–1793. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Basin‐scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{mwanake_basinscale_2022,\n\ttitle = {Basin‐scale estimates of greenhouse gas emissions from the {Mara} {River}, {Kenya}: {Importance} of discharge, stream size, and land use/land cover},\n\tvolume = {67},\n\tissn = {0024-3590, 1939-5590},\n\tshorttitle = {Basin‐scale estimates of greenhouse gas emissions from the {Mara} {River}, {Kenya}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.12166},\n\tdoi = {10.1002/lno.12166},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Mwanake, Ricky M. and Gettel, Gretchen M. and Ishimwe, Clarisse and Wangari, Elizabeth G. and Butterbach‐Bahl, Klaus and Kiese, Ralf},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {1776--1793},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Mälicke, M.\n\n\n \n \n \n \n \n SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(6): 2505–2532. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"SciKit-GStat$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{malicke_scikit-gstat_2022,\n\ttitle = {{SciKit}-{GStat} 1.0: a {SciPy}-flavored geostatistical variogram estimation toolbox written in {Python}},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{SciKit}-{GStat} 1.0},\n\turl = {https://gmd.copernicus.org/articles/15/2505/2022/},\n\tdoi = {10.5194/gmd-15-2505-2022},\n\tabstract = {Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e.,\nfor interpolation, rescaling, data assimilation or modeling.\nAt its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations.\nThe variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method.\nUnfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram.\nNot least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user.\nThis is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all.\nFurthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied.\nSciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow.\nIts main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python.\nDuring the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat.\nToday, the most important ones can be interfaced by SciKit-GStat.\nAdditionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat.\nCommon data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms.\nCommon approaches to estimate variograms are covered and can be used out of the box.\nAt the same time, the base class is very flexible and can be adjusted to less common problems, as well.\nLast but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases.\nWith broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Mälicke, Mirko},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2505--2532},\n}\n\n

\n

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\n Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e., for interpolation, rescaling, data assimilation or modeling. At its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations. The variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method. Unfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram. Not least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user. This is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all. Furthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python. During the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat. Today, the most important ones can be interfaced by SciKit-GStat. Additionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat. Common data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well. Last but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases. With broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.\n

\n\n\n

\n \n\n \n \n Nativel, S.; Ayari, E.; Rodriguez-Fernandez, N.; Baghdadi, N.; Madelon, R.; Albergel, C.; and Zribi, M.\n\n\n \n \n \n \n \n Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(10): 2434. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Hybrid$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{nativel_hybrid_2022,\n\ttitle = {Hybrid {Methodology} {Using} {Sentinel}-1/{Sentinel}-2 for {Soil} {Moisture} {Estimation}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/10/2434},\n\tdoi = {10.3390/rs14102434},\n\tabstract = {Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index ISSM, radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54\\% and 33\\% with respect to that of the neural network or change detection alone, respectively.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Nativel, Simon and Ayari, Emna and Rodriguez-Fernandez, Nemesio and Baghdadi, Nicolas and Madelon, Remi and Albergel, Clement and Zribi, Mehrez},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2434},\n}\n\n

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\n \n\n \n \n Nguyen, T. V.; Kumar, R.; Musolff, A.; Lutz, S. R.; Sarrazin, F.; Attinger, S.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Disparate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{nguyen_disparate_2022,\n\ttitle = {Disparate {Seasonal} {Nitrate} {Export} {From} {Nested} {Heterogeneous} {Subcatchments} {Revealed} {With} {StorAge} {Selection} {Functions}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030797},\n\tdoi = {10.1029/2021WR030797},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Nguyen, Tam V. and Kumar, Rohini and Musolff, Andreas and Lutz, Stefanie R. and Sarrazin, Fanny and Attinger, Sabine and Fleckenstein, Jan H.},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Nogueira, G. E. H.; Schmidt, C.; Partington, D.; Brunner, P.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Spatiotemporal variations in water sources and mixing spots in a riparian zone.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(7): 1883–1905. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{nogueira_spatiotemporal_2022,\n\ttitle = {Spatiotemporal variations in water sources and mixing spots in a riparian zone},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/1883/2022/},\n\tdoi = {10.5194/hess-26-1883-2022},\n\tabstract = {Abstract. Riparian zones are known to modulate water quality in stream corridors. They can act as buffers for groundwater-borne solutes before they enter the stream at harmful, high concentrations or facilitate solute turnover and attenuation in zones where stream water (SW) and groundwater (GW) mix. This natural attenuation capacity is strongly controlled by the dynamic exchange of water and solutes between the stream and the adjoining aquifer, creating potential for mixing-dependent reactions to take place. Here, we couple a previously calibrated transient and fully integrated 3D surface–subsurface numerical flow model with a hydraulic mixing cell (HMC) method to map the source composition of water along a net losing reach (900 m) of the fourth-order Selke stream and track its spatiotemporal evolution. This allows us to define zones in the aquifer with more balanced fractions of the different water sources per aquifer volume (called mixing hot spots), which have a high potential to facilitate mixing-dependent reactions and, in turn, enhance solute turnover. We further evaluated the HMC results against hydrochemical monitoring data. Our results show that, on average, about 50 \\% of the water in the alluvial aquifer consists of infiltrating SW. Within about 200 m around the stream, the aquifer is almost entirely made up of infiltrated SW with practically no significant amounts of other water sources mixed in. On average, about 9 \\% of the model domain could be characterized as mixing hot spots, which were mainly located at the fringe of the geochemical hyporheic zone rather than below or in the immediate vicinity of the streambed. This percentage could rise to values nearly 1.5 times higher following large discharge events. Moreover, event intensity (magnitude of peak flow) was found to be more important for the increase in mixing than event duration. Our modeling results further suggest that discharge events more significantly increase mixing potential at greater distances from the stream. In contrast near and below the stream, the rapid increase in SW influx shifts the ratio between the water fractions to SW, reducing the potential for mixing and the associated reactions. With this easy-to-transfer framework, we seek to show the applicability of the HMC method as a complementary approach for the identification of mixing hot spots in stream corridors, while showing the spatiotemporal controls of the SW–GW mixing process and the implications for riparian biogeochemistry and mixing-dependent turnover processes.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Nogueira, Guilherme E. H. and Schmidt, Christian and Partington, Daniel and Brunner, Philip and Fleckenstein, Jan H.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1883--1905},\n}\n\n

\n

\n\n\n

\n Abstract. Riparian zones are known to modulate water quality in stream corridors. They can act as buffers for groundwater-borne solutes before they enter the stream at harmful, high concentrations or facilitate solute turnover and attenuation in zones where stream water (SW) and groundwater (GW) mix. This natural attenuation capacity is strongly controlled by the dynamic exchange of water and solutes between the stream and the adjoining aquifer, creating potential for mixing-dependent reactions to take place. Here, we couple a previously calibrated transient and fully integrated 3D surface–subsurface numerical flow model with a hydraulic mixing cell (HMC) method to map the source composition of water along a net losing reach (900 m) of the fourth-order Selke stream and track its spatiotemporal evolution. This allows us to define zones in the aquifer with more balanced fractions of the different water sources per aquifer volume (called mixing hot spots), which have a high potential to facilitate mixing-dependent reactions and, in turn, enhance solute turnover. We further evaluated the HMC results against hydrochemical monitoring data. Our results show that, on average, about 50 % of the water in the alluvial aquifer consists of infiltrating SW. Within about 200 m around the stream, the aquifer is almost entirely made up of infiltrated SW with practically no significant amounts of other water sources mixed in. On average, about 9 % of the model domain could be characterized as mixing hot spots, which were mainly located at the fringe of the geochemical hyporheic zone rather than below or in the immediate vicinity of the streambed. This percentage could rise to values nearly 1.5 times higher following large discharge events. Moreover, event intensity (magnitude of peak flow) was found to be more important for the increase in mixing than event duration. Our modeling results further suggest that discharge events more significantly increase mixing potential at greater distances from the stream. In contrast near and below the stream, the rapid increase in SW influx shifts the ratio between the water fractions to SW, reducing the potential for mixing and the associated reactions. With this easy-to-transfer framework, we seek to show the applicability of the HMC method as a complementary approach for the identification of mixing hot spots in stream corridors, while showing the spatiotemporal controls of the SW–GW mixing process and the implications for riparian biogeochemistry and mixing-dependent turnover processes.\n

\n\n\n

\n \n\n \n \n Novick, K. A.; Ficklin, D. L.; Baldocchi, D.; Davis, K. J.; Ghezzehei, T. A.; Konings, A. G.; MacBean, N.; Raoult, N.; Scott, R. L.; Shi, Y.; Sulman, B. N.; and Wood, J. D.\n\n\n \n \n \n \n \n Confronting the water potential information gap.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 15(3): 158–164. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Confronting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{novick_confronting_2022,\n\ttitle = {Confronting the water potential information gap},\n\tvolume = {15},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/s41561-022-00909-2},\n\tdoi = {10.1038/s41561-022-00909-2},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Nature Geoscience},\n\tauthor = {Novick, Kimberly A. and Ficklin, Darren L. and Baldocchi, Dennis and Davis, Kenneth J. and Ghezzehei, Teamrat A. and Konings, Alexandra G. and MacBean, Natasha and Raoult, Nina and Scott, Russell L. and Shi, Yuning and Sulman, Benjamin N. and Wood, Jeffrey D.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {158--164},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n O, S.; Orth, R.; Weber, U.; and Park, S. K.\n\n\n \n \n \n \n \n High-resolution European daily soil moisture derived with machine learning (2003-2020).\n \n \n \n \n\n\n \n\n\n\n . 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

\n

@article{o_high-resolution_2022,\n\ttitle = {High-resolution {European} daily soil moisture derived with machine learning (2003-2020)},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://arxiv.org/abs/2205.10753},\n\tdoi = {10.48550/ARXIV.2205.10753},\n\tabstract = {Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses. The SoMo.ml-EU is available at figshare.},\n\turldate = {2022-11-21},\n\tauthor = {O, Sungmin and Orth, Rene and Weber, Ulrich and Park, Seon Ki},\n\tyear = {2022},\n\tkeywords = {Atmospheric and Oceanic Physics (physics.ao-ph), FOS: Physical sciences},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Panwar, A.; and Kleidon, A.\n\n\n \n \n \n \n \n Evaluating the Response of Diurnal Variations in Surface and Air Temperature to Evaporative Conditions across Vegetation Types in FLUXNET and ERA5.\n \n \n \n \n\n\n \n\n\n\n Journal of Climate, 35(19): 2701–2728. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{panwar_evaluating_2022,\n\ttitle = {Evaluating the {Response} of {Diurnal} {Variations} in {Surface} and {Air} {Temperature} to {Evaporative} {Conditions} across {Vegetation} {Types} in {FLUXNET} and {ERA5}},\n\tvolume = {35},\n\tissn = {0894-8755, 1520-0442},\n\turl = {https://journals.ametsoc.org/view/journals/clim/35/19/JCLI-D-21-0345.1.xml},\n\tdoi = {10.1175/JCLI-D-21-0345.1},\n\tabstract = {Abstract \n             \n              The diurnal variations of surface and air temperature are closely related, but their different responses to evaporative conditions can inform us about land–atmosphere interactions. Here, we evaluate the responses of the diurnal ranges in surface (Δ \n               \n                T \n                s \n               \n              ) and air (Δ \n               \n                T \n                a \n               \n              ) temperature to evaporative fraction at 160 FLUXNET sites and in the ERA5 reanalysis. We show that the sensitivity of Δ \n               \n                T \n                s \n               \n              to evaporative fraction depends on vegetation type, whereas Δ \n               \n                T \n                a \n               \n              does not. On days with low evaporative fraction, Δ \n               \n                T \n                s \n               \n              in FLUXNET is enhanced by up to ∼20 K (∼30 K in ERA5) in short vegetation, but only by up to ∼10 K (∼10 K in ERA5) in forests. Note that Δ \n               \n                T \n                a \n               \n              responds rather similarly to evaporative fraction irrespective of vegetation type (∼5 K in FLUXNET, ∼10 K in ERA5). We find a systematic bias in ERA5’s Δ \n              T \n              response to evaporative conditions, showing a stronger sensitivity to evaporative fraction than in FLUXNET. We then demonstrate with a simple atmospheric boundary layer (SABL) model that the weak response of Δ \n               \n                T \n                a \n               \n              to evaporative fraction can be explained by greater boundary layer growth under dry conditions, which increases the heat storage capacity and reduces the response of air temperature to evaporative fraction. Additionally, using a simplified surface energy balance (SSEB) model we show that Δ \n               \n                T \n                s \n               \n              mainly responds to solar radiation, evaporative fraction, and aerodynamic conductance. We conclude that the dominant patterns of diurnal temperature variations can be explained by fundamental physical concepts, which should help us to better understand the main controls of land–atmosphere interactions. \n             \n             \n              Significance Statement \n              Generally, air temperature is used more widely than the surface temperature, and often they are assumed to be equivalent. However, we show that their responses to changes in vegetation type and evaporative conditions are quite different. Using FLUXNET observations, ERA5 reanalysis, and two simple physical models, we found that these responses are much stronger in surface temperature, especially in short vegetation, and relatively weaker in air temperature. Despite being measured just 2 m above the surface, air temperature carries lesser imprints of evaporation and vegetation than the surface temperature because of boundary layer dynamics. These findings suggest the importance of coupled land–atmosphere processes in shaping surface and air temperature differently and provide insights on their distinctive responses to global changes.},\n\tnumber = {19},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Climate},\n\tauthor = {Panwar, Annu and Kleidon, Axel},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {2701--2728},\n}\n\n

\n

\n\n\n

\n Abstract The diurnal variations of surface and air temperature are closely related, but their different responses to evaporative conditions can inform us about land–atmosphere interactions. Here, we evaluate the responses of the diurnal ranges in surface (Δ T s ) and air (Δ T a ) temperature to evaporative fraction at 160 FLUXNET sites and in the ERA5 reanalysis. We show that the sensitivity of Δ T s to evaporative fraction depends on vegetation type, whereas Δ T a does not. On days with low evaporative fraction, Δ T s in FLUXNET is enhanced by up to ∼20 K (∼30 K in ERA5) in short vegetation, but only by up to ∼10 K (∼10 K in ERA5) in forests. Note that Δ T a responds rather similarly to evaporative fraction irrespective of vegetation type (∼5 K in FLUXNET, ∼10 K in ERA5). We find a systematic bias in ERA5’s Δ T response to evaporative conditions, showing a stronger sensitivity to evaporative fraction than in FLUXNET. We then demonstrate with a simple atmospheric boundary layer (SABL) model that the weak response of Δ T a to evaporative fraction can be explained by greater boundary layer growth under dry conditions, which increases the heat storage capacity and reduces the response of air temperature to evaporative fraction. Additionally, using a simplified surface energy balance (SSEB) model we show that Δ T s mainly responds to solar radiation, evaporative fraction, and aerodynamic conductance. We conclude that the dominant patterns of diurnal temperature variations can be explained by fundamental physical concepts, which should help us to better understand the main controls of land–atmosphere interactions. Significance Statement Generally, air temperature is used more widely than the surface temperature, and often they are assumed to be equivalent. However, we show that their responses to changes in vegetation type and evaporative conditions are quite different. Using FLUXNET observations, ERA5 reanalysis, and two simple physical models, we found that these responses are much stronger in surface temperature, especially in short vegetation, and relatively weaker in air temperature. Despite being measured just 2 m above the surface, air temperature carries lesser imprints of evaporation and vegetation than the surface temperature because of boundary layer dynamics. These findings suggest the importance of coupled land–atmosphere processes in shaping surface and air temperature differently and provide insights on their distinctive responses to global changes.\n

\n\n\n

\n \n\n \n \n Peng, Z.; Tang, R.; Jiang, Y.; Liu, M.; and Li, Z.\n\n\n \n \n \n \n \n Global estimates of 500 m daily aerodynamic roughness length from MODIS data.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 183: 336–351. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{peng_global_2022,\n\ttitle = {Global estimates of 500 m daily aerodynamic roughness length from {MODIS} data},\n\tvolume = {183},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271621003130},\n\tdoi = {10.1016/j.isprsjprs.2021.11.015},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Peng, Zhong and Tang, Ronglin and Jiang, Yazhen and Liu, Meng and Li, Zhao-Liang},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {336--351},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Poorter, H.; Knopf, O.; Wright, I. J.; Temme, A. A.; Hogewoning, S. W.; Graf, A.; Cernusak, L. A.; and Pons, T. L.\n\n\n \n \n \n \n \n A meta‐analysis of responses of C $_{\\textrm{3}}$ plants to atmospheric CO $_{\\textrm{2}}$ : dose–response curves for 85 traits ranging from the molecular to the whole‐plant level.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 233(4): 1560–1596. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{poorter_metaanalysis_2022,\n\ttitle = {A meta‐analysis of responses of {C} $_{\\textrm{3}}$ plants to atmospheric {CO} $_{\\textrm{2}}$ : dose–response curves for 85 traits ranging from the molecular to the whole‐plant level},\n\tvolume = {233},\n\tissn = {0028-646X, 1469-8137},\n\tshorttitle = {A meta‐analysis of responses of {C} $_{\\textrm{3}}$ plants to atmospheric {CO} $_{\\textrm{2}}$},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.17802},\n\tdoi = {10.1111/nph.17802},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {New Phytologist},\n\tauthor = {Poorter, Hendrik and Knopf, Oliver and Wright, Ian J. and Temme, Andries A. and Hogewoning, Sander W. and Graf, Alexander and Cernusak, Lucas A. and Pons, Thijs L.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {1560--1596},\n}\n\n

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\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; Koal, P.; and Sánchez-Azofeifa, A.\n\n\n \n \n \n \n \n Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(2): 380. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Fractional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{putzenlechner_fractional_2022,\n\ttitle = {Fractional {Vegetation} {Cover} {Derived} from {UAV} and {Sentinel}-2 {Imagery} as a {Proxy} for {In} {Situ} {FAPAR} in a {Dense} {Mixed}-{Coniferous} {Forest}?},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/2/380},\n\tdoi = {10.3390/rs14020380},\n\tabstract = {The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE {\\textgreater} 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Koal, Philipp and Sánchez-Azofeifa, Arturo},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {380},\n}\n\n

\n

\n\n\n

\n The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE \\textgreater 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.\n

\n\n\n

\n \n\n \n \n Reiber, L.; Foit, K.; Liess, M.; Karaoglan, B.; Wogram, J.; and Duquesne, S.\n\n\n \n \n \n \n \n Close to reality? Micro-/mesocosm communities do not represent natural macroinvertebrate communities.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 34(1): 65. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Close$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{reiber_close_2022,\n\ttitle = {Close to reality? {Micro}-/mesocosm communities do not represent natural macroinvertebrate communities},\n\tvolume = {34},\n\tissn = {2190-4707, 2190-4715},\n\tshorttitle = {Close to reality?},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-022-00643-x},\n\tdoi = {10.1186/s12302-022-00643-x},\n\tabstract = {Abstract \n             \n              Background \n              The European environmental risk assessment of plant protection products considers aquatic model ecosystem studies (microcosms/mesocosms, M/M) as suitable higher tier approach to assess treatment-related effects and to derive regulatory acceptable concentrations (RAC). However, it is under debate to what extent these artificial test systems reflect the risks of pesticidal substances with potential harmful effects on natural macroinvertebrate communities, and whether the field communities are adequately protected by the results of the M/M studies. We therefore compared the composition, sensitivity and vulnerability of benthic macroinvertebrates established in control (untreated) groups of 47 selected M/M studies with natural stream communities at 26 reference field sites. \n             \n             \n              Results \n               \n                Since 2013 the number of benthic macroinvertebrate taxa present in M/M studies has increased by 39\\% to a mean of 38 families per study. However, there is only an average of 4 families per study that comply with the recommendations provided by EFSA (EFSA J 11:3290, 2013), i.e.: (i) allowing statistical identification of treatment-related effects of at least 70\\% according to the \n                minimum detectable difference \n                (here criteria are slightly modified) and (ii) belonging to insects or crustaceans (potentially sensitive taxa for pesticidal substances). Applying the criterion of physiological sensitivity according to the SPEAR \n                pesticides \n                concept, the number of families decreases from 4 to 2.3 per study. \n               \n             \n             \n              Conclusions \n              Most taxa established in recent M/M studies do not suitably represent natural freshwater communities. First, because their abundances are often not sufficient for statistical detection of treatment-related effects in order to determine an appropriate endpoint and subsequent RAC. Recommendations are given to improve the detectability of such effects and their reliability. Second, the taxa often do not represent especially sensitive or vulnerable taxa in natural communities in terms of their traits. The uncertainties linked to vulnerable taxa in M/M studies are especially high considering their representativity for field assemblages and the comparability of factors determining their recovery time. Thus considering recovery for deriving a RAC (i.e., ERO-RAC) is not recommended. In addition, this paper discusses further concerns regarding M/M studies in a broader regulatory context and recommends the development of alternative assessment tools and a shift towards a new paradigm.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Reiber, Lena and Foit, Kaarina and Liess, Matthias and Karaoglan, Bilgin and Wogram, Joern and Duquesne, Sabine},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {65},\n}\n\n

\n

\n\n\n

\n Abstract Background The European environmental risk assessment of plant protection products considers aquatic model ecosystem studies (microcosms/mesocosms, M/M) as suitable higher tier approach to assess treatment-related effects and to derive regulatory acceptable concentrations (RAC). However, it is under debate to what extent these artificial test systems reflect the risks of pesticidal substances with potential harmful effects on natural macroinvertebrate communities, and whether the field communities are adequately protected by the results of the M/M studies. We therefore compared the composition, sensitivity and vulnerability of benthic macroinvertebrates established in control (untreated) groups of 47 selected M/M studies with natural stream communities at 26 reference field sites. Results Since 2013 the number of benthic macroinvertebrate taxa present in M/M studies has increased by 39% to a mean of 38 families per study. However, there is only an average of 4 families per study that comply with the recommendations provided by EFSA (EFSA J 11:3290, 2013), i.e.: (i) allowing statistical identification of treatment-related effects of at least 70% according to the minimum detectable difference (here criteria are slightly modified) and (ii) belonging to insects or crustaceans (potentially sensitive taxa for pesticidal substances). Applying the criterion of physiological sensitivity according to the SPEAR pesticides concept, the number of families decreases from 4 to 2.3 per study. Conclusions Most taxa established in recent M/M studies do not suitably represent natural freshwater communities. First, because their abundances are often not sufficient for statistical detection of treatment-related effects in order to determine an appropriate endpoint and subsequent RAC. Recommendations are given to improve the detectability of such effects and their reliability. Second, the taxa often do not represent especially sensitive or vulnerable taxa in natural communities in terms of their traits. The uncertainties linked to vulnerable taxa in M/M studies are especially high considering their representativity for field assemblages and the comparability of factors determining their recovery time. Thus considering recovery for deriving a RAC (i.e., ERO-RAC) is not recommended. In addition, this paper discusses further concerns regarding M/M studies in a broader regulatory context and recommends the development of alternative assessment tools and a shift towards a new paradigm.\n

\n\n\n

\n \n\n \n \n Reinermann, S.; Gessner, U.; Asam, S.; Ullmann, T.; Schucknecht, A.; and Kuenzer, C.\n\n\n \n \n \n \n \n Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(7): 1647. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{reinermann_detection_2022,\n\ttitle = {Detection of {Grassland} {Mowing} {Events} for {Germany} by {Combining} {Sentinel}-1 and {Sentinel}-2 {Time} {Series}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/7/1647},\n\tdoi = {10.3390/rs14071647},\n\tabstract = {Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3\\% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84\\%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3\\%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Reinermann, Sophie and Gessner, Ursula and Asam, Sarah and Ullmann, Tobias and Schucknecht, Anne and Kuenzer, Claudia},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1647},\n}\n\n

\n

\n\n\n

\n Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.\n

\n\n\n

\n \n\n \n \n Reitz, O.; Graf, A.; Schmidt, M.; Ketzler, G.; and Leuchner, M.\n\n\n \n \n \n \n \n Effects of Measurement Height and Low-Pass-Filtering Corrections on Eddy-Covariance Flux Measurements Over a Forest Clearing with Complex Vegetation.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 184(2): 277–299. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{reitz_effects_2022,\n\ttitle = {Effects of {Measurement} {Height} and {Low}-{Pass}-{Filtering} {Corrections} on {Eddy}-{Covariance} {Flux} {Measurements} {Over} a {Forest} {Clearing} with {Complex} {Vegetation}},\n\tvolume = {184},\n\tissn = {0006-8314, 1573-1472},\n\turl = {https://link.springer.com/10.1007/s10546-022-00700-1},\n\tdoi = {10.1007/s10546-022-00700-1},\n\tabstract = {Abstract \n             \n              Flux measurements over heterogeneous surfaces with growing vegetation and a limited fetch are a difficult task, as measurement heights that are too high or too low above the canopy adversely affect results. The aim of this study is to assess implications from measurement height in regard to low-pass filtering, footprint representativeness, and energy balance closure for a clear-cut site with regrowing vegetation of varying height. For this, measurements from two open-path eddy-covariance systems at different heights are compared over the course of one growing season. Particular attention is paid to low-pass-filtering corrections, for which five different methods are compared. Results indicate significant differences between fluxes from the upper and lower systems, which likely result from footprint differences and an insufficient spectral correction for the lower system. Different low-pass-filtering corrections add an uncertainty of 3.4\\% (7.0\\%) to CO \n              2 \n              fluxes and 1.4\\% (3.0\\%) to H \n              2 \n              O fluxes for the upper (lower) system, also leading to considerable differences in cumulative fluxes. Despite limitations in the analysis, which include the difficulty of applying a footprint model at this study site and the likely influence of advection on the total exchange, the analysis confirms that information about the choice of spectral correction method and measurement-height changes are critical for interpreting data at complex sites.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Reitz, Oliver and Graf, Alexander and Schmidt, Marius and Ketzler, Gunnar and Leuchner, Michael},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {277--299},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Rink, K.; Şen, Ö. O.; Hannemann, M.; Ködel, U.; Nixdorf, E.; Weber, U.; Werban, U.; Schrön, M.; Kalbacher, T.; and Kolditz, O.\n\n\n \n \n \n \n \n An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems.\n \n \n \n \n\n\n \n\n\n\n Computers & Graphics, 103: 192–200. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rink_environmental_2022,\n\ttitle = {An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems},\n\tvolume = {103},\n\tissn = {00978493},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0097849322000309},\n\tdoi = {10.1016/j.cag.2022.02.009},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Computers \\& Graphics},\n\tauthor = {Rink, Karsten and Şen, Özgür Ozan and Hannemann, Marco and Ködel, Uta and Nixdorf, Erik and Weber, Ute and Werban, Ulrike and Schrön, Martin and Kalbacher, Thomas and Kolditz, Olaf},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {192--200},\n}\n\n

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\n \n\n \n \n Rinne-Garmston, K. T.; Helle, G.; Lehmann, M. M.; Sahlstedt, E.; Schleucher, J.; and Waterhouse, J. S.\n\n\n \n \n \n \n \n Newer Developments in Tree-Ring Stable Isotope Methods.\n \n \n \n \n\n\n \n\n\n\n In Siegwolf, R. T. W.; Brooks, J. R.; Roden, J.; and Saurer, M., editor(s), Stable Isotopes in Tree Rings, volume 8, pages 215–249. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Newer$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@incollection{siegwolf_newer_2022,\n\taddress = {Cham},\n\ttitle = {Newer {Developments} in {Tree}-{Ring} {Stable} {Isotope} {Methods}},\n\tvolume = {8},\n\tisbn = {9783030926977 9783030926984},\n\turl = {https://link.springer.com/10.1007/978-3-030-92698-4_7},\n\tabstract = {Abstract \n            The tree-ring stable C, O and H isotope compositions have proven valuable for examining past changes in the environment and predicting forest responses to environmental change. However, we have not yet recovered the full potential of this archive, partly due to a lack understanding of fractionation processes resulting from methodological constraints. With better understanding of the biochemical and tree physiological processes that lead to differences between the isotopic compositions of primary photosynthates and the isotopic compositions of substrates deposited in stem xylem, more reliable and accurate reconstructions could be obtained. Furthermore, by extending isotopic analysis of tree-ring cellulose to intra-molecular level, more information could be obtained on changing climate, tree metabolism or ecophysiology. This chapter presents newer methods in isotope research that have become available or show high future potential for fully utilising the wealth of information available in tree-rings. These include compound-specific analysis of sugars and cyclitols, high spatial resolution analysis of tree rings with UV-laser, and position-specific isotope analysis of cellulose. The aim is to provide the reader with understanding of the advantages and of the current challenges connected with the use of these methods for stable isotope tree-ring research.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Stable {Isotopes} in {Tree} {Rings}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Rinne-Garmston, Katja T. and Helle, Gerhard and Lehmann, Marco M. and Sahlstedt, Elina and Schleucher, Jürgen and Waterhouse, John S.},\n\teditor = {Siegwolf, Rolf T. W. and Brooks, J. Renée and Roden, John and Saurer, Matthias},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-92698-4_7},\n\tpages = {215--249},\n}\n\n

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\n \n\n \n \n Robinson, K.; Bogena, H. R.; Wang, Q.; Cammeraat, E.; and Bol, R.\n\n\n \n \n \n \n \n Effects of deforestation on dissolved organic carbon and nitrate in catchment stream water revealed by wavelet analysis.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 4: 1003693. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{robinson_effects_2022,\n\ttitle = {Effects of deforestation on dissolved organic carbon and nitrate in catchment stream water revealed by wavelet analysis},\n\tvolume = {4},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2022.1003693/full},\n\tdoi = {10.3389/frwa.2022.1003693},\n\tabstract = {Deforestation can lead to an increase in the availability of nutrients in the soil and, in turn, have an impact on the quality of water in receiving water bodies. This study assesses the impact of deforestation by evaluating the in-stream concentrations of dissolved organic carbon (DOC) and nitrate, their internal relationship, and those with stream discharge in the Wüstebach headwater catchment (Germany). This catchment has monitored stream water and associated environmental parameters for over a decade as part of the TERENO initiative. Additionally, there is a paired undisturbed forested catchment that serves as a reference stream. Our approach included a more advanced correlation analysis, namely wavelet analysis, that assists in determining changes in the correlation and lag time between the variables of interest over different time scales. This study found that after deforestation, there was an immediate increase in in-stream DOC concentrations, followed by an increase in nitrate {\\textasciitilde}1 year later. Overall, the mean DOC concentration increased, and mean nitrate concentration decreased across the catchment post-deforestation. Elevated stream water nutrient levels peaked around 2 to 3 years after the clear-cutting, and returned to pre-deforestation levels after {\\textasciitilde}5 years. The deforestation had no influence on the anti-correlation between DOC and nitrate. However, the correlation between both compounds and discharge was likely altered due to the increased soil nutrients availability as a result of deforestation. Wavelet coherence analysis revealed the “underlying” changing strengths and directions of the main correlations between DOC, nitrate and discharge on different time scales resulting from severe forest management interventions (here deforestation). This information provides new valuable impact insights for decision making into such forest management interventions.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Water},\n\tauthor = {Robinson, Kerri-Leigh and Bogena, Heye R. and Wang, Qiqi and Cammeraat, Erik and Bol, Roland},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {1003693},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Rummler, T.; Wagner, A.; Arnault, J.; and Kunstmann, H.\n\n\n \n \n \n \n \n Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: Benefits of a 2D groundwater representation.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Lateral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rummler_lateral_2022,\n\ttitle = {Lateral terrestrial water fluxes in the {LSM} of {WRF}‐{Hydro}: {Benefits} of a {2D} groundwater representation},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Lateral terrestrial water fluxes in the {LSM} of {WRF}‐{Hydro}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14510},\n\tdoi = {10.1002/hyp.14510},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Hydrological Processes},\n\tauthor = {Rummler, Thomas and Wagner, Andreas and Arnault, Joël and Kunstmann, Harald},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Salomón, R. L.; Peters, R. L.; Zweifel, R.; Sass-Klaassen, U. G. W.; Stegehuis, A. I.; Smiljanic, M.; Poyatos, R.; Babst, F.; Cienciala, E.; Fonti, P.; Lerink, B. J. W.; Lindner, M.; Martinez-Vilalta, J.; Mencuccini, M.; Nabuurs, G.; van der Maaten, E.; von Arx, G.; Bär, A.; Akhmetzyanov, L.; Balanzategui, D.; Bellan, M.; Bendix, J.; Berveiller, D.; Blaženec, M.; Čada, V.; Carraro, V.; Cecchini, S.; Chan, T.; Conedera, M.; Delpierre, N.; Delzon, S.; Ditmarová, Ľ.; Dolezal, J.; Dufrêne, E.; Edvardsson, J.; Ehekircher, S.; Forner, A.; Frouz, J.; Ganthaler, A.; Gryc, V.; Güney, A.; Heinrich, I.; Hentschel, R.; Janda, P.; Ježík, M.; Kahle, H.; Knüsel, S.; Krejza, J.; Kuberski, Ł.; Kučera, J.; Lebourgeois, F.; Mikoláš, M.; Matula, R.; Mayr, S.; Oberhuber, W.; Obojes, N.; Osborne, B.; Paljakka, T.; Plichta, R.; Rabbel, I.; Rathgeber, C. B. K.; Salmon, Y.; Saunders, M.; Scharnweber, T.; Sitková, Z.; Stangler, D. F.; Stereńczak, K.; Stojanović, M.; Střelcová, K.; Světlík, J.; Svoboda, M.; Tobin, B.; Trotsiuk, V.; Urban, J.; Valladares, F.; Vavrčík, H.; Vejpustková, M.; Walthert, L.; Wilmking, M.; Zin, E.; Zou, J.; and Steppe, K.\n\n\n \n \n \n \n \n The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 13(1): 28. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{salomon_2018_2022,\n\ttitle = {The 2018 {European} heatwave led to stem dehydration but not to consistent growth reductions in forests},\n\tvolume = {13},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-021-27579-9},\n\tdoi = {10.1038/s41467-021-27579-9},\n\tabstract = {Abstract \n            Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Nature Communications},\n\tauthor = {Salomón, Roberto L. and Peters, Richard L. and Zweifel, Roman and Sass-Klaassen, Ute G. W. and Stegehuis, Annemiek I. and Smiljanic, Marko and Poyatos, Rafael and Babst, Flurin and Cienciala, Emil and Fonti, Patrick and Lerink, Bas J. W. and Lindner, Marcus and Martinez-Vilalta, Jordi and Mencuccini, Maurizio and Nabuurs, Gert-Jan and van der Maaten, Ernst and von Arx, Georg and Bär, Andreas and Akhmetzyanov, Linar and Balanzategui, Daniel and Bellan, Michal and Bendix, Jörg and Berveiller, Daniel and Blaženec, Miroslav and Čada, Vojtěch and Carraro, Vinicio and Cecchini, Sébastien and Chan, Tommy and Conedera, Marco and Delpierre, Nicolas and Delzon, Sylvain and Ditmarová, Ľubica and Dolezal, Jiri and Dufrêne, Eric and Edvardsson, Johannes and Ehekircher, Stefan and Forner, Alicia and Frouz, Jan and Ganthaler, Andrea and Gryc, Vladimír and Güney, Aylin and Heinrich, Ingo and Hentschel, Rainer and Janda, Pavel and Ježík, Marek and Kahle, Hans-Peter and Knüsel, Simon and Krejza, Jan and Kuberski, Łukasz and Kučera, Jiří and Lebourgeois, François and Mikoláš, Martin and Matula, Radim and Mayr, Stefan and Oberhuber, Walter and Obojes, Nikolaus and Osborne, Bruce and Paljakka, Teemu and Plichta, Roman and Rabbel, Inken and Rathgeber, Cyrille B. K. and Salmon, Yann and Saunders, Matthew and Scharnweber, Tobias and Sitková, Zuzana and Stangler, Dominik Florian and Stereńczak, Krzysztof and Stojanović, Marko and Střelcová, Katarína and Světlík, Jan and Svoboda, Miroslav and Tobin, Brian and Trotsiuk, Volodymyr and Urban, Josef and Valladares, Fernando and Vavrčík, Hanuš and Vejpustková, Monika and Walthert, Lorenz and Wilmking, Martin and Zin, Ewa and Zou, Junliang and Steppe, Kathy},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {28},\n}\n\n

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\n \n\n \n \n Schmidt, L.; Schaefer, D.; Geller, J.; Lünenschloss, P.; Palm, B.; Rinke, K.; and Bumberger, J.\n\n\n \n \n \n \n \n System for Automated Quality Control (Saqc) to Enable Traceable and Reproducible Data Streams in Environmental Science.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"System$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schmidt_system_2022,\n\ttitle = {System for {Automated} {Quality} {Control} ({Saqc}) to {Enable} {Traceable} and {Reproducible} {Data} {Streams} in {Environmental} {Science}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4173698},\n\tdoi = {10.2139/ssrn.4173698},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Schmidt, Lennart and Schaefer, David and Geller, Juliane and Lünenschloss, Peter and Palm, Bert and Rinke, Karsten and Bumberger, Jan},\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Schmitz, M.; Deutschmann, B.; Markert, N.; Backhaus, T.; Brack, W.; Brauns, M.; Brinkmann, M.; Seiler, T.; Fink, P.; Tang, S.; Beitel, S.; Doering, J. A.; Hecker, M.; Shao, Y.; Schulze, T.; Weitere, M.; Wild, R.; Velki, M.; and Hollert, H.\n\n\n \n \n \n \n \n Demonstration of an aggregated biomarker response approach to assess the impact of point and diffuse contaminant sources in feral fish in a small river case study.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 804: 150020. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Demonstration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schmitz_demonstration_2022,\n\ttitle = {Demonstration of an aggregated biomarker response approach to assess the impact of point and diffuse contaminant sources in feral fish in a small river case study},\n\tvolume = {804},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050956},\n\tdoi = {10.1016/j.scitotenv.2021.150020},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Schmitz, Markus and Deutschmann, Björn and Markert, Nele and Backhaus, Thomas and Brack, Werner and Brauns, Mario and Brinkmann, Markus and Seiler, Thomas-Benjamin and Fink, Patrick and Tang, Song and Beitel, Shawn and Doering, Jon A. and Hecker, Markus and Shao, Ying and Schulze, Tobias and Weitere, Markus and Wild, Romy and Velki, Mirna and Hollert, Henner},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {150020},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Schneider, C.; Neuwirth, B.; Schneider, S.; Balanzategui, D.; Elsholz, S.; Fenner, D.; Meier, F.; and Heinrich, I.\n\n\n \n \n \n \n \n Using the dendro-climatological signal of urban trees as a measure of urbanization and urban heat island.\n \n \n \n \n\n\n \n\n\n\n Urban Ecosystems, 25(3): 849–865. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schneider_using_2022,\n\ttitle = {Using the dendro-climatological signal of urban trees as a measure of urbanization and urban heat island},\n\tvolume = {25},\n\tissn = {1083-8155, 1573-1642},\n\turl = {https://link.springer.com/10.1007/s11252-021-01196-2},\n\tdoi = {10.1007/s11252-021-01196-2},\n\tabstract = {Abstract \n            Using dendroclimatological techniques this study investigates whether inner city tree-ring width (TRW) chronologies from eight tree species (ash, beech, fir, larch, lime, sessile and pedunculate oak, and pine) are suitable to examine the urban heat island of Berlin, Germany. Climate-growth relationships were analyzed for 18 sites along a gradient of increasing urbanization covering Berlin and surrounding rural areas. As a proxy for defining urban heat island intensities at each site, we applied urbanization parameters such as building fraction, impervious surfaces, and green areas. The response of TRW to monthly and seasonal air temperature, precipitation, aridity, and daily air-temperature ranges were used to identify climate-growth relationships. Trees from urban sites were found to be more sensitive to climate compared to trees in the surrounding hinterland. Ring width of the deciduous species, especially ash, beech, and oak, showed a high sensitivity to summer heat and drought at urban locations (summer signal), whereas conifer species were found suitable for the analysis of the urban heat island in late winter and early spring (winter signal). \n            The summer and winter signals were strongest in tree-ring chronologies when the urban heat island intensities were based on an area of about 200 m to 3000 m centered over the tree locations, and thus reflect the urban climate at the scale of city quarters. For the summer signal, the sensitivity of deciduous tree species to climate increased with urbanity. \n            These results indicate that urban trees can be used for climate response analyses and open new pathways to trace the evolution of urban climate change and more specifically the urban heat island, both in time and space.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Urban Ecosystems},\n\tauthor = {Schneider, Christoph and Neuwirth, Burkhard and Schneider, Sebastian and Balanzategui, Daniel and Elsholz, Stefanie and Fenner, Daniel and Meier, Fred and Heinrich, Ingo},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {849--865},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Schreiber, M.; Bazaios, E.; Ströbel, B.; Wolf, B.; Ostler, U.; Gasche, R.; Schlingmann, M.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland.\n \n \n \n \n\n\n \n\n\n\n Nutrient Cycling in Agroecosystems. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Impacts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schreiber_impacts_2022,\n\ttitle = {Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland},\n\tissn = {1385-1314, 1573-0867},\n\turl = {https://link.springer.com/10.1007/s10705-022-10239-9},\n\tdoi = {10.1007/s10705-022-10239-9},\n\tabstract = {Abstract \n             \n              Low nitrogen (N) use efficiency of broadcast slurry application leads to nutrient losses, air and water pollution, greenhouse gas emissions and—in particular in a warming climate—to soil N mining. Here we test the alternative slurry acidification and injection techniques for their mitigation potential compared to broadcast spreading in montane grassland. We determined (1) the fate of \n              15 \n              N labelled slurry in the plant-soil-microbe system and soil-atmosphere exchange of greenhouse gases over one fertilization/harvest cycle and (2) assessed the longer-term contribution of fertilizer \n              15 \n              N to soil organic N formation by the end of the growing season. The isotope tracing approach was combined with a space for time climate change experiment. Simulated climate change increased productivity, ecosystem respiration, and net methane uptake irrespective of management, but the generally low N \n              2 \n              O fluxes remained unchanged. Compared to the broadcast spreading, slurry acidification showed lowest N losses, thus increased productivity and fertilizer N use efficiency (38\\% \n              15 \n              N recovery in plant aboveground plant biomass). In contrast, slurry injection showed highest total fertilizer N losses, but increased fertilization-induced soil organic N formation by 9–12 kg N ha \n              −1 \n              season \n              −1 \n              . Slurry management effects on N \n              2 \n              O and CH \n              4 \n              fluxes remained negligible. In sum, our study shows that the tested alternative slurry application techniques can increase N use efficiency and/or promote soil organic N formation from applied fertilizer to a remarkable extent. However, this is still not sufficient to prevent soil N mining mostly resulting from large plant N exports that even exceed total fertilizer N inputs.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Nutrient Cycling in Agroecosystems},\n\tauthor = {Schreiber, Mirella and Bazaios, Elpida and Ströbel, Barbara and Wolf, Benjamin and Ostler, Ulrike and Gasche, Rainer and Schlingmann, Marcus and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = oct,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Schucknecht, A.; Seo, B.; Krämer, A.; Asam, S.; Atzberger, C.; and Kiese, R.\n\n\n \n \n \n \n \n Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(10): 2699–2727. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schucknecht_estimating_2022,\n\ttitle = {Estimating dry biomass and plant nitrogen concentration in pre-{Alpine} grasslands with low-cost {UAS}-borne multispectral data – a comparison of sensors, algorithms, and predictor sets},\n\tvolume = {19},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/19/2699/2022/},\n\tdoi = {10.5194/bg-19-2699-2022},\n\tabstract = {Abstract. Grasslands are an important part of pre-Alpine and Alpine\nlandscapes. Despite the economic value and the significant role of\ngrasslands in carbon and nitrogen (N) cycling, spatially explicit\ninformation on grassland biomass and quality is rarely available. Remotely\nsensed data from unmanned aircraft systems (UASs) and satellites might be an\noption to overcome this gap. Our study aims to investigate the potential of\nlow-cost UAS-based multispectral sensors for estimating above-ground biomass\n(dry matter, DM) and plant N concentration. In our analysis, we compared two\ndifferent sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three\nstatistical models (linear model; random forests, RFs; gradient-boosting\nmachines, GBMs), and six predictor sets (i.e. different combinations of raw\nreflectance, vegetation indices, and canopy height). Canopy height\ninformation can be derived from UAS sensors but was not available in our\nstudy. Therefore, we tested the added value of this structural information\nwith in situ measured bulk canopy height data. A combined field sampling and\nflight campaign was conducted in April 2018 at different grassland sites in\nsouthern Germany to obtain in situ and the corresponding spectral data. The\nhyper-parameters of the two machine learning (ML) approaches (RF, GBM) were\noptimized, and all model setups were run with a 6-fold cross-validation.\nLinear models were characterized by very low statistical performance\nmeasures, thus were not suitable to estimate DM and plant N concentration\nusing UAS data. The non-linear ML algorithms showed an acceptable regression\nperformance for all sensor–predictor set combinations with average (avg; cross-validated, cv)\nRcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and\nrRMSEcv,avg (relative) of 15.9 \\% for DM and with Rcv,avg2 of\n0.40, RMSEcv,avg of 0.48 wt \\%, and rRMSEcv, avg of\n15.2 \\% for plant N concentration estimation. The optimal combination of\nsensors, ML algorithms, and predictor sets notably improved the model\nperformance. The best model performance for the estimation of DM\n(Rcv2=0.67, RMSEcv=41.9 g m2,\nrRMSEcv=12.6 \\%) was achieved with an RF model that utilizes all\npossible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ\nsensor data (Rcv2=0.47, RMSEcv=0.45 wt \\%,\nrRMSEcv=14.2 \\%). DM models with the spectral input of REM\nperformed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors\nwas most influential on model performance, while the effect of the chosen ML\nalgorithm was generally lower. The addition of canopy height to the spectral\ndata in the predictor set significantly improved the DM models. In our\nstudy, calibrating the ML algorithm improved the model performance\nsubstantially, which shows the importance of this step.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Schucknecht, Anne and Seo, Bumsuk and Krämer, Alexander and Asam, Sarah and Atzberger, Clement and Kiese, Ralf},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2699--2727},\n}\n\n

\n

\n\n\n

\n Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.\n

\n\n\n

\n \n\n \n \n Seo, E.; and Dirmeyer, P. A.\n\n\n \n \n \n \n \n Improving the ESA CCI daily soil moisture time series with physically-based land surface model datasets using a Fourier time-filtering method.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{seo_improving_2022,\n\ttitle = {Improving the {ESA} {CCI} daily soil moisture time series with physically-based land surface model datasets using a {Fourier} time-filtering method},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-21-0120.1/JHM-D-21-0120.1.xml},\n\tdoi = {10.1175/JHM-D-21-0120.1},\n\tabstract = {Abstract \n             \n              Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with \n              in situ \n              observations. \n             \n            When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Seo, Eunkyo and Dirmeyer, Paul A.},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Seokhyeon, K.; Sharma, A.; Liu, Y. Y.; and Young, S. I.\n\n\n \n \n \n \n \n Rethinking Satellite Data Merging: From Averaging to SNR Optimization.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–15. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Rethinking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{seokhyeon_rethinking_2022,\n\ttitle = {Rethinking {Satellite} {Data} {Merging}: {From} {Averaging} to {SNR} {Optimization}},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\tshorttitle = {Rethinking {Satellite} {Data} {Merging}},\n\turl = {https://ieeexplore.ieee.org/document/9531937/},\n\tdoi = {10.1109/TGRS.2021.3107028},\n\turldate = {2022-10-26},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Seokhyeon, Kim and Sharma, Ashish and Liu, Yi Y. and Young, Sean I.},\n\tyear = {2022},\n\tpages = {1--15},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Slabbert, E.; Knight, T.; Wubet, T.; Kautzner, A.; Baessler, C.; Auge, H.; Roscher, C.; and Schweiger, O.\n\n\n \n \n \n \n \n Abiotic factors are more important than land management and biotic interactions in shaping vascular plant and soil fungal communities.\n \n \n \n \n\n\n \n\n\n\n Global Ecology and Conservation, 33: e01960. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Abiotic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{slabbert_abiotic_2022,\n\ttitle = {Abiotic factors are more important than land management and biotic interactions in shaping vascular plant and soil fungal communities},\n\tvolume = {33},\n\tissn = {23519894},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2351989421005102},\n\tdoi = {10.1016/j.gecco.2021.e01960},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Global Ecology and Conservation},\n\tauthor = {Slabbert, E.L. and Knight, T.M. and Wubet, T. and Kautzner, A. and Baessler, C. and Auge, H. and Roscher, C. and Schweiger, O.},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {e01960},\n}\n\n

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\n \n\n \n \n Strebel, L.; Bogena, H. R.; Vereecken, H.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(2): 395–411. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Coupling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{strebel_coupling_2022,\n\ttitle = {Coupling the {Community} {Land} {Model} version 5.0 to the parallel data assimilation framework {PDAF}: description and applications},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {Coupling the {Community} {Land} {Model} version 5.0 to the parallel data assimilation framework {PDAF}},\n\turl = {https://gmd.copernicus.org/articles/15/395/2022/},\n\tdoi = {10.5194/gmd-15-395-2022},\n\tabstract = {Abstract. Land surface models are important for improving our understanding\nof the Earth system. They are continuously improving and becoming better in\nrepresenting the different land surface processes, e.g., the Community Land\nModel version 5 (CLM5). Similarly, observational networks and remote sensing\noperations are increasingly providing more data, e.g., from new satellite\nproducts and new in situ measurement sites, with increasingly higher quality\nfor a range of important variables of the Earth system. For the optimal\ncombination of land surface models and observation data, data assimilation\ntechniques have been developed in recent decades that incorporate\nobservations to update modeled states and parameters. The Parallel Data\nAssimilation Framework (PDAF) is a software environment that enables\nensemble data assimilation and simplifies the implementation of data\nassimilation systems in numerical models. In this study, we present the\ndevelopment of the new interface between PDAF and CLM5. This newly\nimplemented coupling integrates the PDAF functionality into CLM5 by\nmodifying the CLM5 ensemble mode to keep changes to the pre-existing\nparallel communication infrastructure to a minimum. Soil water content\nobservations from an extensive in situ measurement network in the\nWüstebach catchment in Germany are used to illustrate the application of\nthe coupled CLM5-PDAF system. The results show overall reductions in root\nmean square error of soil water content from 7 \\% up to 35 \\% compared to\nsimulations without data assimilation. We expect the coupled CLM5-PDAF\nsystem to provide a basis for improved regional to global land surface\nmodeling by enabling the assimilation of globally available observational\ndata.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Strebel, Lukas and Bogena, Heye R. and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {395--411},\n}\n\n

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\n \n\n \n \n Su, Y.; Yang, X.; Gentine, P.; Maignan, F.; Shang, J.; and Ciais, P.\n\n\n \n \n \n \n \n Observed strong atmospheric water constraints on forest photosynthesis using eddy covariance and satellite-based data across the Northern Hemisphere.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 110: 102808. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Observed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{su_observed_2022,\n\ttitle = {Observed strong atmospheric water constraints on forest photosynthesis using eddy covariance and satellite-based data across the {Northern} {Hemisphere}},\n\tvolume = {110},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1569843222000103},\n\tdoi = {10.1016/j.jag.2022.102808},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Su, Yongxian and Yang, Xueqin and Gentine, Pierre and Maignan, Fabienne and Shang, Jiali and Ciais, Philippe},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {102808},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Sunjidmaa, N.; Mendoza-Lera, C.; Hille, S.; Schmidt, C.; Borchardt, D.; and Graeber, D.\n\n\n \n \n \n \n \n Carbon limitation may override fine-sediment induced alterations of hyporheic nitrogen and phosphorus dynamics.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 837: 155689. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{sunjidmaa_carbon_2022,\n\ttitle = {Carbon limitation may override fine-sediment induced alterations of hyporheic nitrogen and phosphorus dynamics},\n\tvolume = {837},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722027851},\n\tdoi = {10.1016/j.scitotenv.2022.155689},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Sunjidmaa, Nergui and Mendoza-Lera, Clara and Hille, Sandra and Schmidt, Christian and Borchardt, Dietrich and Graeber, Daniel},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {155689},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Svenningsen, C. S.; Bowler, D. E.; Hecker, S.; Bladt, J.; Grescho, V.; Dam, N. M.; Dauber, J.; Eichenberg, D.; Ejrnæs, R.; Fløjgaard, C.; Frenzel, M.; Frøslev, T. G.; Hansen, A. J.; Heilmann‐Clausen, J.; Huang, Y.; Larsen, J. C.; Menger, J.; Nayan, N. L. B. M.; Pedersen, L. B.; Richter, A.; Dunn, R. R.; Tøttrup, A. P.; and Bonn, A.\n\n\n \n \n \n \n \n Flying insect biomass is negatively associated with urban cover in surrounding landscapes.\n \n \n \n \n\n\n \n\n\n\n Diversity and Distributions, 28(6): 1242–1254. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Flying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{svenningsen_flying_2022,\n\ttitle = {Flying insect biomass is negatively associated with urban cover in surrounding landscapes},\n\tvolume = {28},\n\tissn = {1366-9516, 1472-4642},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ddi.13532},\n\tdoi = {10.1111/ddi.13532},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Diversity and Distributions},\n\tauthor = {Svenningsen, Cecilie S. and Bowler, Diana E. and Hecker, Susanne and Bladt, Jesper and Grescho, Volker and Dam, Nicole M. and Dauber, Jens and Eichenberg, David and Ejrnæs, Rasmus and Fløjgaard, Camilla and Frenzel, Mark and Frøslev, Tobias G. and Hansen, Anders J. and Heilmann‐Clausen, Jacob and Huang, Yuanyuan and Larsen, Jonas C. and Menger, Juliana and Nayan, Nur L. B. M. and Pedersen, Lene B. and Richter, Anett and Dunn, Robert R. and Tøttrup, Anders P. and Bonn, Aletta},\n\teditor = {Jarvis, Susan},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {1242--1254},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Templer, P. H.; Harrison, J. L.; Pilotto, F.; Flores-Díaz, A.; Haase, P.; McDowell, W. H.; Sharif, R.; Shibata, H.; Blankman, D.; Avila, A.; Baatar, U.; Bogena, H. R.; Bourgeois, I.; Campbell, J.; Dirnböck, T.; Dodds, W. K.; Hauken, M.; Kokorite, I.; Lajtha, K.; Lai, I.; Laudon, H.; Lin, T. C.; Lins, S. R. M.; Meesenburg, H.; Pinho, P.; Robison, A.; Rogora, M.; Scheler, B.; Schleppi, P.; Sommaruga, R.; Staszewski, T.; and Taka, M.\n\n\n \n \n \n \n \n Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds: a large-scale data synthesis.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 160(2): 219–241. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Atmospheric$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{templer_atmospheric_2022,\n\ttitle = {Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds: a large-scale data synthesis},\n\tvolume = {160},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds},\n\turl = {https://link.springer.com/10.1007/s10533-022-00951-7},\n\tdoi = {10.1007/s10533-022-00951-7},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Biogeochemistry},\n\tauthor = {Templer, P. H. and Harrison, J. L. and Pilotto, F. and Flores-Díaz, A. and Haase, P. and McDowell, W. H. and Sharif, R. and Shibata, H. and Blankman, D. and Avila, A. and Baatar, U. and Bogena, H. R. and Bourgeois, I. and Campbell, J. and Dirnböck, T. and Dodds, W. K. and Hauken, M. and Kokorite, I. and Lajtha, K. and Lai, I.-L. and Laudon, H. and Lin, T. C. and Lins, S. R. M. and Meesenburg, H. and Pinho, P. and Robison, A. and Rogora, M. and Scheler, B. and Schleppi, P. and Sommaruga, R. and Staszewski, T. and Taka, M.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {219--241},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Terán, C. P.; Naz, B. S.; Graf, A.; Qu, Y.; Hendricks-Franssen, H.; Baatz, R.; Ciais, P.; and Vereecken, H.\n\n\n \n \n \n \n \n Water conductance rather than photosynthesis controls water-use efficiency in Europe.\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{teran_water_2022,\n\ttype = {preprint},\n\ttitle = {Water conductance rather than photosynthesis controls water-use efficiency in {Europe}},\n\turl = {https://www.researchsquare.com/article/rs-1627127/v1},\n\tabstract = {Abstract \n          Water-use efficiency (WUE) is one of the major axes of ecosystem functioning and an indicator for ecosystem health, but its variability due to climate change and droughts has not yet been thoroughly understood. Here, we use remote sensing and reanalysis data to map the trends and responses to droughts of three WUE indices from 1995 – 2018 in Europe. Further, we conduct a causal network discovery analysis to identify drivers of in WUE change. We found an increasing trends of photosynthesis per canopy conductance (IWUE) in forests and grasslands. IWUE also increased during droughts over whole Europe but this was not translated into an increase of photosythesis per water evaporated (i.e. increased EWUE). We highlight that the WUE indices are predominantly explained by ecohydrological variability, which underlines the role of water demand and supply to ecosystem function in Europe.},\n\turldate = {2022-11-21},\n\tinstitution = {In Review},\n\tauthor = {Terán, Christian Poppe and Naz, Bibi S. and Graf, Alexander and Qu, Yuquan and Hendricks-Franssen, Harrie-Jan and Baatz, Roland and Ciais, Philippe and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2022},\n\tdoi = {10.21203/rs.3.rs-1627127/v1},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Teucher, M.; Thürkow, D.; Alb, P.; and Conrad, C.\n\n\n \n \n \n \n \n Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(2): 393. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Digital$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{teucher_digital_2022,\n\ttitle = {Digital {In} {Situ} {Data} {Collection} in {Earth} {Observation}, {Monitoring} and {Agriculture}—{Progress} towards {Digital} {Agriculture}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/2/393},\n\tdoi = {10.3390/rs14020393},\n\tabstract = {Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Teucher, Mike and Thürkow, Detlef and Alb, Philipp and Conrad, Christopher},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {393},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Theuerkauf, M.; Blume, T.; Brauer, A.; Dräger, N.; Feldens, P.; Kaiser, K.; Kappler, C.; Kästner, F.; Lorenz, S.; Schmidt, J.; and Schult, M.\n\n\n \n \n \n \n \n Holocene lake‐level evolution of Lake Tiefer See, NE Germany, caused by climate and land cover changes.\n \n \n \n \n\n\n \n\n\n\n Boreas, 51(2): 299–316. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Holocene$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{theuerkauf_holocene_2022,\n\ttitle = {Holocene lake‐level evolution of {Lake} {Tiefer} {See}, {NE} {Germany}, caused by climate and land cover changes},\n\tvolume = {51},\n\tissn = {0300-9483, 1502-3885},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12561},\n\tdoi = {10.1111/bor.12561},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Boreas},\n\tauthor = {Theuerkauf, Martin and Blume, Theresa and Brauer, Achim and Dräger, Nadine and Feldens, Peter and Kaiser, Knut and Kappler, Christoph and Kästner, Frederike and Lorenz, Sebastian and Schmidt, Jens‐Peter and Schult, Manuela},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {299--316},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Tittel, J.; Musolff, A.; Rinke, K.; and Büttner, O.\n\n\n \n \n \n \n \n Anthropogenic Transformation Disconnects a Lowland River From Contemporary Carbon Stores in Its Catchment.\n \n \n \n \n\n\n \n\n\n\n Ecosystems, 25(3): 618–632. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Anthropogenic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{tittel_anthropogenic_2022,\n\ttitle = {Anthropogenic {Transformation} {Disconnects} a {Lowland} {River} {From} {Contemporary} {Carbon} {Stores} in {Its} {Catchment}},\n\tvolume = {25},\n\tissn = {1432-9840, 1435-0629},\n\turl = {https://link.springer.com/10.1007/s10021-021-00675-z},\n\tdoi = {10.1007/s10021-021-00675-z},\n\tabstract = {Abstract \n             \n              Rivers transport carbon from continents to oceans. Surprisingly, this carbon has often been found to be centuries old, not originating from contemporary plant biomass. This can be explained by anthropogenic disturbance of soils or discharge of radiocarbon–depleted wastewater. However, land enclosure and channel bypassing transformed many rivers from anabranching networks to single–channel systems with overbank sediment accumulation and lowered floodplain groundwater tables. We hypothesized that human development changed the fluvial carbon towards older sources by changing the morphology of watercourses. We studied radiocarbon in the Elbe, a European, anthropogenically–transformed lowland river at discharges between low flow and record peak flow. We found that the inorganic carbon, dissolved organic carbon (DOC) and particulate organic carbon was aged and up to 1850 years old. The ∆ \n              14 \n              C values remained low and invariant up to median discharges, indicating that the sources of modern carbon (fixed after 1950) were disconnected from the river during half of the time. The total share of modern carbon in DOC export was marginal (0.04\\%), 72\\% of exported DOC was older than 400 years. This was in contrast to undisturbed forested subcatchments, 72\\% of whose exported DOC was modern. Although population density is high, mass balances showed that wastewater did not significantly affect the ∆ \n              14 \n              C-DOC in the Elbe river. We conclude that wetlands and other sources of contemporary carbon were decoupled from the anthropogenically transformed Elbe stream network with incised stream bed relative to overbank sediments, shifting the sources of fluvial carbon in favor of aged stores.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Ecosystems},\n\tauthor = {Tittel, Jörg and Musolff, Andreas and Rinke, Karsten and Büttner, Olaf},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {618--632},\n}\n\n

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\n \n\n \n \n Travova, S. V.; Stepanenko, V. M.; Medvedev, A. I.; Tolstykh, M. A.; and Bogomolov, V. Y.\n\n\n \n \n \n \n \n Quality of Soil Simulation by the INM RAS–MSU Soil Scheme as a Part of the SL-AV Weather Prediction Model.\n \n \n \n \n\n\n \n\n\n\n Russian Meteorology and Hydrology, 47(3): 159–173. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Quality$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{travova_quality_2022,\n\ttitle = {Quality of {Soil} {Simulation} by the {INM} {RAS}–{MSU} {Soil} {Scheme} as a {Part} of the {SL}-{AV} {Weather} {Prediction} {Model}},\n\tvolume = {47},\n\tissn = {1068-3739, 1934-8096},\n\turl = {https://link.springer.com/10.3103/S1068373922030013},\n\tdoi = {10.3103/S1068373922030013},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Russian Meteorology and Hydrology},\n\tauthor = {Travova, S. V. and Stepanenko, V. M. and Medvedev, A. I. and Tolstykh, M. A. and Bogomolov, V. Yu.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {159--173},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Tumajer, J.; Scharnweber, T.; Smiljanic, M.; and Wilmking, M.\n\n\n \n \n \n \n \n Limitation by vapour pressure deficit shapes different intra‐annual growth patterns of diffuse‐ and ring‐porous temperate broadleaves.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 233(6): 2429–2441. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Limitation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{tumajer_limitation_2022,\n\ttitle = {Limitation by vapour pressure deficit shapes different intra‐annual growth patterns of diffuse‐ and ring‐porous temperate broadleaves},\n\tvolume = {233},\n\tissn = {0028-646X, 1469-8137},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.17952},\n\tdoi = {10.1111/nph.17952},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {New Phytologist},\n\tauthor = {Tumajer, Jan and Scharnweber, Tobias and Smiljanic, Marko and Wilmking, Martin},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2429--2441},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Ukkola, A. M.; Abramowitz, G.; and De Kauwe, M. G.\n\n\n \n \n \n \n \n A flux tower dataset tailored for land model evaluation.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(2): 449–461. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{ukkola_flux_2022,\n\ttitle = {A flux tower dataset tailored for land model evaluation},\n\tvolume = {14},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/14/449/2022/},\n\tdoi = {10.5194/essd-14-449-2022},\n\tabstract = {Abstract. Eddy covariance flux towers measure the exchange of water, energy,\nand carbon fluxes between the land and atmosphere. They have become\ninvaluable for theory development and evaluating land models. However, flux\ntower data as measured (even after site post-processing) are not directly\nsuitable for land surface modelling due to data gaps in model forcing\nvariables, inappropriate gap-filling, formatting, and varying data quality.\nHere we present a quality-control and data-formatting pipeline for tower\ndata from FLUXNET2015, La Thuile, and OzFlux syntheses and the resultant\n170-site globally distributed flux tower dataset specifically designed for\nuse in land modelling. The dataset underpins the second phase of the Protocol for the Analysis of Land Surface\nModels (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER), an international model\nintercomparison project encompassing {\\textgreater}20 land surface and\nbiosphere models. The dataset is provided in the Assistance for Land-surface\nModelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For\nforcing land surface models, the dataset provides fully gap-filled\nmeteorological data that have had periods of low data quality removed.\nAdditional constraints required for land models, such as reference\nmeasurement heights, vegetation types, and satellite-based monthly leaf area\nindex estimates, are also included. For model evaluation, the dataset\nprovides estimates of key water, carbon, and energy variables, with the\nlatent and sensible heat fluxes additionally corrected for energy balance\nclosure. The dataset provides a total of 1040 site years covering the period\n1992–2018, with individual sites spanning from 1 to 21 years. The dataset is\navailable at http://doi.org/10.25914/5fdb0902607e1\n(Ukkola et al., 2021).},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Ukkola, Anna M. and Abramowitz, Gab and De Kauwe, Martin G.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {449--461},\n}\n\n

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\n \n\n \n \n Vallentin, C.; Harfenmeister, K.; Itzerott, S.; Kleinschmit, B.; Conrad, C.; and Spengler, D.\n\n\n \n \n \n \n \n Suitability of satellite remote sensing data for yield estimation in northeast Germany.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 23(1): 52–82. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Suitability$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{vallentin_suitability_2022,\n\ttitle = {Suitability of satellite remote sensing data for yield estimation in northeast {Germany}},\n\tvolume = {23},\n\tissn = {1385-2256, 1573-1618},\n\turl = {https://link.springer.com/10.1007/s11119-021-09827-6},\n\tdoi = {10.1007/s11119-021-09827-6},\n\tabstract = {Abstract \n            Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Precision Agriculture},\n\tauthor = {Vallentin, Claudia and Harfenmeister, Katharina and Itzerott, Sibylle and Kleinschmit, Birgit and Conrad, Christopher and Spengler, Daniel},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {52--82},\n}\n\n

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\n \n\n \n \n Vereecken, H.; Amelung, W.; Bauke, S. L.; Bogena, H.; Brüggemann, N.; Montzka, C.; Vanderborght, J.; Bechtold, M.; Blöschl, G.; Carminati, A.; Javaux, M.; Konings, A. G.; Kusche, J.; Neuweiler, I.; Or, D.; Steele-Dunne, S.; Verhoef, A.; Young, M.; and Zhang, Y.\n\n\n \n \n \n \n \n Soil hydrology in the Earth system.\n \n \n \n \n\n\n \n\n\n\n Nature Reviews Earth & Environment, 3(9): 573–587. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{vereecken_soil_2022,\n\ttitle = {Soil hydrology in the {Earth} system},\n\tvolume = {3},\n\tissn = {2662-138X},\n\turl = {https://www.nature.com/articles/s43017-022-00324-6},\n\tdoi = {10.1038/s43017-022-00324-6},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Nature Reviews Earth \\& Environment},\n\tauthor = {Vereecken, Harry and Amelung, Wulf and Bauke, Sara L. and Bogena, Heye and Brüggemann, Nicolas and Montzka, Carsten and Vanderborght, Jan and Bechtold, Michel and Blöschl, Günter and Carminati, Andrea and Javaux, Mathieu and Konings, Alexandra G. and Kusche, Jürgen and Neuweiler, Insa and Or, Dani and Steele-Dunne, Susan and Verhoef, Anne and Young, Michael and Zhang, Yonggen},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {573--587},\n}\n\n

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\n \n\n \n \n Vitali, V.; Martínez-Sancho, E.; Treydte, K.; Andreu-Hayles, L.; Dorado-Liñán, I.; Gutierrez, E.; Helle, G.; Leuenberger, M.; Loader, N.; Rinne-Garmston, K.; Schleser, G.; Allen, S.; Waterhouse, J.; Saurer, M.; and Lehmann, M.\n\n\n \n \n \n \n \n The unknown third – Hydrogen isotopes in tree-ring cellulose across Europe.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 813: 152281. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{vitali_unknown_2022,\n\ttitle = {The unknown third – {Hydrogen} isotopes in tree-ring cellulose across {Europe}},\n\tvolume = {813},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721073575},\n\tdoi = {10.1016/j.scitotenv.2021.152281},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Vitali, V. and Martínez-Sancho, E. and Treydte, K. and Andreu-Hayles, L. and Dorado-Liñán, I. and Gutierrez, E. and Helle, G. and Leuenberger, M. and Loader, N.J. and Rinne-Garmston, K.T. and Schleser, G.H. and Allen, S. and Waterhouse, J.S. and Saurer, M. and Lehmann, M.M.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {152281},\n}\n\n

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\n \n\n \n \n Walker, V. A.; Colliander, A.; and Kimball, J. S.\n\n\n \n \n \n \n \n Satellite Retrievals of Probabilistic Freeze-Thaw Conditions From SMAP and AMSR Brightness Temperatures.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–11. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Satellite$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{walker_satellite_2022,\n\ttitle = {Satellite {Retrievals} of {Probabilistic} {Freeze}-{Thaw} {Conditions} {From} {SMAP} and {AMSR} {Brightness} {Temperatures}},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\turl = {https://ieeexplore.ieee.org/document/9773168/},\n\tdoi = {10.1109/TGRS.2022.3174807},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Walker, Victoria A. and Colliander, Andreas and Kimball, John S.},\n\tyear = {2022},\n\tpages = {1--11},\n}\n\n

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\n \n\n \n \n Walther, S.; Besnard, S.; Nelson, J. A.; El-Madany, T. S.; Migliavacca, M.; Weber, U.; Carvalhais, N.; Ermida, S. L.; Brümmer, C.; Schrader, F.; Prokushkin, A. S.; Panov, A. V.; and Jung, M.\n\n\n \n \n \n \n \n Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(11): 2805–2840. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Technical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{walther_technical_2022,\n\ttitle = {Technical note: {A} view from space on global flux towers by {MODIS} and {Landsat}: the {FluxnetEO} data set},\n\tvolume = {19},\n\tissn = {1726-4189},\n\tshorttitle = {Technical note},\n\turl = {https://bg.copernicus.org/articles/19/2805/2022/},\n\tdoi = {10.5194/bg-19-2805-2022},\n\tabstract = {Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40∘ off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Walther, Sophia and Besnard, Simon and Nelson, Jacob Allen and El-Madany, Tarek Sebastian and Migliavacca, Mirco and Weber, Ulrich and Carvalhais, Nuno and Ermida, Sofia Lorena and Brümmer, Christian and Schrader, Frederik and Prokushkin, Anatoly Stanislavovich and Panov, Alexey Vasilevich and Jung, Martin},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2805--2840},\n}\n\n

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\n \n\n \n \n Wang, B.; Chen, W.; Dai, J.; Li, Z.; Fu, Z.; Sarmah, S.; Luo, Y.; and Niu, S.\n\n\n \n \n \n \n \n Dryness controls temperature-optimized gross primary productivity across vegetation types.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109073. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Dryness$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{wang_dryness_2022,\n\ttitle = {Dryness controls temperature-optimized gross primary productivity across vegetation types},\n\tvolume = {323},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002611},\n\tdoi = {10.1016/j.agrformet.2022.109073},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Wang, Bingxue and Chen, Weinan and Dai, Junhu and Li, Zhaolei and Fu, Zheng and Sarmah, Sangeeta and Luo, Yiqi and Niu, Shuli},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109073},\n}\n\n

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\n \n\n \n \n Wang, B.; Yue, X.; Zhou, H.; and Zhu, J.\n\n\n \n \n \n \n \n Impact of diffuse radiation on evapotranspiration and its coupling to carbon fluxes at global FLUXNET sites.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 322: 109006. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_impact_2022,\n\ttitle = {Impact of diffuse radiation on evapotranspiration and its coupling to carbon fluxes at global {FLUXNET} sites},\n\tvolume = {322},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322001964},\n\tdoi = {10.1016/j.agrformet.2022.109006},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Wang, Bin and Yue, Xu and Zhou, Hao and Zhu, Jun},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {109006},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Wang, Q.; Qu, Y.; Robinson, K.; Bogena, H.; Graf, A.; Vereecken, H.; Tietema, A.; and Bol, R.\n\n\n \n \n \n \n \n Deforestation alters dissolved organic carbon and sulfate dynamics in a mountainous headwater catchment—A wavelet analysis.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Forests and Global Change, 5: 1044447. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Deforestation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_deforestation_2022,\n\ttitle = {Deforestation alters dissolved organic carbon and sulfate dynamics in a mountainous headwater catchment—{A} wavelet analysis},\n\tvolume = {5},\n\tissn = {2624-893X},\n\turl = {https://www.frontiersin.org/articles/10.3389/ffgc.2022.1044447/full},\n\tdoi = {10.3389/ffgc.2022.1044447},\n\tabstract = {Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fe \n              tot \n              ), and calcium (Ca \n              2+ \n              ) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31\\%, W14: 25\\%, W17: 3\\%). We found that water temperature and DOC, sulfate, and Fe \n              tot \n              concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L \n              –1 \n              and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L \n              –1 \n              . After clear-cut, DOC significantly increased by 59, 58\\% in the mainstream (W10, W14), but only 26\\% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca \n              2+ \n              , and Fe \n              tot \n              . The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO \n              4 \n              ) was highly correlated with stream water temperature, runoff, and Fe \n              tot \n              in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R \n              2 \n              ) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25\\% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Forests and Global Change},\n\tauthor = {Wang, Qiqi and Qu, Yuquan and Robinson, Kerri-Leigh and Bogena, Heye and Graf, Alexander and Vereecken, Harry and Tietema, Albert and Bol, Roland},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {1044447},\n}\n\n

\n

\n\n\n

\n Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fe tot ), and calcium (Ca 2+ ) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fe tot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L –1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L –1 . After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca 2+ , and Fe tot . The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO 4 ) was highly correlated with stream water temperature, runoff, and Fe tot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R 2 ) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.\n

\n\n\n

\n \n\n \n \n Wang, R.; Gentine, P.; Li, L.; Chen, J.; Ning, L.; Yuan, L.; and Lü, G.\n\n\n \n \n \n \n \n Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Observational$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{wang_observational_2022,\n\ttitle = {Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-21-0114.1/JHM-D-21-0114.1.xml},\n\tdoi = {10.1175/JHM-D-21-0114.1},\n\tabstract = {Abstract \n            Land-atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land-atmosphere coupling. One of the linkages in land-atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, Northeast Asia, and Southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hotspot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidences for the extreme climate change accelerated by surface energy partitioning.},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Wang, Ren and Gentine, Pierre and Li, Longhui and Chen, Jianyao and Ning, Liang and Yuan, Linwang and Lü, Guonian},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Wang, R.; Li, L.; Gentine, P.; Zhang, Y.; Chen, J.; Chen, X.; Chen, L.; Ning, L.; Yuan, L.; and Lü, G.\n\n\n \n \n \n \n \n Recent increase in the observation-derived land evapotranspiration due to global warming.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(2): 024020. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Recent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{wang_recent_2022,\n\ttitle = {Recent increase in the observation-derived land evapotranspiration due to global warming},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac4291},\n\tdoi = {10.1088/1748-9326/ac4291},\n\tabstract = {Abstract \n             \n              Estimates of change in global land evapotranspiration (ET) are necessary for understanding the terrestrial hydrological cycle under changing environments. However, large uncertainties still exist in our estimates, mostly related to the uncertainties in upscaling \n              in situ \n              observations to large scale under non-stationary surface conditions. Here, we use machine learning models, artificial neural network and random forest informed by ground observations and atmospheric boundary layer theory, to retrieve consistent global long-term latent heat flux (ET in energy units) and sensible heat flux over recent decades. This study demonstrates that recent global land ET has increased significantly and that the main driver for the increased ET is increasing temperature. Moreover, the results suggest that the increasing ET is mostly in humid regions such as the tropics. These observation-driven findings are consistent with the idea that ET would increase with climate warming. Our study has important implications in providing constraints for ET and in understanding terrestrial water cycles in changing environments.},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Wang, Ren and Li, Longhui and Gentine, Pierre and Zhang, Yao and Chen, Jianyao and Chen, Xingwei and Chen, Lijuan and Ning, Liang and Yuan, Linwang and Lü, Guonian},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {024020},\n}\n\n

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\n \n\n \n \n Wang, X.; Chen, J. M.; Ju, W.; and Zhang, Y.\n\n\n \n \n \n \n \n Seasonal Variations in Leaf Maximum Photosynthetic Capacity and Its Dependence on Climate Factors Across Global FLUXNET Sites.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 127(5). May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_seasonal_2022,\n\ttitle = {Seasonal {Variations} in {Leaf} {Maximum} {Photosynthetic} {Capacity} and {Its} {Dependence} on {Climate} {Factors} {Across} {Global} {FLUXNET} {Sites}},\n\tvolume = {127},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021JG006709},\n\tdoi = {10.1029/2021JG006709},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Wang, Xiaoping and Chen, Jing M. and Ju, Weimin and Zhang, Yongguang},\n\tmonth = may,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Wanner, L.; Calaf, M.; and Mauder, M.\n\n\n \n \n \n \n \n Incorporating the effect of heterogeneous surface heating into a semi-empirical model of the surface energy balance closure.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 17(6): e0268097. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Incorporating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wanner_incorporating_2022,\n\ttitle = {Incorporating the effect of heterogeneous surface heating into a semi-empirical model of the surface energy balance closure},\n\tvolume = {17},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0268097},\n\tdoi = {10.1371/journal.pone.0268097},\n\tabstract = {It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time ({\\textasciitilde}30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {PLOS ONE},\n\tauthor = {Wanner, Luise and Calaf, Marc and Mauder, Matthias},\n\teditor = {Roberti, Débora Regina},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {e0268097},\n}\n\n

\n

\n\n\n

\n It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time (~30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements.\n

\n\n\n

\n \n\n \n \n Wanner, L.; De Roo, F.; Sühring, M.; and Mauder, M.\n\n\n \n \n \n \n \n How Does the Choice of the Lower Boundary Conditions in Large-Eddy Simulations Affect the Development of Dispersive Fluxes Near the Surface?.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 182(1): 1–27. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wanner_how_2022,\n\ttitle = {How {Does} the {Choice} of the {Lower} {Boundary} {Conditions} in {Large}-{Eddy} {Simulations} {Affect} the {Development} of {Dispersive} {Fluxes} {Near} the {Surface}?},\n\tvolume = {182},\n\tissn = {0006-8314, 1573-1472},\n\turl = {https://link.springer.com/10.1007/s10546-021-00649-7},\n\tdoi = {10.1007/s10546-021-00649-7},\n\tabstract = {Abstract \n            Large-eddy simulations (LES) are an important tool for investigating the longstanding energy-balance-closure problem, as they provide continuous, spatially-distributed information about turbulent flow at a high temporal resolution. Former LES studies reproduced an energy-balance gap similar to the observations in the field typically amounting to 10–30\\% for heights on the order of 100 m in convective boundary layers even above homogeneous surfaces. The underestimation is caused by dispersive fluxes associated with large-scale turbulent organized structures that are not captured by single-tower measurements. However, the gap typically vanishes near the surface, i.e. at typical eddy-covariance measurement heights below 20 m, contrary to the findings from field measurements. In this study, we aim to find a LES set-up that can represent the correct magnitude of the energy-balance gap close to the surface. Therefore, we use a nested two-way coupled LES, with a fine grid that allows us to resolve fluxes and atmospheric structures at typical eddy-covariance measurement heights of 20 m. Under different stability regimes we compare three different options for lower boundary conditions featuring grassland and forest surfaces, i.e. (1) prescribed surface fluxes, (2) a land-surface model, and (3) a land-surface model in combination with a resolved canopy. We show that the use of prescribed surface fluxes and a land-surface model yields similar dispersive heat fluxes that are very small near the vegetation top for both grassland and forest surfaces. However, with the resolved forest canopy, dispersive heat fluxes are clearly larger, which we explain by a clear impact of the resolved canopy on the relationship between variance and flux–variance similarity functions.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Wanner, Luise and De Roo, Frederik and Sühring, Matthias and Mauder, Matthias},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {1--27},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Weber, U.; Attinger, S.; Baschek, B.; Boike, J.; Borchardt, D.; Brix, H.; Brüggemann, N.; Bussmann, I.; Dietrich, P.; Fischer, P.; Greinert, J.; Hajnsek, I.; Kamjunke, N.; Kerschke, D.; Kiendler-Scharr, A.; Körtzinger, A.; Kottmeier, C.; Merz, B.; Merz, R.; Riese, M.; Schloter, M.; Schmid, H.; Schnitzler, J.; Sachs, T.; Schütze, C.; Tillmann, R.; Vereecken, H.; Wieser, A.; and Teutsch, G.\n\n\n \n \n \n \n \n MOSES: A Novel Observation System to Monitor Dynamic Events across Earth Compartments.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 103(2): E339–E348. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"MOSES:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{weber_moses_2022,\n\ttitle = {{MOSES}: {A} {Novel} {Observation} {System} to {Monitor} {Dynamic} {Events} across {Earth} {Compartments}},\n\tvolume = {103},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {{MOSES}},\n\turl = {https://journals.ametsoc.org/view/journals/bams/103/2/BAMS-D-20-0158.1.xml},\n\tdoi = {10.1175/BAMS-D-20-0158.1},\n\tabstract = {Abstract \n            Modular Observation Solutions of Earth Systems (MOSES) is a novel observation system that is specifically designed to unravel the impact of distinct, dynamic events on the long-term development of environmental systems. Hydrometeorological extremes such as the recent European droughts or the floods of 2013 caused severe and lasting environmental damage. Modeling studies suggest that abrupt permafrost thaw events accelerate Arctic greenhouse gas emissions. Short-lived ocean eddies seem to comprise a significant share of the marine carbon uptake or release. Although there is increasing evidence that such dynamic events bear the potential for major environmental impacts, our knowledge on the processes they trigger is still very limited. MOSES aims at capturing such events, from their formation to their end, with high spatial and temporal resolution. As such, the observation system extends and complements existing national and international observation networks, which are mostly designed for long-term monitoring. Several German Helmholtz Association centers have developed this research facility as a mobile and modular “system of systems” to record energy, water, greenhouse gas, and nutrient cycles on the land surface, in coastal regions, in the ocean, in polar regions, and in the atmosphere—but especially the interactions between the Earth compartments. During the implementation period (2017–21), the measuring systems were put into operation and test campaigns were performed to establish event-driven campaign routines. With MOSES’s regular operation starting in 2022, the observation system will then be ready for cross-compartment and cross-discipline research on the environmental impacts of dynamic events.},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Weber, Ute and Attinger, Sabine and Baschek, Burkard and Boike, Julia and Borchardt, Dietrich and Brix, Holger and Brüggemann, Nicolas and Bussmann, Ingeborg and Dietrich, Peter and Fischer, Philipp and Greinert, Jens and Hajnsek, Irena and Kamjunke, Norbert and Kerschke, Dorit and Kiendler-Scharr, Astrid and Körtzinger, Arne and Kottmeier, Christoph and Merz, Bruno and Merz, Ralf and Riese, Martin and Schloter, Michael and Schmid, HaPe and Schnitzler, Jörg-Peter and Sachs, Torsten and Schütze, Claudia and Tillmann, Ralf and Vereecken, Harry and Wieser, Andreas and Teutsch, Georg},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {E339--E348},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Wei, J.; Knicker, H.; Zhou, Z.; Eckhardt, K.; Leinweber, P.; Wissel, H.; Yuan, W.; and Brüggemann, N.\n\n\n \n \n \n \n \n Nitrogen Immobilization Caused by Chemical Formation of Black Nitrogen and Amide in Soil.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Nitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wei_nitrogen_2022,\n\ttitle = {Nitrogen {Immobilization} {Caused} by {Chemical} {Formation} of {Black} {Nitrogen} and {Amide} in {Soil}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4108591},\n\tdoi = {10.2139/ssrn.4108591},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Wei, Jing and Knicker, Heike and Zhou, Zheyan and Eckhardt, Kai-Uwe and Leinweber, Peter and Wissel, Holger and Yuan, Wenping and Brüggemann, Nicolas},\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Weisner, O.; Arle, J.; Liebmann, L.; Link, M.; Schäfer, R. B.; Schneeweiss, A.; Schreiner, V. C.; Vormeier, P.; and Liess, M.\n\n\n \n \n \n \n \n Three reasons why the Water Framework Directive (WFD) fails to identify pesticide risks.\n \n \n \n \n\n\n \n\n\n\n Water Research, 208: 117848. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Three$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{weisner_three_2022,\n\ttitle = {Three reasons why the {Water} {Framework} {Directive} ({WFD}) fails to identify pesticide risks},\n\tvolume = {208},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135421010423},\n\tdoi = {10.1016/j.watres.2021.117848},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Water Research},\n\tauthor = {Weisner, Oliver and Arle, Jens and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Schreiner, Verena C. and Vormeier, Philipp and Liess, Matthias},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {117848},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Wieser, A.; Güntner, A.; Dietrich, P.; Handwerker, J.; Khordakova, D.; Ködel, U.; Kohler, M.; Mollenhauer, H.; Mühr, B.; Nixdorf, E.; Reich, M.; Rolf, C.; Schrön, M.; Schütze, C.; and Weber, U.\n\n\n \n \n \n \n \n First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding.\n \n \n \n \n\n\n \n\n\n\n Technical Report Catchment hydrology/Instruments and observation techniques, April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"First$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{wieser_first_2022,\n\ttype = {preprint},\n\ttitle = {First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding},\n\turl = {https://hess.copernicus.org/preprints/hess-2022-131/},\n\tabstract = {Abstract. Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.},\n\turldate = {2022-11-21},\n\tinstitution = {Catchment hydrology/Instruments and observation techniques},\n\tauthor = {Wieser, Andreas and Güntner, Andreas and Dietrich, Peter and Handwerker, Jan and Khordakova, Dina and Ködel, Uta and Kohler, Martin and Mollenhauer, Hannes and Mühr, Bernhard and Nixdorf, Erik and Reich, Marvin and Rolf, Christian and Schrön, Martin and Schütze, Claudia and Weber, Ute},\n\tmonth = apr,\n\tyear = {2022},\n\tdoi = {10.5194/hess-2022-131},\n}\n\n

\n

\n\n\n

\n Abstract. Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.\n

\n\n\n

\n \n\n \n \n Wild, B.; Teubner, I.; Moesinger, L.; Zotta, R.; Forkel, M.; van der Schalie, R.; Sitch, S.; and Dorigo, W.\n\n\n \n \n \n \n \n VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(3): 1063–1085. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"VODCA2GPP$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wild_vodca2gpp_2022,\n\ttitle = {{VODCA2GPP} – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing},\n\tvolume = {14},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/14/1063/2022/},\n\tdoi = {10.5194/essd-14-1063-2022},\n\tabstract = {Abstract. Long-term global monitoring of terrestrial gross primary\nproduction (GPP) is crucial for assessing ecosystem responses to global\nclimate change. In recent decades, great advances have been made in\nestimating GPP and many global GPP datasets have been published. These\ndatasets are based on observations from optical remote sensing, are\nupscaled from in situ measurements, or rely on process-based models.\nAlthough these approaches are well established within the scientific\ncommunity, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave\nremote sensing estimates of vegetation optical depth (VOD) to estimate GPP\nat the global scale for the period 1988–2020. VODCA2GPP applies a previously\ndeveloped carbon-sink-driven approach (Teubner et al., 2019, 2021) to\nestimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et\nal., 2020; Zotta et al., 2022), which merges VOD observations from\nmultiple sensors into one long-running, coherent data record. VODCA2GPP was\ntrained and evaluated against FLUXNET in situ observations of GPP and\ncompared against largely independent state-of-the-art GPP datasets from\nthe Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust\nperformance of VODCA2GPP with only a small bias and good temporal agreement.\nThe comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP\nexhibits very similar spatial patterns across all biomes but with a\nconsistent positive bias. In terms of temporal dynamics, a high agreement\nwas found for regions outside the humid tropics, with median correlations\naround 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP\ncorrelates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but\nless well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period\n1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale\nbut rather suggests regionally different long-term changes in GPP. For the\nshorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and\nthe TRENDY-v7 ensemble, significant increases in global GPP were found.\nVODCA2GPP can complement existing GPP products and is a valuable dataset for\nthe assessment of large-scale and long-term changes in GPP for global\nvegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al.,\n2021).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Wild, Benjamin and Teubner, Irene and Moesinger, Leander and Zotta, Ruxandra-Maria and Forkel, Matthias and van der Schalie, Robin and Sitch, Stephen and Dorigo, Wouter},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1063--1085},\n}\n\n

\n

\n\n\n

\n Abstract. Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al., 2021).\n

\n\n\n

\n \n\n \n \n Winter, C.; Nguyen, T. V.; Musolff, A.; Lutz, S. R.; Rode, M.; Kumar, R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Droughts can reduce the nitrogen retention capacity of catchments.\n \n \n \n \n\n\n \n\n\n\n Technical Report Catchment hydrology/Theory development, June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Droughts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{winter_droughts_2022,\n\ttype = {preprint},\n\ttitle = {Droughts can reduce the nitrogen retention capacity of catchments},\n\turl = {https://egusphere.copernicus.org/preprints/2022/egusphere-2022-431/},\n\tabstract = {Abstract. In 2018–2019, Central Europe experienced an unprecedented multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 70 \\% higher, compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times ({\\textless}2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream ({\\textgreater}20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.},\n\turldate = {2022-11-21},\n\tinstitution = {Catchment hydrology/Theory development},\n\tauthor = {Winter, Carolin and Nguyen, Tam V. and Musolff, Andreas and Lutz, Stefanie R. and Rode, Michael and Kumar, Rohini and Fleckenstein, Jan H.},\n\tmonth = jun,\n\tyear = {2022},\n\tdoi = {10.5194/egusphere-2022-431},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Winter, C.; Tarasova, L.; Lutz, S. R.; Musolff, A.; Kumar, R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Explaining the Variability in High‐Frequency Nitrate Export Patterns Using Long‐Term Hydrological Event Classification.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(1). January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Explaining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{winter_explaining_2022,\n\ttitle = {Explaining the {Variability} in {High}‐{Frequency} {Nitrate} {Export} {Patterns} {Using} {Long}‐{Term} {Hydrological} {Event} {Classification}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030938},\n\tdoi = {10.1029/2021WR030938},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Winter, C. and Tarasova, L. and Lutz, S. R. and Musolff, A. and Kumar, R. and Fleckenstein, J. H.},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Wu, S.; Tetzlaff, D.; Yang, X.; and Soulsby, C.\n\n\n \n \n \n \n \n Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(8). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wu_identifying_2022,\n\ttitle = {Identifying {Dominant} {Processes} in {Time} and {Space}: {Time}‐{Varying} {Spatial} {Sensitivity} {Analysis} for a {Grid}‐{Based} {Nitrate} {Model}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Identifying {Dominant} {Processes} in {Time} and {Space}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR031149},\n\tdoi = {10.1029/2021WR031149},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Wu, Songjun and Tetzlaff, Doerthe and Yang, Xiaoqiang and Soulsby, Chris},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n

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\n \n\n \n \n Xi, X.; Gentine, P.; Zhuang, Q.; and Kim, S.\n\n\n \n \n \n \n \n Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 127(5). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{xi_evaluating_2022,\n\ttitle = {Evaluating the {Variability} of {Surface} {Soil} {Moisture} {Simulated} {Within} {CMIP5} {Using} {SMAP} {Data}},\n\tvolume = {127},\n\tissn = {2169-897X, 2169-8996},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021JD035363},\n\tdoi = {10.1029/2021JD035363},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Xi, Xuan and Gentine, Pierre and Zhuang, Qianlai and Kim, Seungbum},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Xie, Z.; Yao, Y.; Zhang, X.; Liang, S.; Fisher, J. B.; Chen, J.; Jia, K.; Shang, K.; Yang, J.; Yu, R.; Guo, X.; Liu, L.; Ning, J.; and Zhang, L.\n\n\n \n \n \n \n \n The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 610: 127990. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{xie_global_2022,\n\ttitle = {The {Global} {LAnd} {Surface} {Satellite} ({GLASS}) evapotranspiration product {Version} 5.0: {Algorithm} development and preliminary validation},\n\tvolume = {610},\n\tissn = {00221694},\n\tshorttitle = {The {Global} {LAnd} {Surface} {Satellite} ({GLASS}) evapotranspiration product {Version} 5.0},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169422005650},\n\tdoi = {10.1016/j.jhydrol.2022.127990},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Xie, Zijing and Yao, Yunjun and Zhang, Xiaotong and Liang, Shunlin and Fisher, Joshua B. and Chen, Jiquan and Jia, Kun and Shang, Ke and Yang, Junming and Yu, Ruiyang and Guo, Xiaozheng and Liu, Lu and Ning, Jing and Zhang, Lilin},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {127990},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Xu, J.; Zhang, X.; Zhang, W.; Hou, N.; Feng, C.; Yang, S.; Jia, K.; Yao, Y.; Xie, X.; Jiang, B.; Cheng, J.; Zhao, X.; and Liang, S.\n\n\n \n \n \n \n \n Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Research, 270: 106056. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{xu_assessment_2022,\n\ttitle = {Assessment of surface downward longwave radiation in {CMIP6} with comparison to observations and {CMIP5}},\n\tvolume = {270},\n\tissn = {01698095},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169809522000424},\n\tdoi = {10.1016/j.atmosres.2022.106056},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Atmospheric Research},\n\tauthor = {Xu, Jiawen and Zhang, Xiaotong and Zhang, Weiyu and Hou, Ning and Feng, Chunjie and Yang, Shuyue and Jia, Kun and Yao, Yunjun and Xie, Xianhong and Jiang, Bo and Cheng, Jie and Zhao, Xiang and Liang, Shunlin},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {106056},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Xu, S.; McVicar, T. R.; Li, L.; Yu, Z.; Jiang, P.; Zhang, Y.; Ban, Z.; Xing, W.; Dong, N.; Zhang, H.; and Zhang, M.\n\n\n \n \n \n \n \n Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale: Patterns and mechanisms.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109085. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Globally$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{xu_globally_2022,\n\ttitle = {Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale: {Patterns} and mechanisms},\n\tvolume = {323},\n\tissn = {01681923},\n\tshorttitle = {Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002738},\n\tdoi = {10.1016/j.agrformet.2022.109085},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Xu, Shiqin and McVicar, Tim R. and Li, Lingcheng and Yu, Zhongbo and Jiang, Peng and Zhang, Yuliang and Ban, Zhaoxin and Xing, Wanqiu and Dong, Ningpeng and Zhang, Hua and Zhang, Mingjun},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109085},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Xue, Z.; Zhang, Y.; Zhang, L.; and Li, H.\n\n\n \n \n \n \n \n Ensemble Learning Embedded With Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental U.S.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–17. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Ensemble$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{xue_ensemble_2022,\n\ttitle = {Ensemble {Learning} {Embedded} {With} {Gaussian} {Process} {Regression} for {Soil} {Moisture} {Estimation}: {A} {Case} {Study} of the {Continental} {U}.{S}.},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\tshorttitle = {Ensemble {Learning} {Embedded} {With} {Gaussian} {Process} {Regression} for {Soil} {Moisture} {Estimation}},\n\turl = {https://ieeexplore.ieee.org/document/9757155/},\n\tdoi = {10.1109/TGRS.2022.3166777},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Xue, Zhaohui and Zhang, Yujuan and Zhang, Ling and Li, Hao},\n\tyear = {2022},\n\tpages = {1--17},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Yang, H.; Wang, Q.; Zhao, W.; Tong, X.; and Atkinson, P. M.\n\n\n \n \n \n \n \n Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion.\n \n \n \n \n\n\n \n\n\n\n Journal of Remote Sensing, 2022: 1–23. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Reconstruction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yang_reconstruction_2022,\n\ttitle = {Reconstruction of a {Global} 9 km, 8-{Day} {SMAP} {Surface} {Soil} {Moisture} {Dataset} during 2015–2020 by {Spatiotemporal} {Fusion}},\n\tvolume = {2022},\n\tissn = {2694-1589},\n\turl = {https://spj.sciencemag.org/journals/remotesensing/2022/9871246/},\n\tdoi = {10.34133/2022/9871246},\n\tabstract = {Soil moisture, a crucial property for Earth surface research, has been focused widely in various studies. The Soil Moisture Active Passive (SMAP) global products at 36 km and 9 km (called P36 and AP9 in this research) have been published from April 2015. However, the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015. In this research, the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme (VIPSTF-SW) to simulate the 9 km AP9 data after failure of the active radar. The method makes full use of all the historical AP9 and P36 data available between April and July 2015. As a result, 8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020, by downscaling the corresponding 8-day composited P36 data. The available AP9 data and \n              in situ \n              reference data were used to validate the predicted 9 km data. Generally, the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product. The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology, climatology, ecology, and many other fields at the global scale.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Remote Sensing},\n\tauthor = {Yang, Haoxuan and Wang, Qunming and Zhao, Wei and Tong, Xiaohua and Atkinson, Peter M.},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {1--23},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Yang, X.; Rode, M.; Jomaa, S.; Merbach, I.; Tetzlaff, D.; Soulsby, C.; and Borchardt, D.\n\n\n \n \n \n \n \n Functional Multi‐Scale Integration of Agricultural Nitrogen‐Budgets Into Catchment Water Quality Modeling.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(4). February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Functional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yang_functional_2022,\n\ttitle = {Functional {Multi}‐{Scale} {Integration} of {Agricultural} {Nitrogen}‐{Budgets} {Into} {Catchment} {Water} {Quality} {Modeling}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021GL096833},\n\tdoi = {10.1029/2021GL096833},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Yang, Xiaoqiang and Rode, Michael and Jomaa, Seifeddine and Merbach, Ines and Tetzlaff, Doerthe and Soulsby, Chris and Borchardt, Dietrich},\n\tmonth = feb,\n\tyear = {2022},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Yang, Y.; Bloom, A. A.; Ma, S.; Levine, P.; Norton, A.; Parazoo, N. C.; Reager, J. T.; Worden, J.; Quetin, G. R.; Smallman, T. L.; Williams, M.; Xu, L.; and Saatchi, S.\n\n\n \n \n \n \n \n CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(4): 1789–1802. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CARDAMOM-FluxVal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yang_cardamom-fluxval_2022,\n\ttitle = {{CARDAMOM}-{FluxVal} version 1.0: a {FLUXNET}-based validation system for {CARDAMOM} carbon and water flux estimates},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{CARDAMOM}-{FluxVal} version 1.0},\n\turl = {https://gmd.copernicus.org/articles/15/1789/2022/},\n\tdoi = {10.5194/gmd-15-1789-2022},\n\tabstract = {Abstract. Land–atmosphere carbon and water exchanges have large uncertainty\nin terrestrial biosphere models (TBMs). Using observations to reduce TBM\nstructural and parametric errors and uncertainty is a critical priority for\nboth understanding and accurately predicting carbon and water fluxes. Recent\nimplementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have\nyielded key insights into ecosystem carbon and water cycling. CARDAMOM\nestimates parameters for an associated TBM of intermediate complexity\n(Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O\nflux observations – have exhibited considerable skill in both representing\nthe variability of assimilated observations and predicting withheld\nobservations. CARDAMOM and DALEC have been continuously developed to\naccommodate new scientific challenges and an expanding variety of\nobservational constraints. However, so far there has been no concerted\neffort to globally and systematically validate CARDAMOM performance across\nindividual model–data fusion configurations. Here we use the FLUXNET 2015\ndataset – an ensemble of 200+ eddy covariance flux tower sites – to\nformulate a concerted benchmarking framework for CARDAMOM carbon\n(photosynthesis and net C exchange) and water (evapotranspiration) flux\nestimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill\nmetrics to evaluate CARDAMOM performance against both assimilated and\nwithheld FLUXNET 2015 photosynthesis, net CO2 exchange, and\nevapotranspiration estimates. We further demonstrate the potential for\ntailored CARDAMOM evaluations by categorizing performance in terms of (i)\nindividual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii)\nlength of assimilation data. The CARDAMOM benchmarking system – along with\nthe CARDAMOM driver files provided – can be readily repeated to support both the\nintercomparison between existing CARDAMOM model configurations and the\nformulation, development, and testing of new CARDAMOM model structures.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Yang, Yan and Bloom, A. Anthony and Ma, Shuang and Levine, Paul and Norton, Alexander and Parazoo, Nicholas C. and Reager, John T. and Worden, John and Quetin, Gregory R. and Smallman, T. Luke and Williams, Mathew and Xu, Liang and Saatchi, Sassan},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1789--1802},\n}\n\n

\n

\n\n\n

\n Abstract. Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across individual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.\n

\n\n\n

\n \n\n \n \n Yin, G.; Verger, A.; Descals, A.; Filella, I.; and Peñuelas, J.\n\n\n \n \n \n \n \n A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology.\n \n \n \n \n\n\n \n\n\n\n Journal of Remote Sensing, 2022: 1–10. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yin_broadband_2022,\n\ttitle = {A {Broadband} {Green}-{Red} {Vegetation} {Index} for {Monitoring} {Gross} {Primary} {Production} {Phenology}},\n\tvolume = {2022},\n\tissn = {2694-1589},\n\turl = {https://spj.sciencemag.org/journals/remotesensing/2022/9764982/},\n\tdoi = {10.34133/2022/9764982},\n\tabstract = {The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) ( \n               \n                 \n                   \n                    R \n                   \n                   \n                    2 \n                   \n                 \n                = \n                0.98 \n                , \n                p \n                {\\textless} \n                0.001 \n               \n              ), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 ( \n               \n                 \n                   \n                    R \n                   \n                   \n                    2 \n                   \n                 \n                = \n                0.97 \n                , \n                p \n                {\\textless} \n                0.001 \n               \n              ). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Remote Sensing},\n\tauthor = {Yin, Gaofei and Verger, Aleixandre and Descals, Adrià and Filella, Iolanda and Peñuelas, Josep},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1--10},\n}\n\n

\n

\n\n\n\n\n\n

\n \n\n \n \n Yu, L.; Zhou, S.; Zhao, X.; Gao, X.; Jiang, K.; Zhang, B.; Cheng, L.; Song, X.; and Siddique, K. H. M.\n\n\n \n \n \n \n \n Evapotranspiration Partitioning Based on Leaf and Ecosystem Water Use Efficiency.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(9). September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evapotranspiration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yu_evapotranspiration_2022,\n\ttitle = {Evapotranspiration {Partitioning} {Based} on {Leaf} and {Ecosystem} {Water} {Use} {Efficiency}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030629},\n\tdoi = {10.1029/2021WR030629},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Yu, Liuyang and Zhou, Sha and Zhao, Xining and Gao, Xiaodong and Jiang, Kongtao and Zhang, Baoqing and Cheng, Lei and Song, Xiaolin and Siddique, Kadambot H. M.},\n\tmonth = sep,\n\tyear = {2022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Yu, P.; Zhou, T.; Luo, H.; Liu, X.; Shi, P.; Zhao, X.; Xiao, Z.; Zhang, Y.; and Zhou, P.\n\n\n \n \n \n \n \n Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing in Ecology and Conservation, 8(3): 409–425. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Interannual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yu_interannual_2022,\n\ttitle = {Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress},\n\tvolume = {8},\n\tissn = {2056-3485, 2056-3485},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/rse2.252},\n\tdoi = {10.1002/rse2.252},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing in Ecology and Conservation},\n\tauthor = {Yu, Peixin and Zhou, Tao and Luo, Hui and Liu, Xia and Shi, Peijun and Zhao, Xiang and Xiao, Zhiqiang and Zhang, Yajie and Zhou, Peifang},\n\teditor = {Disney, Mat and Zhang, Jian},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {409--425},\n}\n\n

\n

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\n \n\n \n \n Yuan, K.; Zhu, Q.; Li, F.; Riley, W. J.; Torn, M.; Chu, H.; McNicol, G.; Chen, M.; Knox, S.; Delwiche, K.; Wu, H.; Baldocchi, D.; Ma, H.; Desai, A. R.; Chen, J.; Sachs, T.; Ueyama, M.; Sonnentag, O.; Helbig, M.; Tuittila, E.; Jurasinski, G.; Koebsch, F.; Campbell, D.; Schmid, H. P.; Lohila, A.; Goeckede, M.; Nilsson, M. B.; Friborg, T.; Jansen, J.; Zona, D.; Euskirchen, E.; Ward, E. J.; Bohrer, G.; Jin, Z.; Liu, L.; Iwata, H.; Goodrich, J.; and Jackson, R.\n\n\n \n \n \n \n \n Causality guided machine learning model on wetland CH4 emissions across global wetlands.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 324: 109115. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Causality$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yuan_causality_2022,\n\ttitle = {Causality guided machine learning model on wetland {CH4} emissions across global wetlands},\n\tvolume = {324},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322003021},\n\tdoi = {10.1016/j.agrformet.2022.109115},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Yuan, Kunxiaojia and Zhu, Qing and Li, Fa and Riley, William J. and Torn, Margaret and Chu, Housen and McNicol, Gavin and Chen, Min and Knox, Sara and Delwiche, Kyle and Wu, Huayi and Baldocchi, Dennis and Ma, Hongxu and Desai, Ankur R. and Chen, Jiquan and Sachs, Torsten and Ueyama, Masahito and Sonnentag, Oliver and Helbig, Manuel and Tuittila, Eeva-Stiina and Jurasinski, Gerald and Koebsch, Franziska and Campbell, David and Schmid, Hans Peter and Lohila, Annalea and Goeckede, Mathias and Nilsson, Mats B. and Friborg, Thomas and Jansen, Joachim and Zona, Donatella and Euskirchen, Eugenie and Ward, Eric J. and Bohrer, Gil and Jin, Zhenong and Liu, Licheng and Iwata, Hiroki and Goodrich, Jordan and Jackson, Robert},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {109115},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Zhan, Q.; Kong, X.; and Rinke, K.\n\n\n \n \n \n \n \n High-frequency monitoring enables operational opportunities to reduce the dissolved organic carbon (DOC) load in Germany’s largest drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Inland Waters, 12(2): 245–260. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-frequency$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhan_high-frequency_2022,\n\ttitle = {High-frequency monitoring enables operational opportunities to reduce the dissolved organic carbon ({DOC}) load in {Germany}’s largest drinking water reservoir},\n\tvolume = {12},\n\tissn = {2044-2041, 2044-205X},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/20442041.2021.1987796},\n\tdoi = {10.1080/20442041.2021.1987796},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Inland Waters},\n\tauthor = {Zhan, Qing and Kong, Xiangzhen and Rinke, Karsten},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {245--260},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Zhang, W.; Jung, M.; Migliavacca, M.; Poyatos, R.; Miralles, D.; El-Madany, T. S.; Galvagno, M.; Carrara, A.; Arriga, N.; Ibrom, A.; Mammarella, I.; Papale, D.; Cleverly, J.; Liddell, M. J.; Wohlfahrt, G.; Markwitz, C.; Mauder, M.; Paul-Limoges, E.; Schmidt, M.; Wolf, S.; Brümmer, C.; Arain, M. A.; Fares, S.; Kato, T.; Ardö, J.; Oechel, W.; Hanson, C.; Korkiakoski, M.; Biraud, S.; Steinbrecher, R.; Billesbach, D.; Montagnani, L.; Woodgate, W.; Shao, C.; Carvalhais, N.; Reichstein, M.; and Nelson, J. A.\n\n\n \n \n \n \n \n The Effect of Relative Humidity on Eddy Covariance Latent Heat Flux Measurements and its Implication for Partitioning into Transpiration and Evaporation.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhang_effect_2022,\n\ttitle = {The {Effect} of {Relative} {Humidity} on {Eddy} {Covariance} {Latent} {Heat} {Flux} {Measurements} and its {Implication} for {Partitioning} into {Transpiration} and {Evaporation}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4106267},\n\tdoi = {10.2139/ssrn.4106267},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Zhang, Weijie and Jung, Martin and Migliavacca, Mirco and Poyatos, Rafael and Miralles, Diego and El-Madany, Tarek S. and Galvagno, Marta and Carrara, Arnaud and Arriga, Nicola and Ibrom, Andreas and Mammarella, Ivan and Papale, Dario and Cleverly, Jamie and Liddell, Michael J. and Wohlfahrt, Georg and Markwitz, Christian and Mauder, Matthias and Paul-Limoges, Eugenie and Schmidt, Marius and Wolf, Sebastian and Brümmer, Christian and Arain, M. Altaf and Fares, Silvano and Kato, Tomomichi and Ardö, Jonas and Oechel, Walter and Hanson, Chad and Korkiakoski, Mika and Biraud, Sébastien and Steinbrecher, Rainer and Billesbach, Dave and Montagnani, Leonardo and Woodgate, William and Shao, Changliang and Carvalhais, Nuno and Reichstein, Markus and Nelson, Jacob A.},\n\tyear = {2022},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Zhang, X.; Zhang, Y.; Tian, J.; Ma, N.; and Wang, Y.\n\n\n \n \n \n \n \n CO $_{\\textrm{2}}$ fertilization is spatially distinct from stomatal conductance reduction in controlling ecosystem water-use efficiency increase.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(5): 054048. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CO$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhang_co_2022,\n\ttitle = {{CO} $_{\\textrm{2}}$ fertilization is spatially distinct from stomatal conductance reduction in controlling ecosystem water-use efficiency increase},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac6c9c},\n\tdoi = {10.1088/1748-9326/ac6c9c},\n\tabstract = {Abstract \n             \n              It is well known that global ecosystem water-use efficiency (EWUE) has noticeably increased over the last several decades. However, it remains unclear how individual environmental drivers contribute to EWUE changes, particularly from CO \n              2 \n              fertilization and stomatal suppression effects. Using a satellite-driven water–carbon coupling model—Penman–Monteith–Leuning version 2 (PML-V2), we quantified individual contributions from the observational drivers (atmospheric CO \n              2 \n              , climate forcing, leaf area index (LAI), albedo and emissivity) across the globe over 1982–2014. The PML-V2 was well-calibrated and showed a good performance for simulating EWUE (with a determination coefficient ( \n              R \n              2 \n              ) of 0.56) compared to observational annual EWUE over 1982–2014 derived from global 95 eddy flux sites from the FLUXNET2015 dataset. Our results showed that global EWUE increasing trend (0.04 ± 0.004 gC mm \n              −1 \n              H \n              2 \n              O decade \n              −1 \n              ) was largely contributed by increasing CO \n              2 \n              (51\\%) and LAI (20\\%), but counteracted by climate forcing (−26\\%). Globally, the CO \n              2 \n              fertilization effect on photosynthesis (23\\%) was similar to the CO \n              2 \n              suppression effect on stomatal conductance (28\\%). Spatially, the fertilization effect dominated EWUE trend over semi-arid regions while the stomatal suppression effect controlled over tropical forests. These findings improve understanding of how environmental factors affect the long-term change of EWUE, and can help policymakers for water use planning and ecosystem management.},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Zhang, Xuanze and Zhang, Yongqiang and Tian, Jing and Ma, Ning and Wang, Ying-Ping},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {054048},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Zhang, Y.; Liang, S.; Zhu, Z.; Ma, H.; and He, T.\n\n\n \n \n \n \n \n Soil moisture content retrieval from Landsat 8 data using ensemble learning.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 185: 32–47. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhang_soil_2022,\n\ttitle = {Soil moisture content retrieval from {Landsat} 8 data using ensemble learning},\n\tvolume = {185},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271622000090},\n\tdoi = {10.1016/j.isprsjprs.2022.01.005},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Zhang, Yufang and Liang, Shunlin and Zhu, Zhiliang and Ma, Han and He, Tao},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {32--47},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Zhang, Y.; Liu, X.; Lei, L.; and Liu, L.\n\n\n \n \n \n \n \n Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(16): 3899. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhang_estimating_2022,\n\ttitle = {Estimating {Global} {Anthropogenic} {CO2} {Gridded} {Emissions} {Using} a {Data}-{Driven} {Stacked} {Random} {Forest} {Regression} {Model}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/16/3899},\n\tdoi = {10.3390/rs14163899},\n\tabstract = {The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO2 emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO2 emissions. The driving variables include the annual features of column-averaged CO2 dry-air mole fraction (XCO2) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO2 (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R2 of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R2 of 0.665, and the national total emissions provided a higher R2 at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R2 = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO2 emissions at a grid scale.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Zhang, Yucong and Liu, Xinjie and Lei, Liping and Liu, Liangyun},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {3899},\n}\n\n

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\n\n\n\n\n\n

\n \n\n \n \n Zhang, Y.; and Ye, A.\n\n\n \n \n \n \n \n Improving global gross primary productivity estimation by fusing multi-source data products.\n \n \n \n \n\n\n \n\n\n\n Heliyon, 8(3): e09153. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhang_improving_2022,\n\ttitle = {Improving global gross primary productivity estimation by fusing multi-source data products},\n\tvolume = {8},\n\tissn = {24058440},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2405844022004418},\n\tdoi = {10.1016/j.heliyon.2022.e09153},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Heliyon},\n\tauthor = {Zhang, Yahai and Ye, Aizhong},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {e09153},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Zhou, L.; Zhou, W.; Chen, J.; Xu, X.; Wang, Y.; Zhuang, J.; and Chi, Y.\n\n\n \n \n \n \n \n Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the Northern Hemisphere.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 135: 108579. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Land$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zhou_land_2022,\n\ttitle = {Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the {Northern} {Hemisphere}},\n\tvolume = {135},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X22000504},\n\tdoi = {10.1016/j.ecolind.2022.108579},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Ecological Indicators},\n\tauthor = {Zhou, Lei and Zhou, Wen and Chen, Jijing and Xu, Xiyan and Wang, Yonglin and Zhuang, Jie and Chi, Yonggang},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {108579},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Zhu, S.; Xu, J.; Zhu, H.; Zeng, J.; Wang, Y.; Zeng, Q.; Zhang, D.; Liu, X.; and Yang, S.\n\n\n \n \n \n \n \n Investigating Impacts of Ambient Air Pollution on the Terrestrial Gross Primary Productivity (GPP) From Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n IEEE Geoscience and Remote Sensing Letters, 19: 1–5. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{zhu_investigating_2022,\n\ttitle = {Investigating {Impacts} of {Ambient} {Air} {Pollution} on the {Terrestrial} {Gross} {Primary} {Productivity} ({GPP}) {From} {Remote} {Sensing}},\n\tvolume = {19},\n\tissn = {1545-598X, 1558-0571},\n\turl = {https://ieeexplore.ieee.org/document/9745487/},\n\tdoi = {10.1109/LGRS.2022.3163775},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Geoscience and Remote Sensing Letters},\n\tauthor = {Zhu, Songyan and Xu, Jian and Zhu, Hao and Zeng, Jingya and Wang, Yapeng and Zeng, Qiaolin and Zhang, Dejun and Liu, Xiaoran and Yang, Shiqi},\n\tyear = {2022},\n\tpages = {1--5},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Zhuang, L.; Schnepf, A.; Unger, K.; Liang, Z.; and Bol, R.\n\n\n \n \n \n \n \n Home-Field Advantage of Litter Decomposition Faded 8 Years after Spruce Forest Clearcutting in Western Germany.\n \n \n \n \n\n\n \n\n\n\n Soil Systems, 6(1): 26. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Home-Field$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zhuang_home-field_2022,\n\ttitle = {Home-{Field} {Advantage} of {Litter} {Decomposition} {Faded} 8 {Years} after {Spruce} {Forest} {Clearcutting} in {Western} {Germany}},\n\tvolume = {6},\n\tissn = {2571-8789},\n\turl = {https://www.mdpi.com/2571-8789/6/1/26},\n\tdoi = {10.3390/soilsystems6010026},\n\tabstract = {Home-field advantage (HFA) encompasses all the processes leading to faster litter decomposition in the ‘home’ environment compared to that of ‘away’ environments. To determine the occurrence of HFA in a forest and adjacent clear-cut, we set up a reciprocal litter decomposition experiment within the forest and clear-cut for two soil types (Cambisols and Gleysols) in temperate Germany. The forest was dominated by Norway spruce (Picea abies), whereas forest regeneration of European Beech (Fagus sylvatica) after clearcutting was encouraged. Our observation that Norway spruce decomposed faster than European beech in 70-yr-old spruce forest was most likely related to specialized litter-soil interaction under existing spruce, leading to an HFA. Elevated soil moisture and temperature, and promoted litter N release, indicated the rapid change of soil-litter affinity of the original spruce forest even after a short-term regeneration following clearcutting, resulting in faster beech decomposition, particularly in moisture- and nutrient-deficient Cambisols. The divergence between forest and clear-cut in the Cambisol of their litter δ15N values beyond nine months implied litter N decomposition was only initially independent of soil and residual C status. We conclude that clearcutting modifies the litter-field affinity and helps promote the establishment or regeneration of European beech in this and similar forest mountain upland areas.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Soil Systems},\n\tauthor = {Zhuang, Liyan and Schnepf, Andrea and Unger, Kirsten and Liang, Ziyi and Bol, Roland},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {26},\n}\n\n

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\n \n\n \n \n Zielhofer, C.; Schmidt, J.; Reiche, N.; Tautenhahn, M.; Ballasus, H.; Burkart, M.; Linstädter, A.; Dietze, E.; Kaiser, K.; and Mehler, N.\n\n\n \n \n \n \n \n The Lower Havel River Region (Brandenburg, Germany): A 230-Year-Long Historical Map Record Indicates a Decrease in Surface Water Areas and Groundwater Levels.\n \n \n \n \n\n\n \n\n\n\n Water, 14(3): 480. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zielhofer_lower_2022,\n\ttitle = {The {Lower} {Havel} {River} {Region} ({Brandenburg}, {Germany}): {A} 230-{Year}-{Long} {Historical} {Map} {Record} {Indicates} a {Decrease} in {Surface} {Water} {Areas} and {Groundwater} {Levels}},\n\tvolume = {14},\n\tissn = {2073-4441},\n\tshorttitle = {The {Lower} {Havel} {River} {Region} ({Brandenburg}, {Germany})},\n\turl = {https://www.mdpi.com/2073-4441/14/3/480},\n\tdoi = {10.3390/w14030480},\n\tabstract = {Instrumental data show that the groundwater and lake levels in Northeast Germany have decreased over the past decades, and this process has accelerated over the past few years. In addition to global warming, the direct influence of humans on the local water balance is suspected to be the cause. Since the instrumental data usually go back only a few decades, little is known about the multidecadal to centennial-scale trend, which also takes long-term climate variation and the long-term influence by humans on the water balance into account. This study aims to quantitatively reconstruct the surface water areas in the Lower Havel Inner Delta and of adjacent Lake Gülpe in Brandenburg. The analysis includes the calculation of surface water areas from historical and modern maps from 1797 to 2020. The major finding is that surface water areas have decreased by approximately 30\\% since the pre-industrial period, with the decline being continuous. Our data show that the comprehensive measures in Lower Havel hydro-engineering correspond with groundwater lowering that started before recent global warming. Further, large-scale melioration measures with increasing water demands in the upstream wetlands beginning from the 1960s to the 1980s may have amplified the decline in downstream surface water areas.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Water},\n\tauthor = {Zielhofer, Christoph and Schmidt, Johannes and Reiche, Niklas and Tautenhahn, Marie and Ballasus, Helen and Burkart, Michael and Linstädter, Anja and Dietze, Elisabeth and Kaiser, Knut and Mehler, Natascha},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {480},\n}\n\n

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\n \n\n \n \n Zuidema, P. A.; Babst, F.; Groenendijk, P.; Trouet, V.; Abiyu, A.; Acuña-Soto, R.; Adenesky-Filho, E.; Alfaro-Sánchez, R.; Aragão, J. R. V.; Assis-Pereira, G.; Bai, X.; Barbosa, A. C.; Battipaglia, G.; Beeckman, H.; Botosso, P. C.; Bradley, T.; Bräuning, A.; Brienen, R.; Buckley, B. M.; Camarero, J. J.; Carvalho, A.; Ceccantini, G.; Centeno-Erguera, L. R.; Cerano-Paredes, J.; Chávez-Durán, Á. A.; Cintra, B. B. L.; Cleaveland, M. K.; Couralet, C.; D’Arrigo, R.; del Valle, J. I.; Dünisch, O.; Enquist, B. J.; Esemann-Quadros, K.; Eshetu, Z.; Fan, Z.; Ferrero, M. E.; Fichtler, E.; Fontana, C.; Francisco, K. S.; Gebrekirstos, A.; Gloor, E.; Granato-Souza, D.; Haneca, K.; Harley, G. L.; Heinrich, I.; Helle, G.; Inga, J. G.; Islam, M.; Jiang, Y.; Kaib, M.; Khamisi, Z. H.; Koprowski, M.; Kruijt, B.; Layme, E.; Leemans, R.; Leffler, A. J.; Lisi, C. S.; Loader, N. J.; Locosselli, G. M.; Lopez, L.; López-Hernández, M. I.; Lousada, J. L. P. C.; Mendivelso, H. A.; Mokria, M.; Montóia, V. R.; Moors, E.; Nabais, C.; Ngoma, J.; Nogueira Júnior, F. d. C.; Oliveira, J. M.; Olmedo, G. M.; Pagotto, M. A.; Panthi, S.; Pérez-De-Lis, G.; Pucha-Cofrep, D.; Pumijumnong, N.; Rahman, M.; Ramirez, J. A.; Requena-Rojas, E. J.; Ribeiro, A. d. S.; Robertson, I.; Roig, F. A.; Rubio-Camacho, E. A.; Sass-Klaassen, U.; Schöngart, J.; Sheppard, P. R.; Slotta, F.; Speer, J. H.; Therrell, M. D.; Toirambe, B.; Tomazello-Filho, M.; Torbenson, M. C. A.; Touchan, R.; Venegas-González, A.; Villalba, R.; Villanueva-Diaz, J.; Vinya, R.; Vlam, M.; Wils, T.; and Zhou, Z.\n\n\n \n \n \n \n \n Tropical tree growth driven by dry-season climate variability.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 15(4): 269–276. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Tropical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zuidema_tropical_2022,\n\ttitle = {Tropical tree growth driven by dry-season climate variability},\n\tvolume = {15},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/s41561-022-00911-8},\n\tdoi = {10.1038/s41561-022-00911-8},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Nature Geoscience},\n\tauthor = {Zuidema, Pieter A. and Babst, Flurin and Groenendijk, Peter and Trouet, Valerie and Abiyu, Abrham and Acuña-Soto, Rodolfo and Adenesky-Filho, Eduardo and Alfaro-Sánchez, Raquel and Aragão, José Roberto Vieira and Assis-Pereira, Gabriel and Bai, Xue and Barbosa, Ana Carolina and Battipaglia, Giovanna and Beeckman, Hans and Botosso, Paulo Cesar and Bradley, Tim and Bräuning, Achim and Brienen, Roel and Buckley, Brendan M. and Camarero, J. Julio and Carvalho, Ana and Ceccantini, Gregório and Centeno-Erguera, Librado R. and Cerano-Paredes, Julián and Chávez-Durán, Álvaro Agustín and Cintra, Bruno Barçante Ladvocat and Cleaveland, Malcolm K. and Couralet, Camille and D’Arrigo, Rosanne and del Valle, Jorge Ignacio and Dünisch, Oliver and Enquist, Brian J. and Esemann-Quadros, Karin and Eshetu, Zewdu and Fan, Ze-Xin and Ferrero, M. Eugenia and Fichtler, Esther and Fontana, Claudia and Francisco, Kainana S. and Gebrekirstos, Aster and Gloor, Emanuel and Granato-Souza, Daniela and Haneca, Kristof and Harley, Grant Logan and Heinrich, Ingo and Helle, Gerd and Inga, Janet G. and Islam, Mahmuda and Jiang, Yu-mei and Kaib, Mark and Khamisi, Zakia Hassan and Koprowski, Marcin and Kruijt, Bart and Layme, Eva and Leemans, Rik and Leffler, A. Joshua and Lisi, Claudio Sergio and Loader, Neil J. and Locosselli, Giuliano Maselli and Lopez, Lidio and López-Hernández, María I. and Lousada, José Luís Penetra Cerveira and Mendivelso, Hooz A. and Mokria, Mulugeta and Montóia, Valdinez Ribeiro and Moors, Eddy and Nabais, Cristina and Ngoma, Justine and Nogueira Júnior, Francisco de Carvalho and Oliveira, Juliano Morales and Olmedo, Gabriela Morais and Pagotto, Mariana Alves and Panthi, Shankar and Pérez-De-Lis, Gonzalo and Pucha-Cofrep, Darwin and Pumijumnong, Nathsuda and Rahman, Mizanur and Ramirez, Jorge Andres and Requena-Rojas, Edilson Jimmy and Ribeiro, Adauto de Souza and Robertson, Iain and Roig, Fidel Alejandro and Rubio-Camacho, Ernesto Alonso and Sass-Klaassen, Ute and Schöngart, Jochen and Sheppard, Paul R. and Slotta, Franziska and Speer, James H. and Therrell, Matthew D. and Toirambe, Benjamin and Tomazello-Filho, Mario and Torbenson, Max C. A. and Touchan, Ramzi and Venegas-González, Alejandro and Villalba, Ricardo and Villanueva-Diaz, Jose and Vinya, Royd and Vlam, Mart and Wils, Tommy and Zhou, Zhe-Kun},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {269--276},\n}\n\n

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