@article{
 title = {Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins},
 type = {article},
 year = {2024},
 volume = {60},
 websites = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034193},
 month = {1},
 day = {8},
 id = {9a4b74e0-0480-3d50-8356-55b54a41fa40},
 created = {2023-12-05T19:00:04.852Z},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:30.795Z},
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 confirmed = {true},
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 abstract = {Optimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.},
 bibtype = {article},
 author = {Demirel, Mehmet Cüneyd and Koch, Julian and Rakovec, Oldrich and Kumar, Rohini and Mai, Juliane and Müller, Sebastian and Thober, Stephan and Samaniego, Luis and Stisen, Simon},
 doi = {10.1029/2022WR034193},
 journal = {Water Resources Research},
 number = {1}
}

Optimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

@inproceedings{
 title = {Spatial pattern oriented optimization of regional scale hydrological models (EGU23-7955)},
 type = {inproceedings},
 year = {2023},
 websites = {https://www.egu23.eu},
 publisher = {Copernicus GmbH},
 city = {Vienna , Austria},
 id = {3c9bde5a-6316-34ce-b956-7e87869e1a4e},
 created = {2023-01-28T00:53:14.789Z},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2023-01-28T00:53:14.789Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Regional scale hydrological models are often constrained by a group of observation stations, typically for discharge, which each represent a lumped catchment response. While multi-station calibration greatly improves model fidelity, other sources of data and different calibration objectives are often required to improve models for other variables and increase robustness for ungauged areas. Satellite data has often been utilized as an additional source of information for multi-objective optimization. However, in many cases satellite-based data for other variables, such as soil moisture, AET, snow cover, storage change etc. has been applied as timeseries of catchment averages, thereby limiting the unique spatial pattern information they carry. In a series of studies a simple alternative approach has been developed to capitalize on the benefits of combining spatial pattern information from satellite data with classical discharge and groundwater head observations. By limiting the constraint by the satellite data to pattern information only a very limited tradeoff with other observations is achieved. Meanwhile, the approach ensures realistic spatial patterns of parameter fields and simulations leading to improved transferability to ungauged basins. In light of equifinality, often encountered for regional scale models constrained by multiple discharge stations, the approach can as such also be seen as an efficient way of identifying spatially consistent solutions among a large range of possible parameter sets. Here we present two cases, one across six central-European basins using a mesoscale hydrological model (mHM) and another using a national scale groundwater-surface model (MIKE-SHE).},
 bibtype = {inproceedings},
 author = {Stisen, Simon and Demirel, Mehmet C and Soltani, Mohsen and Koch, Julian},
 booktitle = {EGU General Assembly}
}

Regional scale hydrological models are often constrained by a group of observation stations, typically for discharge, which each represent a lumped catchment response. While multi-station calibration greatly improves model fidelity, other sources of data and different calibration objectives are often required to improve models for other variables and increase robustness for ungauged areas. Satellite data has often been utilized as an additional source of information for multi-objective optimization. However, in many cases satellite-based data for other variables, such as soil moisture, AET, snow cover, storage change etc. has been applied as timeseries of catchment averages, thereby limiting the unique spatial pattern information they carry. In a series of studies a simple alternative approach has been developed to capitalize on the benefits of combining spatial pattern information from satellite data with classical discharge and groundwater head observations. By limiting the constraint by the satellite data to pattern information only a very limited tradeoff with other observations is achieved. Meanwhile, the approach ensures realistic spatial patterns of parameter fields and simulations leading to improved transferability to ungauged basins. In light of equifinality, often encountered for regional scale models constrained by multiple discharge stations, the approach can as such also be seen as an efficient way of identifying spatially consistent solutions among a large range of possible parameter sets. Here we present two cases, one across six central-European basins using a mesoscale hydrological model (mHM) and another using a national scale groundwater-surface model (MIKE-SHE).

@article{
 title = {Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model},
 type = {article},
 year = {2022},
 keywords = {climate normalization,evapotranspiration,hydrological,model evaluation,modeling,remote sensing,spatial patterns},
 pages = {315},
 volume = {14},
 websites = {https://www.mdpi.com/2072-4292/14/2/315},
 month = {1},
 day = {11},
 id = {b774473c-07a4-3fb6-95e7-0b4756a06a99},
 created = {2022-01-11T10:25:53.699Z},
 file_attached = {true},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2022-01-12T14:17:40.374Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.},
 bibtype = {article},
 author = {Koch, Julian and Demirel, Mehmet Cüneyd and Stisen, Simon},
 doi = {10.3390/rs14020315},
 journal = {Remote Sensing},
 number = {2}
}

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

@article{
 title = {Multi-Constrained Catchment Scale Optimization of Groundwater Abstraction Using Linear Programming},
 type = {article},
 year = {2021},
 pages = {503-516},
 volume = {59},
 websites = {https://onlinelibrary.wiley.com/doi/10.1111/gwat.13083},
 month = {7},
 day = {26},
 id = {21a81928-61e5-3509-9a5a-01da23fcb5a7},
 created = {2021-12-02T07:52:48.304Z},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
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 last_modified = {2021-12-02T10:16:00.879Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Danapour2021},
 private_publication = {false},
 bibtype = {article},
 author = {Danapour, Mehrdis and Fienen, Michael N. and Hoejberg, Anker Lajer and Jensen, Karsten Høgh and Stisen, Simon},
 doi = {10.1111/gwat.13083},
 journal = {Groundwater},
 number = {4}
}

@article{
 title = {Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products},
 type = {article},
 year = {2021},
 pages = {853},
 volume = {13},
 websites = {https://www.mdpi.com/2072-4292/13/5/853},
 month = {2},
 day = {25},
 id = {1d326395-9e37-3d7a-8083-7ad38a5d1e6f},
 created = {2021-12-02T09:25:56.306Z},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T10:12:40.732Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.},
 bibtype = {article},
 author = {Soltani, Mohsen and Koch, Julian and Stisen, Simon},
 doi = {10.3390/rs13050853},
 journal = {Remote Sensing},
 number = {5}
}

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

@misc{
 title = {mesoscale Hydrologic Model - mHM v5.11.1},
 type = {misc},
 year = {2021},
 keywords = {mesoscale hydrological model,multiscale parameter regionalization,seamless predictions},
 websites = {https://zenodo.org/record/4462822},
 month = {2},
 day = {3},
 city = {Leipzig},
 revision = {v5.11.0},
 id = {01d59f4d-1eb7-39e8-bb52-bfb13954c400},
 created = {2021-12-02T09:47:55.852Z},
 accessed = {2021-02-16},
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 hidden = {false},
 citation_key = {Samaniego2021},
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 bibtype = {misc},
 author = {Samaniego, undefined and Brenner, Johannes and Craven, John and Cuntz, Matthias and Dalmasso, Giovanni and Demirel, Mehmet Cuneyd and Jing, Miao and Kaluza, Maren and Kumar, Rohini and Langenberg, Ben and Mai, Juliane and Müller, Sebastian and Musuuza, Jude and Prykhodko, Vladyslav and Rakovec, Oldrich and Schafer, David and Schneider, Christoph and Schron, Martin and Schuler, Lennart and Schweppe, Robert and Shrestha, Pallav Kumar and Spieler, Diana and Stisen, Simon and Thober, Stephan and Zink, Matthias and Attinger, Sabine and Samaniego, Luis and Brenner, Johannes and Craven, John and Cuntz, Matthias and Dalmasso, Giovanni and Demirel, Mehmet Cuneyd and Jing, Miao and Kaluza, Maren and Kumar, Rohini and Langenberg, Ben and Mai, Juliane and Muller, Sebastian and Musuuza, Jude and Prykhodko, Vladyslav and Rakovec, Oldrich and Schafer, David and Schneider, Christoph and Schran, Martin and Schaler, Lennart and Schweppe, Robert and Shrestha, Pallav Kumar and Spieler, Diana and Stisen, Simon and Thober, Stephan and Zink, Matthias and Attinger, Sabine},
 doi = {10.5281/ZENODO.4462822}
}

@article{
 title = {Spatial Patterns in Actual Evapotranspiration Climatologies for Europe},
 type = {article},
 year = {2021},
 pages = {2410},
 volume = {13},
 websites = {https://www.mdpi.com/2072-4292/13/12/2410},
 month = {6},
 day = {19},
 id = {380d289e-2527-3baf-9e63-24640e7c43d9},
 created = {2021-12-02T10:07:31.411Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T10:07:31.411Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.},
 bibtype = {article},
 author = {Stisen, Simon and Soltani, Mohsen and Mendiguren, Gorka and Langkilde, Henrik and Garcia, Monica and Koch, Julian},
 doi = {10.3390/rs13122410},
 journal = {Remote Sensing},
 number = {12}
}

Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.

@article{
 title = {Estimating Net Irrigation Across the North China Plain Through Dual Modeling of Evapotranspiration},
 type = {article},
 year = {2020},
 keywords = {North China Plain,evapotranspiration,hydrologic model,irrigation quantification,remote sensing},
 volume = {56},
 id = {84f14b8e-b0f2-3c81-b858-d52505c7b060},
 created = {2021-12-02T09:37:06.748Z},
 file_attached = {true},
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 last_modified = {2021-12-02T09:37:14.385Z},
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 abstract = {Irrigation is the greatest human interference with the terrestrial water cycle. Detailed knowledge on irrigation is required to better manage water resources and to increase water use efficiency (WUE). This study applies a framework to quantify net irrigation at monthly timescale at a spatial resolution of 1 km2 providing high spatial and temporal detail for regional water resources management. The study is conducted in the Haihe River Basin (HRB) in China encompassing the North China Plain (NCP), a global hot spot of groundwater depletion. Net irrigation is estimated based on the systematic evapotranspiration (ET) residuals between a remote sensing-based model and a hydrologic model that does not include an irrigation scheme. The results suggest an average annual net irrigation of 126 mm yr−1 (15.2 km3 yr−1) for NCP and 108 mm yr−1 (18.6 km3 yr−1) for HRB. It is found that net irrigation can be estimated with higher fidelity for winter crops than for summer crops. The simulated water balance for NCP is evaluated with Gravity Recovery and Climate Experiment (GRACE) data, and the net irrigation estimates can close the water balance gap. Annual winter wheat classifications reveal an increasing crop area with a trend of 2,200 km2 yr−1. This trend is not accompanied by a likewise increasing trend in irrigation water use, which suggests an increased WUE in the NCP, which is further supported by net primary productivity data. The proposed framework has potential to be transferred to other regions and support decision makers to support sustainable water management.},
 bibtype = {article},
 author = {Koch, Julian and Zhang, Wenmin and Martinsen, Grith and He, Xin and Stisen, Simon},
 doi = {10.1029/2020WR027413},
 journal = {Water Resources Research},
 number = {12}
}

Irrigation is the greatest human interference with the terrestrial water cycle. Detailed knowledge on irrigation is required to better manage water resources and to increase water use efficiency (WUE). This study applies a framework to quantify net irrigation at monthly timescale at a spatial resolution of 1 km2 providing high spatial and temporal detail for regional water resources management. The study is conducted in the Haihe River Basin (HRB) in China encompassing the North China Plain (NCP), a global hot spot of groundwater depletion. Net irrigation is estimated based on the systematic evapotranspiration (ET) residuals between a remote sensing-based model and a hydrologic model that does not include an irrigation scheme. The results suggest an average annual net irrigation of 126 mm yr−1 (15.2 km3 yr−1) for NCP and 108 mm yr−1 (18.6 km3 yr−1) for HRB. It is found that net irrigation can be estimated with higher fidelity for winter crops than for summer crops. The simulated water balance for NCP is evaluated with Gravity Recovery and Climate Experiment (GRACE) data, and the net irrigation estimates can close the water balance gap. Annual winter wheat classifications reveal an increasing crop area with a trend of 2,200 km2 yr−1. This trend is not accompanied by a likewise increasing trend in irrigation water use, which suggests an increased WUE in the NCP, which is further supported by net primary productivity data. The proposed framework has potential to be transferred to other regions and support decision makers to support sustainable water management.

@article{
 title = {Missing Data Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types},
 type = {article},
 year = {2020},
 keywords = {Hydrologic models,Hydrometeorology,Numerical analysis/modeling,Pattern detection,Statistical techniques},
 pages = {2325-2341},
 volume = {21},
 websites = {https://journals.ametsoc.org/view/journals/hydr/21/10/jhmD190220.xml},
 month = {10},
 day = {1},
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 hidden = {false},
 private_publication = {false},
 abstract = {Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too limited data in space to feed complex predictive models. In this study we compare different automatic approaches for missing data imputation, including geostatistical interpolation and pattern-based estimation algorithms. We introduce two pattern-based approaches based on the analysis of historical data patterns: (i) an iterative version of K -nearest neighbor (IKNN) and (ii) a new algorithm called vector sampling (VS) that combines concepts of multiple-point statistics and resampling. Both algorithms can draw estimations from variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset. Tested on five case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially homogeneous rain events, geostatistical interpolation tends to minimize the average error, while in mountainous regions with nonstationary rainfall statistics, data mining can recover better the rainfall patterns. The VS algorithm, requiring minimal parameterization, turns out to be a convenient option for routine application on complex and poorly gauged terrains.},
 bibtype = {article},
 author = {Oriani, Fabio and Stisen, Simon and Demirel, Mehmet C. and Mariethoz, Gregoire},
 doi = {10.1175/JHM-D-19-0220.1},
 journal = {Journal of Hydrometeorology},
 number = {10}
}

Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too limited data in space to feed complex predictive models. In this study we compare different automatic approaches for missing data imputation, including geostatistical interpolation and pattern-based estimation algorithms. We introduce two pattern-based approaches based on the analysis of historical data patterns: (i) an iterative version of K -nearest neighbor (IKNN) and (ii) a new algorithm called vector sampling (VS) that combines concepts of multiple-point statistics and resampling. Both algorithms can draw estimations from variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset. Tested on five case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially homogeneous rain events, geostatistical interpolation tends to minimize the average error, while in mountainous regions with nonstationary rainfall statistics, data mining can recover better the rainfall patterns. The VS algorithm, requiring minimal parameterization, turns out to be a convenient option for routine application on complex and poorly gauged terrains.

@article{
 title = {Assessment of regional inter-basin groundwater flow using both simple and highly parameterized optimization schemes},
 type = {article},
 year = {2019},
 keywords = {Data worth,Denmark,Groundwater flow,Highly parameterized optimization,Uncertainty analysis},
 pages = {1929-1947},
 volume = {27},
 websites = {http://link.springer.com/10.1007/s10040-019-01984-3},
 month = {9},
 day = {4},
 id = {84f792c0-08f9-30fa-938b-ff24ee1850fa},
 created = {2021-12-02T09:37:06.776Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.906Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019, The Author(s). The need for regional-scale integrated hydrological models for the purpose of water resource management is increasing. Distributed physically based coupled surface-subsurface models are usually complex and contain a large amount of spatio-temporal information that leads to a relatively long forward runtime. One of the main challenges with regard to regional-scale inverse modeling relates to parameterization and how to adequately exploit the information embedded in the existing observational data while avoiding parameter identifiability issues. This study examined and compared the calibration of a “highly parameterized” model with a “classical” unit-based parameterization scheme in which the dominant geological features were assumed to be known. The physically based coupled surface-subsurface model MIKE SHE was used for conducting the study of five river basins (4,900 km2) in central Jutland in Denmark, characterized by heterogeneous geology and a considerable amount of groundwater flux across topographical catchment boundaries. The results indicated that introducing more flexibility in the parameter estimation process through a regularized approach significantly improved the model performance, in particular head and water balance errors. The highly parameterized calibration results additionally provided very useful insights into the model deficiencies in terms of conceptual model structure and incorrectly imposed boundary conditions. Furthermore, the results from data-worth analysis indicated that the highly parameterized model has more effectively utilized the information in the dataset compared to a traditional unit-based calibration approach.},
 bibtype = {article},
 author = {Danapour, Mehrdis and Højberg, Anker Lajer and Jensen, Karsten Høgh and Stisen, Simon},
 doi = {10.1007/s10040-019-01984-3},
 journal = {Hydrogeology Journal},
 number = {6}
}

© 2019, The Author(s). The need for regional-scale integrated hydrological models for the purpose of water resource management is increasing. Distributed physically based coupled surface-subsurface models are usually complex and contain a large amount of spatio-temporal information that leads to a relatively long forward runtime. One of the main challenges with regard to regional-scale inverse modeling relates to parameterization and how to adequately exploit the information embedded in the existing observational data while avoiding parameter identifiability issues. This study examined and compared the calibration of a “highly parameterized” model with a “classical” unit-based parameterization scheme in which the dominant geological features were assumed to be known. The physically based coupled surface-subsurface model MIKE SHE was used for conducting the study of five river basins (4,900 km2) in central Jutland in Denmark, characterized by heterogeneous geology and a considerable amount of groundwater flux across topographical catchment boundaries. The results indicated that introducing more flexibility in the parameter estimation process through a regularized approach significantly improved the model performance, in particular head and water balance errors. The highly parameterized calibration results additionally provided very useful insights into the model deficiencies in terms of conceptual model structure and incorrectly imposed boundary conditions. Furthermore, the results from data-worth analysis indicated that the highly parameterized model has more effectively utilized the information in the dataset compared to a traditional unit-based calibration approach.

@article{
 title = {Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration},
 type = {article},
 year = {2019},
 keywords = {AMSR-E,ESA CCI SM v04.4,GRACE,HBV,Moselle River,SMAP},
 pages = {2083},
 volume = {11},
 websites = {https://www.mdpi.com/2073-4441/11/10/2083},
 month = {10},
 day = {6},
 id = {d3d98445-7087-3451-9920-dc3add93f622},
 created = {2021-12-02T09:37:06.838Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.529Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.},
 bibtype = {article},
 author = {Demirel, undefined and Özen, undefined and Orta, undefined and Toker, undefined and Demir, undefined and Ekmekcioğlu, undefined and Tayşi, undefined and Eruçar, undefined and Sağ, undefined and Sarı, undefined and Tuncer, undefined and Hancı, undefined and Özcan, undefined and Erdem, undefined and Koşucu, undefined and Başakın, undefined and Ahmed, undefined and Anwar, undefined and Avcuoğlu, undefined and Vanlı, undefined and Stisen, undefined and Booij, undefined},
 doi = {10.3390/w11102083},
 journal = {Water},
 number = {10}
}

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

@article{
 title = {The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models},
 type = {article},
 year = {2018},
 pages = {1873-1886},
 volume = {11},
 websites = {https://gmd.copernicus.org/articles/11/1873/2018/},
 month = {5},
 day = {15},
 id = {cb3ea813-020d-3a99-a8bd-3713f10ec48b},
 created = {2021-12-02T09:37:06.017Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.988Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Koch, Julian and Demirel, Mehmet Cüneyd and Stisen, Simon},
 doi = {10.5194/gmd-11-1873-2018},
 journal = {Geoscientific Model Development},
 number = {5}
}

@article{
 title = {Moving beyond run‐off calibration—Multivariable optimization of a surface–subsurface–atmosphere model},
 type = {article},
 year = {2018},
 pages = {2654-2668},
 volume = {32},
 websites = {http://doi.wiley.com/10.1002/hyp.13177,https://onlinelibrary.wiley.com/doi/10.1002/hyp.13177},
 month = {8},
 day = {15},
 id = {4a062be9-48f4-337e-8aa5-c5e9732f6d70},
 created = {2021-12-02T09:37:06.070Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.511Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multivariable optimization of a complex coupled surface–subsurface–atmosphere model at the catchment scale in an attempt to move beyond simple run‐off calibration. The model is evaluated against five independent observational data sets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM), and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions for all five observation data sets can be reduced simultaneously. Additionally, the multivariable calibration proved more robust, compared with calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio‐temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization, or process description.},
 bibtype = {article},
 author = {Stisen, Simon and Koch, Julian and Sonnenborg, Torben O. and Refsgaard, Jens Christian and Bircher, Simone and Ringgaard, Rasmus and Jensen, Karsten H.},
 doi = {10.1002/hyp.13177},
 journal = {Hydrological Processes},
 number = {17}
}

Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multivariable optimization of a complex coupled surface–subsurface–atmosphere model at the catchment scale in an attempt to move beyond simple run‐off calibration. The model is evaluated against five independent observational data sets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM), and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions for all five observation data sets can be reduced simultaneously. Additionally, the multivariable calibration proved more robust, compared with calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio‐temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization, or process description.

@article{
 title = {Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters},
 type = {article},
 year = {2018},
 keywords = {Disaggregation,Downscaling,SEVIRI,SMOS,Sensitivity analysis,Soil moisture,TVDI},
 pages = {424-441},
 volume = {206},
 websites = {https://www.sciencedirect.com/science/article/pii/S0034425717306259,https://linkinghub.elsevier.com/retrieve/pii/S0034425717306259},
 month = {3},
 day = {1},
 id = {bb449367-68bb-33d6-a068-bbe93f848895},
 created = {2021-12-02T09:37:06.260Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.502Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Abstract The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05° resolution disaggregated SMOS SM product at the sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105 × 105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product were found to be highly sensitive to algorithm input parameters; especially for conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patterns of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July–September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa.},
 bibtype = {article},
 author = {Tagesson, T and Horion, S and Nieto, H and Zaldo Fornies, V and Mendiguren González, G and Bulgin, C.E. and Ghent, D and Fensholt, R},
 doi = {10.1016/j.rse.2017.12.036},
 journal = {Remote Sensing of Environment}
}

Abstract The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05° resolution disaggregated SMOS SM product at the sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105 × 105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product were found to be highly sensitive to algorithm input parameters; especially for conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patterns of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July–September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa.

@article{
 title = {Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model},
 type = {article},
 year = {2018},
 pages = {1299-1315},
 volume = {22},
 websites = {https://hess.copernicus.org/articles/22/1299/2018/},
 month = {2},
 day = {20},
 id = {1659f4db-b749-3598-b759-1bac2bccf2b8},
 created = {2021-12-02T09:37:06.304Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T09:37:06.304Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.},
 bibtype = {article},
 author = {Demirel, Mehmet C. and Mai, Juliane and Mendiguren, Gorka and Koch, Julian and Samaniego, Luis and Stisen, Simon},
 doi = {10.5194/hess-22-1299-2018},
 journal = {Hydrology and Earth System Sciences},
 number = {2}
}

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

@article{
 title = {Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model},
 type = {article},
 year = {2018},
 keywords = {GLUE,actual evapotranspiration,mHM,remote sensing,sensitivity analysis,spatial pattern},
 pages = {1188},
 volume = {10},
 websites = {http://www.mdpi.com/2073-4441/10/9/1188},
 month = {9},
 publisher = {MDPI},
 day = {4},
 id = {b04fe606-3d6f-32a5-9d5e-2a1307453550},
 created = {2021-12-02T09:37:06.532Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.683Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Demirel2018a},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 abstract = {Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.},
 bibtype = {article},
 author = {Demirel, Mehmet and Koch, Julian and Mendiguren, Gorka and Stisen, Simon},
 doi = {10.3390/w10091188},
 journal = {Water},
 number = {9}
}

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

@article{
 title = {Simulating rainfall time-series: how to account for statistical variability at multiple scales?},
 type = {article},
 year = {2018},
 pages = {321-340},
 volume = {32},
 websites = {http://link.springer.com/10.1007/s00477-017-1414-z},
 month = {2},
 day = {11},
 id = {5fbeec94-b9f8-344d-874d-c7094a359591},
 created = {2021-12-02T09:37:06.577Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.911Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Oriani2018},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Oriani, Fabio and Mehrotra, Raj and Mariethoz, Grégoire and Straubhaar, Julien and Sharma, Ashish and Renard, Philippe},
 doi = {10.1007/s00477-017-1414-z},
 journal = {Stochastic Environmental Research and Risk Assessment},
 number = {2}
}

@article{
 title = {Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach},
 type = {article},
 year = {2017},
 pages = {5987-6005},
 volume = {21},
 websites = {https://hess.copernicus.org/articles/21/5987/2017/},
 month = {11},
 day = {30},
 id = {82bf2f70-296d-30e6-825b-2995b522e5e4},
 created = {2021-12-02T09:28:40.968Z},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T10:12:40.856Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.},
 bibtype = {article},
 author = {Mendiguren, Gorka and Koch, Julian and Stisen, Simon},
 doi = {10.5194/hess-21-5987-2017},
 journal = {Hydrology and Earth System Sciences},
 number = {12}
}

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.

@article{
 title = {Simulating Small‐Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data‐Driven Approach Based on Rainfall Radar Images},
 type = {article},
 year = {2017},
 keywords = {elevation,multiple‐point,radar,rainfall,simulation,stochastic},
 pages = {8512-8532},
 volume = {53},
 websites = {http://doi.wiley.com/10.1002/2017WR020876,https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017WR020876},
 month = {10},
 day = {27},
 id = {17e9e11b-b4dd-3264-a7a1-45683a0a51f9},
 created = {2021-12-02T09:37:06.023Z},
 accessed = {2017-10-27},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.659Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high‐resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple‐point simulation techniques dealing with extremely nonstationary heterogeneities and extensive data sets.},
 bibtype = {article},
 author = {Oriani, Fabio and Ohana‐Levi, Noa and Marra, Francesco and Straubhaar, Julien and Mariethoz, Gregoire and Renard, Philippe and Karnieli, Arnon and Morin, Efrat},
 doi = {10.1002/2017WR020876},
 journal = {Water Resources Research},
 number = {10}
}

The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high‐resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple‐point simulation techniques dealing with extremely nonstationary heterogeneities and extensive data sets.

@article{
 title = {Citizen science: A new perspective to advance spatial pattern evaluation in hydrology},
 type = {article},
 year = {2017},
 pages = {e0178165},
 volume = {12},
 websites = {https://dx.plos.org/10.1371/journal.pone.0178165},
 month = {5},
 day = {30},
 id = {e657a025-99b9-39ef-b9fa-4b644bb48841},
 created = {2021-12-02T09:37:06.139Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.683Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics.},
 bibtype = {article},
 author = {Koch, Julian and Stisen, Simon},
 editor = {Schumann, Guy J-P.},
 doi = {10.1371/journal.pone.0178165},
 journal = {PLOS ONE},
 number = {5}
}

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics.

@article{
 title = {Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics},
 type = {article},
 year = {2017},
 keywords = {Atmosphere-land interaction,Hydrologic models,Land surface model,Model output statistics,Remote sensing,Sensitivity studies},
 pages = {1121-1142},
 volume = {18},
 websites = {http://journals.ametsoc.org/doi/10.1175/JHM-D-16-0148.1},
 month = {4},
 day = {1},
 id = {6ecd4844-3083-3449-aaae-0f438ce16734},
 created = {2021-12-02T09:37:06.144Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.519Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.},
 bibtype = {article},
 author = {Koch, Julian and Mendiguren, Gorka and Mariethoz, Gregoire and Stisen, Simon},
 doi = {10.1175/JHM-D-16-0148.1},
 journal = {Journal of Hydrometeorology},
 number = {4}
}

Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.

@article{
 title = {The integrated hydrologic model intercomparison project, <scp>IH‐MIP2</scp> : A second set of benchmark results to diagnose integrated hydrology and feedbacks},
 type = {article},
 year = {2017},
 pages = {867-890},
 volume = {53},
 websites = {http://doi.wiley.com/10.1002/2016WR019191,https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016WR019191},
 month = {1},
 day = {25},
 id = {db883c55-269d-30d3-8b57-cf43f15c60d7},
 created = {2021-12-02T09:37:06.464Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:22.112Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Emphasizing the physical intricacies of integrated hydrology and feedbacks in simulating connected, variably saturated groundwater‐surface water systems, the Integrated Hydrologic Model Intercomparison Project initiated a second phase (IH‐MIP2), increasing the complexity of the benchmarks of the first phase. The models that took part in the intercomparison were ATS, Cast3M, CATHY, GEOtop, HydroGeoSphere, MIKE‐SHE, and ParFlow. IH‐MIP2 benchmarks included a tilted v‐catchment with 3‐D subsurface; a superslab case expanding the slab case of the first phase with an additional horizontal subsurface heterogeneity; and the Borden field rainfall‐runoff experiment. The analyses encompassed time series of saturated, unsaturated, and ponded storages, as well as discharge. Vertical cross sections and profiles were also inspected in the superslab and Borden benchmarks. An analysis of agreement was performed including systematic and unsystematic deviations between the different models. Results show generally good agreement between the different models, which lends confidence in the fundamental physical and numerical implementation of the governing equations in the different models. Differences can be attributed to the varying level of detail in the mathematical and numerical representation or in the parameterization of physical processes, in particular with regard to ponded storage and friction slope in the calculation of overland flow. These differences may become important for specific applications such as detailed inundation modeling or when strong inhomogeneities are present in the simulation domain.},
 bibtype = {article},
 author = {Kollet, Stefan and Sulis, Mauro and Maxwell, Reed M. and Paniconi, Claudio and Putti, Mario and Bertoldi, Giacomo and Coon, Ethan T. and Cordano, Emanuele and Endrizzi, Stefano and Kikinzon, Evgeny and Mouche, Emmanuel and Mügler, Claude and Park, Young-Jin and Refsgaard, Jens C. and Stisen, Simon and Sudicky, Edward},
 doi = {10.1002/2016WR019191},
 journal = {Water Resources Research},
 number = {1}
}

Emphasizing the physical intricacies of integrated hydrology and feedbacks in simulating connected, variably saturated groundwater‐surface water systems, the Integrated Hydrologic Model Intercomparison Project initiated a second phase (IH‐MIP2), increasing the complexity of the benchmarks of the first phase. The models that took part in the intercomparison were ATS, Cast3M, CATHY, GEOtop, HydroGeoSphere, MIKE‐SHE, and ParFlow. IH‐MIP2 benchmarks included a tilted v‐catchment with 3‐D subsurface; a superslab case expanding the slab case of the first phase with an additional horizontal subsurface heterogeneity; and the Borden field rainfall‐runoff experiment. The analyses encompassed time series of saturated, unsaturated, and ponded storages, as well as discharge. Vertical cross sections and profiles were also inspected in the superslab and Borden benchmarks. An analysis of agreement was performed including systematic and unsystematic deviations between the different models. Results show generally good agreement between the different models, which lends confidence in the fundamental physical and numerical implementation of the governing equations in the different models. Differences can be attributed to the varying level of detail in the mathematical and numerical representation or in the parameterization of physical processes, in particular with regard to ponded storage and friction slope in the calculation of overland flow. These differences may become important for specific applications such as detailed inundation modeling or when strong inhomogeneities are present in the simulation domain.

@article{
 title = {Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI},
 type = {article},
 year = {2017},
 pages = {6235-6251},
 volume = {21},
 websites = {https://hess.copernicus.org/articles/21/6235/2017/},
 month = {12},
 day = {8},
 id = {7cd8c86b-b6d0-386e-98dc-159f26f5a27e},
 created = {2021-12-02T09:37:06.521Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.658Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ruiz-Parez2017},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 abstract = {Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.},
 bibtype = {article},
 author = {Ruiz-Pérez, Guiomar and Koch, Julian and Manfreda, Salvatore and Caylor, Kelly and Francés, Félix},
 doi = {10.5194/hess-21-6235-2017},
 journal = {Hydrology and Earth System Sciences},
 number = {12}
}

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

@article{
 title = {A comparative assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature},
 type = {article},
 year = {2017},
 pages = {785-797},
 volume = {553},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S002216941730584X},
 month = {10},
 id = {be00198c-32a1-3bd0-87fa-8ef128d8c038},
 created = {2021-12-02T09:37:06.608Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:22.298Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ahmadalipour2017},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Ahmadalipour, Ali and Moradkhani, Hamid and Demirel, Mehmet C},
 doi = {10.1016/j.jhydrol.2017.08.047},
 journal = {Journal of Hydrology}
}

@misc{
 title = {mesoscale Hydrologic Model - mHM v5.8},
 type = {misc},
 year = {2017},
 keywords = {Fortran,gfortran,hydrologic model},
 websites = {https://doi.org/10.5281/zenodo.1069203},
 id = {6dc4a4eb-72e1-3bf9-a0c4-dc8acbcba2de},
 created = {2021-12-02T09:37:06.612Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.394Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Samaniego2017},
 source_type = {misc},
 user_context = {misc},
 private_publication = {false},
 bibtype = {misc},
 author = {Samaniego, L and Kumar, R and Mai, J and Zink, M and Thober, S and Cuntz, M and Rakovec, O and Schäfer, D and Schrön, M and Brenner, J and Demirel, Mehmet Cüneyd and Kaluza, M and Langenberg, B and Stisen, S and Attinger, S},
 doi = {10.5281/zenodo.1069203}
}

@article{
 title = {Analysis of dam-induced cyclic patterns on river flow dynamics},
 type = {article},
 year = {2017},
 pages = {626-641},
 volume = {62},
 websites = {https://www.tandfonline.com/doi/full/10.1080/02626667.2016.1252841},
 month = {3},
 day = {12},
 id = {4254f8e9-612c-3ab1-a246-b27f16be9435},
 created = {2021-12-02T09:37:06.745Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.531Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Tongal2017},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Tongal, Hakan and Demirel, Mehmet C and Moradkhani, Hamid},
 doi = {10.1080/02626667.2016.1252841},
 journal = {Hydrological Sciences Journal},
 number = {4}
}

@article{
 title = {Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment},
 type = {article},
 year = {2016},
 keywords = {CAL paper},
 pages = {234-249},
 volume = {533},
 websites = {http://www.sciencedirect.com/science/article/pii/S0022169415009415,https://linkinghub.elsevier.com/retrieve/pii/S0022169415009415},
 month = {2},
 id = {91d1428c-cd25-380b-bebc-6479fe5e2926},
 created = {2021-12-02T09:28:40.851Z},
 accessed = {2016-01-04},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
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 last_modified = {2024-01-11T08:40:21.847Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The objective of this study is to inter-compare three spatially distributed hydrological models (HydroGeoSphere, MIKE SHE and ParFlow-CLM) by means of their ability to simulate soil moisture patterns. This study pools the catchment modeling efforts which have been undertaken at the Wüstebach catchment; one of TERENO’s hydrological observatories. The catchment is densely instrumented with a wireless sensor network (SoilNET) which allows continuous measurements of the spatio-temporal soil moisture dynamics. This unique dataset is ideal to benchmark hydrological models as it poses distinct challenges like seasonality and spatial heterogeneity. Two scenarios of soil parametrization assess the modeling implications of moving from homogeneous to heterogeneous porosity. The three given models perform well in terms of discharge and accumulated water balance components. However, their ability to predict soil moisture is found to be more diverging. Interpretations are ambiguous and depend on what performance metric and what level of spatial aggregation is chosen. In comparison to the other models, ParFlow-CLM performs more accurate at predicting the temporal dynamics and the heterogeneity aggregated to catchment scale. Nevertheless, at local scale HydroGeoSphere and MIKE SHE provide more detailed soil moisture predictions. Overall, a clear increase in performance can be attested to the scenario that includes heterogeneous porosity. Next to soil parametrization, topography is among the main drivers of soil moisture variability which was found to have an overemphasized feedback in ParFlow-CLM compared to the other models. This study stresses that further efforts toward spatially distributed input data need to emerge alongside a more suitable soil parametrization that can account for the observed heterogeneity and seasonality of soil moisture.},
 bibtype = {article},
 author = {Koch, Julian and Cornelissen, Thomas and Fang, Zhufeng and Bogena, Heye and Diekkrüger, Bernd and Kollet, Stefan and Stisen, Simon},
 doi = {10.1016/j.jhydrol.2015.12.002},
 journal = {Journal of Hydrology}
}

The objective of this study is to inter-compare three spatially distributed hydrological models (HydroGeoSphere, MIKE SHE and ParFlow-CLM) by means of their ability to simulate soil moisture patterns. This study pools the catchment modeling efforts which have been undertaken at the Wüstebach catchment; one of TERENO’s hydrological observatories. The catchment is densely instrumented with a wireless sensor network (SoilNET) which allows continuous measurements of the spatio-temporal soil moisture dynamics. This unique dataset is ideal to benchmark hydrological models as it poses distinct challenges like seasonality and spatial heterogeneity. Two scenarios of soil parametrization assess the modeling implications of moving from homogeneous to heterogeneous porosity. The three given models perform well in terms of discharge and accumulated water balance components. However, their ability to predict soil moisture is found to be more diverging. Interpretations are ambiguous and depend on what performance metric and what level of spatial aggregation is chosen. In comparison to the other models, ParFlow-CLM performs more accurate at predicting the temporal dynamics and the heterogeneity aggregated to catchment scale. Nevertheless, at local scale HydroGeoSphere and MIKE SHE provide more detailed soil moisture predictions. Overall, a clear increase in performance can be attested to the scenario that includes heterogeneous porosity. Next to soil parametrization, topography is among the main drivers of soil moisture variability which was found to have an overemphasized feedback in ParFlow-CLM compared to the other models. This study stresses that further efforts toward spatially distributed input data need to emerge alongside a more suitable soil parametrization that can account for the observed heterogeneity and seasonality of soil moisture.

@article{
 title = {Calibration of a distributed hydrology and land surface model using energy flux measurements},
 type = {article},
 year = {2016},
 pages = {74-88},
 volume = {217},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0168192315007637,https://linkinghub.elsevier.com/retrieve/pii/S0168192315007637},
 month = {2},
 id = {5c832180-1ecd-39b8-a8e4-4331221c9112},
 created = {2021-12-02T09:37:06.157Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.699Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Larsen2016b},
 private_publication = {false},
 bibtype = {article},
 author = {Larsen, Morten A.D. and Refsgaard, Jens C. and Jensen, Karsten H. and Butts, Michael B. and Stisen, Simon and Mollerup, Mikkel},
 doi = {10.1016/j.agrformet.2015.11.012},
 journal = {Agricultural and Forest Meteorology}
}

@article{
 title = {Where are the limits of model predictive capabilities?},
 type = {article},
 year = {2016},
 keywords = {Distributed hydrological model,Minimum scale of predictive capability,Representative Elementary Scale (RES)},
 pages = {4956-4965},
 volume = {30},
 websites = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.11029},
 month = {12},
 day = {30},
 id = {9d3d11d2-c8cc-34ef-b9b8-06f8acc7cfaa},
 created = {2021-12-02T09:37:06.218Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.820Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Distributed hydrological models can make predictions with much finer spatial resolution than the supporting field data. They will, however, usually not have a predictive capability at model grid scale due to limitations of data availability and uncertainty of model conceptualizations. In previous publications, we have introduced the Representative Elementary Scale (RES) concept as the theoretically minimum scale at which a model with a given conceptualization has a potential for obtaining a predictive accuracy corresponding to a given acceptable accuracy. The new RES concept has similarities to the 25‐year‐old Representative Elementary Area concept, but it differs in the sense that while Representative Elementary Area addresses similarity between subcatchments by sampling within the catchment, RES focuses on effects of data or conceptualization uncertainty by Monte Carlo simulations followed by a scale analysis. In the present paper, we extend and generalize the RES concept to a framework for assessing the minimum scale of potential predictability of a distributed model applicable also for analyses of different model structures and data availabilities. We present three examples with RES analyses and discuss our findings in relation to Beven's alternative blueprint and environmental modeling philosophy from 2002. While Beven here addresses model structural and parameter uncertainties, he does not provide a thorough methodology for assessing to which extent model predictions for variables that are not measured possess opportunities to have meaningful predictive accuracies, or whether this is impossible due to limitations in data and models. This shortcoming is addressed by the RES framework through its analysis of the relationship between aggregation scale of model results and prediction uncertainties and for considering how alternative model structures and alternative data availability affects the results. We suggest that RES analysis should be applied in all modeling studies that aim to use simulation results at spatial scales smaller than the support scale of the calibration data.},
 bibtype = {article},
 author = {Refsgaard, J.C. and Højberg, A.L. and He, X. and Hansen, A.L. and Rasmussen, S.H. and Stisen, S.},
 doi = {10.1002/hyp.11029},
 journal = {Hydrological Processes},
 number = {26}
}

Distributed hydrological models can make predictions with much finer spatial resolution than the supporting field data. They will, however, usually not have a predictive capability at model grid scale due to limitations of data availability and uncertainty of model conceptualizations. In previous publications, we have introduced the Representative Elementary Scale (RES) concept as the theoretically minimum scale at which a model with a given conceptualization has a potential for obtaining a predictive accuracy corresponding to a given acceptable accuracy. The new RES concept has similarities to the 25‐year‐old Representative Elementary Area concept, but it differs in the sense that while Representative Elementary Area addresses similarity between subcatchments by sampling within the catchment, RES focuses on effects of data or conceptualization uncertainty by Monte Carlo simulations followed by a scale analysis. In the present paper, we extend and generalize the RES concept to a framework for assessing the minimum scale of potential predictability of a distributed model applicable also for analyses of different model structures and data availabilities. We present three examples with RES analyses and discuss our findings in relation to Beven's alternative blueprint and environmental modeling philosophy from 2002. While Beven here addresses model structural and parameter uncertainties, he does not provide a thorough methodology for assessing to which extent model predictions for variables that are not measured possess opportunities to have meaningful predictive accuracies, or whether this is impossible due to limitations in data and models. This shortcoming is addressed by the RES framework through its analysis of the relationship between aggregation scale of model results and prediction uncertainties and for considering how alternative model structures and alternative data availability affects the results. We suggest that RES analysis should be applied in all modeling studies that aim to use simulation results at spatial scales smaller than the support scale of the calibration data.

@article{
 title = {Using expert elicitation to quantify catchment water balances and their uncertainties},
 type = {article},
 year = {2016},
 keywords = {catchment hydrology,expert elicitation,multistep elicitation,uncertainty analysis,water balance},
 pages = {5111-5131},
 volume = {52},
 websites = {https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015WR018461},
 month = {7},
 day = {2},
 id = {0d42233c-d3f5-3f7e-b833-0e14cb7c28c9},
 created = {2021-12-02T09:37:06.254Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.350Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Expert elicitation with the participation of 35 experts was used to estimate a water balance for the nested Ahlergaarde and Holtum catchments in Western Denmark. Average annual values of precipitation, evapotranspiration, and surface runoff as well as subsurface outflow and recharge and their uncertainty were estimated in a multistep elicitation, where experts first gave their opinion on the probability distribution of their water balance component of interest, then the average annual values and uncertainty of water balance components and catchment‐scale water balances were obtained by reaching consensus during group discussions. The obtained water balance errors for the 1055 km 2 Ahlergaarde catchment and 120 km 2 Holtum catchment were −5 and −62 mm/yr, respectively, with an uncertainty of 66 and 86 mm/yr, respectively. As an advantage of the expert elicitation, drawing on the intuitive experience and capabilities of experts to assess complex, site‐specific problems, the contribution of independent sources of uncertainties to the total uncertainty was also evaluated similarly to the subsurface outflow component, which traditionally is estimated as the residual of the water balance.},
 bibtype = {article},
 author = {Sebok, E. and Refsgaard, J. C. and Warmink, J. J. and Stisen, S. and Jensen, K. H.},
 doi = {10.1002/2015WR018461},
 journal = {Water Resources Research},
 number = {7}
}

Expert elicitation with the participation of 35 experts was used to estimate a water balance for the nested Ahlergaarde and Holtum catchments in Western Denmark. Average annual values of precipitation, evapotranspiration, and surface runoff as well as subsurface outflow and recharge and their uncertainty were estimated in a multistep elicitation, where experts first gave their opinion on the probability distribution of their water balance component of interest, then the average annual values and uncertainty of water balance components and catchment‐scale water balances were obtained by reaching consensus during group discussions. The obtained water balance errors for the 1055 km 2 Ahlergaarde catchment and 120 km 2 Holtum catchment were −5 and −62 mm/yr, respectively, with an uncertainty of 66 and 86 mm/yr, respectively. As an advantage of the expert elicitation, drawing on the intuitive experience and capabilities of experts to assess complex, site‐specific problems, the contribution of independent sources of uncertainties to the total uncertainty was also evaluated similarly to the subsurface outflow component, which traditionally is estimated as the residual of the water balance.

@article{
 title = {Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics},
 type = {article},
 year = {2016},
 pages = {5430-5452},
 volume = {121},
 websites = {http://doi.wiley.com/10.1002/2015JD024482,https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015JD024482},
 month = {5},
 day = {27},
 id = {5b96b263-243e-34aa-b202-025f30e0d67b},
 created = {2021-12-02T09:37:06.323Z},
 accessed = {2016-05-07},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.763Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Koch2016b},
 private_publication = {false},
 abstract = {Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High‐Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.},
 bibtype = {article},
 author = {Koch, Julian and Siemann, Amanda and Stisen, Simon and Sheffield, Justin},
 doi = {10.1002/2015JD024482},
 journal = {Journal of Geophysical Research: Atmospheres},
 number = {10}
}

Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High‐Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

@article{
 title = {Missing data simulation inside flow rate time-series using multiple-point statistics},
 type = {article},
 year = {2016},
 pages = {264-276},
 volume = {86},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1364815216307745},
 month = {12},
 id = {2898c213-ee8f-3fa6-9f31-e8bb841bf68d},
 created = {2021-12-02T09:37:06.571Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.944Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Oriani2016},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Oriani, Fabio and Borghi, Andrea and Straubhaar, Julien and Mariethoz, Grégoire and Renard, Philippe},
 doi = {10.1016/j.envsoft.2016.10.002},
 journal = {Environmental Modelling & Software}
}

@phdthesis{
 title = {Evaluating spatial patterns in hydrological modelling},
 type = {phdthesis},
 year = {2016},
 pages = {1-63},
 institution = {University of Copenhagen},
 id = {a02ba386-874b-31fe-af94-8ae9cb822d08},
 created = {2021-12-02T09:37:06.651Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T09:37:06.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Koch2016e},
 source_type = {phdthesis},
 user_context = {phdthesis},
 private_publication = {false},
 abstract = {The objective of this Ph.D. study is to investigate possible ways towards a better inte- gration of spatial observations into the modelling process via spatial pattern evaluation. It is widely recognized by the modelling community that the grand potential of read- ily available spatial observations is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay con dence on the predicted catchment inherent spatial variability of hydrological processes in a fully-distributed model.},
 bibtype = {phdthesis},
 author = {Koch, Julian}
}

The objective of this Ph.D. study is to investigate possible ways towards a better inte- gration of spatial observations into the modelling process via spatial pattern evaluation. It is widely recognized by the modelling community that the grand potential of read- ily available spatial observations is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay con dence on the predicted catchment inherent spatial variability of hydrological processes in a fully-distributed model.

@article{
 title = {Assessing the impact of CMIP5 climate multi-modeling on estimating the precipitation seasonality and timing},
 type = {article},
 year = {2016},
 pages = {357-372},
 volume = {135},
 websites = {http://link.springer.com/10.1007/s10584-015-1559-z},
 month = {3},
 day = {14},
 id = {c7e356e2-a2bb-37dd-8955-dfbbb2ed6092},
 created = {2021-12-02T09:37:06.738Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:22.009Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Demirel2016},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Demirel, Mehmet C and Moradkhani, Hamid},
 doi = {10.1007/s10584-015-1559-z},
 journal = {Climatic Change},
 number = {2}
}

@article{
 title = {Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis},
 type = {article},
 year = {2015},
 keywords = {3D hydrological simulation,EOF analysis,Soil moisture,Wavelet coherence analysis},
 pages = {1754-1767},
 volume = {529},
 websites = {http://www.sciencedirect.com/science/article/pii/S0022169415005703},
 month = {8},
 id = {ae5bf936-1ca0-3027-a1d0-3fabaaa927c4},
 created = {2021-12-02T09:37:06.104Z},
 accessed = {2015-10-09},
 file_attached = {true},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T09:37:15.535Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Soil moisture plays a key role in the water and energy balance in soil, vegetation and atmosphere systems. According to Wood et al. (2011) there is a grand need to increase global-scale hyper-resolution water–energy–biogeochemistry land surface modelling capabilities. These modelling capabilities should also recognize epistemic uncertainties, as well as the nonlinearity and hysteresis in its dynamics. Unfortunately, it is not clear how to parameterize hydrological processes as a function of scale, and how to test deterministic models with regard to epistemic uncertainties. In this study, high resolution long-term simulations were conducted in the highly instrumented TERENO hydrological observatory of the Wüstebach catchment. Soil hydraulic parameters were derived using inverse modelling with the Hydrus-1D model using the global optimization scheme SCE-UA and soil moisture data from a wireless soil moisture sensor network. The estimated parameters were then used for 3D simulations of water transport using the integrated parallel simulation platform ParFlow-CLM. The simulated soil moisture dynamics, as well as evapotranspiration (ET) and runoff, were compared with long-term field observations to illustrate how well the model was able to reproduce the water budget dynamics. We investigated different anisotropies of hydraulic conductivity to analyze how fast lateral flow processes above the underlying bedrock affect the simulation results. For a detail investigation of the model results we applied the empirical orthogonal function (EOF) and wavelet coherence methods. The EOF analysis of temporal–spatial patterns of simulated and observed soil moisture revealed that introduction of heterogeneity in the soil porosity effectively improves estimates of soil moisture patterns. Our wavelet coherence analysis indicates that wet and dry seasons have significant effect on temporal correlation between observed and simulated soil moisture and ET. Our study demonstrates the usefulness of the EOF and wavelet coherence methods for a more in-depth validation of spatially highly resolved hydrological 3D models.},
 bibtype = {article},
 author = {Fang, Zhufeng and Bogena, Heye and Kollet, Stefan and Koch, Julian and Vereecken, Harry},
 doi = {10.1016/j.jhydrol.2015.08.011},
 journal = {Journal of Hydrology}
}

Soil moisture plays a key role in the water and energy balance in soil, vegetation and atmosphere systems. According to Wood et al. (2011) there is a grand need to increase global-scale hyper-resolution water–energy–biogeochemistry land surface modelling capabilities. These modelling capabilities should also recognize epistemic uncertainties, as well as the nonlinearity and hysteresis in its dynamics. Unfortunately, it is not clear how to parameterize hydrological processes as a function of scale, and how to test deterministic models with regard to epistemic uncertainties. In this study, high resolution long-term simulations were conducted in the highly instrumented TERENO hydrological observatory of the Wüstebach catchment. Soil hydraulic parameters were derived using inverse modelling with the Hydrus-1D model using the global optimization scheme SCE-UA and soil moisture data from a wireless soil moisture sensor network. The estimated parameters were then used for 3D simulations of water transport using the integrated parallel simulation platform ParFlow-CLM. The simulated soil moisture dynamics, as well as evapotranspiration (ET) and runoff, were compared with long-term field observations to illustrate how well the model was able to reproduce the water budget dynamics. We investigated different anisotropies of hydraulic conductivity to analyze how fast lateral flow processes above the underlying bedrock affect the simulation results. For a detail investigation of the model results we applied the empirical orthogonal function (EOF) and wavelet coherence methods. The EOF analysis of temporal–spatial patterns of simulated and observed soil moisture revealed that introduction of heterogeneity in the soil porosity effectively improves estimates of soil moisture patterns. Our wavelet coherence analysis indicates that wet and dry seasons have significant effect on temporal correlation between observed and simulated soil moisture and ET. Our study demonstrates the usefulness of the EOF and wavelet coherence methods for a more in-depth validation of spatially highly resolved hydrological 3D models.

@article{
 title = {Interpolation of daily raingauge data for hydrological modelling in data sparse regions using pattern information from satellite data},
 type = {article},
 year = {2015},
 pages = {1-16},
 websites = {http://www.tandfonline.com/doi/full/10.1080/02626667.2014.992789},
 month = {9},
 day = {17},
 id = {89633def-f8c3-30b6-9687-276d3c6784fc},
 created = {2021-12-02T09:37:06.322Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2021-12-02T09:37:06.322Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Stisen2015},
 private_publication = {false},
 bibtype = {article},
 author = {Stisen, S. and Tumbo, M.},
 doi = {10.1080/02626667.2014.992789},
 journal = {Hydrological Sciences Journal}
}

@article{
 title = {Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment},
 type = {article},
 year = {2015},
 pages = {2017-2036},
 volume = {19},
 websites = {http://apps.isiknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=1&SID=X2Du82hv7f421JS6CHG&page=1&doc=4,https://hess.copernicus.org/articles/19/2017/2015/},
 month = {4},
 day = {24},
 id = {439401ba-e5d7-3e2e-b6a9-7cfc7769a407},
 created = {2021-12-02T09:37:06.379Z},
 accessed = {2015-11-12},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:43:31.065Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.},
 bibtype = {article},
 author = {Guzinski, R. and Nieto, H. and Stisen, S. and Fensholt, R.},
 doi = {10.5194/hess-19-2017-2015},
 journal = {Hydrology and Earth System Sciences},
 number = {4}
}

Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.

@article{
 title = {Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin},
 type = {article},
 year = {2015},
 keywords = {Drought,Drylands,Geostationary,MIKESHE modeling,MSG,Sahel},
 pages = {57-69},
 volume = {159},
 websites = {http://www.sciencedirect.com/science/article/pii/S0034425714004830,https://linkinghub.elsevier.com/retrieve/pii/S0034425714004830},
 month = {3},
 id = {a9a48f96-ba07-36f5-9345-f12a79992234},
 created = {2021-12-02T09:37:06.461Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:43:31.061Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Satellite remote sensing of vegetation parameters and stress is a key issue for semi-arid areas such as the Sahel, where vegetation is an important part of the natural resource base. In this study we examine if additional information can be obtained on intra-seasonal short term scale by using the Shortwave Infrared Water Stress Index (SIWSI) as compared to Normalized Difference Vegetation Index (NDVI). We perform a spatio-temporal evaluation of NDVI and SIWSI using geostationary remote sensing imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The indices and their seasonally detrended anomalies are evaluated using a gridded rainfall product (RFE2) and modeled actual evapotranspiration (ETa) for the Senegal River basin in 2008. Daily NDVI and SIWSI were found spatially highly correlated to ETa with r=0.73 for both indices, showing the importance of the north/south vegetation gradient in the river catchment. The hypothesis that short term evolution of index anomalies are related to canopy water status was tested by comparing 10-day averages of ETa with short term changes in daily NDVI and SIWSI anomalies, and moderate to strong coefficients of determination where found when anomaly variations where aggregated by Land Cover Classes (LCCs) with R2 values of 0.65 for savanna, 0.60 for grassland, 0.72 for shrubland, and 0.58 for barren or sparsely vegetated areas. This is higher than for the same method applied to NDVI anomalies, with R2 values of 0.57 for savanna, 0.50 for grassland, 0.32 for shrubland, and 0.57 for barren or sparsely vegetated areas. The approach of detrending NIR/SWIR based indices and spatially aggregating the anomalies do offer improved detection of intra-seasonal stress. However, quite coarse spatial aggregation is found necessary for a significant analysis outcome.},
 bibtype = {article},
 author = {Olsen, Jørgen L. and Stisen, Simon and Proud, Simon R. and Fensholt, Rasmus},
 doi = {10.1016/j.rse.2014.11.029},
 journal = {Remote Sensing of Environment}
}

Satellite remote sensing of vegetation parameters and stress is a key issue for semi-arid areas such as the Sahel, where vegetation is an important part of the natural resource base. In this study we examine if additional information can be obtained on intra-seasonal short term scale by using the Shortwave Infrared Water Stress Index (SIWSI) as compared to Normalized Difference Vegetation Index (NDVI). We perform a spatio-temporal evaluation of NDVI and SIWSI using geostationary remote sensing imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The indices and their seasonally detrended anomalies are evaluated using a gridded rainfall product (RFE2) and modeled actual evapotranspiration (ETa) for the Senegal River basin in 2008. Daily NDVI and SIWSI were found spatially highly correlated to ETa with r=0.73 for both indices, showing the importance of the north/south vegetation gradient in the river catchment. The hypothesis that short term evolution of index anomalies are related to canopy water status was tested by comparing 10-day averages of ETa with short term changes in daily NDVI and SIWSI anomalies, and moderate to strong coefficients of determination where found when anomaly variations where aggregated by Land Cover Classes (LCCs) with R2 values of 0.65 for savanna, 0.60 for grassland, 0.72 for shrubland, and 0.58 for barren or sparsely vegetated areas. This is higher than for the same method applied to NDVI anomalies, with R2 values of 0.57 for savanna, 0.50 for grassland, 0.32 for shrubland, and 0.57 for barren or sparsely vegetated areas. The approach of detrending NIR/SWIR based indices and spatially aggregating the anomalies do offer improved detection of intra-seasonal stress. However, quite coarse spatial aggregation is found necessary for a significant analysis outcome.

@article{
 title = {Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site},
 type = {article},
 year = {2015},
 pages = {5523-5535},
 volume = {12},
 websites = {https://bg.copernicus.org/articles/12/5523/2015/},
 month = {9},
 day = {29},
 id = {fdf856f3-93d7-3688-b260-bb6c0281dc68},
 created = {2021-12-02T09:37:06.702Z},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:40:21.572Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Mendiguren2015},
 source_type = {article},
 user_context = {article},
 private_publication = {false},
 abstract = {Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.},
 bibtype = {article},
 author = {Mendiguren, G and Pilar Martín, M. and Nieto, H and Pacheco-Labrador, J and Jurdao, S},
 doi = {10.5194/bg-12-5523-2015},
 journal = {Biogeosciences},
 number = {18}
}

Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

@article{
 title = {Toward a true spatial model evaluation in distributed hydrological modeling: <scp>K</scp> appa statistics, <scp>F</scp> uzzy theory, and <scp>EOF</scp> ‐analysis benchmarked by the human perception and evaluated against a modeling case study},
 type = {article},
 year = {2015},
 pages = {1225-1246},
 volume = {51},
 websites = {http://apps.isiknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=1&SID=X2Du82hv7f421JS6CHG&page=1&doc=2,http://doi.wiley.com/10.1002/2014WR016259,http://doi.wiley.com/10.1002/2014WR016527,http://doi.wiley.com/10.1002/2014WR016607,https},
 month = {2},
 day = {26},
 id = {85c12d45-dcf9-3a4c-aa1f-1ec5d8d3001d},
 created = {2021-12-02T09:47:55.677Z},
 accessed = {2015-11-12},
 file_attached = {false},
 profile_id = {f04515b8-7bd9-3ff6-8226-72a3fe741d01},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:43:31.087Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The hydrological modeling community is aware that the validation of distributed hydrological models has to move beyond aggregated performance measures, like hydrograph assessment by means of Nash‐Suitcliffe efficiency toward a true spatial model validation. Remote sensing facilitates continuous data and can be measured on a similar spatial scale as the predictive scale of the hydrological model thereby it can serve as suitable data for the spatial validation. The human perception is often described as a very reliable and well‐trained source for pattern comparison, which this study wants to exploit. A web‐based survey that is interpreted based on approximately 200 replies reflects the consensus of the human perception on map comparisons of a reference map and 12 synthetic perturbations. The resulting similarity ranking can be used as a reference to benchmark various spatial performance metrics. This study promotes Fuzzy theory as a suitable approach because it considers uncertainties related to both location and value in the simulated map. Additionally, an EOF‐analysis (Empirical Orthogonal Function) is conducted to decompose the map comparison into its similarities and dissimilarities. A modeling case study serves to further examine the metrics capability to assess the goodness of fit between simulated and observed land surface temperature maps. The EOF‐analysis unambiguously identifies a systematic depth to groundwater table‐related model deficiency. Kappa statistic extended by Fuzziness is a suitable and commonly applied measure for map comparison. However, its apparent bias sensitivity limits it's capability as a diagnostic tool to detect the distinct deficiency.},
 bibtype = {article},
 author = {Koch, Julian and Jensen, Karsten Høgh and Stisen, Simon},
 doi = {10.1002/2014WR016607},
 journal = {Water Resources Research},
 number = {2}
}

The hydrological modeling community is aware that the validation of distributed hydrological models has to move beyond aggregated performance measures, like hydrograph assessment by means of Nash‐Suitcliffe efficiency toward a true spatial model validation. Remote sensing facilitates continuous data and can be measured on a similar spatial scale as the predictive scale of the hydrological model thereby it can serve as suitable data for the spatial validation. The human perception is often described as a very reliable and well‐trained source for pattern comparison, which this study wants to exploit. A web‐based survey that is interpreted based on approximately 200 replies reflects the consensus of the human perception on map comparisons of a reference map and 12 synthetic perturbations. The resulting similarity ranking can be used as a reference to benchmark various spatial performance metrics. This study promotes Fuzzy theory as a suitable approach because it considers uncertainties related to both location and value in the simulated map. Additionally, an EOF‐analysis (Empirical Orthogonal Function) is conducted to decompose the map comparison into its similarities and dissimilarities. A modeling case study serves to further examine the metrics capability to assess the goodness of fit between simulated and observed land surface temperature maps. The EOF‐analysis unambiguously identifies a systematic depth to groundwater table‐related model deficiency. Kappa statistic extended by Fuzziness is a suitable and commonly applied measure for map comparison. However, its apparent bias sensitivity limits it's capability as a diagnostic tool to detect the distinct deficiency.

@article{
 title = {Assimilation of <scp>SMOS</scp> ‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in <scp>W</scp> estern <scp>D</scp> enmark},
 type = {article},
 year = {2014},
 pages = {8962-8981},
 volume = {50},
 websites = {http://apps.isiknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=1&SID=X2Du82hv7f421JS6CHG&page=1&doc=5,https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014WR015392},
 month = {11},
 day = {20},
 id = {41a71f07-e217-37dc-8008-1ac3c26e8b99},
 created = {2021-12-02T09:37:06.082Z},
 accessed = {2015-11-12},
 file_attached = {false},
 profile_id = {b0dfdb53-b667-3b16-bb90-3fea29a49cff},
 group_id = {2fdabcb8-a714-3bb4-b42d-1e6a075d4c78},
 last_modified = {2024-01-11T08:42:54.504Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer (MIKE SHE SW‐ET) model using a bias aware ensemble transform Kalman filter (Bias‐ETKF). Satellite‐derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS‐derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.},
 bibtype = {article},
 author = {Ridler, Marc-Etienne and Madsen, Henrik and Stisen, Simon and Bircher, Simone and Fensholt, Rasmus},
 doi = {10.1002/2014WR015392},
 journal = {Water Resources Research},
 number = {11}
}

Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer (MIKE SHE SW‐ET) model using a bias aware ensemble transform Kalman filter (Bias‐ETKF). Satellite‐derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS‐derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.