Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?. Li, F., Bogena, H. R., Bayat, B., Kurtz, W., & Hendricks Franssen, H. Water Resources Research, 60(1):e2023WR035056, January, 2024. ![Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale? [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 12 downloads Abstract Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. , Key Points Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale Soil moisture characterization improved more in a wet year than in a very dry year Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture
@article{li_can_2024,
title = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},
volume = {60},
issn = {0043-1397, 1944-7973},
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},
doi = {10.1029/2023WR035056},
abstract = {Abstract
Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\% in 2016 and 16\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\% RMSE reduction of monthly ET in the wet year and 9\% in the dry year.
,
Key Points
Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale
Soil moisture characterization improved more in a wet year than in a very dry year
Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},
language = {en},
number = {1},
urldate = {2024-02-26},
journal = {Water Resources Research},
author = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},
month = jan,
year = {2024},
pages = {e2023WR035056},
}
Downloads: 12
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This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. , Key Points Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale Soil moisture characterization improved more in a wet year than in a very dry year Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture","language":"en","number":"1","urldate":"2024-02-26","journal":"Water Resources Research","author":[{"propositions":[],"lastnames":["Li"],"firstnames":["Fang"],"suffixes":[]},{"propositions":[],"lastnames":["Bogena"],"firstnames":["Heye","Reemt"],"suffixes":[]},{"propositions":[],"lastnames":["Bayat"],"firstnames":["Bagher"],"suffixes":[]},{"propositions":[],"lastnames":["Kurtz"],"firstnames":["Wolfgang"],"suffixes":[]},{"propositions":[],"lastnames":["Hendricks","Franssen"],"firstnames":["Harrie‐Jan"],"suffixes":[]}],"month":"January","year":"2024","pages":"e2023WR035056","bibtex":"@article{li_can_2024,\n\ttitle = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},\n\tvolume = {60},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},\n\tdoi = {10.1029/2023WR035056},\n\tabstract = {Abstract\n Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\\% in 2016 and 16\\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\\% RMSE reduction of monthly ET in the wet year and 9\\% in the dry year.\n , \n Key Points\n \n \n \n Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale\n \n \n Soil moisture characterization improved more in a wet year than in a very dry year\n \n \n Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-02-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {e2023WR035056},\n}\n\n\n\n\n\n\n\n","author_short":["Li, F.","Bogena, H. 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