The impact of multi-sensor land data assimilation on river discharge estimation
River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingestin...
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Online Access: | http://www.osti.gov/servlets/purl/1883037 https://www.osti.gov/biblio/1883037 https://doi.org/10.1016/j.rse.2022.113138 |
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ftosti:oai:osti.gov:1883037 2023-07-30T04:06:20+02:00 The impact of multi-sensor land data assimilation on river discharge estimation Wu, Wen-Ying Yang, Zong-Liang Zhao, Long Lin, Peirong 2023-06-30 application/pdf http://www.osti.gov/servlets/purl/1883037 https://www.osti.gov/biblio/1883037 https://doi.org/10.1016/j.rse.2022.113138 unknown http://www.osti.gov/servlets/purl/1883037 https://www.osti.gov/biblio/1883037 https://doi.org/10.1016/j.rse.2022.113138 doi:10.1016/j.rse.2022.113138 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.1016/j.rse.2022.113138 2023-07-11T10:14:17Z River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to ... Other/Unknown Material permafrost SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Remote Sensing of Environment 279 113138 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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ftosti |
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unknown |
topic |
54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Wu, Wen-Ying Yang, Zong-Liang Zhao, Long Lin, Peirong The impact of multi-sensor land data assimilation on river discharge estimation |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to ... |
author |
Wu, Wen-Ying Yang, Zong-Liang Zhao, Long Lin, Peirong |
author_facet |
Wu, Wen-Ying Yang, Zong-Liang Zhao, Long Lin, Peirong |
author_sort |
Wu, Wen-Ying |
title |
The impact of multi-sensor land data assimilation on river discharge estimation |
title_short |
The impact of multi-sensor land data assimilation on river discharge estimation |
title_full |
The impact of multi-sensor land data assimilation on river discharge estimation |
title_fullStr |
The impact of multi-sensor land data assimilation on river discharge estimation |
title_full_unstemmed |
The impact of multi-sensor land data assimilation on river discharge estimation |
title_sort |
impact of multi-sensor land data assimilation on river discharge estimation |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1883037 https://www.osti.gov/biblio/1883037 https://doi.org/10.1016/j.rse.2022.113138 |
genre |
permafrost |
genre_facet |
permafrost |
op_relation |
http://www.osti.gov/servlets/purl/1883037 https://www.osti.gov/biblio/1883037 https://doi.org/10.1016/j.rse.2022.113138 doi:10.1016/j.rse.2022.113138 |
op_doi |
https://doi.org/10.1016/j.rse.2022.113138 |
container_title |
Remote Sensing of Environment |
container_volume |
279 |
container_start_page |
113138 |
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1772818872573886464 |