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Title: The impact of multi-sensor land data assimilation on river discharge estimation

Abstract

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 foundmore » 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 R2 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 predict the terrestrial hydrological cycle.« less

Authors:
 [1];  [2];  [3];  [4]
+ Show Author Affiliations
  1. Univ. of Texas, Austin, TX (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Univ. of Texas, Austin, TX (United States)
  3. Univ. of Texas, Austin, TX (United States); Southwest University (China)
  4. Univ. of Texas, Austin, TX (United States); Peking University (China)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); Jackson School of Geosciences; Peking University
OSTI Identifier:
1883037
Report Number(s):
LLNL-JRNL-833763
Journal ID: ISSN 0034-4257; 1052159
Grant/Contract Number:  
AC52-07NA27344; SC002221; 7100603946
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 279; Journal Issue: N/A; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Data assimilation; River discharge; Terrestrial water storage; GRACE; MODIS; AMSR-E

Citation Formats

Wu, Wen-Ying, Yang, Zong-Liang, Zhao, Long, and Lin, Peirong. The impact of multi-sensor land data assimilation on river discharge estimation. United States: N. p., 2022. Web. doi:10.1016/j.rse.2022.113138.
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Wu, Wen-Ying, Yang, Zong-Liang, Zhao, Long, & Lin, Peirong. The impact of multi-sensor land data assimilation on river discharge estimation. United States. https://doi.org/10.1016/j.rse.2022.113138
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Wu, Wen-Ying, Yang, Zong-Liang, Zhao, Long, and Lin, Peirong. Thu . "The impact of multi-sensor land data assimilation on river discharge estimation". United States. https://doi.org/10.1016/j.rse.2022.113138. https://www.osti.gov/servlets/purl/1883037.
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@article{osti_1883037,
title = {The impact of multi-sensor land data assimilation on river discharge estimation},
author = {Wu, Wen-Ying and Yang, Zong-Liang and Zhao, Long and Lin, Peirong},
abstractNote = {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 R2 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 predict the terrestrial hydrological cycle.},
doi = {10.1016/j.rse.2022.113138},
journal = {Remote Sensing of Environment},
number = N/A,
volume = 279,
place = {United States},
year = {Thu Jun 30 00:00:00 EDT 2022},
month = {Thu Jun 30 00:00:00 EDT 2022}
}
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