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

Wu, Wen-Ying; Yang, Zong-Liang; Zhao, Long; Lin, Peirong

doi:10.1016/j.rse.2022.113138

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

Journal Article · Thu Jun 30 00:00:00 EDT 2022 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2022.113138· OSTI ID:1883037

Wu, Wen-Ying ^[1]; Yang, Zong-Liang ^[2]; Zhao, Long ^[3]; Lin, Peirong ^[4]

Univ. of Texas, Austin, TX (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Univ. of Texas, Austin, TX (United States)
Univ. of Texas, Austin, TX (United States); Southwest University (China)
Univ. of Texas, Austin, TX (United States); Peking University (China)

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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Jackson School of Geosciences; Peking University

Grant/Contract Number:: AC52-07NA27344; SC002221; 7100603946

OSTI ID:: 1883037

Report Number(s):: LLNL-JRNL-833763; 1052159

Journal Information:: Remote Sensing of Environment, Vol. 279, Issue N/A; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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