Journal Article

Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin

Dynamiques et écologie des paysages agriforestiers (DYNAFOR) ; École nationale supérieure agronomique de Toulouse (ENSAT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Immunologie et Neurogénétique Expérimentales et Moléculaires (INEM) ; Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Siqueira, Vinícius, Parrens, Marie, Fleischmann, A, Paiva, R, Al Bitar, A, Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université

2024

Overview

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.

• Publisher: American Geophysical Union,CCSD,John Wiley & Sons, Inc,Wiley
• Subject: Amazon River / basins / Computer models / Data assimilation / Data collection / ensemble Kalman filter / Error reduction / Evapotranspiration / Evapotranspiration estimates / Flood predictions / Floods / Forecast improvement / Hydrodynamic models / Hydrodynamics / Hydrologic models / Hydrologic processes / Hydrologic research / hydrologic‐hydrodynamic modeling / Hydrology / Life Sciences / Localization / Mathematical models / Moisture content / multiple observations / prediction / Remote sensing / River basins / Rivers / Satellite data / Satellite observation / Satellites / Seasonal variations / Seasonality / Soil improvement / Soil moisture / soil water / Soil water storage / Uncertainty / Water discharge / Water levels / Water storage / watersheds
• ISBN: 0012866545000