1. Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin
- Author
-
Wongchuig, S., Paiva, R., Siqueira, V., Papa, F., Fleischmann, A., Biancamaria, S., Paris, A., Parrens, M., and Al Bitar, A.
- Abstract
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. The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experimentsMoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed dataMoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experiments MoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed data MoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission
- Published
- 2024
- Full Text
- View/download PDF