Joint assimilation of GRACE Total Water Storage Anomalies and In-Situ Streamflow Data into a Global Hydrological Model

<p><span>Global hydrological models simulate water storages and fluxes of the water cycle, </span><span>motivated to assess water problems such as water scarcity, high flows and more generally the impact of anthropogenic change on the global water system. </span><span>However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure </span><span>which can lead</span> <span>to</span><span> disagreements </span><span>when simulation results are compared to</span><span> observations. To reduce </span><span>and quantify</span><span> these uncertainties, </span><span>some of </span><span>the models are calibrated against in-situ </span><span>streamflow</span><span> observations or compared against </span><span>total water storage anomalies (TWSA) derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. In recent years, TWSA data are integrated into some models via data assimilation </span><span>to directly improve the realism of the models</span><span>.</span></p><p><span>In this study, we present our framework for jointly assimilating satellite and in-situ observations into the WaterGAP Global Hydrological Model (WGHM). </span><span>In addition to GRACE TWSA maps, for the first time here we experimentally jointly assimilate in-situ streamflow observations from gauge stations. </span><span>This</span> <span>is in preparation for the</span><span> Surface Water and Ocean Topography (SWOT) satellite, which will be launched this year and is expected to allow the derivation of streamflow observations globally for rivers wider than 50-100m. </span></p><p><span>GRACE assimilation strongly improves </span><span>the TWSA simulations in the Mississippi River Basin, e.g. the correlation increases to 91%, with which our results are consistent with previous studies. However, we find in this case that the streamflow simulation deteriorat</span><span>es</span><span>, f</span><span>or example, correlation reduces from 92% to 61% at the most downstream gauge station. In contrast, joint</span><span>ly assimilating GRACE data and streamflow observations from GRDC gauge stations improves the streamflow observations by up to 33% in terms of e.g. RMSE and correlation while maintaining the good TWSA simulati</span><span>ons. </span><span>In view of the upcoming SWOT mission, our data suggest that the </span><span>SWOT</span><span> data will help to further improve the structure and simulations of global hydrological models. </span></p>