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

Abstract

<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>

Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

“…The R used in this study is the sum of the surface runoff and subsurface runoff simulated using hydrological models in GLDAS and FLDAS. Since there are no sufficient TWSC observations in most global basins, GRACE TWSC has been widely used for hydrological analysis in previous studies [19,[37][38][39]. GRACE satellite monitors TWSC by mapping variations in the Earth's gravity field [40].…”