|Paper title||Enhancing hydrological model output by joint satellite data assimilation|
|Form of presentation||Poster|
Global hydrological models simulate water storages and fluxes of the water cycle, which is important for e.g. water management decisions and drought/flood predictions. However, models are plagued by uncertainties due to the model input errors (e.g. climate forcing data), model parameters, and model structure resulting in disagreements with observations. To reduce these uncertainties, models are often calibrated against in-situ streamflow observations or compared against 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.
In this study, we present our framework for jointly assimilating satellite and in-situ observations into the WaterGAP Global Hydrological Model (WGHM). For the first time, we assimilate three data sets:
(a) GRACE-derived TWSA,
(b) in-situ streamflow observations from gauge stations; this is in preparation for the Surface Water and Ocean Topography (SWOT) satellite, which will be launched in 2022 and is expected to allow the derivation of streamflow observations globally for rivers wider than 50-100m, and
(c) Global SnowPack snow coverage data derived from the Moderate Resolution Imaging Spectroradimeter (MODIS), which is installed on NASA’s Earth Observing System satellites.
GRACE assimilation strongly improves the TWSA simulations within 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 deteriorates, for example, correlation reduces from 92% to 61% at the most downstream gauge station. In contrast, jointly 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 simulations. We use the snow coverage data first to independently validate the impact of TWSA and streamflow assimilation on the snow simulation, and then, for the first time, assimilate the snow coverage data into the WGHM. We expect that this will not only further enhance the streamflow simulations but also the simulations of single WGHM water storages like the snow storage.