Coordination Funds

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Contemporary global hydrological (or land surface) models provide conflicting estimates of e.g., mean annual evapotranspiration or low, mean, and high flows in river basins, resulting in strongly differing estimates of current water availability or of climate change impacts on freshwater resources. The central objective of the proposed Research Unit (RU) is to improve our understanding of global freshwater resources and to obtain better estimates of continental water fluxes (streamflow, groundwater recharge, actual evapotranspiration, and renewable water resources) and storages (in snow, soil, groundwater and surface water bodies as well as in glaciers). It is our hypothesis that a major improvement can only be achieved through combining state-of-the-art hydrological modelling and multiple new and optimally processed geodetic and remote sensing data in an ensemble-based calibration and data assimilation (C/DA) approach that allows a flexible parameter (calibration) and state (data assimilation) adjustment tailored to the modelling purpose. Such an approach, which also takes into account uncertainty due to model structure and input, has not been implemented yet. Therefore, in this RU we formulated two primary goals for the first phase: (1) develop a multi-observation ensemble-based C/DA methodology to combine, in the second phase at the global scale, observational data of model output variables (time series of gauge-based streamflow and GRACE and GRACE-FO total water storage anomalies as well as remotely-sensed data on snow cover, extent and water level of surface water bodies and streamflow) with hydrological models in an optimal manner, and (2) exploit this methodology with the global hydrological model WaterGAP to provide an improved quantitative assessment of freshwater fluxes and storages including their uncertainties in response to climate and anthropogenic forcing, for critical basins and for estimating ocean mass change due to continental water storage changes. The C/DA approach encompasses an Ensemble Kalman Filter approach (EnCDA) for both data assimilation and parameter calibration and a Pareto-optimal calibration approach (POC) that enables optimization of temporally constant parameters but needs to be expanded, in the second phase, to take into account the uncertainty of calibration data and estimation of model output uncertainty. Sensitivity analyses, uncertainty information provided by C/DA as well as model validation against independent data will allow evaluating the added value of applying the C/DA approach.