Project Group 1

Developing an Ensemble Kalman Filter calibration and data assimilation (EnC/DA) approach for integrating geodetic and remote sensing data into a global hydrological model

Main objective of project P1 is the development and implementation of an effective Ensemble Kalman Filter for the integration of data assimilation and parameter calibration with hydrological modelling. Foci within the first phase will be (1) simultaneously integrating multiple data sets (with different spatial resolution and not well-known random and systematic errors) and creating the operators that map the model states on observables, and (2) developing and investigating the ensemble-based model parameter calibration (in a common framework and comparing with the POC approach of P3). Work will depart from the existing GRACE assimilation approach and integrate in-situ streamflow, and later space-borne observations of surface water level, extent and volume, snow cover area and streamflow. This will then be utilized to combine hydrological modelling and several geodetic and remote sensing data sets created in this research unit (RU); with the aim to create a new quantitative description of global-scale water fluxes and storages, including a characterization of the uncertainty of this description.