The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into the WaterGAP global hydrological model

We describe the new Global Land Water Storage data set (GLWS2.0), which contains total water storage anomalies (TWSA) over the global land with a spatial resolution of 0.5°, covering the time frame 2003 to 2019 without gaps, and including an uncertainty quantification.

GLWS is produced by assimilating 4° gridded GRACE and GRACE-FO-derived TWSA into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF). The resulting data set represents thus an optimal synthesis of GRACE data, the hydrological model and implicitly all data sets that went into the model. This synthesis seeks to fit GRACE and GRACE-FO TWSA grids within error bars (from propagating full level-2 error variance covariance matrices), and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via a 32-member ensemble, where we account for the uncertainty of the precipitation and temperature data and for the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, GLWS represents the mean of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean depends on the ensemble and the previous GRACE estimates, and thus differs from published WaterGAP standard runs even if there is no GRACE data within a particular month. Due to the dynamical constraints, the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. GLWS indeed contains all water storages that are represented in WaterGAP (e.g. groundwater), but here we will focus only on TWSA.

The main updates with respect to the release 1 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation. GLWS1.0 and GLWS2.0 have already been provided to several research groups within the DFG GlobalCDA research unit and beyond for evaluation purposes.

In this presentation we describe the methods and data sets that went into GLWS2.0, and the validation of the resulting 0.5° TWSA grids from a geodesy applications perspective, including comparisons to GRACE and GRACE-FO data and to GNSS-derived site displacements. We will also show some extended experiments with jointly assimilating river discharge data, model parameter estimation, and the integration of machine-learning based model prediction in our assimilation approach.