Abstract. The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii) temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with overrespectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared.

A concentration of surface mass has a distinct, localized signature in Gravity Recovery and Climate Experiment (GRACE) K-band range rate (KBRR) data. This fact is exploited in the regional solutions for mass concentration parameters (mascons) made at the Goddard Space Flight Center (GSFC). In this paper we explore an experimental set of regionally constrained mascon blocks over Southern Africa where a system of 1.25 Â 1.5 and 1.5 Â 1.5 blocks has been designed. The blocks are divided into hydrological regions based on drainage patterns of the largest river basins, and are constrained in different ways. We show that the use of regional constraints, when solving mascon parameters of different hydrological regions independently, yields more detail and variation than comparable spherical harmonic solutions and mascon solutions using isotropic constraints. We validate our results over Lake Malawi with water level from altimetry. Results show that weak constraints across regions in addition to intra-regional constraints are necessary, to reach reasonable mass variations.

The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration and validation of a rainfall-runoff model of the ungauged Okavango catchment in Southern Africa: (i) Surface soil moisture (SSM) estimates derived from SAR measurements onboard the Envisat satellite; (ii) Radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum width of about one hundred meters; and (iii) Temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are compared to simulated moisture conditions in the top soil layer. They cannot be used for model calibration but support bias identification in the precipitation data. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim data sets are considered and compared

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