Abstract
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 over-respectively 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.
Highlights
Hydrological modelling faces the challenge of decreasing availability of in-situ monitoring data
Through the sensitivity analysis we find that discharge and water levels are more sensitive than the water storage and that the topsoil water content has little sensitivity (Fig. 5)
The only two parameters to which the total storage variation is more sensitive than the other model outputs are SURLAG and CH N1
Summary
Hydrological modelling faces the challenge of decreasing availability of in-situ monitoring data. The number of meteorological stations as well as the number of operational discharge monitoring stations has been decreasing continuously since the 1970s (Fekete and Vorosmarty, 2007; Jones and Moberg, 2003; Peterson and Vose, 1997). Whereas data from such stations are vital for the calibration and validation of hydrological models, many major river basins of the world are currently poorly monitored. Precipitation, evapotranspiration, surface soil moisture, total terrestrial water storage variations, river and lake levels have all been studied through remote measurements (see Tang et al, 2009, for a review). In this study we use remotely sensed datasets of precipitation, surface soil moisture, river stages and total water storage for a hydrological model of a poorly gauged basin – the Okavango basin in Southern Africa
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
