Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Climatic and hydrological changes will likely be intensified in the Upper Blue Nile (UBN) basin by the effects of global warming. The extent of such effects for representative concentration pathways (RCP) climate scenarios is unknown. We evaluated projected changes in rainfall and evapotranspiration and related impacts on water availability in the UBN under the RCP4.5 scenario. We used dynamically downscaled outputs from six global circulation models (GCMs) with unprecedented spatial resolution for the UBN. Systematic errors of these outputs were corrected and followed by runoff modelling by the HBV (Hydrologiska ByrånsVattenbalansavdelning) model, which was successfully validated for 17 catchments. Results show that the UBN annual rainfall amount will change by −2.8 to 2.7% with a likely increase in annual potential evapotranspiration (in 2041–2070) for the RCP4.5 scenario. These changes are season dependent and will result in a likely decline in streamflow and an increase in soil moisture deficit in the basin.

By increased rural-urban migration in many African countries, the assessment of changes in catchment hydrologic responses due to urbanization is critical for water resource planning and management. This paper assesses hydrological impacts of urbanization on two medium-sized Zimbabwean catchments (Mukuvisi and Marimba) for which changes in land cover by urbanization were determined through Landsat Thematic Mapper (TM) images for the years 1986, 1994 and 2008. Impact assessments were done through hydrological modeling by a topographically driven rainfall-runoff model (TOPMODEL). A satellite remote sensing based ASTER 30 metre Digital Elevation Model (DEM) was used to compute the Topographic Index distribution, which is a key input to the model. Results of land cover classification indicated that urban areas increased by more than 600 % in the Mukuvisi catchment and by more than 200 % in the Marimba catchment between 1986 and 2008. Woodlands decreased by more than 40% with a greater decrease in Marimba than Mukuvisi catchment. Simulations using TOPMODEL in Marimba and Mukuvisi catchments indicated streamflow increases of 84.8 % and 73.6 %, respectively, from 1980 to 2010. These increases coincided with decreases in woodlands and increases in urban areas for the same period. The use of satellite remote sensing data to observe urbanization trends in semi-arid catchments and to represent catchment land surface characteristics proved to be effective for rainfall-runoff modeling. Findings of this study are of relevance for many African cities, which are experiencing rapid urbanization but often lack planning and design.