Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data

Abstract. Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10- year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.