Impact of rainfall distribution on the parameterisation of a soil-moisture balance model of groundwater recharge in equatorial Africa

Robust calibration of hydrological models, driven by gridded precipitation data derived from either Regional Climate Models or statistical downscaling of General Circulation Models, is essential to the quantitative analysis of the impact of climate change on catchment hydrology and freshwater resources. Predicted warming in equatorial Africa, accompanied by greater evaporation and more frequent heavy precipitation events, will have significant but uncertain impacts on terrestrial hydrology. In this study, we examine how the spatial representation of precipitation influences the parameterisation and calibration of a soil-moisture balance model (SMBM) in the humid tropics of equatorial Uganda. The advantages of SMBMs are that they explicitly account for changes in soil moisture and partition effective precipitation into groundwater recharge and runoff. Despite comparable mean annual totals (<7% difference) between gridded (i.e., interpolated) and point-based precipitation data, application of more uniformly distributed, gridded precipitation to a semi-distributed, SMBM calibrated using point- based precipitation over a 15-year period (1965 to 1979), underestimates runoff and recharge by 30% and 55% respectively. Calibration of the SMBM using gridded precipitation requires an 18% reduction in potential evapotranspiration and a 36% increase in the runoff-coefficient that are inconsistent with local, point-based observations of these parameters. Although current efforts seek to improve the distribution and duration of key hydrological measurements ( e.g., soil moisture, groundwater levels) in data-poor regions, the parameterisation of gridded hydrological models remains largely empirical due to the discrepancy between gridded and locally observed hydrological parameter.