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Cape Verde is a semi-arid country conformed by a group of islands located off the west coast of Africa, with highly variable rainfall that appears during a single rainy season. Santiago Island, the biggest of the country, is characterized for abrupt changes of relief within small distances. The influence of geographic location and topographic parameters, such as slope gradient, exposition and elevation on the variability of rainfall in Santiago Island was studied using monthly rainfall data of 30 seasons (1981 to 2010), with daily rainfall data for 14 seasons (1997 to 2010). The number of rainfall days and the percentage of maximum daily rainfall within the monthly and seasonal totals were evaluated. Few rainy days can control the monthly and seasonal rainfall patterns of Santiago Island. Multivariate linear regressions among daily, monthly and seasonal rainfall and elevation, slope gradient, aspect, and geographic east and west coordinates as predictors were carried out. Elevation explains most of the variance in the rainfall. The coefficients of determination show an inverse relationship with the rainfall depth: moderate rainfall totals (120-150 mm monthly, 250-300 mm seasonal) produced the best correlations for seasonal and monthly rainfall, while very low (<50 mm for monthly, <200 mm for seasonal) and very high amounts (>250 mm for monthly, >350 mm for seasonal) resulted in poor correlations. Long-term mean rainfall was interpolated using ordinary kriging and kriging with external drift. In Santiago Island, high and more extreme rainfall events are less influenced by elevation, while low and medium rainfall events are significantly influenced by orography, with most of the rainfall appearing on high elevations.

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Accurate runoff and soil erosion modeling is constrained by data availability, particularly for physically based models such as OpenLISEM that are data demanding, as the processes are calculated on a cell‐by‐cell basis. The first decision when using such models is to select mapping units that best reflect the spatial variability of the soil and hydraulic properties in the catchment. In environments with limited data, available maps are usually generic, with large units that may lump together the values of the soil properties, affecting the spatial patterns of the predictions and output values in the outlet. Conversely, the output results may be equally acceptable, following the principle of equifinality. To studyhow the mapping method selected affects the model outputs, four types of input maps with different degrees of complexity were created: average values allocated to general soil map units (ASG1), average values allocated to detailed map units (ASG2), values interpolated by ordinary kriging (OK) and interpolated by kriging with external drift (KED). The study area was Ribeira Seca, a 90 km2 catchment located in Santiago Island, Cape Verde (West Africa), a semi‐arid country subject to scarce but extreme rainfall during the short tropical summer monsoon. To evaluate the influence of rainfall on runoff and erosion, two storm events with different intensity and duration were considered. OK and KED inputs produced similar results, with the latter being closer to the observed hydrographs. The highest soil losses were obtained with KED (43 ton ha− 1 for the strongest event). To improve the results of soil loss predictions, higher accurate spatial information on the processes is needed; however, spatial information of input soil properties alone is not enough in complex landscapes. The results demonstrate the importance of selecting the appropriate mapping strategy to obtain reliable runoff and erosion estimates. Copyright © 2013 John Wiley & Sons, Ltd.

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