Impact of precipitation data density and duration on simulated flow dynamics and implications for ecohydrological modelling of semi-arid catchments of Southern Africa

Abstract

Understanding flow regime dynamics is important for the management and conservation of river systems and their related ecosystems under current and future pressures like climate and land use change. For this reason, the accuracy and detail with which hydrological models are able to replicate streamflow and other spatially distributed processes is critical for global and regional water management programs. One of the most critical factors for model performance is the spatial and temporal representation of precipitation data, a procedure known as regionalisation. It is therefore imperative to evaluate the sensitivity of models to data from different stations in order to identify locations that exert the strongest control on simulated streamflow. In this study, a spatially distributed rainfall/runoff model, the J2000, calibrated under near natural river flow conditions, was used to analyse the overall precipitation station impact on simulated streamflow and its flow components. For this purpose, different analysis methods including Regional Sensitivity Analysis (RSA) were used. The study was applied to the Berg River catchment, a water stressed region in the Western Cape, South Africa that was recently affected by serious drought conditions. The results show that during peak flows precipitation stations in the headwater part of the basin had the largest systematic impact, often controlling the data input for only a small proportion of the overall spatial extent of the model, but still having the largest impact on the overall modelled streamflow. In contrast, precipitation stations in lowland areas showed to be more important during low-flow periods, although they often control the data input for a larger part of the modelled catchment. The study highlights the importance of good overall data coverage for improved low-flow simulations, as well as identification of critical areas in regions responsible for peak runoff generation processes. The inclusion of systematic station weights has the potential to improve the ecohydrological representation of rainfall/runoff models in data scarce regions, as well as precipitation regionalisation approaches. The results are indicative of known microclimatic variability in semi-arid Southern Africa, highlighting the significance of catchment heterogeneity and areas of dominant sub-basins for overall runoff generation.