Abstract
Abstract Study region Four headwaters in Southern Africa. Study focus The streamflow regimes in Southern Africa are amongst the most variable in the world. The corresponding differences in streamflow bias and variability allowed us to analyze the behavior and robustness of the LISFLOOD hydrological model parameters. A differential split-sample test is used for calibration using seven satellite-based rainfall estimates, in order to assess the robustness of model parameters. Robust model parameters are of high importance when they have to be transferred both in time and space. For calibration, the modified Kling-Gupta statistic was used, which allowed us to differentiate the contribution of the correlation, bias and variability between the simulated and observed streamflow. New hydrological insights Results indicate large discrepancies in terms of the linear correlation ( r ), bias ( β ) and variability ( γ ) between the observed and simulated streamflows when using different precipitation estimates as model input. The best model performance was obtained with products which ingest gauge data for bias correction. However, catchment behavior was difficult to be captured using a single parameter set and to obtain a single robust parameter set for each catchment, which indicate that transposing model parameters should be carried out with caution. Model parameters depend on the precipitation characteristics of the calibration period and should therefore only be used in target periods with similar precipitation characteristics (wet/dry).
Highlights
Hydrological models are widely used for water resources modelling, both drought and flood forecasting, and climate change impact assessment studies, among others
We applied the differential split-sample test to investigate the robustness of model parameters of a fully distributed hydrological model, by calibrating the model forced by seven satellite-based precipitation estimates
We selected four regions in Southern Africa with highly variable climate conditions, which resulted in large precipitation differences between the seven precipitation products
Summary
Hydrological models are widely used for water resources modelling, both drought and flood forecasting, and climate change impact assessment studies, among others. Coron et al (2014) showed the inability of three models of increasing complexity in reproducing the water balance on different sub-periods. Another explanation for the lack of model robustness can be miscalibration (i.e. poor optimization algorithm) or overcalibration (i.e. insufficient calibration period, too many parameters, wrong objective function) of model parameters, as shown by Wagener et al (2003), Hartmann and Bárdossy (2005), Son and Sivapalan (2007), Gupta et al (2009), Ebtehaj et al (2010), Efstratiadis and Koutsoyiannis (2010), Andréassian et al (2012), Gharari et al (2013) and Zhan et al (2013). Fenicia et al (2009) showed the major role of changes in land use management and forest age on the catchments’ behavior
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