Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment

Precipitation is a key variable in the hydrological cycle and essential input data rainfall-runoff modeling. Rain gauge are considered as one of best sources precipitation but before further use, must be spatially interpolated. The process interpolation particularly challenging over mountainous areas due to complex orography usually sparse network rain gauges. This paper investigates two deterministic methods (inverse distance weighting (IDW), first-degree polynomial) their impact on outputs semi-distributed modeling catchment. performed analysis considers aspect grid size, which often neglected other than fully-distributed inverse power (IDP) value IDW was also analyzed. It has been found that simulation results were obtained using size smaller or equal 750 m polynomial an method. indicate IDP method more size. Evaluation done Kling-Gupta efficiency (KGE), alternative Nash-Sutcliffe (NSE). KGE generally tends provide higher less varied values NSE makes it useful for evaluation results.