Optimization of hydrologic response units (Hrus) using gridded meteorological data and spatially varying parameters

Although complex hydrological models with detailed physics are becoming more common, lumped and semi-distributed models are still used for many applications and offer some advantages, such as reduced computational cost. Most of these semi-distributed models use the concept of the hydrological response unit or HRU. In the original conception, HRUs are defined as homogeneous structured elements with similar climate, land use, soil and/or pedotransfer properties, and hence a homogeneous hydrological response under equivalent meteorological forcing. This work presents a quantitative methodology, called hereafter the principal component analysis and hierarchical cluster analysis or PCA/HCPC method, to construct HRUs using gridded meteorological data and hydrological parameters. The PCA/HCPC method is tested using the water evaluation and planning system (WEAP) model for the Alicahue River Basin, a small and semi-arid catchment of the Andes, in Central Chile. The results show that with four HRUs, it is possible to reduce the relative within variance of the catchment up to about 10%, an indicator of the homogeneity of the HRUs. The evaluation of the simulations shows a good agreement with streamflow observations in the outlet of the catchment with an Nash–Sutcliffe efficiency (NSE) value of 0.79 and also shows the presence of small hydrological extreme areas that generally are neglected due to their relative size.