Abstract

The management and allocation of water resources have been considered as one of the most significant endeavors in human society due to water’s vital role in all natural and environmental systems. However, in most practical applications, precipitation records at the location of interest are often either limited or unavailable due to the lack of adequate network of rainfall measurements. Moreover, the estimation and prediction of hydrological variables with climate change conditions for ungauged sites remains a crucial challenge for water resources applications. This study demonstrates a statistical procedure to downscale climate change model outputs at ungauged stations. The proposed model is consistent of three steps: i) regionalization approach using PCA/OFA for identifying homogeneous regions of daily precipitation series, ii) stochastic weather model for estimating daily precipitation series at ungauged locations, and iii) a statistical downscaling model (SDRain).

Keywords: Water; Environment; Ungauged Locations; Downscaling of Daily Rainfall Time; Statistical Procedure