Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Performance evaluation of artificial neural networks in statistical downscaling of monthly precipitation (Case study: Minab watershed)

Statistical downscaling of precipitation

Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Abstract Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, ...

Comparison of LARS-WG and SDSM Downscaling Models for Prediction Temperature and Precipitation Changes under RCP Scenarios

Various methods developed to convert large-scale data to regional climatic data. In few studies , the results of these methods have been statistically compared. The main purpose of this study was to compare SDSM and LARS-WG models for Downscaling output data of CANE-SM2 and HADGEM2-ES general circulation models under RCP2.6, RCP4.5 and RCP8.5 scenarios. For this study, precipitation, minimum an...

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface charact...

Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing

We present a novel statistical downscaling method that provides accurate and relatively transparent simulations of local-scale precipitation characteristics. The method combines large-scale upper-air circulation with surface precipitation fields, and is based on a nonhomogeneous stochastic weather typing approach. Here we applay the method to downscale precipitation at 37 rain gauges in the sta...