Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Abstract

AbstractTwo statistical downscaling (SD) models, the nonhomogeneous hidden Markov model (NHMM) and the statistical down‐scaling model (SDSM), which have been widely applied and proved skillful in terms of downscaling precipitation, were evaluated based on observed daily precipitation over the Tarim River basin, an arid basin located in China. The evaluated metrics included residual functions, correlation analyses, probability density functions (PDFs) and distributions. Overall, both models exhibited stability with little model performance difference between the calibration and validation periods. There was little difference for model performance on dry‐spell length (dsl) and wet‐spell length (wsl) between NHMM and SDSM. NHMM showed skill in simulating wet‐day precipitation amount (wpa), whilst SDSM performed relatively poorly on extreme values of wpa, especially for dry stations with annual precipitation lower than 200 mm. Both NHMM and SDSM captured the spatial distribution characteristics of precipitation for most of the stations, as 11 months had an at‐station correlation coefficient being greater than 0.9 and 0.8 for NHMM and SDSM in calibration period. NHMM showed slightly better model performance than SDSM on simulating monthly precipitation, as the former was able to model precipitation well for all months, whereas the later was well only for certain months. SDSM was able to capture the inter‐site correlation characteristics of observed series, whilst the NHMM multi‐site simulation over estimated inter‐site correlation. Both NHMM and SDSM had less skill downscaling annual series because of stochastic components in precipitation amounts modelling. Copyright © 2011 Royal Meteorological Society