Comparison of two statistical climate downscaling models: a case study in the Beijing region, China

Abstract

Climate change can have a significant impact on the hydrological cycle. This article focuses on the comparison of the statistical down scaling model (SDSM) and the automated statistical downscaling model (ASD), which are applied to global climate model (GCM) predictions for the Beijing region. Through the analysis of the evaluation indices in the calibration and validation periods, the results show that both downscaling models simulate the temperature and evapotranspiration well, but the simulation of precipitation is not as good as that of other climate factors. The overall performance of ASD model is slightly superior to that of SDSM model, especially in the process of predictor's selection. The future climate change downscaled by the two models shows an analogous trend as well. The temperature and evapotranspiration show a general increasing trend. The precipitation shows a different trend with an increasing trend in the south and a decreasing trend in the north.

Irrigation Scheme Selection Based on Water Footprint Analysis of Winter Wheat Production in Piedmont Plains of Hebei Province under Future Climate Scenarios

“…The ocean-atmosphere coupled model of Had CM3 was used to generate the future climate in the future emission models of scenarios A2 and B2 among SRES scenarios proposed by IPCC. The SDSM model has good simulation effects for maximum and minimum temperatures, and accurate predictions could be made for future temperature change [34,35]. Meanwhile, this model was found to have poor inferior simulation effects for wind speed, humidity, sunshine hour, and precipitation compared with those for temperature indices, but its simulation accuracy remained higher than the basic standard value requirement of 0.5.…”

Implication of Climate Change On Crop Water Requirement: A Regional Case Study in The Semi-Arid Region of Western Maharashtra, India

“…The grid resolution is 2.5 degrees of latitude by 2.5 degrees of longitude. This data is available on the Canadian Climate Impact Scenarios (CCIS) website (Guo et al 2018).…”

“…The selection of predictors is a crucial step in the statistical downscaling process. The selection of predictors is governed by there four rules (Amin et al, 2014;Guo et al, 2018) stated below: (Amin et al 2014;Guo et al 2018) as below:…”

“…Studies in the Tibetan plateau by (Wang et al 2013) projected ET0 during 2011-2100 from the HadCM3 and CGCM3 model, where SDSM performed satisfactorily in downscaling ET0, and the continuous increment in ET0 observed in the 21st century. (Guo et al 2018) analyzed the evaluation indices (R 2 , Ens) during calibration and validation periods, the results indicated fair simulation of temperature and evapotranspiration by SDSM model. The SDSM is the combination of multiple regression and a stochastic weather generator (Gebremeskel et al 2004;Mahmood and Babel 2013).…”

“…Downscaling approaches have emerged as effective methods to reduce the problem of indistinct scales by deriving regional climate detail from global climate data (Gagnon et al 2005). Various researchers (Wang et al 2013;Xu et al 2014;Tao et al 2015;Manasa and Shivapur 2016;Kundu et al 2017;Zhou et al 2017;Guo et al 2018) downscaled ET0 using SDSM and other models and found significant variations in contrast to current conditions. The statistical downscaling is based on few assumptions, these are the predictorpredictand relationships are valid under future climatic conditions and predictor variables with their changes well-characterized by GCMs (Wilby and Wigley 2000;Saraf and Regulwar 2016).…”