Evaluating climate change impacts on the hydrology of watershed in northwestern China using a stepwise‐clustered downscaling approach

Abstract

In this study, a stepwise-clustered downscaling model (SCDM) is advanced for transferring atmospheric simulation outputs to acquire high-resolution climate projections at a large-scale watershed system. SCDM can operate different temporal resolutions of atmospheric variables with continuous and discrete complexities. SCDM coupling with hydrological model is used for evaluating climate change impacts on hydrology of the Kaidu watershed in northwestern China. The daily and monthly series of large-scale atmospheric simulation outputs for the Kaidu watershed are extracted from the ensemble of GCMs during past, recent and future periods. Results reveal that (1) SCDM is capable of downscaling climate projections for different stations, and can help understand the spatial heterogeneity of climate change, (2) the performance of SCDM is more acceptable for temperature than precipitation, (3) increase trends of T min and T max (minimum and maximum temperatures) from recent to future are projected. Besides, results from multiple downscaled climate change projections are used for driving a daily climate-streamflow hydrological model. Results disclose that the streamflow would increase because temperature change will cause more glacier melt in future.

An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

“…Alternatively, statistical models were widely used for runoff simulation due to their ability in capturing arbitrary input–output relationships, without explicit descriptions of the underlying physical processes (Li et al ., 2015; Zhuang et al ., 2017; Huang et al ., 2020). For instance, Li et al .…”

Stepwise‐clustered heatwave downscaling and projection for Guangdong Province

“…The stepwise‐clustered heatwave downscaling approach (i.e., SCHW) is developed in this study based on the stepwise clustering analysis (i.e., SCA). SCA is a nonparametric statistical method firstly proposed by Huang (1992) and has been widely used in previous studies (Huang et al ., 2006; Qin et al ., 2007; Sun et al ., 2009; Wang et al ., 2013, 2015; Fan et al ., 2017; Zhuang et al ., 2017; Guo et al ., 2018; Zhai et al ., 2019). Most of previously proposed statistical downscaling tools assume that each predictand of interest is a function of predictors (Beckmann and Adri Buishand, 2002; Wilby et al, 2002; Hessami et al, 2008; Gibson et al ., 2017), while the function might not be able to improve significantly the projection quality compared to the direct outputs of raw GCMs owing to the complexity of the climate system (Wilby et al, 2002; Wang et al ., 2013).…”