Future Climate Projections Using SDSM and LARS-WG Downscaling Methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region

Abstract

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators Tmax, Tmin and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (Wi) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for Tmax and Tmin are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in Tmax (2.37–4.66 °C), Tmin (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin.

Trend Analysis and Projection of Climate Variables Using the LARS-WG Model in Coastal Areas of China

“…(Shayanmehr et al, 2022). The graphic comparison of mean observed and simulate data was also done to see the ability of the model to downscale future Prec., Tmin and Tmax as indicated in figure 3 (Munawar et al, 2022;Zubaidi et al, 2019). The graphical representations demonstrated that LARS-WG effectively replicated the observed Prec., Tmin, and Tmax, indicating the model's capability to downscale climate variables in the study area.…”

“…(10) to (13). These metrics are employed to compare the simulated values of Prec., Tmin and Tmax with their corresponding observed values (Ahmadi et al, 2021;Baez-Villanueva et al, 2018;Munawar et al, 2022;Shayanmehr et al, 2022). Models that exhibit higher values of R 2 and Kling-Gupta efficiency (KGE) and lower values of RMSE and MAD demonstrate good performance in simulating future climate variables.…”

“…Climate change's profound influences on water resources and crops have become a critical concern in China due to the direct connection between agricultural production and changes in climate variables (Ding et al, 2022;Fan et al, 2021;Wang et al, 2022). The increasing concentrations of CO2 and other greenhouse gases forced air temperatures to increase in ranges of 0.65 to 1.06 o C from 1880 to 2012, which triggered climate change and extreme climate events (Mo et al, 2017;Munawar et al, 2022). The rising air temperatures and irregular patterns of rainfall distribution present a challenge to both water availability and crop productivity in the 21st century (Shayanmehr et al, 2022;Wang et al, 2019;Wu et al, 2017;Zhang et al, 2021).…”

Trend analysis of historical and future precipitation projections over a diverse topographic region of Khyber Pakhtunkhwa using SDSM

“…Initially, the daily rainfall data were computed and converted to monthly, seasonal and total annual. For future projections of precipitation for the 21st century, GCM CCSM4 (Community Climate System Model version 4) has been selected following the methodology as presented in the author's previous research (Munawar et al 2022a). The daily large-scale predictors of GCM CCSM4 for RCP4.5 and RCP8.5 and the daily predictors for NCEP (National Centers for Environmental Prediction) were downloaded from a website (http://climate-scenarios.canada.ca/).…”

“…Despite improvements in the GCMs efficiency to represent climate processes in better ways these uncertainties cater to produce better climate projection but remain a subject of ample concern at the regional/local level (Hussain et al 2021). To cope with these uncertainties, downscaling techniques have been widely applied (Munawar et al 2022a). GCMs' coarse spatial resolution limits their use for sustainable environmental management (Araya-Osses et al 2020).…”