Projecting Future Maximum Temperature Changes in River Ganges Basin Using Observations and Statistical Downscaling Model (SDSM)

Abstract

AbstractClimate change is the most considerable peril to the environment and a significant concern for humans of twenty-first century, which affects life all around the globe. River Ganges basin is highly vulnerable to climate change, which plays a vital role in socio-economic activities and the water supply for livelihood and irrigation in the northern part of India. A statistical downscaling model (SDSM) is used to forecast future changes in the maximum temperature of the Ganges basin (India) due to climate change under the 2.6, 4.5, and 8.5 RCPs scenarios of the CanESM2 model outputs for the forthcoming annual change (2006–2099) as well as monthly changes in maximum temperatures. In addition to that, the use of 26 large-scale climatic variables that were produced using data from the NCEP reanalysis (1961–2005) and the Canadian Earth System Model (CanESM2, 1961–2100) is employed. It is critical to identify the best predictors to use for the simulation phase for each location using SDSM. The investigation revealed that there was an increase of between 0.4 and 3.4 °C in the maximum average temperature over the basin at study stations. Furthermore, according to the RCP 8.5 scenario of the CanESM2 model, these maximum temperature changes will be raised to a greater extent than under the RCP 2.6 and RCP 4.5 circumstances. The months of March–July were predicted to have significant raise temperature (maximum) in all scenarios and the least variation in maximum temperature projection during August and September. According to an uncertainty analysis linked to model scenarios, the CanESM2 model under scenario RCP 4.5 performed the best in the simulation of future maximum temperatures of all scenarios. The downscaled data provide better management of projected temperature and, ultimately finer scale adaptation plans in the basin.KeywordsClimate projectionGCMStatistical downscalingSDSMPredictorsMaximum temperature