Abstract

The modern and quite commonly used techniques for projection of climate change are the General Circulation Models or Global Climate Models (GCMs). However, these models predict climate at a much coarser spatial resolution. Often many studies involve the assessment of climate change impacts at smaller scales, viz., a catchment area of a river or even point scales, viz., a gauging station. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. In the present study, the effect of climate change on meteorological parameters, viz., precipitation and temperature at four metrological stations, viz., Srinagar, Pahalgam, Qazigund, and Kupwara of Kashmir Valley, were examined. The data of mslpas (mean sea level pressure), tempas (mean temperature at 2 m), p500-as (500 hpa geopotential height), humas (specific humidity at 2 m), and p5_uas (500 hpa zonal velocity) obtained from Canadian third-generation Climate Model (CGCM3) were used as predictors along with National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis climatic data set. The locally observed temperature and precipitation were used as predictands. The methods of Multiple Linear Regression (MLR) and Statistical Downscaling Model (SDSM) were used as downscaling techniques. The large-scale GCM predictors were related to observed precipitation and temperature. It was found that the temperature of the Kashmir Valley is likely to increase in the coming decades while as the precipitation is going to decrease with each coming decade.

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