Abstract
Statistical downscaling method is mainly practised to relate atmospheric circulation to surface variables for forecast and prediction of the regional climate. As we know in Rajasthan drought is the foremost problem due to scanty of rainfall. The core objective of the present study stands to prognosis rainfall variation also assess the recital of Multiple Linear Regression (MLR) to access the variation in rainfall. The data were analyzed using higher resolution atmospheric data which includes daily National Centers for Environmental Prediction (NCEP)/ National Center for Atmospheric Research (NCAR) reanalysis data and daily mean climate model result intended for A2 and B2 scenarios of the Hadley Centre Climate Model (HadCM3) model. The period from 1961-1990 used as a baseline due to the availability of adequate period which is required to establish a reliable climatology. Results of the study show an increasing trend of future precipitation tended for both A2 and B2 scenarios. From the study, it has been found that MLR model is more superior to downscale precipitation in most districts under study area.
Highlights
The natural as well as socioeconomic variability of state Rajasthan which includes water resource management, agriculture, forestry, tourism etc. are highly influenced by key component of hydrological cycle i.e. precipitation
A Multiple Linear Regression (MLR) model was used in the study to downscale the precipitation in data scarce arid and semi-arid regions of Rajasthan state of India, which considered as most susceptible areas to climate change
The dataset of National Centers for Environmental Prediction (NCEP) reanalysis from twenty grid points which surrounds the study range were used to select the predictors based on principal component analysis (PCA)
Summary
The natural as well as socioeconomic variability of state Rajasthan which includes water resource management, agriculture, forestry, tourism etc. are highly influenced by key component of hydrological cycle i.e. precipitation. Are highly influenced by key component of hydrological cycle i.e. precipitation. The natural as well as socioeconomic variability of state Rajasthan which includes water resource management, agriculture, forestry, tourism etc. It is necessitated for predicting future precipitation change since it is an input for climate impact model to assess the consequences of global change in climate. GCMs under climate input model often found inadequate due to limited depiction of mesoscale atmospheric processes, topography and sea distribution.
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