alexa MLR Based Statistical Downscaling of Temperature and Pr
ISSN: 2573-458X

Environment Pollution and Climate Change
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Research Article

MLR Based Statistical Downscaling of Temperature and Precipitation in Lidder Basin Region of India

YasirAltaf1*, Ahanger Manzoor Ahmad1and Fahimuddin Mohd2

1Department of Civil Engineering, NIT Srinagar, Jammu and Kashmir, India

2DHI Environment Pvt (India) Ltd., New Delhi, India

*Corresponding Author:
Yasir Altaf
Research Scholar, Department of Civil Engineering
NIT Srinagar, Jammu and Kashmir, India
Tel: 00918803002425
E-mail: [email protected]

Received date: November 11, 2016; Accepted date: January 23, 2017; Published date: January 30, 2017

Citation: Altaf Y, Ahmad AM, Mohd F (2017) MLR Based Statistical Downscaling of Temperature and Precipitation in Lidder Basin Region of India. Environ Pollut Climate Change 1:109.

Copyright: © 2017 Altaf Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

In the present study, the multiple linear regression technique was employed to relate the monthly predictors obtained from the Global Climate Model namely CGCM3 (Canadian Centre for Climate Modelling and Analysis) with the monthly Predictands such as the locally observed precipitation and temperature at Pahalgam meteorological station which is located in the Lidder River Basin. Appropriate predictand-predictor relationships were found out for the region by carrying out sensitivity analysis .Regression equations were developed and subsequently future monthly and annual projections for precipitation, maximum temperature and minimum temperature for the entire 21st century were made. It was observed that at the end of the 21st century the CGCM3 model predicted an increase of 18.07% annual precipitation, whereas the average maximum and minimum annual temperatures were predicted to increase by 0.62°C and 0.76°C, respectively.

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