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Paper Details

Paper Title
MODELING THE PRECIPITATION AMOUNTS DYNAMICS FOR DIFFERENT TIME SCALES IN SRINAGAR City, J&K-India USING MULTIPLE REGRESSION APPROACH
Abstract
Water resources are very important for ecosystems and water deficit may cause serious social and economic issues. The aim of this study is to analyze the performances ofprediction procedure based on Multiple Linear Regression Model (MLRM), for the precipitation amounts for yearly and monthly time scales, in Srinagar City located in Jammu and Kashmir, India. For this purpose we have used as predictand monthly amounts of precipitation and as predictors Mean Sea level pressure(mslpas),Mean temperature at 2m(tempas),Specific humidity at 2m (humas), Geopotential height at 500 hPa (p500-as), Surface zonal velocity(u) and Surface meridional velocity(v). The selection of predictors is based on the backward elimination or predicative (p-test) analysis.All data sets used in this study are gridded data with a spatial resolution of 2º x 2º lat/lon and are obtained from Canadian Global Climate model version 3(CGCM3) .The analysis is made for a period of 45 years between1970 and 2015, the period 1970– 2010 being used to build the MLRM and the period 2011 – 2015 for testing the prediction performances of the MLRM. Using MLRM we have obtained some good correlation between predicted and measured precipitation amount. The correlation coefficient varies between 0.57 and 0.84, with the smallest values in winter and the greatest values in spring. The total annual precipitation shows a decrease of 41.42% at the end of 21st century for Srinagar city.
KeyWord
Precipitation amount prediction, Multiple linear regression
Others Details
Paper Id : 89682
Author Name : Mehnaza Akhter
Co-Author Name(s) :
Volume/Issue No : Volume 04 Issue 07
Page No : 36-42
DOI Number : DOI:10.21090/IJAERD.89682
Publication Date : 2017-07-06
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website : http://www.ijaerd.com/index.php
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406