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Time Series Models of Monthly Rainfall and Temperature to Detect Climate Change for Jorhat (Assam), India

Paper Topic: 
Climate Change Impacts and Societal Adaptation in the Indian Subcontinent

Pages :
494 - 507

Corresponing Author: 
Dabral, P. P.
Dabral P.P., Saring T. and Jhajharia D.
Paper ID: 
Paper Status: 
Date Paper Accepted: 
Paper online: 

In the present study, monthly rainfall, maximum temperature and minimum time series models were developed for Jorhat (Assam) situated in northeast India using monthly rainfall, maximum temperature and minimum temperature data from the year 1965 to 2000. A trend free time series of rainfall and temperature was obtained by eliminating the trend component in the original time series, and then was used in identifying the periodic component. Fourier series analysis was used to identify periodic component. First five harmonics explained total variance of 79.4, 72.6 and 73.7% for monthly rainfall, maximum temperature and minimum temperature respectively. In the stochastic dependent component modelling, Autoregressive (AR) models of order 12 were found suitable on the basis of minimum value of AICC and BIC statistics. Portmanteau test formulated by McLeod and Li was carried out for checking the independence of stochastic dependent component which indicated that series consist of independent and identically distributed variables. Independent stochastic components were further modelled using normal distribution function.  Nash-Sutcliffe coefficient also indicated high degree of models fitness to the observed data. Developed time series models were validated using eight years (2001-2008) data. Using the developed time series models, monthly rainfall, maximum temperature and minimum temperature were forecasted up to the year 2050. Assessment of changes in monthly rainfall, maximum temperature and minimum temperature in generated series (2009 to 2050) were predicted using linear regression which indicated no significant trend, i.e., the climate at Jorhat (Assam) in next four decades will remain more or less stable. 


Time series, Rainfall, Maximum and Minimum temperature, Autoregressive models, Climate change detection