Assessment of Temperature for Future Time Series Over Lower Godavari Sub-Basin, Maharashtra State, India

Abstract

Climate change can cause various negative impacts on water resources system, ecosystem, etc. To deal with these effects, it is necessary to study the climate change. There are various ways to study climate change in which one of the way is the study of downscaling. Downscaling is the procedure in which prediction of information is done for local scale area from the available information of a large scale area. In the downscaling of climatic variables, General Circulation Model (GCM) plays an important role. GCM gives larger scale climatic variables. With the help of this downscaling, we can predict different climatic variables such as temperature, precipitation for future time period over the selected area. To perform this downscaling there are different ways, we can classify it as statistical downscaling and dynamical downscaling. In statistical downscaling, we can find relation between predictant and predictors and this statistical relation we use for the future prediction of the selected climatic variable. In dynamical downscaling, we use Regional Climatic Model (RCM), and with the help of this, we carry out downscaling procedure. In this study, statistical downscaling has studied for temperature parameter (Tmax and Tmin) by considering the basic equation given by Wilby in (Inter-research 10:163–178 [1]). The study area selected for this study is lower Godavari Sub-basin, Maharashtra State, India (Latitude: 19° 11′, Longitude: 76° 33′). In this study, in the first step, statistical downscaling has been done with the help of statistical downscaling model (SDSM) software by using HadCM3 GCM with A2a and B2a scenarios for temperature parameter for the future time period up to 2099. In second step, the statistical downscaling again performed by using basic equation given by Wilby (Inter-research 10:163–178 [1]) in excel which is named as “Excel Model.” Temperature values predicted up to 2099. These results are considered with three different series such as 2020s, 2050s, and 2080s. Downscaled results of temperature parameter by “SDSM” model and “Excel Model” were compared for future series. After study of these results, it is concluded that SDSM gives higher value of change in mean monthly daily value of Tmax and Tmin than that of “Excel Model.”