Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India

Abstract

General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.5 0 C to 2.5 0 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods.

Effect of Temperature on Frost-Free Days and Length of Crop Growing Season across Southern Ontario

“…This data is available for a seven meteorological stations of Canadian southern Ontario locations on Environment Canada's National Climate Data and Information Archive. The available length of data for this study is 70 years (1940 to 2010), more than sufficient for longterm trend analysis (Qian et al, 2012;Shukla et al, 2015;Shukla et al, 2023). However, in this analysis all the winter months were included since the data was readily available.…”

“…Although there is an abundance of research on the consequences and modeling of climate change at a global level, there is still much work to be done to translate these consequences to the regional level, which would allow for the development of practical solutions in effected sectors. A wave of research beginning in the 1980's focused on analyzing daily temperature extremes on a regional level (Karl et al, 1993;Brazdil et al, 1996;Manton et al, 2001;Klein Tank & Können, 2003;Rogers et al, 2007;Shukla & Khare, 2013;Neelin et al, 2017). This research relates to the overall yearly trends, seasonal trends analysis of climatic variable for many stations around the world using variety of different methods.…”

Evaluation of Rainfall Tendency for the Twentieth Century over Indira Sagar Region in Central India

“…As a result, the availability of water may fluctuate significantly due to irregular rainfall patterns. Therefore, analyzing the trend of rainfall time series is crucial for designing hydraulic structures and managing local water resources efficiently (Mishra et al, 2013;Khare et al, 2014;Shukla et al, 2015). Several studies have been conducted to understand the effects of climate change on rainfall in various regions of the world (Kunkel et al, 1999;Osborn et al, 2000;Ventura et al, 2002;Xu et al, 2003;Partal & Kahya, 2006;Ampitiyawatta & Guo, 2009;Karpouzos et al, 2010;Tabari & Talaee et al, 2011;Mishra et al, 2014;Meena et al, 2015;Mishra et al, 2016;Shukla et al, 2017: Preethi et al, 2017.…”

Hydrologic responses to climate and land use/cover changes in world heritage site of Ngorongoro conservation area and surrounding catchments, northern Tanzania

“…In the present study, the SDSM was applied in the watersheds around the NCA to downscale the CanESM daily rainfall and temperature to a point-scale. Two datasets were involved in this process: (1) the predictands of interest, i.e., locally observed rainfall and temperature and (2) the corresponding large-scale predictors from NCEP and CanESM2 in the study area's grid box (Shukla & Singh, 2021;Shukla et al, 2015). Model calibration and respective downscaling were performed through the steps as suggested by Wilby et al (2002):…”