Monthly, seasonal and annual trends of rainfall and temperature (both minimum and maximum) have been analyzed using the Mann–Kendall trend test (a non-parametric test) and Sen's slope estimator for Sagar division, India from 1988 to 2018. Sagar division is a drought-prone zone of Madhya Pradesh, India. The same analysis has been performed for two drought indices, the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). Both indices were calculated to see the trend in the drought for 35 rain-gauge stations belonging to the study area. The study revealed that the minimum temperature had increased more as compared to the maximum temperature in the last 31 years. The strong similarity in the results of Sen's slope of SPI and RDI were seen for both significant and non-significant trends. Analysis of Variance (ANOVA) test validates the substantial similarity between SPI and RDI based on Sen's slope. It also indicated the suitability of RDI for future projection of drought using the general circulation models (GCMs) or regional climate models (RCMs) in meteorological drought as well as the agricultural drought category. In contrast, the SPI indicated the meteorological drought only. The distribution of trends of temperature and drought indices were presented using the kriging interpolation.
The central India region has been seriously affected by repeated droughts in recent decades due to climate change, which is the main reason for conducting this research. It is still uncertain how the numerous climate models could precisely estimate the future climate for central India. The study mainly focuses on the forcing global climate models (GCMs) and the regional climate models (RCMs). The models have been checked using the coefficient of correlation (r2), Nash Sutcliffe efficiency (NSE) and an improved method, Skill score (SS). The performance is also spatially checked on ArcGIS using the kriging interpolation. The bias-corrected GCMs performed more authentic than the CORDEX RCMs in signifying maximum and minimum temperatures for the Bundelkhand region in central India. Bias-corrected GCMs, EC-EARTH, CCSM4 and GFDL-ESM-2M affirmed the best models on multiple time scales for maximum and minimum temperature in the study region. Maximum NSE and r2 have been observed for seasonal minimum temperature. GCM-EC-EARTH has shown 97% to 98% accuracy, while GCM-GFDL-ESM-2M has demonstrated 84% to 97% accuracy among other selected models. The research outcomes will also assist policymakers in developing strategies and policies for the future climate of central India with the help of more precise projected climatic data.
The present study evaluates the reliability of the latest generation five best general circulation models (GCMs) under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their corresponding regional climate models (RCMs) of Coordinated Regional Climate Downscaling Experiment (CORDEX) for the Bundelkhand region in central India. The study is performed on a microscale due to frequent drought events and more climate susceptibility in the study region. Observed daily precipitation data of 35 years (1971–2005) from the Indian Meteorological Department (IMD) have been chosen to check the performance of the models. Bilinear interpolation has been adopted to prepare all the data to obtain them on a common grid platform at a half-degree (0.5° × 0.5°) resolution. The data of the models have been bias-corrected using quantile mapping. Uncertainty of the models has been assessed using Nash–Sutcliffe efficiency (NSE), coefficient of determination (r2) and a modified method known as skill score (SS). The study concluded that the bias-corrected GCMs played a better role than the CORDEX RCMs for the Bundelkhand region. Earth System Model, ESM-2M of the Geophysical Fluid Dynamics Laboratory (GFDL) has shown better accuracy than all the CORDEX RCMs and their driving GCMs for the study region.
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