Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials.
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Water is essential for irrigation, drinking and industrial purposes from global to the regional scale. The groundwater considered a signifcant water resource specifcally in regions where the surface water is not sufcient. Therefore, the research problem is focused on district-wise sustainable groundwater management due to urbanization. The number of impervious surface areas like roofng on built-up areas, concrete and asphalt road surface were increased due to the level of urban development. Thus, these surface areas can inhibit infltration and surface retention by the impact of urbanization because vegeta tion/forest areas are decreased. The present research examines the district-wise spatiotemporal groundwater storage (GWS) changes under terrestrial water storage using the global land data assimilation system-2 (GLDAS-2) catchment land surface model (CLSM) from 2000 to 2014 in West Bengal, India. The objective of the research is mainly focused on the delineation of groundwater stress zones (GWSZs) based on ten biophysical and hydrological factors according to the defciency of groundwater storage using the analytic hierarchy process by the GIS platform. Additionally, the spatiotemporal soil moisture (surface soil moisture, root zone soil moisture, and profle soil moisture) changes for the identifcation of water stress areas using CLSM were studied. Finally, generated results were validated by the observed groundwater level and groundwater recharge data. The sensitivity analysis has been performed for GWSZs mapping due to the defcit of groundwater storage. Three correlation coefcient methods (Kendall, Pearson and Spearman) are applied for the interrelationship between the most signifcant parameters for the generation of GWSZ from sensitivity analysis. The results show that the northeastern (max: 1097.35 mm) and the southern (max: 993.22 mm) parts have high groundwater storage due to higher amount of soil moisture and forest cover compared to other parts of the state. The results also show that the maximum and minimum total annual groundwater recharge shown in Paschim Medinipore [(361,148.51 hectare-meter (ham)] and Howrah (31,510.46 ham) from 2012 to 2013. The generated outcome can create the best sustainable groundwater management practices based upon the human attitude toward risk.
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