One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.

On a local and regional level, climate change has had a significant impact on precipitation in the global climatic state. The purpose of this research is to examine the trend and character of urban precipitation in the world’s most densely inhabited metropolis. From 1981 to 2020, 40 years of monthly and annual precipitation data from 50 major metropolitan cities throughout the world, based on population statistics, were analysed. The monthly and annual precipitation analysis was done using a homogeneity test, shifting point test, non-parametric Modified Mann Kendall test, and also through computing the magnitude of the trend using Sen’s slope estimate. According to the findings of the study, the most homogeneous data was obtained in May (90 %) and the least in September (74%). In 2002, the highest number of breakpoints were found in July (9 cities) and August (8 cities). The month of January has the largest significant positive trend (10 cities) whereas annually it has 20 cities. The monthly maximum of the significant negative trend was discovered in February (4 cities) and annually in 2 main cities. In November, the maximum positive and minimum positive Sen’s slope values were found to be 82% and 56%, respectively. The findings of this study are important for future water resource projections, flood or drought predictions, and engineering, scientific, industrial, agricultural, and social studies. The goal of this research is to come up with a good plan for dealing with urban flash floods and droughts as precipitation acts as the key parameter of the hydrological cycle.

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km² with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system.

Cold waves, cold nights and warm nights are major threats to human beings during winter due to climate change in different parts of India. The analysis of these has been studied for four major metropolitan cities (Chennai, Mumbai, Kolkata, and Delhi) of India during the period 1985–2020. The authors have used the 90th and 10th percentile threshold to identify the cold nights, warm nights and cold waves during the winter season. The degree of discomfort and cold stress category are identified using the Humidity Index (HD), and the Universal Thermal Climate Index (UTCI). The results indicate that the cold night event in Mumbai is ~0.36% higher than both Kolkata and Chennai cities but it is ~0.42% higher than Delhi. The number of cold wave events in Delhi is 53.5% higher than in Kolkata during the period of the study. It is also observed from the study of UTCI that the possibility of slight cold stress in the Delhi region during cold nights is 59.36% more than in other metropolitan cities. The study indicates that in the winter season, the climate of Delhi is more dynamic but for Kolkata it is relatively less dynamic.