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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.

The research is a mapping of terrestrial environmental services of each of the land use/land cover (LULC) types in the Ganga Basin, which is among the densely populated river systems in the world. The region is highly vulnerable to extreme events, with intensified agricultural activities aimed at ensuring food security. Shift-point detection is used to identify critical periods of abrupt land-use change, while land-use transition matrices reveal the direction and magnitude of class conversions over time. Trend magnitude analysis quantifies the intensity of change, and directional distribution captures spatial dispersion patterns. Multivariate statistical methods integrate multiple drivers and variables, providing a holistic understanding of the socio-environmental interactions shaping LULC dynamics. ESA-linked land cover data available since 1992, with a spatial resolution of 300 m, were analyzed to assess spatiotemporal changes from 1992 to 2020. The results indicate that the most pronounced shifting patterns occurred between 2002 and 2008. Land-use transitions over the study period were classified into 47 conversion classes. Over 29 years, the Ganga Basin experienced a substantial increase in settlements (270.9%) and a decline in grassland (− 8.14%). Settlements exhibited the largest mean-center shift (89.08 km), followed by wetland–marsh areas (65.83 km). Modified Mann–Kendall and Sen’s slope analyses show significant increasing trends for settlements (292.05 km²/year), forests (120.45 km²/year), water bodies (17.86 km²/year), bare areas (3.50 km²/year), and wetlands (0.27 km²/year), while agriculture (− 406.35 km²/year), grassland (− 38.12 km²/year), and sparse vegetation (− 13.54 km²/year) show significant declining trends. Decadal analysis further reveals maximum growth of settlements (83.55%), wetlands (9.82%), and bare areas (1.72%) between 2001 and 2010. Overall, habitation areas expanded markedly from 1992 to 2020, whereas snow and ice remained largely unchanged. These findings support governments, policymakers, and communities in spatial planning and land management, enabling informed decision-making and implementation of development programs aligned with Agenda 2030 and the UN Sustainable Development Goals.

Urban expansion has been significant and rapid over the last 30 years, with the outward growth of the Kolkata Metropolitan Area (KMA). Much of this growth has followed a lowdensity, disparate development pattern, commonly known as urban sprawl. This study aims to examine the spatial expansion pattern in the Kolkata Metropolitan Development Area (KMDA) between 1990 and 2020 through the application of advanced geoinformatics tools and spatial metrics. We analyzed Landsat Satellite images from 1990, 2000, 2010, and 2020 to evaluate urban areas, including their extent and trends. Patterns of directional expansion, assessed using standard deviation ellipses and wedge analysis, showed a clear north-to-south axis of growth in the study area. The expansion of urbanization by 2020 was therefore more concentrated in the south-western region. Urban growth rates were measured using the Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Landscape Expansion Index (LEI). The urban land cover of the study area increased by 446.71 km2 during the study period. The highest growth rate was from 1990 to 2000 (5.42%), followed by a decline in subsequent decades. LEI analysis revealed edge expansion as the prevalent growth type, which is a typical feature of urban sprawl. A mixture of infilling and peripheral growth patterns points to the processes of urban diffusion and clustering. Results for the Department of Labrador were obtained using the Area-Weighted Mean Patch Fractal Dimension (AWMPFD), which classified the urban spatial patterns into four types: major core, secondary core, suburban fringe, and dispersed settlements. Central aggregation and peripheral fragmentation are related straightforwardly. Multiple correspondence analysis (MCA) further confirmed this spatial distribution pattern, which has valuable implications for both resource managers and urban planners.

The northern Ganga basin is one of the most densely populated basins in the world. Most agricultural and industrial contaminants drained in the river length are likely to be accumulated in the lower part of the Ganga basin. In this study, we have used ten parameters obtained from 495 sampling locations, besides using long-term climate data (GLDAS_NOAH025_M) to understand the irrigation suitability using the TOPSIS model. Multi-criteria decision making (MCDM) model using TOPSIS has been used to make the best choices from the available finite number of alternatives based on their ranking. The entropy weights for the irrigation suitability parameters such as electrical conductivity (Ec), sodium adsorption ratio (SAR), magnesium hardness (MH), sodium percent (Na%), total hardness (TH), Kelly’s ratio (KR), permeability index (PI), chloride concentration (Cl − ), groundwater level fluctuation (GWLF), and the Lang factor (Df) are found to be 0.08, 0.14, 0.02, 0.02, 0.04, 0.08, 0.01, 0.32, 0.29, and 0.01, respectively. We find that SAR, Cl − , and GWLF control the water quality for irrigation in the Lower Ganga basin since these parameters have relatively higher entropy weights (more than 0.10). The results obtained from the computed performance index or the closeness coefficient show that the area percent having very good and good groundwater quality for irrigation in the Lower Ganga basin is 77.03% and 22.97% respectively. The land-use change dynamics for the between 2000 and 2015 estimated using the transition matrix shows a positive percentage change for settlement (133.50%), wetland (35.04%), and bare area (0.98%); however, several other classes such as the agriculture (− 0.85%), forest (− 0.49%), grassland (− 14.38%), sparse vegetation (− 11.39%), and water (− 4.12%) show a decreasing trend. The highest amount of percentage change was observed in settlement areas which were contributed by other land-use classes such as agriculture (694.43 km 2 ), water (41.61 km 2 ), forest (16.77 km 2 ), and grassland (1.86 km 2 ). The results may be useful to the concerned organization for the proper planning and management of water resource for sustainable development.

The evaluation of irrigation suitability plays a crucial role for the socio-economic development of the society, especially in the region of Sundarban. For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop yield. The entropy information theory, several irrigational indices, multivariate statistics, GIS, and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. In total, 33 groundwater samples were collected in 2018 (April and May), and they were evaluated for major cations, anions, as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH), and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na +  ≥ Mg 2+  ≥ Ca 2+  ≥ K 2+ and HCO 3 −  ≥ Cl −  ≥ NO 3 −  ≥ SO 4 2−  ≥ F − , respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. In order to identify the specific association and potential source of the dissolved chemical in the groundwater, statistical techniques like correlation and principal component analysis were also employed. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). The EWQII reports that no samples fall into the very good (no restriction) category, whereas 30.30%, 30.30%, and 39.40% of the sample wells record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, respectively. Based on this study, the bulk of the groundwater samples taken from the study area are unsuitable for cultivation. The findings of this study will also help decision-makers develop adequate future plans for irrigation and groundwater resource management.

The land use transition plays an important role for terrestrial environmental services, which had a mixed impact of positive and negative on the groundwater and terrestrial water resource. The health of ecological systems and groundwater depends on the mapping and management of land use. The Ganga basin is one of the most densely populated and agriculture-intensive river systems in the South Asia and the world. The multi-temporal spatial database includes land use (ESA-CCI), satellite-based gravity anomaly (GRACE/GRACE-FO), and well log (CGWB) adopted in this study for assessment of the impact of land use transition on groundwater depth, groundwater drought, and terrestrial water storage. The methodology includes the computation of land use transition, trend magnitude by Sen’s slope, Innovative Trend Analysis (ITA) for graphical visualization, clustering techniques employ to identify pattern & structure, and finally space-time transformation was assessed based on multi-dimensional scaling using Alternating Least Squares Scaling (ALSCAL). The land use transition over two decades shows an increase in forest (2.23%), wetland (2.2%), settlement (208.4%), bare area (3.18%), water (5.18%), and a decrease in agriculture (-1.16%), grassland (-4.5%), & vegetation (-2.8%). The non-parametric climatological trend of groundwater depth, drought, and terrestrial water loss was maximally observed during the post-monsoon season in the Ganga basin. The seasonal climatological trend statistics shows that, the upper Ganga and northern (left) of the Ganga basin shows an alarming rate of groundwater depletion, with increased in the severity of groundwater drought in near future with the loss in terrestrial water storage. The ITA shows the monotonic decreasing trend depicting loss of groundwater and terrestrial water resources. Bi-dimensional regression, ALSCAL shows that the model is efficient based on the input data having stress value and RSQ (proportion of variance) of 0.09 and 0.97 with excellent linear fit. The impact assessment of land use transition was obtained in low dimensional space showing that the conversion from sparse vegetation, agriculture, grassland, wetland and forest to settlement has the maximum impact on groundwater and TWSA loss, although the persistent settlement area is also responsible. The results are extremely useful for the policymakers, scientists, concern Govt. section, and local communities must work together to manage groundwater sustainably. Water resource management can also help to lessen the effects of climate change on groundwater and terrestrial water loss by focusing on the environmental, economic, social, and institutional dimensions of UN-SDG.

River water pollution and the subsequent degradation of water quality for irrigation and drinking are reported worldwide, especially in tropical regions with excess population pressure. The present study intends to investigate irrigation and drinking water quality and assess their suitability in the subtropical Damodar River in India using hydrochemical indices during pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) periods. The water quality index (WQI) results reveal that the river’s water is unsuitable for drinking, as 68.92% (52.95% in PRM, 86.54% in MON, and 66.88% in POM) of samples are found to be unfit for consumption in the temporal dimension. However, in the spatial dimension, the percentage of unsuitable water samples is primarily high near the village of Mujher Mana station, with 97.20% of samples (97.87% in PRM, 97.91% in MON, and 95.83% in POM) deemed unfit for drinking. This suggests the Damodar River water in MON and near the village of Mujher Mana needs treatment before drinking. The study’s findings from the irrigation hazards indices and the local farmers’ feedback indicate that the river water is suitable for irrigation use. Moreover, SAR, %Na, KR, and PS are high at Mujher Mana village, RSC at Raniganj downstream (Ds), PI at Barakar, and MAR at Durgapur upstream (Us) in terms of spatial extent. The ANOVA test indicates a significant variation in river water quality across different spatio-temporal dimensions in the study area. Water pollution is mainly attributed to the discharge of untreated industrial and urban effluents directly into rivers, without undergoing water treatment. Therefore, it is imperative to address the issue promptly to reinstate the river water quality.

This study assesses drought in the Ganga River Basin using standard precipitation index (SPI), Gravity Recovery and Climate Experiment Drought Severity Index (GRACE-DSI), and vegetation condition index (VCI) via geographic information system (GIS) and non-parametric tests. Future SPI trends show increasing drought severity from 1986 to 2020. GRACE-DSI analysis (2002–2020) indicates a potential rise in drought conditions. VCI computations reveal vegetation health dynamics. Findings suggest an impending severe drought in the Ganga Basin, necessitating urgent water resource management. Drought causes are linked to precipitation changes and resource over-exploitation in changing climate conditions. The study emphasizes implementing measures like water conservation, sustainable land use, and groundwater recharge strategies to mitigate drought impacts.

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.