Climate Change is a natural phenomenon, but due to current anthropogenic and natural uncertainties and shifts, these changes lead to occurrence of various types of extreme events. The current study focuses on the Weather and Climatic Extreme Events, concerning the precipitation and its dynamic shifts in the Wainganga River, India. The basin is a major tributary of the River Godavari, the biggest peninsular river in India. It offers various flora and fauna, food, agriculture, and agroforestry products and acts as the major income source for the local population. The extreme event analysis uses a multi-model ensemble approach of six Global Climate Models and an extreme event indices technique. The study incorporates fourteen rainfall-based extreme indices, and the analysis is carried out using spatiotemporal analysis for the whole river basin, the MK test to analyse the trend of rainfall extremes, and box plots for determining variable shifts in rainfall for the Wainganga River Basin. The study findings show that the western region of the basin projects a decrease in rainfall, which can lead to the occurrence of drought-based extreme events. At the same time, the eastern ridge projects heavy and intense rainfall trends, projecting to be a hotspot for flood-based events. The study also focuses on how future projections might show short-duration rainfall dominating over the longer duration, contributing prominently to the total rainfall. The study can be helpful to the decision-makers and policymakers who need to take appropriate steps for sustainable and efficient planning and strategies.

Urban water management is crucial for the expeditious development of cities, particularly in the context of human-induced climate change. Conventional hydrological rainfall-runoff models often struggle to address the interplay of growing urban sprawl, water availability, and community needs, specifically in regional areas with a complicated terrain and limited/no real time data. This study examines the implications of utilizing Water Accounting Plus (WA+) by a thorough assessment of water inflows, utilization, and losses to empower local communities to adopt integrated approaches, including runoff estimation and rainwater harvesting, in Dehradun, the capital city of Uttarakhand state in India. The city faces repetitive waterlogging, fierce freshwater depletion, and urban floods, which tends to increase similar to 70-80% by 21st century. WA + can lead to assistance by identifying the bottlenecks in the system, keeping a tab on rainfall-runoff events. By integrating hydrological modelling along with GIS tools and community driven rainwater harvesting, similar to 67.01 Mm(3)/annum of runoff can be harnessed from the streets, roof-tops, and other open spaces, which often goes dormant causing severe water logging. This can help mitigate the urban floods and enhance groundwater augmentation ensuring prolonged water availability as water demands are going to increase by 1.25 times by 2041. This work draws attention to Dehradun city and similar peri-urban areas with inadequate real time flow data heading for a more adaptive and sustainable approach in line with policy goals like the Jal Jeevan Mission and Atal Mission for Rejuvenation and Urban Transformation (AMRUT), progressing towards more resilient and self-sustaining water management system.

Rapid urbanization significantly alters natural hydrological processes by modifying land-use patterns, thereby affecting surface runoff generation and streamflow dynamics in urban watersheds. This study investigates the long-term impacts of urban land-use change on surface runoff in Vadodara City, India, using the Soil and Water Assessment Tool (SWAT). Land-use maps for the years 1980, 2001, and 2017 were derived from multi-temporal Landsat satellite imagery, while a high-resolution Cartosat-1 Digital Elevation Model (30 m) and FAO soil data were employed for watershed characterization. To isolate the effect of land-use change, model simulations were performed by keeping climatic conditions constant and varying only land-use scenarios across the selected periods. The urban watershed was delineated into hydrological response units based on unique combinations of land use, soil type, and slope, and runoff was simulated at the catchment outlet. Results indicate a substantial increase in urban area within the study catchment, expanding from 1.18 km² in 1980 to 53.88 km² in 2017, accompanied by a corresponding rise in simulated discharge. Surface runoff volume increased from 36.8 MCM in 1980 to 38.85 MCM in 2017, demonstrating a strong positive relationship between urban expansion and runoff generation. Additionally, peak discharge magnitudes and flow velocities were observed to increase with progressive urbanization, implying reduced infiltration and enhanced surface flow. The findings highlight that land-use change alone, independent of climatic variability, can significantly modify urban hydrological responses. This study underscores the necessity of incorporating land-use dynamics into urban water management and flood mitigation planning to enhance resilience against future hydrological extremes.

Climate change significantly threatens agricultural water security, especially in semi-arid regions with high cropping intensity and limited water resources. This study assesses current and future irrigation water requirements in the Hemavathy command area of Karnataka, India, using the CROPWAT 8.0 model integrated with historical meteorological data from the Indian Meteorological Department (1985–2014) and projected climate data from the MIROC5 regional climate model under RCP 4.5 and RCP 8.5 scenarios (2020–2035). Spatial interpolation of climatic variables was performed using the Thiessen polygon method to generate area-weighted averages across the command area. Gross Irrigation Requirement (GIR) was estimated based on seasonal cropping patterns and effective rainfall using the Soil Conservation Service (SCA) method. Beyond standard irrigation demand estimation, the study introduces a Water Stress Index (WSI) to quantify the balance between irrigation demand and water availability, performs sensitivity analysis of key drivers (ET0, rainfall, crop coefficient), and evaluates adaptation scenarios including improved irrigation efficiency and crop pattern diversification. Results indicate a significant increase in irrigation demand under future climate scenarios, with projected requirements reaching up to 31.80 Thousand-Million m³ (TMC) under RCP 4.5, as compared to 27.13 TMC in 2012–2013 under historical conditions, accompanied by higher WSI indicating elevated irrigation stress. Sensitivity analysis highlights reference evapotranspiration (ET0) and rainfall as dominant controls on irrigation stress. Sensitivity analysis highlights ET0 and rainfall as dominant controls on irrigation requirements, while adaptation scenarios demonstrate that integrated strategies can reduce irrigation demand by over 30

Reliable hydrological drought monitoring in data-scarce regions such as India is constrained by limited long-term streamflow records. Global reanalysis products like GloFAS offer a potential alternative, but their applicability remains insufficiently evaluated. This study presents the first nationwide assessment of GloFAS for streamflow representation and hydrological drought monitoring using discharge from 109 gauging stations (>25 years). Model performance was evaluated across daily to annual timescales, monsoon and non-monsoon periods, and different flow conditions, and SDI (1, 3, 6, 12 months) was benchmarked against GloFAS. Performance improved with aggregation: correlations increased from rho = 0.50 (daily) to 0.78 (annual), and from rho = 0.43 (SDI-1) to 0.58 (SDI-12). GloFAS reproduced medium-to-high flows well but underestimated low flows, limiting drought onset detection. At SDI-12, detection skill was moderate (POD = 0.73, CSI = 0.60). GloFAS can reliably detect drought occurrence but is less effective in distinguishing drought severity, with potential for application in ungauged basins.

Accurate daily streamflow prediction is a major challenge in data scarce basins such as the Gilgel Abay watershed in Ethiopia's Upper Blue Nile region, where data-oriented models lack physical understandability and physically based models often struggle to capture peak flows. This study addresses this limitation by introducing a hybrid framework that pairs the process-based SWAT model with a Multi-Layer Perceptron (MLP) to improve predictive reliability. Using land use, soil, topography, and daily weather records datasets, SWAT was first calibrated (2000-2010) and then validated (2011-2014). This is followed by an MLP model trained using rainfall, temperature, and lagged streamflow. The hybridization was achieved by integrating key SWAT-simulated hydrological components with meteorological variables and lag-based predictors to improve learning of flow dynamics. SWAT demonstrated reasonable skill (NSE = 0.77) but underestimated peak flows, whereas MLP performed better (NSE = 0.92) by capturing nonlinear temporal patterns. The coupled SWAT-MLP model outperformed both individual models, achieving NSE and R2 of 0.94 with significantly reduced error, demonstrating its robustness and reliability. The findings highlight the potential of hybrid modeling to strengthen daily streamflow prediction in similar data-limited watersheds, with future application recommended for operational water management and extension to nearby sub-basins.

The current study is based on a rural water sustainability index (RWSI), in which tools like multicriteria decision analysis are used to simulate the future forecast of water availability. These are used to develop strategies to supply water to rural communities. The Delphi method (DM) is adopted to select subindicators for expert-based views. Subsequently, the square root judgment scale is modified to incorporate an expanded analytic hierarchy process (AHP) methodology to determine priority values. Despite the narrow range of the initial values, the proposed method effectively redistributed these scores on a final scale of 1 to 10. To use the RWSI technique and estimate water sustainability, researchers must first engage in public and expert consultation to modify the weight of indicators and subindicators. This process allows for adjustments based on the specific conditions and requirements of different countries and areas. Once these adjustments are made, the RWSI method can generate water sustainability maps for other locations as well. This method can be applied to water-stressed regions worldwide. The tool is applied to simulate the future forecast of water availability in the Bundelkhand region of Madhya Pradesh, India.

The spatial distribution of crucial weather parameters, including rainfall, temperature, evapotranspiration, relative humidity, and solar radiation, is changing over time due to the influence of climate change. These parameters play a significant role in hydro-meteorological studies. This study focuses on the Hemavathy project area at Goruru Dam, situated in a semi-arid climate zone with extensive agricultural activities. Covering an agricultural command area of 265,074 hectares, irrigation is facilitated through the left bank canal (LBC) and right bank canal (RBC). Before analysing supply and demand dynamics, the study investigates the spatial variability and trends of key climate variables, such as rainfall and temperature, using statistical methods like the Mann–Kendall test and Sen’s slope estimator for the period 1985–2014. Findings indicate an upward trend in rainfall during annual and pre-monsoon seasons, whereas the monsoon season displays a downward trend near the reservoir, potentially impacting reservoir inflow. Conversely, the lower catchment tail end of the Tumkuru branch canal exhibits an upward rainfall trend. Post-monsoon, rainfall declines across the study area, affecting moisture availability during the Rabi season. Annual maximum temperature shows an upward trend during monsoon, post-monsoon, and winter seasons, except for a downward trend in the pre-monsoon period across the entire study area. These temperature trends are likely to intensify water demand in the region.

In a socio-environmental system, natural and human subsystems influence each other when an external trigger occurs, leading to abrupt or gradual changes. The assessment of regime shifts caused by floods in urban socio-environmental systems is conducted in Guwahati, India’s most urbanized city in Assam. Here, we examined regime shifts based on temporal and spatial patterns in the human and natural subsystems within city boundaries. The objectives of this study are: (1) to analyze regime shifts in natural subsystems using annual time series of hydrometeorological variables (precipitation, runoff, and temperature) and structural break regression models, such as penalized maximum likelihood with white-noise detection (including sensitivity analysis), and (2) to identify regime shifts in land use and population factors in human subsystems using spatial covariance techniques. The results show that precipitation and runoff data align well, with Bayesian Information Criteria (BIC) values of 8.837 and 7.977, respectively. Regime shifts in rainfall are observed in 1947, 1954, 1987, 2003, and 2004, corresponding to pluvial flooding, while shifts in runoff are seen in 1988 and 1998, associated with fluvial flooding. In the human system, land-use and population shifts indicate a shift in 2003, based on covariance maps. It was determined that 2003 was the year when both regime shifts occurred in the human and natural subsystems. These findings reveal resilience during periods between shifts and highlight critical thresholds at which the system transitions between states. This study enhances understanding of regime shifts and resilience, which can assist policymakers in developing robust, adaptive infrastructure planning and governance strategies to better withstand future extreme events. This study was conducted to identify regime shifts in urban socio-environmental systems in Northeast India, capturing pre- and post-flooding social and environmental conditions. It focuses on Guwahati, a city in the Indian state of Assam that experiences heavy flooding every year due to a combination of hydrometeorological and human factors. The research aims to analyze shifts in natural and complex socio-environmental systems, highlighting how they respond to the region’s human influences. A methodology was introduced that integrates structural break and spatial covariance matrix analyses. The results indicate significant changes in rainfall, runoff, land use, and population during the flood year. Detected major regime shifts in Guwahati’s social-environmental system caused by severe flood events. Structural break-regression models effectively identified hydrometeorological shifts, signalling major floods in natural systems. Spatial covariance analysis showed land-use and population changes affecting urban resilience in the human system.

This study focused on managing the Indira Sagar Canal Command Area (ISCCA) by simulating groundwater flow using Visual MODFLOW version 4.2. The model examined hydraulic head changes under transient pumping conditions from 2001 to 2010, with calibration based on six years of data and validation using the remaining years. Data from 48 observation wells supported this analysis. Convergence was achieved with a maximum of 50 outer iterations and 100 inner iterations, with a residual threshold set to 0.01. Sensitivity analysis identified groundwater recharge as the most critical parameter, followed by aquifer hydraulic conductivity. Specific storage and yield parameters showed less sensitivity. The consistency between observed and computed groundwater head contours validated the model's accuracy in replicating groundwater dynamics. The groundwater balance calculated from the model closely matched actual field conditions, confirming the model's reliability. Additionally, the study highlighted the impact of topography and base flow on groundwater flow within the canal command area. Key findings include the successful development of a reliable Groundwater Model (GWM) for the ISCCA, which accurately simulates aquifer behaviour, recharge, and withdrawal patterns. The calibration and validation process demonstrated the model's potential for future integration with climate models to enhance groundwater predictions and support sustainable water management.

Drought is among the most pervasive and devastating natural disasters, particularly in countries like India, where agriculture forms the backbone of the economy and supports most of the workforce. A prolonged deficiency in precipitation, often exacerbated by rising temperatures, adversely impacts water availability, agricultural productivity and socio-economic stability. The situation is further worsened by the heterogeneous agro-climatic conditions in India, where regional variations in rainfall, cropping patterns and hydrological regimes mean that drought affects different zones in distinct ways. India's agro-climatic zones are increasingly experiencing intensified drought conditions due to rapid climatic changes and threatening agricultural productivity. This study investigates the spatio-temporal dynamics of drought across India's agro-climatic zones from 1951 to 2022, employing the Standardised Precipitation Evapotranspiration Index (SPEI) at both annual (SPEI-12) and monsoon season specific (SPEI-4) scales. The rainfall and temperature trends, as well as trend analysis of SPI-12 and SPEI-12, have been carried out using the modified Mann-Kendall (MMK) trend test. Long-term analysis (1951-2022) shows a declining rainfall trend in 9 out of 14 agro-climatic zones, with the most pronounced losses in the Upper and Middle Gangetic Plains, while warming trends are evident across almost all zones, intensifying evapotranspirative demand. The analysis reveals significant shifts in drought patterns, with increased frequency, intensity and spatial expansion of drought-prone areas, particularly post-1990. The MMK test revealed statistically significant increases in drought frequency and intensity post-1990, particularly in the Trans-Gangetic Plains, Gangetic Plains and Southern Plateau and Hills. SPEI-based droughts demonstrated sharper upward trends compared to SPI, confirming the role of temperature-driven evaporative stress. The spatial analysis highlights emerging vulnerabilities in resilient regions, driven by declining monsoon reliability, rising temperatures and the additional impact of unsustainable anthropogenic practices, including the over-extraction of groundwater and deforestation, which will worsen the situation. The study underscores the inadequacy of reactive drought management policies, which lack integration of climate change considerations and evolving drought dynamics. The findings emphasise the urgent need for proactive, region-specific drought management strategies, including advanced monitoring systems, sustainable water resource management and climate-resilient agricultural practices. By integrating rainfall, temperature, SPI and SPEI trend analysis with robust MMK testing, this study provides evidence-based insights to guide climate-resilient and drought-adaptive policy frameworks across India's agro-climatic zones.

In ancient India, air, water, and solar radiation were represented as gods and key components of water availability, food production, and energy generation. However, the linkage between modern science and ancient wisdom has not been well explored. In this study, we estimate the water-food-energy potential using various openly available data products and perform statistical analysis to establish the scientific basis behind the geographical alignment of eight prominent Shiva temples along the 79 degrees meridian east to the north, known as the Shiva Shakti Aksh Rekha (SSAR). Results indicate a strong correlation between the SSAR belt and water-energy-food productivity potential, where 18.5% of the area can produce 44 x 106 tons of rice annually. With an estimated renewable energy generation potential of 596.6 GW, the SSAR belt could significantly contribute to India's target of 500 GW of renewable energy production by 2030. This study highlights the potential role of traditional geographical alignments in supporting future water, energy, and food security in highly populated countries such as India.

The primary objective of the study is to provide an analysis of Roof-Top Harvested Rainwater Filtration Systems (RTHRWFS), importantly emphasising the findings that will be pivotal for future innovation and development in filtration technologies for a promising and effective adaptation of RTHRWFS by the masses. The work addresses a gap in the global Roof-top Rainwater Harvesting (RTRWH) implementation using detailed Prismatic and Bibliometric Analysis. The study has thoroughly examined distinct technical approaches, the structural cost, and public consciousness hindering global acknowledgement of RTRWH systems. It discusses the influence of the composition of rainwater, roof materials, and implementation of the first flush on the concentration of roof runoff. Further, our study delves into the probable areas of the sustainability and pursuance of these practices, potential improvement in existing system designs, and possible proficiency gaps. The work presents a framework for evaluating the sustainability of roof-top harvested rainwater intending towards high (installation, operation and maintenance) cost, less public recognition, low filtration rate, liability towards microbiological contamination etc. It also points out the symbolic roadblocks pertinent to skills and technological implementation, hence curtailing the extensive adaption of these systems globally. Our paper reiterates the critical areas in filtration technologies, focusing on the evolution of competent, economic and sustainable filtration methodologies catering to the long-term storage needs and diverse demands along with ensuring public awareness related to contaminants primarily present in rainwater, reassuring embracement of filtration systems for better domestic and potable use. We present a framework for evaluating the sustainability of Roof-top Harvested Rainwater. The need for accomplished filtration systems for filtering Roof-top Harvested Rainwater is emphasised. Critical areas for future research in harvested rainwater filtration technologies are highlighted.

An integrated surface-groundwater (SW-GW) model effectively captures the spatiotemporal variation of the groundwater dynamics under the impact of climate change and anthropogenic activities. Hence, this study examines the efficacy of SWAT-MODFLOW using CMIP6 climate models in simulating spatio-temporal patterns of groundwater dynamics at Upper Godavari Sub-Basin in India. The models were calibrated and validated using the SUFI-2 algorithm and indicated the efficacy of model with high R2 and NSE. For future scenarios, the NESM3 model showed the least variation of quartiles based on R2, RMSE, MAE and HSS, depicting more accuracy for present-day conditions than other models. Both the northern and the southern areas of the sub-basin experienced higher runoff under SSP585, whereas central areas along with the northwestern region experienced significant soil moisture decline under SSP126 and SSP245. Hence, the findings successfully captured the anomaly of head and SW-GW interaction and proved to be an important method in simulating the SW-GW dynamics under climate change scenarios.

Climate change has contributed to shifting extreme events' frequency, intensity, spatial extent, duration, and timing, affecting communities and regions worldwide. Yet, disaster governance grapples with addressing the effects of emerging and unexpected spatiotemporal patterns of hydroclimatic variability on the built and natural systems. This study aims to create a workflow to identify sources of flood disaster governance improvement using flood risk attributes for three major flood events at state and district levels in India. The flood risk-based framework quantifies vulnerability, exposure, and hazard to evidence the potential critical drivers of flood disaster improvement in the affected areas. Three major flooding events occurred between 2005 and 2020 in India's Maharashtra, Uttarakhand, and Assam states are characterized by their hazard, vulnerability, and exposure risk attributes. A comprehensive compilation of precipitation anomalies, augmented by media data and hazard mapping flow accumulation (F), rainfall intensity (I), geology (G), land use (U), slope (S), elevation (E) and distance from the drainage network (D) and global sensitivity analysis (FIGUSED-GSA), presiding over the estimation of flood exposure (using runoff Peak-over-threshold return periods), socio-economic vulnerabilities (using the equal weightage method), and risk (as a product of hazard, exposure and vulnerability). These methods will be useful for the data scarce regions as well. The estimates of flood risk and its components will aid in highlighting the areas of possible actions needed to create more effective flood governance frameworks at both the state and district level.

Evaporation holds a significant position in the global hydrological cycle and is one of the intricate phenomena widely affected by various hydrometeorological parameters. Evaporation accounts for 66 R^2 , NSE and MARE measure ANN-PSO and ANN-GA and the performance of the models. The study enlightens on two major outcomes: the application of the various AI-based models for estimation of evaporation and the intercomparison of their outputs with the hybrid-AI models. All models show R^2 > 0.8, 0.15 ≤ MARE ≤ 0.25 and NSE ≥ 0.73, signifying robust performances. ANN-PSO and ANN-GA outperformed other models by integrating AI learning with optimization algorithms, addressing single-algorithm limitations. The study’s findings can assist researchers and act as a tool for the local stakeholders in managing water resources.

Groundwater is a critical lifeline for sustaining water resources in Upper Godavari Sub Basin, India's arid regions. However, due to impeding water requirement and demand in these regions along with anthropogenic complexities has raised serious concerns for this vital resource. Along with anthropogenic activities, Climate change also threatens this precious resource due to scarcity of surface water mostly during the summer season of the year. Hence, to comprehend this important issue, the groundwater resource assessment needs to be done for present as well as future scenarios. Therefore, the present study assesses the groundwater resource using a SWAT-MODFLOW model which is a combination of advanced hydrological model with cutting-edge numerical groundwater model. Individual surface and groundwater models are developed in SWAT and MODFLOW respectively and then are linked using the linkages files to get the more enhanced surface and groundwater interaction in the form of recharge, groundwater level and interaction of rivers with sub surface. The surface and groundwater models are calibrated and validated using the streamflow and groundwater level data. The calibrated model thus presents the current scenario of groundwater allocation which is then simulated with different bias corrected climate variables for getting the status of groundwater for future SSPs scenarios. From a range of CMIP6 climate models, the best model is selected based on the statistical index such as NSE, the correlation coefficient, R2, MAE, RMSE, MSE, and NRMSE which was NESM3 in the present case with a highest correlation and R2 with IMD precipitation and temperature dataset. The best selected climate model (NESM3) is then bias corrected using the empirical quantile method. Along with the numerical approach, to map the groundwater level data, soft computing approach using RFR and GBR is also employed to predict the groundwater level data for future scenarios. The optimization of these models was done by the Particle PSO. The study findings in Upper Godavari Sub Basin, India, revealed significant changes in groundwater levels across different seasons, with particularly significant increases observed during the dry season. The study showed that MODFLOW-GBR-PSO is more accurate in predicting groundwater level than MODFLOW-GBR, MODFLOW-RFR-PSO and MODFLOW-RFR. The result also predicted decreased rainfall for the SSP 585 scenario which in turn lead to drop in groundwater level and recharge in the distinct parts of the sub basin. Hence, from the above result a proper mitigation and framework needs to be prepared to counterfort the diminishing groundwater resource for the betterment of environment. Key words: climate change, hydrological model, SWAT, Nash-Sutcliffe efficiency (NSE), Root mean square error (RMSE), Random Forest regression (RFR) and Gradient Boosting Regression (GBR), Swarm Optimization method (PSO).

A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub-basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA-55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long-term variability of the best reanalysis product, trend analysis was also performed using the Mann-Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5-2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF-HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.