Indian Summer Monsoon is highly susceptible to extreme rainfall events, which vary markedly across different regions. The complex topography of the country further complicates the interpretation of regional disparities in these extremes and their underlying drivers. This study investigates the regional variability of monsoon behavior across four climatically homogeneous zones: Southwest India (SWI), Southeast India (SEI), Central India (CI), and Northwest India (NWI). We analyze the frequency of extreme rainfall events, defined at the 99.9th percentile (R99.9), and explore the underlying physical mechanisms for these occurrences. To better understand the thermodynamic influences, Clausius–Clapeyron (CC) scaling is performed to assess the relationship between atmospheric moisture-holding capacity and surface temperature across the zones. Our results indicate a significant increase in extreme rainfall events over the past four decades in all regions except CI, which exhibits a slight decline in recent decades. These events are predominantly associated with large-scale precipitation, suggesting a strong influence of synoptic systems. Notably, SEI is an exception, where convective precipitation plays a dominant role, highlighting the importance of convection in this region. Furthermore, the vertically integrated moisture transport (VIMT), specific humidity, and vertical velocity (omega), along with the convective processes, were investigated. A nonlinear, region-specific relationship between total column water vapor (TCWV) and surface temperature has been observed suggesting that atmospheric dynamics plays a dominant role in modulating the extreme rainfall. The findings offer critical insights for enhancing regional monsoon predictability and inform strategies for improved societal and environmental resilience.

Floods represent extreme natural occurrences that lead to devastating damage and adversely impact communities. To effectively mitigate and manage the consequences of such events, decision makers and disaster management authorities rely on reliable data related to flood depth, discharge, magnitude and specialized datasets. This research article prepared to employ the upgraded software model of HEC-RAS to perform one-dimensional hydrodynamic flood modeling along the mountainous ‘Sutlej’ river, spanning of 23 km from ‘Karcham Dam’ to ‘Nathpa Dam’ in Himachal Pradesh, India, with a specific focus on leveraging geospatial techniques. The study seeks to showcase the geospatial analytical capabilities of HEC-RAS v5. River data, including bank lines, flow path lines, and cross-section cut lines, as extracted from ALOS-PALSAR Digital Elevation Model (DEM) for flood modeling purposes. Steady flow analysis was conducted to simulate a one-dimensional hydrodynamic model. The model’s outcomes presented as flood elevation ranging from 1779.6m to 1596.2m (R.L) can be visualized in the geospatial RAS Mapper window, Flood depth maps for maximum flood highlight the susceptibility of low-lying areas on both sides of Sutlej River when river discharge exceeds 6740 m3/s. The model simulation results depicted different arrival time of flood at different places on downstream with water surface elevations varying from 1790.8m to 1520.2m and backwater affect starts approximately 2 km before reaching Nathpa Dam reservoir. The velocity of flood water varies from 0 to 8m/s. By effectively integrating geo-spatial techniques with HEC-RAS, it is witnessed to create more accurate data-driven models and the results from the one-dimensional modeling demonstrate promise and accuracy.

Rainfall and temperature variability serve as crucial indicators of hydroclimatic hazards, including floods, droughts, heatwaves, and cold spells. While such events may arise from a single variable reaching extreme levels, they often result from the interplay of multiple climatic factors. This study examines the spatio-temporal variability of compound extreme events (CEEs) over the semi-arid Ken and Betwa River catchments in Central India. Although these regions primarily receive rainfall during the Indian Summer Monsoon (ISM) season, they have experienced a post-2000 drying trend along with rising temperatures. A significant negative correlation between rainfall and temperature indicates rainfall suppression under hotter conditions due to enhanced atmospheric stability and reduced moisture availability. The analysis further shows that extreme wet and dry events have declined in the Betwa basin, while the Ken basin exhibits an increase in extreme dry events and a decrease in wet extremes. Cold extremes (T10) have also shown a decreasing trend across both regions. Investigation of different combinations of rainfall and temperature extremes reveals that moderate and extreme warm-dry CEEs have intensified over the past four decades, emerging as the most dominant compound events. The persistence of these events is largely driven by wind patterns and convective inhibition energy (CIN) in the case of moderate events, and by moisture transport and divergence for extreme ones. The intensification of such CEEs poses substantial risks to regional agriculture, eco-hydrological systems, and socioeconomic stability. Composite Resilience Index (CRI) was developed at the district level, integrating indicators like the Human Development Index, Multidimensional Poverty Index, and literacy rates. Results reveal that Ashoknagar, Shivpuri, Lalitpur, and Chhatarpur are the relatively low-resilience districts, while Bhopal, Sagar, Jhansi, and Hamirpur exhibit higher resilience. Overall, the findings underscore the urgent need for climate-informed policies and adaptive strategies to ensure water sustainability and socio-economic stability in the Ken–Betwa river catchments under a warming climate.

The Indian Summer Monsoon Rainfall (ISMR) and its extremes exhibit substantial complexity due to the region’s intricate land–ocean distribution and diverse topography, and remain crucial for the livelihoods of billions across the Indian subcontinent. This study examines the influence of increased horizontal resolution on persistent dry biases over India and associated physical mechanisms, using the High-Resolution Model Intercomparison Project (HighResMIP) data under the PRIMAVERA framework. The analysis demonstrates that higher-resolution models show marked improvements in reproducing the mean ISMR and its extremes, with rainfall distributions more consistent with observations. Enhanced resolution also contributes to a more realistic simulation of intraseasonal variability, particularly the active and break phases of the monsoon. However, considerable inter-model spread remains even at finer resolutions, highlighting the ongoing role of model physics and parameterization schemes in determining simulation fidelity. These findings underscore the added value of increased horizontal resolution in reducing systematic biases and enhancing the representation of monsoon variability, while emphasizing the need for continued improvements in the physical representation of key processes within climate models. This figure summarizes the study, which investigates the role of enhanced horizontal resolution in simulating Indian Summer Monsoon (ISM) extremes and intraseasonal variability. Rainfall data from the India Meteorological Department (IMD) and 850 hPa wind data from ERA5 are used to evaluate the performance of models participating in HighResMIP, listed in the blue box. The study conducts extreme rainfall analyses using both observed and model datasets, and Kernel Density Estimation (KDE) is applied to examine rainfall distribution. The results confirm that finer-resolution models reproduce the observed distribution more accurately and capture extreme rainfall events over the Indian landmass. Spatial bias maps are analyzed to understand regional variability, revealing that coarser models exhibit notable dry biases over northern India, which are reduced in most high-resolution models, except for NorESM2-MM, where biases remain pronounced. Performance gains are assessed not only for mean rainfall but also for extremes, and the ETCCDI indices demonstrate that finer-resolution models are more skillful in representing extreme rainfall events. The study further examines intraseasonal variability, including active and break spells during July–August over the core monsoon zone, and evaluates the models’ ability to depict the low-level jet (LLJ) at 850 hPa, a key feature of ISM circulation. The results highlight that high-resolution models consistently outperform their coarser counterparts, capturing mean rainfall, extremes, intraseasonal variability, and seasonal wind patterns more realistically. Overall, the findings demonstrate that high-resolution models offer substantial improvements for monsoon simulation, providing better representation of mean and extreme rainfall as well as LLJ dynamics. However, model physics and parameterization schemes continue to play a critical role in modulating model performance, indicating that resolution alone is not sufficient for achieving optimal skill. Lower-resolution models exhibit larger dry biases and poorer representation of monsoon mean and extreme rainfall. High-resolution models better simulate the rainfall intensity and distribution over the Indian landmass. Intraseasonal variability is more realistically represented in high-resolution models than in their coarser counterparts. High-resolution models skilfully represent the large-scale dynamics and fine-scale features as compared to the coarser ones. Despite the improvements in the high-resolution model, physics and parameterization schemes remain critical in modulating the skill of monsoon simulations.

Groundwater plays a vital role in sustaining agriculture and water security in semi-arid regions of India, where increasing extraction pressures necessitate a clear understanding of its variability. This study examines the spatio-temporal dynamics of groundwater levels in the alluvial aquifers of Anand district, Gujarat, over a 20-year period (2000–2020) using pre and post-monsoon data from India-WRIS. Geostatistical analysis through Ordinary Kriging within a GIS framework was employed to map and analyze groundwater distribution. Results indicate significant spatial and temporal variability, with pre-monsoon groundwater declining from 4.4-22.0 m below ground level (bgl) in 2000 to 5.6-26.1 m bgl in 2020, reflecting progressive depletion. Spatially, northern and western regions consistently showed shallower water levels, while southeastern areas remained critically deeper. Post-monsoon analysis revealed an initial recovery phase (2000–2008) due to effective recharge, followed by a reversal after 2012, with expansion of deeper zones exceeding 26 m bgl by 2020. The study highlights a shift from recovery to depletion driven by combined natural and anthropogenic factors, emphasizing the need for sustainable groundwater management strategies, including regulated extraction and improved recharge interventions.

The reflectivity (Z)-rain rate (R) relationship is crucial for describing the microphysical characteristics of precipitating clouds and plays a vital role in assessing the performance of polarimetric Doppler radar and rain gauge measurements. For the first-time, the power-law Z-R relationship ( Z=aR^b ) is determined for stratiform and convective rainfall during the pre-monsoon, monsoon, and post-monsoon seasons at Mahabaleshwar, a tropical station in the Western Ghats, using the in-situ Joss-Waldvogel Disdrometer (JWD) measurements from 2014 to 2019 at the High-Altitude Cloud Physics Laboratory (HACPL: 17.56 oN, 73.4 oE; 1400 m above MSL). The proportion of convective precipitation to the total precipitation during the pre-monsoon, monsoon, and post-monsoon seasons are 42

In this study, we employ explainable machine learning (ML) models to predict daily streamflow ( Q_flow ) by leveraging hydro-meteorological parameters. The predictive matrix incorporates crucial factors such as daily rainfall, temperature, relative humidity, solar radiation, wind speed, and the one-day lag value of Q_flow . Notably, among these parameters, the one-day lag value of Q_flow , along with rainfall, solar radiation, temperature, and relative humidity emerge as highly influential predictors. We apply various ML models, including bagging ensemble learning, boosting ensemble learning, Gaussian process regression (GPR), and automated machine learning (Auto ML). Following a rigorous evaluation, the bagging ensemble learning model stands out as the most effective with a correlation coefficient (R = 0.80) and root-mean-square error (RMSE = 218). Further, we compare the Q_flow predicted using ML models with a process-based hydrological model (SWAT) that was executed using a similar set of climatic variables as the input parameters. In our case, the predictive strength of the ML model (R = 0.80; RMSE = 218) to estimate ( Q_flow ) is greater than the SWAT (R = 0.82; RMSE = 281). In conclusion, by emphasizing the importance of explainable ML models and highlighting the significance of specific hydro-meteorological parameters, our study contributes to advancing the field of hydrology and water resource management.

We propose a hybrid machine learning algorithm (i.e., P2CA−PSO−ANN) to model malaria outbreak in three districts (Barmer, Bikaner, and Jodhpur) of Rajasthan in the Western India. We have used different meteorological variables (i.e., relative humidity, temperature, and rainfall) as input features to predict malaria. We have also considered the combined impact of these variables through a linear data fusion. We then extract the uncorrelated information from the feature set by applying Probabilistic Principal Component Analysis (P2CA). We trained the fully connected feed-forward Artificial Neural Network (ANN) by optimising its hyperparameters iteratively through a bio-inspired optimisation algorithm (Particle Swarm Optimisation). We train and evaluate the performance of this algorithm using monthly meteorological variables from 2009 - 2012. This accurately predicts the malaria cases with the coefficient of correlation (R = 0.99), and Root Mean Square Error (RMSE = 1.76). Finally, we compare our model with different benchmark algorithms (Generalised Regression Neural Network (GRNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest, and Radial Basis Neural Networks (RBNN)) in terms of accuracy. We observed the performance of hybrid machine learning model relatively high. This study can be used as an early warning intelligent system to predict the malaria outbreaks solely from meteorological data.

Abstract Certain parts of Odisha suffer from acute water scarcity at times due to nature's vagaries as well as improper management of water resources. Despite this, the groundwater situation, in general, does not appear to be very dire. Considering the above, an attempt has been made to perform hydrogeomorphic studies in two administrative blocks within Keonjhar District, Keonjhargarh and Patana. The study area extends between latitudes 21°26ꞌ38″N to 21°51ꞌ35″N and longitudes 85°37ꞌ6″E to 86°02ꞌ03″E with a total area of 1053.455 sq. Km. Survey of India Toposheets of 1:50,000 scale and LISS-III satellite data having a resolution of 30 meters are used to prepare several thematic layers, viz., lithological map, slope map, land-use and land cover map, lineament map and lineament density map, drainage map, and geomorphological map. The raster maps of these factors are assigned a fixed score and weight using the Multi Influencing Factor (MIF) technique. All the thematic layers have been integrated with one another in ArcGIS software to delineate groundwater potential zones using the weighted overlay analysis method. The final integrated layer has been classified into five classes, i.e., ‘Very Good,’ ‘Good,’ ‘Moderate,’ ‘Poor,’ and ‘Very Poor, to delineate groundwater potential zones that are validated using hydrological data. The present study has provided insight into the role played by geomorphological, lithological, climatological, and hydrogeological factors that affect the occurrence, distribution, movement, recharge-discharge, and quality of groundwater in the study area.

The Betwa River basin, a semi-arid catchment that has been classified as a major hotspot of groundwater depletion in Central India. The rainfall and streamflow intermittency have affected agricultural practices due to the variability of groundwater availability for irrigation. This study evaluates the spatial and temporal variations of groundwater level (GWL) in the last 25 years (1993–2018) in the catchment. We applied a nonparametric Seasonal Trend decomposition based on the Loess (STL) method to decompose the GWL time series into the seasonal, trend, and remainder components. We observed that the GWL in the northeastern regions of the basin has depleted about 3–5 mbgl in the last two decades. During the same period, the basin has experienced a reduction in the rainfall magnitude (2.07 mm/yr). We observed that the overexploitation of groundwater for irrigation and rainfall variability have greatly impacted the GWL condition in the study area. Further, if the groundwater extraction continues at present rates, the Betwa River basin may experience severe depletion in the future.

We estimate the combined effect of climate and landuse-landcover (LU-LC) change on the streamflow of the Betwa River; a semi-arid catchment in Central India. We have used the observed and future bias-corrected climatic datasets from 1980–2100. To assess the LU-LC change in the catchment, we have processed and classified the Landsat satellite images from 1990–2020. We have used Artificial Neural Network (ANN) based Cellular Automata (CA) model to simulate the future LU-LC. Further, we coupled the observed and projected LU-LC and climatic variables in the SWAT (Soil and water assessment tool) model to simulate the streamflow of the Betwa River. In doing so, we have setup this model for the observed (1980–2000 and 2001–2020) and projected (2023–2060 and 2061–2100) time periods by using the LU-LC of the years 1990, 2018, and 2040, 2070, respectively. We observed that the combined effect of climate and LU-LC change resulted in the reduction in the mean monsoon stream flow of the Betwa River by 16% during 2001–2020 as compared to 1982–2000. In all four CMIP6 climatic scenarios (SSP126, SSP245, SSP370, and SSP585), the mean monsoon stream flow is expected to decrease by 39–47% and 31–47% during 2023–2060 and 2061–2100, respectively as compared to the observed time period 1982–2020. Furthermore, average monsoon rainfall in the catchment will decrease by 30–35% during 2023–2060 and 23–30% during 2061–2100 with respect to 1982–2020.

The long term extreme rainfall event analysis at the catchment scale is crucial for mitigation and prevention of flood like situations and water resource management. This study evaluates the different rainfall events categorized by the India Meteorological Department (IMD) over the lower Kosi and Punpun river basin using the gridded rainfall data of 0.25° × 0.25° resolution for the period of 1979-2018. Further, the linkage between Outgoing Longwave Radiation (OLR) and rainfall events is examined. The daily accumulated rainfall shows large variation over the Kosi river basin in compare to the Punpun basin. The number of days of moderate and heavy rainfall over the lower Kosi River has significantly decreased. In addition, light and moderate rainfall over Punpun River has also decreased; however, it is non-significant. No significant trend in very heavy rainfall is found for both the river basin. It is observed that the number of days in moderate rainfall events shows an acceptable negative correlation with Outgoing Longwave Radiation (OLR) over basins. This study will be helpful for the mitigation and prevention of disasters and sustainable water resource management under changing climate.

We use Soil and Water Assessment Tool (SWAT) to simulate the combined effects of land use/land cover (LU/LC) and climate change on the hydrological response of the Upper Betwa River Catchment (UBRC), a semi-arid region in Central India. We execute this model for two different time periods, 1982–2000 and 2001–2018, using the LU/LC data of 1990 and 2018, respectively. We classified the Landsat satellite images of 1990 and 2018 to obtain the dominant LU/LC classes (water body, built-up, forest, agriculture, and open land) in the catchment. The water body, built-up areas, and cropland have increased by 63%, 65%, and 3%, respectively, whereas forest cover and open land decreased by 16% and 23% in the UBRC from 1990 to 2018. The observed climate data in UBRC shows an increase in the average temperature and decrease in the total rainfall during the period between 1980 to 2018. Once the model is set up, we perform the calibration and validation by using the SWAT Calibration Uncertainty Program (SWAT-CUP). We considered two time periods (1991–1994 and 2001–2007) for the calibration and (1995–1998 and 2008–2014) for the validation. For both these time periods, the calibration and validation result of our model is satisfactory. The output of our calibrated model shows a relative decrease in rainfall (12%), surface runoff (21%), and percolation (9%) in the catchment during the period between 2001–2018 as compared to 1982–2000. Finally, we simulate the surface runoff and percolation in the UBRC using the future climate change scenario. We used the bias-corrected multi-model ensemble of CMIP6 GCMs for four different climate scenarios (2023–2100) by assuming no change in the existing LU/LC. We do this for two different time slices: one from 2023–2060 and the other from 2061–2100. For all the climate scenarios, rainfall and surface runoff in the catchment are expected to decrease by 15–40% and 50–79% as compared to the baseline period of 1982–2018. Percolation in the catchment will have a mixed response. It is expected to decrease by 18% in the middle part of the catchment and increase about 25% in the remaining parts of the catchment.

We propose an innovative methodology to estimate the formative discharge of alluvial rivers from remote sensing images. This procedure involves automatic extraction of the width of a channel from Landsat Thematic Mapper, Landsat 8, and Sentinel-1 satellite images. We translate the channel width extracted from satellite images to discharge using a width–discharge regime curve established previously by us for the Himalayan rivers. This regime curve is based on the threshold theory, a simple physical force balance that explains the first-order geometry of alluvial channels. Using this procedure, we estimate the formative discharge of six major rivers of the Himalayan foreland: the Brahmaputra, Chenab, Ganga, Indus, Kosi, and Teesta rivers. Except highly regulated rivers (Indus and Chenab), our estimates of the discharge from satellite images can be compared with the mean annual discharge obtained from historical records of gauging stations. We have shown that this procedure applies both to braided and single-thread rivers over a large territory. Furthermore, our methodology to estimate discharge from remote sensing images does not rely on continuous ground calibration.

Rainfall indices during the summer monsoon season may be used for assessment of drought and flood characteristics as well as for agricultural practices and water resources. Therefore, rainfall indices are evaluated over the meteorological subdivisions, namely Bihar, East Uttar Pradesh (UP), and West UP, over the eastern Gangetic Plain of India, which is densely populated and largely depends on agricultural production for the livelihood of the population. For the period 1980–2018, the present study considers the gridded rainfall data of the India Meteorological Department (IMD) at a resolution of 0.25° × 0.25° and outgoing longwave radiation (OLR) data of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) at a resolution of 1° ×1°. Rainfall indices including total rainfall in wet days (PRCPTOT) when daily rainfall is greater than or equal to 1 mm, the number of consecutive dry days (CDD) when daily rainfall is ≤ 1 mm, the number of consecutive wet days (CWD) when daily rainfall is ≥ 1 mm, the number of days when daily rainfall is ≥ 10 mm (Rx10) and the number of days when daily rainfall ≥ 20 mm (Rx20) are analyzed over the meteorological subdivisions. The PRCPTOT, CDD, CWD, Rx10, and Rx20 show an increasing/decreasing trend at a 95% confidence level. The rainfall indices CDD and CWD show positive and negative correlations with OLR, respectively, over the meteorological subdivisions.

Groundwater is an important natural freshwater resource and plays a significant role in the socio-economic development of any country. The Betwa River basin in central India has experienced severe exploitation of groundwater resource in the past few decades. About 80 % of groundwater in this region is extracted for the agriculture purpose. Also, the scarcity in rainfall throughout the year and seasonal flow in the Betwa River has increased the agricultural dependence on the groundwater. This has led the Betwa River basin into a major hot spot of groundwater depletion. This study estimates the trend of groundwater level and storage change to assess the groundwater dynamics in the Betwa River basin. We used in-situ groundwater level data for a period between 1987-2018 to calculate the trend in groundwater level using the Seasonal and Trend decomposition using Loess (STL) method. Further, we performed the Ordinary Kriging to understand the spatial and temporal trends of groundwater during the pre-monsoon and post-monsoon. Eventually, we use the water table fluctuation (WTF) method to estimate groundwater storage in the study area. Our results suggest a decline in groundwater storage change as 701 and 626 MCM in the post and pre-monsoon period respectively from 2008-2018. During the same period, we observed that the Betwa basin has experienced about 3-5 m decline in the groundwater level.

Climate and land-use change have altered the regional hydrological cycle. As a result, the mean summer monsoon rainfall has decreased by 10 % over central India during 1950-2015. This study evaluates the combined effect of climate and land-use change on the hydrological response of the upper Betwa River basin in Central India. We use Landsat satellite images from 1990 to 2018 to compute the changes in various land-use types; waterbody, built-up, forest, agriculture, and open land. In the past two decades, we found that the water body, built-up, and cropland have increased by 63 %, 65 %, and 3 %, respectively. However, forest and open land have decreased by 16 % and 23 %. Further, we observed a significant increase in annual average temperature and a decrease in the mean rainfall in the study area during 1980-2018.We then coupled the land-use change with weather parameters (precipitation, temperature, wind speed, solar radiation, and relative humidity) and setup the SWAT (Soil and water assessment tool) model to simulate the hydrological responses in the catchment. We have run this model for two different time steps, 1980-2000 and 1998-2018, using the land-use of 1990 and 2018. Calibration and validation are performed for (1991-1994, 2000-2004) and (1995-1998, 2005-2008) respectively using SUFI-2 method. Our results show that the surface runoff and percolation decreased by -21 and -9 %, whereas evapotranspiration increased by 3 % in the upper Betwa River basin during 2001-2018. A decrease in rainfall, runoff, and percolation will have considerable implications on regional water security.