Akash Tiwari

Groundwater is a major drinking water resource supporting nearly half of the world's population but its quality is being compromised by various anthropogenic factors. This study provides an in‐depth analysis of groundwater quality in Hisar district, Haryana, India by integrating hydrogeochemical parameters and advanced analytical techniques. It evaluates the groundwater quality using samples collected from 76 locations (wells, tube wells, and hand pumps) in the pre‐monsoon season. Groundwater Quality Index (GWQI) and Fuzzy GWQI (FGWQI) assessments depicted water quality varying from “unsuitable” to “excellent” across the district. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to isolate the underlying geogenic and anthropogenic factors, while saturation indices computed via PHREEQC confirmed the supersaturation and active precipitation of carbonate minerals. A random forest‐based sensitivity analysis quantified the relative influence of individual parameters on the computed indices. The hydrochemical evaluation indicated that the dominant hydrogeochemical process influencing groundwater quality is water–rock interaction and Ca ^{2 +} –Mg ^{2 +} –HCO ₃ ⁻ is the dominant water type. Chloro‐alkaline index analysis highlighted an exchange of sodium and potassium ions in the groundwater with magnesium and calcium ions in the aquifer matrix. A 5‐year historical validation (2019–2023) using pixel‐wise linear regression trend mapping revealed zones of continuous degradation concentrated in the core urban and intensely cultivated regions. Health risk was also assessed using target hazard quotients and hazard index which indicated associated noncarcinogenic health risks especially for children. The study's findings will help policy‐makers in decision‐making and formulating plans related to the problems associated with groundwater management.

Population growth and wider development activities in a city yield land‐use land cover (LULC) changes. Changes in LULC in turn relate to land surface temperature (LST) changes. This study examines the spatio‐temporal variation in LST in Gaya City, Bihar, India brought about by changes in LULC. It also analyses changes in the village‐wise mean LST located within a 5 km buffer zone of the formal city boundary. The mono window algorithm was employed to calculate LST while further zonal statistics were conducted to examine the village‐level mean LST. Pearson's correlation coefficient method was used in analysing the correlation among different spectral indices between the year 2006 and 2022. ArcMap‐10.8 and SPSS‐14.0 software were used to complete all algorithm and statistical calculations. Our analysis revealed that as built‐up and sandy areas in Gaya City increased by 175 per cent and 54 per cent respectively during the study period (with most of the increment occurring within the city boundary), there was a corresponding increase of more than 9°C in mean LST. The correlation between spectral indices and LST strengthened in 2022 relative to the reference year of 2006.

Rapid and uncontrolled urban growth in the Kumaun Himalayas in absence of proper land use policy has pushed built-up areas towards the tectonically and ecologically sensitive regions, reducing the availability of suitable built-up land while simultaneously increasing the vulnerability of both communities and environment. The identification of areas for sustainable built-up growth is of paramount importance to address the challenges arising from unregulated urban expansion. In this study GIS-based Fuzzy-AHP technique and machine learning algorithms (SVM and BN) were employed to delineate the potential built-up sites selection in Hawalbagh Block, Uttarakhand (India) using nine socio-physical drivers, including slope, aspect, LU/LC, distance to road, distance to drainage, distance to lineament, distance to landslide, distance to settlement, and lithology. The suitability maps generated by the three methods were validated using AU-ROC analysis, which demonstrated that each approach produces outstanding results with AU-ROC values more than 0.90. The comparison of the approaches shows that SVM (AUROC=0.99) outperforms BN (0.95) and GIS-based Fuzzy-AHP (0.90). The suitability maps were classified into five suitability classes. Assuming that very high and high suitability classes are acceptable for built-up expansion, the study identified potential built-up locations in the study region covering an area of 148.86 km2, 85.23 km2, and 55.25 km2 according to the Fuzzy-AHP technique, SVM model, and BN model, respectively. The suitability zonation in this study can serve as a foundation for the development of land-use policy or the formulation of master plans aimed at achieving a sustainable mountain ecology in the Kumaun Himalayas.

This study analyses the trends of changing climatic elements in the hydrological regime of the Indian Himalayan Region with specific focus on Agro‐ecological zone II & III of Himachal Pradesh for the period of 1901−2021. The upper, middle, and lower catchment areas of Sutlej River Basin were studied to reveal regional trends in climatic parameters. The Mann‐Kendall test and Sen Slope analysis were used to estimate annual and seasonal trends and their magnitude. The results showed a significant decreasing trend in the lower catchment area, with a break point year estimated to be 1953 for the entire basin. Two blocks of analysis, 1901−1953 and 1954−2021, showed significant variations. Annual rainfall data revealed a statistically significant decreasing trend at different stations. The lower catchment area received a significant increase in rainfall compared to higher altitude stations. In terms of seasonal variation, the pre‐monsoon season showed a significant decrease, while the entire basin recorded a significant increase in average monthly temperature. The study concludes by generating future time series predictions using an ANN model for the period of 2022−2050. Overall, the study's findings indicate a significant change in climatic variables with signs of increasing monthly temperatures and decreasing annual rainfall.

Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km²) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being.