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.

Desertification processes in arid, semi-arid, and dry sub-humid conditions have been enhanced in recent decades. The geospatial database and associated satellite data can be effectively employed for regional planning to address desertification and land degradation. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model has been used to map environmentally sensitive areas due to desertification in the Satara and Sangli districts of Maharashtra, India. This was achieved by combining Landsat-8 multispectral data, Census data, soil data, and climatic variables like temperature, rainfall, and evapotranspiration. The algorithm of MEDALUS is the geometric mean of four indicators, namely soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and socio-economic quality index (SEQI). The findings indicated that the majority of the study area comes under the potential category of desertification (60.32%) followed by fragile (27.87%) and critical (11.81%). Areas with a high propensity for desertification were found over the low to very low climatic quality and moderate to high soil quality including lower socio-economic quality. The lower socio-economic quality is mainly due to high to very high population density (>100 people/km²), low to moderate illiteracy rate (<16%), and low to moderate work participation rate (<50%) that incentivize unsustainable land use practices. The study provides a valuable tool for understanding and managing natural resources. It offers a detailed analysis of the environmental sensitivity of the study area, taking into account various factors like land use, vegetation cover, slope, and soil erosion potential. The developed comprehensive map of the area helps in identifying the most sensitive regions and developing appropriate conservation strategies. The information obtained from the study can be utilized to develop and implement successful measures to prevent or alleviate desertification, which is crucial for sustaining the health of ecosystems and the welfare of local residents.

Groundwater is the most salient and utilitarian water resource for living organisms. However, major parts of the Ken River Basin (KRB) in Central India are grappling with the overexploitation of groundwater resources, primarily due to extensive agricultural activities, raising problems in achieving Sustainable Development Goals (SDGs) 2 and 6. This study focused on delineating groundwater potential zones (GPZ) by employing remote sensing and GIS-based thematic datasets, complemented by the application of the Analytic Hierarchy Process (AHP). The thematic layers consisting of geomorphology, precipitation, geology, soil texture, lineament density, slope, LULC, and drainage density were considered and further weights were allocated with respect to their water storing capacity and characteristics to groundwater occurrences to develop GPZs. The zones were generated by classifying overlayed maps into four categories namely, very low, low, moderate, and high. The key findings indicated that 13.84%, 62.34%, 23.37%, and 45% of areas were found under high, moderate, low, and very low GPZs respectively. Furthermore, the zonation map was validated with 48 boreholes’ yield, which revealed a noteworthy 77.08% borewells in concurrence with predicted zones. Area Under the Receiver Operating Characteristic (AUROC) curve showcased a commendable 70.1% accuracy using the ROC curve. These results were highly beneficial in formulating sustainable groundwater management plans and policies, contributing towards the attainment of targets outlined in SDGs 2 and 6, particularly in regions resembling the KRB.

Flash droughts are rapidly developing soil moisture deficits that intensify within weeks and pose growing threats to agriculture, ecology, hydropower, water, and food security. Their behaviour in Himalayan basins remains poorly understood due to complex topographic features, highly heterogeneous soil moisture & land-atmosphere energy fluxes, and the coexistence of snow-fed & rain-fed hydrological regimes. Addressing to this gap and supporting Sustainable Development Goals (SDGs) related to zero hunger (SDG 2), clean water availability (SDG 6), and climate action (SDG 13), this study investigates flash drought characteristics and their elevation dependence in the Indian Indus Basin. Multi-source daily root-zone soil moisture (RZSM) datasets from ERA5, SMAP, FLDAS_CA, and GLDAS (versions 2.1 and 2.2) bilinearly interpolated at 0.1° resolution were evaluated against in-situ measurements from the NGARI network of the International Soil Moisture Networks (ISMN), which identified the ERA5-GLDAS2.1-GLDAS2.2 ensemble as the most reliable RZSM data (R² = 0.7003). The validated RZSM data were converted to 8-day (octad) means, and soil moisture percentiles were computed using an empirical Weibull distribution. The RZSM percentile octads were then used to identify flash droughts in the basin. Flash drought events were detected when percentiles declined from ≥40 to ≤20 within three octads at an intensification rate ≥6.5 percentiles per octad, and persisted below the 20th percentile up to eleven octads. From these events, the mean annual and seasonal onset speeds, durations, severities, and frequencies were quantified. Piecewise linear regression with breakpoints selected using the change in Akaike information criterion (ΔAIC) revealed distinct elevation-dependent regimes. Mean annual flash drought severity, frequency, and duration increased from low to mid-elevation zones (up to ~2000 m) and declined toward higher elevations. Mean annual onset speed was maximum (~17-30 percentile/octad) at low elevations, indicating rapid soil moisture depletion under strong atmospheric demand, whereas higher elevations exhibited slower onset (~6.5-17 percentile/octad), likely due to snowmelt-driven soil moisture replenishment and reduced evaporative demand. Similar elevation dependence regimes of flash drought characteristics were observed seasonally, with maximum frequency (~10-15%) and onset speed (~15-20 percentile/octad) in the monsoon, but the highest duration (~6.5-10 octads) and severity (~100-150) in the post-monsoon. These non-linear elevation responses highlighted the critical role of topography in modulating flash drought evolution in the complex Himalayan basin. This study presents the first elevation-based characterization of flash droughts and demonstrates the value of high-resolution reanalysis-based soil moisture data for enhancing flash drought monitoring in data-scarce mountain basins.