This article identifies potential flood hazard areas through multi‐criteria analysis in Allahabad district, India. The study has incorporated eight criteria, namely, flow accumulation, draining capability, elevation, groundwater depth, land use, runoff coefficient, slope, and geology for preparing hazard index. The weights of the criteria were obtained through the analytical hierarchy process (AHP) method based on their relative importance for occurring floods. Finally, a flood hazard index (FHI) was prepared by combining the parameter ratings and corresponding weights. The credibility of the present methodology was tested through validation with the satellite‐based inundation map of August 20, 2016. A normalized difference water index (NDWI) was prepared from Landsat‐8 OLI data and the inundated area was delineated by a binary classification of NDWI based on a threshold calculated following Otsu's method. The analysis found 81% of inundation is associated with high to very high flood hazard zones. Agricultural land is more prone to flood than other land use types. The results showed that the GIS‐based multi‐criteria analysis framework could be effectively applied for flood hazard analysis to support decision making in disaster management.

Hypsometric analysis is an important tool to identify and justify the erosional status, erosional processes, geologic and tectonic influences, and stage of landscape development of a basin. In this study, scale dependence of hypsometric integral (Ea) and hypsometric curve parameters were derived and analysed for various order sub-basins of Sirsa river. Here, Ea of higher order sub-basins are quite scale dependent and controlled by lithology; albeit Ea of all order sub-basins is independent of tectonic uplift. Based on Ea, all fourth and fifth order sub-basins were categorised in three classes. The analysis of hypsometric curve parameters showed that sub-basins of first category (Ea < 0.35) is characterised by upward-concave curve, fluvial process dominated and early old stage landscape. Sub-basins of second category (0.35 < Ea < 0.45) reflects concave-convex curve, dominance of fluvial process and mature landscape; whereas third category sub-basins (Ea > 0.45) are diffusive process dominated and belong to late youth to early mature stage.

Floods, while contributing to soil fertility and agricultural productivity in low-lying deltaic areas and floodplain regions, remain one of the most destructive natural hazards, posing significant risks to human life, property, the economy, and the environment. The annual occurrence of floods in the lower Dwarkeswar River Basin highlights the critical need for flood risk assessment to support effective risk reduction policies. This research employs the analytical hierarchy process within a geographic information system environment to evaluate flood risk, with a focus on Ghatal CD Block in West Medinipur, West Bengal. By integrating flood hazard elements and vulnerability indicators, a comprehensive risk map is developed, delineating high-risk zones such as Ghatal, Erpala, and Mansuka, as well as low-risk areas like Birsingha, Sultanpur, and Dewanchak. In the present study, the flood hazard map was validated using AUC curve, achieving an accuracy of 75%, incorporating data from primary surveyed flood locations (n = 42) within the study area. These ground truth points played a crucial role in assessing the hazard map’s precision. The study extends beyond mere hazard identification, recognizing the importance of vulnerability assessment in understanding the holistic risk landscape. The findings indicate that 36% of the study area falls within high to very high-risk zones emphasizing the necessity of targeted interventions and mitigation strategies. Ultimately, this research contributes to advancing flood risk assessment methodologies and reinforces the importance of informed decision-making in mitigating the multifaceted challenges posed by floods, thereby enhancing disaster resilience and preparedness.

Over the past four decades, India's rapid economic development, urban expansion, and unregulated industrialization have been major contributors to severe air pollution. This study aims to examine the relationship between various modes of urban growth and air pollution in Kolkata Urban Agglomeration (KUA), India's third-largest urban area. Using Google Earth Engine (GEE) and Sentinel-5P satellite data, concentrations of pollutants—CO, NO₂, SO₂, HCHO, and O₃ were analyzed from 2018 to 2023. The study identified annual and seasonal variations in pollutant levels and mapped pollutant hotspots. Additionally, it investigated how different urban growth patterns like infilling, edge expansion, and leapfrog development impact air quality. Results indicate that Leapfrog development significantly increases NO2 (1.46), SO2 (1.70), and HCHO (1.70) concentrations by 28.91%, while infilling impacts CO (1.58) and NO2 (0.88). Edge expansion has a lower impact, significantly affecting O3 (0.92), indicating varying pollutant contributions across urban growth patterns.