The Urban Heat Island (UHI) phenomenon significantly affects us by exacerbating heat discomfort, increased energy consumption, and urban air pollution. The severity of UHI associated with heat waves and heat stress-related mortality is now one of the major concerns, particularly in densely populated cities. Therefore, the significance of the UHI has emerged as a crucial issue due to cities’ fast growth and urban development, necessitating a comprehensive review of the present status of UHI research, particularly concerning regional comparisons. This paper delineates the characteristics of UHI, focusing on its intensity, impact, determining factors, and potential mitigation strategy. It synthesizes an insight into important aspects of UHI from a comprehensive analysis of over 400 national/international research articles. The findings indicate a lack of UHI research studies in the central and eastern regions of the country, bringing out the need for further investigation in these areas. The observed UHI intensity varies across the country between 2 and 10 °C, with the northwest seeing a more pronounced temperature gradient. Following a detailed review, a few suggestions for future research to minimize the impact of UHI on public health, energy consumption, and the economy are proposed, as are strategies for mitigation. While studies on UHIs in India have primarily relied on observational data, there is still a substantial need for more research on employing numerical model-based and machine-learning approaches. Furthermore, the availability of mitigation research on Indian cities is limited. Additional research is needed to ascertain the intricate mechanisms behind the UHI effect on cities vulnerable to various climatic risks and hazards.

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km²) was validated against the satellite-based burnt area (1.07 km²) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.

Flood Response System (FRS) is a network-enabled solution developed using open-source software. The system has query based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. FRS effectively facilitates the management of post-disaster activities caused due to flood, like displaying spatial maps of area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of damage. The inputs to FRS are provided using two components: (1) a semi-automated application developed indigenously, to delineate inundated areas for Near-Real Time Flood Monitoring using Active Microwave Remote Sensing data and (2) a two-dimensional (2D) hydrodynamic river model generated outputs for water depth and velocity in flooded areas for an embankment breach scenario. The 2D Hydrodynamic model, CCHE2D (Center for Computational Hydroscience and Engineering Two-Dimensional model), was used to simulate an area of 600 km² in the flood-prone zone of the Brahmaputra basin. The resultant inundated area from the model was found to be 85% accurate when validated with post-flood optical satellite data.

The Indian subcontinent is annually affected by floods that cause profound irreversible damage to crops and livelihoods. With increased incidences of floods and their related catastrophes, the design, development, and deployment of an Early Warning System for Flood Prediction (EWS-FP) for the river basins of India is needed, along with timely dissemination of flood-related information for mitigation of disaster impacts. Accurately drafted and disseminated early warnings/advisories may significantly reduce economic losses incurred due to floods. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. HPC, remote sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. The model is open-source, supports geographic file formats, and is capable of simulating rainfall run-off, river routing, and tidal forcing, simultaneously. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta, 9225 sq km) with actual and predicted discharge, rainfall, and tide data. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time.

Forests are vital for life on Earth but are threatened by forest fires, which have significant impacts on climate change both locally and globally. This study examines a forest fire that lasted from 15 to 26 February 2019 in Karnataka, India, using the Weather Research and Forecasting model with Chemistry (WRF-Chem) model to analyze the effects and atmospheric spread of fire-emitted aerosols. Model simulations are analyzed to understand the horizontal and vertical transport and radiative effects of the fire. The results show high aerosol levels and smoke particles reaching up to 3.5 km altitude and above. The fire raised near-surface air temperatures by ~1–1.5 °C. The net atmospheric forcing due to the fire over the affected area ranged from approximately 10 to 14 W/m², resulting in heating rates between about 0.002 and 0.005 K/day in the impacted region.