In recent decades, major growth is observed in wildfire incidents across the globe. These ecological disasters, triggered by natural and/or anthropogenic factors, can have long-lasting effects on the environment, ecosystems, and biodiversity. Advancements in remote sensing technology provided an impetus to forest fires research, enabling precise determination of the geographical locations susceptible to fire and assess fire risk. This study focuses on India, whose total forest cover (71.22 Mha) is about 21.67% of the country’s total geographical area. Around 90% of the forest fires in India are attributable to anthropogenic factors. Therefore, the generation of a forest fire risk map (FRM) is essential for devising strategies to mitigate/manage forest fires and avert their disastrous impacts. An attempt is made to develop FRM for the Budhabalanga river basin, which contains the Similipal national park, a part of the UNESCO World Network of Biosphere Reserves. For this purpose, covariates affecting forest fire are identified viz. fuel (vegetation), topographic features (elevation, aspect, and slope), and human activities. The covariates are assigned weights, depicting their relative importance in influencing the fire, byusing Analytical Hierarchical Process (AHP). The AHP is a widely used technique in multi-criteria decision-making models. The Similipal national park has experienced a prolonged dry spell and below-average monsoon in 2020. The consequent dry conditions led to a significant forest fire event in the last week of February 2021, which lasted nearly three weeks. Fine (30m) resolution satellite data (Landsat-8) are used to calculate the Normalized Difference Vegetation Index (NDVI) corresponding to the study area to assess vegetation conditions before the fire event. Furthermore, Cartosat-1 Digital Elevation Model (24m resolution) is used to extract topographic-related information. The FRM generated for the basin using the assigned weights was 80% accurate when validated with 375m resolution NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire point data for the analyzed fire event. Hence, the methodology considered for developing FRM appears promising. It can be extended to other river basins for identifying fire risk zones and devise timely strategies to mitigate fire risk.

Monsoon low pressure systems (LPS) are synoptic scale tropical disturbances that form over the Indian subcontinent along the quasi-stationary trough axis during the monsoon (June to September) period. Around 14 LPS form every year, accounting for around 44% of monsoon precipitation and 78% of extreme precipitation events over the country. Many past studies have investigated the influence of various topographical features on the Indian monsoon. This study investigates the influence of the Himalayan and Tibetan orography (HTO) on various LPS-related characteristics/features (genesis location, number, tracks, and intensity). The NCAR Community Earth System Model (CESM1.2.2) is used to study the influence of HTO on monsoon and LPS activity over India. Simulations from CESM1.2.2 are obtained at 0.9°×1.25° horizontal resolution by considering the present-day height (h) of HTO, and altered heights (zero, 0.5h, and 1.5h). A 9.3% increase in the average monsoon precipitation is simulated over India when the height of HTO is increased to 1.5h, while a decrease in the same by 11.5% (44%) is simulated when the height of HTO is reduced to 0.5h (zero). These results are consistent with previous modeling studies. The changes in monsoon precipitation are attributed to a strong (weak) mean meridional temperature gradient (MTG) associated with an increase (a decrease) in the height of HTO and the prevention of cold dry mid-latitude air mixing with the warm humid air over India. Furthermore, we find that the simulated number of LPS per year increases when the height of HTO is reduced. The number of LPS is 17.2, 16.1, 13.6, and 12.4, respectively, in the simulations where the height of HTO is zero, 0.5h, h, and 1.5h. The mean meridional width of the LPS active region also increases when the height of HTO is reduced (Fig. 1). Contrary to the expectation of a southward shift in the LPS median track with a decrease in MTG (when the height of HTO is reduced), a slight northward shift is simulated in the track’s location. We attribute this to an increase (a decrease) in barotropic instability on reducing (increasing) the height of HTO, which results in a larger latitudinal spread in the location of genesis and tracks. The increased (decreased) barotropic instability also causes an increase (a decrease) in the frequency of LPS over the Indian subcontinent.

In recent decades, the intensity, frequency, and variability of extreme events (e.g., heat waves, droughts, severe storms, and floods) have increased globally due to the intensification of the changing climate, land-use/landcover and anthropogenic activities. An increase in extreme events results in severe economic crises and enormous disruptions in various social and environmental sectors. Hence, effective and efficient prediction and mitigation of flood and drought events are desirable. It requires a better understanding of the recent trends and variability of the extremes, which is quite challenging owing to the complex and non-stationary behaviour of the associated covariates (hydroclimatic variables, atmospheric circulation patterns), and their spatiotemporal variability and nonlinear interactions. This study proposes a new Non-stationary Standardized Potential Evapotranspiration Index (NSPEI) to assess the wetness and dryness condition and couples it with an entropy-based measure to quantify the variability of the extremes. NSPEI considers non-stationarities of both precipitation and temperature in addition to their ability to identify extreme events at different time/accumulation scales (multi-scalar). Hence, water deficits/excess could be better assessed over different accumulation periods, which helps identify and monitor various droughts (e.g., agricultural, meteorological) and water saturation conditions (e.g., floods, runoff). Furthermore, as the drought/wetness triggering variables and causes for extreme conditions may vary worldwide, a standardized drought variability index is introduced in this study to assess better drought and wetness variability across the world at multiple timescales (monthly, seasonal, annual and decadal). In addition, the influence of LULC and location indicators (latitude, longitude, and elevation) on drought and wetness variability is assessed across different continents for various time scales and severity of both wetness and dryness conditions. The analysis and outcomes of this study enhance understanding of global drought and wetness variability patterns and provide reliable information on water availability for devising effective water management strategies towards mitigation of the extremes in various continents.