Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.

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Doppler sodar measurements were made at the tropical Indian station, i.e., Gadanki (13.5° N, 79. 2° E). According to wind climatologies, the wind pattern changes from month to month. In July and August, the predominant wind direction during the monsoon season was the southwest. In September, it was the northwest and south. While the winds in November came from the northeast, they came from the northwest and southwest in October. The winds in December were out of the southeast. The diurnal cycle of winds at 60-m above the ground was visible, with disturbed wind directions in September and October. This may be connected to the Indian subcontinent’s southeastern monsoon recession. To better understand the monsoon circulation on a monthly basis, the present work is innovative in that it uses high-resolution winds measured using the Doppler sodar at the atmospheric boundary layer. The convergence of a sea breeze and the background wind might result in a sudden change in wind direction, and forecasting such a chaotic atmospheric event is crucial in the aviation sector. As a result, the wind shear that is produced may pose a serious threat to airplanes that are landing. In the current study, we present a few cases of sea breeze intrusions. The physics underlying these intrusions may help modelers better understand these chaotic wind structures and use them as inputs in their models. Based on surface-based atmospheric characteristics, there have been two reports of deep sea breeze intrusions that we report in this research. The sea breeze days were marked by substantial (moderate) drops in temperature (dewpoint temperatures) and increased wind speed and relative humidity. The India Meteorological Department (IMD) rainfall data showed a rise in precipitation over this location on 23 July (4.8 mm) and 24 July (9.5 mm) when sea breeze intrusions over Gadanki were noticed. Sea breeze intrusions could have brought precipitation (intrusion-laden precipitation) to this area due to conducive meteorological conditions. A simple schematic model is proposed through a diagrammatic illustration that explains how a sea breeze triggers precipitation over adjacent locations to the seacoast. The skew-T log-P diagrams have been drawn using the balloon-borne radiosonde measured atmospheric data over Chennai (a nearby location to Gadanki) to examine the thermodynamic parameters to gain insights into the underlying mechanisms and meteorological conditions during sea breeze intrusion events. It is found that the convective available potential energy (CAPE), which is presented as a thermos diagram, was associated with large values on 23 July and 24 July (898 J/kg and 1250 J/kg), which could have triggered thunderstorms over Chennai.