Probabilistic assessment of phenophase-wise agricultural drought risk under different sowing windows: a case study with rainfed soybean.

A new methodology for crop-growth stage-specific assessment of agricultural drought risk under a variable sowing window is proposed for the soybean crop. It encompasses three drought indices, which include Crop-Specific Drought Index (CSDI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The unique features of crop-growth stage-specific nature and spatial and multi-scalar coverage provide a comprehensive assessment of agricultural drought risk. This study was conducted in 10 major soybean-growing districts of Madhya Pradesh state of India. These areas contribute about 60% of the total soybean production for the country. The phenophase most vulnerable to agricultural drought was identified (germination and flowering in our case) for each district across four sowing windows. The agricultural drought risk was quantified at various severity levels (moderate, severe, and very severe) for each growth stage and sowing window. Validation of the proposed new methodology also yielded results with a high correlation coefficient between percent probability of agricultural drought risk and yield risk (r = 0.92). Assessment by proximity matrix yielded a similar statistic. Expectations for the proposed methodology are better mitigation-oriented management and improved crop contingency plans for planners and decision makers.

Indian summer heat wave of 2015: a biometeorological analysis using half hourly automatic weather station data with special reference to Andhra Pradesh.

Heat wave is a hazardous weather-related extreme event that affects living beings. The 2015 summer heat wave affected many regions in India and caused the death of 2248 people across the country. An attempt has been made to quantify the intensity and duration of heat wave that resulted in high mortality across the country. Half hourly Physiologically Equivalent Temperature (PET), based on a complete heat budget of human body, was estimated using automatic weather station (AWS) data of four locations in Andhra Pradesh state, where the maximum number of deaths was reported. The heat wave characterization using PET revealed that extreme heat load conditions (PET >41) existed in all the four locations throughout May during 2012-2015, with varying intensity. The intensity and duration of heat waves characterized by "area under the curve" method showed good results for Srikakulam and Undi locations. Variations in PET during each half an hour were estimated. Such studies will help in fixing thresholds for defining heat waves, designing early warning systems, etc.