Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9-6.0 °C) and minimum temperatures (1.1-6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980-2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

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.