Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives

Drought is a complicated and diverse natural calamity that causes huge global economic losses. Drought, particularly in dominant agricultural areas, is becoming more severe, posing a threat to global food security. Drought stress has a wide range of effects on a region, and it is largely dependent upon climate variability, groundwater availability and soil moisture condition. Droughts are influenced by a wide range of factors, including climatic factors like rainfall and temperature and economic factors like population density, irrigated land, and so on. In India, the recurrence of drought is largely owed to sole physical and climatic susceptibilities. As a result, this research study makes an intensive effort to assess drought vulnerability assessment in India considering meteorological, hydrological, agricultural and socio-economic drought groups. The statistical method of analytical hierarchy process (AHP) and machine learning algorithms of random forest (RF) and Bayesian additive regression trees (BART) were used for respective modelling purposes. The modelling outcomes were assessed using receiver operating characteristics area under the curve (ROC-AUC), mean square error (MSE), root mean square error (RMSE), and the Taylor diagram. The evaluation measure indicates that BART is the most optimal (AUC = 0.901, r = 0.931) followed by RF (AUC = 0.872, r = 0.901). The findings suggest that the method used to determine drought vulnerability in the region is successful, which will aid planners in developing drought mitigation measures.(c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Automated summary

Drought is a complex natural disaster that causes significant global economic losses, especially in agricultural areas, posing a threat to global food security. This study successfully assessed drought vulnerability in India, considering meteorological, hydrological, agricultural, and socio-economic factors, using statistical and machine learning models. The findings indicate the effectiveness of the method and will aid in developing drought mitigation measures.