Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India - Senapati, Ujjal; Das, Tapan Kumar (Environmental science and pollution research international, 2024)
The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km 2 , 186.68 km 2 , 568.69 km 2 , 547.05 km 2 , and 266.89 km 2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit ( R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI ( B = 0.391, p < 0.001) and EI ( B = 0.223, p < 0.001) are positively associated with ADV, ACI is negatively associated with ADV ( B =  − 0.721, p < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.