Assessment of Climatic Drivers and Meteorological Drought Risk Mapping Using a Multi-Criteria Decision-Making Approach in Peri-Urban Bangalore

Extreme climate change and atmospheric variations amplifies common natural disasters like floods and droughts. This study provides key information for sustainable development with analysis of climatic factors causing drought, using linear correlation, multiple linear regression, and follows an expert knowledge-based multi-parametric analytical hierarchy process (AHP). It offers insights into the most influential factor in four different stations in the peri-urban areas of Bangalore urban district, along with risk percentage mapping for the area crucial for sustainable urban development. Results revealed precipitation to be the leading factor in all seasons at the four stations, except at the Kengeri station. In contrast, in the winter season, relative humidity was the leading factor. The other important factor for Yelahnka and Kengeri stations turned out to be the average temperature, except for the winter season in Kengeri. At Anekal station, average temperature was the second most influential factor during the post-monsoon and summer seasons, while relative humidity ranked second during winter and monsoon seasons. At Kuguru station, relative humidity was a crucial factor during winter, summer, and post-monsoon seasons, whereas wind speed was the second most important factor during the monsoon season, with annual average temperature being the second leading factor overall. The drought susceptibility map is constructed based on four influencing climatic factors i.e., precipitation, average temperature, relative humidity, and windspeed. Seasonal monitoring can be crucial for understanding and predicting moisture conditions. This study helps in the appropriate identification of climatic factors, which improves the accuracy in weather forecasting, and mapping will help inform decisions related to agriculture, drought management, and water resources, thereby integrating climatic variability into sustainable development.