Comprehensive risk assessment of drought disasters from the perspective of multi-source geospatial big data: evidence from China's grain production bases

ABSTRACT

Liaoning Province, a major grain production base in China, has faced increasingly frequent extreme drought events under global climate change, impacting local economic and social sustainability. Effective prevention requires comprehensive risk assessments. However, existing risk assessment studies often suffer from low spatial resolution and limited integration of geographic big data. This study integrates multi-source geographic big data, using ten indicators across risk, vulnerability, and exposure dimensions. A comprehensive drought disaster risk assessment model was established by combining the analytic hierarchy process (AHP) and the entropy weight method. Theil–Sen median analysis evaluated drought risks from 2001 to 2021 and predicted future trends. Results revealed spatial heterogeneity in drought risks, with ‘higher in the west and north, lower in the east and south’ distribution. Chaoyang City, in the western hilly region, had the highest risk, with a vulnerability index above 0.65, while Panjin City in the east showed lower risk and a vulnerability index below 0.45. Over 20 years, the overall risk declined across the province. This method aligns with actual drought losses, validating its effectiveness and enhancing understanding of drought risk patterns to mitigate impacts.