Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence

Drought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding patterns drought predicting likelihood inter-seasonal persistence can provide substantial operational guidelines for water resource management agricultural production. This study examines by identifying frequency in northeastern region Pakistan. The Standardized Precipitation Index (SPI) a three-month time scale used to examine meteorological drought. Furthermore, Bayesian logistic regression calculate probability odds ratios occurrence current season, given previous season’s SPI values. For instance, at Balakot station, summer-to-autumn value ratio significant (6.78). It shows that unit increase summer season will cause 5.78 times autumn occurrence. average varies from 37.3 89.1%, whereas 21.9 91.7% region. Results indicate some areas region, like Kakul Garhi Dupatta, are more prone vulnerable persistence. results reveal negative relationship between spring winter SPI, demonstrating overall winter-to-spring less Overall has concluded region’s forecast challenging due uncertain However, model provides accurate precise regional forecasts. outcome present scientific evidence develop early warning systems manage crops