A seasonally blended and regionally integrated drought index using Bayesian network theory

Abstract

Abstract Among the list of all‐natural hazards, the unique characteristic of drought is its multiplex nature. Besides, the inherited regional characteristics and seasonal variation of drought make it more complicated. Conventional drought indices are inadequate to integrate seasonal and local elements. Therefore, to integrate seasonal components and regional factors, the present study emphasizes the following features: the existence of multiple drought monitoring indicators, the regional broadcasting of drought‐related statistics, and seasonal fluctuations. The seasonally blended regionally integrated drought index (SBRIDI) based on two‐stage Bayesian network theory comprehensively captures seasonal and regional variability embedded in individual drought indicators and regionally scattered homogeneous meteorological stations. It extracts information from a widely accepted standardized precipitation index and other multiscalar variants (standardized precipitation evapotranspiration index, standardized precipitation temperature index). It offers a better estimate of drought severity and pattern in the whole study region. The application of the SBRIDI is based on six meteorological stations located in the Gilgit Baltistan region of Pakistan. Among 216 time series datasets, the stage I Bayesian network selects 72 seasonally relevant standardized drought indices datasets, and the stage II Bayesian network selects 12 of the seasonally most prominent meteorological station datasets. Temporal graphical evidence shows that the SBRIDI can assess the drought risk and depicts the spatial extent of drought conditions at the regional level. The SBRIDI can highlight hotspots of drought‐prone regions, which could help freshwater management agencies and stakeholders.

Keywords

• Bayesian network
• conditional probability
• drought
• multiscalar drought indices
• regional drought monitoring