Adaptive flood hazard index: a probabilistic framework for flood risk assessment
摘要
Climate change and global warming are expected to worsen in the future, significantly increasing the severity and frequency of extreme events. This study specifically focuses on floods, which are considered a complex natural hazard. Flooding poses substantial risks to ecosystems, communities, and economies worldwide. In this regard, flood monitoring and forecasting play a vital role in developing effective flood mitigation policies. Although several studies have been conducted in this context, accurate flood forecasts remain elusive. This research proposes a novel index: the adaptive flood hazard index (AFHI), designed to monitor floods more accurately and effectively. The framework of AFHI is based on three phases: (1) computation of standardized precipitation index (SPI) using the K components Gaussian mixture model (KCGMM) to classify the characteristics of the flood, (2) assessment of first-order steady-state probabilities (SSPs) of the Markov chain, (3) development of AFHI by incorporating the severity and probabilities of flood using SSPs. The research utilizes precipitation data from 22 CMIP6 GCMs across 94 locations in Pakistan, spanning longitudes