Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling - Xuemei Wu; Yuting Zhao; Wenjiang Zhang; Xiaodong Li; Guanghua Qin; Hongxia Li (Engineering Applications of Computational Fluid Mechanics)
Accurate early warning of flash floods is critical for prompt decision-making in mitigating disaster impact. However, most current applications of flash-flood warning are based on deterministic approaches, and the inherent uncertainty that exists has not been fully considered. This study proposed a probabilistic flash-flood warning approach by incorporating hydrological modelling uncertainty. The Monte Carlo (MC)-based parameter selection method, together with probability density analysis, was used in assessing the probability of warning criteria being exceeded. Moreover, an optimal decision rule was introduced to enhance the reliability of the flash-flood warning. The results show that the proposed approach provides more informative results by generating the probability distribution estimation and probabilistic thresholds, enabling the user to choose their own decision rule. The probabilistic approach with the optimal threshold has a better performance (CSI = 0.58) than the deterministic approach (CSI = 0.41), especially in the reduction of the number of false alarms (from 37 to 19 events), which shows better reliability and confidence. The results highlight the improvement of the proposed approach by incorporating the uncertainty in hydrological modelling, which can effectively quantify the potential impact risk and aid decision-making to issue warnings. Specifically, a range of possible outcomes are transformed into actionable decisions for issuing reasonable flash-flood warnings with a lead time of 1–3 h. This study provides new insights into the application of the probabilistic approach in flash-flood warning and is expected to enhance practical applications.