A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Despite the significant progress in probabilistic forecasting science last two decades, particularly quantification of predictive uncertainty (PU), most operational flood early warning systems (FEWSs) continue to be based on deterministic forecasts. Thereupon, additional work is needed demonstrate advantages using PU over enhance uptake forecasts decision-making. In this paper, a Monte-Carlo (MC)-based sensitivity analysis done explore how outcomes peak water level-based and -probabilistic strategies behave when factors controlling forecast quality are perturbed. The reliability evaluated through probability detection (POD) false alarm ratio (FAR) criteria: flooding threshold-based criterion (FTC), new floodplain property-based (FPC) inundation level forecasting. results show that advantage strategy one greater relatively high. robust biases mean variance by maintaining POD FAR stability, while not case for strategy. Likewise, it was concluded if undertaken (FPC), improved would achieve same as FTC, reflecting more demanding FPC criterion. Also, levels correlation acceptable values shown. These provide insight into under several scenarios guidance design FEWSs.