<scp>M</scp>onte <scp>C</scp>arlo‐based flood modelling framework for estimating probability weighted flood risk

TLDR

The study develops a GPU‑accelerated Monte Carlo framework that estimates probability‑weighted flood risk by running many 2D hydraulic simulations. The framework executes thousands of GPU‑based 2D flood simulations with randomly sampled peak flows to generate spatially resolved hazard maps, demonstrated with 1 % annual probability events and 1,000 runs for the Swananoa River. Compared to a single simulation, the probabilistic approach reduces a 28 % underestimation of risk and, as simulations increase from 1 to 1,000, expands low‑danger and judgment zones by 87.4 % and 36.8 % while raising high‑danger areas by 9.3 %, yielding more accurate and detailed flood‑risk maps.

Abstract

Abstract This study presents a new M onte C arlo‐based flood inundation modelling framework for estimating probability weighted flood risk using a computationally efficient graphics processing unit ( GPU ) two dimensional ( 2D ) hydraulic model. The 2D flood model is programmed in the GPU framework providing a unique ability to run numerous simulations in a short period of time, permitting the integration of 2D hydraulic modelling into M onte C arlo analysis. The framework operates by performing many 2D flood simulations of randomly sampled input parameters to develop a spatially varied flood hazard map. The probabilistic framework is demonstrated using a 1% annual probability flood event and simulating 1000 different flood simulations by randomly selected peak flows of the S wannanoa R iver in B uncombe C ounty, USA . The results, in general, display benefits of probabilistic flood risk approach compared with a single simulation approach. The latter approach underestimated 28% of flood risk relative to the former. As the number of simulations increased from 1 to 1000, areas identified as low danger and judgment zone increased by 87.4% and 36.8% respectively, whereas the high danger zone increased by 9.3%. In conclusion, the new M onte C arlo flood risk modelling framework has the ability to provide improved accuracy of flood risk and greater insight into the spatial distribution of flood risk.