Probabilistic Dam Break Flood Mapping via Monte-Carlo Simulations using a 2D Local-Inertial Model
Maria de A. R. A. Castro, Marcus N. Gomes Jr, Peter A. Troch
University of Arizona, Department of Hydrology and Atmospheric Sciences, James E. Rogers Way, 316A, Tucson, 85719, Arizona, United States of America
Abstract: Dam-break flood hazard assessment is essential to enhance preparedness and safeguard downstream areas in case of failure. Deterministic event-based approaches typically do not consider the inherent uncertainty arising from the effects governing a dam-break scenario. A probabilistic dam-break model was developed based on the Monte-Carlo method, coupling a 2D local-inertial hydrodynamic model (HydroPol2D) with Bayesian-generated governing parameters to the dam-break flow propagation problem to capture a set of scenarios with different reservoir initial volumes, breach hydrographs, and terrain roughness. The Tapacurá dam in Pernambuco, Brazil, was assessed under ensemble probabilistic scenarios. To validate the numerical approach, the local-inertial model (HydroPol2D) was benchmarked with the HEC-RAS 2D full momentum model for spatial resolutions of 10 (LiDAR) and 30 meters (Copernicus DEM). Results obtained from the probabilistic maps point out that the expected inundation area tends to increase 6% for all probabilities, on average, as model spatial resolution decreases from 10 m to 30 m (threefold). By this increase in the pixel size, the computational times are reduced, on average, by a factor of three. For both resolutions, several internal points of the domain were assessed, with flood inundation probability results for the 10 m resolution including (i) a school (Prob = 0.4 ± 0.4), (ii) an emergency care unit (Prob = 0.3 ± 0.5), (iii) a public market (Prob = 0.6 ± 0.5), (iv) a historical cinema (Prob = 0.2 ± 0.4), (v) a hospital (Prob = 0.4 ± 0.5), and (vi) a soccer stadium (Prob = 0.05 ± 0.2). The large standard deviations arise from the uncertain nature of the dam-break characteristics, emulated by different fitted probability distribution functions to represent HydroPol2D parameters and boundary conditions.