Modeling and Risk Analysis of Dam-Break Flooding in a Semi‐Arid Montane Watershed.

Abstract

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from dam-failure event correspond to various breach parameters such as width, slope and formation time. This study aimed to simulate dam-breach failure scenario of Yabous dam (NE Algeria) and analyze its influence on areas (urban and natural environments) downstream the dam. The simulation was completed using the sensitivity analysis method in order to assess the impact of breach parameters on the dam-break scenario. The propagation of flood wave associated to dam-break was simulated using the one-dimensional HEC-RAS hydraulic model. This study ap-plied a sensitivity analysis of three breach parameters (slope, width, and formation time) in five sites selected downstream the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1579.2m3/s at about 8.5km downstream the dam. This study estab-lished the map of flood-prone areas that illustrated zones threatened with the flooding wave trig-gered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height and width revealed positive and similar changes for the increase in adjustments (±25% and ±50%) of breach width and slope in the 5 sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width.

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

“…The developed process offers possibilities for the application of Iber in other types of analyses in which cases need to be systematically executed, such as probabilistic studies or sensitivity analyses [58][59][60][61]. Similar processes are already in application to generate training sets for ML surrogate models in hydrological studies [62][63][64], as well as to analyse the effect of breach properties on the floodplain [65,66].…”