Probabilistic Risk Assessment of Dam Breach Floods: A Stochastic Framework Integrating Multi-Model Uncertainty and HEC-RAS Coupling

Deterministic empirical formulas often fail to capture the epistemic uncertainties of dam failure mechanisms, leading to biased risk estimations. To address this, we propose the Probabilistic Hydro-Risk Forecasting System (PHRFS), a stochastic framework integrating Latin Hypercube Sampling (LHS) with HEC-RAS 2D hydrodynamic modeling. Applied to the Honghu-Erji Reservoir (Inner Mongolia), an ensemble of 60 stratified scenarios was generated based on a multi-model envelope of breach parameters and simulated over a 12.5m ALOS PALSAR DEM using the Full Momentum Shallow Water Equations with the Eulerian–Lagrangian method (SWE-ELM). The SWE-ELM simulations reveal a heavy-tailed distribution of peak discharge (mean 2.65×104m3/s; max 5.85×104m3/s), significantly exceeding deterministic estimates. Sensitivity analysis identifies a physical dichotomy: breach depth (Db) primarily controls flood magnitude (r≈0.7), whereas formation time (tf) governs the arrival timeline. This temporal uncertainty propagates downstream, creating a distinct longitudinal gradient in SWE-ELM-derived warning time—ranging from rapid onset (33.0±19.3min) in proximal zones to a substantial lead time (33.1±4.2h) in the distal Hailar District. Consequently, a moderate coupling (r=0.65) emerges between economic loss and loss of life, while their spatial patterns remain strongly differentiated by warning time and exposure. Upstream settlements face high mortality risks due to insufficient evacuation windows, whereas the downstream urban center faces high economic exposure (∼1.57 billion CNY) but limited life loss (∼12.15 persons). These findings provide a scientific basis for differentiating emergency strategies, shifting from immediate life-saving in upstream reaches to asset protection in downstream areas.