Stochastic Event Flood Model

RTI’s Stochastic Event Flood Model (SEFM) is a purpose-built probabilistic flood-hazard platform designed to support modern dam safety and portfolio risk assessments. SEFM simulates thousands of plausible flood events that vary in magnitude, timing, and antecedent watershed conditions to generate hydrologic hazard curves for peak flow, inflow volume, and reservoir stage.

These curves provide the robust flood-loading inputs required for Semi-Quantitative and Quantitative Risk Analyses (SQRA and QRA), enabling defensible, risk-informed decision making and prioritization of risk-reduction investments.

Why a stochastic event approach?

Traditional deterministic design floods—such as the Probable Maximum Flood (PMF) or selected design storms—can overlook the probabilities of flood events that drive key failure modes or life-loss risk. SEFM addresses this gap by incorporating the full range of precipitation frequency information along with antecedent watershed conditions, rain-on-snow dynamics, and temperature variability. Using a Monte Carlo simulation framework, SEFM captures the natural variability and uncertainty in watershed response to produce transparent, reviewable estimates of flood probability, including extremely rare events. 

SEFM is particularly valuable:

●    Dam safety risk assessments for watersheds with complex or highly variable hydrologic behavior 
●    Capturing extreme but plausible flood loadings that deterministic approaches may under-represent 
●    Incorporating seasonal processes, including snowpack and temperature effects in mountainous basins 
●    Providing defensible flood-hazard inputs for SQRA and QRA workflows 

Proven Applications for the Stochastic Event Flood Model

RTI engineers routinely apply SEFM to support federal agencies and major power utilities to evaluate flood risk for individual and large portfolios of dams. We can integrate a variety of hydrologic and hydraulic models into SEFM to develop probabilistic flood loading relationships for many use cases that can benefit from risk-informed decision making.