Forecast and risk analysis of floodplain regarding uncertainty factors

Abstract

Today, it seems necessary to study and analyze the uncertainty in any plan, so that without considering and analyzing the uncertainty, the occurrence of undesired situations whose occurrence challenges the objectives of a plan is unexpected. The uncertainty is an integral part of hydrological and hydraulic models, and a proper evaluation of uncertainties in hydrological and hydraulic models may help avoid risky decisions, and high costs in product life cycle and design of structures. The purpose of this study is to predict and analyze the flood risk zone in different probabilities and investigate the role of uncertainty related to inlet flow hydrograph and Manning’s roughness coefficient in river flood zone in Seymareh river. The first step was flood zoning using the Hydrologic Engineering Center-River Analysis System model (Hec-Ras). Then, using synthetic data generation, 3049-year-old series of synthetic peak discharges were generated for 2-, 5-, 10-, 25-, 50- and 100-year return periods. Finally, using the discharge-probability curve, the probable boundaries of the river flood plain were determined at 90 and 10% probability levels, respectively. The results showed that the higher the degree of uncertainty of inflow discharges, the greater the rate of changes in flood and flood zone. In the next step, parameter space sampling (roughness coefficient of flood zone and channel) was performed using Monte Carlo simulation and the model was run 500 times. The simulated flood zone was evaluated based on observational flood zone using the F factor. The response-level curve obtained from Monte Carlo sampling showed that the highest F performance was when the channel roughness coefficient was 0.046 and the flood plain roughness coefficient was 0.058. Then, the uncertainty was determined using the cumulative distribution function of flood zones of the upper and lower limits. The results showed that taking into account the uncertainty threshold of the discharge in all return periods based on probabilities of 18 to 38% is able to cover all risks arising from inaccurate estimation of the flow rate in each return period.