Abstract:Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub-watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario.

Watershed management includes methods to create, enhance, and maintain vegetation to reduce run‐off and provide flood control in the watershed. The assignment of priority for watershed management measures requires the use of mathematical techniques to attain the most suitable strategies. In the present study, a framework was presented for assigning the optimal combinations of watershed management measures based on simulation‐based optimisation approach. For this purpose, a one‐dimensional hydrodynamic model was used to calculate the potential damages of different flood scenarios under various combinations of watershed management measures and was coupled with the NSGA‐II multi‐objective optimisation model to provide the optimal Pareto solutions between two conflicting objectives of minimising the investment costs of flood mitigation measures and the expected flood damages of the watershed. The proposed model was then applied to a small watershed in the centre of Iran as a case study, and the optimal trade‐off solutions were calculated for flood risk mitigation. Using these trade‐offs, for each level of funding, decision makers can select the optimal combination of watershed management measures considering decision criteria.

No abstract