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Abstract:The proper assessment of design flood is a major concern for many hydrological applications in small urban watersheds. A number of approaches can be used including statistical approach and the continuous simulation and design storm methods. However, each method has its own limitations and assumptions being applied to the real world. The design storm method has been widely used for a long time because of the simplicity of the method, but three critical assumptions are made such as the equality of the return periods between the rainfall and corresponding flood quantiles and the selections of the rainfall hyetograph and antecedent soil moisture conditions. Continuous simulation cannot be applied to small urban catchments with quick responses of runoff to rainfall. In this paper, a new flood frequency analysis for the simulated annual peak flows (FASAP) is proposed. This method employs the candidate rainfall events selected by considering a time step order of five minutes and a sliding duration without any assumptions about the conventional design storm method in an urban watershed. In addition, the proposed methodology was verified by comparing the results with the conventional method in a real urban watershed. OPEN ACCESSWater 2014, 6 3842 Keywords: design storm method; continuous simulation method; flood frequency analysis; urban drainage basin

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Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.