Author(s): Kai Wang; Min Xu; Mingkai Qian; Shunfeng Peng; Shijin Xu; Fengsheng Li
Linked Author(s): Min Xu
Keywords: Flood forecast; Open source and open shell; Huai River Basin; Deft-FEWS
Abstract: Flood forecasting plays a vital role in decision-making in terms of flood prevention and disaster reduction. However, flood forecasting involves complex processes which are very time-consuming, especially on condition that multiple models and multi-sources data are employed for forecasting. Traditionally most forecasting systems have taken a model-centric approach in their development, which is inflexible to changing model needs and data availability. The challenges for developing a modern hydrological forecasting and warning system are found in the integration of large data sets, specialized modules to process the data, and open interfaces to allow easy integration of existing modelling capacities. This paper introduces open source software, Delft-FEWS developed by Deltares in the Netherlands. Contrary to the most common approach of building a flood forecasting system around a specific model, Delft-FEWS provides a platform for data handling and is open to connect a wide range of monitored and forecasted weather inputs on the one side and of hydrological and hydraulic flood routing models on the other side. In practice, operational forecast can also be automated in Delft-FEWS, which facilitates the forecast procedure. In order to test the system applicability and potential, Delft-FEWS is applied to the Huai River Basin, where several models are used for hydrological and hydraulic forecasts separately. The application shows that Delft-FEWS is a useful and efficient tool for flood forecasting in the Huaihe River Basin, China. Particularly, as an open system – joint development approach, Delft FEWS shows a promising potential for application as a platform in the Huaihe River Basin because of its openness in integration of external models and its powerful capacity in data management, which will facilitate probability flood forecasting for the Huaihe River Basin.
Year: 2013