Abstract. In recent years, an important development in flood management is a focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research by the multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g. tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial approach for dynamic adaptation of support to the needs of the decision maker. The overall methodology combines various engineering and geoinformation methods to analyse flood risk as well as calculate major damage processes. The main innovation is the application of model-driven concepts, which are promising for loose-coupling of GIS and multidisciplinary models. This paper defines the new system as Model-driven Decision Support System (MDSS) and proposes its framework. Two characteristics differentiate the MDSS are as follows: (1) it is made accessible to a non-technical specialist and (2) it has a higher level of adaptability and reusability. Furthermore, the MDSS was employed to manage the flood risk in Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is reasonable, reliable and flexible, thus has bright prospects of application for comprehensive flood risk management.
Rethinking data‐driven decision support in flood risk management for a big data age
