Multicriteria assessment framework of flood events simulated with vertically mixed runoff model in semiarid catchments in the middle Yellow River

Li, Dayang; Liang, Zhongmin; Zhou, Yan; Li, Binquan; Fu, Yupeng

Flood forecasting in semiarid regions is always poor, and a single-criterion assessment provides limited information for decision making. Here, we propose a multicriteria assessment framework called flood classification–reliability assessment (FCRA) that combines the absolute relative error, flow classification and uncertainty interval estimated by the hydrologic uncertainty processor (HUP) to assess the most striking feature of an event-based flood: the peak flow. A total of 100 flood events in four catchments of the middle reaches of the Yellow River are modeled with hydrological models over the period of 1983–2009. The vertically mixed runoff model (VMM) is compared with one physically based model, the MIKE SHE model (originating from the Système Hydrologique Européen program), and two conceptual models, the Xinanjiang model (XAJ) and the Shanbei model (SBM). Our results show that the VMM has a better flood estimation performance than the other models, and the FCRA framework can provide reasonable flood classification and reliability assessment information, which may help decision makers improve their diagnostic abilities in the early flood warning process.

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Zitierform:

Li, Dayang / Liang, Zhongmin / Zhou, Yan / et al: Multicriteria assessment framework of flood events simulated with vertically mixed runoff model in semiarid catchments in the middle Yellow River. 2019. Copernicus Publications.

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