摘要：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 floodclassification–reliability assessment (FCRA) that combines the absoluterelative error, flow classification and uncertainty interval estimated bythe hydrologic uncertainty processor (HUP) to assess the most strikingfeature of an event-based flood: the peak flow. A total of 100 flood events infour catchments of the middle reaches of the Yellow River are modeled withhydrological models over the period of 1983–2009. The vertically mixedrunoff model (VMM) is compared with one physically based model, the MIKE SHEmodel (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.
