On the criteria of model performance evaluation for real-time flood forecasting

Ke-Sheng Cheng,Yii-Chen Wu,Yi-Ting Lien,Yuan-Fong Su

doi:10.1007/s00477-016-1322-7

Abstract

Model performance evaluation for real-time flood forecasting has been conducted using various criteria. Although the coefficient of efficiency (CE) is most widely used, we demonstrate that a model achieving good model efficiency may actually be inferior to the naïve (or persistence) forecasting, if the flow series has a high lag-1 autocorrelation coefficient. We derived sample-dependent and AR model-dependent asymptotic relationships between the coefficient of efficiency and the coefficient of persistence (CP) which form the basis of a proposed CE–CP coupled model performance evaluation criterion. Considering the flow persistence and the model simplicity, the AR(2) model is suggested to be the benchmark model for performance evaluation of real-time flood forecasting models. We emphasize that performance evaluation of flood forecasting models using the proposed CE–CP coupled criterion should be carried out with respect to individual flood events. A single CE or CP value derived from a multi-event artifactual series by no means provides a multi-event overall evaluation and may actually disguise the real capability of the proposed model.

Highlights

Like many other natural processes, the rainfall–runoff process is composed of many sub-processes which involve complicated and scale-dependent temporal and spatial variations
The coefficient of efficiency (CE) is most widely used, we demonstrate that a model achieving good model efficiency may be inferior to the naıve forecasting, if the flow series has a high lag-1 autocorrelation coefficient
coefficient of persistence (CP) of the artifactual series is positive whereas two events are associated with negative CP values

Summary

Introduction

Like many other natural processes, the rainfall–runoff process is composed of many sub-processes which involve complicated and scale-dependent temporal and spatial variations It appears that even less complicated hydrological processes cannot be fully characterized using only physical models, and many conceptual models and physical models coupled with random components have been proposed for rainfall–runoff modeling (Nash and Sutcliffe 1970; Bergstrom and Forsman 1973; Bergstrom 1976; Rodriguez-Iturbe and Valdes 1979; Rodriguez-Iturbe et al 1982; Lindstrom et al 1997; Du et al 2009). In addition to pure physical and conceptual models, empirical data-driven models such as the artificial neural networks (ANN) models for runoff estimation or forecasting have gained much attention in recent years These models usually require long historical records and lack physical basis. This requirement cannot be met, as many hydrologic records do not go back far enough (ASCE 2000)

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