Parameter uncertainty assessment of a flood forecasting model using multiple objectives

Abstract

The calibration of hydrological models used for flood forecasting is a difficult task that has received widespread attention. Assessing the uncertainty of flood forecasting models is fundamental to properly support decision-making activities. Because of the demand of the different decision-making objectives of a flood management system, we assess the uncertainty of flood forecasting models from a multiobjective perspective. Multicriteria decision analysis (MCDA) is adopted to evaluate the flood likelihood within a generalised likelihood uncertainty estimation (GLUE) framework. By combining four criteria representing different flood characteristics, the acceptability of the simulations is fully examined. The method is applied to an uncertainty analysis of a variable infiltration capacity model for the Xitiaoxi catchment. The results of 10,000 Monte Carlo simulations show that no single criterion can describe the characteristics of floods. Compared with two single-criterion methods using the Nash–Sutcliffe efficiency, the multicriteria method shows advantages not only in assessing the prediction bounds but also in providing median GLUE estimates. The results indicate that a single criterion may be inadequate in parameter identification within the GLUE framework and that MCDA is an alternative approach to reduce the prediction uncertainty in flood forecasting.