Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set

Abstract

ABSTRACT This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers’ experience.