Abstract
Conceptual rainfall–runoff models are widely used to understand the hydrologic responses of catchments of interest. Modellers calculate the model performance statistics for the calibration and validation periods to investigate whether these models serve as satisfactory representations of the natural hydrologic phenomenon. Another useful method to investigate model suitability is sensitivity analysis (SA), which investigates structural uncertainty in the models. However, a comprehensive method is needed, which led us to develop a model suitability index (MSI) by combining the results of model performance statistics and SA. Here, we assessed and compared the suitability of three rainfall–runoff models (GR4J, IHACRES and Sacramento model) for seven Korean catchments using MSI. MSI showed that the GR4J and IHACRES models are suitable, having more than 0.5 MSI, whereas the Sacramento has less than 0.5 MSI, representing unsuitability for most of the Korean catchments. The MSI developed in this study is a quantitative measure that can be used for the comparison of rainfall–runoff models for different catchments. It uses the results of existing model performance statistics and sensitivity indices; hence, users can easily apply this index to their models and catchments to investigate suitability.
Highlights
Conceptual rainfall–runoff models are widely used to understand and predict the hydrologic responses of catchments of interest
If the maximum and minimum total sensitivity index (TSI) are on the diagonal line as they are equal for a parameter, that parameter has the same TSI for all periods so these parameters are insensitive regardless of the data period
It is worth noting that the NSElog* target function is a statistical measure, which calculates the overall performance by comparison of modelled and observed streamflow, it sometimes produces ‘type I error’
Summary
Conceptual rainfall–runoff models are widely used to understand and predict the hydrologic responses of catchments of interest. ), sensitivity analysis (SA) (Shin et al ; Van Hoey et al ; Massmann & Holzmann ), uncertainty analysis (Wagener et al ; Blasone et al ; Clark et al ; Hailegeorgis & Alfredsen ; Shin et al ), regionalisation (Li et al ; Singh et al ; Zhang et al ) and climate change (Chiew et al ; Vaze et al ; Vansteenkiste et al a; Shin et al ). These various studies demonstrate the usefulness of conceptual rainfall– runoff models. Hydrologic models with more parameters tend to have higher performance statistics compared to parsimonious models, but more complex models could have a serious interaction between parameters, which causes uncertainty in the results (Shin et al )
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