Abstract
Abstract. The parameters of hydrological models are usually calibrated to achieve good performance, owing to the highly non-linear problem of hydrology process modelling. However, parameter calibration efficiency has a direct relation with parameter range. Furthermore, parameter range selection is affected by probability distribution of parameter values, parameter sensitivity, and correlation. A newly proposed method is employed to determine the optimal combination of multi-parameter ranges for improving the calibration of hydrological models. At first, the probability distribution was specified for each parameter of the model based on genetic algorithm (GA) calibration. Then, several ranges were selected for each parameter according to the corresponding probability distribution, and subsequently the optimal range was determined by comparing the model results calibrated with the different selected ranges. Next, parameter correlation and sensibility were evaluated by quantifying two indexes, RC Y, X and SE, which can be used to coordinate with the negatively correlated parameters to specify the optimal combination of ranges of all parameters for calibrating models. It is shown from the investigation that the probability distribution of calibrated values of any particular parameter in a Xinanjiang model approaches a normal or exponential distribution. The multi-parameter optimal range selection method is superior to the single-parameter one for calibrating hydrological models with multiple parameters. The combination of optimal ranges of all parameters is not the optimum inasmuch as some parameters have negative effects on other parameters. The application of the proposed methodology gives rise to an increase of 0.01 in minimum Nash–Sutcliffe efficiency (ENS) compared with that of the pure GA method. The rising of minimum ENS with little change of the maximum may shrink the range of the possible solutions, which can effectively reduce uncertainty of the model performance.
Highlights
Hydrological process modelling is an important tool for research on water resource management, flood control and disaster mitigation, water conservancy project planning and design, hydrological response to climate change, and so on (Zanon et al, 2010; Papathanasiou et al, 2015)
According to the characteristics of the box plots, the probability distributions of the calibrated values are normal for parameters CI, SM, and Kc, while those are exponential for other parameters
Despite the fact that both normal and uniform distributions are accepted for parameter KC, the probability distribution of parameter KC is regarded as a normal distribution
Summary
Hydrological process modelling is an important tool for research on water resource management, flood control and disaster mitigation, water conservancy project planning and design, hydrological response to climate change, and so on (Zanon et al, 2010; Papathanasiou et al, 2015). The initial hydrological model was a black-box model in 1932 (Sherman, 1932) and conceptual and physically based models were subsequently put forward in 1960s (Freeze and Harlan, 1969). The three kinds of hydrological models have been significantly improved in recent years, with their structures becoming more mature. The physically based model has a definite physical mechanism of the water cycle and all parameters can be measured in situ (Abbott et al, 1986; Huang et al, 2014). Conceptual models express hydrological processes in the form of some abstract models which come from some physical phenomenon and experience. Some parameters of conceptual models need calibrating. Conceptual models have better performance in modelling the streamflow at the catchment outlet than physically based distributed
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