Abstract
The objective of this study is to examine the impact of the number of calibration repetitions on hydrologic model performance and parameter uncertainty in varying climatic conditions. The study is performed in a pristine alpine catchment in the Western Tatra Mountains (the Jalovecký Creek catchment, Slovakia) using daily data from the period 1989–2018. The entire data set has been divided into five 6-years long periods; the division was based on the wavelet analysis of precipitation, air temperature and runoff data. A lumped conceptual hydrologic model TUW (“Technische Universität Wien”) was calibrated by an automatic optimisation using the differential evolution algorithm approach. To test the effect of the number of calibrations in the optimisation procedure, we have conducted 10, 50, 100, 300, 500 repetitions of calibrations in each period and validated them against selected runoff and snow-related model efficiency criteria. The results showed that while the medians of different groups of calibration repetitions were similar, the ranges (max–min) of model efficiency criteria and parameter values differed. An increasing number of calibration repetitions tend to increase the ranges of model efficiency criteria during model validation, particularly for the runoff volume error and snow error, which were not directly used in model calibration. Comparison of model efficiencies in climate conditions that varied among the five periods documented changes in model performance in different periods but the difference between 10 and 500 calibration repetitions did not change much between the selected time periods. The results suggest that ten repetitions of model calibrations provided the same median of model efficiency criteria as a greater number of calibration repetitions and model parameter variability and uncertainty were smaller.
Highlights
Conceptual rainfall-runoff models are used for a wide range of purposes including reservoir operations, flood and drought prediction, risk analysis, climate change impact studies, etc. [1,2].These models usually contain parameters that need to be estimated through calibration
The results show that the median difference between the groups of calibration repetitions was very small even for snow root mean square error (RMSE), which was not used in model calibration
While larger volume error (VE) range was observed in drier periods, the largest snow RMSE range for most of the calibration groups was found in the recent period (2013–2018) that had many snow poor winters with shallower snowpacks and on average smaller snow water equivalent (SWE) maximum (Table 1)
Summary
Conceptual rainfall-runoff models are used for a wide range of purposes including reservoir operations, flood and drought prediction, risk analysis, climate change impact studies, etc. [1,2].These models usually contain parameters that need to be estimated through calibration. Many parameters sets with different values of the same parameters can provide similar results in terms of model efficiency, which is termed as “the equifinality principle” [3,4]. The problem of equifinality and its reduction in hydrological modelling has been discussed in many studies (e.g., [3,4,5,6,7]). Previous investigations examined the potential of the Monte Carlo approach, multiple-objective calibration or the influence of the length of data series or climatic variability during calibration/validation periods on the improvement of parameter representativeness and reduction of uncertainty in parameters of the models. Several authors (e.g., [8,9,10,11]) used the Monte Carlo (MC) calibration approach to examine
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