Abstract
In hydrological modelling, traditionally one calibration was performed over a certain calibration period before the model is used to study the hydrological system. This implies that a constant model structure and parameterization are assumed. However, if the catchment system is subject to changes that are not incorporated in the model, the parameter values found in a calibration period may not be optimal for other periods, which is called systemic change. The aim of this study was to identify systemic change and its possible causes with the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. Systemic change was studied for the main model components, by selecting a key parameter from each component (minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor, Manning's n). These parameters were calibrated for 10-year rolling periods between 1901-2010. The results showed that at the downstream locations, the changes in optimal parameter values were small, while at the upstream locations, the optimal values of most parameters changed considerably over the different rolling calibration periods, signifying systemic change. Especially the degree day factor showed large variations, varying over time between 0.5 and 2.5 times its default value at Basel and Maxau (upstream and middle part of the Rhine basin). Based on correlation analysis, it was found that climate change as well as changes in land use and river structure are possible causes of changes in optimal parameter values through time.
Highlights
The amount of damage caused by riverine flooding has increased over the past years due to population growth and economic development in flood prone areas (Winsemius et al, 2016)
To be able to adapt to the consequences of climate change, more knowledge is needed on how hydrological systems respond to changing conditions (Blöschl et al, 2019)
To be able to identify the temporal stability of the optimal parameter values, the model is calibrated in three different ways: Default model: no calibration is performed and PCR-GLOBWB is run with its default parameterization; One calibration: the model is run with 576 different parameter sets to determine the optimal parameter values for 1901-2010; Rolling calibration: the model is run with 576 different parameter sets to determine the optimal parameter values for each 10-year rolling calibration period (i.e., 1901-1910, 1902-1911, 1903-1912, ..., 20012010)
Summary
The amount of damage caused by riverine flooding has increased over the past years due to population growth and economic development in flood prone areas (Winsemius et al, 2016). Traditionally one calibration with discharge measurements, or possibly additional hydrological information (e.g. Fernandez-Palomino et al, 2020; Ilampooranan et al, 2021; Melišová et al, 2020), is performed over a certain period, after which the model is validated with observations of another period (split-sample test) (Klemeš, 1986; Pechlivanidis et al, 2011) This method implies the modeller assumes that the model equations and parameters are stationary. Fundamental changes in behaviour of the modelled system, represented by the model equations, can occur over time that are not represented by the used model structure or parameterization These changes can not be simulated and may result in model output that deviates from observed values. We refer to this problem as ‘systemic change’, following Verstegen et al (2016)
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