Systemic Change in Hydrology: Spatio-temporal parameter variability of the PCR-GLOBWB hydrological model in the Rhine-Meuse basin
Summary
In hydrological modelling, usually one calibration is performed over a certain calibration period before the model is used to simulate future systems and support decision making. This means a constant model structure and parameterization are assumed and potential systemic changes are ignored. However, if the catchment system is subject to changes that are not incorporated in the model, the parameters found in a single calibration might not be applicable to other periods. The aim of this study was, therefore, to identify systemic change in the Rhine-Meuse basin and its potential causes, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. The minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor and Manning’s n were calibrated for 10-year rolling periods between 1901-2010, resulting in 100 optimal parameter sets. The results showed that most parameters changed at the upstream locations when they were calibrated for the different rolling calibration periods. Especially degree day factor showed large variations, ranging between 0.5 and 2.5 times its default value at Basel and Maxau, located at the upstream and middle part of the Rhine basin. In addition, using a rolling calibration increased the model performance up to 24% in comparison with one calibration for the entire 1901-2010 period. Based on correlation analysis, it was found that climate change as well as land use change and river structures are likely causes behind changes in parameter values through time, as these processes are not or only in a simplified manner incorporated in PCR-GLOBWB. This study shows, therefore, that a model calibrated for a certain period does not necessarily represent another period correctly, especially for longer time frames, which should be kept in mind when interpreting discharge predictions.