Climate change-induced changes in weather patterns call for the development of hydrological models that perform well under increasingly extreme and varied conditions. Multiple research studies have demonstrated that hydrological models perform poorly when applied to climate conditions that differ from those during the calibration period of the model (Duethmann, Bloschl, & Parajka, 2020). The need for robust hydrological models was emphasised after the Geul catchment, and large parts of Belgium and Germany flooded in July 2021. During this flood, the hydrologists from Waterboard Limburg (WL) indicated that the current model would not have been able to correctly forecast the flood under such an extreme rain event anyway (Expertise Netwerk Waterveiligheid, 2021).
The underlying assumption in this research is that hydrological models cannot perform to the same standard during changing weather conditions because they are overfitted during the calibration period. Meaning that the parameter values are the mathematical best fit for streamflow predictions, but do not represent the internal hydrological processes between precipitation and discharge. One way of trying to improve the internal processes of a hydrological model is to create more relations between these processes and external measurements (Kirchner, 2006). In this research, a secondary calibration data set is used, namely evaporation data.
There are two goals in this thesis, first building a hydrological model of the Geul which represents the streamflow response of the catchment in a meaningful way. Second, comparing if a difference in calibration methodology, specifically comparing calibration on discharge with calibration on both discharge and evaporation, would improve the predictive power of the model. These goals are set up to be able to answer the main research question of this thesis:
Is the predictive power of a discharge calibrated hydrological model of the Geul catchment in the Netherlands, greater than the predictive power of an evaporation calibrated model, in addition to discharge?
The methodology used in this thesis to calibrate and evaluate is Generalised Likelihood Uncertainty Estimation (GLUE), with NSE efficiency as objective function.
The first goal, building a hydrological model of the Geul which represents the stream flow response of the catchment satisfactory, is successfully reached. The model calibrated on streamflow (Model Q) and the model calibrated on both streamflow and evaporation (Model QE) were able to predict streamflow satisfactorily, with DeQ scores between 0.632 and 0.649 across all runs.
The second goal was to compare the performance of Model Q and Model QE. Model QE outperformed Model Q on monthly runoff coefficient, monthly average evaporation and cumulative evaporation, increasing the NSE score of Model Q of the monthly runoff coefficient from 0.774 to 0.801, the NSE log score of the monthly average evaporation from 0.884 to 0.891 and the cumulative evaporation from 0.760 to 0.774. However, Model QE did not outperform Model Q on streamflow.
Therefore, it cannot be concluded that additional calibration on evaporation data increases the predictive power for the streamflow of the model. However, the model now more accurately represents the observed evaporation data, without trading this in for less predictive power of streamflow. Therefore, Model QE can be assumed to be a more accurate description of the hydrological processes in the Geul catchment than Model Q.