Impact of dataset size on the signature-based calibration of a hydrological model

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are...

Added value of distribution in rainfall-runoff models for the Meuse basin

Why do equal precipitation events not lead to equal discharge events across space and time? The easy answer would be because catchments are different, which then leads to the second question: Why do hydrologists often use the same rainfall-runoff model for different catchments? Probably because specifying and distributing hydrological processes...

Improving model consistency and process realism in conceptual rainfall-runoff models

Hydrological models are used for several purposes, from reservoir management to stormflow prediction. However, their predictive power is often questionable. As most rainfall-runoff models are highly dependent on calibration, models often show deteriorated performance during the validation period. This is caused by too much focus on and trust in...

Process consistency in models: The importance of system signatures, expert knowledge, and process complexity

Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this...