Abstract
Tightly constraint parameter ranges are seen as an important goal in constructing hydrological models, a difficult task in complex models. However, many studies show that complex models are often good at capturing the behaviour of a river. Therefore, this study explores the trade-offs between tightly constrained parameters and the ability to predict hydrological signatures, that capture the behaviour of a river. To accomplish this we built five models of differing complexity, ranging from a simple lumped model to a semi-lumped model with eight spatial subdivisions. All models are built within the same modelling framework, use the same data, and are calibrated with the same algorithm. We also consider two different methods for the potential evapotranspiration. We found that that there is a clear trade-off along the axis of complexity. While the more simple models can constrain their parameters quite well, they fail to get the hydrological signatures right. It is the other way around for the more complex models. The method of evapotranspiration only influences the parameters directly related to it. This study highlights that it is important to focus not only on parametric uncertainty. Tightly constrained parameters can be misguiding as they give credibility to oversimplified model structures.
Highlights
How complex should a hydrological model be? This question is still unsolved in hydrology
Catchment Modelling Framework (CMF) is one of the few existing modelling frameworks that allows the isolation of the effects of the model structures and processes like the potential evapotranspiration (PET)
When we look at the model performances as indicated by the Kling-Gupta Efficiency (KGE) (Fig. 3) the two most simple model structures Lumped 1 and Lumped 2 seem to perform fairly well, showing only a very small range of the KGE at a high level, both during the calibration and validation
Summary
How complex should a hydrological model be? This question is still unsolved in hydrology. This was noted by other authors[13], who state that many comparisons of lumped and semi-distributed models are hindered by different selections of included processes This can be avoided by using a fixed modelling framework, which standardizes all steps of model structure development. This implies a trade-off between the realism of the model and its ability to constrain its parameters To explore this dilemma, this study will look at five different model structures, ranging from a simple lumped model to a semi-lumped model that takes vegetation and topography into account. The ROPE algorithm[20] is used to calibrate the models, as it is capable of generating parameter sets with a small range of potential parameter values[20] Using those tools, the aim of this study is to explore the trade-offs between the ability of a model structure to constrain its parameters, and the realism of the model structure. Realism is expressed as the performance of a model to simulate a variety of hydrologic signatures[21,22] for which the model has not been calibrated
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