Articles | Volume 21, issue 8
Special issue:
Opinion article
01 Aug 2017
Opinion article |  | 01 Aug 2017

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

Markus Hrachowitz and Martyn P. Clark

Abstract. In hydrology, two somewhat competing philosophies form the basis of most process-based models. At one endpoint of this continuum are detailed, high-resolution descriptions of small-scale processes that are numerically integrated to larger scales (e.g. catchments). At the other endpoint of the continuum are spatially lumped representations of the system that express the hydrological response via, in the extreme case, a single linear transfer function. Many other models, developed starting from these two contrasting endpoints, plot along this continuum with different degrees of spatial resolutions and process complexities. A better understanding of the respective basis as well as the respective shortcomings of different modelling philosophies has the potential to improve our models. In this paper we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the seemingly competing modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy, although some models may be more suitable for specific applications than others and vice versa, but rather on the way a model is implemented. Based on the premises that any model can be implemented at any desired degree of detail and that any type of model remains to some degree conceptual, we argue that a convergence of modelling strategies may hold some value for advancing the development of hydrological models.

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Short summary
Physically based and conceptual models in hydrology are the two endpoints in the spectrum of modelling strategies, mostly differing in their degree of detail in resolving the model domain. Given the limitations both modelling strategies face, we believe that to achieve progress in hydrological modelling, a convergence of these methods is necessary. This would allow us to exploit the respective advantages of the bottom-up and top-down models while limiting their respective uncertainties.
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