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Journal Article

How to tailor my process-based hydrological model? Dynamic identifiability analysis of flexible model structures


Pilz,  Tobias
Potsdam Institute for Climate Impact Research;

Francke,  Till
External Organizations;

Baroni,  Gabriele
External Organizations;

Bronstert,  Axel
External Organizations;

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Pilz, T., Francke, T., Baroni, G., Bronstert, A. (2020): How to tailor my process-based hydrological model? Dynamic identifiability analysis of flexible model structures. - Water Resources Research, 56, 8, e2020WR028042.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_24376
In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semi‐distributed, process‐based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro‐meteorological boundary conditions. For instance, we tested the Penman‐Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in‐depth analysis of process implementations and catchment functioning.