Details

Autor(en) / Beteiligte

• Pilz, Tobias
• Francke, Till
• Baroni, Gabriele
• Bronstert, Axel

Titel

How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures

Ist Teil von

• Water resources research, 2020-08, Vol.56 (8), p.n/a

Ort / Verlag

Washington: Blackwell Publishing Ltd

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• 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 semidistributed, 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. Key Points A flexible simulation environment coupled with dynamic identifiability analysis forms a diagnostic tool for process‐based model building Alternative model configurations in terms of process representation, numerical solver, and parametrization are compiled Identifiability of model structures varies in space and time driven by the governing hydro‐meteorological conditions