1. Process consistency in models: The importance of system signatures, expert knowledge, and process complexity
- Author
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Chantal Gascuel-Odoux, Tanja Euser, Laurent Ruiz, Jim Freer, Shervan Gharari, Hubert H. G. Savenije, Markus Hrachowitz, Ophélie Fovet, Remko C. Nijzink, Department of Civil Engineering and Geosciences [Delft], Delft University of Technology (TU Delft), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), École des sciences géographiques, University of Bristol [Bristol], UNESCO IHE Inst Water Educ, Delft, Netherlands, Institute for Water Education (UNESCO–IHE), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)
- Subjects
010504 meteorology & atmospheric sciences ,HYDROLOGICAL MODELS ,Calibration (statistics) ,Process (engineering) ,Computer science ,conceptual model ,media_common.quotation_subject ,hydrological signatures ,[SDV]Life Sciences [q-bio] ,0207 environmental engineering ,02 engineering and technology ,Equifinality ,computer.software_genre ,01 natural sciences ,AGRICULTURAL CATCHMENTS ,Consistency (database systems) ,expert knowledge ,RUNOFF-MODEL ,020701 environmental engineering ,Representation (mathematics) ,HESS-OPINIONS ,0105 earth and related environmental sciences ,Water Science and Technology ,media_common ,CLIMATE-CHANGE ,EVENT RUNOFF ,GROUNDWATER DYNAMICS ,UNGAUGED BASINS ,Runoff model ,Conceptual model ,Predictive power ,FLEX-TOPO ,model constraints ,HEADWATER CATCHMENT ,Data mining ,computer - Abstract
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 study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by “prior constraints,” inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter-balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge-driven strategy of constraining models.
- Published
- 2014