Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments

By L. Oudin, Vazken Andréassian, Charles Perrin, C. Michel, N. Le Moine. 2008

551 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3216a6e11c231512ede2cd6cb9abc19736ef6d23

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches (2014, background): “The most notable studies have had some diverging results, which are part of the reason why more conclusive evidence is required [Merz et al., 2006; Oudin et al., 2008].”
Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, methodology): “…in temporal transferability mirror those of spatial transferability, whereby rainfall-runoff models are developed for ungauged catchments using parameters calibrated at suitable donor sites identified based on physical similarity and/or spatial proximity [Oudin et al., 2008; Parajka et al., 2013].”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “, 2009; Pokhrel and Gupta, 2010] and regionalization [Yadav et al., 2007; Oudin et al., 2008; Bulygina et al., 2011] shows promise.”
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration (2013, background): “…where insufficient data for model calibration (e.g., Koren et al., 2003; Duan et al., 2006; Winsemius et al., 2009) or only comparatively unreliable regionalization tools are available (e.g., Wagener and Wheater, 2006; Bárdossy, 2007; Parajka et al., 2007; Oudin et al., 2008; Laaha et al., 2014).”
Large-sample hydrology: a need to balance depth with breadth (2013, background): “One reason for such disagreement, though not the only one (see Oudin et al., 2008) is the likely use of insufficiently large catchment samples, which would cause the conclusions to be overly dependent on differences in the characteristics represented by each data set.”