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Reducing Structural Uncertainty in Conceptual Hydrological Modeling in the Semi-arid Andes : Volume 11, Issue 10 (31/10/2014)

By Hublart, P.

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Book Id: WPLBN0004011950
Format Type: PDF Article :
File Size: Pages 50
Reproduction Date: 2015

Title: Reducing Structural Uncertainty in Conceptual Hydrological Modeling in the Semi-arid Andes : Volume 11, Issue 10 (31/10/2014)  
Author: Hublart, P.
Volume: Vol. 11, Issue 10
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Jourde, H., Ruelland, D., Dezetter, A., & Hublart, P. (2014). Reducing Structural Uncertainty in Conceptual Hydrological Modeling in the Semi-arid Andes : Volume 11, Issue 10 (31/10/2014). Retrieved from

Description: UM2 – UMR HydroSciences Montpellier, Place E. Bataillon, 34395 Montpellier CEDEX 5, France. The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a multiple-hypothesis framework with a multi-criteria assessment scheme to reduce structural uncertainty in the conceptual modeling of a meso-scale Andean catchment (1515 km2) over a 30 year period (1982–2011). The modeling process was decomposed into six model-building decisions related to the following aspects of the system behavior: snow accumulation and melt, runoff generation, redistribution and delay of water fluxes, and natural storage effects. Each of these decisions was provided with a set of alternative modeling options, resulting in a total of 72 competing model structures. These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain 8 model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modeling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.

Reducing structural uncertainty in conceptual hydrological modeling in the semi-arid Andes

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