On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation
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Published:2022-05-24
Issue:10
Volume:26
Page:2733-2758
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ISSN:1607-7938
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Container-title:Hydrology and Earth System Sciences
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language:en
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Short-container-title:Hydrol. Earth Syst. Sci.
Author:
Pelletier AntoineORCID, Andréassian VazkenORCID
Abstract
Abstract. The role of aquifers in the seasonal and multiyear dynamics of streamflow is undisputed: in many temperate catchments, aquifers store water during the wet periods and release it all year long, making a major contribution to low flows. The complexity of groundwater modelling has long prevented surface hydrological modellers from including groundwater level data, especially in lumped conceptual rainfall–runoff models. In this article, we investigate whether using groundwater level data in the daily GR6J model, through a composite calibration framework, can improve the performance of streamflow simulation. We tested the new calibration process on 107 French catchments. Our results show that these additional data are superfluous if we look only at model performance for streamflow simulation. However, parameter stability is improved, and the model shows a surprising ability to simulate groundwater levels with a satisfying level of performance in a wide variety of hydrogeological and hydroclimatic contexts. Finally, we make several recommendations regarding the model calibration process to be used according to the hydrogeological context of the modelled catchment.
Funder
Agence Nationale de la Recherche
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference104 articles.
1. Ardia, D., Arango, J. O., and Gomez, N. G.: Jump-Diffusion Calibration using
Differential Evolution, Wilmott Magazine, 55, 76–79, https://doi.org/10.1002/wilm.10034, 2011a. a 2. Ardia, D., Boudt, K., Carl, P., Mullen, K. M., and Peterson, B. G.: Differential Evolution with DEoptim: An Application to Non-Convex
Portfolio Optimization, R J., 3, 27–34, 2011b. a 3. Ardia, D., Mullen, K. M., Peterson, B. G., and Ulrich, J.: DEoptim:
Differential Evolution in R, version 2.2-5, CRAN [code],
https://CRAN.R-project.org/package=DEoptim (last access: 17 May 2022), 2020. a 4. Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil
moisture and streamflow data in a conceptual rainfall–runoff model, J. Hydrol., 280, 145–161, https://doi.org/10.1016/s0022-1694(03)00229-4, 2003a. a 5. Aubert, D., Loumagne, C., Oudin, L., and Hégarat-Mascle, S. L.: Assimilation of soil moisture into hydrological models: the sequential method, Can. J. Remote Sens., 29, 711–717, https://doi.org/10.5589/m03-042, 2003b. a
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