Abstract
Groundwater modeling is an efficient tool for the groundwater management. The accuracy of the groundwater model output depends on both quantity and quality of the input data. In many areas, the data scarcity is often mentioned as a common problem of the model setup. Hence, one of the most important issues in groundwater modeling is to find an approach to build valid and operational models with limited data sets, so that the model is able to provide reliable simulation results that can be applied in the decision-making process. In the current project, a groundwater model is developed on the lower Var river valley. Some simplifications and assumptions are made in order to adapt to data scarcity. The model has been validated with a simulation of 3 years, which contains an extreme flood event and several dry periods. The average Nash coefficient for the 6 points is 0.74 and the average value of the mean absolute error is 0.21 m. The model evaluation indicated that the simplifications and assumptions are able to provide relevant results. This numerical model is therefore implemented into a decision support system for the groundwater management in the lower Var river valley, and the simulated results may help the decision-makers to have a better understanding of the behavior of the aquifer during extreme hydrological events.
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Acknowledgements
This research is currently developed within the AquaVar project with the support of Métropole Nice Côte d’Azur, Agence de l’Eau Rhône Mediterranéen, Nice Sophia Antipolis University, Conseil Départemental 06 and Méteo France. The work benefited from the data provided by the Métropole Nice Côte d’Azur, Conseil Départemental 06, Méteo France and H2EA. DHI is acknowledged for the sponsored MIKE Powered by DHI licence files. The authors also wish to thank Prof. Philippe Audra for his valuable inputs.
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Du, M., Zavattero, E., Ma, Q., Gourbesville, P., Delestre, O. (2018). Groundwater Modeling for a Decision Support System: The Lower Var Valley, Southeastern France. In: Gourbesville, P., Cunge, J., Caignaert, G. (eds) Advances in Hydroinformatics . Springer Water. Springer, Singapore. https://doi.org/10.1007/978-981-10-7218-5_18
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DOI: https://doi.org/10.1007/978-981-10-7218-5_18
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