AquaVar decision support system for water resource management: Lessons learned from the first five years of operation

Fanny Picourlat , Lian Guey Ler , Jérémy Targosz , Paguedame Game , Hézouwé Amaou Tallé , Morgan Abily , Félix Billaud

River ›› 2025, Vol. 4 ›› Issue (1) : 44 -54.

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River ›› 2025, Vol. 4 ›› Issue (1) : 44 -54. DOI: 10.1002/rvr2.120
RESEARCH ARTICLE

AquaVar decision support system for water resource management: Lessons learned from the first five years of operation

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Abstract

Decision support systems (DSS) based on physically based numerical models are standard tools used by water services and utilities. However, few DSS based on holistic approaches combining distributed hydrological, hydraulic, and hydrogeological models are operationally exploited. This holistic approach was adopted for the development of the AquaVar DSS, used for water resource management in the French Mediterranean Var watershed. The year 2019 marked the initial use of the DSS in its operational environment. Over the next 5 years, multiple hydrological events allowed to test the performance of the DSS. The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events (storms Alex and Aline). For a moderate flood, the real-time functionality was able to simulate forecast discharges 26 h before the flood peak, with a maximum local error of 30%. Finally, simulations for the drought period 2022–2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions, which give rise to unprecedented hydrological processes. The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized, addressing various topics such as DSS modularity, evolution, data positioning, technology, and governance.

Keywords

decision support system / distributed physically based models / holistic approach / water resource management

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Fanny Picourlat, Lian Guey Ler, Jérémy Targosz, Paguedame Game, Hézouwé Amaou Tallé, Morgan Abily, Félix Billaud. AquaVar decision support system for water resource management: Lessons learned from the first five years of operation. River, 2025, 4(1): 44-54 DOI:10.1002/rvr2.120

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References

[1]

Chochon, R., Martin, N., Lebourg, T., & Vidal, M. (2022). Analysis of extreme precipitation during the Mediterranean event associated with the Alex storm in the Alpes-Maritimes: Atmospheric mechanisms and resulting rainfall, In Advances in hydroinformatics: Models for complex and global water issues—practices and expectations (pp. 397–418). Springer Nature Singapore.

[2]

Cunge, J. A., & Erlich, M. (1999). Hydroinformatics in 1999: What is to be done? Journal of Hydroinformatics, 1 (1), 21–31.

[3]

Danish Hydraulic Institute (DHI). (2017). MIKE 21 Flow model & MIKE 21 Flood Screening Tool – Hydrodynamic Modure – Scientific Documentation.

[4]

Danish Hydraulic Institute (DHI). (2021). MIKE 11 – A modeling system for rivers and channels, Reference manual.

[5]

Danish Hydraulic Institute (DHI). (2023). MIKE SHE – User Guide and Reference Manual.

[6]

Diersch, H. J. (2001). Treatment of free surfaces in 2d and 3d groundwater modelling-user’s manual/reference manual/white paper. release 4.9. Tech. rep., WASY Ltd, Berlin.

[7]

Diersch, H. J. G. (2013). FEFLOW: Finite element modeling of flow, mass and heat transport in porous and fractured media. Springer Science & Business Media.

[8]

Du, M. (2016). Integrated hydraulic modeling of groundwater flow and river-aquifer exchanges in the lower valley of Var River [Doctoral dissertation, Université Côte d’Azur].

[9]

Game, P. I. (2023). Modélisation hydrologique, hydraulique et hydrogéologique déterministes pour les systèmes d’aide à la décision en temps réel: Application au bassin versant des Paillons, France [Doctoral dissertation, Université Côte d’Azur].

[10]

Giupponi, C., Balabanis, P., Cojocaru, G., F. Vázquez, J., & Mysiak, J. (2024). Decision support tools for sustainable water management: Lessons learned from two decades of using MULINO-DSS. Cambridge Prisms: Water, 2, e4.

[11]

Goharian, E., & Burian, S. J. (2018). Developing an integrated framework to build a decision support tool for urban water management. Journal of Hydroinformatics, 20 (3), 708–727.

[12]

Gorry, G. A., & Scott Morton, M. S. (1971). A framework for management information systems. Sloan Management Review, 13 (1), 56–79.

[13]

Gourbesville, P., & Ghulami, M. (2022). Assessment of smart heating and cooling system based on thermal use of shallow aquifer, In Advances in hydroinformatics: Models for complex and global water issues—Practices and expectations (pp. 1023–1034). Springer Nature Singapore.

[14]

Gourbesville, P., & Ma, Q. (2022). Smart river management: What is next? River, 1 (1), 37–46.

[15]

Gourbesville, P. (2023). Added value of deterministic models in Decision Support Systems. IOP Conference Series: Earth and Environmental Science (Vol. 1136, No. 1, p. 012011). IOP Publishing.

[16]

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91.

[17]

Graham, D. N., & Butts, M. B. (2005). Flexible, integrated watershed modelling with MIKE SHE. In V. P. Singh, & D. K. Frevert (Eds.), Watershed models (pp. 245–272). CRC Press.

[18]

Haagsma, I. G., & Johanns, R. D. (1994). Decision support systems: An integrated approach. In P. Zannetti (Ed.), Environmental systems (II, pp. 205–212).

[19]

Havnø K., Madsen, M. N., & Dørge, J. (1995). MIKE 11 – a generalized river modeling package. In V. P. Singh (Ed.), Computer models of watershed hydrology (pp. 733–782). Water Resources Publications.

[20]

Holz, K. P., Cunge, J., Lehfeldt, R., & Savic, D. (2013). Hydroinformatics Vision 2011, Advances in hydroinformatics: SIMHYDRO 2012–new frontiers of simulation (pp. 545–560). Springer Singapore.

[21]

IGN. (2022). BD ALTI® Version 2.0-Descriptif de contenu. https://geoservices.ign.fr/sites/default/files/2021-07/DC_BDALTI_2-0.pdf

[22]

IPCC. (2023). Summary for Policymakers. In Core Writing Team, H. Lee, and J. Romero (Eds.), Climate change 2023: Synthesis report. Contribution of Working Groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change (pp. 1–34). IPCC.

[23]

Ma, Q. (2018). Deterministic hydrological modelling for real time decision support systems: Application to the Var catchment, France [Doctoral dissertation, Université Côte d’Azur].

[24]

Ma, Q., & Gourbesville, P. (2022). Integrated water resources management: A new strategy for DSS development and implementation. River, 1 (2), 189–206.

[25]

Ma, Q., Chang, S., Lu, G., & Gourbesville, P. (2024). Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France. River, 3, 362–372.

[26]

Picourlat, F., Ler, L. G., Targosz, J., Masselis, G., Dominguez, A. G., Billaud, F., Gourbesville, P., & Roux, P. (2024). A combined pipe and overland flow model to support urban flood risk management: Case study of the espartes watershed. In P. Gourbesville, & G. Caignaert (Eds.), Advances in Hydroinformatics—SimHydro 2023 Volume 1. SimHydro 2023. Springer Water. Springer.

[27]

Poch, M., Comas, J., Rodríguez-Roda, I., Sànchez-Marrè M., & Cortés, U. (2004). Designing and building real environmental decision support systems. Environmental Modelling & Software, 19 (9), 857–873.

[28]

Pons, A. F., Bonnifait, L., Criado, D., Payrastre, O., Billaud, F., Brigode, P., & Cardelli, B. (2024). Consensus hydrologique de la tempête ALEX du 2 et 3 octobre 2020 dans les Alpes-Maritimes, LHB, 110(1).

[29]

Power, D. J. (1997). What is a DSS. The On-Line Executive Journal for Data-Intensive Decision Support, 1 (3), 223–232.

[30]

Price, R. K., & Solomatine, D. P. (2009). A brief guide to hydroinformatics. UNESCOIHE Institute for Water Education.

[31]

Smith, R. E., & Woolhiser, D. A. (1971). Overland flow on an infiltrating surface. Water Resources Research, 7 (4), 899–913.

[32]

Tardieu, J., & Leroy, M. (2003). Radome, le réseau temps réel d’observation au sol de Météo-France. La Météorologie, 8 (40), 40–43.

[33]

Toreti, A., Bavera, D., & Acosta Navarro, J., et al. (2022). Drought in Europe – August 2022 – GDO analytical report, Publications Office of the European Union. https://data.europa.eu/doi/10.2760/264241

[34]

Toreti, A., Bavera, D., & Acosta Navarro, J., et al. (2023). Drought in Europe – August 2023 – GDO analytical report, Publications Office of the European Union. https://data.europa.eu/doi/10.2760/928418

[35]

Wang, M. (2023). Hydrological and hydraulic modelling of for Cagne catchment for a new decision support system within the AquaVar framework [Doctoral dissertation, Université Côte d’Azur].

[36]

Yan, J., & Smith, K. R. (1994). Simulation of integrated surface water and ground water systems-model formulation 1. JAWRA Journal of the American Water Resources Association, 30 (5), 879–890.

[37]

Zavattero, E. (2019). Intégration de modélisation à surface libre dans un système d’aide à la décision: Application à la Basse Vallée du Var, France [Doctoral dissertation, Université Côte d’Azur (ComUE)].

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2025 The Author(s). River published by Wiley-VCH GmbH on behalf of China Institute of Water Resources and Hydropower Research (IWHR).

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