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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-363
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-363
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Sep 2020

22 Sep 2020

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This preprint is currently under review for the journal HESS.

Predicting Sediment Discharge at Water Treatment Plant Under Different Land Use Scenarios Coupling Expert-Based GIS Model and Deep Neural Network

Edouard Patault1, Valentin Landemaine2, Jérôme Ledun3, Arnaud Soulignac4, Matthieu Fournier1, Jean-François Ouvry3, Olivier Cerdan2, and Benoit Laignel1 Edouard Patault et al.
  • 1Normandie UNIV, UNIROUEN, UNICAEN, CNRS, M2C, FED-SCALE, Rouen, France
  • 2BRGM, 3 avenue Claude Guillemin, BP6009, F-45060 Orléans Cedex 2, France
  • 3AREAS, 2 avenue Foch, F-76460 Saint-Valéry-en-Caux, France
  • 4BRGM, 1039 rue de Pinville, F-34000 Montpellier, France

Abstract. Excessive sediment discharge at karstic springs and thus, water treatment plants, can be highly disruptive. It is essential for catchment stakeholders and drinking water supplier to reduce the impact of sediment on potable water supply, but their strategic choices must be based on simulations, integrating surface and groundwater transfers, and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical description for the modelling process, and they can be seen as a black-box due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new coupled modelling approach with an erosion-runoff GIS model (WaterSed) and a deep neural network. The model was used in the Radicatel catchment (106 km2 in Normandy, France) where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five designed storm projects under current land use and compared to three land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes were used as input data for the deep neural network model. The model structure was found by a classical trial and error procedure, and the model was trained on two significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly-backward chaining nested cross validation revealed that the model is able to generalize on new datasets. Simulations made for the three land use scenarios suggested that ploughing up 33 % of grasslands would not increase significantly sediment discharge at the water treatment plant (5 % in average). In the opposite, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the range of 10–44 and 24–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 to 80 %). The coupled modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plant under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

Edouard Patault et al.

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