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Last update: May 2021

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The TNTBretagne2020 project


Update and regional application of the TNT2 model to support public policies on nitrogen-pollution remediation



Context and objectives

Non-point-source pollution from agriculture remains an acute issue in many regions in Europe. In Brittany (France), nitrate pollution remains the focus of most remediation efforts, despite more than 30 years of action. A major impact of this pollution is the occurrence of green tides (massive macroalgae blooms) at many coastal sites. Scientific predictions of effects of agroecosystem change on the nitrogen cycle and nitrogen losses can help to design more effective remediation policies.

The main objectives of the TNTBretagne2020 project are:

  • improve the applicability of the TNT2 model to any catchment in Brittany
  • design digital services to collect and organize the data needed to run the model
  • calibrate the model for several catchments that have major environmental issues (e.g. green tides, contaminated water supply.


The TNT2 model is a fully distributed agro-hydrological model designed to model nitrogen transfer and transformation in an agricultural catchment, including farms, fields, and non-agricultural areas (e.g. riparian wetlands, hedges, woods). Based on advanced web-based techniques, all relevant public databases are used to build a GIS project that contains the information needed to run the model. We then calibrate the model and run scenarios. This project is associated with the projects of two partners to connect the TNT2 model to the MARS-3D_Ulves model to simulate green algae growth in bays with green tides.

Expected results

The results will provide a comprehensive overview of the hydrological functioning of catchments in Brittany and their responses to human changes. They are meant to be used primarily by policy makers and stakeholders to design effective actions to decrease nitrogen pollution. They will also be useful for other scientific projects on water resources in Brittany or elsewhere. The data management and visualisation methods developed will also be made available to the public.

SAS staff involved

Patrick Durand, Jordy Salmon-Monviola, Hervé Squividant, Tom Loree, Wafa Malik