An integrated framework to model nitrate contaminants with interactions of agriculture, groundwater, and surface water at regional scales: The STICS–EauDyssée coupled models applied over the Seine River Basin

International audienceNutrient enrichment from natural and anthropogenic activities is one of the major environmental pollution stressors. This study presents an integrated framework that couples a mesoscale atmospheric analysis system (SAFRAN), an agronomic model (STICS) and a distributed hydro(geo)logic model (EauDyssée) to estimate nitrogen flux at the regional scale. The EauDyssée modeling framework was developed to include nitrate transport from soils to rivers via surface runoff and stream-aquifer interactions. Further, an in-stream nitrate model was developed based on the large-scale river routing of the framework. The utility of the integrated framework was demonstrated on the Seine River Basin (SRB), which has an area of 88,000 square kilometers. The SRB is one of the most productive agricultural areas in France and encompasses the megalopolis of Paris. This basin is a complex hydrosystem with multiple aquifers. The STICS-EauDyssée integrated framework was implemented for a long-term simulation covering 39 years (1971–2010) at a daily time step. Comparison of groundwater nitrate concentrations with observations showed an overall absolute bias of less than 10 mg/L. Model results showed that simulated nitrate flux to rivers highly depend on the inflow produced by surface and subsurface waters. Results also demonstrated that approximately 10% of leaching nitrate flux is delivered to the river network from stream-aquifer interactions. The statistical analysis indicates the modeling system performs better in the eastern region of the SRB in comparison to the western part of the basin, due to the hydrological complexity of the western part of the basin. Comparison of maximum nitrate concentration between dry and wet years (1990 and 2001) showed an increase of 60%, largely driven by more annual water yield in the wet year compared to the dry year