Eutrophication is one of the most common causes of water quality impairment of inland and marine waters. Its best-known manifestations are toxic cyanobacteria blooms in lakes and waterways and proliferations of green macro algae in coastal areas. The term eutrophication is used by both the scientific community and public policy-makers, and therefore has a myriad of definitions. The introduction by the public authorities of regulations to limit eutrophication is a source of tension and debate on the activities identified as contributing or having contributed decisively to these phenomena. Debates on the identification of the driving factors and risk levels of eutrophication, seeking to guide public policies, have led the ministries in charge of the environment and agriculture to ask for a joint scientific appraisal to be conducted on the subject. Four French research institutes were mandated to produce a critical scientific analysis on the latest knowledge of the causes, mechanisms, consequences and predictability of eutrophication phenomena. This paper provides the methodology and the main findings of this two years exercise involving 40 scientific experts.

Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by ''prior constraints,'' inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter-balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge-driven strategy of constraining models.

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Abstract:Nitrate concentrations in streamwater of agricultural catchments often exhibit interannual variations, which are supposed to result from land-use changes, as well as seasonal variations mainly explained by the effect of hydrological and biogeochemical cycles. In catchments on impervious bedrock, seasonal variations of nitrate concentrations in streamwater are usually characterized by higher nitrate concentrations in winter than in summer. However, intermediate or inverse cycles with higher concentrations in summer are sometimes observed. An experimental study was carried out to assess the mechanisms that determine the seasonal cycles of streamwater nitrate concentrations in intensive agricultural catchments. Temporal and spatial patterns of groundwater concentrations were investigated in two adjacent catchments located in south-western Brittany (France), characterized by different seasonal variations of streamwater nitrate concentrations. Wells were drilled across the hillslope at depths ranging from 1Ð5 to 20 m. Dynamics of the water table were monitored and the groundwater nitrate and chloride concentrations were measured weekly over 2 years. Results highlighted that groundwater was partitioned into downslope domains, where denitrification induced lower nitrate concentrations than into mid-slope and upslope domains. For one catchment, high subsurface flow with high nitrate concentrations during high water periods and active denitrification during low water periods explained the higher streamwater nitrate concentrations in winter than in summer. For the other catchment, the high contribution of groundwater with high nitrate concentrations smoothed or inverted this trend. Increasing bromide/chloride ratio and nitrate concentrations with depth argued for an effect of past agricultural pressure on this catchment. The relative contribution of flows in time and correlatively the spatial origin of waters, function of the depth and the location on the hillslope, and their chemical characteristics control seasonal cycles of streamwater nitrate concentrations and can influence their interannual trends.