This study provides an innovative process-based modelling approach using the SWAT model and shows its application to support the implementation of the European environmental policies in large river basins. The approach involves several pioneering modelling aspects: the inclusion of current management practices; an innovative calibration and validation methodology of streamflow and water quality; a sequential calibration starting from crop yields, followed by streamflow and nutrients; and the use of concentrations instead of loads in the calibration. The approach was applied in the Danube River Basin (800,000 km2), the second largest river basin in Europe, that is under great nutrients pressure. The model was successfully calibrated and validated at multiple gauged stations for the period 1995–2009. About 70% and 61% of monthly streamflow stations reached satisfactory performances in the calibration and validation datasets respectively. N-NO3 monthly concentrations were in good agreement with the observations, albeit SWAT could not represent accurately the spatial variability of the denitrification process. TN and TP concentrations were also well captured. Yet, local discrepancies were detected across the Basin. Baseflow and surface runoff were the main pathways of water pollution. The main sinks of TN and TP diffuse emissions were plant uptake which captured 58% of TN and 92% of TP sources, then soil retention (35% of TN and 2% of TP), riparian filter strips (2% both for TN and TP) and river retention (2% of TN and 4% of TP). Nitrates in the aquifer were estimated to be around 3% of TN sources. New reliable “state-of-the-art” knowledge of water and nutrients fluxes in the Danube Basin were thus provided to be used for assessing the impact of best management practices and for providing support to the implementation of the European Environmental Directives.

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The paper is aimed at a methodological development of change-point detection, applicable in identifying abrupt changes in temporal or spatial data sequences. In earlier papers we developed a method for detecting a change in the parameters of a discrete distribution, with the simultaneous estimation of the (deterministic but unknown) distribution parameters before and after the change. In this paper we not only extend this method to the case of normal distributions, but also provide a new algorithm for the iterative refining of the estimation of the change-point, based on a "cleaning" of mixed-up parts of the samples. The appropriate size of reduced part of the sample is analytically calculated for the case of normal distributions. This "cleaning" is combined with our original change-point detection method. Our new algorithm is not only validated on artificial data, but also applied to a real environmental data set collected and analysed by other authors in a seafloor observatory. Our results detecting abrupt changes of bacterial mat coverage of a seafloor area are in harmony with the biological fluctuations and changes in the abiotic environment, analysed recently by other authors using a different method. We also provide a comparison with other existing change-point detection methods: a one-dimensional version of the gradient method widely used for edge detection, and a maximum type statistical method well-known in environmental studies. Although normality conditions of our method are rather restrictive, its application potential for environmental data sets is also demonstrated.

HighlightsAn effective DSS integrating several models and methods has been developed.The E-water tool enable the identification of site-specific agronomic practices for nutrients and water management.Identified optimal solutions take into account food demand thus coping with food security issue.The main features of the DSS are tested by applying it to various scenarios in the Mékrou river basin.

The use of geographical information system (GIS) datasets in combination with mathematical modeling has been proven a powerful tool for modeling refinement at higher tier assessment in all crop situations. This study constitutes the first attempt to predict pesticide exposure in surface-water and groundwater systems of a rice watershed using a combination of GIS and modeling. A rice-cultivated watershed in the region of Lombardy, province of Pavia, Italy was selected. The community of Tromello is bypassed by the river Terdoppio, which along with drainage canals and streams creates a rice watershed of 467 ha, comprising 201 paddies. The watershed was conceptualized using a combination of the rice water quality (RICEWQ 1.6.4v) and river water quality (RIVWQ 2.02) models. Spatial GIS data (land use, soil properties and hydrology), on-site scouting and personal interviews with the farmers were used for modeling parameterization. Application of RICEWQ in each of the paddies provided groundwater predicted environmental concentrations for the herbicides propanil and molinate. Groundwater predicted concentrations did not exceed 0.1 µg/L in any of the propanil-treated paddies, unlike molinate, whose predicted concentrations exceeded 0.1 µg/L in 7 of the 31 molinate-treated paddies. Pesticide mass and water releases from paddies were used as inputs for canals, streams and the river, and the fate of propanil and molinate was simulated with RIVWQ. Comparison of the predicted concentrations of molinate and propanil for the year 1999 at the most downstream point of the simulated segment of the river Terdoppio, with measured values obtained from approximately the same point of the river showed a relatively good agreement. These results indicate that the combination of GIS with validated models can be a useful option for higher tier exposure assessment and modeling refinement in rice-paddy areas.