Contribution to the assessment of European River Basin Management Plans

5.2 Spatial and temporal resolution

The indicators reflect land use, vegetation, infrastructure and dams at a given instant in time. This can be considered representative of some contiguous years.

The indicators used for pan-European assessment are proposed at a resolution of 1 km to balance detail and generality.

5.3 Input

Land cover is characterized by Corine (currently, version 2006 has been used pending full validation of version 2012).

Infrastructure is characterized using OpenStreetMap¹⁴.

Floodplains are characterized on the basis of morphology and other land surface characteristics following Weissteiner et al., 2013.

5.4 Preliminary assessment

The above indicators have been evaluated for Europe in Pistocchi et al., 2015 (see Figure ). The indicators related to dams and stream barriers are not considered reliable due to the lack of sufficient information.

5.5 Previous applications

None.

5.6 Strengths

Conceptual simplicity and robustness.

Figure – example maps of hydromorphological alteration indicators.

5.7 Weaknesses

Not capturing hydromorphological processes explicitly, but only through proxies of complex phenomena. Dams and stream barriers insufficiently mapped at pan-European scale yet.

5.8 State of play

Improvements to the dams layer underway (also in the context of the AMBER H2020 project). Riparian indicators to be updated using new Copernicus layers¹⁵.
6. Groundwater quality

6.1 Methodology

At European scale, we address the threat to groundwater quality represented by agriculture, as urban areas and contaminated sites are best addressed at a more local scale. We use the EPIC model¹⁶ results regarding leaching of nitrogen to identify areas with higher downward fluxes of fertilizers. Although specific to fertilizers, this indicator is expected to be representative of any soluble chemical applied on agricultural soils, which may eventually percolate to aquifers. So, for instance, we expect that a similar indicator computed for certain pesticides would yield similar maps. Fertilizers, on the other hand, are chemicals for which application rates are much better known, and monitoring data are more abundant.

We selected the biophysical model EPIC because it simulates crop production under different farming practices and operations including fertilization and irrigation application rates and timing and because it considers nutrient losses to the environment (N leaching and runoff). In addition, it has been thoroughly evaluated and applied from local to continental scale (Gassman et al. 2005) and used in global assessments (Liu et al. 2007). Furthermore the model is already integrated in a GIS system working at European scale. For the EPIC run we will use the model-computed annual nitrate leaching below the root zone as an indicator of potential groundwater contamination.

6.2 Spatial and temporal resolution

While EPIC works at daily scale for Europe, we consider the annual leaching load. Model results are referred to a 5 km grid but will be aggregated at subbasin scale as for GREEN.

6.3 Input

A geodatabase was developed to support the application of EPIC for the entire continent. The geodatabase includes all the data required for EPIC modelling: meteorological daily data, soil profile data, crop distribution and management data, and scenario information. The landuse was defined using a map of 1 × 1 built from the combination of CAPRI (Britz, 2004), SAGE (Monfreda et al., 2008), HYDE 3 (Klein Goldewijk and Van Drecht, 2006) and GLC2006 databases.

6.4 Preliminary assessment

The above indicator has been evaluated for Europe in Pistocchi et al., 2015. It is important to note that the EPIC model predicts value at field scale, and the simulated losses are to be considered as below the root zone, not taking into account other processes occurring in the unsaturated and saturated zones, in-stream processes and other factors (groundwater body depth and type, sub lateral flow, impermeable layers, etc.). In some situations the model has shown to underestimate leaching concentrations and loads while overestimating runoff. For this reason, one might think of the total load predicted by EPIC (leaching and runoff) and the concentration in both leaching and runoff water losses as more representative indicators. The indicator, although conceptually well-founded and based on the best available information to parameterize the EPIC model at European scale, requires further refinement to reflect specific conditions, and/or more context-dependent interpretation.

Figure – nitrogen loads (above) and concentrations (below) from agricultural diffuse sources, as modelled by EPIC (average by RBD).

6.5 Previous applications

The model has been used to assess different scenarios in the context of the implementation of the Nitrates Directive by Member States. In particular the following scenarios were tested:

• 
  Optimal mineral fertilization
  
• 
  Introduction of a catch crop
  
• 
  Evaluation of the closed fertilization periods by comparing actual closed periods
  
• 
  Evaluation of the derogation on nitrate leaching
  

6.6 Strengths

Physically based, and sensitive to farming practices in particular timing, type and intensity of fertilization.

6.7 Weaknesses

Parameterization is data intensive and may be limited by lack of data making model predictions incapable to reflect specific local situations. The proxy of leaching fluxes does not account for dilution in the aquifer and further contaminant attenuation below the root zone.

6.8 State of play

Improvements have been done to better consider the denitrification process and N2O emission to the atmosphere.

7. Groundwater quantitative status

7.1 State of play

The LISFLOOD model has been recently upgraded to incorporate a better description of the groundwater component of the water cycle. On the basis of this development, indicators for groundwater quantitative status (essentially, the balance between groundwater recharge and abstractions) are under evaluation. The possibility to compute a reasonable indicator of groundwater quantitative status depends on the availability of robust estimates of groundwater recharge (stemming from the LISFLOOD model within the limits of its calibration) as well as on the possibility to obtain European-scale estimates of the abstractions from groundwater, presently limited by the available Eurostat-reported data.

8. Urban runoff and combined sewer overflows

8.1 State of play

The LISFLOOD model computes urban runoff as 90% of rainfall on urban areas, on a daily basis. Total annual urban runoff may be regarded as a proxy for total annual load of contaminants from urban diffuse sources including the wash-out of roof and road surfaces, where contaminants accumulate during dry weather due to atmospheric deposition and emissions from traffic and other human activities.

Another urban source of pollution is represented by combined sewer overflows (CSOs). Typically, CSOs occur in relatively large urban areas where, during rainfall events, stormwater discharge exceeds a multiple of dry weather (“black”) discharge, so that sewers are unable to convey the total combined discharge. Combined sewers are typically designed to convey between 2 and 10 times the black discharge; beyond these thresholds, dilution was assumed in the past to be sufficient to allow the release of excess flow directly to receiving water bodies.

A CSO indicator is being developed that consists of computing the sum of daily runoff volumes, for each urban area, in excess of a multiple (between 1 and 10) of “black” discharge, estimated on the basis of the area’s population. This is expected to represent a proxy of the actual CSO load.

As urban storms usually develop over a time scale of hours, while LISFLOOD works with daily precipitation, the thresholds of the ratio between runoff volume and “black” water discharge need to be calibrated against evidence of functioning of CSOs.

Both the total urban runoff and the CSO indicator represent loads of contaminants and can be interpreted in terms of concentrations taking into account the diluting discharge of the receiving water body.

9. Other pressure indicators

9.1 State of play

9.1.1 Alien species

The JRC maintains a European Alien Species Information Network (EASIN) that brings together information on alien species in Europe. This provides a basis for the development of possible indicators of ecological pressure from alien species on water bodies. Such indicators are still being explored.

9.1.2 Plastic litter

Plastic litter is addressed in the context of the Marine Strategy Framework Directory. The JRC has started an exploratory project (RIMMEL) on the characterization of plastic litter loads from rivers. The project will try to quantify floating macro-litter loads through rivers to marine waters, by collecting existing data, developing a European observation network, deploying a camera system and using the resulting data to build a statistical inverse model of litter loading based on the characteristics (flows, population, economic factors) of the catchments upstream of the observation points. This would be the first-ever European scale quantification of loads of floating litter to the European seas. As ongoing work, anyway, the assessment of this pressure is not likely to be established well enough in order to be used in the assessment of the 2^nd RBMPs.

9.1.3 Aggregated pressures in coastal and transitional waters

Coastal and transitional waters often suffer from a combination of pollution, morphological and management pressures that are difficult to disentangle. A combined indicator (“Land use simplified index” - LUSI) has been proposed in the past (see Pistocchi et al., 2015) to represent this combination of pressures. This indicator requires further discussion and testing.

References

• 
  Bouraoui F., Grizzetti B., Aloe A., 2009. Nutrient discharge from rivers to seas for year 2000. JRC Report EUR 24002 EN. ISBN 978-92-79-13577-4. pp.72.
  
• 
  Bouraoui F., Grizzetti B., Aloe A., 2011. Long Term Nutrient Loads Entering European Seas. JRC Report EUR 24726 EN Luxembourg. pp.82.
  
• 
  Bouraoui, F., Thieu, V., Grizzetti, B., Britz, W., Bidoglio, G., 2014. Scenario analysis for nutrient emission reduction in the European inland waters. Environmental Research Letters 9.
  
• 
  European Commission, 2007. SEC(2007) 339. Commission Staff working document accompanying document to the Report from the Commission to the Council and the European Parliament on the implementation of the Council Directive 91/676/EEC concerning the protection of the waters against pollution caused by nitrates from agricultural sources for the period 2000-2003. COM(2007)120.
  
• 
  European Environment Agency, 2010. Report on the Status of Environment (SOER) 2010. Thematic Assessment Freshwater Quality.
  
• 
  Grizzetti B., Bouraoui F., Billen G., van Grinsven H., Cardoso A.C., Thieu V., Garnier J., Curtis C., Howarth R., Jones P., 2011. Nitrogen as a threat to European water quality. In The European Nitrogen Assessment, Sutton M., Britton C., Erisman J.W., Billen G., Bleeker A., Greenfelt P., van Grinsven H., Grizzetti B., (eds), Cambridge University Press.
  
• 
  Grizzetti, B., Bouraoui, F., Aloe, A., 2012. Changes of nitrogen and phosphorus loads to European seas. Global Change Biology 18, 769-782.
  
• 
  Grizzetti, B., Passy, P., Billen, G., Bouraoui, F., Garnier, J., Lassaletta, L., 2015. The role of water nitrogen retention in integrated nutrient management: Assessment in a large basin using different modelling approaches. Environmental Research Letters 10.
  
• 
  La Notte, A., Liquete, C., Grizzetti, B., Maes, J., Egoh, B., Paracchini, M., 2015. An ecological-economic approach to the valuation of ecosystem services to support biodiversity policy. A case study for nitrogen retention by Mediterranean rivers and lakes. Ecological Indicators 48, 292-302.
  
• 
  Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M.A., De Vries, W., Weiss, F., Westhoek, H., 2015. Impacts of European livestock production: Nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environmental Research Letters 10.
  
• 
  Liquete, C., Kleeschulte, S., Dige, G., Maes, J., Grizzetti, B., Olah, B., Zulian, G., 2015. Mapping green infrastructure based on ecosystem services and ecological networks: A Pan-European case study. Environmental Science and Policy 54, 268-280.
  
• 
  Malagó A., Venhor M., Gericke A., Vigiak O., Bouraoui F., Grizzetti B., Kovacs A., 2015. Modelling nutrient pollution in the Danube River Basin: a comparative study of SWAT, MONERIS and GREEN models. JRC Report EUR 27676. Publications Office of the European Union. Luxembourg.
  
• 
  Pistocchi A., Aloe A., Bizzi S., Bouraoui F., Burek P., de Roo A., Grizzetti B., van de Bund W., Liquete C., Pastori M., Salas F., Stips A., Weissteiner . C., Bidoglio G., 2015. Assessment of the effectiveness of reported Water Framework Directive Programmes of Measures.Part I – Pan-European scale screening of the pressures addressed by member states. JRC Report EUR 27465 EN ISBN:978-92-79-51888-1. Luxembourg: Publications Office of the European Union. 88 pp.
  
• 
  Pistocchi, A., GIS Based Chemical Fate Modeling: Principles and Applications. Wiley, Hoboken, April 2014, ISBN: 978-1-1180-5997-5, 520 pp
  
• 
  Pistocchi, A., Loos, R., A Map of European Emissions and Concentrations of PFOS and PFOA, Environmental Science & Technology 2009 43 (24), 9237-9244
  
• 
  Pistocchi, A., Marinov, D., Gawlik, B., Pontes, S., Continental Scale Inverse Modeling of Common Organic Water Contaminants in European Rivers. Environmental Pollution, Volume 162, March 2012, Pages 159-167. http://dx.doi.org/10.1016/j.envpol.2011.10.031
  
• 
  Weissteiner C. J., Bouraoui F., Aloe A.: Reduction of nitrogen and phosphorus loads to European rivers by riparian buffer zones. Knowledge and Management of Aquatic Ecosystems, 408, 08, 2013. DOI: 10.1051/kmae/2013044
  

List of abbreviations

BOD = biochemical oxygen demand

CSO = combined sewer overflow

EASIN = European alien species information network

GREEN = geospatial regression equation for European nutrient losses

LUSI = Land use simplified index

RBMP=river basin management plan

RPR = reported percentage (of water bodies) at risk (of not achieving WFD goals)

TSS = total suspended sediments

WFD = Water Framework Directive 60/2000/EC
List of figures

List of tables

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Supporting legislation


doi:xx.xxxx/xxxx

ISBN xxx-xx-xx-xxxxx-x

1^ http://ec.europa.eu/environment/water/blueprint/index_en.htm; see also JRC supporting studies:

http://ec.europa.eu/environment/water/blueprint/pdf/EUR25552EN_JRC_Blueprint_Optimisation_Study.pdf

http://ec.europa.eu/environment/water/blueprint/pdf/EUR25551EN_JRC_Blueprint_NWRM.pdf

2^ http://publications.jrc.ec.europa.eu/repository/handle/JRC96943

3^ http://cdr.eionet.europa.eu/help/WFD/WFD_521_2016/Guidance/WFD_ReportingGuidance.docx

4^ In general, indicators representing concentration are not pressure indicators; only when they exceed a threshold they can indicate that the pressure is high. We will refer to concentrations as a proxy of the intensity of pressure from specific pollution sources, to be interpreted in the light of available environmental quality standards as far as possible.

5^ http://easin.jrc.ec.europa.eu/

6^ http://www.capri-model.org/

7^ http://publications.jrc.ec.europa.eu/repository/handle/JRC99193

8^https://circabc.europa.eu/sd/a/6a3fb5a0-4dec-4fde-a69d-5ac93dfbbadd/Guidance%20document%20n28.pdf

9^ https://ipchem.jrc.ec.europa.eu/RDSIdiscovery/ipchem/index.html

10^ http://esdac.jrc.ec.europa.eu/content/soil-erosion-water-rusle2015

11^ http://www.eea.europa.eu/data-and-maps/data/waterbase-rivers-10

12^ http://ec.europa.eu/environment/water/blueprint/pdf/EUR25552EN_JRC_Blueprint_Optimisation_Study.pdf

13^ http://publications.jrc.ec.europa.eu/repository/handle/JRC96943

14^ http://www.openstreetmap.org

15^ http://land.copernicus.eu/local/riparian-zones

16^ http://epicapex.tamu.edu/epic/