Modelling Approaches to Link Agricultural Practices and Water Quality in Germany

1. Modelling Approaches to Link Agricultural Practices and Water Quality in Germany martin.bach@umwelt.uni-giessen.de Eionet Workshop „Pollutant Emissions to Water“ 11th – 12th September 2008, EEA ,Copenhagen

2. Agricultural Practices and Water Quality in Germany National scale modelling approaches (Germany) • Nutrients (N, P) • MONERIS • Pesticides • DRIPS • FOOTPRINT

3. MONERIS • Source apportionment tool • Origin: OSPARCOM marine conventions • Implementation WFD context: •  Pressures (nutrient & HM inputs, river load estimation) •  Not: status of water bodies  Responsibility of 16 Federal State (Länder) Water Authorities (monitoring programmes) •  Priorisation of measures (point vs diffuse sources; localization) Source: Behrendt et al. (2008)

4. MONERIS – Nutrient Flux Scheme Ag practices affect MONERIS results Source: Behrendt et al. (2008)

5. N Soil Surface Balance Surplus Frankfurt Hamburg Munich Berlin Regional levels (examples) A) Germany, Districts (NUTS 3), 1999 kg N/ ha Ag Area 21 - 50 51 - 80 81 - 110 111 - 150 151 - 200 201 - 260 B) State Baden-Württemberg,Municipalities (NUTS 5), 1999 (Bach et al., 1999, 2005)

6. Nitrogen Soil Surface Surplus in the WFD context Nitrogen emission into river basins from diffuse sources N surplus, municipalities State Baden-Württemberg Re-aggregation (Behrendt et al., 1999) (Bach & Frede, 2005) <www.ewaonline.de/journal/2005_01.pdf>

7. Diffuse N-Emissions (1998-2000, acc. MONERIS) Source: Behrendt et al. (2003)

8. Diffuse P-Emissions (1998-2000, acc. MONERIS) Source: Behrendt et al. (2003)

9. MONERIS – Diffuse Sources Nutrientsurplus: Rightindicator - toestimate N (and P) riverload? - topredicttheeffectsofmeasurestoreduce N riverload?

10. N-Fluxes and Turn-Over Root zone  Vadose Zone  GW  SW N Input  N Withdrawal  Root zone  Vadose Zone  Groundwater  Surface Waters N Surplus • Factors, Processes • N fertilization (amount, timing) • N mineralization • N volatilisation • Leaching rate (soil, climate) • Depth vadose zone • Groundwaterresidence time • Denitrification vadose zone & GW • N retentionsurfacewaterbodies ? %Retention N Load in river discharge

11. N Retention in Soil, Vadose Zone, andGroundwater(acc. MONERIS) N Retention in soil, vadosezone, and groundwater > 95 % 90 - 95 % 80 - 90 % 70 - 80 % 60 - 70 % (< 60%) If: N riverload(N retention) then: (N riverload) (N retention)minorrelevance: N surplus Source: Behrendt et al., 2003; UBA-texte 82/03, Abb. 4.19

12. Open Question: Functional Relation N Surplus and NO3Leaching (riverload)? 150 b = 0,7 Which function?  Measures (concepts,costs!) b = 0,5 100 NO3 Leaching [kg N/ha LF] 50 b = 0,3 0 0 50 100 150 200 N0 N Surplus (soil balance) [kg N/ha LF]

13. Resume - Diffuse NutrientEmissions • MONERIS is state of the art as source apportionment tool. • Assessment of nitrogen soil surface balances for regional levels NUTS 3 and NUTS 5 is well established in Germany and gives reasonable figures. • Agriculture affects nutrient river loads not only via indicator „surplus“  e.g. erosion (crop rotation, soil tillage), drainage etc. • Farm Structure Survey (FSS): 100% coverage only in 4 year intervals  appropriate diffuse source reporting frequency (future changes?). • Indicator "N balance surplus" valid for the conception of measures and prognosis of effects?

14. O Cl NHC N(CH3)2 Cl Agricultural Practices and Water Quality in Germany National scale modelling approaches (Germany) • Nutrients (N, P) • MONERIS • Pesticides • DRIPS • FOOTPRINT

15. Pesticide Modelling Approaches DRIPS - Drainage, Runoff, and Spraydrift Input of Pesticides in Surface Waters* • Regionalized assessment of surface waters exposureto pesticide contamination,PECswspecific for: substance, crop, area, river basins • • Probabilistic elements - Spatial variability of landscape features - River discharge (temporal variability) • • DRIPS Results: - Pesticide losses / river input - Probabilistic PECsw calculation - River basin PECsw assessment *) Details ref. Bach et al. 1999, 2002

16. Annual Pesticide Input into surface waters with surface runoff (acc. model DRIPS) Hazard Levels Source: Bach et al. (2000)

17. Pesticide Modelling Approaches FOOTPRINTwww.eu-footprint.eu Functional Tools for Pesticide Risk Assessment and Management MACRO, PRZM Multiple (gridded) combinations of Koc and DT50 underconstruction 'Pesticide loss and PEC on different scales‘ FOOT-CRS Catchment & Regional Scale FOOT-NES National & EU Scale FOOT-FS Farm Scale

18. WFD - RBD Analysis Surface Water Bodies Germany The causes for failing WFD objectives mentioned most frequently (based on the number of surface water bodies that are at risk of failing the objectives) Number of times mentioned Chemical substances, physicochemical conditions (Annex VIII) Hydromorphologyincluding river continuity Priority substances (Annexes IX and X) Nutrients Source: BMU (2005)

20. Thankyouforyourattention

21. WFD - RBD Analysis Key results for Germany‘s water bodies„The main source of nutrient and pollutant pressures on bodies of surface and ground water is agricultural activity followed by wastewater and rainwater drainage systems.“ Source: BMU, 2005. Water Framework Directive – Summary of River Basin District Analysis 2004 in Germany. Fed. Min. for the Environment, Nature Conservation and Nuclear Safety (BMU), Berlin [p.12]

22. WFD - River Basin District Analysis Status of SW and GW Bodies in Germany Results of the characterization of surface and ground water bodies Source: BMU (2005)

23. N- and P-Emissions into German Surface Waters, Point and Diffuse Sources (acc. MONERIS) Total Phosphorus Emissions [t/a] Total Nitrogen Emissions [t/a] WWTP Industry Atmospericdeposition Urbansurfaces Agriculture Source: Federal Environment Agency (2005) Naturalbackground 1975 1985 1995 2000 1975 1985 1995 2000

25. MONERIS Calculation scheme for nutrient fluxes via Surface runoff

26. MONERIS Calculation scheme for nutrient fluxes via Tile drainage

27. MONERIS Calculation scheme for nutrient fluxes via Atmospheric deposition on surface waters

28. MONERIS Calculation scheme for nutrient fluxes via Ground water and naturalinterflow

29. MONERIS Calculation scheme for nutrient fluxes via Soil erosion

30. MONERIS Calculation scheme for nutrient fluxes from Urban areas

31. MONERIS Calculation scheme for nutrient fluxes via Point sources

32. MONERIS Calculation scheme for nutrient losses via Retention

34. Methodological variations of nutrient balances FSS based balances • different primary statistical database, different categories ; especially national (NUTS 0) vs. regional (NUTS 3) vs. communal (NUTS 5 = LAU 2) • different nutrient conversion coefficients • with vs. without accounting of N deposition from atmosphere • regional balances: different approaches for commercial fertilizer estimation, e.g. normative or recommended quantitites • "net I surplus" diminshed by 'unadvoidable' losses  "net IIsurplus"

35. Soil Surface Nutrient Balance (net) – 'Standard' method ILR & FAL Primary data on croppingacreage, livestock andyields: GENESIS-tablesofthe Federal Statistical Office Germany Nitrogen coefficients: tablesof "Musterverwaltungsvorschrift" (partoftheFertilizingOrdonance) NH3 volatilization: coefficientsoftheFertilizingOrdinance(Dünge-Verordnung)  statutorynorms (!) Atmospheric N deposition: "internal N cycle" (EMEP depositiondataalternatively)

36. Regional Gross Nutrient Balances Top down approach Data base: FSS Problems, tasks National NUTS 0 • Primary FSS data gaps (data secrecy) • Uncertainty of regional commercial fertilizer quantities • Uncertainty of non-marketed fodder crops and grass • Uncertainty of regional fodder imports (concentrates) and market exports • No data on manure import/export District NUTS 3 Municipality NUTS 5

37. Regional Gross Nutrient Balances Bottom-up approach National NUTS 0 • Farm based data, farm-gatebalances • Data sources: • German Federal GovernmentAgriculture Report,Farm Accountig Data Network (FADN): representativepanel (but monetarybooking) • Book-keepingcompanies (e.g. LAND-DATA GmbH):bookingofphysicalamountof N fertilizers • Nutrientbalancerecords (Nährstoffvergleich) acc. toFertilizingOrdonance (Düngeverordnung) District NUTS 3 Municipality NUTS 5 Farmholdings

38. Regional Gross Nutrient surplus Bottom-up approach • Farm based data, farm-gatebalances • Lessonstolearn, ref. to: • Osterburg et al. (2004, 2006a,b) analyses; • Dämmgen (ed., 2006), EMEP National Inventory Report; empiricaldata, coefficients & relations on: • N withdrawalwithforage = {milk production, grazing} • N commercialfertilizerconsumption = {livestock density, farmstructure, region} • Ammoniavolatilisation (manure, slurry) = {livestock category, storagesystem, application, tillage} • Manureimport/export = {livestock density, farmstructure} National NUTS 0 District NUTS 3 Municipality NUTS 5 Farmholdings

40. NO3 leaching N Fluxes in the "Agrosphere“ Germany (N mass balance) Volati- lization NH3 N2 N2O NO Agriculture Input (fertilizer,N fixation,atmospher. deposition, others) Denitri-fication N2 (N2O) Harvest yield River discharge Crop land Soil surface surplus  Pool soil-N Vadose zone Groundwater  Pool GW-N

41. N-Fluxes "Agrosphere„ Germany – Gaps? -1012

42. Resume • AssessmentofnitrogenSoilSurfaceBalancesfor regional levels NUTS 3 and NUTS 2 is well established in Germany andgivesreasonablefigures. • DisaggregationofNational Gross Balance:Problems withmissingdata andseveralnutrientfluxes on the regional level (commercialfertilizers, fodderimports, marketexports, manureimport/ export). • Startingpoint: combineto-down andbottom-upapproaches, useoffarm data andfarm-gatebalancestoderivetransferfunctionsforspecificnutrient (especially N) fluxes.

43. WFD - River Basin District Analysis GW Factors Factors that can result in failure to meet WFD objectives mentioned of groundwater bodies (based on the number of GW bodies that are at risk of failing the objectives) Source: BMU (2005)

44. EUROHARP Model Comparison Nitrogen Phosphorus Example: River Zelivka catchment (Czech Rep.) Source: Behrendt et al. (2008) Scenarios:

45. EUROHARP Model Comparison Principle: All models were applied on three identical catchments plus three (of 13) additional basins selected by lot Agricultural N input, individual model [kg N/(ha.a])] Agricultural N-input, average of all models [kg N/(ha.a])] deviation line MONERIS Source: Behrendt et al. (2008)

46. Other MONERIS-like Models used in Germany • STOFFBILANZ(M. Grünwald, TU Dresden, DE) • MOBINEG(Hydrotec Engineering, Aachen; DE) • ... • MODIFFUS(U. Prasuhn, AgroscopeFAL Reckenholz, CH)

47. Pilot DSS River Elbe Source: Berlekamp et al. (2005)

48. GREAT-ER GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers) GIS coupled with chemical models for Modeling fate and behaviour of chemical substances in rivers (Matthies et al., University Osnabrück) source: http://www.usf.uni-osnabrueck.de/usf/arbeitsgruppen/ ASW/Great-Er_Preprocessing.en.html

49. Agricultural Practices and Water Quality in Germany Impulses • OSPARCOM & other international marine conventions • WFD

50. DRIPS Results:Pesticide Losses Calculated diffuse losses of pesticides from crop land Identification of „hot spots“ (full consideration of spatial variability of all input parameters on pixel base 1 x 1 km²) Benefit for users: Identify critical environmental parameter combinations and/or plant protection management