1. Justus-Liebig-University Giessen Institute of Landscape Ecoology and Resources Management Martin Bach 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. Institute of Landscape Ecology and Resources Management Agricultural Practices and Water Quality in Germany National scale modelling approaches (Germany) Nutrients (N, P) MONERIS Pesticides DRIPS FOOTPRINT

3. Institute of Landscape Ecology and Resources Management MONERIS Source: Behrendt et al. (2008) 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)

4. Institute of Landscape Ecology and Resources Management MONERIS – Nutrient Flux Scheme Source: Behrendt et al. (2008) Ag practices affect MONERIS results

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

6. Institute of Landscape Ecology and Resources Management Nitrogen Soil Surface Surplus in the WFD context N surplus, municipalities State Baden-Württemberg Nitrogen emission into river basins from diffuse sources (Behrendt et al., 1999) (Bach & Frede, 2005) Re-aggregation

7. Institute of Landscape Ecology and Resources Management Source: Behrendt et al. (2003) Diffuse N-Emissions (1998-2000, acc. MONERIS)

8. Institute of Landscape Ecology and Resources Management Source: Behrendt et al. (2003) Diffuse P-Emissions (1998-2000, acc. MONERIS)

9. Institute of Landscape Ecology and Resources Management Nutrient surplus: Right indicator - to estimate N (and P) river load? - to predict the effects of measures to reduce N river load? MONERIS – Diffuse Sources

10. Institute of Landscape Ecology and Resources Management N-Fluxes and Turn-Over Root zone Vadose Zone GW SW N Input N Withdrawal Root zone Vadose Zone Groundwater Surface Waters N Surplus N Load in river discharge Factors, Processes N fertilization (amount, timing) N mineralization N volatilisation Leaching rate (soil, climate) Depth vadose zone Groundwater residence time Denitrification vadose zone & GW N retention surface water bodies ? %Retention

11. Institute of Landscape Ecology and Resources Management N Retention in Soil, Vadose Zone, and Groundwater (acc. MONERIS ) Source: Behrendt et al., 2003; UBA-texte 82/03, Abb. 4.19 > 95 % 90 - 95 % 80 - 90 % 70 - 80 % 60 - 70 % (< 60%) If: N river load (N retention) then: (N river load) ( N retention) minor relevance: N surplus N Retention in soil, vadose zone, and groundwater

12. Institute of Landscape Ecology and Resources Management Open Question: Functional Relation N Surplus and NO 3 Leaching (river load)? Which function? Measures (concepts, costs!) 150 050100150200 0 50 100 b = 0,3 b = 0,7 b = 0,5 N0N0 N Surplus (soil balance) [kg N/ha LF] NO 3 Leaching [kg N/ha LF]

13. Institute of Landscape Ecology and Resources Management 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? Resume - Diffuse Nutrient Emissions

14. Institute of Landscape Ecology and Resources Management Agricultural Practices and Water Quality in Germany National scale modelling approaches (Germany) Nutrients (N, P) MONERIS Pesticides DRIPS FOOTPRINT Cl NHC N(CH 3 ) 2 O

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

16. Institute of Landscape Ecology and Resources Management Hazard Levels Annual Pesticide Input into surface waters with surface runoff (acc. model DRIPS) Source: Bach et al. (2000)

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

18. Institute of Landscape Ecology and Resources Management Source: BMU (2005) 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) WFD - RBD Analysis Surface Water Bodies Germany Hydromorphology including river continuity Nutrients Chemical substances, physicochemical conditions (Annex VIII) Priority substances (Annexes IX and X) Number of times mentioned

19. Institute of Landscape Ecology and Resources Management

20. Thank you for your attention

21. Institute of Landscape Ecology and Resources Management 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] Key results for Germanys 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. WFD - RBD Analysis

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

23. Institute of Landscape Ecology and Resources Management Source: Federal Environment Agency (2005) N- and P-Emissions into German Surface Waters, Point and Diffuse Sources (acc. MONERIS) WWTP Industry Atmosperic deposition Urban surfaces Agriculture Natural background Total Nitrogen Emissions [t/a] Total Phosphorus Emissions [t/a] 1975 1985 1995 2000

24. Institute of Landscape Ecology and Resources Management

25. MONERIS Calculation scheme for nutrient fluxes via Surface runoff

26. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes via Tile drainage

27. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes via Atmospheric deposition on surface waters

28. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes via Ground water and natural interflow

29. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes via Soil erosion

30. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes from Urban areas

31. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient fluxes via Point sources

32. Institute of Landscape Ecology and Resources Management MONERIS Calculation scheme for nutrient losses via Retention

33. Institute of Landscape Ecology and Resources Management

34. 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 II surplus" Methodological variations of nutrient balances FSS based balances

35. Institute of Landscape Ecology and Resources Management (1)Primary data on cropping acreage, livestock and yields: GENESIS-tables of the Federal Statistical Office Germany (2)Nitrogen coefficients: tables of "Musterverwaltungsvorschrift" (part of the Fertilizing Ordonance) (3)NH3 volatilization: coefficients of the Fertilizing Ordinance (Dünge-Verordnung) statutory norms (!) (4)Atmospheric N deposition: "internal N cycle" (EMEP deposition data alternatively) Soil Surface Nutrient Balance (net) – 'Standard' method ILR & FAL

36. Institute of Landscape Ecology and Resources Management 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 Regional Gross Nutrient Balances Top down approach Data base: FSS Problems, tasks National NUTS 0 District NUTS 3 Municipality NUTS 5

37. Institute of Landscape Ecology and Resources Management National NUTS 0 District NUTS 3 Municipality NUTS 5 Farm holdings Regional Gross Nutrient Balances Bottom-up approach Farm based data, farm-gate balances Data sources: German Federal Government Agriculture Report, Farm Accountig Data Network (FADN): representative panel (but monetary booking) Book-keeping companies (e.g. LAND-DATA GmbH): booking of physical amount of N fertilizers Nutrient balance records (Nährstoffvergleich) acc. to Fertilizing Ordonance (Düngeverordnung)

38. Institute of Landscape Ecology and Resources Management National NUTS 0 District NUTS 3 Municipality NUTS 5 Farm holdings Regional Gross Nutrient surplus Bottom-up approach Farm based data, farm-gate balances Lessons to learn, ref. to: Osterburg et al. (2004, 2006a,b) analyses; Dämmgen (ed., 2006), EMEP National Inventory Report; empirical data, coefficients & relations on: o N withdrawal with forage = {milk production, grazing} o N commercial fertilizer consumption = {livestock density, farm structure, region} o Ammonia volatilisation (manure, slurry) = {livestock category, storage system, application, tillage} o Manure import/export = {livestock density, farm structure}

39. Institute of Landscape Ecology and Resources Management

40. River discharge Volati- lization NH 3 N 2 N 2 O NO NO 3 leaching Pool GW-N Denitri- fication N 2 (N 2 O) N Fluxes in the "Agrosphere Germany (N mass balance) Pool soil-N Input (fertilizer, N fixation, atmospher. deposition, others) Harvest yield Agriculture Soil surface surplus Vadose zone Groundwater Crop land

41. Institute of Landscape Ecology and Resources Management NO 3 Retention vadose zone & GW - ??? N Immobilisation soil organic matter - ?? 0...20 kg N/(ha.a)? N Mineralization ploughing of grassland + ?? >500 kg N/(ha.a)? NO 3 (from AA) in river discharge - 513 MONERIS ( Behrendt et al., 2002) N 2 O, NO Volatilization (from AA) NH 3 Volatilization (from AA) N 2 Denitrification (from AA) - 111 - 82 - 306 (Dämmgen, 2007) N FluxesAmount 1000 t N Source, remarks Manure fertilizing Mineral fertilizing Legume N fixation Atmospheric N deposition 1016 1803 227 391 alternatively: 91 alternatively: 501 Sum Input Harvest withdrawal = 3488 - 2115 Surplus soil surface balance (2003)+ 1373 (Bach, 2007) -1012 N-Fluxes "Agrosphere Germany – Gaps?

42. Institute of Landscape Ecology and Resources Management Assessment of nitrogen Soil Surface Balances for regional levels NUTS 3 and NUTS 2 is well established in Germany and gives reasonable figures. Disaggregation of National Gross Balance: Problems with missing data and several nutrient fluxes on the regional level (commercial fertilizers, fodder imports, market exports, manure import/ export). Starting point: combine to-down and bottom-up approaches, use of farm data and farm-gate balances to derive transfer functions for specific nutrient (especially N) fluxes. Resume

43. Institute of Landscape Ecology and Resources Management 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. Institute of Landscape Ecology and Resources Management Example: River Zelivka catchment (Czech Rep.) EUROHARP Model Comparison Nitrogen Phosphorus Scenarios: Source: Behrendt et al. (2008)

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

46. Institute of Landscape Ecology and Resources Management STOFFBILANZ (M. Grünwald, TU Dresden, DE) MOBINEG (Hydrotec Engineering, Aachen; DE)... MODIFFUS (U. Prasuhn, Agroscope FAL Reckenholz, CH) Other MONERIS-like Models used in Germany

47. Institute of Landscape Ecology and Resources Management Pilot DSS River Elbe Source: Berlekamp et al. (2005)

48. Institute of Landscape Ecology and Resources Management 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 GREAT-ER

49. Institute of Landscape Ecology and Resources Management Agricultural Practices and Water Quality in Germany Impulses OSPARCOM & other international marine conventions WFD

50. Institute of Landscape Ecology and Resources Management 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) DRIPS Results: Pesticide Losses Benefit for users: Identify critical environmental parameter combinations and/or plant protection management

51. Institute of Landscape Ecology and Resources Management DRIPS Results: River Basin Concentration Map: Spatial distribution PEC sw of Isoproturon (IPU), 90th-percentile, from surface runoff, drainage and spraydrift inputs (year 2000) - Identify location of worst case catchments - Judge frequency of worst case occurence Benefit for users: Identify river basins prone to high pesticide conc. / treshold exceedance

52. Institute of Landscape Ecology and Resources Management DRIPS results: Probabilistic PECsw Calculation Example: Cumulated probability PEC sw for IPU, month April, at station Frankfurt-Nied (Nidda river basin) 0.11 0.0 0.2 0.4 0.6 0.8 1.0 PEC-Q95 PEC-Q50 PEC-Q5 Wahrscheinlichkeit PEC sw [log µg L ] 90%- Perc. 50%- Perc. cumulated probability FDC wet month FDC dry month average FDC normal month PEC sw (log µg L -1 ) within-month variability 0.72 3.11.7