Justus-Liebig-University Giessen Institute of Landscape Ecoology and Resources Management Martin...

Justus-Liebig-University Giessen Institute of Landscape Ecoology

and Resources Management

Martin Bach

Modelling Approaches to

Link Agricultural Practices and

Water Quality in Germany

[email protected]

Eionet Workshop „Pollutant Emissions to Water“ 11th – 12th September 2008, EEA ,Copenhagen

Institute of Landscape Ecology and Resources Management

Agricultural Practices and Water Quality in Germany

National scale modelling approaches (Germany)

Nutrients (N, P)

MONERIS

Pesticides

DRIPSFOOTPRINT


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)


MONERIS – Nutrient Flux Scheme


Ag practices affect MONERIS results


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


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) <www.ewaonline.de/journal/2005_01.pdf>

Re-

agg

reg

atio

n

Institute of Landscape Ecology and Resources ManagementSource: Behrendt et al. (2003)

Diffuse N-Emissions (1998-2000, acc. MONERIS)

Institute of Landscape Ecology and Resources ManagementSource: Behrendt et al. (2003)

Diffuse P-Emissions (1998-2000, acc. MONERIS)


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


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


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


Open Question: Functional Relation N Surplus and NO3 Leaching (river load)?

Which function? Measures (concepts,costs!)

150

0 50 100 150 2000

50

100

b = 0,3

b = 0,7

b = 0,5

N0

N Surplus (soil balance) [kg N/ha LF]

NO

3 L

each

ing

[k

g N

/ha

LF]


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



National scale modelling approaches (Germany)

Nutrients (N, P)

MONERIS

Pesticides

DRIPSFOOTPRINT

Cl

Cl

NHC N(CH3)2

O


Pesticide Modelling Approaches

DRIPS - Drainage, Runoff, and Spraydrift Input of Pesticides in Surface Waters*

• Regionalized assessment of surface waters exposureto pesticide contamination,PECsw 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 PECsw calculation - River basin PECsw assessment

*) Details ref. Bach et al. 1999, 2002


Hazard Levels

Annual Pesticide Input

into surface waters with surface runoff(acc. model DRIPS)

Source: Bach et al. (2000)


Pesticide Modelling Approaches

FOOTPRINT www.eu-footprint.eu

Functional Tools for Pesticide Risk Assessment and Management

MACRO, PRZMMultiple (gridded) combinations of Koc and DT50

FOOT-CRSCatchment & Regional

Scale

FOOT-FSFarm Scale

FOOT-NESNational & EU Scale

'Pesticide loss and PEC on different scales‘

Institute of Landscape Ecology and Resources ManagementSource: 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

Hydromorphologyincluding

river continuity

Nutrients Chemical substances, physicochemical

conditions (Annex VIII)

Priority substances (Annexes IX and X)

Num

ber o

f tim

es m

entio

ned



Thank you for your attention


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 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.“

WFD - RBD Analysis


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


So

urc

e:

Fe

de

ral E

nvi

ron

me

nt

Ag

en

cy (

20

05

)

N- and P-Emissions into German Surface Waters, Point and Diffuse Sources (acc. MONERIS)

WWTP

Industry

Atmospericdeposition

Urbansurfaces

Agriculture

Naturalbackground

Total Nitrogen Emissions [t/a] Total Phosphorus Emissions [t/a]

1975 1985 1995 2000 1975 1985 1995 2000



MONERIS

Calculation scheme for nutrient fluxes via

Surface runoff


MONERIS


Tile drainage


MONERIS


Atmospheric deposition on surface waters


MONERIS


Ground water and naturalinterflow


MONERIS


Soil erosion


MONERIS

Calculation scheme for nutrient fluxes from

Urban areas


MONERIS


Point sources


MONERIS

Calculation scheme for nutrient losses via

Retention



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


(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


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 BalancesTop down approach

Data base: FSS

Problems, tasks

NationalNUTS 0

DistrictNUTS 3

MunicipalityNUTS 5


NationalNUTS 0

DistrictNUTS 3

MunicipalityNUTS 5

Farmholdings

Regional Gross Nutrient BalancesBottom-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)


NationalNUTS 0

DistrictNUTS 3

MunicipalityNUTS 5

Farmholdings

Regional Gross Nutrient surplusBottom-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}



River discharge

Volati-lization

NH3

N2

N2ONO

NO3 leaching

Pool GW-N

Denitri-ficationN2 (N2O)

N Fluxes in the "Agrosphere“ Germany (N mass balance)

Pool soil-N

Input(fertilizer,N fixation,atmospher. deposition,

others)

Harvestyield

Agriculture

Soil surface surplus

Vadose zone

Groundwater

Crop land


NO3 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)?

NO3 (from AA) in river discharge - 513 MONERIS (Behrendt et al., 2002)

N2O, NO Volatilization (from AA)NH3 Volatilization (from AA)N2 Denitrification (from AA)

- 111 - 82 - 306

(Dämmgen, 2007)

N Fluxes Amount1000 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?


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


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)


Example: River Zelivka catchment (Czech Rep.)

EUROHARP Model Comparison

Nitrogen

Phosphorus

Scenarios:

So

urc

e:

Be

hre

nd

t e

t a

l. (2

00

8)


Principle: All models were applied on three identical catchments plus three (of 13) additional basins selected by lot

EUROHARP Model Comparison

MONERISline deviation

Agricultural N-input, average of all models [kg N/(ha.a])]

Ag

ricu

ltura

l N in

pu

t, in

div

idua

l mo

del [

kg N

/(h

a.a

])]



• 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


Pilot DSS River Elbe

Source: Berlekamp et al. (2005)


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)

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GREAT-ER



Impulses

OSPARCOM & other international marine conventions

WFD


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


DRIPS Results: River Basin Concentration

Map: Spatial distribution PECsw 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


DRIPS results: Probabilistic PECsw Calculation

Example: Cumulated probability PECsw for IPU, month April, at station Frankfurt-Nied (Nidda river basin)

0.1 10.0

0.2

0.4

0.6

0.8

1.0

PEC-Q95 PEC-Q50 PEC-Q5

Wah

rsch

einl

ichk

eit

PECsw [log µg L-1]

90%- Perc.

50%- Perc.

cum

ula

ted

pro

ba

bilit

y

FDC „wet“ month FDC

„dry“ month

average FDC „normal“ month

PECsw (log µg L-1)

within-month variability

0.72 3.11.7

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