gerda: risk assessment for pesticide inputs into surface waters via surface runoff, erosion and...
Post on 22-Nov-2023
0 Views
Preview:
TRANSCRIPT
1)
Institut für Agrarökologie
3)
4)5) 6)
GERDA – A new software tool for pesticide exposure assessment for surface waters
in Germany
Stefan Reichenberger6, Martin Bach1, Benjamin Daniels5, Dietlinde Großmann2, Djamal Guerniche3, Udo Hommen4, Mirjam Kaiser2, Michael Klein4, Roland Kubiak3, Alexandra Müller2, José Pires6, Thomas G. Preuss5, Kai Thomas3, Matthias Trapp3
This project was funded by the German Federal Environment Agency (Umweltbundesamt, UBA), Project number 371163427 , Environmental Research Plan of the Federal Ministry of Environment
2)
Table of Contents
1. Objectives
2. Modelling concept
3. The GERDA software package
4. Projects and substances
5. Mitigation options
6. Running an assessment
7. Output
8. Conclusions and Outlook
Objectives
• Develop a tool for exposure assessment in Germany for pesticide inputs into surface waters via– surface runoff
– erosion
– drainage
– spray drift
– potentially: lateral subsurface flow
– volatilisation/deposition
• The tool should– take into account all agriculturally relevant agro-pedo-climatic
conditions in Germany (representativity)
– allow the user to model the effects of mitigation measures
Modelling concept (1)
• GERDA produces hourly 30-year time series of PECsw + PECsed for– spray drift and drainage inputs
– spray drift, surface runoff and erosion inputs
• Pesticide losses from the field via tile drains: MACRO 5.2
• Pesticide losses via surface runoff and erosions: PRZM 4.51
• Pesticide fate in surface water: – model STEP3 from STEPS-1-2-3-4 (Klein, 2007)
– runs for FOCUS-type stream and ditch
• Parameterisation of Ganzelmeier drift functions, soils and crops according to FOOTPRINT (Dubus et al., 2009)
• Mitigation options: – Spray drift: drift-reducing technology, minimum distances (= no-spray buffers)
– Surface runoff / erosion: Simulation of vegetated buffer strips (VFS) using VFSMOD 4.2.4
Modelling concept (2)
Agro-pedo-climatic scenarios contained in GERDA• Climate: 12 climate scenarios
• Soils– all 102 FOOTPRINT soil types (FSTs) that are agriculturally relevant for Germany (PRZM)
– 36 of these have artificial drains (MACRO)
– 35 exhibit lateral subsurface flow (MACRO; not modelled in v.1)
• Crops: 49 different crops have been parameterised (final list of available crops still under discussion)
Further options• kinetic sorption in MACRO and PRZM (Streck-Altfelder model)
• one metabolite per applied active substance; the metabolite can be formed in soil, water/sediment or both
The GERDA software package
• GERDA was programmed as an extension of the existing STEPS-1-2-3-4 software (Klein, 2007)
• The installation package contains 1296 spatial cumulative distribution functions (CDFs) of “worst-case-ness” (or “vulnerability”) which have been determined quantitatively based on MACRO, PRZM and STEPS modelling and German national geodata (cf. last year’s presentation)
• The CDFs correspond to unique combinations of– pesticide input pathway: drainage or surface runoff / erosion (2)– Koc: 5 levels (surface runoff + erosion) or 4 levels (drainflow) (4/5)– DT50 in soil: 3 levels (3)– application month (12)– seasonality: winter or spring crop (2)– descriptor variable: median (annual PECsw,max) or median (annual AUC) (2)
• CDFs are only used for selecting scenarios for modelling!
Projects and substances
• A project in GERDA comprises
– an active substance (with or without metabolite)
– a crop– an application
scheme
• The GERDA input interface allows the user to
– add – modify – copy – save– delete
projects + substances
Mitigation options
Spray drift mitigation:• specify reduction efficiency of drift-reducing technology
• enter minimum distance (width of no-spray buffer) between treated area and surface water body
Surface runoff and erosion mitigation:• Simulation of grassed buffer strips (vegetated filter strips, VFS) using VFSMOD 4.2.4
• This version of VFSMOD is able to simulate a shallow water table parameterisationof lower boundary condition and initial water table depth (WTD) according to site hydrology, climate and season
• VFSMOD simulations are done separately for stream and ditch scenarios
• Two parameters to be entered by the GERDA user– VL: length of the VFS in flow direction (“buffer width”)
– FWIDTH: effective flow width of the VFS, perpendicular to the slope (allows accounting for flow concentration)
• Upstream Catchment definition: fraction of stream UC equipped with VFS (fb) set to 0.6 instead of 0.2 in SWAN
GERDA allows more realistic buffer strip simulations than SWAN (water table, FWIDTH, fb)
How to run an assessment
1) Click on the tab GERDA
2) Select a project
3) GERDA internally determines the relevant CDFs
4 CDFs (2 exposure descriptors * 2 pathways)
4) Select a cumulative area percentage of worst-case-ness in space
(“landscape percentile”); regulatory relevant value to be decided
5) GERDA internally reads off the soil-climate combinations which correspond
to the selected cumulative area percentage of worst-case-ness.
≤ 2 soil-climate combinations for MACRO, ≤ 2 for PRZM
6) Click on “Runoff(PRZM)-Simulations” to start PRZM
7) Click on “Drainage(MACRO)-Simulations” to start MACRO
8) Once PRZM- and/or MACRO results are available, click on STEPS-button
Output
Three types of output for each STEPS simulation:• Report
– echo of all user inputs and water body parameters
– PECsw,max (30 annual values + 80th percentile thereof)
– PECsed,max (80th percentile of 30 annual values)
– TWACsw/sed for various periods (80th percentile of 30 annual values)
• 30-year hourly time series of PECsw + PECsed (text file)
• Time series diagrams of PECsw and PECsed for each simulation year
Conclusions and Outlook
• The tool GERDA is in line with the FOCUS surface water approach, while constituting a major scientific improvement in comparison to the latter.
– well-defined worst-case-ness in time (due to 30-year simulations)
– well-defined worst-case-ness in space (due to worst-case-ness CDFs)
• Therefore, in contrast to FOCUSsw, GERDA enables informed regulatory decisions on a national level.
• Apart from that, GERDA also allows more realistic buffer strip simulations than SWAN.
• GERDA v.1.0 will be finalised within the next two months.
Introduction: VFSMOD
• VFSMOD (z.B. Muñoz-Carpena and Parsons, 2011; http://abe.ufl.edu/carpena/vfsmod/) is a numerical, event-based model for the dynamic simulation of grassed buffer strips(vegetated filter strips, VFS)
• Various versions– vfsm.exe: command line
– VFSMOD-W: Windows-GUI + vfsm.exe
– SWAN-VFSMOD (developped by ECPA for FOCUS step4) with VFSMOD als .dll
• Characteristics of VFSMOD– 1 simulation = 1 surface runoff event
– mechanistic simulation of infiltration and sedimentation
– since 2009: reduction of pesticide load with a multiple regression equation (Sabbagh et al., 2009); deltaP = f(deltaE, deltaQ, Fph, C)
– mechanistic solute transport only available in research version
• Relevant outputs– deltaQ: relative reduction of total incoming water flow (incoming surface runoff + rainfall on VFS)
– deltaR: relative reduction of incoming surface runoff
– deltaE: relative reduction of incoming sediment load
– deltaP: relative reduction of incoming pesticide load
Introduction: SWAN-VFSMOD
• SWAN 3.0 contains two options:
a) user-defined fixed efficiencies of VFS (inherited from SWAN v. 1)
b) dynamic simulation of VFS (SWAN-VFSMOD)
• many events in one p2t-File many VFSMOD simulations per p2t file
– long time simulations of soil moisture in advance using the tool ThetaFAO (Muñoz-Carpena, 2012a)
– carry-over of residues from one surface runoff event to the next (Muñoz-Carpena, 2012b)
– however: no ageing (e.g. progressive silting up) of the buffer strip is simulated: the VFS is assumed to be maintained between events so that it is in perfect condition at the start of each event
• Advantage of SWAN-VFSMOD: more realistic simulation of the efficiency of VFS than with fixed efficiency values
• Disadvantage: SWAN-VFSMOD cannot fix the main problems inherent in FOCUSsw, especially the lack of representativeness of the simulated 12-month period
• further critical SWAN assumption (v. 1 und 3.0):
– The non-treated (with the simulated pesticide!) area of the upstream catchment of the FOCUS stream (80 % of 100 ha) doesn’t have buffer strips relatively high dilution with the unchanged surface runoff volumes from these areas
– An analytically derived relationship shows that, with the FOCUS scenario assumptions (Ac = 100 ha, Af = 1 ha, ft = 0.2) and the additional SWAN assumptions (fb = ftb = ft), deltaPECsw is approximately equal to deltaP.
Differences of VFSMOD in GERDA to SWAN-VFSMOD
• Bug (unit error) in SWAN : incoming flow sediment concentration CI underestimated by a factor of 2.22 fixed in GERDA
• field dimensions for pond were not consistent in SWAN with FOCUSsw scenario definition
– SWIDTH: 100 m 60 m
– SLENGTH: 100 m 75 m
– FWIDTH (base value): 100 m 30 m
• runoff hydrograph: triangular instead of rectangular (triangular recommended by VFSMOD developer in technical note)
• presence of shallow water table depending on site hydrology– additional parameters needed (VG alpha, N, m)
– calculated using ptfs of Wösten et al. (1998)
Effect of lowerBC and WTD for: R1 stream, Koc = 100, 240 months, appmonth = 10, VL = 20 m, FWIDTH = 100 m
13.09
9.31
12.99
8.02 8.02 8.027.53 7.53 7.53
5.57
4.96
3.50
4.92
3.01 3.01 3.01 2.83 2.83 2.83
2.09
0
1
2
3
4
5
6
0
2
4
6
8
10
12
14
1 1 1 2 2 2 3 3 3 n.a.
1 2 3 1 2 3 1 2 3 0
PE
Csed
(µ
g/k
g d
ry m
att
er)
PE
Csw
(µ
g/L
)
water table depth (m)lower boundary condition
PECswmax
PECsedmax
Effekt of VL and FWIDTH for: R1 stream, Koc = 100, 240 months, appmonth = 10, lowerBC = 1, WTD = 2 m
35.06
32.09
30.8029.87 30.29
28.40
25.03
22.09
28.45
25.19
20.21
17.07
18.72
14.55
11.88
9.75
14.55
11.89
8.02
5.33
11.3010.01
8.487.72 7.91
6.97 6.52 6.326.93 6.50 6.15 5.88 6.01
5.414.43
3.65
5.414.44
3.012.01
0
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
5 10 20 30 5 10 20 30 5 10 20 30 5 10 20 30 5 10 20 30
1 1 1 1 5 5 5 5 10 10 10 10 50 50 50 50 100 100 100 100
PE
Csed
(µ
g/k
g d
ry m
att
er)
PE
Csw
(µ
g/L
)
VL (length in flow direction; m)FWIDTH (effective flow width; m)
PECswmax
PECsedmax
Control: PECsw,max = 71.2 µg/L, PECsed,max = 13.1 µg/kg
30.98 30.99
29.18
27.3128.01
24.32
20.74
19.28
24.23
20.65
18.4517.57 17.86
16.38
14.25
12.53
16.37
14.25
11.08
8.80
13.93 13.93
13.14
12.3212.62
11.01
9.428.77
10.97
9.38
8.408.00 8.14
7.46
6.48
5.69
7.45
6.48
5.02
3.98
0
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
5 10 20 30 5 10 20 30 5 10 20 30 5 10 20 30 5 10 20 30
1 1 1 1 5 5 5 5 10 10 10 10 50 50 50 50 100 100 100 100
PE
Csed
(µ
g/k
g d
ry m
att
er)
PE
Csw
(µ
g/L
)
VL (length in flow direction; m)FWIDTH (effective flow width; m)
PECswmax
PECsedmax
Effekt of VL and FWIDTH for: R4 stream, Koc = 100, 12 months, appmonth = 10, lowerBC = 1, WTD = 2 m
Control: PECsw,max = 30.97 µg/L, PECsed,max = 13.92 µg/kg
Footways Pesticide Application Timer
• The method works as follows:
1) Target application date = n
2) IF rainfall on day n-1 <= 10 mm AND rainfall on day n <= 2 mm, then final application date = target application date. ELSE move to day n+1 and repeat.
• Consequences:
– application days are only shifted towards later dates, not towards earlier dates
– no iterations after relaxation of rules
References• BGR (2007). Nutzungsdifferenzierte Bodenübersichtskarte der Bundesrepublik Deutschland 1 : 1000000 (BÜK
1000 N 2.3). Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover (Digitales Archiv FISBo BGR).
• Dubus I.G., Reichenberger S., Allier D., Azimonti G., Bach M., Barriuso E., Bidoglio G., Blenkinsop S., Boulahya F., Bouraoui F., Burton A., Centofanti T., Cerdan O., Coquet Y., Feisel B., Fialkiewicz W., Fowler H., Galimberti F., Green A., Grizzetti B., Højberg A., Hollis J.M., Jarvis N.J., Kajewski I., Kjær J., Krasnicki S., Lewis K.A., Lindahl A., Lobnik F., Lolos P., Mardhel V., Moeys J., Mojon-Lumier F., Nolan B.T., Rasmussen P., Réal B., Šinkovec M., Stenemo F., Suhadolc M., Surdyk N., Tzilivakis J., Vaudour-Dupuis E., Vavoulidou-Theodorou E., Windhorst D. & Wurm M. (2009). FOOTPRINT – Functional tools for pesticide risk assessment and management. www.eu-footprint.org. Final report of the EU project FOOTPRINT (SSPI-CT-2005-022704), 221 p.
• FAO (1998). World Reference Base for Soil Resources, by ISSS-ISRIC-FAO. World Soil Resources Report No. 84, Rome.
• Hollis J., Jones R., Marshall C., Holden A., Van De Veen J., Montanarella L. (2006). SPADE 2: The Soil Profile Database for Europe version 1.0. Report for the European Crop Protection Association and EC Joint Research Centre,. European Soil Bureau Research Report No. 19, EUR 22127. Office for the Official Publications of the European Communities, Luxembourg.
• Jager T., Albert C., Preuss T.G., Ashauer R. (2011). General Unified Threshold Model of Survival – a Toxicokinetic-Toxicodynamic Framework for Ecotoxicology. Environ. Sci. Technol. 2011, 45, 2529–2540
• Klein M. (2007). Long-term surface water simulations with STEPS-1-2-3-4. Proc. XIII Symposium Pesticide Chemistry, p. 950-957.
• Le Bas C., King D., Jamagne M., Daroussin J. (1998). The European Soil Information System. In: Heineke H., Ecklemann W., Thomasson A., Jones R., Montanarella L, Buckley B, editors. Land Information Systems: Developments for planning the sustainable use of land resources. European Soil Bureau Research Report No. 4, EUR 17729 EN, 33-42. Office for Official Publications of the European Communities, Luxembourg.
General concept of FSTs
• The system of FOOTPRINT Soil Types (FSTs) has been derived during the EU project FOOTPRINT (2006-2009), mainly by John Hollis (UK).
• Objective: characterize a limited number of soil types suitable for modelling the environmental fate of pesticides in Europe such that they represent– all relevant pollutant transfer pathways (surface runoff, erosion,
lateral subsurface flow, drainage and leaching) from soil to surface water and groundwater
– the complete range of soil sorption potential relevant to ‘reactive’ pollutants
• Applicability of the FST system– pesticides– other contaminants (e.g. nitrate)
General concept of FSTs (2)
• The FST typology is a functional classification that groups soils according to their hydrological, textural and sorption potential characteristics.– The system contains 986 potentially occurring FSTs („FSTmap“)– 367 of these occur in the Soil Geographical Database of Europe
SGDBE (Le Bas et al., 1998) for the EU24– 269 of these have been identified as agriculturally relevant and been
parameterised during FOOTPRINT for MACRO and PRZM („FSTmodelled“)
• If an FST resulting from the classification does not belong to the 269 FSTmodelled, the most similar FSTmodelled is used for the simulation (correspondence table).
• More details can be found in the FOOTPRINT Final Report (Dubus et al., 2009).
General concept of FSTs (3)
• The FST system consists of three parts, which are basically independent of each other:– The FST flowchart to classify a given soil typological unit into
the FST system (i.e. assign an FST to a given STU)– The parameterisation method for MACRO and PRZM– The standard profiles for each FST (in FOOTPRINT such profiles
were derived by John Hollis from SPADE-1 and SPADE-2)
The FST code
• The FST name is a code consisting of– a capital letter (L-Z): FOOTPRINT Hydrologic Group FHG
– a number (1-6): topsoil texture code
– a number (0-6): subsoil texture code
– one or more lowercase letters: organic matter profile code
FOOTPRINT
hydrological
code
L 4 4 n Organic matter
profile code
Subsoil texture code
Topsoil texture
code
FOOTPRINT
hydrological
code
L 4 4 n Organic matter
profile code
Subsoil texture code
Topsoil texture
code
The FOOTPRINT Hydrologic Group (FHG)FOOTPRINT
hydrological codeDescription
MACRO bottom boundary
condition
PRZM Soil
Hydrologic
Group
LPermeable, free draining soils on permeable sandy, gravelly, chalk or limestone substrates
with deep groundwater (below 2 m depth).Unit hydraulic gradient A
MPermeable, free draining soils on hard but fissured substrates (including karst) with deep
groundwater (below 2 m depth).Unit hydraulic gradient B
NPermeable, free draining soils on permeable soft loamy or clayey substrates with deep
groundwater (below 2m depth).Unit hydraulic gradient B-C
OPermeable soils on sandy or gravelly substrates with intermediate groundwater (at 1 - 2 m
depth)Zero flow A
PPermeable soils on soft loamy or clayey substrates with intermediate groundwater (at 1 - 2 m
depth)Zero flow B-C
Q All soils with shallow groundwater (within 1 m depth) and artificial drainage Zero flow A
RPermeable, free draining soils with large storage, over hard impermeable substrates below 1
m depthZero flow B
SPermeable, free draining soils with moderate storage, over hard impermeable substrates at
0.5 - 1 m depthZero flow B-C
TShallow, permeable, free draining soils with small storage, over hard impermeable substrates
within 0.5 m depthZero flow C
U Soils with slight seasonal waterlogging ('perched' water) over soft impermeable clay substrates Zero flow B-C
VSoils with prolonged seasonal waterlogging ('perched' water) over soft impermeable clay
substratesZero flow C
W Free draining soils over slowly permeable substratesPercolation rate regulated by
water table heightB
The FOOTPRINT Hydrologic Group (FHG)
FOOTPRINT
hydrological codeDescription
MACRO bottom boundary
condition
PRZM Soil
Hydrologic Group
XSlowly permeable soils with slight seasonal waterlogging ('perched' water) over
slowly permeable substrates
Percolation rate regulated by water
table heightB
YSlowly permeable soil with prolonged seasonal waterlogging ('perched' water)
over slowly permeable substrates
Percolation rate regulated by water
table heightB-C
Z All undrained peat or soils with peaty tops Not modelled D
5 fundamental types of site hydrology
• Soils L, M, N: free draining (better: free percolation)• Soils O, P, Q: groundwater in the profile• Soils R, S, T, U, V: impermeable substrate
– R: deep soil over hard substrate– S, T: shallow soil over hard substrate– U, V: deep soil over soft substrate
• Soils W, X, Y: slowly permeable substrate• Soils Z: undrained peat not modelled
Implications of the FHG for the MACRO modelling
• Leaching:– L, M, N, W, X, Y soils have leaching flux concentrations
– O, P, Q soils have resident concentrations
– R, S, T, U, V soils have neither of them
• Lateral water movement:– Q, U, V, Y soils have artificial drains
– O, P, R, S, T, W, X soils have lateral subsurface flow (écoulement hypodermique)
– L, M, N soils have neither of them
– Artificial drains and lateral subsurface flow are technically modelled in the same way in MACRO (albeit with different parameter values). However, the interpretation is / can be different.
Implications of the FHG for the PRZM modelling
• The FHG determines the PRZM soil hydrologic group und thus the setof SCS Curve Numbers for the modelling.
• The Curve Numbers in turn determine the frequency and magnitude ofsurface runoff events in PRZM.
Classification of the German soil map into FSTs
• The land-use differentiated German Soil Map 1:1000000 (BGR, 2007) contains 1936 Soil Typological Units STU („Bodenform“).
• 432 STUs with reference profile classification according toFST flowchart set up by John Hollis
• 1504 STUs without reference profile classificationaccording to soil systematic unit
• STUs are only defined for land use categories arable land, grassland, forest and heterogeneous agricultural areas no information on soils under permanent crops(vineyards, orchards, hops)
The topsoil texture code
• Refers to texture of uppermost horizon (usually A)
• Texture triangle according to FAO (1998)
• Silt: 2-50 µm particle diameter
• Classes– 1 = coarse (sand or sandy loam)
– 2 = medium (loamy)
– 3 = medium fine (silty)
– 4 = fine (clayey)
– 5 = very fine (very clayey)
– 6 = peat
The subsoil texture code
• Refers to texture of the subsoil (usually the layer belowthe topsoil and usually a B horizon)
• Classes
– 0 = no subsoil present
– 1 = coarse (sand or sandy loam)
– 2 = medium (loamy)
– 3 = medium fine (silty)
– 4 = fine (clayey)
– 5 = very fine (very clayey)
– 6 = peat
The organic matter profile code
FOOTPRINT organic
profile code Description SOIL (from SGDBE)
aAlluvial soils with an uneven distribution of organic matter down
the profileFluvisols, fluvic subgroups
gWith a thick (artificially deepened) topsoil relatively rich in organic
matterPlaggen soils
hWith an organic-rich topsoil
Chernozems, phaeozems humic & mollic
subgroups
iWith a clay increase in the subsoil
Planosols, luvisols, podzoluvisols, luvic &
planic subgroups
n With a 'normal' organic profile
f Permafrost soils (non-agricultural) with an uneven distribution of
organic matter down the profile
Gelic subgroups
oSoils in volcanic material with organic-rich upper layers
Andosols
pPodzols' with a relatively organic rich topsoil and an relatively
organic rich subsoil layerPodzols
r Soils where the organic profile is limited by rock within 1 m depth Rendzinas rankers and lithosols
t With a peaty topsoil Histosols & histic subgroups
u Undeveloped' soils with relatively small organic matter content. Regosols
0
20
40
60
80
100
120
140
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Organic carbon %
Dep
th (
cm
)
"n"
"u"
"h"
"p"
"g"
The organic matter profile code: example depth profiles
FST properties and parameters
• Generic FST profiles and horizon properties were derived during theFOOTPRINT project as follows:– Classify all Soil Typological Units (STUs) in the SGDBE (Le Bas et al., 1998) into FSTs
for the EU 25 (without Malta and Cyprus)
– Use the profile and horizon data of the STUs in the SPADE-1 and SPADE-2 databases(Hollis et al., 2006) to derive mean FST profiles and horizons one table with soil properties for all 269 modelled FSTs („FOOTPRINT soilproperties database“) (no model parameters yet!)
• Parameterization methodology is fully documented and published:– FOOTPRINT DL21 (Jarvis et al., 2007)
– FOOTPRINT DL20 (Reichenberger et al., 2008)
– FOOTPRINT Final Report (Dubus et al., 2009)
• The FST system does not depend on the profile and horizonproperties derived from SPADE-1 and SPADE-2.
Climate Clustering - Variables
Hortonian runoffNo. days March-June with >19.0 mm prec. (equ. >5 mm runoff on bare soil) No. days March-June with >30.5 mm prec. (equ. >5 mm runoff row crops, covered soil) No. days Sept-Dec with >19.0 mm prec. (equ. >5 mm runoff on bare soil) No. days Sept-Dec with >30.5 mm prec. (equ. >5 mm runoff row crops, covered soil )
Erosion No. days March-June with >26.6 mm prec. (equ. >10 mm runoff on bare soil) No. days March-June with >40.3 mm prec. (equ. >10 mm runoff row crops, covered soil) No. days Sept-Dec with >26.6 mm prec. (equ. >10 mm runoff on bare soil) No. days Sept-Dec with >40.6 mm prec. (equ. >10 mm runoff row crops, covered soil)
Saturation excess runoffNo. days Nov-March with saturation AND >11.8 mm prec. (>2 mm runoff on bare soil) No. days Nov-March with saturation AND >16.8 mm prec. (>2 mm runoff on winter crops)
Leaching and drainageMean temperature April-JuneMean temperature Sept-NovMean cumulative precipitation Oct-MarchMean annual precipitation
Identification of 18* climate variables most relevant forrunoff, erosion, and drainage
No. Days April-June with >2 mm precipitationNo. Days April-June with >20 mm precipitationNo. Days April-June with >50 mm precipitationNo. Days Sept-Nov with >20 mm precipitation
*) 8 defined by Blenkinsop et al. (2008), 10 derived from Curve Number approach
Intersection and subsequent analysis
• The German soil map and the map of the 12 GERDA climate zones were intersected.
• Results:– area of resulting FST/climate combinations: 129480 km2.– 1153 combinations of FST and climate zone with known areas– 395 combinations of drained FST and climate zone– 126 FSTs, 46 of which are drained
• For the pesticide fate modelling: – 973 combinations of FSTmodelled and climate zone– 311 combinations of drained FSTmodelled and climate zone– 102 FSTmodelled, 36 of which are drained
TKTD modelling
• Toxicokinetic-Toxicodynamic (TKTD) modelling is able to describe the dynamics of survival for dynamic exposure time series most suitable tool available to rank different exposure time series according to their worst-case-ness (i.e. % mortality).
• Running 440000 TKTD simulations for 30 years computationally not feasible need to identify exposure descriptors that enable ranking time series
• Toxicokinetic-Toxicodynamic (TKTD) simulations were carried out by partner RWTH Aachen – model used: GUTS (Jager et al., 2011)
– period: 16-month FOCUS PECsw time series
– 3 model parameters varied with Monte Carlo
• Results:– The minimum rank of PECsw,max and AUC (area under the curve) is a protective
descriptor of the relative worst-case-ness of a PECsw exposure time series
– TWA (7d) is not a protective descriptor
Descriptors to rank STEP-3 PECsw series
• Slightly different situation: 30 years vs. 16 months
• TWA (7d) is proposed by EFSA draft guidance documentfor invertebrates, fish and macrophytes
• One possible combination of descriptors would be:– median (annual PECsw,max) acute toxicity
– median (annual AUC) chronic toxicity
– median (annual TWA (7d)) intermediate
• The 440000 PECsw time series have been storedCan extract and experiment with other descriptors.
top related