Modeling of pesticide fate in a
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Modeling of pesticide fate in a luxembourgish catchment with the Soil and Water Assessment Tool (SWAT) - combining modeling and monitoring. Stefan Julich 1& ² , Tom Gallé², Marion Frelat², Michael Bayerle², Hassanya El Khabbaz² & Denis Pittois²

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M 3 , WFD and POCIS

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M 3 wfd and pocis

Modeling of pesticide fate in a luxembourgish catchment with the Soil and Water Assessment Tool (SWAT) - combining modeling and monitoring

Stefan Julich1&² , Tom Gallé², Marion Frelat², Michael Bayerle², Hassanya El Khabbaz² & Denis Pittois²

1 TechnischeUniversitätDresden, Instit. for Soil Science & Site Ecology

² CRTE, CRP Henri Tudor - Luxembourg


M 3 wfd and pocis

M3, WFD and POCIS

  • M3 is a demonstration project for WFD policy implementation of the LIFE+ programme

  • M3 tests state-of-the-art monitoring and modelling tools for programme of measures (POM) evaluation

  • Can passive samplers (POCIS) help to validate and improve simulations of pesticide fate ?


Inroduction of study area

Inroduction of study area

elevation

slope

landuse

Land use: ~ 30% managed pasture

~ 30% agriculture (corn, winter cereals, summer cereals)

~ 6% urban area


Sources and dynamics of pesticide emissions

Sources and dynamics of pesticide emissions


Emission modeling of pesticides

Emission modeling of pesticides

Main challenges in model calibration

  • Monitoring data covering the dynamics of the emissionpathways/sources

  • Pesticide application date, amount, formulation and spatial distribution are unkown

  • Compound fateproperties t1/2 and KOC are spatially variable

  • Calibration atcatchmentoutlet, no distributed calibration possible

t1/2 = 15 d

Log KOC = 1.51


Case study wark monitoring setup

Case studyWark:Monitoring setup

4

6

3

5

2

1


The watershed

The watershed

  • Warkwatershed ca. 82km²

  • First simulationconcentrate on Terbuthylazine

  • Applied on corn (~ 465 ha)

  • Statisticsindicate a applicationof340 g/ha on average

  • Same applicationdatefor all fields


Data needs for pesticide fate modelling

Data Needs forpesticidefatemodelling ?

  • Whichcropsaregrown?

  • Whichsubstancesareapplied?

  • Whenarethepesticidesapplied?

  • Whatistheamountoftheapplication?


Loads observed vs simulated

Loads – observed vs. simulated

4

6

3

5

2

1

  • Event 1  toolessmobilization (surfacerunoff vs. Parameterization)

  • Event 2  viceversa


M 3 wfd and pocis

Floodwave

24/5-10/6

4

24/5-10/6

3

2

6

5

1


Loads observed vs simulated1

Loads – observed vs. simulated

Mertzig

4

6

3

  • Mertzigforbotheventstolessexport applicationamounts?

  • Niederfeulenfirsteventisunderestimated  toolessmobilizationthroughsurfacerunoff?

5

2

Niederfeulen

1


Conclusions outlook

Conclusions & outlook

  • Results indicate  simulations do not match with the measurements

  • under or over estimation of surface runoff

  • POCIS measurement are useful to gain spatially distributed information of pesticide export to rivers

  • Comparison of simulation with POCIS results show the value of adequate Input Data like spatial information of application amounts and application times


Conclusions outlook1

Conclusions & outlook

  • Integration of more events in the model validation (measurements are still in progress)

  • Improve hydrological modelling

  • Simulate pesticide with spatially different application dates and amounts


Event loads with long term pocis

Event loadswith long term POCIS

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6

3

5

2

1


Performance of pocis during floodwaves

Performance of POCIS duringfloodwaves

  • Rsdetermined in the fieldwith composite samples over a day

  • Rs on different river sites RSD 20-30 %

  • No explanation for Rsvariability


Losses during longer exposure

Lossesduring longer exposure

Memory test

  • Spiked POCIS exposed for differentlength of time in a river

  • Losses for compounds with log KOW <1

  • ke: 0.04-0.1 d-1 (t1/2: 7-17 d)

Exposure time


Load calculation bias

Loadcalculationbias

  • Loadcalculation on eventlevelbiasedonly on floodwave close to application


Time proportional calculation

Time-proportionalcalculation


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