A method to derive crop-specific leaching scenarios

Transcription

1 A method to derive crop-specific leaching scenarios Andreas Huber, November 22, 2005 DuPont Crop Protection Bad Homburg EU Modelling Workshop, Paris.

2 2 Preamble GIS methods can be used to estimate leaching risk based on current understanding of leaching processes, however the validation of any GIS method with measured data is still difficult.

3 Introduction 3 Where could GIS data be used in leaching assessments? Tier 1 * 1) standard scenarios with lab or field degradation data Tier 2 * 2a) Modeling with refined parameters 2b) Modeling with refined scenarios Tier 2 2c) Higher tier leaching Experiments (set in context) Tier 3 * 3a) Combined modeling refined parameters & scenarios 3b) Advanced spatial Modeling Tier 3 3c) Model-based extrapolation of leaching experiments 3d) Other modeling approaches Tier 4 * * Mitigation possible 4) groundwater monitoring This assessment scheme is still under discussion in FOCUSgw

4 Proposed endpoints in Tier 2 and 3 assessments 4 Regulatory endpoint = PECgw < 0.1 µg/l Tier 2 Endpoint of assessment = new use-specific scenarios selected on the basis of their relative rank of leaching risk Regulatory endpoint = calculated with a standard FOCUS leaching model as it is used in Tier I Tier 3 Endpoint of assessment = Regulatory endpoint

5 Case study 5 Problem: Compound X is proposed for use in sugar beets in France. How should a refined crop scenario look? Sugar beet cropping area in France FOCUS gw scenario > 0.1 ppb in Tier 1

6 Case study: Selection of indicators 6 The parameter should be available across the EU in the same resolution and quality AND The parameter should be sensitive in leaching models MARS climate data 50 x 50 km Organic carbon 1 x 1 km

7 Case study: Selection of indicators 7 Proposed Indicators in the case study Temperature (seasonal temperature for DT50 < 10 d, annual average for DT50 > 10) Average winter rainfall Soil properties Organic carbon ph (if relevant) other soil properties (if relevant) e.g. no correlation between OC and sorption

8 Case study: Selection of indicators 8 Why winter rainfall and not annual rainfall? Hypothesis: Groundwater recharge occurs primarily in winter Water balance April - September Negative (= ETP higher than precipitation) Positive (=ETP lower than precipitation) Source: Interpolated meteorological data, JRC MARS database - EU Commission - JRC

9 Case study: Methodology 9 Area of interest MARS climate grid cells covering sugar beet area Sugar beet area (ha) Source for Ag. statistics: SCEES, 2000; canton level

10 Case study: Methodology 10 Unit of analysis MARS units were selected because daily climate data only available for 50 km grids; consequently the mean OC was calculated for each unit Organic carbon contents in % < > 4.3 Organic carbon content in % < > 2.6

11 Case study: Methodology 11 Calculation of percentiles if all factors are of equal importance Organic carbon content in % < > 2.6 Percentile of leaching vulnerability < > = th percentile = 1.23 in distribution N(0,1) 84th percentile = 0.99 in distribution N(0,1) 51st percentile = in distribution N(0,1) 90th percentile = 2.25 in distribution N(0, 3)

12 Case study: Methodology 12 Are these factors of equal importance? => Analysis of variation of input parameters in sugar beet area Organic carbon content (%) Temperature ( C) Frequency Frequency Winter rainfall (mm) Temperature ( C) Winter rainfall (mm) Organic carbon (%) Average Median Stdev Coefficient of variation

13 Case study 13 Variation of landscape parameters results in: OC > winter rainfall > temperature Question: Is organic carbon more important than temperature to assess leaching risk? Solution: Perform a sensitivity analysis with properties of the compound. Example: Sensitivity analysis with 4 FOCUS dummy compounds (Piacenza, PEARL, 1 kg/ha) Dummy A Dummy B Dummy D Dummy 3 (FOCUSsw) DT K OC

14 Case study 14 PECgw (ppb) Results of sensitivity analysis Base case + 23 % rain - 23 % rain + 5 % temp - 5 % temp + 35 % OC Dummy A (Koc 103): all factors have more or less a similar impact - 35 % OC Dummy 3 (Koc 1) : 5 % change of temperature has same impact as 35 % change of OC Dummy A Dummy B Dummy D Dummy 3

15 Case study 15 Does weighting affect the location of candidate scenarios? Recommendation: Perform sensitivity analysis to justify use of weights Map for Dummy A OM: 0.29 Temp: 0.38 Rain: 0.34 Map if all factors get equal weight Risk > 80th percentile Map for Dummy 3 OM: 0.18 Temp: 0.73 Rain: 0.09

16 Case study: Selection of candidate scenarios 16 Method: CNT_CANTOCANTON SUGAR_BEECICECD temp_avg11wi_rain11 MEAN_OC_MEDIAN_O OC_PERC_1RAIN_PER_1TEMP_PER_PERC_TOT_ 5 DUNKERQUE GRAVELINE HONDSCHOO CALAIS-ES ARMENTIER BAILLEUL BASSEE (L HAUBOURDI LANNOY MERVILLE QUESNOY-S STEENVOOR LOMME SECLIN-NO CAMBRIN LAVENTIE BETHUNE-E BERGUES BOURBOURG CASSEL HAZEBROUC HAZEBROUC WORMHOUT AIRE-SUR ARDRES AUDRUICQ BETHUNE-N CALAIS-CE FAUQUEMBE HUCQUELIEOutput = LILLERS LUMBRES NORRENT-F SAINT-OMETable with percentiles ARQUES AUCHEL CALAIS-NO ETAPLES for each grid cell 16 GUINES MARQUISE SAMER ARLEUX BOUCHAIN CAMBRAI-E CAMBRAI-O CARNIERES CYSOING DENAIN DOUAI-SUD DOUAI-SUD MARCHIENN ORCHIES PONT-A-MA SECLIN-SU DOUAI-NOR SECLIN CAMBRAI ARRAS-NOR ARRAS-SUD BERTINCOU CARVIN CROISILLE MARQUION VIMY VITRY-EN LENS-NORD NOEUX-LES DAINVILLE DOUVRIN SAINS-EN HENIN-BEA BAVAY MAUBEUGE Look for grid cells closest to the target percentile of total vulnerability temperature winter rainfall organic carbon content (other properties) (in most typical production area) Number of scenarios depend on compound and landscape properties

17 Case study: Selection of candidate scenarios 17 Defining the scenario: Climate files: Extracted directly from MARS (=> same database as for FOCUS Tier 1 scenarios) Soil profile: Extracted from SPADE database in EU soil map Plausibility checks required to ensure that scenario is still located in the cropping area!

18 Case study: Selection of candidate scenarios 18 Selected grid cells Sugar beet area Soil units with profile data Scenario at the edge of sugar beet area

19 Case study: Selection of candidate scenarios 19 Regulatory endpoints in Tier selected_gridcells Selected soil units; profile number in SPADE I

20 Conclusions 20 Advantages of the method More realistic crop scenarios tailored to the specific use Method based readily available EU GIS databases Highly transparent when applied according to Good Modelling Practices - Tracking of all decisions - Reporting of calculation results and input parameters Simple method which is flexible enough to be applied for numerous regions, compound properties and uses Flexibility to include other pesticide or landscape parameters if needed (e.g. ph, clay, other properties.) Regulatory endpoint (PECgw) is calculated with a leaching model that is applied in Tier-1 => consistency within the tiered assessment scheme

21 Conclusions 21 Disadvantages of the method Basic GIS skills are needed to use the methodology (ArcGIS) Demanding in terms of reporting to make all decisions transparent Impact of compound properties have to be approximated by means of a sensitivity analysis. This approach is subject to uncertainty. Expert judgement is needed to decide about the inclusion of parameters in the sensitivity analysis (e.g. DT50 < 10 d may require seasonal rainfall and temperature data) The assessment is currently done on the basis of the 50 km grid size of MARS because daily climate data is only available at this size.

22 Conclusions 22 Outlook Minor, regional crops may not require a complete analysis and cropping area alone may suffice (e.g. hops) Method provides an interim solution as long as detailed soil databases are not available across Europe (e.g. 1 : 200,000 soil maps) In certain regions of Europe more detailed databases are available which would allow for regional scale applications of process-based models; these methods are superior if the database is considered accurate The added information in terms of regional leaching risk justifies the various uncertainties inherent to the method The method is not used to predict a PECgw; the regulatory endpoint is still based on calculations with a standard leaching model and is thus highly compound specific.

23 23 Thank you

24 24 Distribution of percentiles Percentile (%) Dummy A Dummy grid cells