GIS integrated subsurface leaching assessment model for various agrochemicals mobility and persistence in Nebraska

Transcription

1 GIS integrated subsurface leaching assessment for various agrochemicals mobility and persistence in Nebraska By Mohana Sundaram S Postdoc Research Associate Nebraska Water Center University of Nebraska-Lincoln

2 Leaching assessment s Different s with different degrees of complexity to assess the chemical transport Simple (TIER-I) GUS index Leaching index Convective mobility Diffusive mobility Persistence RF/AF index Lumped s No time stepping Intermediate (TIER-II) CRACKP LEACHP GLEAMS MACRO PELMO PRZM2 1 Dimensional s Physical, chemical and biological processes Less data requirement Complex (TIER-III) HYDRUS 2D/3D TOUGH2 VS2DTI Multidimensional domain Complex physical, chemical, biological processes Needs more data

3 Applications of screening s (TIER-I) Contaminant leaching risk assessment (Volatile and Non-volatile) Pesticide registration Better than limited field perspective studies conducted by registrants Ex: Hawaii Monitoring waiver studies for drinking water wells for VOCs Pharmaceutical leaching assessment from recycled water use/septic tanks

4 CLERS - Conceptual process Kohne et al., 2009 Phase partitioning for gas, for solid, for liquid The new CLERS mainly consider sorption, decay, and convective transport in the vadose zone with volatilization loss from soil surface rather than lateral transfer, root uptake, etc (see the right picture).

5 Modification in RF and AFR in CLERS The new CLERS include partitioning for vapor phase (often neglected for pesticides from many previous studies) and volatilization loss from the soil surface. Prediction errors of the generally increase at low Peclet numbers (i.e., low downward advective flux). Vapor phase partitioning ρb foc K RF =1+ θ FC oc d RF AFR ln θ FC = + k q t Expanded ρb foc K ERF =1+ θ 1 EAF = 1+ σ q FC oc na K + θ FC ln 2 d ERF θ exp q t1 2 H FC q = q +σ where, σ = K H l D a g Volatilization loss Comparison of two s whether or not vapor loss is lumped into first-order rate process

6 Uncertainty in the AFR value Several sources of uncertainty Soil data, pesticide data, spatial and temporal variability in parameters, lack of process descriptions in AF (e.g. preferential flow, hydrodynamic dispersion, recharge variations) Evaluated by first-order uncertainty in several studies AF estimates associated with large uncertainties that limit the application of AF ( CV + CV + CV + CV + CV ) FC = AFR RF d q θ δ (1) t 1/ 2 CV x Coefficient of variation of ' x' parameter Suitable for relative exposure assessments only

7 Uncertainty bands and Reference chemicals Uncertainty bands can be expressed as: AFR ± δ (2) AFR Reference chemicals: Reference chemicals are identified based on their likely and unlikely leaching in the groundwater Leacher (e.g., atrazine): Traces of chemical often observed in groundwater Non-Leacher (e.g., endosulfan): Traces of chemical not observed in groundwater

8 AFR vulnerability classification AFR Class no. AFR Class Condition 1 Highly leachable CM<LM 2 Moderate leachable LM>(CM-CS)<(LM+LS) 3 Less likely (LM+LS)>(CM-CS)<(NLM-NLS) 4 Unlikely (NLM-NLS)> (CM-CS)<NLM 5 Very unlikely CM>NLM LM-LS LM LM+LS NLM-NLS NLM NLM+NLS AFR CM- Chemical mean AFR value of the Chemical LM Mean AFR value of the Leacher NLM - Mean AFR value of the Non-Leacher; CS - Standard deviation AFR value of the Chemical; LS - Standard deviation AFR value of the Leacher NLS - Standard deviation AFR value of the Non-Leacher

9 Soil data base The United States Department of Agriculture (USDA), Natural Resources Soil Service (NRSS) leads the soil survey and maintains the soil database for the nation. Three soils at different spatial scales are i) SSURGO, ii) STATSGO, and iii) NATSGO data bases We prepared SSURGO and STATSGO data bases for the CLERS. SSURGO soil data base gives a detailed spatial information than STATSGO soil data base

10 SSURGO soil data base SSURGO soil has 3 levels of information which can be related from bottom level to top level, map unit levels which is related to component levels Which is related to layer levels CLERS requires the soil parameters such as bulk density, organic carbon and field capacity which are available at layer levels Therefore, this layer level information is aggregated to map unit level by weighted averaging approach

11 Weighted average approach for soil parameters at map unit level For example, the soil layer contains the following (Ref: Table: Component: MUKEY (mapping unit) COKEY(Component key ) Component(%) : : Table: Chorizon: COKEY(component key) Horizon depth (cm) Clay(%) : : : : : : Weighted average clay for each component: : [ ( ( 20/152) *9.5 ) + ( ( 56/152 ) * 12.5 ) + ( ( 76/152 ) * 1.5 ) ] = : [ ( ( 20/152) *9.5 ) + ( ( 10/152 ) * 11.5 ) + ( ( 122/152 ) * 3.5 ) ]= 4.82 Weighted average clay for each map unit: : * 6.60 = : * 4.82 = = = 5.89

12 Pesticide database Three databases for pesticides linked in the CLERS 1. Hornsby et al., 1996 data base: 2. Stenemo et al., 2007 data base: 3. PPDB (Lewis et al., 2016) These three data bases contain volatile and non-volatile compounds chemical properties such as, K H, T 1/2, K oc mean and standard deviation values.

13 Recharge data base Recharge is estimated in 2 steps, Step:1 A remote sensing based evapotranspiration estimated using complementary relationship method Step:2 Baseflow index based recharge estimated at regional scale using estimated ET information from Step:1

14 Step:1 Evapotranspiration estimation A complementary relationship based ET estimation was adopted to estimate annual ET (Bouchet, 1963) Under energy limiting condition ET p decreases to ET w Under water limiting condition, ET a increases to ET w EE TTTT = RR TT λλff TT (TT pp TT (3) EE TTTT = bb1 + bb2 1 + γγγγ Δpp RR TTTT (4) EE TT = 2EE TTTT EE TTTT (5)

15 Annual Evapotranspiration Annual average estimated ET ranged from 322 mm/year to 750 mm / year Western part being the lowest estimated ET while eastern part being the highest State wide average ET is about 539 mm/year which is 89% of the total rainfall A good correlation between led ET against water balance ET with RMSE = 42 mm/year R 2 = 0.91

16 Step: 2 Base flow Baseflow can be done with streamflow records using computer based baseflow separation methods such as PART program (Rutledge, 1993) QQ bb is base flow (L 3 T -1 ); QQ ss is surface runoff (L 3 T -1 ) BBBBBB = QQ bb QQ bb + QQ ss (6) The Groundwater Toolbox in ArcGIS has four different base flow separation methods, BFI (standard and modified BFI methods) HYSEP (Fixed interval, Sliding interval and local minimum) PART RORA 86 stream gauging locations matched with a minimum of 10 years continuous daily streamflow data set PART equates the streamflow to baseflow when there is no surface runoff event and interpolates linearly other times

17 Baseflow estimation as base recharge The long term base recharge can be calculated from rainfall, ET and BFI values as follows, BBBBBB PP EEEE = BBBBBB qq = qq bb RR (7) ET values from PY-CRAE This estimated ET and rainfall values were used with BFI values to calculate baseflow or base recharge A good agreement between independent estimates of baseflow and recharge was observed with R 2 = 0.83 Recharge from Remote Sensing (mm/year) Annual recharge y = x R² = Base flow from USGS (mm/year)

18 Modification in the CLERS script The PYTHON script for this CLERS is slightly modified from the original version of Ki et al., Modifications in the current CLERS version include, Soil data base selection 1. Script takes the argument for choosing the geographic locations and corresponding data base 2. Script takes the argument for reading pesticide data from script integrated pesticide data base 3. Script takes the argument for choosing different soil data bases such as STATSGO and SSURGO for accessing soil related parameters

19 Case study : CLERS for Nebraska Chemicals to be analyzed: Chemical name Type Remarks Sulfentrazone Herbicide Leacher in this study S-metolachlor Herbicide - Atrazine Herbicide/Fungicide - Acetochlor Herbicide - Glyphosate Herbicide - Trifloxystrobin Herbicide Non-Leacher in this study Soil data base to be selected: SSURGO soil data base Recharge : Zonal average recharge calculated based on SSURGO soil polygons Landuse mask: Irrigated (corn, soybean, sugar beets, sorghum)

20 Chemical properties Chemical K oc (m 3 /kg) Standard deviation (K oc ), (m 3 /kg) T 1/2 (days) Standard deviation (T 1/2 ), (days) Ref: PPDB (Lewis et al., 2016) Henry s coefficient (K h ) Sulfentrazone S-metolachlor Atrazine Acetochlor Glyphosate Standard deviation (K h )

21 Soil and recharge datasets for Nebraska (SSURGO)

22 Chemical leaching in Nebraska Red: high leaching Blue: low leaching

23 Relative leaching in Nebraska

24 Validation of CLERS with Four AWDN weather stations locations selected randomly across the state 1. A A A A Textural classes at 4 locations obtained based on SSURGO soil data ROSETTA in was run for retention parameters

25 Soil specific parameters and estimated retention parameters Parameters AWDN: A AWDN: A AWDN: A AWDN: A BD_M (kg/m 3 ) BD_SD (kg/m 3 ) OM_M (-) OM_SD (-) OC_M (-) OC_SD (-) FC_M (cm 3 /cm 3 ) FC_SD (cm 3 /cm 3 ) Total sand (%) Total clay (%) Total silt (%) Theta_r (cm 3 /cm 3 ) Theta_s (cm 3 /cm 3 ) alpha (1/cm) n Ks (cm/day) l

26 Atmospheric Boundary conditions Potential ET (cm/day) AWDN station: A Days since June, 1, 2016 ET Rainfall Rainfall (cm/day) Potential ET (cm/day) AWDN station: A Days since June, 1, 2016 ET Rainfall Rainfall (cm/day) 1.4 AWDN station: A AWDN station: A Potential ET (cm/day) ET Rainfall Rainfall (cm/day) Potential ET (cm/day) ET Rainfall Rainfall (cm/day) Days since June, 1, 2016 Days since June, 1, 2016

27 Chemical concentration at 153 DAA 99% percentage of all applied chemicals except sulfentrazone were degraded and the maximum of 3% left in the soil after 150 DAA Highly mobile components order Sulfentrazone S-metolachlor Atrazine Acetochlor Glyphosate

28 Chemical leached concentration at 50 cm depth AFR value and error limit Leaching order from CLERS

29 Comparison of leaching order: AWDN site CLERS HYDRUS- 1D A A Leaching Rank AWDN site CLERS HYDRUS- 1D Sulfentrazone Sulfentrazone 1 A Sulfentrazone Sulfentrazone 1 S-metolachlor S-metolachlor Atrazine Acetochlor Glyphosate Atrazine Acetochlor Glyphosate S-metolachlor S-metolachlor Atrazine Acetochlor Glyphosate Atrazine Acetochlor Glyphosate Sulfentrazone Sulfentrazone 1 A Sulfentrazone Sulfentrazone 1 S-metolachlor S-metolachlor Atrazine Acetochlor Glyphosate Atrazine Acetochlor Glyphosate S-metolachlor S-metolachlor Atrazine Acetochlor Glyphosate Atrazine Acetochlor Glyphosate Leaching Rank

30 Funding Agency for this project: Nebraska Environmental Trust Thank You

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