PATIALLY DISTRIBUTED MODELING FRAMEWORK TO … · BACKGROUND MIDWEST CASE STUDY: 22,186 AC...
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BACKGROUND MIDWEST CASE STUDY: 22,186 AC WATERSHED IN NORTHERN MISSOURI METHODOLOGY A SPATIALLY -DISTRIBUTED MODELING FRAMEWORK TO INTEGRATE EFFECTS OF A GRICULTURAL BEST MANAGEMENT PRACTICES: MIDWEST C ASE S TUDY Daniel B Perkins 1 , Andy Jacobson 1 , Rohith Gali 1 , Colleen Moloney 1 , Wenlin Chen 2 , and Richard A Brain 2 1 Waterborne Environmental, Inc.; 2 Syngenta Environmental Safety Upland Model (PRZM Hydrology & Chemical Load) Chemical reduction efficiency at each catchment Flowing Water Model (e.g. SWAT 1,2,3 or RIVWQ 4,5 ) Soils Sub-watershed Map Units Land Use PROPOSED FRAMEWORK Watershed Corn Acres Waterway Corn Acres % of Total Corn in Waterways 2013 6,208 1,611 26% 2014 6,921 1,938 27.8% Watershed Corn Acres Filter Strip Corn Acres % of Total Corn in Filter Strips 2013 6,208 474 7.6% 2014 6,921 660 9.5% Grassed Waterways Vegetative Filter Strips REFERENCES OBJECTIVE FIELD-SCALE TO WATERSHED-SCALE IMPLICATIONS AND RECOMMENDATIONS: • Integration of BMPs, such as grassed waterways and vegetative buffer strips, is an important consideration for estimating load and concentration of agricultural chemicals. • Grassed waterways and vegetative filter strips can be successfully integrated into a spatially- distributed, watershed-scale hydrologic and chemical transport modeling framework using aerial imagery. Other important BMPs could also be simultaneously included in a similar manner. • This approach represents a first-tier, but other, higher-tier BMP modeling techniques could be implemented within this framework to add more realism, sophistication, and robustness, alternate flowing water models, paired with field-scale data and literature. • With increasing simulated years and associated weather patterns, agrochemical reduction potential resulting from BMPs would be expected to vary • Framework improvement will include process-level relationships between important environmental factors and conditions, such as: slope, chemical solubility, rainfall amount and intensity, soil type, BMP management and implementation (e.g. grass type, mowing, re-seeding, and width) Runoff reduction efficiency at each catchment Applied as a % reduction from published, relevant literature BMP FIELD-SCALE reduction of agrochemicals via BMPs such as grassed waterways is expected to be a function of local-scale factors, such as: • Grass type • Slope • Antecedent water conditions • Rainfall intensity and amount • Area of watershed influenced by the BMP(s) • Crop rotation – depending on agrochemical labeling • Receiving water body flow properties and conditions • Spatial patterns of rainfall amount and intensity • Non-agricultural area and crop area in which an agrochemical may not be applied • Agrochemical application timing Single field within a watershed WATERSHED-SCALE reduction of agrochemicals via BMPs would ideally consider all of the field-scale reduction factors, listed above, as well as: MODELING TECHNIQUES USED TO INCORPORATE SPATIALLY-EXPLICIT BMPS: ℎ = − 1 − 2 + 1 1 + 2 2 Where RFLUX catchment is the PRZM runoff flux representing any given catchment; A HRUi is the area of the total hydrologic response units in the catchment; A BMP1 is the hydrologic contributing area of BMP1; A BMP2 is the hydrologic contributing area of BMP2; RF BMP1 is the reduction fraction of BMP1; and RF BMP2 is the reduction fraction of BMP2 • RFLUX catchment and runoff volume for any given catchment (or catchments that converge to a common stream flow node) is aggregated at the stream node level (discrete points along the stream reach) • At stream nodes, RFLUX catchment and runoff volume are combined with stream baseflow and routed from stream node to node using Manning’s flow and Muskingum routing principles • Watershed-scale, in-stream concentrations are then reported watershed outlet node Watershed with many fields/BMPs Applied as a % reduction from published, relevant literature Develop a spatially-distributed model framework that includes critical watershed-scale information, such as: • Spatially-distributed rainfall • Spatially-distributed runoff as a function of ‘hydrologic response unit’ concept 1,2,3 • Flowing receiving water body • Integrated spatial BMP locations and associated chemical load and concentration reduction potential in a simplistic first approach (e.g. benchmarked to literature values) 1. Arnold, Jeffrey G., et al. "SWAT: Model use, calibration, and validation." Transactions of the ASABE 55.4 (2012): 1491-1508. 2. Larose, M., et al. "Hydrologic and atrazine simulation of the Cedar Creek watershed using the SWAT model." Journal of environmental quality 36.2 (2007): 521-531. 3. Holvoet, Katrijn, et al. "Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT." Physics and Chemistry of the Earth, Parts A/B/C 30.8 (2005): 518- 526. 4. Chung, S.O., E.W. Christen, and W.C. Quayle, 2005. Preliminary Modeling of Pesticide Fate in Drainage Channels Using the RIVWQ Model. In Zerger, A. and Argent, R.M. (eds) MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2005. ISBN: 0-9758400-2-9. 5. Parker, R., J.G. Arnold, Michael Barrett, Lawrence Burns, Lee Carrubba, S.L. Neitsch, N.J. Snyder, R. Srinivasan. 2007. Evaluation of Three Watershed-Scale Pesticide Environmental Transport and Fate Models. JAWRA Journal of the American Water Resources Association. Volume 43, Issue 6, pages 1424–1443, December 2007 Stewardship and land management efforts are important and impactful on crop production, nutrient management, and agrochemical use and potential off-site transport reduction. Within agricultural landscapes of the U.S., numerous structural and cultural BMPs are currently in place to address agrichemical and soil loss reduction, natural resource conservation, and yield increase. A Midwest watershed was used as a case study for development of a spatially-distributed modeling framework that quantitatively evaluates potential reduction in pesticide load and/or concentration from grassed waterways and vegetative filter strips at the watershed scale. Previous integration of BMPs in hydro-chemical modeling has been conducted at the field scale, but the current framework incorporates watershed-scale hydrologic and chemical transport processes. Within the upland model, spatially-explicit sub-areas were modeled to represent various structural BMPs, based on their physical location. In this framework, a baseline scenario (no BMPs) would be developed and used to define comparative difference between addition (adoption) and elimination of BMPs in a step-wise fashion. As part of the baseline scenario, relevant literature values and publicly-available environmental data could be used to inform model parameterization. In this case study, spatial integration of BMPs (grassed waterways and vegetative filter strips) in the upland model was determined from aerial image analysis. This modeling framework represents a watershed-scale, framework for evaluation of effects from BMP adoption rate and viability, weather-dependent behavior, and frequency and extent of BMP efficacy (alone and in combination) on agrochemical transport dynamics. This modeling framework can be further built on to include higher-tier sub-models of BMPs and other best-available data on a watershed-by-watershed basis to increase confidence in estimated concentration and load.
Transcript of PATIALLY DISTRIBUTED MODELING FRAMEWORK TO … · BACKGROUND MIDWEST CASE STUDY: 22,186 AC...
BACKGROUND MIDWEST CASE STUDY: 22,186 AC WATERSHED IN
NORTHERN MISSOURI
METHODOLOGY
A SPATIALLY-DISTRIBUTED MODELING FRAMEWORK TO INTEGRATE EFFECTS OF AGRICULTURAL BEST MANAGEMENT PRACTICES: MIDWEST CASE STUDYDaniel B Perkins1, Andy Jacobson1, Rohith Gali1, Colleen Moloney1, Wenlin Chen2, and Richard A Brain2
1 Waterborne Environmental, Inc.; 2 Syngenta Environmental Safety
Upland Model(PRZM Hydrology
& Chemical Load)
Chemical reduction
efficiency at each
catchment
Flowing Water Model(e.g. SWAT1,2,3 or RIVWQ4,5)
Soils
Sub-watershed
Map Units
Land Use
PROPOSED FRAMEWORK
Watershed Corn Acres
Waterway Corn Acres
% of Total Corn in Waterways
2013 6,208 1,611 26%
2014 6,921 1,938 27.8%
WatershedCorn Acres
Filter Strip Corn Acres
% of Total Corn in Filter Strips
2013 6,208 474 7.6%
2014 6,921 660 9.5%
Grassed WaterwaysVegetative Filter Strips
REFERENCES
OBJECTIVE
FIELD-SCALE TO WATERSHED-SCALE
IMPLICATIONS AND RECOMMENDATIONS:• Integration of BMPs, such as grassed waterways and vegetative buffer strips, is an important
consideration for estimating load and concentration of agricultural chemicals. • Grassed waterways and vegetative filter strips can be successfully integrated into a spatially-
distributed, watershed-scale hydrologic and chemical transport modeling framework using aerial imagery. Other important BMPs could also be simultaneously included in a similar manner.
• This approach represents a first-tier, but other, higher-tier BMP modeling techniques could be implemented within this framework to add more realism, sophistication, and robustness, alternate flowing water models, paired with field-scale data and literature.
• With increasing simulated years and associated weather patterns, agrochemical reduction potential resulting from BMPs would be expected to vary
• Framework improvement will include process-level relationships between important environmental factors and conditions, such as: slope, chemical solubility, rainfall amount and intensity, soil type, BMP management and implementation (e.g. grass type, mowing, re-seeding, and width)
Runoff reduction
efficiency at each
catchment
Applied as a % reduction from published, relevant
literature
BMP
FIELD-SCALE reduction of agrochemicals via BMPs such as grassed waterways is expected to be a function of local-scale factors, such as:
• Grass type• Slope• Antecedent water conditions• Rainfall intensity and amount
• Area of watershed influenced by the BMP(s)• Crop rotation – depending on agrochemical labeling• Receiving water body flow properties and conditions• Spatial patterns of rainfall amount and intensity• Non-agricultural area and crop area in which an agrochemical may
not be applied• Agrochemical application timing
Single field within a watershed
WATERSHED-SCALE reduction of agrochemicals via BMPs would ideally consider all of the field-scale reduction factors, listed above, as well as:
MODELING TECHNIQUES USED TO INCORPORATE SPATIALLY-EXPLICIT BMPS:
𝑅𝐹𝐿𝑈𝑋𝑐𝑎𝑡𝑐ℎ𝑚𝑒𝑛𝑡 = 𝑖𝑛𝑅𝐹𝐿𝑈𝑋𝐻𝑅𝑈𝑖 𝐴𝐻𝑅𝑈𝑖 − 𝐴𝐵𝑀𝑃1𝑖 − 𝐴𝐵𝑀𝑃2𝑖 + 𝑅𝐹𝐿𝑈𝑋𝐻𝑅𝑈𝑖 𝐴𝐵𝑀𝑃1𝑖 𝑅𝐹𝐵𝑀𝑃1𝑖 + 𝑅𝐹𝐿𝑈𝑋𝐻𝑅𝑈𝑖 𝐴𝐵𝑀𝑃2𝑖 𝑅𝐹𝐵𝑀𝑃2𝑖
𝐴𝐻𝑅𝑈𝑖
Where RFLUXcatchment is the PRZM runoff flux representing any given catchment; AHRUi is the area of the total hydrologic response units in the catchment; ABMP1 is the hydrologic contributing area of BMP1; ABMP2 is the hydrologic contributing area of BMP2; RFBMP1 is the reduction fraction of BMP1; and RFBMP2 is the reduction fraction of BMP2
• RFLUXcatchment and runoff volume for any given catchment (or catchments that converge to a common stream flow node) is aggregated at the stream node level (discrete points along the stream reach)
• At stream nodes, RFLUXcatchment and runoff volume are combined with stream baseflow and routed from stream node to node using Manning’s flow and Muskingum routing principles
• Watershed-scale, in-stream concentrations are then reported watershed outlet node
Watershed with many fields/BMPs
Applied as a % reduction from published, relevant
literature
Develop a spatially-distributed model framework that includes critical watershed-scale information, such as:
• Spatially-distributed rainfall• Spatially-distributed runoff as a function of ‘hydrologic response unit’ concept1,2,3
• Flowing receiving water body• Integrated spatial BMP locations and associated chemical load and concentration reduction
potential in a simplistic first approach (e.g. benchmarked to literature values)
1. Arnold, Jeffrey G., et al. "SWAT: Model use, calibration, and validation." Transactions of the ASABE 55.4 (2012): 1491-1508.2. Larose, M., et al. "Hydrologic and atrazine simulation of the Cedar Creek watershed using the SWAT model." Journal of environmental quality 36.2 (2007): 521-531.3. Holvoet, Katrijn, et al. "Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT." Physics and Chemistry of the Earth, Parts A/B/C 30.8 (2005): 518-
526.4. Chung, S.O., E.W. Christen, and W.C. Quayle, 2005. Preliminary Modeling of Pesticide Fate in Drainage Channels Using the RIVWQ Model. In Zerger, A. and Argent, R.M. (eds)
MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2005. ISBN: 0-9758400-2-9.5. Parker, R., J.G. Arnold, Michael Barrett, Lawrence Burns, Lee Carrubba, S.L. Neitsch, N.J. Snyder, R. Srinivasan. 2007. Evaluation of Three Watershed-Scale Pesticide
Environmental Transport and Fate Models. JAWRA Journal of the American Water Resources Association. Volume 43, Issue 6, pages 1424–1443, December 2007
Stewardship and land management efforts are important and impactful on crop production, nutrient management, and agrochemical use and potential off-site transport reduction. Within agricultural landscapes of the U.S., numerous structural and cultural BMPs are currently in place to address agrichemical and soil loss reduction, natural resource conservation, and yield increase.
A Midwest watershed was used as a case study for development of a spatially-distributed modeling framework that quantitatively evaluates potential reduction in pesticide load and/or concentration from grassed waterways and vegetative filter strips at the watershed scale. Previous integration of BMPs in hydro-chemical modeling has been conducted at the field scale, but the current framework incorporates watershed-scale hydrologic and chemical transport processes. Within the upland model, spatially-explicit sub-areas were modeled to represent various structural BMPs, based on their physical location. In this framework, a baseline scenario (no BMPs) would be developed and used to define comparative difference between addition (adoption) and elimination of BMPs in a step-wise fashion. As part of the baseline scenario, relevant literature values and publicly-available environmental data could be used to inform model parameterization. In this case study, spatial integration of BMPs (grassed waterways and vegetative filter strips) in the upland model was determined from aerial image analysis.
This modeling framework represents a watershed-scale, framework for evaluation of effects from BMP adoption rate and viability, weather-dependent behavior, and frequency and extent of BMP efficacy (alone and in combination) on agrochemical transport dynamics. This modeling framework can be further built on to include higher-tier sub-models of BMPs and other best-available data on a watershed-by-watershed basis to increase confidence in estimated concentration and load.
