Phosphorus index project overview collick
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71st SWCS International Annual Conference July 24-27, 2016 Louisville, KY
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Phosphorus index project overview collick
- 3. Spring Creek Mahantango Creek Conewago Creek Allegheny Plateau Dressler Run Nanticoke River (Bucks Branch) Upper Manokin River Factory Brook Valley & Ridge Piedmont Coastal Plain Shenandoah River Chesapeake Conservation Innovation Grant Watersheds
- 5. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners
- 6. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners
- 8. This is what we must represent177 144 44 1 <1 Soil P – mg kg-1 Runoff – liters P loss – kg P ha-1 yr-1 92 Buda et al. JEQ, 2009 Lowest field is now a CREP buffer that continues to yield largest P loads 4620 DPDPDP 8 DPDPDP78
- 9. Hydrologic Routine Testing – Mahantango Creek • Similar outlet discharge hydrographs • Better spatial distribution of runoff with TopoSWAT • Improved identification of nutrient sources with TopoSWAT Standard SWAT WE38 outlet TopoSWAT Collick et al., 2014
- 10. Detailed field‐scale modeling https://i.ytimg.com/vi/wx0SJM7FeEc/mqdefault.jpg http://www.extension.org/sites/default/files/w/4/4a/ Spreading_manure.jpg • Pre‐process SWAT to field‐scale • Every field can have a different set of practices • Field specific management scenarios
- 11. Application rate Collick et al. JEQ, 2016 6000 gal ac‐1 9000 gal ac‐1 NewOld NewOld 6 3 0 Runoff total P kg ha‐1 New Old Runoff total P kg ha‐1 1/15 1/31 2/14 0.1 0 0.2 6000 gal ac‐1 Field 1 Field 2 P routine updates are strongly suggested for models Application timing
- 12. 0.00 0.25 0.50 0.75 1.00 1/1/2010 2/20/2010 4/11/2010 0.0 1.0 2.0 3.0 4.0 1/1/2010 2/20/2010 4/11/2010 0 10 20 30 40 50 Measured Old New No manure applied Poultry litter applied, January (2 tons ac‐1) Runoff P, mg L‐1 Rainfall, mm Runoff P, mg L‐1
- 13. Next generation: Forecasting models SWAT with weather forecasterWatershed stakeholder decision support system architecture • Forecast runoff risks (6‐hrs to 3 days) across Chesapeake Bay watershed • Provide information for land management decision‐making to reduce nonpoint source pollution risks • Enabled for smart phones and other GPS‐ enabled devices Sommerlot et al., 2016 Env. Model. & Soft.
- 16. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners
- 17. Upper Manokin River, MD Coastal Plain
- 19. 0 40 80 120 160 20020 Meters 0 40 80 120 160 20020 Meters 0 40 80 120 160 20020 Meters 0 40 80 120 160 20020 Meters Figure : The delineations of the four ditches on the 1m LiDAR (A), 3m NED DEM (B) and 10m NED DEM (C). Only three drainage areas are apparent from the 10m DEM. A. 1m DEM B. 3m DEM C. 10m DEM 0 1 2 30.5 Kilometers '4 0 1 2 30.5 Kilometers HUC12 Manokin River – Taylor Branch 10m DEM 3m DEM UMES Experimental Farm D. Manokin Watershed Ditch 5 Ditch 7 Ditch 6 Ditch 8 Ditch 5 Ditch 7 Ditch 6 Ditch 8 Ditch 5 Ditch 6 & Ditch 7 Ditch 8 19
- 20. Water Balance Modeling Efforts • Early effort to apply SWAT to the Manokin watershed at the channel scale at the UMES farm using these datasets to parameterize the model initialization and corroborate the model results • However, the processes occurring on this flat terrain, the discrepancy of flow in the field channels could not be adequately represented in SWAT • Potential to couple water balance model with nutrient cycling and transport model
- 21. General field layout with field ditches and a schematic of the water balance model View from above Lateral view The model (Collick et al., 2006) seems appropriate for Manokin fields (the low slope gradient may still cause problems), which are divided by field drainage ditches that flow into larger public drainage association (PDA) channels
- 23. Testing the coastal plain P Index Drainage intensity and distance to drains Majority of P loss occurs in subsurface flow Empirical work Geophysical techniques to map shallow flow paths ERI now supported by NIFA funds CIG proposal pending for subsurface processes
- 25. Factory Brook – Limited watershed monitoring • TopoSWAT without calibration • Automation of farm nutrient management plan data • APLE model comparison with a large farm field dataset from across New York
- 27. Dissolved P loss Particulate P loss % % Comparison of application method, same amount of P applied Spring, 6000, surface apply 100 100 Spring, 6000, incorporation within 1 day 66 95 Spring, 6000, injection 31 85 Comparison of surface versus incorporation and injection, same N supply Spring, 15500, surface apply 100 100 Spring, 6000, incorporation within 1 day 23 52 Spring, 6000, injection 11 47 Comparison for cover crop use Fall, 6000, surface apply 100 100 Fall, 6000, surface apply + cover crop 110 81 Relative differences between methods to be compared to P index coefficients Ketterings et al., 2016 in review, JEQ
- 29. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners
- 31. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . ... . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . ... . .. .. . . .. P Loss (Modeled or Measured) Representative* Scenarios Modeled Monitored Scenario . Modeled Scenario Integrating Modeling and Monitoring * Major Panel Activity P Index
- 32. NY P index and TopoSWAT – 1st Run R² = 0.29 R² = 0.57 0 5 10 15 20 25 30 35 40 45 0.0 100.0 200.0 300.0 400.0 500.0 600.0 Soluble P from TopoSWAT, kg ha‐1 Dissolved P index Corn and alfalfa fields Grass fields Very preliminary results from uncalibrated TopoSWAT run of Factory Brook 1st Run indicates further vetting of results and rerunning when necessary
- 33. Pennsylvania P Index Screening tool • Special Protection Watershed • Significant management changes • STP > 200 ppm Mehlich 3 • Distance to Stream < 150 feet Source factors • Soil Test P (Mehlich 3) • Manure P (Rate, Timing x Method, P Availability Coeff.) • Fertilizer P (Rate, Timing x Method) Transport factors • Erosion (RUSLE2) • Runoff Potential (Drainage Class) • Subsurface drainage • Distance to the Stream Modified connectivity • Riparian Buffer • Grassed waterway • Direct connection
- 36. SSURGO data -- clay % Clay
- 37. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners
- 38. Allegheny Plateau New York and Pennsylvania Ridge and Valley/Piedmont Pennsylvania and West Virginia Coastal Plain Delaware Assess opinions regarding… – Current P Index factors (importance and reliability) – P Index modifications (boundaries and screening tool) Evaluation and Revision of Phosphorus Indices Questionnaire for Nutrient Management Experts Ketterings et al., 2016 in press, JSWCS
- 39. Lessons and Achievements • Model Improvement: Better representation of critical source areas and nutrient transport • Greater understanding of challenges/limitations in modeling P risk • Model component comparisons (i.e., erosion) to pinpoint where changes are likely needed in P index • Informed evaluations and modifications by nutrient management planners and stakeholders • Expanded and enhanced collaborations between partners