Improving spatial representation easton
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71st SWCS International Annual Conference July 24-27, 2016 Louisville, KY
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Improving spatial representation easton
- 1. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering This material is based on work supported by the NSF under Grant No. CBET-1360280 Improving the Spatial Representation of Soil Properties and Hydrology Using Topographically Derived Initialization Processes in the SWAT Model Zachary M. Easton1, Daniel Fuka1, Amy Collick2 1Biological Systems Engineering, Virginia Tech 2Ag Food and Resource Sciences, UMES
- 2. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering Outline • Introduction to Topography, Soils, and TopoSWAT • Variable Area Hydrology (VSA) Case • USDA WE‐38, PA • Hortonian Hydrology Case • Reisel, TX • What does this imply for modeling field management?
- 5. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering Infiltration Excess or Hortonian Runoff Scientific Background Ontl, T. A. & Schulte, L. A. (2012) Soil Carbon Storage. Nature Education Knowledge 3(10):35
- 8. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems EngineeringScientific Background Saturation Excess Runoff Modified for VSA. Ontl, T. A. & Schulte, L. A. (2012) Soil Carbon Storage. Nature Education Knowledge 3(10):35
- 9. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering tan ln a High: Low: Topographic Index 33.10 3.52 Topographic Index TI10= Low Infiltration TI= High Infiltration Easton et al. 2008. J. Hydrol Easton et al. 2011. Hydrol. Proc TI Classes Scientific Background TI10= High Infiltration TI= High Infiltration HortonianVSA TI= Low Infiltration
- 14. Watershed Science and Engineering Biological Systems EngineeringVirginiaTech Invent the Future ln a tan High: Low: Topographic Index 33.10 3.52 Z1=f(λ) Saxton and Rawls, 2006; Easton et al. 2008; Collick et al. 2014; Fuka et al. 2016 Wetness Index Classes 10 1 Zi = soil depth Z2=f(λ) Z3=f(λ)Z4=f(λ) Z5=f(λ) Z6=f(λ) Z7=f(λ) Z10=f(λ) Z8=f(λ) Z9=f(λ) Topographic Index Wetness Index Classes
- 16. Watershed Science and Engineering Biological Systems EngineeringVirginiaTech Invent the Future Topography to explain soil depth and fractions Using measured pedon data from ARS long term watershed TI Class (Dry to Wet) TI Class (Dry to Wet) SandorClay(%) SoilDepth(cm) Collick et al. 2014. Hydrol Proc
- 17. Watershed Science and Engineering Biological Systems EngineeringVirginiaTech Invent the Future High: Low: Topographic Index 33.10 3.52 Soil Depth as explained by Topography Collick et al. 2014. Hydrol Proc TI Classes 10 1 Zi = local soil depth Z2= 80 cm Z6=100 cm Z10= 130 cm
- 20. Watershed Science and Engineering Biological Systems EngineeringVirginiaTech Invent the Future 2 4 6 8 10 800 1000 1200 1400 1600 TI Class DepthtoCHor.(mm) R^2=0.28 y=50.80 x + 1025.52 (a) 2 4 6 8 10 5 10 15 20 25 TI ClassMeas.Ksat(mm/hr) R^2=0.19 −y= 0.88 x + 17.00 (b) 2 4 6 8 10 25 30 35 40 45 TI Class FieldCapacityat33kpa(%) R^2=0.79 −y= 2.17 x + 46.00 (c) 2 4 6 8 10 1.65 1.70 1.75 1.80 1.85 TI Class BulkDensity(g/cm^3) R^2=0.17 −y= 0.01 x + 1.75 (d) 2 4 6 8 10 0.48 0.50 0.52 0.54 0.56 TI Class Porosity(%) R^2=0.50 y=0.01 x + 0.47 (e) 2 4 6 8 10 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 TI Class AWC(volume) R^2=0.71 −y= 0.01 x + 0.13 (f) Soil Depth AWC Field CapacityKsat PorosityBulk Density TI Class SoilDepth(cm) Ksat(mm/hr) Fieldcapacity(33kpa%) BulkDensity(gcm-3) Porosity(%) AWC(cm3cm-3) Fuka et al. 2016 Hydrol Proc TI Class TI Class TI Class TI ClassTI Class
- 21. Watershed Science and Engineering Biological Systems EngineeringVirginiaTech Invent the Future High: Low: Topographic Index 33.10 3.52 Available Water Content as explained by Topography TI Classes AWC2= 0.12 AWC6=0.09 AWC10= 0.06 Fuka et al. 2016. Hydrol Proc
- 22. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering TI adjusted FAO Soils ( ) vs base SSURGO Soils ( ) ● ● ● ● ● ● ● ● 800 1000 1200 1400 1600 1800 2000 800 1000 1200 1400 1600 1800 2000 Depth to C Hor.(mm) Est.ZMX R^2=0.28 y=0.33 x + 538.88 (a)Soil Depth ● ● ●● ● ● ● ● 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Meas. Ksat(mm/hr) Est.Ksat(mm/hr) R^2=0.59 −y=2.07 x + 15.38 (b)Ksa t SoilBasedSoilDepth(mm) SoilBasedKsat(mm/hr) Pit Soil Depth (mm) Pit Ksat (mm/hr) How does SSURGO perform? Fuka et al. 2015 Hydrol Proc
- 23. Watershed Science and Engineering VirginiaTech Invent the Future Biological Systems Engineering Discussion • TI initialization exhibited strong correlations with field level measurements • Resulted in improved predictions of field level contaminant transport • SSURGO initialization captured soil properties and the hydrologic response poorly • Due in part to the overestimated soil depth and as a result AWC • Adjusting model parameters based on topography can result in more accurate soil characterization and improve model performance at the field scale • A solution that combines BOTH Hortonian AND Saturation Excess processes into watershed models is needed, and our next release.