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Watershed Impact Assessment

Watershed Impact Assessment. Watershed Modeling Theory and Concepts. Chris McColl Center for Spatial Information RGIS USDA-CSREES. Modeling Components of N-SPECT. Runoff Modeling Curve Number Approach Sediment Erosion Modeling Universal Soil Loss Equation (USLE, RUSLE, MUSLE)

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Watershed Impact Assessment

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  1. Watershed Impact Assessment Watershed Modeling Theory and Concepts Chris McColl Center for Spatial Information RGIS USDA-CSREES

  2. Modeling Components of N-SPECT • Runoff Modeling • Curve Number Approach • Sediment Erosion Modeling • Universal Soil Loss Equation (USLE, RUSLE, MUSLE) • Pollutant Modeling • N-SPECT pollution coefficient development program • Pollution concentration estimates

  3. Runoff Estimation in N-SPECT • Uses NRCS Urban Hydrology for Small Watersheds: TR 55 – Curve Number Approach • One parameter empirical model Source: USDA-NRCS, 1986

  4. CN Approach Background • The entire rainfall-runoff response for various soil-plant cover complexes is represented by a single index called the Curve Number. • A higher curve indicates response from a watershed with a fairly uniform soil with a low infiltration capacity. • A lower curve is the response expected from a watershed with a permeable soil, with a relatively high spatial variability in infiltration capacity. • Developed an index of “storm-runoff generation capacity”, (the Curve Number), which would vary from 0 to 100 (implying percent of rainfall).

  5. SCS – Curve Number Loss Model • Attempts to estimate precipitation excess as a function of cumulative precipitation, soil cover, land use, and antecedent moisture (USACE, 2000). CN grid values Ia = 0.2S

  6. Parameters • Pe = total runoff (inches) • P = precipitation (inches) • Ia = Initial abstraction (inches), which is all losses before runoff begins - includes water retained in depressions, interception, evaporation, and infiltration). • S = Maximum potential retention after runoff begins (inches) • Can be rewritten as: • CN is the only parameter needed!

  7. Designers of the procedure must have known that they needed R to respond to P in approximately as follows: SCS (1972) storm runoff relationship

  8. CNs were then evaluated for many watersheds and related to: • Soil type (SCS soil types classified into Soil Hydrologic groups on the basis of their measured or estimated infiltration behavior); • Vegetation cover and or land use practice;  • Antecedent soil-moisture content.

  9. Hydrologic Soil Groups are defined in NRCS County Soil Survey reports

  10. Coastal Change Analysis Program (C-CAP)

  11. SCS CN – Strengths & Limitations • Perceived benefits: Simplicity, predictability, and stability that allow it to be easily applied to a variety of locations across the conterminous U.S. • It only relies on one parameter which can be quickly estimated from readily accessible land use, soil type, and antecedent moisture condition data. • Note that predicted values are not in accordance with classical unsaturated flow theory. The infiltration rate will approach zero during a storm of long duration, instead of establishing a constant rate of infiltration, which is typically expected (USACE, 2000). • As a result, the SCS CN loss model can generate over-estimated, sharp, peaky hydrographs because the slow and gradual withdrawal of water from storage and subsurface drainage systems are not described by the model (Madramootoo, 1988).

  12. Strengths & Limits Cont’d • Method entrenched in runoff prediction practice and is acceptable to regulatory agencies and professional bodies. • Method packaged in handbooks and computer programs(e.g N-SPECT, L-THIA, SWAT, HEC-HMS). • Appears to give ‘reasonable’ results. • No easily available competitor that does any better. • The task for a watershed analyst or regulator is to decide how to interpret and use the results.

  13. N-SPECT Runoff Process

  14. Estimating Erosion in N-SPECT • N-SPECT estimates suspended sediment concentrations using RUSLE / MUSLE (Revised Universal Soil Loss Equation / Modified Universal Soil Loss Equation). • The equation was empirical derived during the 1970s as a tool for soil conservation. • The USLE was developed by statistical analyses of many plot-years of rainfall, runoff, and sediment loss data from many small plots located around the country (Wischmeier and Smith, 1978). • Based on: • Rainfall pattern, soil type, topography, crop system and management practices. • Predicts: • Long term average annual rate of erosion • Subroutine in models such as: • SWRRB (Williams, 1975), EPIC (Williams et al., 1980), ANSWERS (Beasly et al., 1980), AGNPS (Young et al., 1989), N-SPECT(NOAA-CSC).

  15. Standard USLE plot: • 22.1m (72.6 ft) long • 9% slope • 4m (13.12 ft) wide.

  16. RUSLE A = R x K x LS x C x P • A = average annual soil loss • Units are feet*tonfeet*inch/acre*hour*year • R = rainfall/runoff erosivity factor • K = soil erodibility factor • L = slope length factor • S = slope steepness factor • C = cover management factor • P = supporting practices factor

  17. R (rainfall and runoff erosivity index) • The erosion index (EI) for a given storm is a product of the kinetic energy of the falling raindrops and its maximum 30 minute intensity. • The sum of these EI values over a year divided by 100 give the annual R factor. • R factor =EI over a year / 100 • Units are feet*tonfeet*inch/acre*hour*year A =R x K x LS x C x P

  18. Average annual values of the rainfall erosion index (R).

  19. Washington State R Factor Valueshttp://www.nrcs.usda.gov/technical/efotg/

  20. Developing R factor grid

  21. K(soil erodibility) • Soil erodibility is a measure of the susceptibility of a given soil to erosion by rainfall and runoff. • The properties of a soil that influence its erodibility are: • soil texture, • soil structure, • organic matter content, and • soil permeability. • Soil erodibility (K) factors have been computed by the Natural Resources Conservation Service. • GIS data available online: SSURGO (Soil Survey Geographic Database). A =R x K x LS x C x P

  22. Soil-erodibility nomograph.

  23. SSURGO GIS Data

  24. K Factor Development

  25. LS(slope length-gradient) • The topographic factors L and S are used to adjust the erosion rated based upon the length and steepness of the slope. • The erosivity of runoff increases with the velocity of the runoff water. • Steep slopes produce high runoff velocities. • Soil loss increases with increasing slope due to the greater volume of runoff accumulating on the longer slope lengths. • The slope length is the distance from the point of origin of the runoff to the point where the slope steepness decreases sufficiently to cause deposition or to the point where runoff enters a well-defined channel. • Often the L and S factors are combined into a single topographic factor, LS. A =R x K x LS x C x P

  26. LS(slope length-gradient) • L = (lamda / 72.6)m A =R x K x LS x C x P

  27. Topographic LS factor

  28. LS Factor in N-SPECT • LS factor is generated using an AML script in ArcGIS that was written by Dr. Bob Hickey. • Slope length is calculated by deriving the downslope angle for each cell in degrees. • First step of process identifies breaks in slopes based on grid neighborhood analysis • Second step creates a flow direction grid where slope break points are recoded as zeros, all other cells retain their original flow direction values; • Third step: A weight grid is created where each output cell receives a 1 if water flows through the cell in a cardinal direction, and 1.4142 is water flow in a diagonal direction; • Fourth step: ArcINFO ‘FLOWLENGTH’ command is used to sum these values along the flow paths until a break point, ridge, or pour point is encountered. • This yields the estimate of the slope length lamda to calculate the L factor in: • L = (lamda/72.6)m A =R x K x LS x C x P

  29. The ‘m’ exponent?

  30. S Factor • S factor is calculated using the following equations: • Where slope < 9% S = 10.8*(sin theta)+0.03 • Where slope > 9% S = 16.8*(sin theta)-0.50 A =R x K x LS x C x P

  31. Crop Tillage Factor Factor Fall Plow Grain Corn 1.00 0.40 Silage Corn, Beans & Canola Spring Plow 0.90 0.50 Cereals (Spring & Winter) Mulch Tillage 0.60 0.35 Seasonal Horticultural Crops Ridge Tillage 0.35 0.50 Fruit Trees Zone Tillage 0.25 0.10 No-Till Hay and Pasture 0.25 0.02 C(crop/management) • Ratio of soil loss from land use under specified conditions to that from continuously fallow and tilled land. A =R x K x LS x C x P

  32.  Support Practice P Factor Up & Down Slope 1.00 Cross Slope 0.75 Contour farming 0.50 Strip cropping, cross slope 0.37 Strip cropping, contour 0.25 P(conservation practices)– Not included in this version of N-SPECT • Ratio of soil loss by a support practice to that of straight-row farming up and down the slope. A =R x K x LS x C x P

  33. Digital Elevation Model (CN) (DA) (ZL)

  34. Pollution Concentration • N-SPECT estimates pollutant concentrations by using land cover as a proxy. • This requires the development of pollutant coefficients to land cover classes. • N-SPECT’s pollutant concentration estimation procedure does not explicitly take duration or intensity of rainfall in to account

  35. Generating Pollutant Coefficients • Basic equation form • Observed Pollutant load = (C1*LC1) + (C2*LC2) + …. • Where the coefficent value (C) is unknown for each land cover type • C is solved using iterative “bootstrap” method

  36. Generating Pollutant Coefficients • Process for a WS (not-tested) • Obtain WQ data • Obtain LC data • Need Discharge and Precip. Data (USGS) • Find sampling sites and run AML scripts Available @ http://waterdata.usgs.gov/ct/nwis

  37. Water Quality Data Sources • Washington Administrative Code (WACs) • http://apps.leg.wa.gov/WAC/default.aspx?cite=173-204-320 • EPA’s ‘Surf your watershed’ • http://cfpub.epa.gov/surf/locate/index.cfm • USGS’s water quality data for the nation • http://waterdata.usgs.gov/nwis/qw • Department of Ecology water quality studies • http://www.ecy.wa.gov/programs/wq/links/wq_assessments.html

  38. Generating Pollutant Coefficients • Process continued • run snap_gages.aml (Snaps sampling station to stream network) • Run landcover.aml (Isolates landcover for a given point) • Add x,y • Run Fortran program (Availability???) which solves for coefficients

  39. Existing Coefficient Values • Appendix B (pg. 59) within the N-SPECT technical manual.

  40. Pollution Concentrations

  41. Water Quality Standards • To evaluate whether pollutions levels are acceptable must incorporate regulated standards for comparison.

  42. Questions? • Tutorial 3 & 4 (Developed by NOAA-CSC) (~ 45 min) • Analysis with management scenario (local effects) • Analysis with management scenario (accumulated effects)

  43. Sediment Delivery Ratio (SDR) • RUSLE produces an estimate of gross erosion, but does not indicate how much eroded soil is actually transported by streams. • SDR = 1.366 * 10 -11 * (DA) -0.0998 * (ZL)0.3629 * (CN)5.444 • Where: • DA = drainage area • ZL = the relief-length ratio • CN = curve number • Drainage Area (DA) parameter represents the area of each individual cell, rather than a watershed/catchment • Relief-Length (ZL) is calculated based on the DEM and flow direction grid • Elevation change along the downslope flow path is calculated from the DEM and divided by the distance from the center of the current cell to the center of the next cell along the flow path. • Curve Number grid is derived from the land cover grid and SSURGO shape file. • True annual sediment yield = SDR*A

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