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Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS

Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS. E. Lynn Usery Michael P. Finn. USGS DoD Environmental Program Conference. usery@usgs.gov mfinn@usgs.gov. http://mcmcweb.er.usgs.gov/carto_research. Outline. Objectives and Introduction

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Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS

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  1. Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS E. Lynn Usery Michael P. Finn USGS DoD Environmental Program Conference usery@usgs.gov mfinn@usgs.gov http://mcmcweb.er.usgs.gov/carto_research

  2. Outline • Objectives and Introduction • GIS Databases for Parameter Extraction • AGNPS Parameter Generation • AGNPS Output Visualization • Resolution and Resampling Effects • Conclusions

  3. Objectives • Develop GIS databases as input to Agricultural Non-Point Source (AGNPS) Pollution Model • Create a tool for generating input, executing the model, and analyzing output • Determine effects of resolution and resampling

  4. Introduction -- AGNPS • Operates on a cell basis and is a distributed parameter, event-based model • Requires 22 input parameters • Elevation, land cover, and soils data are the base for extraction of input parameters

  5. Georgia Watersheds Agricultural areas with some woodland, wetlands, and small urban areas

  6. Project Design • Assumptions • AGNPS parameters can be generated with GIS • Parameters are affected by resolution of GIS data • Hypotheses • Lower resolution cannot provide same parameters as higher resolution • Resampling GIS data degrades quality

  7. GIS Databases for Parameter Extraction • National Elevation Dataset (30-m) • National Land Characteristics Data (30 m) • Augmented with recent Landsat TM data • Soils databases from USDA soil surveys • Scanned separates, rectified, vectorized, tagged • Resampled the 30-m data to 60, 120, 210, 240, 480, 960, and 1920 meters • 210-m roughly matches 10 acre grid size

  8. AGNPS Parameter Generation • AGNPS Data Generator • Input parameter generation • Details on generation of parameters • Extraction methods

  9. AGNPS Data Generator • Created to provide interface between GIS software (Imagine) and AGNPS • Developed interface for Imagine 8.4, running on WinNT/2000

  10. AGNPS Data Generator

  11. Input Parameter Generation • 22 parameters; varying degrees of computational development • Simple, straightforward, complex

  12. Creating AGNPS Input • Input Data File Creation • Format generated parameters into AGNPS input file • Use a “stacked” image file to create AGNPS data file (“.dat”) -- ASCII

  13. Input Parameter Generation

  14. Details on Generation of Parameters • Cell Number • Receiving Cell Number • SCS Curve Number • Uses both soil and land cover to resolve curve number

  15. Details on Generation of Parameters • Slope Shape Factor

  16. Details on Generation of Parameters • Slope Length • A concern; max value should be 300 ft. • Parameters 10, 11, 12, 14, 15, 16, and 17 • Uses Spatial Modeler to lookup attributes from soils or land cover • Parameters 13, 18, 19, 20, and 21 • Hard coded on advice from experts

  17. Details on Generation of Parameters • Type of Channel • Uses TARDEM program • Creates a Strahler steam order

  18. Extraction Methods • Used object-oriented programming and macro languages • C/ C++ and EML • Manipulated the raster GIS databases with Imagine • Extracted parameters for each resolution for both boundaries using AGNPS Data Generator

  19. Creating AGNPS Output • AGNPS creates a nonpoint source (“.nps”) file • ASCII file like the input; tabular, numerical form

  20. AGNPS Output

  21. AGNPS Output

  22. Creating AGNPS Output Images • Output Image Creation • Combined “.nps” file with Parameter 1 to create multidimensional images • Users can graphically display AGNPS output • Process: create image with “x” layers, fill layers with AGNPS output data, set projection and stats for image • Multi-layered (bands) images per model event

  23. Creating AGNPS Output Images Red – Peak Flow Upstream Green – Upstream Runoff Blue – Overland Runoff

  24. Creating AGNPS Images Red – Total Soluble Nitrogen Green – Sediment Attached Nitrogen Blue – Drainage Area

  25. Results • Resolution effects • Tested with two independent collections • Elevation at 3 m and 30 m resolution • Land cover at 3 m and 30 m resolution • Comparison of values

  26. Elevation

  27. Sampling of Points for Land Cover and Elevation Comparisons for Little River, GA

  28. Regression Results • 3 m to 30 m comparison • Elevations -- R2 of 0.81 • Land cover – McFadden’s pseudo R2 of 0.139, meaning little correlation • Derived parameters, e.g., slope, problematic because of degraded data source

  29. Results • Resampling effects

  30. Experimental Approach • Analysis requires DEM, slope, and land cover at 30, 60, 120, 210, 240, 480, 960, 1920 m cells • Starting point is 30 m DEM and land cover • Calculate slope at 30 m cell size from DEM • Resample land cover • How to generate slope at 60 m and larger cell sizes? How to aggregate land cover?

  31. Method of Calculation • Slope calculated from DEM • 30, 60, 120, 210, 240, 480, 960, 1920 m cells • Compute slope from 30 DEM • Aggregate DEM from 30 m to each lower resolution • Compute slope from aggregated elevation data

  32. Sample of Slope Generation Approaches compute aggregate 30 m DEM 30 m slope 60 m slope aggregate compute 60 m slope 30 m DEM 60 m DEM aggregate compute 30 m DEM 120 m DEM 120 m slope 30 m DEM compute 30 m slope aggregate 120 m slope

  33. Results - DEM

  34. Results - DEM

  35. Image Results -- DEM 30-480 m Pixels 210-480 m Pixels

  36. Results -- Slope Slope % 30 to 480m Pixels 7.8816 7.8232 7.5870 7.8251 8.1604 8.5415 8.2065 7.9530 7.7434 7.7092 Slope % 210 to 480m Pixels 7.9514 7.8969 7.6244 7.7855 8.1263 8.5087 8.2157 7.8606 7.6390 7.6081 Regression Output: Constant 0.2762 Std Err of Y Est 1.1626 R Squared 0.7690 No. of Observations 500 Degrees of Freedom 498 X Coefficient(s) 0.8860 Std Err of Coef. 0.0218

  37. Results -- Slope • Slope • Method of calculation affects results • Higher resolution aggregation directly to large pixel sizes yields better results than multistage aggregation (e.g., 30 m to 960 m is better than 30 m to 60 m to 120 m to 240 m to 480 m to 960 m) • Even multiples of pixels hold results while odd pixel sizes introduce error

  38. Slope Image Comparison 30 m to 480 m pixels 210 m to 480 m pixels

  39. Results - Land Cover -- 210 m Pixels

  40. Results - Land Cover -- 480 m Pixels

  41. Results-Land Cover -- 960 m Pixels

  42. Image Results - Land Cover 30-480 m Pixels 240-480 m Pixels

  43. Image Results - Land Cover 30-210 m Pixels 120-210 m Pixels

  44. Statistical Testing • Selected 500 random points over the watershed • Compared elevation, slope, and land cover values at the 500 points • Computed R2 and pseudo R2 between resolutions • Plotted R2 and pseudo R2 against resampled resolutions from 30 m data

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