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

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

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

4. Introduction -- AGNPS n Operates on a cell basis and is a distributed parameter, event-based model n Requires 22 input parameters n 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

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

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

9. AGNPS Parameter Generation n AGNPS Data Generator n Input parameter generation n Details on generation of parameters n Extraction methods

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

11. AGNPS Data Generator

12. Input Parameter Generation n 22 parameters; varying degrees of computational development F Simple, straightforward, complex

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

14. Input Parameter Generation

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

16. Details on Generation of Parameters n Slope Shape Factor

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

18. Details on Generation of Parameters n Type of Channel F Uses TARDEM program F Creates a Strahler steam order

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

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

21. AGNPS Output

22. n AGNPS Output

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

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

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

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

27. Elevation

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

30. Regression Results n 3 m to 30 m comparison n Elevations -- R 2 of 0.81 n Land cover – McFadden’s pseudo R 2 of 0.139, meaning little correlation n Derived parameters, e.g., slope, problematic because of degraded data source

31. Results n Resampling effects

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

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

34. 30 m DEM120 m DEM120 m slope 60 m slope 30 m DEM30 m slope60 m slope 30 m DEM60 m DEM 30 m DEM30 m slope120 m slope Sample of Slope Generation Approaches compute aggregate compute

35. Results - DEM

37. Image Results -- DEM 30-480 m Pixels210-480 m Pixels

38. 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: Constant0.2762 Std Err of Y Est1.1626 R Squared0.7690 No. of Observations500 Degrees of Freedom498 X Coefficient(s)0.8860 Std Err of Coef.0.0218

39. Results -- Slope n Slope F Method of calculation affects results F 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) F Even multiples of pixels hold results while odd pixel sizes introduce error

40. Slope Image Comparison 30 m to 480 m pixels210 m to 480 m pixels

41. Results - Land Cover -- 210 m Pixels

42. Results - Land Cover -- 480 m Pixels

43. Results-Land Cover -- 960 m Pixels

44. Image Results - Land Cover 30-480 m Pixels240-480 m Pixels

45. Image Results - Land Cover 30-210 m Pixels120-210 m Pixels

46. Statistical Testing n Selected 500 random points over the watershed n Compared elevation, slope, and land cover values at the 500 points n Computed R 2 and pseudo R 2 between resolutions n Plotted R 2 and pseudo R 2 against resampled resolutions from 30 m data

51. Effects on Model Outputs Total Soluble Nitrogen

52. Effects on Model Outputs Total Soluble Phosphorus

53. Conclusions n Automatic generation of AGNPS parameters from elevation, land cover, and soils n Resolution affects results F Elevation and derivatives (slope) hold values well because of averaging methods of resampling F Land cover (categorical data) is inconsistent across resolutions because of nearest neighbor resampling F Model outputs follow input degradation with resolution, but indicate a threshold base don model formulation with respect to areas of aggregation

54. Conclusions n Resampling retains values better with even multiples of original pixel sizes n Aggregation directly from higher resolution to lower retains values better than multiple intermediate resampling

55. Research Web Site n http://mcmcweb.er.usgs.gov/carto_research

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