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

U.S. Department of the InteriorU.S. Geological Survey

Automatic Generation of Parameter Inputs and Visualization of Model

Outputs for AGNPS using GIS

USGS DoD Environmental Program Conference

E. Lynn UseryMichael P. Finn

http://mcmcweb.er.usgs.gov/carto_research

[email protected]

[email protected]

Outline

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

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

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

Georgia Watersheds

Agricultural areas with some woodland, wetlands, and small urban areas

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

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

AGNPS Parameter Generation

AGNPS Data Generator Input parameter generation Details on generation of parameters Extraction methods

AGNPS Data Generator

Created to provide interface between GIS software (Imagine) and AGNPS

Developed interface for Imagine 8.4, running on WinNT/2000

AGNPS Data Generator

Input Parameter Generation

22 parameters; varying degrees of computational development Simple, straightforward, complex

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

Input Parameter Generation

Details on Generation of Parameters

Cell Number Receiving Cell Number

SCS Curve Number Uses both soil and land cover to resolve curve number


Slope Shape Factor


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


Type of Channel Uses TARDEM program Creates a Strahler steam order

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

Creating AGNPS Output

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

AGNPS Output

AGNPS Output

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

Creating AGNPS Output Images

Red – Peak Flow Upstream

Green – Upstream Runoff

Blue – Overland Runoff

Creating AGNPS Images

Red – Total Soluble NitrogenGreen – Sediment Attached NitrogenBlue – Drainage Area

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

Elevation

Easting Northing 3-m LC 30-m LC 3-m Elev 30-m Elev

239589 3504260 Crop Mature Planted Pine 119 122 241209 3503180 Crop Crop 125 124 256449 3486470 Urban Crop 102 103 252039 3491360 Mature Deciduous Wetland 84 85 240369 3516350 Mixed Deciduous/Pine Mature Planted Pine 132 132 253959 3486830 Urban Crop 90 85 253539 3496400 Urban Crop 111 111 246369 3497360 Mixed Deciduous/Pine Wetland 95 94 247779 3512330 Urban Urban 130 130 256179 3491270 Crop Crop 97 97 244239 3498170 Mixed Deciduous/Pine Mature Planted Pine 106 106 238449 3515090 Young Planted Pine Mature Planted Pine 132 130 254589 3486920 Mature Planted Pine Crop 84 85 244749 3504560 Crop Crop 121 119 250929 3495140 Crop Crop 107 100 247719 3498890 Crop Crop 115 112 244359 3507260 Crop Disturbed or Harvested land 116 115 255579 3491240 Mixed Deciduous/Pine Wetland 95 94 252339 3500660 Crop Crop 113 115 247719 3508160 Crop Crop 117 116

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

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

Results

Resampling effects

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?

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

30 m DEM 120 m DEM 120 m slope

60 m slope

30 m DEM 30 m slope 60 m slope

30 m DEM 60 m DEM

30 m DEM 30 m slope 120 m slope

Sample of Slope Generation Approaches

compute aggregate

aggregate

aggregate

aggregate

compute

compute

compute

Results - DEM

Regression Output:0.980539Constant3.105509Std Err of Y Est0.959085R Squared

34No. of Observations32Degrees of Freedom

0.983164X Coefficient(s)0.035898Std Err of Coef.

120-210m30-210m76766153464978767578464569707167575660636465606038385152

Results - DEM

Regression Output:-1.38617Constant2.274152Std Err of Y Est0.97968R Squared

10No. of Observations8Degrees of Freedom

1.010755X Coefficient(s)0.051466Std Err of Coef.

210-480m30-480m65636365404061614849787756623334616132335356

Image Results -- DEM

30-480 m Pixels 210-480 m Pixels

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

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

Slope Image Comparison30 m to 480 m pixels 210 m to 480 m pixels

Results - Land Cover -- 210 m Pixels

210_30res

2424.048

14632.4352

8492.98272

8625.20352

17536.88544

4689.43104

15527.12928

25465.72608

5641.4208

612.62304

22213.0944

210_120res

2500.71948

14413.31792

8679.74592

8812.05912

17169.84292

4600.08892

14894.05588

25624.6564

5680.64672

648.33468

22171.28188

210 % diff

-3.16

1.50

-2.20

-2.17

2.09

1.91

4.08

-0.62

-0.70

-5.83

0.19

Land Cover Category

Pecan Groves

Recently Disturbed Land / Harvested Cropland

Pastures

Cypress Dominant Weltands

Mature Deciduous

Young Planted Pine

Mature Planted Pine

Mixed Dominant Deciduous / Pine

Roads / Urban Complex

Open Water

Crops (Cotton, Peanuts)

Results - Land Cover -- 480 m Pixels

210-240d30-240d30-210d480_240res480_210res480_30res

-36.45-10.4419.062764.80002026.30562503.3376

8.773.29-6.0013570.560014874.925214032.4704

-5.332.507.438755.20008312.45828979.8624

6.511.98-4.858847.36009463.76829025.7952

-7.010.346.8717372.160016233.471017431.4976

-8.06-11.70-3.364976.64004605.24004455.4816

3.11-4.35-7.7015505.920016003.209014859.2608

0.65-0.23-0.8925735.680025904.475025676.4352

6.98-8.04-16.145483.52005894.70725075.5744

-30.51-20.387.76691.2000529.6026574.1600

-0.992.973.9222440.960022220.283023127.1648

Land Cover Category

Pecan Groves


Pastures


Mature Deciduous

Young Planted Pine

Mature Planted Pine



Open Water


Results-Land Cover -- 960 m Pixels

Land Cover Category

Pecan Groves


Pastures


Mature Deciduous

Young Planted Pine

Mature Planted Pine



Open Water


210-480d30-480d30-210d960_480res960-210res960_30res

-19.69-3.1213.842755.974 2302.61752672.64

11.542.93-9.7413688.0042 15473.589614100.48

-18.79-12.415.389737.7748 8197.31838663.04

0.26-12.68-12.989554.0432 9578.88888478.72

-9.94-0.208.8617821.9652 16210.427217786.88

17.7611.60-7.484317.6926 5249.96794884.48

9.015.76-3.5714331.0648 15749.903715206.4

0.942.651.7326916.6794 27170.886527648

21.4223.773.004777.0216 6078.91026266.88

40.1640.190.06275.597460.5235460.8

-8.52-6.741.6324987.523026.17523408.64

Image Results - Land Cover


Image Results - Land Cover


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

Resample Coeffient of DeterminationElevation -- Little River Watershed

60120

480

1920

960

210240

0.000.100.200.300.400.500.600.700.800.901.00

0 500 1000 1500 2000

Resampled Size(From 30 meters)

R2

Resample Coeffient of DeterminationLand Slope -- Little River Watershed

60

1920

480

120

240

210

9600.000.100.200.300.400.500.600.700.80

0 500 1000 1500 2000


R2

Resample Coeffient of DeterminationFlow Direction -- Little River Watershed

Multinominal Regression

60

1920480

120

240210

9600.00

0.10

0.20

0 500 1000 1500 2000


Pseudo R2

Comparison of Land Cover Values across Resamplings for Little River (Values are percentages of 30-m land cover category areas).

60-m 120-m 210-m 240-m 480-m 960-m 1920-m Water 100.12 94.50 121.23 97.04 98.56 65.71 0.00 Urban 104.04 94.34 100.28 76.73 68.51 35.97 89.98 Transitional 100.54 96.96 92.18 90.15 81.95 69.34 90.20 Deciduous 103.35 101.65 102.12 94.36 120.21 156.28 97.74 Pine 100.11 98.48 97.90 96.80 86.37 69.99 48.35 Mixed 98.18 101.29 94.74 98.41 86.33 65.95 148.47 Crop 98.99 97.72 95.42 95.80 91.40 88.33 74.46 Wetlands 99.50 100.27 99.98 102.78 101.91 105.47 82.45

Effects on Model OutputsTotal Soluble Nitrogen

Little River Watershed

60

120

480960 1920

210

240

0.00

0.05

0.10

0.15

0.20

0.25

0 500 1000 1500 2000

Cell Size

R2

Effects on Model OutputsTotal Soluble Phosphorus

Little River Watershed

60

120

480960 1920

240

210

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 500 1000 1500 2000

Cell Size

R2

Conclusions

Automatic generation of AGNPS parameters from elevation, land cover, and soils

Resolution affects results Elevation and derivatives (slope) hold values well because of

averaging methods of resampling Land cover (categorical data) is inconsistent across resolutions

because of nearest neighbor resampling Model outputs follow input degradation with resolution, but

indicate a threshold base don model formulation with respect to areas of aggregation

Conclusions

Resampling retains values better with even multiples of original pixel sizes

Aggregation directly from higher resolution to lower retains values better than multiple intermediate resampling

Research Web Site


U.S. Department of the InteriorU.S. Geological Survey

Automatic Generation of Parameter Inputs and Visualization of Model

Outputs for AGNPS using GIS

USGS DoD Environmental Program Conference

E. Lynn UseryMichael P. Finn


[email protected]

[email protected]

Documents

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