Release) Public for (Not DRAFTetalweb.joewheaton.org.s3-us-west-2.amazonaws.com/BRAT/...BRAT consists of two capacity models, a beaver-dam building capacity model and an ungulate capacity

GIS INSTRUCTIONAL MANUAL: BEAVER RESTORATION AND ASSESSMENT TOOL (BRAT)

CASE STUDY: ESCALANTE RIVER WATERSHED

VERSION 1.0 (RELEASE DATE: JANUARY 21, 2013)

Prepared by:

WILLIAM W. MACFARLANE, Research Associate

JOSEPH M. WHEATON, Assistant Professor

Ecogeomorphology & Topographic Analysis Lab Watershed Sciences Department Utah State University 5210 Old Main Hill Logan, UT 84322-5310

FEBRUARY 17, 2013

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GIS Instructional Manual: Beaver Restoration and Assessment Tool (BRAT)

Recommended Citation:

Macfarlane WW, and Wheaton JM. 2013. GIS Instructional Manual: Beaver Restoration and

Assessment Tool (BRAT) Case Study: Escalante River Watershed. Ecogeomorphology and

Topographic Analysis Lab, Utah State University, Prepared for Walton Family Foundation,

Logan, Utah, XXX pp.

Available at: http: XXX.

© 2013 Macfarlane and Wheaton All Rights Reserved

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CONTENTS

Introduction ................................................................................................................................................................... 4

GIS Data ......................................................................................................................................................................... 5

GIS and FIS Data Processing ........................................................................................................................................... 5

Data Capture .............................................................................................................................................................. 6

BEAVER DAM-BUILDING CAPACITY MODEL GIS PROCESSING ................................................................................... 6

NHD Data GIS Processing (Water Input) .................................................................................................................... 7

LANDFIRE LANDCOVER GIS PROCESSING AND CLASSIFICATION (Vegetation Input) ............................................... 12

Total Stream Power Calculations (Stream Power Input) ......................................................................................... 18

Beaver Dam Capacity FIS ......................................................................................................................................... 22

Joining the Output Beaver FIS with the GIS ............................................................................................................. 24

Ungulate Capacity Model GIS Processing ................................................................................................................ 26

LANDFIRE LANDCOVER GIS PROCESSING AND CLASSIFICATION (Vegetation Input) ............................................... 27

Slope Map Generation ............................................................................................................................................. 29

Distance to water .................................................................................................................................................... 29

Ungulate Capacity FIS .............................................................................................................................................. 33

References ................................................................................................................................................................... 37

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INTRODUCTION

This manual provides step-by-step instruction on the GIS and Fuzzy Inference System

(FIS) processing tasks that were performed for the pilot/proof of concept project in the Escalante

River Watershed, southern Utah to develop and test a portable assessment tool called The Beaver

Restoration and assessment Tool (BRAT). As such the manual provides the beta version

processing steps that we developed and performed during the pilot projects’ contract period

(January 1, 2012 through January 1, 2013). Updated instructions will likely be available from the

authors in the near future. For updated instructional information please contact Wally Macfarlane

at [email protected].

The manual was generated with the assumption that the end user possesses a good

working knowledge of the Geographic Information System (GIS) software package ArcGIS 10.0

(ESRI, 2012) and at least some exposure to the MathWorks Matlab R2012b Fuzzy Logic

Toolbox (MathWorks, 2012). It was also assumed that you have access to both these software

packages. In addition, there is a couple other freeware software packages that you will be

prompted to download during the processing steps.

The manual is divided into tasks and subset by steps within each task. The tasks and steps

are sequentially organized. Therefore, we suggest that you follow the manual in the order it is

presented for best results. The manual contains various screen shots to help clarify the processing

steps and to ensure that our method is repeatable. However, since this is pilot project and the first

time the approach has been used it is highly likely that questions will arise that require further

clarification. In addition, you may find other way, potentially better ways, to perform the tasks

that we have outlined. Therefore, we encourage your questions and suggestions in these regards.

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BRAT consists of two capacity models, a beaver-dam building capacity model and an

ungulate capacity model, that work together to assess beaver restoration potential. The beaver-

dam building capacity model uses both existing and potential vegetation layers to show current

capacity and potential capacity. Potential capacity is capacity without limiting factors such as

ungulate over grazing.

BRAT consists of an innovative combination of vector and raster based geoprocessing

FIS processing. The processing steps are described in the following pages and are organized by

heading, subheading, task, and step.

GIS AND FIS DATA

All the GIS data used in this pilot study is stored in a geodatabase named Escalante

Watershed.gdb. For your convenience and as an instruction aid all the features classes and grids

that were generated during the steps outlined in this manual are included in the geodatabase. The

feature class data in the geodatabase is structured into different containers based on the data type.

For example, all NHD derived data is in a container called NHD. All the raster datasets are

lumped together just inside the geodatabase.

All the FIS data used in this pilot study is stored in a folder name FIS.

GIS AND FIS DATA PROCESSING

Both the beaver dam-building and ungulate capacity models consisted of a series of

straight-forward geoprocessing steps that build upon one another to generate spatially explicit

output data. The resulting GIS data was then input into Fuzzy Inference Systems (FIS) to assess

the relative importance of these inputs. Final output beaver-dam capacity data were in the form

of NHD stream (line) layers attributed to reflect dams per km across the watershed at a reach-

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level (250 m). Whereas ungulate capacity data were in the form of raster data attributed by 0-4

with 0: low and 4: high indicating the level of capacity across the landscape.

DATA CAPTURE

Existing, readily and freely available GIS datasets from GIS data clearinghouses were

collected and used as model inputs. These datasets included environmental parameter data to

build both the beaver dam-building capacity model and the ungulate capacity model.

Specifically, National Hydrography Dataset (NHD) Plus data http://www.horizon-

systems.com/nhdplus/, LANDFIRE land cover data http://www.landfire.gov/, USGS Digital

Elevation (DEM) data http://ned.usgs.gov/ and USGS StreamStats Regional Regressions

equations http://pubs.usgs.gov/sir/2007/5158/. For NHD data download both stream and water

body data, for the LANDFIRE data download both Us_110evt (existing vegetation type) and

us_110bps (potential vegetation type) for USGS NED download the 10 m DEM data for your

area of interest and for the USGS StreamStats data select the correct region and associated

equations for base flow (PQ80 for the month with the lowest flows) and 2-year incurrence peak

interval.

BEAVER DAM-BUILDING CAPACITY MODEL GIS PROCESSING

The beaver dam-building capacity model includes three inputs. Water (NHD streams),

Vegetation (LANDFIRE landcover data), and Stream Power (based on USGS regional

equations) (Figure 1).

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http://www.horizon-systems.com/nhdplus/

http://www.horizon-systems.com/nhdplus/

http://www.landfire.gov/

http://ned.usgs.gov/

http://pubs.usgs.gov/sir/2007/5158/

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Figure 1. Diagram showing beaver dam capacity model inputs.

NHD DATA GIS PROCESSING (WATER INPUT)

The NHD data processing consisted of four straightforward GIS processing tasks:

Task 1: Subset the NHD stream layer to perennial streams;

Task 2: Divide the perennial streams by 250 m lengths;

Task 3: Buffer the segmented streams (250 m lengths) by 30 m width, and

Task 4: Buffer the segmented streams (250 m lengths) by 100 m width (Figure 2).

NHD Perennial Streams

LANDFIRE Land Cover

USGS Regional Regression Equations

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Figure 2. Diagram showing NHD stream GIS processing tasks.

Task 1: Subset the NHD stream layer (NHD_All_Streams) to perennial

(NHD_Perennial_Streams). This is accomplished through the following steps:

Step 1: Open NHD_All_Streams in ArcMap

Step 2: Select by attribute: the Attribute field: StreamRive Make a selection of:

‘perennial stream or river’

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Step 3: Export the selection as NHD perennial stream layer.

Output: NHD_Perennial_Streams

Task 2: Divide the NHD perennial streams by 250 m lengths. This is accomplished through the

following steps:

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Step 1: Merge all segments of NHD perennial streams into one segment (needed for step

3 to work as one step). Use the merge command under Editor.

Step 2: Download Editing Labs Divide Line By Length Add-in

http://www.arcgis.com/home/item.html?id=d5d27ee47330434b9a96b91136a0118f

Installation and use:

1. Download and double-click the file.

2. To use the add-in, you must first add it to a toolbar in ArcMap. Click the Customize

menu and click Customize Mode. Click the Commands tab and type Divide Line in the

search box. Drag the Divide Line By Length command from the Editing Labs category

onto any toolbar, such as the Editor or Advanced Editing toolbar.

3. Click the Edit tool on the Editor Toolbar and select the line that you would like to split.

4. Click Divide Line By Length on the toolbar to which you added it.

5. Type the length value you want to use to divide the line.

6. Press ENTER to split the line. If the length entered does not divide evenly into the line’s

length, the remaining leftover distance is not allocated among the new features.

Note: Make sure your Add-in Manger settings are as follows:

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Step 3: Selected the entire drainage network and clicked on the Divide Line by Length

Tool and then in the pop-up box type 250. Because the projection is in meters the

command divides the selected line into 250 m segments.

Output: Segmented_250m_NHD_Perennial.shp

Tasks 3: Buffer NHD perennial streams

Step 1: Created a 100 meter (riparian and vicinity level) buffer using the Buffer

command under the Geoprocessing Tab. Note: Use End Type “FLAT”

250

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Step 2: Repeat the above step but this time generate a 30 m (bank and channel level)

buffer. Note: Use End Type “FLAT”

Outputs: Buf_100m_Seg_250m_NHD_Perennial

Buf_30m_Seg_250m_NHD_Perennial

LANDFIRE LANDCOVER GIS PROCESSING AND CLASSIFICATION (VEGETATION INPUT)

Figure 3 show how the LANDFIRE land cover data for both existing (2008) and potential

vegetation is classified by beaver vegetation food/building material preferences established in the

literature.

Figure 3. Diagram showing LANDFIRE land cover data classification for the beaver dam

capacity model.

Task 1: Classify the LANDFIRE land cover data

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Step 1: Load LANDFIRE land cover type rasters: Us_110evt.img (existing vegetation

type) and Us_110bps.img (potential vegetation type) and add a field called “Code”

representing dam-building material preferences (0-4) (Table 1).

Step 2: Use the Table 1 to classify the field SAF_SRM in Us_110evt and the field

GROUPNAME in Us_110bps.

Table 1. Suitability of LANDFIRE Land Cover as Dam-building Material

O, Unsuitable Material - LANDFIRE land cover = agriculture, developed, roads, barren, non-

vegetated, sparsely vegetated, grasslands or water.

1, Barely Suitable Material - LANDFIRE land cover = herbaceous wetland/riparian or shrubland,

Transitional herbacous.

2, Moderately Suitable Material - LANDFIRE land cover = introduced woody riparian,

woodland or conifer.

3, Suitable Material - LANDFIRE land cover = Maple, gambel oak or other deciduous upland

trees, aspen/conifer.

4, Preferred Material - LANDFIRE land cover = cottonwood, willow, aspen or other native

woody riparian.

Outputs:

Us_110evt_Code.img

Us_110bps_Code.img

Step 3: For Us_110evt_Code.img use the Lookup command to generate a new raster with

“Code” as the “lookup” field.

Step 4: For Us_110bps_Code.img use the Lookup command to generate a new raster

with “Code” as the “lookup” field.

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Outputs:

Us_110evt_Look_Up_Code.img

Us_110bps_Look_Up_Code.img

Step 5: Perform Zonal Statistics: Using the Zonal Statistics command (ArcToolbox under

Spatial Analyst Tools -> zonal).

Statistics type: Mean

Conduct this step a total of four times.

1st: Use Buf_30m_Seg_250m_NHD_Perennial as the zone data. Use the LANDFIRE

land cover (Existing) Us_110evt_Look_Up_Code.img as the input value raster.

2nd: Use Buf_100m_Seg_250m_NHD_Perennial as the zone data. Use the LANDFIRE

land cover (Existing) Us_110evt_Look_Up_Code.img as the input value raster.

3rd

: Use Buf_30m_Seg_250m_NHD_Perennial as the zone data. Use the LANDFIRE

land cover (Potential) Us_110bps_Look_Up_Code.img as the input value raster.

4th: Use Buf_100m_Seg_250m_NHD_Perennial as the zone data. Use the LANDFIRE

land cover (Potential) Us_110bps_Look_Up_Code.img as the input value raster.

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Outputs:

Existing_30m_Veg_Cap.img

Existing_100m_Veg_Cap.img

Potential_30m_Veg_Cap.img

Potential_100m_Veg_Cap.img

Task 2: Use the above generated raster datasets that contain the food/dam-building material

suitability values (0-4) to the Segmented_250m_NHD _Perennial feature class.

Step 1: Download Geospatial Modeling Environment (GME) Open Source GIS software

http://www.spatialecology.com/gme/gmedownload.htm

GMES has dependencies on R and ArcGIS. Therefore, select the download that matches

the version or R or ArcGIS you are running.

Note: GME’s predecessor, was called HawthsTools, it’s likely that you may have used

this software in the past.

Step 2: Install GME software

1. Download and extract the zip file.

2. You must use the setup.exe program, not the gme.msi program, to install GME.

3. GME is a stand-alone program that can be started from the Windows Start button

-- Programs -- SpatialEcology.

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Step 3: Click on the Geospatial Modeling Icon and run the software.

Step 4: Use the isectlinerst command

Conduct this step a total of four times.

1st for the in use Segmented_250m_NHD_Perennial, for the raster use

Existing_30m_Veg_Cap.img and for the prefix use ex_30.

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2nd

for the in use Segmented_250m_NHD _Perennial for the raster use

Existing_100m_Veg_Cap.img and for the prefix use ex_100.

3rd

for the in use Segmented_250m_NHD _Perennial for the raster use

Potential_30m_Veg_Cap.img and for the prefix use pot_30.

4th for the in use Segmented_250m_NHD_Perennial for the raster use

Potential_100m_Veg_Cap.img and for the prefix use pot_100.

Step 5: For the output feature class Segmented_250m_NHD _Perennial remove the fields

with the suffix MIN, MAX, END. Keep the field with the suffix Length Weighted

Mean (LWM). LWM is calculated by multiplying the length of each segment by the

raster cell value of that segment, summing this value across all segments, and finally

dividing that sum by the total length of the polyline:

where l is the length of a segment, v is the value of the raster cell for that segment, and L

is the total line length.

Below is what the resulting feature class attribute table should look like. Do the same for the

30m buffer data.

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Below is what the resulting feature class attribute table should look like. Do the same for the

30m buffer data.

The above generated tables (30 m and 100 m) become the inputs for the FISl

Outputs: Need to list

TOTAL STREAM POWER CALCULATIONS (STREAM POWER INPUT)

Stream power is an expression of flow strength at a given point in a river (Worthy, 2005).

Total stream power was calculated at base flow to gauge the maximum stream power at which

beaver can build dams and at the 2-year recurrence interval to gauge the likelihood of dams

persisting from year –to-year due to stream power.

Equation:

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Where Ω is the stream power, ρ is the density of water (1000 kg/m3), g is acceleration due to

gravity (9.8 m/s2), Q is discharge (m

3/s), and S is the channel slope.

Calculating discharge required discharge data and the generation of drainage area grids.

Discharge estimates were obtained from USGS StreamStats Regional Regression Equations

(region 6 for Escalante River Watershed). The base flow equation (Qp80 for August) QP80 =

9.4102E-02 DRNAREA 0.7404

was obtained from Kenney et al. (2008). Whereas, the peak 2-year

recurrence interval flow equation PK2 = 4,150 DRNAREA 0.553

(ELEV/1,000) 2.45

was obtained

from Wilkowske et al. (2008).

Task 1: Calculate Drainage Area (using a flow accumulation raster)

Step 1: Clip the DEM to the watershed or area of interest

Use the Extract by Mask command.

Step 2: Fill the pits in the DEM

Navigate to the Spatial Analyst > Hydrology Tools > Fill in the Toolbox or search for the

Fill tool.

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In the Fill dialog (below), specify the original input DEM and the name of the output

raster you wish to create (make sure the name designates that it is a filled DEM). Make

sure that all subsequent Hydrology Analyses are run on this filled DEM.

Output: (name)_DEM_fill

Step 3: Calculate flow directions

Again in the Toolbox, go to Spatial Analyst Tools -> Hydrology Tools -> Flow

Direction.

Specify the pit-filled DEM (NOT the original DEM) as the Input raster, and specify the

new output raster as a flow direction raster. You will use this flow-direction grid for other

hydrology analyses.

Output: (name)_DEM_flow_direction.img

Step 4: Calculate flow accumulation

Spatial Analyst Tools > Hydrology > Flow Accumulation.

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The dialog box prompts you to input your flow direction raster. The output should be

named something that designates the grid as a flow accumulation raster.

Output: (name)_DEM_flow_accumulation.img

The flow accumulation raster provides the requisite information on Drainage Area.

Task 2: Calculate channel slope

Step 1: Generate a slope map in percent using the slope function within the spatial

analysis extension

Step 2: Extract raster DEM elevations to a polyline

Click on the Geospatial Modeling Icon and run the software.

The following tool isectlinerst (Intersect Lines With Raster) will do the trick.

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For “prefix” use “Elev” for elevation.

Task 3: The equations were carried out in the raster calculator

BEAVER DAM CAPACITY FIS

The beaver dam capacity model consists of two FISs, a vegetaion_FIS and a

combined_FIS. The VEG FIS is a two input FIS that uses the 30 m (riparian vegetation) and 100

m (adjacent vegetation)buffer data to calculated a dam-building capacity based on land cover

classification values (i.e., beaver preferences 0-4 values) (Figure 4). Note: if you are new to

fuzzy logic and the Matlab Fuzzy Logic Toolbox a good place to start would be to read:

http://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy.pdf.

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Figure 4. Vegetation FIS, two inputs, based on the 30 m and 100 m LANDFIRE land cover

beaver dam capacity classification data.

The Combined_FIS is a three input FIS that uses the output from the above VEG_FIS

and the two stream power calculations base flow and two-year peak flow as the other inputs

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Figure 5. Combined beaver dam capacity FIS based on output from the vegetation FIS and

stream power calculations baseflow and Peak 2-year interval.

A successful run of the software produces the statement below:

Done writing output file

K:\etal\Projects\USA\Utah\Escalante\BRAT_FIS\Inputs\BRAT_BeaverCapacity_Escalante_pote

ntial_veg_input_formatted.csv.

Program Completed.

JOINING THE OUTPUT BEAVER FIS WITH THE GIS

Task 1: Prepare the shapefile and join the FIS Output with the NHD perennial stream layer

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In this case, FIS output is: BRAT_ Beaver Capacity Escalante

_potential_veg_input_formatted.csv.

Prior to joining the csv table and the shapefile the following preparation steps must be taken.

Step 1: Add the NHD perennial streams layer to ArcMap

Step 2: Export it as a new shapefile: I called it /dlvdata/Escalante_BRAT.shp.

Step 3: Open the attribute table of the newly generated shapefile and delete all the fields

except FID, Shape and you must have at least one other field in this case I had a field

called ENABLED

Step 4: Use Add Data to load BRAT_BeaverCapacity_Escalante_potential _veg_ input_

formatted.csv.

Step 4: Join Escalante_BRAT.shp and BRAT_BeaverCapacity_Escalante_potential_ veg_input

_formatted.csv based on the field FID.

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In order to make the join permanent it needs to be exported as a new shapefile.

Output: ______

This becomes the final data that is attributed and shown on the maps. Beaver combined FIS field

gives the dams per km per stream segment.

UNGULATE CAPACITY MODEL GIS PROCESSING

The ungulate capacity model includes three inputs. Distance to Water (NHD streams and

water bodies), Vegetation (LANDFIRE landcover data), and Slope (based on USGS 10-m DEM)

(Figure 6).

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Figure 4. Diagram showing ungulate capacity model inputs.

LANDFIRE LANDCOVER GIS PROCESSING AND CLASSIFICATION (VEGETATION INPUT)

Figure 7 shows how the LANDFIRE land cover data for existing (2008) was classified by

ungulate forage preferences established in the literature.

NHD Perennial Streams and water bodies

LANDFIRE Land Cover

USGS Regional Regression Equations

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Figure 7. Diagram showing LANDFIRE land cover data classification for ungulate capacity

model.

Task 1: Classify the LANDFIRE land cover data

Step 1: Load LANDFIRE land cover type raster: Us_110evt.img (existing land cover

type)

Step 2: Export the Existing LANDFIRE Raster as Grazing_Veg_Capacity.img

Step 3: Add a new field and name it “Code” to Grazing_Veg_Capacity.img to represent

ungulate grazing preferences (0-4) (Table 2).

Step 4: Assign the ungulate land cover preferences (0-4) to the field named “Code” based

on Table 2 using field SAF_SRM in Us_110evt.img

Table 2. Suitability of LANDFIRE land cover classes for ungulate grazing

O = Unsuitable - LANDFIRE land cover = Cropland, developed, roads, barren, or water.

1 = Barely Suitable - LANDFIRE land cover = sparsely vegetated.

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2 = Moderately Suitable - LANDFIRE land cover = conifer forest.

3 = Suitable - LANDFIRE land cover = woodland or evergreen shrubland.

4 = Preferred - LANDFIRE land cover = grasslands, scrubland steep or riparian

Output file: Grazing_Veg_Capacity.img

Step 4: Using the raster to ASCII command to convert the .img raster to an ASCII raster

for used in metlab.

Output file: Grazing_Veg_Capacity.asc

SLOPE MAP GENERATION

Task 1: Created a slope map

Step 1: Open the 30 m DEM in ArcMap

Step 2: Generate a slope map using the Slope Tool (ArcToolbox under Spatial Analyst

Tools -> Surface). Use the Percent option for the output measurement.

Step 3: Use Extract by Mask with your project area boundary layer to confine the slope

map to the project area.

Output file: Ungulate_Capacity_Slope.img

Step 4: Use the raster to ASCII command to convert the .img raster to an ASCII raster

format for use in Matlab.

Output file: Ungulate_Capacity_Slope.asc

DISTANCE TO WATER

Task 1: Convert NHD waterbodies (nhd24kwb_a_140700005 and nhd24kar_a_140700005_1) to

polyline for use in the ungulate capacity model (distance to water.)

Step 1: Use the Polygon to Line command as follows:

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Output: NHD_Lakes_As_Polyline

Step 2: Use the Merge command to combine the resulting polyline lakes with the NHD

perennial streams include streams outside your project area.

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Output: NHD_Lakes_Perennial_Streams

Task 2 Use the Euclidean Distance command to calculate line of sight distance of the landscape

to water

Step 1: Load the input layer, which is the layer that was generated in the previous task

NHD_Lakes_Perennial_Streams

Step 2: Assign an output grid name

Output: Euclidean_Distance_Perennial_and_Lakes_30m.img DRAFT

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Step 3: Clip the output raster Euclidean_Distance_Perennial_and_Lakes_30m.img by your area

of interest.

Use Extract by Mask

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Output: Distance_to_Water.img

Step 4: Convert raster to ASCII raster using the Raster to ASCII command.

Output: Distance_to_Water.asc

UNGULATE CAPACITY FIS

Task 1: Run the grazing capacity three input FIS

Step 1: click on the MATLAB R2012a icon to start the program.

Step2: Browse to the location were GrazingCapacity_3input is stored

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Step 3: In the command window type FIS_IT and press enter

The below dialog box should open and prompt you to select and FIS. DRAFT (N

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Step 4: Select GrazingCapacity_3input.fis

The below dialog box should open and prompt you to select RiparianVegPrefCover input raster.

Step 5: Path to the Escalante Watershed.gdb and selected the grazing_veg_capacity.asc

The below dialog box should open and prompt you load Slope Input raster.

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Step 5: Path to the Escalante Watershed.gdb and selected the ungulate_capacity_slope.asc

The below dialog box should open and prompt you load WaterSource Input raster

Step 6: Path to the Escalante Watershed.gdb and selected the distance_to_water.asc

The FIS should now start Calculating… this may take a few minutes.

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The below dialog box should open and prompt you to save your FIS output

Then it will need to save the FIS Grid to a File this will take even a bit longer.

Step 7: Open ArcMap

Step 8: Calculate Statistics on the Output FIS grid

REFERENCES

Kenney, T.A., Wilkowske, C.D., Wright, S.J., 2008. Methods for Estimating Magnitude and

Frequency of Peak Flows for Natural Streams in Utah, U.S. Geological Survey, Prepared in

cooperation with Utah Department of Transportation and the Utah Department of Natural

Resources, Divisions of Water Rights and Water Resources.

http://pubs.usgs.gov/sir/2007/5158/pdf/SIR2007_5158_v4.pdf

Wilkowske, C.D., Kenney, T.A., and Wright, S.J., 2008, Methods for estimating monthly and

annual streamflow statistics at ungaged sites in Utah: U.S. Geological Survey Scientific

Investigations Report 2008-5230, 63 p. Available at http://pubs.usgs.gov/sir/2008/5230

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http://pubs.usgs.gov/sir/2008/5230

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Worthy, M. 2005. High-resolution total stream power estimates for the cotter river, namadgi

national park, australian capital territory. Pages 338-343 in Regolith 2005 – Ten Years of the

Centre for Resource and Environment Studies. Australian National University, Canberra,

Australia.

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