Spatial Model of Flood Inundations in Moorhead and East Grand Forks Minnesota

7/27/2019 Spatial Model of Flood Inundations in Moorhead and East Grand Forks Minnesota

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Using ArcGIS to Model Flood Inundation and Impact

Near East Grand Forks and Moorhead, Minnesota

GIS 5578 GIS Programming

Matt Taraldsen, Josh Dunsmoor

1.Project IntroductionFlooding is both the most common and the costliest natural disaster in the United States

(FEMA). From 2000 2010, a yearly average of $2.6 Billion of flood damage was

reported, as well as an average of 71 deaths (National Weather Service). Damage

caused by flooding not only includes automobiles and houses, but sometimes entire

cities may experience significant damage. Once such area with high susceptibility to

flood risk is the Red River of the North Basin. This area experiences nearly annual flood

stage readings, and boasts a record crest of nearly 41 feet, experienced in 2009 near

Fargo, North Dakota and 54 feet, experienced in 1997 in the Grand Forks area (North

Dakota State University). The purpose of this study was to build a model in ArcGIS,

which features an ASCII DEM file as an input to produce inundation maps. The

inundation maps would then be overlaid on Tiger Census data to gauge potential

population impacts.

The motivation for this project comes from mutual interest of natural disaster response

and mitigation. Matt has a background in meteorology, while Josh has a strong interest

in using GIS in disaster modeling and mitigation. This project will allow not only for

modeling of the natural processes of flooding, but also allow an estimation of


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inundations at different flood levels. By integrating population data in this study, we can

learn the potential impacts to the areas population.

Further justification of the project comes from the large impacts associated with

flooding. As stated above, flooding is a major issue in the Red River Valley, and utilizing

GIS allows for further analysis of flooding potential. This project also presents an

opportunity for professional development of both group members. As a meteorologist,

Matt would like to conduct flood studies in a professional environment after graduation,

and Josh would like to apply his risk management perspective to manage and mitigate

disaster using GIS. This study would facilitate GIS skill development in natural sciences

and potential environmental impacts.

2.Background DataThe purpose of this project will be to develop a simple model demonstrating flood

inundation based from a DEM file. Several more complicated studies have been

conducted and were used for reference in this project. The forecasting and monitoring

of floods is a federally mandated task of the National Weather Service (Department of

Commerce, 2010). The forecasts, warnings, and advisories issued by the National

Weather Service are intended for use to mitigate the threat to life and property posed

by events such as floods. For this model, the focus has been given to river flooding

which is high flow, overflow, or inundation by [river] water which causes or threatens

damage (Department of Commerce, 2010). This is separated from flash flooding, which

occurs within six hours of a caustic event and usually arises from events such as


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thunderstorms. The National Weather Service further defines the levels of flooding

(Table 1).

National Weather Service Flood TerminologyTerm Definition

Monitor/Action

Stage

Bank full condition. NWS will issue flood forecasts, and monitoring of levee and other

protection systems mandatory.

Flood Stage Minimal or no property damage - but public threat.

Moderate

Flood Stage

Some inundations of roads and structures near streams. Some people or property

relocated to higher ground.

Major Flood

Stage

Extensive inundation of structures and roads. Significant evacuations of people to

higher elevations.

Record Flood

Stage

Flooding equals or exceeds previous maximum flood stage for a given location. Not

necessarily above other flood stages.

Table 1 - National Weather Service flood terminology (Dept. of Commerce, 2010)

Flood stages are then quantitatively assigned to a given flood area based on the

impacts listed above. The stages will be assigned by the local National Weather Service

forecast office, and corresponding area River Forecast Center (Dept. of Commerce,

2010).

Flood Stages are occasionally altered as new data is available. Due to the frequent

flooding in the Red River valley, new data is available. Lidar data has recently been

collected for the Red River Valley (MN Geospatial Intelligence Office, 2012). Table 2

illustrates the current flood stages for the Fargo and Grand Forks area (National

Weather Service, 2012). These flood stages were assigned in 2010 (Grand Forks) (FEMA,

2010), and 2012(Moorhead) (FEMA, 2012).


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Flood Information for Fargo and Grand Forks (in feet)Gauge

Elevation

Action

Stage

Flood

Stage

Moderate

Stage

Major

Stage

Record

Crest

Fargo 861 17 18 25 30 41

Grand

Forks 779 27 28 40 46 54Table 2 - Flood Data for Grand Forks and Fargo (From National Weather Service, 2012)

Both Grand Forks and Moorhead have had major flood events in the past. For a

comparison of how the predicted major and record floods compared, record (at the

time) crests in 1997 and then again in 2009 will be utilized.

3.Area of Interest

The study focused on the red river in three counties in Minnesota: Polk, Norman and

Clay. Within these counties are the cities of East Grand Forks and Moorhead, both of

Figure 1 - Clay, Norman, and Polk Counties with population centers. East Grand Forks and Moorhead are

highlighted


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which have experienced major flooding in both 1997 and 2009. In 2000, Moorhead had

a population of 38,000, with a Fargo-Moorhead metro population of 174,000 residents.

East Grand Forks had a population of 7,500, and 60,000 in the wider Grand Forks-East

Grand Forks area (US Census). MN DNR 30m DEMs were secured for both the Grand

Forks and the Moorhead areas. As these two cities represent the largest population

centers along the Red River, they will be the primary focus for this survey.

4.MaterialsData utilized in this project came from the Minnesota Department of Natural Resources

(DNR) Data Deli (http://deli.dnr.state.mn.us/). North Dakota does not widely distribute

data, and the data that was located did not correspond with the area of interest. An

attempt was made to obtain Lidar data from the Red River Decision Information

Network (http://gis.rrbdin.org/lidarapps.htm), however, data usage was limited on the

site and lidar data was not easily accessible. The decision was made to download the

30 meter resolution DEM from the Minnesota DNR. Raw ASCII files were downloaded

for the East Grand Forks and Moorhead, Minnesota area.

In addition to the flooding inundation, this project also intends to view the potential

impacts of flood stages. Once the DEM was secured, 2010 Census population data

(http://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.html )was downloaded. This

includes population and population density data. These data were overlaid with the

inundation polygons to create and estimate of impacted population.
http://deli.dnr.state.mn.us/http://deli.dnr.state.mn.us/http://deli.dnr.state.mn.us/http://gis.rrbdin.org/lidarapps.htmhttp://gis.rrbdin.org/lidarapps.htmhttp://gis.rrbdin.org/lidarapps.htmhttp://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.htmlhttp://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.htmlhttp://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.htmlhttp://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.htmlhttp://gis.rrbdin.org/lidarapps.htmhttp://deli.dnr.state.mn.us/


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While a majority of sources and data were found online, they represent the most

accurate and official documentation of the US Government. The National Weather

Service maintains all of their data online to ensure Emergency Management, storm

spotters, other meteorologists, or any other interested party can easily and readily

access the data. The data on these websites is considered official

climatic/meteorological data from the National Weather Service. Paper copies are only

available for purchase from the National Climatic Data Center (NCDC), and currently are

not available through the University of Minnesota. Due to the lack of budget for the

project, the decision was made to only use publicly accessible online data.

5.Data PreparationMethodology for this paper was to combine ArcGIS Model Builder with custom scripts

from Python to develop a working model. Once the data was collected from the DNR

and the Census Bureau, the next step was to create an Area of Interest. Data from the

DNR was centered on both Moorhead and East Grand Forks. The Moorhead area was

represented by one raster while the Grand Forks area was covered by two separate

rasters. The first step was to mosaic these two rasters to create two files to represent

the area of interest.

The decision was then made to keep the two rasters separate as they both

represented different parts of the river. The Red River slowly decreases in elevation as it


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flows north, with the gauge at Fargo located 861 feet and the Grand Forks gauge at 779

feet above sea level. Table 2 illustrates the flood elevations for each stage, in each city.

These values were utilized in custom python scripts to select impact areas.

6.MethodologyOnce the data and research was complete, the decision was made to use Model Builder

to build rasters. By using Model Builder, any DEM for another location could be used for

similar study. The Grand Forks mosaic DEM raster was ingested into model builder, and

converted from an ASCII file to a point file (Figure 2). This was time intensive, but

allowed for a selection of the different elevations from the raster file. A similar approach

was taken with the Moorhead area (Figure 3)

Figure 2 - Model Builder for Initial Grand Forks Setup

Figure 3 - Model Builder for the Initial Moorhead Setup


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Two python scripts, one for each city, (Appendix 1 and 2) were developed to take the

converted point raster and select all the points from the different flood elevations from

Table 1. These selected layers are then converted to an individual raster. These

individual rasters will represent the inundation levels for the different flood stages

around Moorhead and Grand Forks.

Figure 4 - Moorhead Model Builder


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Figure 4 and Figure 5 illustrate the next step in the model builder. Once the scripts

(Appendix 1 and 2) created five separate rasters the next step was to transform them.

Model builder was them employed to complete the following:

A) Convert the point files into a rasterB) Convert the raster into polygonsC) Aggregate the polygon layers (100m radius) to create a solid polygon layerD) Intersect the aggregated polygons with the census tracts to form an impacted area

The model was designed to build successively off of each layer. Therefore, most layers

featured the previous iteration of the model as input. Table 2 lists each step with input

and output parameters. Also included is a remarks column, stating the intended use of

each layer. The impacted area will then feature not only the DEM data but also the

Figure 5Grand Forks Model Builder


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census data including housing and population data. This will provide an estimation of

the impacts of a flood at different levels.

Grand Forks/Moorhead Model Steps

Model Layer Input Parameters

Output

Parameters Remarks

Step 1

GF DEM N/A N/A raw ASCII DEM file

Raster to Point

GF/ Moorhead DEM

ASCII file Point Raster

Converts ASCII raster to a

point raster

Grand Forks

Script/Moorhead Point Raster

GF/Moor Action

PointsGF/Moor Major

Points

GF/Moor Record

Points

GF/Moor Moderate

Points

GF/Moor Flood

Points

Takes the point file and

selects attributes based on

individual flood elevations.

Individual selections are then

saved as flood layers.

Completed with arcpy script

(Appendix 1)

Step 2

Point to Raster (x5)

GF/Moor Action Points

GF/Moor Major Points

GF/Moor Record

Points

GF/Moor Moderate

Points

GF/Moor Flood Points

GF/Moor Action

RasterGF/Moor Major

Raster

GF/Moor Record

Raster

GF/Moor

Moderate Raster

GF/Moor Flood

Raster

Converts point files

into a raster.

Raster to Polygon (x5)

GF/Moor Action

Raster

GF/Moor Major Raster

GF/Moor Record

Raster

GF/Moor Moderate

Raster

GF/Moor Flood Raster

GF/Moor Action

Polygon

GF/Moor Major

Polygon

GF/Moor Record

Polygon

GF/Moor

Moderate Polygon

GF/Moor Flood

Polygon

Converts raster into

polygon layers


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Aggregate Polygons

(x5)

GF/Moor Action

Polygon

GF/Moor Major

Polygon

GF/Moor Record

Polygon

GF/Moor Moderate

Polygon

GF/Moor Flood

Polygon

GF/Moor Action

Polygon

Aggregated

GF/Moor Major

Polygon

Aggregated

GF/Moor Record

Polygon

Aggregated

GF/Moor Moderate

Polygon

Aggregated

GF/Moor Flood

Polygon

Aggregated

Combines all Polygon Layers

into one large polygon for

overlay

Intersect (x5)

GF/Moor Action

Polygon Aggregated

GF/Moor MajorPolygon Aggregated

GF/Moor Record

Polygon Aggregated

GF/Moor Moderate

Polygon Aggregated

GF/Moor Flood

Polygon Aggregated

Grand Forks Census

Tracts (GF only)

Moorhead Census

Tracts (Moorhead only)

GF/Moor Action

Impacted Area

GF/Moor Flood

Impacted Area

GF/Moor Moderate

Impacted Area

GF/Moor Major

Impacted Area

GF/Moor Record

Impacted Area

Intersects the census tract

information

with the aggregated

polygons. Impacted

population tallys can then be

extracted from

the attributes table.Table 2 - Model input and output parameters and remarks

7.Data ErrorsData collection proved to be the most challenging part of the project. North Dakota in

particular did not have data readily available. The Minnesota DNR did have data available,

but was in a different format from the available Census data. All files have a projection of

Transverse Mercator in the NAD 1983 UTM 15N coordinate system.

The next issues were the large size of the files utilized, and file types. Initially the project

was intended to ingest a raster data and select attributes from this file. However, once


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the DNR ASCII file was downloaded this was no longer an option. Upon investigation

into other flood projects from the University of Texas (Lopez, 2003) it was found a point

layer would be sufficient to select attributes from. However, this point layer created a

large file that took over 30 minutes to render in model builder. This slowed down the

project considerably but gave more representative elevation results.

8.Preliminary ResultsFigure 6 illustrates the amount of flooding near East Grand Forks at different levels. Of

note was the large spatial extent of the major flood stage polygon. The bottom left of

Figure 6 illustrates the amount of the city of East Grand Forks is inundated in a major

flood. Nearly the entire city is inundated in a major flood with only minor additional

flooding from the record crest. This is validated by the fact that after 1997 flood, all but

eight homes were damaged by flood waters in East Grand Forks. When compared to

Moorhead, it appears that the northern edge of East Grand Forks is especially

susceptible to widespread inundation. While the areas covered by both the major and

record floods are significant, the areas are likely exaggerated in the model. The areas

north of East Grand Forks have a lower elevation than the gauge within the city limits,

and our model cannot account for those immediate changes. Even with the northernly

bias, it is clear that major sections of East Grand Forks (over 10,000 people) would be

directly impacted by a major or record flood. This is consistent with both the major

floods of 1997 and 2009.


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Figure 6 - Flood levels near East Grand Forks, MN


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East Grand Forks ImpactsAction Stage Flood Stage Moderate Flood Major Flood Record Flood

Population Impacted 2322 3257 3977 10823 10889

% Increase from Previous NA 140.27% 122.11% 272.14% 100.61%

Area Covered (ha) 1405 4059 7137 22505 23932

% Increase from Previous NA 288.90% 175.83% 315.33% 106.34%

Table 3 - Grand Forks Impact

Unlike Grand Forks, which had an immense flooding impact within the city, Moorhead was relatively

unscathed by flooding. Most of the flooding (bottom Figure 6) inundates the areas to the north of the

city which is comparatively sparsely populated. There is still some inundation within the city, but

compared to Grand Forks there is a comparatively smaller area covered. A quantitative result is

displayed in table 4. As in Grand Forks, the Moorhead area also shows a large area of inundation north

of the city. Some of this may again be due to the lower elevation of the river as it flows north, but

historic flood maps show areas to the north of the city are more susceptible to flooding. The inundated

areas north of the city are likely exaggerated, but unlike Grand Forks these areas are sparsely populated.

This limited the amount of populations that were impacted in the model. However, as with Grand Forks,

a record or major crest has a very large impact. The model estimated that nearly 18,000 people would

be impacted by flood waters in a record crest. In a major flood this number is slightly less, but still over

15,000 people are likely to be significantly impacted by flood inundation. Again, this is consistent with

the major floods of 1997 and 2009.


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Figure 7 - Flood stages in Moorhead, MN


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Moorhead Impacts

Action

Stage

Flood

Stage

Moderate

Flood

Major

Flood

Record

Flood


% Increase from

Previous NA 121.04% 109.61% 317.90% 113.16%

Area Covered (ha) 939 3697 8394 20821 22177

% Increase from

Previous NA 393.72% 227.05% 248.05% 106.51%

Table 4 - Flooding Impacts in the Moorhead Area

9.Model TestTo ensure the model was applicable to other situations, a test run was conducted. While the

flood stages utilized in the paper were the most recent, there was theoretically still some error

in the model. By using a 30m resolution DEM, at least some error is generated in the inundation

fields. The difference between the use of lidar generated DEM, and larger scale DEM can

sometimes be rather significant. (Wang et. al, 2010). Not only can there be error in the

measured DEM, but our model also did not capture the dynamics of a flood (Erich et. al, 2002).

As a flood progresses, there are often strong dynamics besides the simple inundation of

structures. This includes the potential for multiple crests, flood control efforts of the affected

municipalities, and environmental sources such as ice in the channel or wave action. Therefore,

a hypothetical flood was generated in our model. This flood will be 1 foot above all current

levels (Table 5). This tested the potential impact of just a one foot change in flood elevation due

to the dynamics of moving water. The same methodology was followed as the original run, but


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all levels in the model were increased by one foot.

Figure 8 - Test run comparison to first run output


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Test Stages for Fargo and Grand Forks (in feet)

Gauge

Elevation

Action

Stage

Flood

Stage

Moderate

Stage

Major

Stage

Record

Crest

Fargo 861 18 19 26 31 42

Grand

Forks 779 28 29 41 47 55Table 5 - Fargo and Grand Forks Test Values

The same methodology was used for the test run as with the first run of the test

(Section 6). The scripts were altered to reflect the new levels, and then run (Figures 10

and 11). A new model was then created, which analyzed all ten flood stages for both

Figure 9 Fargo/Grand Forks test model

Figure 11 - Test script for MoorheadFigure 10 - - Test script for Grand Forks


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locations at one time. The model followed the same methodology as the first model set

up, just with new test layers for outputs.

The test revealed a few interesting trends. First, as expected there were large increases

in impacts for both the major and record crest. What was surprising was that there was

actually less of an impact if the action and flood stages were raised. This was not

expected, and the model was run several times to ensure these results are correct. It is

speculated by the group that this error is a byproduct of the intersection of the flood

layers with the census tracts. With the new stage, a different set of intersections were

created which may have altered which census tracts were impacted. This would alter

the population totals for each stage.

Moorhead Test ImpactsAction

Stage

Flood

Stage

Moderate

Flood

Major

Flood

Record

Flood

Population Impacted 3764 4556 4994 15876 17966Test population

impacted 3874 3721 4598 17444 18881

% Change 102.92% 81.67% 92.07% 109.88% 105.09%

Area Covered (ha) 939 3697 8394 20821 22177

Test Area Covered (ha) 1031 4094 9613 21586 23011

% Change 109.80% 110.74% 114.52% 103.67% 103.76%Table 6 - Moorhead test results

Grand Forks Test Impacts

ActionStage

FloodStage Moderate Flood

MajorFlood

RecordFlood


Test population impacted 2181 2266 3981 10857 11127

% Change Population 93.90% 69.60% 100.10% 100.30% 102.20%

Area Covered (ha) 1405 4059 7137 22505 23932

Test Area Covered (ha) 1328 3664 8482 21716 23545


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% Change Area 94.52% 90.27% 118.85% 96.49% 98.38%Table 7 Grand Forks test results

With the exception of the errors with some stages having inconsistent impacts the model

worked well. The largest changes were seen in the major flood category, where a nearly 10%

greater impact was noted by increasing the crest by just one foot. In Grand Forks the changes

were comparatively small, but there was a 2% change in the population impacted when the

record crest was adjusted upwards by one foot. Significant increases in Moorhead were also

noted in are inundated between flood stages. The biggest increase was from flood to moderate

flood stage, which increased the inundation area by 14%.

10. Final Conclusions

For both Grand Forks and Moorhead, the results were consistent with what were expected.

While there were some inconsistencies with the population impacts between the various flood

stages, the model clearly created polygons that increased in spatial coverage as the flooding

became more significant. Also of note was the large amount of area inundated and people

impacted for both the major and record floods. While this was likely biased to the river

naturally decreasing in elevation away from the gauge sites, having over 10,000 people

impacted in Grand Forks and over 17,000 in Moorhead was still indicative of a high impact flood

event. The use of python and model builder were especially useful for this project. The python

script allowed for a relatively rapid collection and generation of the raster layers, which would

have taken considerable more time for a human analyst to complete. The model builder, while

sometimes cumbersome, allowed for all flood layers to be generated simultaneously, and again

saved time. For future research the inclusion of higher resolution lidar data would be desired


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as a comparison of the two data sources could then be completed. Also, including information

about the flood levies in both Grand Forks and Moorhead would be valuable as a much more

accurate inundation map could be produced. Additional analysis of the census data could also

be performed to mitigate and plan response for future floods. Using the data to locate elderly

or under-privileged populations would allow emergency crews to focus early efforts in their

direction. Finally, including more in depth information about soil infiltration, timing of

inundation in different weather conditions, and also dynamically mapping the flood inundation

are all potential areas of research but were not achievable with the methodology of this

particular project.

11.Task ContributionsThe proposal for this project was a collaboration of the two of us as we both drew on our

interest in natural disaster management. Given Matts meteorological experience, he

determined the needed data sets and sources. Once data was collected, it was passed off to

Josh who developed the work flow needed to get from the input ASCII file to our output

flood stage raster files. With the workflow determined, the model was assembled in model

builder by Matt, and the manual scripting needed to develop the flood stage selections was

written by Josh. From there, it was a collaborative effort to display the information and

report and display the findings.


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Appendix:

Appendix 2: Python code for selecting Moorhead data

## Date: 4/22/2012

## This tool will query the point shape file storing the elevation data. This script will

## query the points, create the selections, and write the output files for creating

## the flood level polygons in Moorhead. There are 5 flood levels, action, flood, moderate

## major, and record.

import arcpy

import sys

arcpy.env.Workspace = "E:\Programming\Project_Data\moorhead_point.shp"

inFC = arcpy.env.Workspace

moorElev = 861

outAction = "E:\Programming\Project_Data\Output_Rasters\moor_outaction.shp"

outFlood = "E:\Programming\Project_Data\Output_Rasters\moor_outfld.shp"

outModerate = "E:\Programming\Project_Data\Output_Rasters\moor_outmod.shp"

outMajor = "E:\Programming\Project_Data\Output_Rasters\moor_outmjr.shp"

outRecord = "E:\Programming\Project_Data\Output_Rasters\moor_outrec.shp"

arcpy.Select_analysis (inFC, outAction, '"GRID_CODE" < 879')

arcpy.Select_analysis (inFC, outFlood, '"GRID_CODE" < 886')

arcpy.Select_analysis (inFC, outModerate, '"GRID_CODE" < 891')

arcpy.Select_analysis (inFC, outMajor, '"GRID_CODE" < 902')

arcpy.Select_analysis (inFC, outRecord, '"GRID_CODE" < 903')

Appendix 2: Python code for selecting Grand Forks data

## Date: 4/22/2012

## This tool will query the point shape file storing the elevation data. This script will


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## query the points, create the selections, and write the output files for creating

## the flood level polygons for the East Grand Forks, MN area. There are 5 flood levels,

action, flood, moderate

## major, and record.

import arcpy

import sys

arcpy.env.Workspace = "E:\Programming\Project_Data\grand_forks_point.shp"

inFC = arcpy.env.Workspace

moorElev = 861

outAction = "E:\Programming\Project_Data\Output_Rasters\gf_outaction.shp"

outFlood = "E:\Programming\Project_Data\Output_Rasters\gf_outfld.shp"

outModerate = "E:\Programming\Project_Data\Output_Rasters\gf_outmod.shp"

outMajor = "E:\Programming\Project_Data\Output_Rasters\gf_outmjr.shp"

outRecord = "E:\Programming\Project_Data\Output_Rasters\gf_outrec.shp"

arcpy.Select_analysis (inFC, outAction, '"GRID_CODE" < 807')

arcpy.Select_analysis (inFC, outFlood, '"GRID_CODE" < 819')

arcpy.Select_analysis (inFC, outModerate, '"GRID_CODE" < 825')

arcpy.Select_analysis (inFC, outMajor, '"GRID_CODE" < 833')

arcpy.Select_analysis (inFC, outRecord, '"GRID_CODE" < 834')

Appendix 3: Location of Files

File name, sub-folders, and remarks within Programming File

Folder Name Sub-Folder Remarks

Census Data Contains raw census data

Flood Maps Contains all figures of flood inundation utilized in paper

Scripts Contains all arcpy scripts utilized

MN_Data Contains raw data from MN DNR data deli

MN_Data MN Counties Shapefile of MN county outlines


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MN_Data MN Cities Shapefile of MN municipal boundaries

Model Figures Contains figures from model builder

Project_Data Grand Forks and Moorhead point files

Project_Data Aggregated_Polygons Final layer of polygons before overlaid with census tracts

Project_Data Grand Forks DEM Raw ASCII DEM file for Grand Forks

Project_Data Intersected_Pop

The final layer of the model - combined census tracts and

aggregated polygons for both Grand Forks and Moorhead

Project_Data Moorhead DEM Raw ASCII DEM file for Moorhead

Project_Data Output_Raster Converted raster layers to polygons

Project_Data Temp_Rasters Converted point files into raster layer

Project_Data Toolbox

Contains python scripts and model builder files

utilized in the model

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.

"Census 2000 TIGER/Line Data." Free Data. ESRI Corporation. Web. 22 Mar. 2012.

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Spatial Model of Flood Inundations in Moorhead and East Grand Forks Minnesota

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