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USING PYTHON AND XML FOR FLOOD PLAIN DELINEATION MODELING AND DYNAMIC
INUNDATION ANALYSIS OF THE MISSOURI RIVER VALLEY IN
HOLT COUNTY, MISSOURI
A RESEARCH PAPER PRESENTED TO
THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES
IN CANDIDACY FOR THE DEGREE OF
MASTER OF SCIENCE
By
JEFFREY K. HERZER
NORTHWEST MISSOURI STATE UNIVERSITY
MARYVILLE, MISSOURI
November 1, 2016
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Abstract
This paper describes how ArcGIS and Python scripting were used to create a simple
floodplain delineation model and to drive analysis on the model with real-time river gage
data retrieved from the USGS XML data feed. Model output is assessed against photographs
of the study area during severe flooding in 2011. Floodplain delineation models compare
water elevations against land elevations; flooded areas exist where water surface elevation
values exceed those of the terrain surface. The model's terrain surface was created from a
LiDAR-derived 1m Digital Elevation Map, while the water surface was created by drawing
an overlay of polygons representing elevation "offset zones”, with values based on the
average drop in terrain elevation upstream and downstream from the Rulo, Nebraska river
gage. Offset values were assigned to the terrain surface polygons within each offset zone.
The output of the model is a "Potential for Flooding" (PFlood) map -- called
"potential" because the model does not calculate "flood cell discontinuity", those areas that
flood or remain dry based on which levees remain intact. Model output provides highly
detailed assessments of which levees may remain above the water surface and the
inundation depth of those below the water surface at a given river gage value.
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Table of Contents
I. Introduction ................................................................................................................................ 1
II. Objective ....................................................................................................................................... 2
III. Study Area ................................................................................................................................... 2
IV. Research Background ............................................................................................................. 6
a. Floodplain Modeling................................................................................................ 6
b. River Gage and Flow Data .................................................................................. 10
1. XML ......................................................................................................................... 10
2. WaterML2 ............................................................................................................ 10
3. JSON ........................................................................................................................ 11
c. The Proof of Concept Projects .......................................................................... 14
1. Web Development ............................................................................................ 14
2. MassDOT Traffic Data Feed ........................................................................... 16
V. Data Sources ............................................................................................................................ 18
VI. Development ........................................................................................................................... 19
a. The Geodatabase .................................................................................................... 19
b. Floodplain Delineation Model .......................................................................... 19
1. Modeling the Ground Surface ....................................................................... 19
2. Modeling the Water Surface .......................................................................... 24
a. The XML Feed.......................................................................................................... 27
b. Analysis ..................................................................................................................... 31
VII. Python Code ............................................................................................................................ 32
a. Enter Gage Stations ............................................................................................... 32
b. Get XML Feed .......................................................................................................... 33
c. Build Floodplain Model ....................................................................................... 33
d. Pflood from XML .................................................................................................... 33
e. PFlood Generate Scenario .................................................................................. 33
VIII. Model Output ........................................................................................................................ 35
a. Symbolization ......................................................................................................... 35
b. Offset and No-Offset ............................................................................................. 35
c. Modified Offset ....................................................................................................... 39
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IX. Analysis Scenarios .................................................................................................................. 42
a. SCENARIO A: Rulo/Big Lake ................................................................................... 45
b. SCENARIO B: Missouri Highway 111/118 (Fred Guthrie Site) .................. 55
c. SCENARIO C: Tarkio River Southwest of Corning, Missouri ....................... 64
d. SCENARIO D: Corning, Missouri and Corning Conservation Area ........... 71
X. Conclusions and Further Development ........................................................................... 81
In Memoriam: Trooper Frederick F. Guthrie and K-9 Reed ......................................... 84
Acknowledgements ...................................................................................................................... 85
APPENDICE ..................................................................................................................................... 86
APPENDIX A: Enter Gage Stations Tool .................................................................... 87
APPENDIX B: Get XML Feed Tool ................................................................................ 93
APPENDIX C: Build Floodplain Model Tool ............................................................ 97
APPENDIX D: PFlood from XML Tool ..................................................................... 101
APPENDIX E: PFlood Generate Scenario Tool ..................................................... 105
References .................................................................................................................................... 108
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Table of Figures
Figure 1: Holt County, in the northwestern corner of Missouri ......................................................... 4
Figure 2: I-29 runs along the eastern edge of the Missouri River Valley at Corning, MO., view
to the north 21 JUN 2011 ........................................................................................................................ 5
Figure 3: I-29 at Corning, MO., roughly two miles from the Missouri River, view to the
southwest 21 JUN 2011............................................................................................................................ 5
Figure 4: Approaches to Floodplain Modeling in GIS (illustrations by the author) .................... 9
Figure 5: Levee Structure, adapted from Choung (2014) – illustration by the author .............. 9
Figure 6: Data stream query parameters passed through a URL .................................................... 12
Figure 7: XML tags are named so data is self-describing .................................................................... 12
Figure 8: XML tags with namespaces ......................................................................................................... 12
Figure 9: The NWIS feed in JSON format ................................................................................................... 13
Figure 10: River gage web map with dynamic content from NWIS data ..................................... 15
Figure 11: Dynamic map, PHP back-end code ........................................................................................ 15
Figure 12: Data display from the (MassDOT) XML traffic data feed .............................................. 16
Figure 13: Massachusetts DOT Road Condition XML Feed ................................................................ 17
Figure 14: MassDOT XML Display, workflow algorithm ..................................................................... 18
Figure 15: Workflow, XML-driven Floodplain Modeler ...................................................................... 20
Figure 16: Ground surface model from slope analysis ........................................................................ 22
Figure 17: Output of raster to polygon conversion .............................................................................. 23
Figure 18: Raster to Polygon coverage - Number of Polygons vs. Polygon Minimum Area 24
Figure 19: Floodplain delineation map with projected water surface ......................................... 25
Figure 20: Elevation Drops Between River Gages, Omaha to Kansas City ................................... 26
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Figure 21: Water Elevation Offset Zones, Polygon Feature Class ................................................... 27
Figure 22: XML data tree from USGS river gage feed (Part 1) .......................................................... 28
Figure 23: XML data tree from USGS river gage feed (Part 2) .......................................................... 29
Figure 24: XML data tree from USGS river gage feed (Part 3) .......................................................... 30
Figure 25: Accessing parameters within XML nodes ........................................................................... 31
Figure 26: PFlood (potential for flood) formulas .................................................................................. 32
Figure 27: Workflows in the Floodplain Delineation Toolbox ......................................................... 34
Figure 28: The floodplainColors schema-only layer package file (.lpk) ....................................... 34
Figure 29: Elevation Values Selected for “Immersion Scenarios” ................................................... 36
Figure 30: Floodplain Immersion Scenarios with Elevation Offsets .............................................. 37
Figure 31: Floodplain Immersion Scenarios Without Elevation Offsets ...................................... 38
Figure 32: Floodplain Immersion Scenarios with Modified Elevation Offsets........................... 40
Figure 33: PFlood Projection vs. Landsat Image from 17 July 2011 (Source: USGS) .............. 41
Figure 34: Study Area Scenario Locations w/ Nearby Levee Breach Sites (insets) ................. 43
Figure 35: Mill Creek and Union Township Levee Districts (Source: USACE) ........................... 44
Figure 36: SCENARIO A, Map and Aerial View ....................................................................................... 47
Figure 37: SCENARIO A, Vicinity of Big Lake and US 159 between Rulo, NE and Fortescue,
MO .................................................................................................................................................................. 48
Figure 38: SCENARIO A, Big Lake pre-flood, view to the north 16 JUN 2011 (Max Stage:
24.12) ............................................................................................................................................................ 49
Figure 39: SCENARIO A, Big Lake inundated, view to the north 21 JUN 2011 (Max Stage
26.51) ............................................................................................................................................................ 49
Figure 40: SCENARIO A, US 159 between Rulo and Big Lake ........................................................... 50
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Figure 41: SCENARIO A, water over US 159, view to the west 21 JUN 2011 (Max Stage
26.51) ............................................................................................................................................................ 51
Figure 42: SCENARIO A, roadway and rail bed, south shore of Big Lake ..................................... 52
Figure 43: SCENARIO A, roadway and rail bed, south shore of Big Lake, view to the north
14 OCT 2011 (Max Stage 14.19) ....................................................................................................... 53
Figure 44: SCENARIO A, US 159 between Rulo and Big Lake, post-flood, view to the west
14 OCT 2011 (Max Stage 14.19) ....................................................................................................... 53
Figure 45: SCENARIO A, Google Streets images from 2013 show high water marks .............. 54
Figure 46: SCENARIO B, Map and Aerial View; Fred Guthrie Site circled .................................... 56
Figure 47: SCENARIO B, Looking south towards Big Lake from the Guthrie site 01 AUG
2011(Max Stage 24.03) .......................................................................................................................... 57
Figure 48: SCENARIO B, Scour hole at the Guthrie site (r. center) and sand deposition along
Highway 111 (l. center), view south towards Big Lake 14 OCT 2011 (Max Stage 14.19)
......................................................................................................................................................................... 57
Figure 49: SCENARIO B, Fred Guthrie Site at Junction of Highway 111 and 118 ..................... 58
Figure 50: SCENARIO B, Looking west towards the Guthrie site with water over Highway
111 01 AUG 2011 (Max Stage 24.03) ............................................................................................... 59
Figure 51: SCENARIO B, Water is deep enough for shallow draft boats to operate freely
around the Guthrie site 01 AUG 2011 (Max Stage 24.03) ...................................................... 59
Figure 52: SCENARIO B, Level of immersion at Jct Missouri 111/118 on the morning of 7
AUG 2011, view to the south (top) and to the west (center and bottom) .......................... 60
Figure 53: SCENARIO B, potential for flooding caused by breaks in agricultural levees ....... 61
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Figure 54: SCENARIO B, The flood’s areal coverage grows as agricultural levees are
breached inland and individual flood cells are filled 21 JUN 2011 (Max Stage 26.51) 62
Figure 55: SCENARIO B, areas east and south of the Union Township levee breach .............. 63
Figure 56: SCENARIO C, Map and Aerial View (Google Maps, Google Earth) ............................. 65
Figure 57: SCENARIO C, The leveed Tarkio River ................................................................................. 66
Figure 58: SCENARIO C, Tarkio River, view to the north, as overflow begins to seep through
the levees 21 JUN 2011 (Max Stage 26.51) and post-flood 14 OCT 2011 (Max Stage
14.19) ............................................................................................................................................................ 67
Figure 59: SCENARIO C, The leveed Tarkio River and railroad bridge at County Road 125 68
Figure 60: SCENARIO C, Tarkio River and railroad bridge at County Road 125 21 JUN 2011
(Max Stage 26.51) ..................................................................................................................................... 69
Figure 61: SCENARIO C, Minute elevation differences seen at very high map scales ............. 70
Figure 62: SCENARIO D, Map and Aerial View (Google Maps, Google Earth) ............................ 72
Figure 63: SCENARIO D, Missouri River Levee Breach to Corning, MO and Interstate 29 .... 73
Figure 64: SCENARIO D, Flooding in Corning, MO 23 JUN 2011 (Max Stage 26.96) ............... 74
Figure 65: SCENARIO D, Corning, MO, high water marks of around 1 meter 11 JAN 2012 .. 75
Figure 66: SCENARIO D, Corning, MO. ....................................................................................................... 76
Figure 67: SCENARIO D, Looking southwest from the I-29 Corning interchange to the town
of Corning and the Missouri River 8 JUL 2011 (Max Stage 25.81) ....................................... 77
Figure 68: SCENARIO D, High water failed to top the Interstate 29 mainline south of the
Corning interchange, view to the north 23 JUN 2011 (Max Stage 26.96) ......................... 77
Figure 69: SCENARIO D, Interstate 29 and the Corning Interchange ............................................ 78
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Figure 70: SCENARIO D, I-29 Corning Interchange, view to the east 23 JUN 2011 (Max Stage
26.96) ............................................................................................................................................................ 79
Figure 71: SCENARIO D, Corning Interchange flood map, extremely high scale; island of
land noted by arrow, compare to feature in Figure 70. ............................................................. 79
Figure 72: SCENARIO D, Green vegetation marks areas that stayed dry along I-29 just south
of the Corning interchange, looking south 14 OCT 2011 (Max Stage 14.19) .................. 80
Figure 73: Flight track constructed from GPX files ............................................................................... 83
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I. Introduction
Flooding is part of life along major rivers like the Mississippi and Missouri in the
central United States, but more recently, major flood events have become more frequent
and more destructive. The Great Flood of 1993 has been described as the largest and most
significant extreme flood event ever to occur in the United States (Larson, 1996). This
disaster was surpassed less than twenty years later by the Missouri River Flood of 2011,
where a series of significant weather events sent unprecedented volumes of water into the
Missouri River basin, overwhelming the system of dams and reservoirs used for flood
control (Kahn, 2011; National Weather Service, 2012). Since 1993, GIS technology has put
powerful analytic capabilities within reach of the smallest public entities. Using water level
forecasts, flood area projections and critical crest levels for levees and other flood control
structures, highly localized risk scenarios can be generated to the point where flood control
protection measures can be evaluated for individual properties. This study applied GIS
technology and readily available source data to determine the accuracy and usefulness of
such an effort on a county-wide scale.
The primary goal of this project is to demonstrate how ArcGIS and Python scripting
were utilized to create a simple floodplain delineation model and to retrieve data from an
EXtensible Markup Language (XML) feed to drive analysis on the model. Floodplain
analysis involves creating and comparing terrain and water surfaces to see where the two
underlie/overlie each other. More sophisticated floodplain delineation models incorporate
scientific principles and include factors such as friction, channel cross-section, water mass,
velocity and momentum; however, “no consensus exists concerning the level of model and
data complexity required to achieve a useful prediction of inundation extent” (Horritt &
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Bates, 2001). This project does not seek to create a universal model for floodplain analysis,
but rather a simple, useful floodplain model with data readily at hand, in this case, high
resolution (1m) LiDAR-derived digital elevation maps and real-time river gage and flow
volume data, both available via the Internet. The result is a set of Python tools that can be
used to create and run analysis through a basic floodplain delineation model for any area
where sufficient data exists.
II. Objective
The objective of this study was to use Python scripting to generate a simple dynamic
floodplain delineation model and create scenarios using data received through a live XML
Internet feed. The secondary objective was to create a floodplain delineation model where
iterations could be generated quickly and where analyses could be easily conducted from
wide areas down to individual properties and flood control structures.
III. Study Area
Holt County, Missouri, in the extreme northwestern corner of the state (Figure 1)
suffered extensive damage in not only the 1993 and 2011 flood events, but also during four
consecutive years (2007 to 2010) where the U.S. Army Corps of Engineers (USACE) altered
spring river elevations to mimic natural seasonal “pulses”. The Corps’ Missouri River
Recovery Program (MRRP) seeks to change the lower Missouri River from a tightly
controlled waterway free of flooding, to an environment where more natural flows support
riverine and sandbar habitat for several threatened or endangered species (U.S. Army
Corps of Engineers, n.d.). To produce a pulse, greater volumes of water are released from
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the Gavins Point Dam near Yankton, South Dakota, the last in a series of flood control dams
and reservoirs between the Missouri headwaters and the river's mouth at the Mississippi
River north of St. Louis. Officials in Holt County have blamed the spring releases as the
immediate cause of frequent major flooding, particularly the 2011 event, which kept most
of Holt County’s bottomlands under water for the greater part of four months. Crop losses
in these years exceeded $150-million dollars, infrastructure and property were repeatedly
destroyed and much farmland was devalued or destroyed by sand deposition and scour
holes (Mahoney, 2011).
Holt County is located roughly halfway between Omaha, Nebraska to the northwest
and Kansas City, Missouri to the southeast. It is bordered on the west and south by the
Missouri River and the states of Nebraska and Kansas. The Missouri River floodplain is an
area 3 to 12 miles (5 to 19 km) wide and covers much of the western half of Holt County; its
confines are readily apparent in Figure 1. The main highway through the area is Interstate
29, part of the Dwight D. Eisenhower National System of Interstate and Defense Highways,
and the main route for regional traffic between Kansas City and Omaha, Nebraska. I-29
runs along the far eastern edge of the river valley (Figure 2) and is less than two miles from
the Missouri River in some locations (Figure 3). Inundation around the I-29 mainline or
flooding across the roadway itself are indicators of extreme flood events.
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Figure 1: Holt County, in the northwestern corner of Missouri
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Figure 2: I-29 runs along the eastern edge of the Missouri River Valley at Corning,
MO., view to the north 21 JUN 2011
Figure 3: I-29 at Corning, MO., roughly two miles from the Missouri River, view to
the southwest 21 JUN 2011
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IV. Research Background
a. Floodplain Modeling
Hydrology is the study of water in the environment, a science whose purpose is to
understand the Earth’s complex water systems and how water will behave as it moves
across the land (British Hydrological Society, 2014). Floodplains are components in this
system. Wright (2007) describes four approaches to delineating areas that are subject to
inundation: through detailed engineering studies, often assisted by computer modeling,
which consider factors such as energy, flow depths, velocities, roughness coefficients,
acceleration and energy loss; the use of historic flood data including aerial photography
and sketches; use of topographic maps, where land elevations are considered relative to
the water stream, and; through detailed soil maps, where flood surface areas are apparent
through varying soil types in areas not substantially altered by human activity. At a most
basic level, flood models generated in a GIS require a characterization of both land and
water surfaces and a comparison of their elevations. The following are some approaches
currently available in GIS field for floodplain model:
Surface Difference Models (Figure 4a) compare displacements between
Triangulated Irregular Network (TIN) terrain and water surfaces to determine where one
surface elevation is above, below, or the same as the other (Esri, n.d.). 1-Dimensional (1D)
Models (Figure 4b) are constructed from surveyed “glass slide” cross sections of an in-bank
river channel, indicated by the lateral lines. Cross sections may be extended to include the
elevations of adjoining terrain. 1D is used to analyze water flow in primarily one direction
and considers conservation of water mass and momentum. Water level, velocity and flow
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rate are calculated for each cross section. 1D models are useful in flood forecasting. They
are not a good tool for complex flood routes, such as those found in urban areas.
2-Dimensional (2D) Models (Figure 4c) are built from digital terrain models (DTMs)
and/or channel bathymetry and represent conditions across an x, y mesh or grid. They are
good for “breach analysis”, the results of failures in levees and other flood control
structures; they can be applied to braided/split stream water flows, shallow flood plain
flows and to flows with minimal depth-varied water velocities created by river bends, sand
bars, etc. (Swift, 2014). Direct outputs from 2D models include flood maps and depth grids.
1D and 2D models can be “linked” to create a Combined 1D/2D models (Figure 4d)
using 1D to represent channels and 2D for floodplains. Ground surfaces are created from
digital elevation maps (DEMs) and water surfaces are generated from 1D profiles of the
flood plain, with each profile assigned a z-value representing the height of water across a
river at a given location (Esri, n.d.). Combined models are useful for flood mapping and
flood control structure design. Water surfaces are typically generated using hydrology tools
like HEC-RAS, an industry-standard River Analysis System (RAS) application developed by
the U.S. Army Corps of Engineers’ Hydrologic Engineering Center (HEC) (U.S. Army Corps of
Engineers, Hydrologic Engineering Center, n.d.). Within ArcGIS, Esri’s ArcHydro toolbox
(Esri, 2011), makes it possible to model both land and water surfaces. However, both HEC-
RAS and ArcHydro require working knowledge of hydrology, which is beyond the scope of
this study.
A critical factor in modeling the ground surface is accurately representing both
levees and “flood cells”, the protected areas within the floodplain that fill and drain
individually as levees are breached. Extracting the linear features that comprise levees and
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cells from remotely sensed data has always been problematic. According to Choung
(2014), the task of mapping levees has historically been done using ground surveying
methods or only one type of remote sensing dataset. Choung’s (2014) research on levees
along 22 kilometers/13.7 miles of South Korea’s Nakdong River used two datasets,
airborne topographic LiDAR data and multispectral orthoimages, to extract and map linear
features on the basis that levee surfaces consist of multiple objects with different geometric
and spectral patterns.
The levee structure shown in Figure 5 consists of a berm on the side facing land and
two slopes leading to a crown at the top elevation. Levees are covered by various materials,
such as an asphalt or gravel road surface on the crown and concrete or vegetation on the
slope surfaces. All will register at different elevations with different spectral signatures.
Choung (2014) also generated a Digital Surface Model (DSM) from LiDAR points using the
linear interpolation method, then subjecting the data to a number of steps such as slope
classification, elevation and area analysis, median filtering, morphological filtering,
clustering algorithms, and a “break line” detection method for mapping levees. Steinfeld, et
al. (2013) assessed three semi-automated techniques for detecting and mapping
earthworks in two- and three-dimensional imagery of a floodplain in Australia, and found
DEM analysis was the most accurate compared to Landsat TM, SPOT satellite, and aerial
photography. They also reported how such assessments across time and broad extents are
limited by significant costs and technical challenges.
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Figure 4: Approaches to Floodplain Modeling in GIS (illustrations by the author)
Figure 5: Levee Structure, adapted from Choung (2014) – illustration by the author
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b. River Gage and Flow Data
Data from the USGS National Water Information System (NWIS) web site is offered
over the Internet through a REST (REpresentational State Transfer) web service and
retrieved using Hypertext Transfer Protocol (HTTP) through a URL that passes query
parameters for desired format, station locations and parameter codes (Figure 6). Available
data formats include XML using the WaterML 1.1 or WaterML 2.0 schemas and JSON.
1. XML
XML is a platform-independent tool for storing, transporting and exchanging data
(W3Schools , n.d.). XML data is organized in a “tree” hierarchy using markup language
<tags> similar to HyperText Markup Language (HTML) tags used to construct web pages.
Unlike HTML, XML tags can be named by the author, so data can be self-describing as seen
in Figure 7. XML is extensible, meaning tags can be added or removed from the tree
without affecting most applications. XML data streams can pass incremental updates and
changes to individual tags within the data. XML is used primarily in ArcGIS for
administering geodatabases, using three types of XML documents: a workspace document,
a record set document, and a data changes document (Esri, 2008). ArcCatalog’s XML
Workspace Document tool will import single uncompressed XML documents and zip-
formatted archives containing multiple files. Conversely, ArcGIS does not recognize “plain”
XML, which is simply information wrapped in tags. Scripting code like that created in
Python must be written to send, receive, parse, store and display XML data in ArcGIS.
2. WaterML2
Like XML, Water Markup Language, currently in version 2, is a tag-based,
hierarchical markup language. WaterML is an industry-specific exchange standard used by
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hydrologists and was developed over a period of years by a group of national and
international organizations including the National Oceanic and Atmospheric
Administration (NOAA) and USGS. WaterML was built on Geography Markup Language
(GML) and provides “a common exchange format for hydrological time-series” (KISTERS
North America, Inc., 2016). The USGS-NWIS feed uses the WaterML v.1.1 schema, however
data is returned in XML using namespaces to avoid conflicts when tag names in two
schemas are the same. The example in Figure 8 from W3 Schools (n.d.) shows two
instances of <table> tags that refer to a web page data structure and a piece of furniture.
They are made unique by a letter prefix and colon before the tag name. The XML
namespace is further distinguished by using an "xmlns" attribute as on Lines 2 and 9 of the
code, and declaring a Uniform Resource Indicator (URI) source address. The URI is not
required to link an actual address or resource because it is not called when XML is used.
3. JSON
JSON is a lightweight, self-describing, hierarchical data interchange format whose
syntax is a subset of JavaScript. With parameters expressed in pairs like value”:“6.33”
, JSON is billed as being more easily read by humans than XML (W3 Schools, n.d.). Figure 9
shows data from the NWIS feed in its “tree” hierarchy with tiers of “addresses” based on a
“root” level (left column) through which data is retrieved and read into Python variables.
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Figure 6: Data stream query parameters passed through a URL
Figure 7: XML tags are named so data is self-describing
Figure 8: XML tags with namespaces
1 <root>
2 <h:table xmlns:h="http://www.w3.org/TR/html4/">
3 <h:tr>
4 <h:td>Apples</h:td>
5 <h:td>Bananas</h:td>
6 </h:tr>
7 </h:table>
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9 <f:table xmlns:f="http://www.w3schools.com/furniture">
10 <f:name>African Coffee Table</f:name>
11 <f:width>80</f:width>
12 <f:length>120</f:length>
13 </f:table>
14 </root>
13
Figure 9: The NWIS feed in JSON format
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c. The Proof of Concept Projects
1. Web Development
The NWIS JSON feed was used to power web page content on a site developed for
the Kankakee River National Water Trail. A map (Figure 10) displays real-time river gage
and discharge information at 12 sites along more than 150 miles/250 km of the Kankakee,
Iroquois and Yellow Rivers in Illinois and Indiana.
The JSON feed was parsed using PHP (PHP: Hypertext Preprocessor), an open
source, server-side scripting language commonly used in web development (W3Schools,
n.d.). Figure 11, Line 2 shows the PHP code that drills down through the data hierarchy to
return data for specific gage sites at the node “addresses”:
$json->value->timeSeries[#]->values->value->value
where [#] is a numeric value indicating the data’s level in the hierarchy. Cell color, font
style and certain text characters on the map change based on whether water elevations are
approaching or above flood stage, and to what degree. The code in shows the if/elseif/else
conditional logic that generates these changes. The map is available online at
http://kankakeeriverwatertrail.org/map.php.
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Figure 10: River gage web map with dynamic content from NWIS data
Figure 11: Dynamic map, PHP back-end code
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2. MassDOT Traffic Data Feed
The second proof-of-concept was a study of the Massachusetts Department of
Transportation’s (MassDOT) XML traffic data internet feed and porting the live data to
ArcGIS to support “real time” display shown in Figure 12. The data feed, seen in Figure 13,
describes location specific “events” such as construction or maintenance, with each
identified spatially using lat-long coordinates. XML data is arranged in a “tree” hierarchy
and the data of interest is four levels down from the “root”. The right margin shows the
“addresses” needed to access data from tree “branches”. The ElementTree XML Python API
(Python Software Foundation, n.d.), uses these to navigate through the tree hierarchy, read
parameters in the stream, save them to variables and write values into database fields
using a cursor. The processing algorithm described in Figure 14 describes how the XML
data was handled once it was parsed.
. Figure 12: Data display from the (MassDOT) XML traffic data feed
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Figure 13: Massachusetts DOT Road Condition XML Feed
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Figure 14: MassDOT XML Display, workflow algorithm
V. Data Sources
1m Bare Earth, LiDAR-derived Digital Elevation Model: Missouri Spatial Data Information Center http://www.msdis.missouri.edu/data/lidar/County_LiDAR/index.html USGS National Water Information System (NWIS) XML data feed: http://waterdata.usgs.gov/nwis
Set the workspace path name using the 'raw' string
Set the target feature class
Set the target field class path
Allow output results to be overwritten
Get user input to set the maximum number of event records to
be returned
Designate the URL to query and retrieve data from the XML
data source
Open the XML data URL
Parse the XML data
Load the parsed XML data into a dictionary object
Count the total number of event records
Count the total number of event parameters in the XML schema
Validation: if returnEvents > eventCount:Display Error
Message
List fields to be accessed
Initialize the cursor
Initialize an iterative counter LOOP: while i <
returnEvents: write retrieved data into variables
Read variables into an array for database entry
Increment the counter to read the next event
Insert returned records into the destination table
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VI. Development
The utility is comprised of four component parts (Figure 15): 1) the Geodatabase; 2)
the Floodplain Delineation Model; 3) the XML Data Parser, and; 4) the Analysis Tool. Each
component is executed through a Python scripted tool in a Floodplain Delineation Toolbox.
a. The Geodatabase
The geodatabase initially contains two source files and three database tables. The
three source files are: a LiDAR-derived DEM (raster); a hand-drawn polygon shapefile
defining the area to be clipped from the DEM, and; a hand-drawn Water Elevation Offset
Zone polygon shapefile. The two tables hold data on river gage sites (gageSites) and
statistics (gageStatistics). Once the river gage site registered in the database, time stamped
data on gage height and discharge volume can be read from the XML feed and stored in the
gageStatistics table.
b. Floodplain Delineation Model
1. Modeling the Ground Surface
To meet the project goals, the ground surface layer had to be highly detailed, easily
generated and not overly cumbersome to process, and the system of levees had to be
represented with precision. A Triangulated Irregular Network (TIN) surface did not
provide sufficient detail over such a wide area, while the 1D and 2D approaches were
beyond the scope of the researcher’s ability. Where Choung’s (2014) task was to extract
levee data and generate polygons representing levee structures, the task in this project was
simply to ensure linear flood control structures of various sizes were identifiable. Two
methods were evaluated for this task, one using Slope Analysis and the other using the
Raster to Polygon conversion tool.
20
Figure 15: Workflow, XML-driven Floodplain Modeler
21
Slope Analysis (Figure 16) produced excellent linear detail and the least extraneous
(non-linear) data in the range between 21 and 29 percent. The drawback is that Slope
Analysis does not produce discreet areas with individual elevation values needed to
compare against the water surface. The output of Raster to Polygon conversion (Figure 17)
is a highly detailed, continuous coverage of polygons representing discrete elevation zones
in one-meter increments. However, this method had one major drawback: the conversion
produced more than 3-million polygons, a fatal threat to the goals of easy data generation
and short processing times. Visually rendering the polygon data took at least two minutes
per redraw and simply opening the attribute table took more than 5 minutes. Operations
involving this data frequently crashed the computer, a Dell Precision T3610 64-bit
workstation with an Intel® Xeon® E5-1620 v2 @ 3.7GHz processor and 16 GB of RAM
using Windows 7. The issue was easily remedied by deleting polygons with shape areas (as
indicated in the Shape_Area field of the data table) below a minimum threshold. In one
instance, deleting polygons with shape areas of less than 50 square meters reduced the
total number of polygons from 3,164,578 to only 60,373 (Figure 18). The time needed to
draw polygons across the entire coverage was reduced to around four seconds. At small
scales, elevation polygons draw more quickly. Eliminating small polygons resulted in a
large number of no-data “holes” which could be considered as minute transitions in
elevation or areas of uncertainty in floodwater coverage. Raster to Polygon conversion was
the appropriate choice as it provided a highly detailed representative ground surface and
met the project objectives of fast analyses at both wide area and highly localized scales.
22
Figure 16: Ground surface model from slope analysis
23
Figure 17: Output of raster to polygon conversion
24
Figure 18: Raster to Polygon coverage - Number of Polygons vs. Polygon Minimum Area
2. Modeling the Water Surface
Water surface elevations are easily characterized across short distances with a flat
plane having a single elevation value. The problem becomes more complex across larger
areas, where surges caused by rainfall or changes in discharge from dams will cause
“ripples” on the plane. The floodplain delineation map in Error! Reference source not
found.Figure 19, developed from an Esri tutorial on Floodplain Delineation from LiDAR
Points (Esri, n.d.) has one-dimensional elevation cross sections or "surface profiles" with
multiple surfaces projected from center stream elevations. A process based on this
approach was used to develop a water profile in this project.
Among the factors considered: the points where the Missouri River meets Holt
County’s northern and southern boundaries are 38.8 air miles (62.4 km) apart but cover 56
25
linear river miles (90 km); between these points, the elevation drops from ~883 feet/269
meters to ~826 feet/252 meters above sea level; the rate of elevation drop is not constant
along the 250 river miles between Omaha, Nebraska and Kansas City, Missouri as seen in
Figure 20, though the total drop of 242 feet neatly averages to nearly 1 foot per mile (0.97),
and; the initial water surface created for use in analysis was based on a one-foot-per-mile
(or 1 meter per three mile) elevation drop rate for simplicity.
To recreate this elevation drop rate, rectangular polygon “strips” approximately 3
miles wide, each representing a 1-meter elevation offset, were arbitrarily drawn over the
area into a new feature class, using river mile markers as references (Figure 21). Positive
offset values were assigned to upstream zones and negative values were assigned to
downstream zones. The elevation offset values were assigned to ground surface polygons
within each zone using overlay analysis. A field named to hold calculated “PFlood” values
was added to the table to hold values for the calculated flood potential of each polygon.
Figure 19: Floodplain delineation map with projected water surface
26
Figure 20: Elevation Drops Between River Gages, Omaha to Kansas City
27
Figure 21: Water Elevation Offset Zones, Polygon Feature Class
a. The XML Feed
Figures 22, 23 and 24 show the XML data “tree” returned from the URL noted in
Figure 6, which requests data formatted in WaterML1.1, for site ID 06813500 (Rulo,
Nebraska) and figures for parameters 00060 (stream flow in cfs) and 00065 (river gage in
28
feet). Data is returned for each of the three data “branches” that extend from the root level;
they are referred to and represented in Python code as root[0], root[1] and root[2]. The
data we need may be contained within a tag, in an attribute within a tag or in the text
between opening and closing tags. The line of XML at the top of Figure 25 shows data
returned for the node at address root[1][0][1]; the following lines show how tag, attribute
and text within the node are retrieved. In the data tree figures, columns on the left margin
indicate the hierarchy of “addresses” used to retrieve data from branches or “nodes” of the
tree. For example, the site name “Missouri River at Rulo, NE” is located at address
root[1][0][0]. Some data is retrieved as deeply as five levels down from the root, as in the
case of site latitude at address root[1][0][3][0][0].
Figure 22: XML data tree from USGS river gage feed (Part 1)
29
Figure 23: XML data tree from USGS river gage feed (Part 2)
30
Figure 24: XML data tree from USGS river gage feed (Part 3)
31
Figure 25: Accessing parameters within XML nodes
b. Analysis
With the floodplain model and XML feed in place, the potential for flooding (PFlood)
in each individual ground surface polygon was calculated based on river gage values --
called “potential” because our model does not account for which flood control structures
protecting an area may or may not be intact. Figure 26 shows the calculations used to
determine PFlood:
Figure 26a: Water Surface Elevation asl at Zero Offset (WSEØ) was determined by
adding the river gage figure in feet to the datum elevation at Rulo, Nebraska (838.16
ft.), then converting the sum to meters.
Figure 26b: Zonal Water Surface Elevation for each zone (WSEz) is the sum of the
zero offset elevation (WSEØ) and a zone’s Elevation Offset value (ZOff).
Figure 26c: Flood Potential (PFlood) for individual polygons was determined by
comparing the polygon’s elevation (ElevPoly) to the Zonal Water Surface Elevations
(WSEz). The result is either a positive (not flooded) or negative (amount of
inundation) value.
32
Figure 26d: The formula used in Python code. The result is multiplied by -1 so that
negative values indicate elevations inundated by water and positive values indicate
elevations above water.
VII. Python Code
Five Python scripts comprising a “Floodplain Delineation” toolbox were developed
to execute the process. Figure 27 shows how individual tools relate to each other in a
systemic context. A schema-only layer package was developed so consistent symbolization
could be applied to model output.
a. Enter Gage Stations
USGS gage recording stations must be registered in the utility database before they
can be queried and their data used. The user must enter the gage’s 8-digit Site ID number,
its datum elevation and floodstage values in feet. The tool validates user input, queries the
USGS feed to confirm the ID is active, confirms data is available, retrieves fields and enters
them into the database.
Figure 26: PFlood (potential for flood) formulas
a. WSEØ = (Datum elevation + river gage) * 0.308
b. WSEz = WSEØ + ZOff
c. PFlood = ElevPoly – WSEz
d. PFlood = (WSEØ + [ZOff] - [ElevPoly]) * -1
33
b. Get XML Feed
With a Site ID as input, confirms the station is registered in the database; accesses
data by constructing and submitting a query URL; downloads and parses the XML data
stream to retrieve/process river gage, discharge and timestamp statistics; enters the data
into the database.
c. Build Floodplain Model
From three input files – a Digital Elevation Map (DEM) raster, clipping polygon and
“offset zones” source file -- produces a polygon elevation layer representing both land
elevations and adjusted water elevations with offsets to account for the general
downstream elevation drop. The average run time for this script on the development
workstation was 40-45 minutes.
d. Pflood from XML
Queries the database and retrieves the most recent recorded gage height in feet for
Rulo, Nebraska, Site ID 06813500; generates a floodplain scenario based on the returned
value.
e. PFlood Generate Scenario
Generates a floodplain scenario for Rulo, Nebraska based on a river gage height in
feet entered by the user.
A consistent color symbolization schema can be applied using a Layer Package
stored in the geodatabase and unpacked in ArcCatalog (Figure 28). As scenario iterations
are generated from either the “PFlood Generate Scenario” or “PFlood from XML” tools,
symbolization is updated when the floodplainModel feature class display is refreshed.
34
Figure 27: Workflows in the Floodplain Delineation Toolbox
Figure 28: The floodplainColors schema-only layer package file (.lpk)
35
VIII. Model Output
a. Symbolization
Symbolization is done with negative numbers representing inundation and positive
numbers representing land above the projected water surface. All values above ~2 meters
are classified as dry. Areas between 0 and 2 meters are considered as being at immediate
risk of inundation, with 0-1 meter areas symbolized in red and 1-2 meter areas symbolized
in yellow. Inundation is symbolized in shades of blue, with darker blues representing
greater inundation depths. In the example figures, inundation was symbolized in 3-meter
increments.
b. Offset and No-Offset
Floodplain delineation maps were generated for the four immersion scenarios listed
in Figure 29: the record crest set in 2011; a level one foot over Rulo’s 17-foot flood stage; a
typical annual low gage value, and; a random historic low water mark.
To analyze the utility of elevation offsets, PFlood values in the first map sequence
(Figure 30) were calculated using offsets while values in second map sequence (Figure 31)
were not. On a wide scale (Macroanalysis), both sequences show how the potential for
flooding spreads from south to north as river gages increase. The maps indicate areas in
the extreme south would be at constant risk of overflow were it not for the levees that line
the river banks. This would be consistent with the general nature of the Missouri River and
its morphology as a controlled waterway. Flood control structures were built to keep the
river confined to its channel and to restrain its natural tendency for frequent overflow.
From here, the map sets begin to diverge. The Offset maps tend to be more “forgiving” on
the upper end, showing a more gradual south-to-north overflow at higher river gage
36
heights, but greater flood depths in lower terrain elevations at the southern end of the
extent. The No-Offset maps show not only a faster spread of flood potential, but potential
immersion over a far greater area at higher river gage heights. The exception is in the very
low water scenario, where the No-Offset map shows significantly less flood potential. The
breaklines between zone offset areas are also more apparent.
Figure 29: Elevation Values Selected for “Immersion Scenarios”
Gage (ft) Elev (ft) Elev (m) Notes
1.46 839.6 255.9 Random recorded low water level 12/19/1989
10 848.2 258.5 Typical annual low water value
18 856.2 261.0 One foot above floodstage level of 17 feet
27.26 865.4 263.8 Record high level 6/27/2011
37
Figure 30: Floodplain Immersion Scenarios with Elevation Offsets
38
Figure 31: Floodplain Immersion Scenarios Without Elevation Offsets
39
c. Modified Offset
Offset and No-Offset values were similar in that the terrain elevation drop was
evenly distributed -- by design in the first case, and by being ignored in the second. To
further investigate these differences, a third “Modified Offset” sequence map was produced
(Figure 32), using values more consistent with the per-mile average elevation drops
between river gages, diagrammed in Figure 20. The average 0.6 foot-per-mile drop
between the Brownville, Nebraska and Rulo gages is the most gradual while the 1.71 per-
mile average between Rulo and St. Joseph, Missouri is the steepest. With 3-mile wide offset
ones, values in increments of 1.8 feet (0.55m) were assigned to Zones 1 through 5, with
values in increments of 5.13 feet (1.56m) assigned to Zones -1 through -4.
The Modified Offset maps would seem to do a better job at the lowest river stage
scenario, with practically no threat of immersion anywhere, the outline of Big Lake left
visible at center left, and low elevations adjacent to the Squaw Creek National Wildlife
Refuge visible east of Big Lake. A comparison of a PFlood projection with actual conditions
on January 17, 2011 (Figure 33) shows immersion at the northern end of the extent, unlike
the initial offset map. Again, while these inundation potential maps are not meant to be
hydrologically accurate, it would seem the Modified Offset values do a better job of creating
projections, therefore this is the data set we will use in Microanalysis. The feature class
Zones_Modified has been hardcoded into the Build Floodplain Model script.
40
Figure 32: Floodplain Immersion Scenarios with Modified Elevation Offsets
41
Figure 33: PFlood Projection vs. Landsat Image from 17 July 2011 (Source: USGS)
42
IX. Analysis Scenarios
Scenarios covering four locations within the Study Area (Figure 34) were created
from the modified elevation offsets using river gages in Figure 29. As note on the maps,
river gages are referred to in feet while PFlood figures are measured in meters. In each
three-panel simulation, panel a. represents a river gage of 10 feet, panel b. represents 18
feet (1 foot over floodstage), panel c. represents 27.26 feet. Model output is compared
against conditions observed and photographed in 2011 to look for similarities and to
determine what kinds of conclusions might reasonably be drawn from the flood delineation
model.
Scenarios A and B are within a 17,366-acre leveed area owned by the Union
Township Levee District (Figure 35, bottom) described by the State of Missouri as “a
mainline levee and the first line of defense for much of northwestern Holt County” (Office
of Missouri Governor Jay Nixon, 2012). A levee at Lower Cotter Bend, just downstream
from the mouth of the Tarkio River at River Mile 507 in the northwestern corner of the
district was breached by high water in 2010 and the gap of 1100 feet/335 meters was not
repaired until 2012 (U.S. Army Corps of Engineers, 2012). Scenarios C and D occurred as
the result of a levee breach at the mouth of Mill Creek at approximately River Mile 515.5,
within the Corning Conservation Area. The breach was at the southern edge and just
outside of the federally constructed and non-federally operated L-536-550 Turkey Crk LB,
Rock Crk LB, Mo Riv LB, & Mill Crk RB Levee System (Mill Crk RB Levee System, Figure 35,
top) as described by the U.S. Army Corps of Engineers (2014).
43
Figure 34: Study Area Scenario Locations w/ Nearby Levee Breach Sites (insets)
44
Figure 35: Mill Creek and Union Township Levee Districts (Source: USACE)
45
a. SCENARIO A: Rulo/Big Lake
This area is immediately east of Rulo, Nebraska along US Highway 159 and includes
all of Big Lake. Missouri Highway 111 runs north from US 159 and along the eastern edge
of Big Lake (Figure 36, 37). Figure 37a shows how low the general area is and why flood
protection is required. Much of the area is within 2 meters of flooding at a low river gage. A
description of flood impacts from the National Weather Service’s Advanced Hydrologic
Prediction Service (National Weather Service, 2015) notes levees protecting Big Lake begin
to overtop at a river gage of 26 feet, and; at 27.26 feet, the record historic crest reached in
2011, “significant flooding will encompass a very large area”. In Figure 37c, the entire area
is inundated with nearly all flood control structures under water. Figure 38 shows how Big
Lake is dry at a river gage of 24.12. Figure 39 shows conditions five days later, after the
water has risen and weakened levees failed, the area is flooded at river gage 26.51.
The 2011 Flood overtopped Big Lake, flooded homes along its banks (Figure 38, 39)
and washed out the Burlington Northern-Santa Fe (BNSF) railroad tracks running adjacent
to US 159 (Figure 40, 41, 42). In Figure 40c, the flood pattern along a curve of US 159 west
of Big Lake closely greatly resembles that observed on 21 June (Figure 41) with the river
gage at 26.51; as in the PFlood map, the railroad tracks along the northern edge of the
curve are above water at the west end while the roadway running parallel to the south is
immersed.
Figure 42 confirms deep immersion at the south end of Big Lake as seen in Figure
43, 44, and 45. By the time the photos were taken in October, the destroyed railroad tracks
had been replaced. US 159, the roadway furthest south, has been washed out and destroyed
46
by a large scour hole. High water marks of approximately 2 meters on buildings in Figure
45 compare with the 3-meter immersion depth projected by the model.
47
Figure 36: SCENARIO A, Map and Aerial View
48
Figure 37: SCENARIO A, Vicinity of Big Lake and US 159 between Rulo, NE and
Fortescue, MO
49
Figure 38: SCENARIO A, Big Lake pre-flood, view to the north 16 JUN 2011 (Max Stage:
24.12)
Figure 39: SCENARIO A, Big Lake inundated, view to the north 21 JUN 2011 (Max Stage
26.51)
50
Figure 40: SCENARIO A, US 159 between Rulo and Big Lake
51
Figure 41: SCENARIO A, water over US 159, view to the west 21 JUN 2011 (Max Stage
26.51)
52
Figure 42: SCENARIO A, roadway and rail bed, south shore of Big Lake
53
Figure 43: SCENARIO A, roadway and rail bed, south shore of Big Lake, view to the
north 14 OCT 2011 (Max Stage 14.19)
Figure 44: SCENARIO A, US 159 between Rulo and Big Lake, post-flood, view to the west 14 OCT 2011 (Max Stage 14.19)
54
Figure 45: SCENARIO A, Google Streets images from 2013 show high water marks
55
b. SCENARIO B: Missouri Highway 111/118 (Fred Guthrie Site)
Located approximately two miles north of Big Lake at the junction of Missouri
Highways 111 and 118 and approximately 5 miles/8 km southwest of the levee breach at
River Mile 507. Missouri State Highway Patrol Trooper Fred Guthrie and his K-9 Reed
were swept away by fast currents where Missouri 111 runs west from the junction (see In
Memoriam on the last page). The intersection is circled in Figure 46a and is seen while
submerged in Figure 47 and post-flood in Figure 48. The huge scour hole that formed at
this location, seen in the Figure 48 photo, is also visible in Figure 49a.
The degree of flooding at the Guthrie site was greatly similar to levels seen in Figure
49 . Figure 49b clearly shows Missouri 111 trailing off into high water as seen in Figure 50,
a photo taken hours after Trooper Guthrie and Reed had disappeared. Water depth was
enough to allow motorized, shallow draft boats to operate freely (Figure 51). At the
111/118 intersection, Figure 52 shows roadways to be up to 2 meters above water level, in
the same locations seen in the model. Figure 53 and Figure 54, covering the area just north
of the Guthrie site, show the potential for flooding caused by breaks in agricultural levees.
Areas south and east of the levee break in the Union Township system are covered in
Figure 55; the levee break occurred at the upper left corner of the mapped area.
56
Figure 46: SCENARIO B, Map and Aerial View; Fred Guthrie Site circled
57
Figure 47: SCENARIO B, Looking south towards Big Lake from the Guthrie site 01 AUG
2011(Max Stage 24.03)
Figure 48: SCENARIO B, Scour hole at the Guthrie site (r. center) and sand deposition
along Highway 111 (l. center), view south towards Big Lake 14 OCT 2011 (Max Stage 14.19)
58
Figure 49: SCENARIO B, Fred Guthrie Site at Junction of Highway 111 and 118
59
Figure 50: SCENARIO B, Looking west towards the Guthrie site with water over
Highway 111 01 AUG 2011 (Max Stage 24.03)
Figure 51: SCENARIO B, Water is deep enough for shallow draft boats to operate freely
around the Guthrie site 01 AUG 2011 (Max Stage 24.03)
60
Figure 52: SCENARIO B, Level of immersion at Jct Missouri 111/118 on the morning of
7 AUG 2011, view to the south (top) and to the west (center and bottom)
61
Figure 53: SCENARIO B, potential for flooding caused by breaks in agricultural levees
62
Figure 54: SCENARIO B, The flood’s areal coverage grows as agricultural levees are
breached inland and individual flood cells are filled 21 JUN 2011 (Max Stage 26.51)
63
Figure 55: SCENARIO B, areas east and south of the Union Township levee breach
64
c. SCENARIO C: Tarkio River Southwest of Corning, Missouri
The Tarkio River, or the “Big Tarkio”, flows onto the flood plain from the hills north
and east of Corning, Missouri (Figure 56) and is channelized with high levees on either side
to where it meets the Missouri River southwest of Craig, Mo. These levees not only protect
a large area including the town of Craig from Missouri River overflows, they also keep the
Tarkio River from spilling onto the flood plain, On July 7, 2011, the Tarkio River
contributed to an already disastrous situation in Holt County after rising more than 15
feet/4.5 meters in 5 hours and spilling through a levee breach created two weeks earlier
(Norvell, 2011).
Figure 57 shows the Tarkio River levee remaining largely above water even in the
worst case scenario (Figure 57c). Photos in Figure 58 confirm the levees as formidable
structures and the size of the channel between them. In high water conditions, the river
itself is clearly defined between the levees in Figure 59 with the levees themselves
remaining well above the potential flood level. This is confirmed in Figure 60, a photo taken
on 21 June when the river stage was 26.51 feet. In our map developed from thousands of
polygons, it is possible to examine elevation differences at very high scale. Figure 61 shows
minute elevation differences on the levee that might be potential locations for overtopping
at higher water levels. We can even estimate the size of the indicated gap at 12.5 meters.
65
Figure 56: SCENARIO C, Map and Aerial View (Google Maps, Google Earth)
66
Figure 57: SCENARIO C, The leveed Tarkio River
67
Error! Reference source not found.
Figure 58: SCENARIO C, Tarkio River, view to the north, as overflow begins to seep through the levees 21 JUN 2011 (Max Stage 26.51) and post-flood 14 OCT 2011 (Max Stage 14.19)
68
Figure 59: SCENARIO C, The leveed Tarkio River and railroad bridge at County Road 125
69
Figure 60: SCENARIO C, Tarkio River and railroad bridge at County Road 125 21 JUN 2011 (Max Stage 26.51)
70
Figure 61: SCENARIO C, Minute elevation differences seen at very high map scales
71
d. SCENARIO D: Corning, Missouri and Corning Conservation Area
The area shown in Figure 62 ncludes: the site of the Mill Creek levee breach on the
Missouri River, visible as a sand deposition plume at the upper left of the diagrams in
Figure 63; the town of Corning, Mo, two miles east, and; the Interstate 29 interchange and
mainline.
The extreme flood scenario in Figure 63c shows a water depth of 0-3m in the town
of Corning. This would seem to be confirmed by photos in Figure 64 and Figure 65 which
show flood conditions and post-flood high water marks on buildings. The conditions
projected at the Corning Interchange and along the I-29 mainline in Figure 66 match
conditions documented in Figure 67 and Figure 68.
Figure 69 clearly indicates the I-29 mainline remains well above the flood waters. It
compares well to the actual flood levels in Figure 70, Figure 71, and Figure 72. In addition,
documented flood conditions at the Corning Interchange (Figure 70) closely match an
extremely high scale projection of the area in Figure 71 down to the very small patch of
land remaining above water.
Error! Reference source not found.
Figure 62: SCENARIO D, Map and Aerial View (Google Maps, Google Earth)
72
Figure 63: SCENARIO D, Missouri River Levee Breach to Corning, MO and Interstate 29
73
Figure 64: SCENARIO D, Flooding in Corning, MO 23 JUN 2011 (Max Stage 26.96)
74
Error!
Reference source not found.
75
Figure 65: SCENARIO D, Corning, MO, high water marks of around 1 meter 11 JAN 2012
Figure 66: SCENARIO D, Corning, MO.
76
Figure 67: SCENARIO D, Looking southwest from the I-29 Corning interchange to the
town of Corning and the Missouri River 8 JUL 2011 (Max Stage 25.81)
Figure 68: SCENARIO D, High water failed to top the Interstate 29 mainline south of the
Corning interchange, view to the north 23 JUN 2011 (Max Stage 26.96)
77
Figure 69: SCENARIO D, Interstate 29 and the Corning Interchange
78
Figure 70: SCENARIO D, I-29 Corning Interchange, view to the east 23 JUN 2011 (Max
Stage 26.96)
Figure 71: SCENARIO D, Corning Interchange flood map, extremely high scale; island of
land noted by arrow, compare to feature in Figure 70.
79
Figure 72: SCENARIO D, Green vegetation marks areas that stayed dry along I-29 just
south of the Corning interchange, looking south 14 OCT 2011 (Max Stage 14.19)
80
X. Conclusions and Further Development
The major objectives of this project were successfully met: a simple floodplain
delineation model was created with Python script; data tables were populated with and
analysis was driven by “live” XML river gage data; the model quickly and efficiently
returned precise results, viewable from low to very high scales, across a wide area.
Avenues for further development of this application include:
1) Use on more sophisticated flood models: The model used in this study is based on
elevations alone and does not purport to be hydrologically accurate. Industrial-grade
flood models can be studied to determine where and how live data or other XML inputs
could be used.
2) Flood area/flood cell discontinuity: Our method for determining a flood’s areal
coverage considers only comparative elevations between terrain and a projected water
surface, and does not consider containment within flood control structures. A method
for analyzing flood cell contiguity would be useful, though the model has proven useful
enough without this capability.
3) As part of the research for this project, some linear structures were extracted manually
at individual elevations for demarcation from the rest of the polygons. The process was
deemed too time consuming for this effort. Steinfeld, et al. (2013) studied three semi-
automated GIS and traditional visual interpretation techniques for detecting
earthworks. The color symbolization used in this project to emphasize polygons within
2 meters of inundation is a workaround for this issue.
81
4) Temporal applications: Flood events are comprised of incidents that occur geospatially
and have consequences over time. For example, the length of time a flood control
structure has been exposed to standing water may signal a loss of structural integrity
and impending breaches; the amount of time land is inundated may affect soil health
and plant life recovery. Timestamped entries in the database support temporal
analysis.
5) Mobile data gathering technology: There are number of field data collection
applications such as Collector for ArcGIS, which run on tablet devices such as
iPhones/iPads. One such application, MotionX-GPS® by Fullpower Technologies, Inc.,
was evaluated as part of this study as a way of providing location-specific data. Motion-
X records tracks and waypoints as .GPX (GPs eXchange) files, an open-source XML
format described in the ArcGIS help file as the standard for saving the results from a
GPS receiver (Herzer, 2012). The track in Figure 73 was recorded during the search for
Trooper Fred Guthrie and “Reed” as the aircraft orbited the main search area. GPX files
can either be parsed with Python or imported directly into ArcGIS using the Conversion
> From GPS > GPX to Features tool.
6) Changes to source data: The U.S. Geological Survey has outlined changes to USGS water
data offered online, scheduled to be implemented state-by-state over a 7-month period
that began in late summer 2016 (U.S. Geological Survey, 2016). Some nationwide
changes were rolled out in July 2016. These changes may impact users with advanced
applications that use water data and bookmarked URLs. The list includes replacing data
descriptors with time-series identifiers, changes in URLs for time-series graphs, in tab-
82
separated (RDB) output and in some less common time-series parameter codes, e.g. for
stream velocity.
Figure 73: Flight track constructed from GPX files
* * * * *
83
In Memoriam: Trooper Frederick F. Guthrie and K-9 Reed
Missouri State Highway Patrol
On August 1, 2011, Trooper Frederick F. Guthrie Jr., and his Patrol K-9 Reed were assigned to Missouri River flood duty. They were working in the area of Big Lake on Missouri Highway 118 at Missouri Highway 111 in Holt County, Missouri, when they were apparently swept away by swift flood water.
At approximately 6:25 p.m. on Tuesday August 2, 2011, K-9 Reed was located in swift moving flood water approximately 100 yards from where Trooper Guthrie's patrol truck and boat were located.
The search for Trooper Guthrie continued for months. On January 12, 2012, his body was recovered under approximately three-and-a-half feet of packed sand and silt, after a brush pile was removed during an excavation process. The recovery site was south of where K-9 Reed was found months earlier, near the original search site.
Trooper Guthrie, 46, was the 30th member of the Missouri State Highway Patrol to make the ultimate sacrifice while serving and protecting the citizens of Missouri. K-9 Reed was a five-year veteran with the Patrol.
This study is dedicated to their memory. Source: Missouri State Highway Patrol
84
Acknowledgements
For my wife Jeannine, who has supported and endured my every career move and
bright idea utterly without complaint.
With thanks to my friend and colleague Sgt. Kevin G. Haywood Sr., Missouri State
Highway Patrol and to my Highway Patrol family.
Ground truth photographs were taken by the author working as a member of the
Missouri State Highway Patrol Communications Division during and flying in a Highway
Patrol aircraft piloted by Sgt. Kevin G. Haywood, Sr. Reconnaissance flights to look for levee
breaches and flooding between Iatan, Missouri and the Iowa state line began in early June
2011. Information was shared with the Missouri State Emergency Management Agency
(SEMA) and local law enforcement agencies. The author also participated in the months
long search for Highway Patrol Trooper Fred F. Guthrie and his K-9 “Reed”, who were
swept away by flood water north of Big Lake on August 1, 2011.
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