CDA 2010 Annual Conference Paper_FINAL

CDA 2010 Annual Conference Congrès annuel 2010 de l’ACB CANADIAN DAM ASSOCIATION Niagara Falls, ON, Canada ASSOCIATION CANADIENNE DES BARRAGES 2010 Oct 2-7

MODELING LARGE TERRAIN DATASETS FOR FLOOD INUNDATION MAPPING

Sarah Christian, P.H., Senior Hydrologist, HDR|DTA, Portland, Maine, USA

Dan Soucie, Senior GIS Specialist, HDR|DTA, Portland, Maine, USA

Joel M. Bilodeau, H.I.T., Associate Hydrologist, HDR|DTA, Portland, Maine, USA

Scott Taylor, P.Eng., Lead Engineer, Dam Safety, TransAlta, Calgary, Alberta, Canada

ABSTRACT: Advancements in computational power and the availability of highly detailed digital representations of the earth’s surface

have transformed inundation mapping methodology for emergency planning; however, the immense amount of data

involved requires the development of efficient management and flexible manipulation techniques. In 2009, HDR|DTA was

retained by TransAlta to create Emergency Preparedness Plan maps for 15 postulated dam breach scenarios. Light Detection

and Ranging datasets were obtained to hydraulically model and map the inundation zones encompassing 1,770 kilometers

along the Bow and North Saskatchewan rivers and tributaries in Alberta, Canada. The sheer scale of this project elicited

many interesting questions, including, what is the best way to organize and model detailed digital terrain datasets over large

geographic expanses? Is the same level of detail required to develop cross sections for the hydraulic model and for mapping

the results as areas of flood inundation? How does the data resolution affect these endeavors and what

benefits/consequences might occur if these data are generalized or re-sampled to a coarser resolution? HDR|DTA employed

a Geographic Information System to manage nearly 1 billion elevation readings and a two-model approach to utilize these

large datasets. It was determined that a more generalized terrain model was acceptable for modeling the flood breach in the

one-dimensional HEC-RAS hydraulic modeling platform, and a more detailed terrain model was optimal for modeling the

resultant flood inundation extents. This paper will serve as an example approach for the practical utilization of modern

detailed terrain datasets.

RÉSUMÉ: Le pouvoir computationnel des ordinateurs, de plus en plus puissant, et la possibilité de représenter numériquement la

surface de la terre de manière extrêmement détaillée ont transformé la méthode de cartographier les inondations dans la

planification de situations d’urgence ; toutefois, l’énorme quantité de données exige le développement de techniques de

gestion efficaces et de techniques d’analyses flexibles. En 2009, la société TransAlta a embauché HDR |DTA pour créer des

cartes de Planifications en cas de Situations d’Urgence pour 15 scenarios de rupture de barrage hydraulique. Les données de

détection et télémétrie par la lumière ont été obtenues pour faire des prédictions hydrauliques et créer des cartes de zones

d’inondation sur un territoire de 1770 kilomètres au bord de la rivière Bow and North Saskatchewan à Alberta, Canada. Ce

projet de grande échelle a généré des questions nombreuses et intéressantes telles que la meilleure manière d’organiser les

données numériques détaillées sur un si vaste terrain et de prédire différents scenarios. Le même niveau de détails est-il

exigé pour développer des coupes transversales à partir du modèle hydraulique et pour cartographier les résultats par

secteurs de zone d’inondation? Quelle est l’influence de la résolution de données sur les résultats et quelles conséquences ou

avantages y aurait-il à généraliser ses données ou à réduire la résolution? HDR|DTA a employé un Système d’Information

Géographique pour gérer environ 1 billion de données d’altitudes et a analysé cette large banque de données de 2 manières

différentes. Il a été conclu qu’un modèle de terrain plus général était acceptable pour prédire les différents scenarios de

rupture de barrage et d’inondations pour le modèle hydraulique HEC-RAS en une dimension, et qu’un modèle de terrain

plus détaillé était nécessaire pour prédire l’étendue des inondations. Cet article présentera un exemple d’utilisation et de

gestion de données de terrain détaillées et nombreuses.

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CDA 2010 Annual Conference, Niagara Falls, ON, Canada – October 2 to 7, 2010

1 INTRODUCTION

Early in the discipline of map making, cartographers grappled with the dilemma of a paucity of data, which led

to generalization of map features and a well-placed title cartouche or scale bar supplementing those empty areas

on a map that contained inadequate information. With the advent of the digital age, a new paradigm in map

making now exists. Gathering data via remote sensing using airplanes, helicopters, and satellites has filled those

once-blank areas on maps with highly precise and detailed information. Responsible management and

manipulation of the increasingly higher volumes of information, while keeping true to the initial dataset, is a

difficult balance. This paper explains the need for, and impacts of, data management and reduction methods as

applied to dam breach inundation modeling and the creation of Emergency Preparedness Plan (EPP) maps.

In 2009, HDR|DTA was contracted to supply TransAlta with EPP dam breach inundation maps for 12 dams in

the Bow River basin and 3 dams in the North Saskatchewan River basin in Alberta, Canada. The sheer scale of

the project area proved to be the first challenge faced during the initial project setup. The Bow River and its

tributaries, including the South Saskatchewan River, contained over 1,000 linear river kilometers to be modeled

and mapped, while the North Saskatchewan River and its tributaries contained nearly 900 kilometers. The two

river systems are shown in Figure 1.

Figure 1: Project location

The project area is comprised of a wide variety of valley types and river channel slopes. The topography ranges

from steep, mountainous, incised V-shaped valleys to more gradually sloped, glacially carved U-shaped valleys

to broad, relatively flat flood plains in the lower portions of each river system. These differences in topography

presented an additional challenge for creating an accurate terrain model with the varying degrees of detail needed

to map inundation extents.

Digital representation of the topography was obtained using Light Detection and Ranging (LiDAR) technology.

The resulting data were provided in Environmental Systems Research Institute (Esri) American Standard Code

for Information Interchange (ASCII) grid file format, containing X, Y, and Z coordinates (latitude, longitude,

and elevation). A total of 827 ASCII files covered individual sections of the Bow River watershed, and 803 files

covered the North Saskatchewan River. From the beginning, it was apparent that data processing and

organization would play a key role in effectively utilizing these data for inundation modeling purposes. Time

restrictions would also factor in the decision-making process, as all work needed to be completed within a four-

month period. It was quickly determined that some generalization of terrain data would be necessary to achieve

adequate computational speed while modeling and to keep the immense volume of digital data to a manageable

size on the computer servers. The overall goal was to generalize the digital data while maintaining the high level

of precision in results made available with the original detailed LiDAR terrain data. By using a derivative

product of the original LiDAR grid (a Triangulated Irregular Network, or TIN) for development of the hydraulic

models, HDR|DTA produced final inundation mapping results that were comparable to mapping using the

original LiDAR raster grid surface to develop the hydraulic model.

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The study consisted of four sequential steps: 1) the development of a Digital Terrain Model (DTM), 2) the

development of U.S. Army Corps of Engineers (USACE) HEC-RAS model cross sections using HEC-GeoRAS,

3) hydraulic simulations of the breach formations and downstream progression of the resulting flood waves using

the one-dimensional HEC-RAS model, and 4) inundation mapping. This paper primarily focuses on decisions

made during the initial step and the ensuing challenges and solutions HDR|DTA encountered, including the

potential impact of those decisions on modeling and mapping results. A test study using a smaller, simplified

HEC-RAS model based on the more detailed Bow River model validated the decision to generalize terrain data

and verified the techniques performed while developing the hydraulic model cross sections and, ultimately, the

EPP maps themselves. All processes discussed herein can be performed on industry standard, commercially

available GIS desktop applications.

2 BACKGROUND

The first step of a modern inundation study is to develop a DTM. The DTM is required both for development of

model cross sections for use in the dam breach and hydraulic water routing application, and for mapping of the

inundation results. For this study, HDR|DTA used the unsteady, one-dimensional HEC-RAS model for breach

simulation and flood routing. A geographic information system (GIS) was employed to manage the DTM,

develop geometric inputs and process the hydraulic results from the HEC-RAS model. The platform for running

the GIS was Esri ArcMAP version 9.2, including the Spatial Analyst and 3D Analyst extensions. The USACE

companion software application, HEC-GeoRAS, was used for the pre- and post-modeling tasks within the GIS.

The DTM was developed from multiple LiDAR data sets, collected via an airborne LiDAR system. LiDAR data

collection operates using a laser or light mounted to an aircraft, which can emit up to several thousand laser

pulses per minute. The range is defined by the time these laser pulses are emitted from the aircraft, reflected from

an object such as the earth’s surface, and returned to the aircraft’s sensor. Since the laser pulse is traveling at the

known speed of light, a precise distance calculation can be performed by the sensor. An onboard Inertial

Measuring Unit records the aircraft sensor’s angular orientation to the ground as a Global Positioning System

(GPS) measures the sensor’s horizontal and vertical accuracy. Typically, the X, Y, Z coordinates collected by the

sensor are compared and corrected against one or more known ground-surveyed GPS base stations. When the

LiDAR system is working most accurately, vertical elevations within several centimeters are possible, depending

on the varying reflectivity of differing terrain surfaces. One key advantage of airborne LiDAR technology is the

ability to collect multiple laser returns. LiDAR pulses can penetrate openings in tree canopy and ground

vegetation to return “bare earth” elevations. Other laser pulses detect “first return” elevations, which reflect first

contact with tree canopy, ground vegetation, and man-made features such as buildings and bridge overpasses. A

multitude of additional analyses can be performed from these bare earth and first return data collections,

including calculations of slope, aspect, and line of sight, as well as development of hill shading files, which can

add the perspective of relief to a base map. Very large quantities of “point cloud” X, Y, Z information (i.e., raw

data) can be collected in a short time frame.

LiDAR data are typically received from the vendor in one of several standard formats, including ASCII, Binary,

and LAS (a binary public file format that helps facilitate the exchange of LiDAR data between vendor and

customer). Each file type has its advantages, disadvantages, and user loyalties. One disadvantage of ASCII and

Binary file formats is file size; however, these file types are most easily manipulated within the ArcMAP

platform. Having the ability to request the file format most suitable for intended uses may not be an option,

especially when receiving these LiDAR data as a third party recipient. When possible, it can be useful to request

LiDAR data, which includes breaklines for definition of important features, such as river banks and roads. In

addition, it is important to request the LiDAR metadata explaining data collection and processing methods, as

well as the guaranteed vertical and horizontal accuracy. For this project, HDR|DTA received over 1,600

individual bare earth Esri Binary and Esri ASCII grid files from TransAlta. An equivalent amount of data was

also received as first return Digital Surface Model (DSM) data with accompanying hillshade files. The bare earth

data files alone totaled over 16 gigabytes (GB) of data. A typical file only covered a minor portion of the

modeled area. Each file contained thousands of evenly spaced elevation readings presented as 2X2-meter raster

pixels and represented contiguous data with little to no overlap.

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Early in the study, it was necessary to process and piece together or “mosaic” the LiDAR files into two all-

encompassing DTMs representing the Bow and North Saskatchewan River systems. Digital representations of

the three-dimensional earth surface within industry standard computer software packages such as GIS can take

several modeled forms depending on user intent, data storage and display limitations, and the processing power

of computers used in the procedure. Three standard DTM storage formats are typically available: contour

isolines, or lines connecting equivalent elevation; Digital Elevation Models (DEMs) in the form of raster grid

files; and TINs.

Raster grid files consist of a matrix of pixel cell values. In the case of the LiDAR data used in this project, each

pixel cell value or posting has a unique elevation. When combined with thousands or millions of contiguous

cells, the composite of all cells represents a continuous surface, referred to as a DEM, as shown in Figure 2. The

uniform pixel posting size determines the level of detail contained within the data set. A smaller posting size is

beneficial for representing dramatic changes in slope, whereas a larger pixel size is more appropriate in areas of

flatter terrain. The raster grid file format contains data over a square or rectangular area extent. In the case of

inundation mapping along a river system, grid cells containing elevation values are only required in the

proximity of the river centerline. Remaining areas in the rectangular region contain “null” or “no data” values

that can increase file size to the extent of compromising computer performance and requiring additional server

storage space. For example, the composites of the Esri ASCII grid files pieced together for the Bow River and

South Saskatchewan River contained 40,690,020 grid cells with actual elevation values and 10,338,852,782 cells

with null or no-data values.

Figure 2: Area depicted by raster cells

TINs are an efficient method for modeling terrain elevation surfaces, with the ability to add more or less

definition across an entire surface where needed based on the topography. The “irregular” aspect of the TIN

results from a weeding or sampling of elevation values. For example, the point cloud LiDAR data can be re-

sampled to eliminate points of similar value within a small geographic area. The DTM as represented by a TIN

model can be refined by adding additional surface features such as polygons for rivers and lakes with breaklines

depicting ridges or river banks. A TIN consists of three separate entities (nodes, edges, and faces), as shown in

Figure 3. Nodes are encoded with X, Y, and Z-values to represent coordinate location and elevation. Edges

connect the nodes, which are triangulated to the next-closest node across the terrain surface. Polygon faces are

located within the triangular areas between the three nodes and edges. These non-overlapping faces are described

by height, slope, and aspect and can consist of different sizes and shapes, resulting in a continuous surface of

varying detail.

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Figure 3: Elements of a TIN

Smaller TIN triangles may be tightly grouped to represent areas with greater slope change. Conversely,

topography with more gradual slopes can be adequately represented by larger TIN triangles, as demonstrated in

Figure 4. Since the project area was so well-covered by the 2X2-meter LiDAR data, adequate elevation data was

available for detailed triangulation. One significant advantage of a TIN model over a raster grid cell-based

surface is that TINs only need to contain relevant data and are not constrained to a rectangular shape, potentially

eliminating billions of irrelevant data readings. By utilizing a TIN, even the relevant data within the original

LiDAR collection coverage area can be re-sampled to remove unnecessary nodes of similar elevation readings

based on their proximity to one another.

Figure 4: Photograph of terrain surface (left) and TIN representation (right)

Each type of DTM has its advantages and disadvantages when applied to hydraulic modeling over large

geographic expanses. It is important to note that the DTM is not confined to its native format, but can move

freely between each data type by creating derivative products from one to the other. In GIS, contour isolines can

easily be converted to one or several TIN surfaces, a DEM grid surface can be modeled as a TIN surface, or a

TIN and/or DEM grid can be modeled as contour isolines. The 3D Analyst extension in ArcGIS provides easy-

to-use tools that allow data model conversions between any one of the three DTM model types. However, HEC-

GeoRAS only works directly with grid and TIN terrain data models. Ultimately, as discussed in the following

section, HDR|DTA chose to develop comprehensive TIN representations of each river system for development

of hydraulic model input, while retaining the highly accurate original LiDAR raster grid format for mapping of

the results.

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3 DEVELOPMENT OF HYDRAULIC MODEL GEOMETRY

HDR|DTA’s standard procedure for previous dam breach modeling and inundation mapping projects was to

create a DTM to cover the entire geographic extent of a study river system, which typically extended less than

200 kilometers downstream from the dam of interest. In those cases, preparation of the DTM consisted of

projecting raster datasets into an appropriate coordinate system, piecing together contiguous datasets where

needed, and possibly converting elevation units. The DTM was typically represented as a raster grid cell-based

USGS DEM with a 10X10-meter pixel posting size, assumed to represent adequate precision for the one-

dimensional HEC-RAS modeling software. HEC-RAS model cross sections were drawn or “cut” from the DTM

perpendicular to the flow path using HEC-GeoRAS. The pixel posting size, along with the cross-section width,

controlled the number of spot elevations contained in each cross section.

The 2X2-meter pixel posting size represented in the TransAlta LiDAR data resulted in cross sections along the

Bow and North Saskatchewan River basins consisting of well over 1,000 spot elevations, or nodes. HEC-RAS

imposes a 500-node limit per cross section and the model provides several functions for filtering excess nodes

with varying levels of user control. It was not practical to filter over 1,000 cross sections individually using

visual inspection to ensure that no important morphologic features were lost. However, as a rule, the default

tactic was to maintain the original or native pixel posting size in the DTM used to develop the hydraulic model,

even if post-processing of cross sections is required. Using this methodology, HDR|DTA attempted to build two

continuous raster grid files by piecing together over 800 gridded 2X2-meter, LiDAR-based files for each river

system. The immense geographic extent of the basins posed a significant challenge for the desktop GIS

application. Processing run times were long and prone to fatal errors. After many hours of failed attempts, it was

determined that a different path would need to be explored.

The solution to this software stalemate resided within HEC-GeoRAS itself, which offers the functionality to

utilize multiple DTM terrain tiles. Each terrain tile must be contiguous with a small amount of overlap. HEC-

GeoRAS supports both grids and TINs with this terrain tiling utility. The possibility for dissecting the terrain

LiDAR data into more manageable extents also provided greater flexibility of project workflows. Setting up the

utility in HEC-GeoRAS is relatively simple as long as certain rules are adhered to (e.g., tiled terrain DTMs

should have minimal overlap and contain at least one common cross section that is entirely located in each

adjoining DTM and does not overlap across two contiguous DTMs). An index polygon feature class is created

that bounds each DTM file. The tile index file’s attribute table contains a field storing the path name of the DTM

location. Ideally, terrain tiles should adjoin at straightaway areas along a river reach, avoiding tributary

confluences and bends in the river, which increases the likelihood of creating a cross section that cuts across

adjoined DTMs. After some planning, HDR|DTA divided the North Saskatchewan River system into five terrain

tiles and the Bow River system into four terrain tiles, with the number of tiles influenced by the orientation of the

river. An attempt was made to follow the most rectangular orientation to limit the amount of null or no-data pixel

cell values in the raster grid files. Figure 5 shows the tile coverage of each river system.

Figure 5: Terrain tiles

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Trial tests for assigning cross-section elevations on just one raster grid terrain tile proved to be a tedious process,

with very long run times in HEC-GeoRAS; assigning elevations to cross-section geometry with multiple

adjoining raster grid files proved to be impossible. The benefits of converting the high-resolution raster grid-

based terrain tiles to TIN representations began to gain greater consideration, with the possibility of using

multiple TIN terrain tiles for assigning elevations to the cross-section geometry for hydraulic modeling in HEC-

RAS, yet retaining the original 2X2-meter bare earth grid for modeling the inundation polygon.

The HEC-GeoRAS User’s Manual (USACE 2009) recommends the use of TINs as “the preferred method for

surface modeling for river hydraulics because it is well suited to represent linear features, such as channel banks,

roads, and levees”. The advantages in terms of file size, processing times, and the portrayal of data in logical

slope change breaks were attractive for use in GIS. The question still remained as to how much detail should be

retained in the TIN to avoid compromising the HEC-RAS hydraulic model. When transforming raster grid data

to a TIN using 3D Analyst, the level of detail and precision is controlled by setting the vertical, or Z-tolerance,

an important variable to understand. The vertical tolerance prevents elevations represented in the output TIN

from deviating from the input DTM, in this case the LiDAR grid data, by the specified value at all locations

represented by the resolution of the input data, as demonstrated in Figure 6.

Figure 6: Z-tolerance schematic

The default and maximum TIN tolerance in 3D Analyst is one-tenth the maximum elevation range represented in

the terrain dataset. The Z-tolerance determines the number of nodes in the resultant TIN and controls the

accuracy of representation of topographic features. The addition of breaklines to define important features can

significantly improve the definition of the TIN. Given that breaklines were not provided as part of the original

LiDAR data sets from TransAlta and there was limited time to incorporate ancillary data to the TIN, HDR|DTA

decided that the detail offered in the original 2X2-meter grid together with a sufficiently low Z-tolerance would

suit the project modeling purposes. For initial tests, a 0.5-meter Z-tolerance was used, which resulted in a highly

detailed TIN that compared favorably with the original raster grid file, but with a much smaller file size.

Assignment of elevations to the cross-sectional geometry was achieved with much faster computer processing

times in HEC-GeoRAS. HDR|DTA was able to successfully process one continuous geometry model covering

the five TIN terrain tiles for the North Saskatchewan River and tributaries for import into HEC-RAS using the

0.5-meter Z-tolerance TIN model. However, an important limitation was encountered when processing the 0.5-

meter Z-tolerance TINs for the larger Bow River model.

Larger TIN files in excess of 15 to 20 million nodes cannot be created, displayed, or used in ArcGIS Desktop.

ArcGIS Desktop uses the Operating System’s memory management system, which sets a 2 GB per process limit

for Microsoft Windows applications (Esri 2010) Although each of the four separate terrain tiles in the Bow

River model contained less than 15 million nodes, the cumulative total for all four tiles exceeded the allowable

limit. It was possible to display and assign elevations to cross sections for any set of three terrain tiles, but the

fourth tile exceeded the limit. HDR|DTA considered dividing the hydraulic model into pieces, but decided

against that procedure for several reasons. Dividing a hydraulic model involves careful implementation of

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boundary conditions to avoid introducing model artifacts and it is difficult to ascertain whether model results

have been impacted by boundary condition choices. In addition, each sequential model requires the upstream

model output for its input hydrograph, introducing the potential for data accounting errors, especially during the

calibration process, where each model component must be rerun, and when running multiple scenarios. The

ultimate decision was to retain a single model by creating five 1-meter Z-tolerance TINs for the Bow River

model, which was below the 15-20 million node threshold. This decision led to the investigation of the impact of

Z-tolerance choice, or TIN resolution, on the HEC-RAS hydraulic model results and inundations maps.

4 TEST MODEL

To test the impact of digital terrain smoothing on hydraulic modeling results, HDR|DTA developed a smaller,

simplified HEC-RAS model based on the larger, more detailed model of the Bow River system. The test model

simulated a breach of Minnewanka Dam and routed the flow down Cascade Creek into the Bow River, with a

total model length of approximately 100 kilometers, whereas the detailed study model routed flows

approximately 816 kilometers downstream from Minnewanka Dam. The simplified test model consisted of a

single reach and did not explicitly model any of the downstream dams, reservoirs, or tributaries. Therefore, test

model results were not representative of any of the actual breach flow scenarios developed for emergency

planning. The results were simply a demonstration of the potential impact of cross-section smoothing in this type

of terrain.

Three separate DTMs were developed for the hydraulic modeling comparisons: the original raster grid with 2X2-

meter pixel posting size as represented in the LiDAR data, and two separate TINs with Z-tolerances of 1 meter,

as used in the detailed Bow River study, and 5 meters, to demonstrate the impact of resolution loss on hydraulic

modeling. A comparison of file sizes illustrates the advantages of the TINs over the raster grid. The raster grid

had a file size of 5.4 GB, while the file sizes of the 1-meter and 5-meter Z-tolerance TINs were 169 megabytes

(MB) and 31.5 MB, respectively. A review of the data indicated that significant detail was compromised within

the 5-meter TIN, potentially affecting the overall accuracy of the hydraulic model. The impact of tolerance level

is demonstrated in Figures 7 and 8, which show the results of subtracting the high-resolution LiDAR grid

elevations from the 1-meter and 5-meter TINs.

Figure 7: 1-meter TIN minus LiDAR grid elevations Figure 8: 5-meter TIN minus LiDAR grid elevations

Linear features such as braided river beds and roads are evident in the 5-meter TIN subtraction, indicating that

these features were not captured by the elevation data. Important information could be retained within the lower

resolution grid by creating breaklines to delineate all features of interest, especially river banks and channel

inverts, but this would involve significant additional work. The impact of TIN tolerance on cross-section

geometry is demonstrated in Figure 9, which shows three overlain cross sections from each of the terrain models

taken directly from the HEC-RAS model. The 5-meter TIN is shown in pink, the 1-meter TIN is shown in blue,

and the LiDAR grid is shown in black. Bank stations are represented as red dots. A close-up of a portion of this

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cross section is shown in Figure 10 to demonstrate the impact of scale on the level of elevation precision

required; the same cross-section resolution adequate for accurate inundation modeling of large volumes of water

moving through the landscape may not by appropriate for modeling low volume flows contained within channel

banks.

Figure 9: Example HEC-RAS cross section developed from LiDAR, 1-meter, and 5-meter TINs

Figure 10: Close-up of example HEC-RAS cross section in Figure 9

It is interesting to note that the terrain tile used in the test model had minimum and maximum elevations of

2,450 meters and 1,146 meters, with a range of 1,304 meters. The Esri default Z-tolerance of one-tenth the

maximum range would result in a vertical tolerance of 130 meters, far exceeding the required level of accuracy.

It is imperative to determine an appropriate TIN Z-tolerance level to create a file with a manageable size that

does not exceed the 15 to 20 million-node threshold in ArcMAP, and most importantly, reflects a sufficient

amount of real world ground feature detail without significantly affecting the hydraulic model results.

5 IMPACT OF CROSS-SECTION RESOLUTION ON FLOW MODELING

Three separate HEC-RAS geometries were developed based on cross sections derived from the original LiDAR

grid, the 1-meter resolution TIN, and the 5-meter resolution TIN. The same cross-section locations were used as

in the detailed Bow River model, with an average spacing of 1 kilometer. Intermediary cross sections were

interpolated at an interval of 100 meters to prevent model instability and numerical diffusion of peak flows.

Detailed stream channels were not added beyond what was already represented in the digital cross sections, but

the geometries employed HEC-RAS-generated uniform pilot channels to improve model instability. Bank station

locations across each transect were maintained between models as closely as possible; however, elevations of the

bank stations varied depending on the geometry resolution. In the primary test model, the Manning’s roughness

coefficient variability was defined by the bank stations, with a channel coefficient of 0.035 and overbank

coefficient of 0.10. An additional set of geometries was created with a uniform Manning’s coefficient of 0.07

throughout to test the impact of accurate channel definition.

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A breach scenario and constant base flow scenario were run using each geometry. The breach flow hydrograph

from the detailed model 1-hour breach scenario of Minnewanka Dam was used as the upstream flow boundary

condition for the test model breach scenarios, and a base flow of 100 cubic meters per second (cms) was used for

the test model base flow scenarios. Differences in water surface elevation (WSE), flood wave and peak flow

arrival times, velocities, and incremental rise (the maximum increase in WSE between base flow and breach flow

conditions) were used to compare model results between the TIN geometries and the original LiDAR grid

geometry. Results for each of the geometries at 10 cross sections spread throughout the modeled reach are shown

in Tables 1 through 3. The results demonstrate that cross-section smoothing to the 1-meter TIN resolution had

minimal impact on the measured variables, while the 5-meter TIN showed significant differences from the

LiDAR grid results, which could translate into an even greater impact if the model were extended for the entire

816-kilometer reach.

Table 1: LiDAR grid model breach scenario results summary

Section

Model

River

Station

Flood Wave

Arrival Time

Time to

Peak

Peak

Elevation

Incremental

Rise

Peak

Flow

Velocity at

Peak

Elevation

(Hr:Min)* (Hr:Min) (m)** (m) (cms)*** (m/s)****

1 88943 1:00 1:43 1,370.5 8.5 17,909 3.9

2 79178 1:55 3:08 1,331.7 7.7 14,918 2.1

3 69222 3:30 4:50 1,313.6 8.1 13,127 1.7

4 59610 5:01 8:14 1,301.9 9.5 10,210 1.0

5 48874 6:32 9:41 1,296.5 8.0 8,637 1.5

6 39196 7:39 10:24 1,267.9 10.4 8,522 6.1

7 29163 8:13 10:57 1,222.8 10.8 8,509 2.4

8 19212 8:55 11:54 1,201.9 9.8 8,408 5.6

9 9282 9:48 12:54 1,178.3 6.7 8,323 1.6

10 78 10:30 13:44 1,164.8 16.0 8,238 2.3

* Hr:Min is hour and minute

** m is meters

*** cms is cubic meters per second

**** m/s is meters per second

Table 2: 1-meter TIN model breach scenario results summary

Section

Model

River

Station

Flood Wave

Arrival Time

Time to

Peak

Peak

Elevation

Incremental

Rise

Peak

Flow

Velocity at

Peak

Elevation

(Hr:Min) (Hr:Min) (m) (m) (cms) (m/s)

1 88943 1:00 1:43 1,370.4 8.6 17,957 3.9

2 79178 1:55 3:08 1,331.7 7.8 14,887 2.1

3 69222 3:30 4:55 1,313.5 8.0 13,150 1.7

4 59610 4:58 8:12 1,301.9 9.5 10,221 1.0

5 48874 6:31 9:44 1,296.6 8.0 8,630 1.5

6 39196 7:38 10:33 1,268.0 10.4 8,516 6.2

7 29163 8:13 10:59 1,222.8 10.7 8,503 2.4

8 19212 8:55 12:01 1,202.0 9.9 8,398 5.6

9 9282 9:50 12:58 1,178.3 6.4 8,314 1.6

10 78 10:31 13:46 1,164.7 15.9 8,224 2.3

11 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


Table 3: 5-meter TIN model breach scenario results summary

Section

Model

River

Station

Flood Wave

Arrival Time

Time to

Peak

Peak

Elevation

Incremental

Rise

Peak

Flow

Velocity at

Peak

Elevation

(Hr:Min) (Hr:Min) (m) (m) (cms) (m/s)

1 88943 1:02 1:41 1,370.4 8.7 18,026 3.9

2 79178 1:57 3:08 1,331.2 7.0 14,777 2.1

3 69222 3:30 4:51 1,312.9 8.1 13,136 1.6

4 59610 4:58 8:19 1,301.7 9.7 10,101 1.1

5 48874 6:29 9:51 1,296.7 8.1 8,441 1.3

6 39196 7:35 10:40 1,268.2 10.7 8,326 5.8

7 29163 8:14 11:06 1,223.2 11.2 8,315 2.4

8 19212 9:08 12:03 1,202.3 9.8 8,221 5.4

9 9282 10:08 13:03 1,178.3 6.1 8,152 1.5

10 78 10:48 13:59 1,166.1 15.8 8,046 1.9

Note the difference in elevation of the bank stations and invert between the 5-meter TIN, and the 1-meter and

LiDAR cross sections apparent in Figure 10. These elevation discrepancies result from TIN creation without

inclusion of breakline features. Differences in bank station and invert location and elevation can have a

significant impact on model results for several reasons. The invert elevations are used in the energy equation to

solve for the water surface profile from one cross section to the next. The bank stations are generally used as

dividing lines between channel and overbank Manning’s roughness coefficients. Differences in flow volume

attributed to channel versus overbank can have a significant impact on calculation of conveyance using

Manning’s equation. This impact was observed when comparing model results using a uniform Manning’s

coefficient across the cross section versus using a different coefficient for channels and overbanks. Note that the

uniform Manning’s scenario is not intended to represent a composite approximation of the variable scenario and

comparison of results between the two scenarios is not meaningful.

Table 4 shows peak flow and stage results at the final model cross section for the two scenarios. The uniform

Manning’s coefficient TIN models resulted in nearly identical stages and arrival times within 2 minutes of the

LiDAR grid model results. The variable Manning’s coefficient results showed significant differences for the 5-

meter TIN model, with a 1.3-meter increase in peak stage and a 15-minute delay in peak arrival time at the final

model cross section. The increase in stage and travel time is likely a result of overall under-representation of

channel area in the 5-meter TIN cross sections, thus a larger portion of the flow occurs along the rougher

overbanks.

Table 4: Sensitivity to Manning’s coefficient (n) results at last model cross section

Peak Flow

Peak

Elevation

Time to

Peak

(cms) (m) (Hr:Min)

LiDAR Grid 0.07 0.07 7,636 1168.8 16:12

1-m TIN 0.07 0.07 7,612 1168.8 16:14

5-m TIN 0.07 0.07 7,582 1168.8 16:13

LiDAR Grid 0.035 0.10 8,238 1164.8 14:52

1-m TIN 0.035 0.10 8,224 1164.7 14:54

5-m TIN 0.035 0.10 8,046 1166.1 15:06

Channel n Overbank nModel

A less obvious impact of reduced resolution and inaccurate bank station placement can occur when the

interpolation function is used to generate cross sections. For extremely large models, it is not feasible to cut

DTM cross sections at an interval that will guarantee model stability and it is usual to interpolate between cross

sections where needed throughout the model. The interpolation function uses five points to generate chords, or

12 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


breaklines, between the upstream and downstream cross sections: the channel invert, the two bank stations, and

the two end points of the cross sections. Therefore, differences in locations of the bank stations and inverts, as

well as in the extent of terrain coverage over the width of the cross section, will perpetuate and can result in the

generation of very different interpolated cross section shapes from those generated between two LiDAR cross

sections with different bank station locations.

When developing this test model, HDR|DTA discovered that these types of discrepancies caused significant

differences in WSE, emphasizing the need to be very cautious when interpolating between cross sections cut

from the digital terrain model. An example is shown in Figures 11 through 13. The 5-meter resolution cross

section, shown in pink, has bank stations defined on either side of the wide, flat portion of the cross section,

whereas the higher resolution cross sections have a much narrower defined channel on the left-hand side of the

flat region. Figure 12 shows the impact on the interpolated cross section between the upstream and downstream

cross sections. It is important to note that due to the extreme vertical exaggeration represented in these figures,

small horizontal changes can have a very significant impact on channel volume. To avoid introducing model

artifacts, it is prudent to increase the number of cross sections cut in areas where a more precise estimate of

WSEs is desired.

Figure 11: Upstream cross section

Figure 12: Interpolated cross section

Figure 13: Downstream cross section

13 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


Table 5 summarizes various model parameter result differences between the TIN geometries and the LiDAR-

based geometry for all the cross sections. The maximum WSE difference at peak breach flows for the 1-meter

TIN model was 0.38 meters, while the maximum difference for the 5-meter TIN model was 2.15 meters.

Maximum differences at base flows were 0.48 meter and 3.47 meters, respectively. HDR|DTA considers the 1-

meter TIN model result differences to be acceptable for dam breach modeling and well within the range of

expected model uncertainty. Note that the differences increase for the base flow simulations, and are especially

significant for the 5-meter TIN model. This is an expected result due to differences in scale of the sampled area

of the cross sections for the two flow scenarios. At low flows, cross-section geometry discrepancies have a larger

impact on relative flow area, wetted perimeter, and the resulting WSE.

Table 5: Summary of model differences from LiDAR-derived model at maximum profile (all cross sections)

Water

Surface

Elevation

Energy

Gradeline

Elevation

Minimum

Channel

Elevation

Total Flow

Area

Channel

Flow Area

Top

Width

Wetted

Perimeter

Invert

Slope

Incremental

Rise

(m)* (m) (m) (% ) (% ) (% ) (% ) (m/km)** (m)

Mean 0.04 0.02 0.02 0.3% -0.6% 0.2% 0.1% 0.00 0.02

Standard Deviation 0.10 0.13 0.14 2.1% 7.2% 1.8% 1.8% 0.26 0.15

Maximum Difference 0.38 0.82 0.75 13.2% 37.8% 12.8% 12.7% 0.80 0.53

Minimum Difference -0.34 -0.71 -0.33 -9.0% -27.5% -20.4% -20.3% -1.00 -0.45

Mean 0.12 -0.01 0.31 4.0% -4.9% 1.6% 1.4% 0.00 -0.15




Mean 0.01 0.02 0.02 1.3% -3.5% 0.2% 0.1% 0.00 0.02




Mean 0.27 0.29 0.31 62.7% -6.3% 44.2% 44.0% 0.00 -0.15




*m is meters

**m/km is meters per kilometer

Brea

ch

Scen

ario

s

1-m

TIN

5-m

TIN

Ba

se F

low

Scen

ario

s

1-m

TIN

5-m

TIN

An additional statistical analysis was performed to attempt to link WSE results with terrain morphology by

calculating Pearson correlation coefficients between 1-meter and 5-meter TIN cross-section WSE differences and

top width (to represent flood plain versus steep, incised channel), invert slope, flow area, wetted perimeter, and

relative differences in minimum channel elevation. The 1-meter TIN WSE differences showed no significant

correlations to any of the tested parameters, with all coefficients less than 0.1. The 5-meter TIN WSE differences

showed the highest correlation with top width and wetted perimeter, with an equivalent Pearson coefficient value

of -0.41 for each, which indicates the tendency for modeled WSEs to be too high in narrow valleys and too low

in the flood plains. This correlation is a result of complex interactions between model hydrology and channel

morphology that are not explained by correlation of local differences at each cross section. When considering the

impact on flood mapping, the effect in the narrow valleys is countered by the fact that discrepancies in WSE in

steep channels will not result in large changes in flooded area. The impact in floodplains will be more

pronounced, where a small change in WSE can result in a large change in inundation area. Figures 14 and 15

display the differences in WSE determined by subtraction of the LiDAR grid model results from the 1-meter and

5-meter TIN model results, as mapped within inundation extents. The inverse correlation of WSE discrepancies

with top width is evident in the 5-meter TIN (Figure 15) where many of the narrow valleys show up as red and

the floodplains are blue. The 1-meter TIN model differences are smaller with a more random distribution.

14 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


Figure 14: WSE difference: 1-meter TIN minus LiDAR Figure 15: WSE difference: 5-meter TIN minus LiDAR

Overall, HDR|DTA considered the results from the 1-meter TIN to be an excellent approximation of the model

results that can be achieved with the higher resolution LiDAR data. Some small differences in WSEs and flood

wave travel times were apparent through statistical analysis, but the impact on inundation mapping and

emergency planning was insignificant. The 5-meter TIN models resulted in more significant discrepancies,

which were more apparent in the flatter, floodplain regions of the inundation maps. However, these differences

did not result in as large an impact to the areal extent of flooding within the modeled terrain as might be expected

from the degree of smoothing apparent in the model cross sections. The end use of the results must be considered

when determining acceptable model resolution; even small WSE differences can become very important in the

context of mapping sensitive areas and especially when modeling downstream dams, where flood elevations

determine whether or not a sequential failure will occur.

6 INUNDATION MAPPING RESULTS

The hydraulic model results were translated into EPP maps using HEC-GeoRAS. An example EPP map is shown

in Figure 16, where the blue line represents the inundation boundary or polygon, and important flood data, such

as peak elevations and flows, and flood wave arrival times are included in text boxes at representative cross

sections. (Note that this map is an example only and does not depict actual predicted flood extents.) As was done

for the original study, the inundation extents resulting from each of the test models were mapped on the original

2X2-meter raster grid base derived from the LiDAR data. It is important to retain the high resolution grid for the

inundation mapping in order to depict flood extents as accurately as possible within the terrain. Additional

information may be added to the base map as desired, such as aerial photographs, labels for important locations,

contours, hillshade images, and other features.

Figure 16: Example EPP map

15 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


Mapping of the test model results showed very little difference between inundation polygons developed from the

LiDAR grid and the 1-meter TIN model results. The difference in total polygon area was 0.08% and the area not

included in the intersection of the two polygons represented 0.17%. At most locations, the polyline smoothing

algorithm chosen to generate the flood boundary accounted for more variance in flooding extent than the

differences in maximum water surface elevation. The inundation maps developed from the 1-meter TIN depicted

a level of accuracy that was highly sufficient for the purposes of emergency planning.

A slightly greater difference was observed in the polygon results derived from the 5-meter TIN model. The

overall difference between the two inundation polygon areas was 0.95% and the total area not included in the

intersection of the two polygons represented 1.69% of the LiDAR polygon area. The regions with the greatest

horizontal differences in inundation extent occurred in the floodplains, with several areas showing differences of

up to 75 meters or more. This level of impact of the lower-resolution modeling may not be acceptable when used

for emergency planning, especially in sensitive areas.

7 SUMMARY AND CONCLUSIONS

The availability of high-resolution terrain data has opened new possibilities in the areas of emergency mapping

and planning. However, the tremendous volume of data involved requires a judicious approach in its appropriate

use. Through the course of this project, HDR|DTA realized that it was not necessary, or even possible, to utilize

all available data for every aspect of the modeling process. While the high-resolution data could be employed for

detailed mapping of hydraulic model results, it was necessary to carefully re-sample the data into TIN format to

create the hydraulic model. The results of the comparison between hydraulic modeling using the 1-meter and 5-

meter TINs demonstrated the potential impacts and pitfalls due to the level of smoothing of elevation data.

The vertical resolution required in the creation of TINs for development of the hydraulic model input depends

upon the type of terrain being modeled in terms of scales of topographic roughness, as well as the scale of flows

being modeled. High volume flood flows are not as sensitive to small-scale topographic elevation discrepancies

as average or low flows. Each project area must be considered individually and may require some

experimentation to determine an appropriate vertical resolution. It may be possible to reduce required TIN

resolution with careful inclusion of breaklines to delineate important features such as river banks, channels, and

roadways. During this study, HDR|DTA determined that conversion of the DTM format from the 2X2-meter

gridded DEMs to 1-meter TINs allowed retention of key information contained in the higher resolution data set,

while significantly reducing the computational burden for development of the hydraulic model and allowing

timely completion of the project, with little impact on overall results.

8 REFERENCES

Environmental Systems Research Institute. 2010. ArcGIS Desktop (Version 9.3/9.3.1.) [Software]. Available

from http://www.esri.com/products/index.html

Robinson, A. H., J. L. Morrison, P. C. Muehrcke, A. J. Kimerling, and S. C. Guptill. 1995. Elements of

Cartography, Sixth Edition. John Wiley & Sons, Inc. New York, New York.

U.S. Army Corps of Engineers. 2009. HEC-GeoRAS. GIS Tools for Support of HEC-RAS using ArcGIS®.

User’s Manual, Version 4.2. September 2009.

Zeiler, M. 1999. Modeling Our World: The ESRI Guide to Geodatabase Design. ESRI Press. Redlands,

California.

Documents

CDA 2010 Annual Conference Paper_FINAL