1. 1
cracCra
Micheal O Flatharta
m.t.oflatharta@student.vu.nl
2552984
moa380
DEVELOPMENT OF A
SIMPLIFIED MODELLING
FRAMEWORK TO ASSESS
FLOODS OF DIFFERENT
MAGNITUDES IN
CALGARY, CANADA

2. 2
Abstract
In June of 2013, a massive flood hit the City of Calgary in Canada that caused thousands of
people to be displaced from their homes, millions of dollars in damage and is today considered
the costliest natural disaster in the history of Canada. After this event, there were many
questions about how to effectively prepare for a flood and what measures could be
implemented to reduce their damage. Therefore, the goal of this project was to develop a
simplified modelling framework which aided stakeholders in the decision making process by
giving them a model that could produce meaningful flood maps on the fly. Because this model
works quickly, more flood scenarios can be viewed and more flood prevention methods tested.
To develop the model, rating curves created by the hydraulic model HEC-RAS and discharge
values from the hydrologic model STREAM were combined in an agent-based model called
Netlogo. Along with the Netlogo model, a design software called Infraworks was used to
create a fly over video of a digital representation of the City and a 3D print of the catchment
was created. All three of these products, the Netlogo model, the Infraworks model and the 3D
print are intended to be used together in a workshop setting to allow stakeholders to change
variables in the model and view the consequences of those changes at many different scales.

3. 3
Table of Contents
1) Acknowledgments ---------------------------------------------------------------------- 4
2) Introduction ------------------------------------------------------------------------------ 5
3) Literature Review ----------------------------------------------------------------------- 11
3.1) One dimensional models ……………………………………..………… 12
3.2) Unsteady Flow assumption……………………………………………... 13
3.3) HEC-RAS…………..………………………………………………….... 15
3.4) Rating Curves…..……………………………………………………...... 16
3.5) Manning values ………...………………………………………………. 17
4) Methodology----------------------------------------------------------------------------- 19
4.1) HEC-RAS………………………………………………………………. 19
4.2) Three-Dimensional Rating Curves …………………………………….. 23
4.3) Netlogo ………………………………………………………………… 24
5) Results ------------------------------------------------------------------------------------ 26
6) Discussion ------------------------------------------------------------------------------- 31
6.1) Golder report …………………………………………………………….31
6.2) Infraworks and 3D print………………………………………………….32
6.3) Recommendations…………………………………………………....…..33
7) Conclusions ------------------------------------------------------------------------------ 35
8) References ------------------------------------------------------------------------------- 38

4. 4
1) Acknowledgments
I would first like to give a big thank you to Dr Scott Heckbert at Alberta Innovates Technology
Futures in Edmonton, Canada. His advice and support was always invaluable, especially during
my time in Canada.
Also thanks to my thesis supervisor Dr Hans De Moel of the Faculteit der Aard – en
Levenswetenschappen at the Vrije University in Amsterdam. His guidance and comments on the
thesis always made sure it was heading in the right direction and I am very grateful for his help.
Finally, I would like thank my parents for providing me with support and encouragement
throughout my years of study. This would not have been possible without them.
Thank you.

5. 5
2) Introduction
In recent years, flooding has become a major
issue around the world. In 2015 alone there
was the Southern India flood (Reuters, 2015)
in which over 500 people were killed and 1.8
million others were displaced from their
homes. In Texas and Oklahoma there was
severe flooding which caused 100,000
people to be without power (Sider and
Frosch, 2015) and this year alone France and
Germany experienced some of its worst
flooding in years. For example, the River
Seine, which runs through Paris, was the
highest it had ever been for the last 30 years
and it forced the Louvre and Orsay museums
too close. In the town of Triftern in Germany,
rivers and streams burst their banks and the
resulting floodwaters dragged along cars,
trees and furniture from homes(Chrisafis,
2016). All of these events made news
headlines around the world and caused
governments to reevaluate their flood
policies. The European Environment Agency
(2016) reported that between 1980 and 2010
37 countries registered 3,563 floods in total.
The highest number of which were reported
in 2010 with 27 countries being affected that
year alone. They also stated that annual flood
losses could be expected to increase fivefold
by 2050 and up to 17 fold by 2080. These
kinds of predictions can be seen from
governments around the world and there is
now more interest into how to prevent,
mitigate and/or predict the effect of floods,
especially in high populated areas.
There are many different measures which can
be taken in response to a flood and so
countries around the world develop
frameworks to classify these measures. This
gives governments a series of steps to work
towards. One example of a framework is the
multi-layer safety in the Netherlands. In this
system flood control measures are classified
into three layers. The first layer consists of
flood prevention measures such as dykes and
storm-surge barriers. The goal being to
prevent the flood from reaching and
damaging high value areas and property. This
for example, could involve huge construction
projects such as the Oosterscheldekering,
translated the “Eastern Scheldt storm surge
barrier” in the Netherlands. This massive
barricade is designed to prevent the North
Sea from flooding large parts of the
Netherlands. The next two layers consider
methods to reduce the cost of floods when
they do occur. The second layer specifically
deals with cost reduction through spatial
planning. This involves modifying homes
and business to reduce the damage a flood
could cause to a property. For example,
installing non-return valves on pipes to

6. 6
prevent sewage flowing back into a property,
mounting shelves and electrical sockets
higher up on the wall or replacing ground
floor carpet or wood flooring with tile which
does not need to be replaced if it becomes
inundated with water. These types of
measures can substantially reduce the cost of
a flood for property owners, insurance
companies and governments. For example, a
study about an unembanked area in
Rotterdam in the Netherlands observed that if
all of the buildings within the flood risk area
were dry-proofed (which means that the
building is water tight to the flood level) then
the risk to residential areas is reduced by 87%
(Moel et al., 2013). Finally, the third layer
deals with emergency measures. This
involves organizations being prepared in
advance for a flood. It would involve having
protocols in place which deal with
evacuations and identifying areas where
emergency crews would need to deployed to
be the most effective. For example, in 2012
on the 6th
of January 800 residents in a
northern part of the Netherlands were
evacuated when an inland dyke started
leaking (Staff, 2012). While the dyke did not
fail crisis management, measures ensured
that the evacuation went smoothly and that
there was no panic. All three layers when
applied support a risk based approach to
flood risk. The modelling framework
discussed in this report would be a part of the
second layer and intended to help
stakeholders make informed decisions about
the extent of a flood quickly. It could be used
to inform property owners about the risk they
are under and how to best reduce that risk. To
develop the model in this project a site was
required which had a substantial amount of
data available to calibrate with. Which is why
the City of Calgary was chosen.
The City of Calgary is the largest City in
Alberta, Canada (Figure 1). There are two
rivers which flow through the catchment
called the Bow and Elbow River. The Bow
River has a total length of 587 km and an
average discharge of 129m3
/s. While
upstream of the Elbow River is the Glenmore
Reservoir which has been regulating the
discharge of the Elbow River since 1933. The
average discharge below the Glenmore
Reservoir is 12m3
/s. Due to the much higher
discharge of the Bow River the water level at
the downstream portion of the Elbow River
does not have a unique relationship with its
own discharge. In other words, the water
level at the most downstream portion of the
Elbow is effect by both the discharge of the
Elbow River as well as the discharge of the
Bow River. This is known as the backwater
effect.

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Figure 1 – City of Calgary. The Bow and Elbow River and their direction of flows.
Bow River
Glenmore
Reservoir
Downtown
Calgary

8. 8
Another important feature of the catchment is
the lack of elevation change. Especially at the
downstream portion of the river. The range of
altitude varies from 1099 meters above sea
level (m.a.s.l.) to 1002 m.a.s.l. with most of
that elevation difference occurring upstream
of the Elbow River (Figure 2). The lack of
elevation in the downstream portion of the
catchment may have contributed to the extent
of floods which have previously hit the city.
In June 2013 there was a massive flood event
in Alberta, Canada. It is considered one of the
worst floods in recent Canadian history and
economists projected that recovery costs
combined with damage losses cost over $6
billion Canadian dollars (CAD), including a
record $2 Billion CAD in insured losses
(Government of Canada, 2013). After this
event the City of Calgary and the
Government of Canada invested money into
flood prevention methods like those
discussed in the frameworks section. These
measures were recommended by private
consultancy companies who created a series
of reports suggesting ways to improve early
detection methods, engineering projects
which could buffer the effects of a flood
along the Bow and Elbow River and policies
which could incentivize home owners to buy
flood insurance or buy property outside of the
floodplain. However, for consultancy
companies to propose meaningful and
impactful flood prevention methods they
require a considerable amount of data about
the hydrology of the area, the topography of
the catchment, the source of the flood water,
etc. This is a time consuming process which
can take between a few days to a few weeks
to collect, process and then present the data
and results. This makes it difficult to change
variables on the fly and view different
scenarios.
Therefore, a model which can produce results
quickly would be of great benefit to
stakeholders. Which is why the goal of this
project is to develop a simplified modelling
Figure 2 – Elevation change in Calgary,
Canada showing the two rivers flowing
through the city.

9. 9
framework which can assess floods of
different magnitudes in Calgary, Canada.
This model is able to show the effects of
many different environmental conditions and
represent that as a flood map quickly. The
model accomplishes this by linking the
output of a hydraulic model (called HEC-
RAS) with a hydrological model (called
STREAM). HEC-RAS calculates the
relationship between water discharges to
water elevation at regular intervals all along
the Bow and Elbow River. While the
STREAM model calculates the discharge
along those rivers based on hydrologic
drivers such as precipitation,
evapotranspiration and temperature of snow
melt. The outputs of these two models are
then combined in another model called
Netlogo. With STREAM calculating
discharge in the catchment and HEC-RAS
calculating the relationship between
discharge and water elevation it is possible to
then create accurate flood maps on the fly.
This gives the model a strong advantage at
stakeholder meetings, as the consequences of
different flood measures can be viewed and
then be revised or rejected immediately. This
is in contrast to traditional methods which
would involve discussing an idea at a
meeting, commissioning a feasibility study
which could take a few weeks and then
reviewing the report produced by the study. It
is difficult to run different flooding scenarios
quickly and so the model discussed in this
report allows stakeholders to quickly get an
answer about the effects of a flood under
different environmental conditions.
Due to how the model works there is also one
other major advantages to using it, which is
that it can model the flood effects caused by
climate change and land use change. This is
due to the STREAM model, which was
designed to analyses the effects of climate
change and land use change (Aerts et al.,
1999). Different climate conditions can be
modelled and then measures to combat them
can be tested. For example, according to the
2014 climate change report by the Natural
Resources Canada team, on average warmer
temperatures and more rainfall are expected
for the country, with increase in extreme heat
and heavy rainfall events (Lemmen et al.,
2014). This could be input into the STREAM
model as an increase in precipitation and
evapotranspiration. The model would then be
run and the consequences for Calgary would
be seen immediately.
In addition, for this project a fly over video
has been created using a design software
called Infraworks, as well that a scale model
3D print of the city of Calgary was made.

10. 10
These are intended to aid stakeholders
visualize the consequences of a flood and
help them make a more informed decision.
The Netlogo model, the Infraworks model
and the 3D print are intended to be used in a
workshop setting together to allow
stakeholders to change a variety of variables
and then see the results of those changes on
the Netlogo model, the Infraworks model and
on the 3D print. The different tools can show
how the flood looks in different ways.

11. 11
3) Literature Review
In June 2013 the province of Alberta in
Canada experienced severe flooding. When
the flood was scaled to Canada, it could be
directly comparable to Hurricane Katrina
(costs equated to 1% of US GDP). Calgary
(Figure 1) is the largest city in Alberta with a
population of 1,149,552 in 2013 (Calgary
Census 2013) and an elevation ranging from
1002m.a.s.l. to 1099m.a.s.l. (Figure 2). This
relatively flat area has been flooded
repeatedly in the past (1979 Fort Calgary,
1897, 1915, 1929 and 2005). Therefore, it is
of great interest to the public to discover what
measures can be installed to prevent future
floods or reduce the cost of their damage.
This promoted studies into the Bow and
Elbow River.
The Bow River has a drainage area of 7,868
km2
at Calgary while the Elbow River has a
drainage area of 1,236km2
below the
Glenmore Dam. Due to the presence of the
dam the discharge of the Elbow River has
been regulated since 1933 while the Bow has
a natural river discharge. There are two
discharge measuring gauges within the study
area, one below the Glenmore reservoir and
another along the Bow just before the Elbow
River joins (Figure 3). Both of these are
maintained by the government of Canada
water office and the data is regularly checked
for errors and irregularities. These gauges
were also in operation during the flood which
occurred in 2013 in Calgary.
On the 17th
of June 2013 heavy rainfall began
falling over the eastern slopes of the
Canadian Rockies, southwest of Calgary. The
rain fell on the mountains which were at that
time covered in dense snow pack. Some areas
were already saturated from less intense rain
which had fallen there previously. The
precipitation melted the snowmelt, which
then in turn increased the volume of water
flowing down the narrow and steep mountain
streams.
Figure 3 – Discharge stations in the Catchment.

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The first effects of the flood were observed in
Calgary on the 20th
of June. At the peak of the
flood the Bow had a discharge of 1750 m3
/s
and the Elbow had 700m3
/s. At the Glenmore
dam water flowed through at 30 times its
usual rate. 110,000 people were displaced
from twenty-six communities and five people
died. A state of emergency was declared on
June 20th
and was not lifted till the 4th
of July.
To represent the City of Calgary
appropriately different types of models were
studied to try and find which one would best
represent both the City while also keeping to
the goal of the project. That being to build a
simplified modelling framework.
3.1) 1D models
A one-dimensional (1D) model assumes that
a series of variables such as velocity and
depth only changes along one axes (Figure
4).
In contrast, a two-dimensional (2D) model
solves for variables along two axes (Figure
5). This means that velocity and depth can
vary along not only the river channel but also
perpendicular to it. Due to it only solving
equations along a single axis one-
dimensional models tend to be faster and
require less data then two dimensional
models. HEC-RAS is a one dimensional (1D)
unsteady flow model and therefore interprets
the river as a series of nodes along a line
(Figure 4). At each node a calculation is
performed which calculates the water
elevation. In the case of HEC-RAS, the ST.
Venant Equations are used (See Equation 1,
2)).
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
+
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
= 𝑞𝑞𝑙𝑙
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
+
𝜕𝜕(𝑄𝑄2
𝐴𝐴⁄ )
𝜕𝜕𝜕𝜕
+ 𝑔𝑔𝑔𝑔
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
+ 𝑔𝑔𝑔𝑔𝑆𝑆𝑓𝑓 = 0
Where A = cross-sectional area perpendicular
to the flow; Q = discharge; ql = lateral inflow;
g = acceleration due to gravity; H= elevation
of the water surface above a specified datum;
Sf= longitudinal boundary friction slope; t =
temporal coordinate; and x = longitudinal
coordinate. The St. Venant Equations are a
combination of the continuity equation
Figure 5 – Two dimensional model
framework
Figure 4 – One-dimensional model
framework.
Equation 1 and 2 – Saint Venant Equations
[1]
[2]

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(Equation 1) and the momentum equation
(Equation 2).
Horritt and Bates (2002) compared the
accuracy of 1D and 2D models for predicting
river flood inundation. They found that HEC-
RAS, which was their representation of a 1D
model, performed well compared to the more
complicated 2D models. This difference in
accuracy was attributed to how the different
models responded to friction
parameterization. 2D models require more
data as there are a greater number of variables
it must model. Those variables must then be
calibrated correctly, but this can be difficult.
In contrast, HEC-RAS and 1D models in
general have fewer variables to calibrate
correctly. HEC-RAS in particular places a lot
of weight on the friction parameters. This
means that while 1D and 2D models can
perform equally well if they are both
perfectly calibrated, a 1D model, which is
perfectly calibrated, can produce better
results than a poor or averagely calibrated 2D
model. This shows that for this project the
friction parameters are incredibly important.
HEC-RAS represents these parameters as
values called manning values, which will be
discussed further in the chapter. To
completely describe the geometry in HEC-
RAS another important parameter is the
digital elevation model (DEM).
A two-meter resolution DEM was used in this
project. Horritt and Bates asserted that with
accurate and high-resolution DEM data even
simple models will perform adequately. This
is a repeated statement found in many other
flood inundation studies Brandt (2016), Cook
and Merwade (2009), Mark et al., (2004).
However, a city can be considered an area of
complex topography. There are obstructions
such as buildings, roads and footpaths that
alter the way water flows. Very high-
resolution data would be required to run the
model, which would then need to be able to
work with HEC-RAS. The ability of ArcGIS
to be able to work with that much data
without crashing is a limiting factor in the
project, as ArcGIS is used to extract the DEM
for HEC-RAS. The two-meter DEM was the
ideal resolution for the project as it allowed
the important topographic features of the
catchment to be adequately represented and
be practically used in ArcGIS. To account for
the effect of obstructions the manning values
will be increased.
3.2) Unsteady Flow assumption
A rivers discharge can be modelled in HEC-
RAS in two ways, either using a steady flow
or an unsteady flow (Figure 6). A steady flow
model means that discharge does not change
over time while an unsteady flow represents

14. 14
discharge changing over time. There are a
number of advantages and disadvantages to
each of these methods in HEC-RAS. Bales
and Wagner (2009) discussed how a steady-
flow model assumes that the flood has been
constant for a period sufficiently long so that
the entire area which could be flooded. Even
if it did not have enough time to flood to that
extent. This would cause the model to
overestimate the inundation area. The degree
of inaccuracy or overestimation would
depend on whether the flood had a very short
time to peak discharge or a more gradual
increase to the peak. A flood with a very short
peak is more likely to be overestimated in
HEC-RAS.
The reason why a steady flow is used in any
project is because it makes it a more stable
model, this has been tested in HEC-RAS and
a steady flow does indeed crash less often and
much less easily than an unsteady flow. This
type of model is also more robust and easier
to calibrate then an unsteady flow model. A
stable model in HEC-RAS means that is
capable of solving the St. Venant equations
without any errors occurring in the equations.
Instability can be due to a number of issues
but they all result in HEC-RAS being unable
to model the catchment correctly.
Working with a steady flow also makes it
easier to produce return period maps. A
return period is an estimation of the
likelihood of a flood occurring in any given
year. For example, a 100-year return period
flood map shows the extent of a flood which
statistically should only have a 1% chance of
occurring that year. The peak discharge of the
flood is input into a steady HEC-RAS model
and that creates a return period map. When
another return period map is required, first
the discharge associated with that return
period is calculated, which is done by
observing the entire discharge dataset of a
catchment and using statistical methods to
calculate the return period discharge values.
If there are missing values in the discharge
data or if there isn’t enough data available,
then there are methods to extrapolate that
data. The next step is then to input the
discharge value into a steady HEC-RAS
Unsteady Flow
Time
Discharge
Steady Flow
Figure 6 – Different ways to model flow in
HEC-RAS.

15. 15
model which produces a return period map
with a different likelihood of occurring. The
inaccuracies which a steady flow produce are
within an acceptable range by most measures.
An issue with these return period maps
however is that they produce a deterministic
flood map as there result.
Merwade (2008) considered the inherent risk
in circulating these types of deterministic
maps. This can be considered in the context
of a homeowner who assumes that their
property has zero chance of flooding if it lies
outside the return period flood map. It could
convince them that they do not require flood
insurance even though those maps may not
have been recently updated or do not take
climate change and land change use into
account. These errors could cause unexpected
property damage. Due to a steady flow
usually overestimating floods however, there
is a sort of buffer which helps reduce the
consequences of climate change and land
change use. Therefore, a steady flow model
produces flood maps that are less accurate to
a specific flood events but could generally
indicate which areas should be more alert
about floods.
The model produced in this report uses the
unsteady flow component of HEC-RAS. It
solves the full, dynamic, 1-D Saint Venant
Equation using an implicit, finite difference
method. The unsteady flow is more
physically correct than then energy equation
used in steady flow according to Goodell
(HEC-RAS blog). Therefore, it should
produce more representative flood maps.
While unsteady flow does make a model
more unstable, with enough data and time, it
can be stabilized to produce more accurate
and representative results. A drawback of an
unsteady flow model is that it requires more
discharge data then a steady flow model.
However, there is plenty of data in the
catchment and therefore an unsteady flow
model was easily the best choice for this
project. It was also important so that rating
curves could be produced for the Netlogo
model.
3.3) HEC-RAS
HEC-RAS stands for Hydrologic
Engineering Centers River Analysis System.
It was developed by the US Army Corp of
Engineers and it is widely used all over North
America. The software can model both
steady and unsteady flow. It computes the
geometry of the catchment with a series of
cross-sections and for this project, HEC-RAS
was used to create a series of rating curves all
along the Bow and Elbow River at each
cross-section under an unsteady flow. The

16. 16
HEC-RAS model creates the rating curves by
solving the Saint Venant equations
formulated for natural channels (Brunner,
1995) (See Equations 1 and 2). The main
variable that controls how well the HEC-RAS
model performs is a friction component
called the manning value. This meant it was
important to ensure reasonable and accurate
values for the catchment to prevent
equifinality. i.e. calibrating the model to one
flood event but there being many different
configurations of the friction values that
produce the same result.
As Hicks and Peacock (2005) discuss HEC-
RAS is widely used in Canadian practice and
many water resource engineers have some
familiarity with it. It is also a standard tool for
floodplain delineation studies, which means
that most government agencies already have
a HEC-RAS model for many of the river
basins in their areas. With relative ease these
models could be repurposed to create,
unsteady flow models such as the one
discussed it this report. These models would
then be run and produce rating curves for that
catchment and then be input into the Netlogo
model.
3.4) Rating Curves
A rating curve shows the unique relationship
water discharge has to water elevation at one
specific location along a river. HEC-RAS
was used to create these rating curves along
the Bow and Elbow River. Rating curves
would traditionally have been constructed by
measuring the discharge of a river and the
water elevation at one location. This would
then be repeated under different flow
conditions at different times. The data would
be collected and placed on a single graph with
a logarithmic function fitted to it. This
allowed the data to then be extrapolate. While
this method does produce the best rating
curves it is a time consuming process and it
would not be practical to produce the
hundreds required for this project using the
traditional method. This is why HEC-RAS
was used to create them.
The HEC-RAS rating curves are created by
calculating the water elevation and relating it
to the discharge values that were input into it.
Reistad (2007) compared the HEC-RAS
computed rating curves with traditionally
derived rating curves for a catchment in
Norway. Their results showed that the HEC-
RAS rating curves tended to reliably model
the higher water levels but at lower stages
they were inaccurate. However, for that study
they only modelled two months of
measurements in July and August. An entire
year of data may have produced better results
in the rating curve. The goal of this project is

17. 17
to model floods (i.e. high discharge values)
and therefore the inaccuracies at lower
discharges should not be a big issue. Another
issue with modeling rating curves is data
availability. Domeneghetti (2012) discussed
rating curve uncertainty and its effects on
models. They identified that the effectiveness
of models to represent the hydraulic behavior
of rivers was closely related to the
availability and the reliability of stream flow
data. Poor data collection could cause
inaccuracies in rating curves. These could
appear either due to uncalibrated gauges or
too great of a time step between
measurements. For this project, there were
daily discharge values at both rivers and both
were checked regularly for errors or
inconsistencies. Another source of error that
Domeneghetti discussed was that the cross-
sectional geometry of the rating curve
location may change over time. This could be
due to erosion of the banks and channel or
sediment deposition at slower parts of the
river. These processes make the rating curve
less accurate over time, with big events like a
flood perhaps causing them to become
problematic immediately due to massive
sediment transport. Due to the rivers flowing
through the city there is effort to prevent the
river from eroding the surrounding property.
3.5) Manning values
HEC-RAS calculates the water elevation
along the Bow and Elbow River. There are
three data types required by the model which
are, geometry data representing the
catchment, discharge data and friction values
otherwise known as manning values.
Geometry data was obtained from the City of
Calgary as two-meter DEM data and the
discharge data was available from the water
office of Canada website. However, for the
manning values there is no preexisting data
available for Calgary. Therefore, it is the
friction values that require the most focus
when they are input into HEC-RAS.
Within the Calgary catchment, the floodplain
can vary from thick forest and brush to urban
areas with buildings and roads right along the
river. Each of these areas have different
manning values and they can quickly
alternate along both rivers. HEC-RAS only
allows three manning n values at each cross-
section. One on the left of the channel, one in
the channel and one on the right of the
channel. This means that even if the
floodplain consists of a mix of trees, roads
and buildings only one value can be chosen
to represent that side making it difficult to
find the right value. Syme (2008) observed
that it was difficult to represent the myriad of

18. 18
flow behaviors that occurs as water flows
down roads, through/over/under fences and
around/through houses. The issue of how to
treat buildings in the model is especially
important. Buildings do not tend to cover the
full extent of a floodplain but have a very
strong effect on the flow and therefore have a
very high manning value. While asphalt
roads in-between properties can have a very
low manning value. This mix can cause
calibration issues. In Figure 7 there is clearly
a complex floodplain that consists of areas
with very high manning values (such as
buildings and trees) and very low manning n
values (such as roads covered in asphalt and
short grassy areas). To model these features
on the floodplain three methods were
discussed by Syme, 1) block out the building
i.e. define a location with a building as a no
flow area 2) Alter the energy loss coefficients
3) Increase the manning values. Due to the
number of buildings in the city and the lack
of data on building location the blocking out
method was ruled out. The energy loss
coefficient was also determined to be
unsuitable as no previous literature was found
to help pick representative values. This left
increasing the manning values as the most
viable option. The guideline values for
increasing manning values were based on
Syme (2008). However, Syme used 2D
modeling and was able to divide the
floodplain into more than three manning
values per cross-section. The 1D aspect of
HEC-RAS does not allow this and so the
effect of buildings, roads, shrubbery, etc. had
to be rolled up to one representative figure.
The accuracy of the manning values were
determined based on how well they recreated
the 2013 flood event. If there were areas
which under or overestimated the flood the
manning value at that location was changed
to better match the flood.
Figure 7 –The Bow River flowing through Calgary.
Arrow shows direction of flow. Source: John
Lehmann/The Globe and Mail.

19. 19
4) Methodology
The overview of the steps of the model can
be seen in Figure 8. There are three major
aspects in the building of this model. HEC-
RAS creates a series of rating curves along
the Bow and Elbow River. The STREAM
model creates discharge for the catchment
and finally the output of both these models
are tied together in an agent-based model
called Netlogo. At the junction where the
Bow and Elbow River meet, a backwater
effect can be observed in the rating curves. To
model this correctly in Netlogo, 3D rating
curves were built and implemented into the
model.
4.1) HEC-RAS
HEC-RAS is used to generate rating curves
all along the Bow and Elbow River. To create
representative curves three inputs are
required, geometry data, discharge data and
manning values. Geometry data was
processed by using an add-on in ArcGIS
called HEC-geoRAS. This simplified the
inputting of data and it integrates seamlessly
with HEC-RAS. Two-meter resolution DEM
was obtained from the government of
Calgary and then loaded into ArcGIS. From
that data four layers were drawn, which were
the river, the banks, the flow-paths and the
cross-sections (Figure 9). The rivers, banks
and flow paths layers are drawn from the
upstream part of their respective rivers to the
downstream direction and the last layer to be
drawn is the cross sections. There are rules to
follow when drawing the cross-section
otherwise, HEC-RAS cannot interpret the
cross-sections correctly. Some of these rules
are to draw them left to right, when facing the
downstream direction, make sure to cover the
full extent of the floodplain, they must be
perpendicular to the flow paths and in areas
with a steep incline, there should be more of
them drawn closer together. Poorly drawn
cross-sections create instabilities in the
Figure 8 – Overview of the methods used in
this report.

20. 20
model and it is then difficult to identify the
problems within HEC-RAS. It is a time
consuming process to fix these issues. At this
point, a review of all the cross sections is
required to make sure they accurately
described the actual geometry of the
catchment.
Next, three manning values are attributed to
each cross-section, one for the left overbank,
one for the channel and one for the right
overbank. There are 516 cross-sections along
the rivers meaning 1548 manning values
were required for this catchment. To gain
insight into which values best represented
each area a fieldtrip to the City of Calgary
was arranged. On the 25th
to the 26th
of
February a trip to the city of Calgary was
made to view both rivers as well as the areas
most affected by the flood. High-resolution
aerial photography before the flood was also
obtained. With both the fieldtrip and the
aerial photography, it is possible to create a
series of manning values to describe the
catchment which is representative of
floodplains in 2013.
At the upstream portion of the Elbow River,
there are steep but small banks with well-kept
vegetation on either side of the channel.
These short grassy green areas tend to be flat
and allow water to flow over it without much
resistance that gives it a low manning value.
The river channel itself can be described as a
winding stream, which is clear of debris and
vegetation. As the water flows further down
the stream, residential areas begin to appear
on either side of the river. These buildings
tend to present a problem as to how to
accurately describe them with a manning n
value. The smooth asphalt lets the water pass
without much resistance but the buildings act
as a barrier to the flow. To give an accurate
account of the effect of buildings in a
floodplain a much higher manning n-value is
used in the downstream portion of the river,
especially at the junction where the Bow and
Figure 9 – DEM showing all the HEC-RAS
layers.

21. 21
Elbow meet. This value is then calibrated
based on how well it fit the actual 2013 flood
extent shape file, which was provided by the
City of Calgary. At the upstream portion of
the Bow River, the channel is wider than the
Elbow River and has a much greater
discharge. The floodplain tends to vary from
heavy vegetation too residential areas. Much
like the Elbow, as it flows towards the
junction the floodplain becomes more
urbanized and therefore, has higher manning
values. Further downstream past the junction,
the channel begins to become vegetated and
less populated again. This drops the manning
value down again. Once all the cross-sections
have a manning value, the geometry data is
finished and is ready to be used in the next
step of HEC-RAS.
The next step is to input the discharge data for
the 2013 period. The daily discharge data was
downloaded from the government of
Canada’s water office and placed into HEC-
RAS.
Figure 10 – Bow River hydrograph
Figure 11 – Elbow River hydrograph

22. 22
The start time for the simulation is set at the
1st
March to the 31st
of August. Any longer
time period tends to cause HEC-RAS to
crash. This time frame covers the 2013 flood
event but also the average discharge in both
rivers. This is so that a large series of
conditions could be modelled by HEC-RAS,
not just the peak flood of 2013 (Figure 10 and
Figure 11). The final phase of this simulation
is then to run the model. The computational
interval is set to one hour which means it
calculates the water elevation at the Bow and
Elbow River at every cross-section for every
hour within the simulation period. The short
computation interval also helps make the
model more stable then a longer
computational interval would.
When the model is finished running it
produces an inundation map, as well as rating
curves at each cross-section. At this point, the
manning n values need to be calibrated. To do
this the inundation area created by HEC-RAS
is exported to ArcGIS so that it can then be
compared with the actual 2013 flood area.
Locations with too low a water level are
given a higher manning value in HEC-RAS.
Areas with too high a water elevation are
given a lower manning value. Once these
differences are identified the HEC-RAS
model is run again with the updated manning
values. This produces a more accurate
inundation map then the previous HEC-RAS
run. The results of this run are then used in
the next step.
The rating curves at each cross section are
exported out of HEC-RAS and then copied
into Excel. This is to convert the series of xy
points which HEC-RAS created, into two
equations to describe the rating curve (Figure
12).
Two equations are required to describe the
curve fully. One to define the area within data
set (a fifth order polynomial equation was
used) and the other is used to extrapolate the
curve (the logarithmic equation). The reason
two equations were required is due to an
inflection point along the curve (Figure 12).
This point prevents one equation being able
to describe the entire curve accurately. Any
one equation used either accurately describes
the lower part of the curve but miss the higher
discharge or vice versa. The source of the
Inflection
Point
Figure 12 –Rating curve for a cross-section
along Bow River.

23. 23
inflection is because the rate of water
elevation change is reasonably constant
within the banks of the channel. However,
once the water spills over the banks of the
river a much greater volume of water is
required to increase the water elevation by
the same height as before. This means there
is a different rate of water elevation change
and this is where that inflection point is
located. The extrapolation curve (the
logarithmic equation) fits the data after that
inflection which helps create a smooth
logarithmic curve.
4.2) Three-dimensional rating curves
Areas next to the junction where the Bow and
Elbow River meet experience a strong
backwater effect according to HEC-RAS
(Figure 13). The Bow River clearly has a
strong effect on the water elevation of the
Elbow and this can be seen along the first
twenty-two cross-sections next to the
junction along the Elbow River (Figure 14).
In order to model this effect correctly in the
Netlogo model a method was designed to
show the backwater effect. This is done by
running the HEC-RAS model with the Bow
River having a steady discharge while the
Elbow River has an unsteady discharge. This
creates rating curves along the Elbow that
described the water elevation of the Elbow
changing when the Bow had a specific steady
flow. The Bow steady flow is first set at a low
discharge value of, for example 10m3/s. Then
the Elbow discharge has its naturally
changing discharge input into HEC-RAS.
This means that the effect which the Bow has
on the Elbow is a constant. It allows the
0 100 200 300 400 500 600 700
1034
1035
1036
1037
1038
1039
1040
1041
1042
Project_021 Plan: 3D_Rating_Curves 11/07/2016
River = Elbow Reach = Full
Q Total (m3/s)
W.S.Elev(m)
Legend
W.S. Elev
Figure 13 –Rating curve that shows the effect
of backwater effect at the Elbow River.
Figure 14 – Twenty-two cross-sections show
the backwater effect along the Elbow River.

24. 24
change in the water elevation along the
Elbow to only be affected by the changing
discharge of the Elbow River. After this the
rating curves are exported out of HEC-RAS
and the model is run again. But this time the
Bow River is set to a higher constant
discharge values of 20m3/s. The rating
curves produced are again exported out and
then added to the rating curves produced in
the first run. This is repeated ten times with
the Bow discharge having a different value
during each run. When each of these curves
are then placed on a three-dimensional plot
they show how the Elbow Rivers water
elevation changes not only due to its own
discharge but also due to Bow River
discharge. To help Netlogo read the data, the
curves are converted into a series of x, y and
z points. This makes it into a look up table
that Netlogo can easily read and quickly find
the correct water elevation. Finally, these
tables are imported into Netlogo so that it can
calculate the water elevation at each cross-
section accurately while also considering the
backwater effect.
4.3) Netlogo
Netlogo is an agent-based model which was
used in this project. This program uses agents
which can be programed to follow a set of
instructions. There are four types of agents in
Netlogo which are called turtles, patches,
links and observer. Each of these agent types
have unique features to them specifically and
they also interact with each other uniquely.
Turtles are agents which can move around in
the Netlogo world which is divided up into a
grid of other agents called patches. Links
connect two turtles together and finally there
is the observer which doesn’t have a location.
It observes the Netlogo world and gives the
other agents instructions on what to do.
Netlogo was designed with a specific type of
model in mind: mobile agents acting
concurrently on a grid space with behavior
dominated by local interactions over short
times (Railsback et al., 2006). This made it an
ideal program to build this model on. The
discharge acts as a mobile agent which moves
through the catchment (or from Netlogo
perspective through the grid space). While
the rating curves interacted with the
discharge on a local scale. When the
discharge and the rating curves are combined
they show the water extent at that location.
For Netlogo to be able to translate the water
discharge to a water elevation, first each
cross-section along the Bow and Elbow River
is given two equations or in the case of the
backwater cross-sections a lookup table.
These describe how the water elevation
changes under various discharges at that
specific cross-section. The 3D rating curves

25. 25
cannot be easily converted into equations and
so those cross-sections are attributed with a
look up table which consists of a list of
coordinates made of an x-axis, y-axis and z-
axis columns (x-axis = Elbow discharge, y-
axis= Bow discharge and z-axis = water
elevation). To find the correct water elevation
Netlogo searches through this list and finds
which two discharges match and then outputs
the water elevation.
Next, the DEM data is input into Netlogo.
The catchment is divided up into patches in
Netlogo and an attribute called elevation is
added to every patch. This describes the
topography of the catchment to the Netlogo
model. The patches surrounding the cross
section lines are linked (Figure 15). When a
discharge value reached a point in the river
with a cross-section it calculates the water
elevation using the rating curve equations or
the look up table. It then looked at the patches
linked to that cross section and if the water
elevation is higher than the elevation of that
patch all of those patches are considered to be
flooded. To reduce the processing burden on
the model a maximum of ten patches away
from the cross-section are set. This ten-patch
limit prevented the model from performing
unnecessary and time-consuming
calculations and therefore helps it run faster.
Figure 15- Patches associated with cross-
sections. Each colour is linked with one cross-
section.

26. 26
5) Results
Table 1 shows the accuracy of the HEC-RAS
model compared to the 2013 flood and
another flood that occurred in Calgary in
2005. While the 2005 flood event was less
extensive than the 2013 event, it provides a
good validation test for the HEC-RAS model.
The visual check of the inundated areas is in
Figure 16. It shows which areas did the best
job modelling the flood and which areas did
poorly. The downstream portion of the Bow
River is especially well represented, even
though there are small isolated areas which
overestimate the flood. At the upstream
portion of the Bow River the flood is also
well modelled with the majority of the actual
flood and the HEC-RAS flood overlapping
each other. Along the Elbow however there is
less overlapping of the two layers. This is
especially true where the Elbow River meets
the Bow River. This difference could be due
to that area being a part of downtown Calgary
and so there are many obstacles and
obstructions along the flood path. This
creates complex flow patterns is difficult to
model in HEC-RAS. Overall the HEC-RAS
model has a 93% hit which means that 93%
of the actual flood raster cells are overlapped
by the modelled raster cells. This is a positive
result which indicates the model does an
accurate job of representing the real world
event. The 16% miss, which indicates how
many raster cells do not have overlapping
cells, is most likely predominately due to the
areas along the Elbow River in which the
HEC-RAS model especially overestimates
the flood. The overestimations along the
upstream and downstream Bow also
contribute to the miss figure but not to the
same degree as the Elbow River. After
checking the accuracy of the HEC-RAS
model, the Netlogo Model was run to
compare the actual 2013 flood with the
Netlogo model of the 2013 flood. To do this
the peak discharge values for the Bow and
Elbow, were run through the Netlogo model,
which produced Figure 17.
2013 Flood 2005 Flood
Raster Cell No.
Actual
flood extent 3394957 162374
Modelled
flood extent 3708008 194779
Overlapping
cells 3157184 136718
Missing
Cells 550824 58061
Accuracy
Hit 93% 84%
Miss 16% 36%
Table 1 – A hit and miss table showing the
accuracy of the HEC-RAS model under two
different flood events.

27. 27
At this time the Netlogo model is still being
developed however, there are clear
similarities between Figure 16 and Figure
17. Especially at the downstream portion of
the Bow River. It seems that the shape of the
HEC-RAS modelled flood made it across to
the Netlogo model. The major difference
between the two being that the Netlogo
model is slightly less extensive. This is a
positive result, as the HEC-RAS model did
seem to overestimate the size of the actual
flood. This is especially clear at the upstream
portion of the Bow River and the area with
the red box drawn around it. Other areas still
need to be debugged. For example, the Elbow
River still overestimates the flood in some
areas, but in different ways to the HEC-RAS
version (blue boxes in Figure 16 & Figure
17). At this site, the Netlogo model seems to
have flooded an area on the other side of
elevated land. In the actual catchment, the
water would not be able to reach it but the
Netlogo model just sees it as an area of lower
elevation to be flooded. This is an effect that
will be programed out of the model. Overall,
as preliminary results, these flood maps look
Figure 16- Comparison between inundated
areas for the 2013 flood. Blue box shows
areas which require some work.
Figure 17- Netlogo run of the 2013 flood.
Red square shows area of improvement
compared to HEC-RAS model

28. 28
very promising. Unfortunately, the water
elevation for the 3D rating curves could not
be visually shown on the Netlogo model at
this time. The model can however look up
what the water elevation should be at specific
cross-sections when it is manually checked.
The next step would be to show that visually
in the Netlogo model.
To validate the HEC-RAS model another
flood event has been used to see how well the
model runs when it is not calibrated to that
specific event and so the 2005 flood event
provides an excellent test case. The Bow
River once again seems to be well
represented in the HEC-RAS model (Figure
18). The upstream Bow especially does not
seem to have major differences and perhaps
even models the Bow even better than it did
for the 2013 flood event. This is most likely
due to the 2005 flood being smaller and so
there is less flood area to be incorrectly
modelled. In contrast, the Elbow River
performs worse than before. The same area
seem to be overestimated again but this time
the difference between the actual 2005 flood
and the HEC-RAS model is more striking
(Red box in Figure 18). The accuracy of the
HEC-RAS model this time was an 84% hit
(Table 1). However, the miss is 36%, which
is a much higher difference then between the
hit and miss for the 2013 flood model. This
increase in inaccuracy can be attributed to
one area that most likely skews the results.
Along the Elbow River, one area
overestimates the flood much more than any
other location (Red box in Figure 18). This
area also overestimated the flood in the 2013
event but because that flood was larger, there
was a comparatively smaller difference
between the model and the actual then in the
2005 HEC-RAS model. This area will need
to be reevaluated to see what can be done to
better model it.
The next step is to view how the Netlogo
model behaves when the 2005 flood event is
inserted. The peak discharge values for the
Bow and Elbow River during the 2005 event
are input into the Netlogo model, which were
600m3/s and 250m3/s respectively and it
produces the results in Figure 19. Once
again, just like in the 2013 event, the Netlogo
model seems to produce a less extensive
flood then in the HEC-RAS model and it
seems to fit the actual flood event better. The
Bow River, both upstream and downstream,
is very well represented and modelled. The
Elbow River seems to be less extensively
flooded then in the HEC-RAS model but is
still much larger when compared to the actual
flood extent (Figure 19, red bow).

29. 29
Figure 20- 3D rating curve at one cross-section along the Elbow River.
Figure 18- Comparison between inundated
areas for the 2005 flood. The red box shows
an area which HEC-RAS has overestimated
the flood.
Figure 19- Netlogo run of the 2005 flood.

30. 30
Therefore, the Elbow River needs to
reevaluated to find out what measures can be
taken to make it more representative of actual
flood events.
Each of the cross-sections in Netlogo have a
unique rating curve attached to it. There are
however, twenty-two cross sections that need
to take the backwater effect into account
(Figure 14). These are implemented into
Netlogo as long lookup tables. To show the
backwater effect more effectively the data in
the lookup table was used to create a graph
(Figure 20). From this data it is clear to see
that when the Bow River has a high discharge
the water elevation along the Elbow is also
higher. For example, if the discharge at a
point along the Elbow River is 100m3
/s the
water elevation could be between 1037.9
meters and 1039.8 meters based on if the Bow
discharge is 10m3
/s or 1000 m3
/s. That is a
difference of almost two meters with the only
variable being what the discharge of the Bow
River is next to the Elbow. As the discharge
at the Elbow becomes higher, there is less of
a change in water elevation due to the Bow.
For example, if the Elbow River has a
discharge of 600 m3
/s the difference in water
elevation is one meter, with the BOW River
having a variable discharge of 10m3
/s to 1000
m3
/s. This is most likely due to the Elbow
River having more energy at higher
discharges and so the Bow River cannot exert
as strong a backwater effect as it can when
the Elbow has a lower discharge. The
Netlogo model has had the 3D rating curves
implemented and when they are tested by
inserting a discharge into the Bow and Elbow
River, the correct water elevation is
produced. For example, the Elbow discharge
was set at 100 m3
/s and the Bow set to 200
m3
/s. When the model is run, the cross-
section returned a value of 1038.9 m.a.s.l.
The lookup tables worked correctly. The next
step for these 3D rating curve locations is to
try to have the water elevation be visually
represented on the Netlogo model.

31. 31
6) Discussion
The results show that the Netlogo model has
done a good job of modelling the 2013 and
2005 floods. There is some calibrating
required but overall the model performs well
at this stage of its development. The problem
area along the Elbow River is most likely due
to that location having a very flat topography.
The DEM used by HEC-RAS and Netlogo is
unable to notice the small increases in
elevation, which in the actual flood stop the
water from advancing but in the models just
show a flat topography with no obstacles for
the water. The model may be improved if a
higher resolution DEM is used so that it
includes those subtle changes in the models.
For now, however the Netlogo model is able
to represent the parts of the Elbow and the full
length of the Bow quite well. Therefore, the
model must be compared with other models
created for the City of Calgary.
6.1) Golder Report
After the City of Calgary experienced the
severe flooding in 2013 they commissioned a
report from Golder Associates (Golder
Associates, 2015). The goal of the report was
to update the 2012 flood maps that they had
produced previously. For the project they
used HEC-RAS as they did in 2012 but this
time included new bridges in the city which
were installed after 2012, they included an
updated DEM and created another set of
flood maps which showed the areas which
would be flooded if critical control structures
failed. To do this they used there HEC-RAS
model in a steady state. This is in contrast
with how HEC-RAS was used in this project,
which was as an unsteady flow model. The
Golder report consists of thirteen return
period flood maps of 2,5,8,10,20,35,50,75,
100, 200, 350, 500 and 1,000 years. They also
calculated the corresponding return period
discharges for these events. The biggest
advantage the method Golder used was that it
produces clear deterministic maps, which
showed the effects of a specific flood event
on the City of Calgary. The maps were
produced using the best data available and
great expertise was used to create and update
their HEC-RAS model. However, the
drawback that their results have is that it does
not consider a changing environment very
well. For example, the effect of land change
use or climate change.
The dynamic model discussed in this report
intends to be used in addition to the maps
produced by Golder. While the Golder report
shows the impact of many different return
periods, they can only show the conditions
under which they were modelled at the time.
In other words, the effect of land change use

32. 32
cannot be observed in the static Golder maps.
If the change in land use were to be included
in the Golder report, another study would
have to be commissioned. It would also be
time consuming to compile the new data to
create the updated flood maps. In contrast,
the dynamic model discussed in this report
can much more easily accommodate these
changes in the initial conditions. For
example, if an area of trees were proposed to
be cut down and converted to agricultural
land, the question may be, would this
increase the likely hood of a flood or worsen
there effect? The dynamic Netlogo model can
quickly produce a map showing how much
the catchment is inundated, when the trees are
present in the catchment and then contrast it
with the inundated area with the agricultural
land. By combing the Golder maps with the
dynamic Netlogo, model stakeholders can
make more informed decisions about the
catchment quickly.
6.2) Infraworks and 3D print
An important part of this project is
communicating the consequences of a flood
to stakeholders, policy makers and to the
public. To aid in this a design software called
Infraworks has been used to create a flyover
video of a digital representation of the
catchment. It shows the effect of floods under
the modelled conditions with high quality
imagery with a 30cm resolution. This has
been input into Infraworks as well as the two-
meter DEM used previously in HEC-RAS.
By combining these two in the software,
along with a shape file of the modelled flood
extent from HEC-RAS, it is possible to show
the effects of the flood at different scales.
Many different 3D objects can be placed in
the Infraworks program. For example,
different styles of houses, a variety of
vegetation, vehicles, etc. With all of these
features working together, a realistic looking
city has been built in Infraworks. By then
adding different flood scenarios the extent of
flooding can be seen both on the small scale,
like street level, but also at the full catchment
scale. This program helps policy makers see
more clearly the consequences of different
flood measures, as well as view how different
areas are effected by the same flood event. It
also helps homeowners view how their
property would be effected in the event of a
flood.
In addition to the fly over video from
Infraworks, a three-dimensional print has
been created of the Calgary catchment, with
the same DEM data as that used in HEC-
RAS. The Netlogo model, the Infraworks
model and the 3D print are all intended to
work together. The Netlogo model produces
a flood extent based on some variable that

33. 33
stakeholders want to view the results of, for
example, how big of a flood would occur if
the precipitation increased by 5%? Then the
data from Netlogo is imported into the
Infraworks model to view the results at
different scales and finally a projector is set
up above the 3D model, which projects the
flood extent image on the 3D print. This
shows the modelled flood changing over time
as it runs an animation of the flood
developing and then receding. It is intended
to give stakeholders more ways of looking at
the data.
6.3) Recommendations
There are two points, which may improve
how the model functions. First, an area that
needs to be looked at closer is along the
Elbow River. The model seems to
overestimate its inundation level more than
along the Bow River. Especially when the
2005 flood is concerned. This could be due to
the DEM representing the Elbow River
floodplain as a much flatter area relative to
the Bow River floodplain. Therefore,
relatively small increases in the water
elevation may cover much more ground than
would be expected in the model. While in the
actual catchment, obstructions and small
elevation increases stop the water from
progressing further. To resolve this issue
higher resolution DEM in Netlogo can be
used. This may increase the time it takes to
model the catchment but the increase in
accuracy would be worth it.
Another aspect of the model that could be
improved is dealing with the isolated water
bodies that are produced by Netlogo. In some
locations, water appears where it is
unconnected to the river. This is because
Netlogo recognizes that area as being below
the water level calculated by the model
however; when the topography is examined
there could be a hill which prevents the water
from flowing into it. To fix this Netlogo can
be programed to only inundate areas if it is
connected to the main body of the river. This
would prevent isolated flooded areas from
appearing in the area.
The next step that the model could take would
be to model the effects of climate change. The
STREAM model will calculate the discharge
in the catchment based on conditions like the
precipitation, snowmelt and
evapotranspiration. If these conditions were
altered to include the effects of climate
change, it could predict the effects of a whole
host of different environmental conditions.
For example, if the precipitation was 10%
higher and the evapotranspiration dropped by
5% what effect would that have on floods in
Calgary? These kinds of scenarios could be

34. 34
fed into the model and help inform
stakeholders about the consequences of
climate change and show them the effect of
measures in the catchment.

35. 35
7) Conclusions
In this study, a simplified modeling
framework has been developed which is
capable of quickly creating flood maps of
different magnitudes in Calgary, Canada.
This model has been built in response to the
increasing frequency of floods around the
world. The Netlogo model is intended to be a
part of a flood framework and will be used to
identify areas that require flood measures as
well as be capable of modelling different
flood conditions quickly. This is in contrast
to alternative modelling methods that take
longer to run and produce results. This model
is intended to produce results quickly, give
stakeholders more chances to view different
flooding scenarios and to test different flood
prevention measures, which can save them
both time and money. It is on these points
where the Netlogo model stands out from
other flood models.
To design this model HEC-RAS has been
used to create a series of rating curves all
along the Bow and Elbow River. Due to
HEC-RAS’s widespread use, especially in
North America, many people have used it
previously. It is also a standard tool for
floodplain delineation studies in Canada; so
many government agencies already have a
HEC-RAS model for many of the river basins
in Canada. This means that rating curves for
these other HEC-RAS models can be
implemented into the Netlogo model quite
easily, lessening the amount of work it would
require to setup the model in another
catchment.
With the Netlogo model at this stage it is
possible to calculate the water elevation for
all of the cross-sections in the catchment as
well as, those effected by the backwater
effect and it can produce a flood map quicker
than the time it takes HEC-RAS to complete
a full run. When the HEC-RAS model was
run to produce the rating curves created for
this project, the total time it took was two and
a half hours. In contrast, the Netlogo model
took approximately five minutes. That
includes the time it took for the cross-sections
to calculate its nearest patches, for it to then
calculate the water elevation at all 516 cross-
sections and finally find out which
surrounding patches would be inundated with
water. The difference in processing time is
huge and the model will clearly save time.
With more optimization, the model will only
get faster.
An important feature of the model is its
ability to account for the backwater effect.
The 3D rating curves are especially
significant when the City of Calgary is
concerned, as they are located along
downtown Calgary. This is a dense, urban

36. 36
area which if flooded would cost millions of
dollars in damages. The 3D rating curves are
intended to accurately model that location
and ideally help reduce the cost of a flood if
it hit the city.
The results show that for the 2013 flood the
HEC-RAS model produces a 93% hit and
only a 16% miss. Which means that the
model has a small tendency to overestimate
floods. This was also seen when the 2005
flood event was run through the model, which
has an 84% hit and a 36% miss. This
difference in accuracy between the 2013 and
the 2005 can be attributed the issue of the area
along the Elbow River overestimating the
flood substantially. This problem is localized
to one area however, and the rest of the
Elbow and the entirety of the Bow River
seem to have been modelled quite well and is
representative of the actual flood event, both
the 2013 and the 2005 event. When the peak
discharges of the Bow and Elbow River are
input into the Netlogo model it produces a
similar inundation map as that produced by
HEC-RAS, it does not however take as long.
This comes back to what the goal of the
project is, to develop a simplified modelling
framework that could be used by stakeholders
to quickly obtain results.
To further aid stakeholders a fly over video
of the catchment has also been created using
a program called Infraworks. This video and
the program itself is intended to show the
consequences of a flood in a different way to
those concerned. Rather than there just being
a 2D map with a set scale, the Infraworks
model is able to help show the catchment at
different scales and give a better sense of
what the effects of a proposed flood measure
would be on the catchment. A 3D print has
also been created. It shows the entire city of
Calgary, the Bow and Elbow Rivers and even
roads and railways lines. It is intended to give
stakeholders a physical representation of the
catchment and show the inundated area. The
projection onto the 3D print allows them to
be able to physically touch the catchment and
perhaps get a better sense of how the flood
measures effect the catchment.
All of the products developed and created in
this project, the Netlogo model, the
Infraworks model and the 3D print are
intended to be used together in a workshop
setting. Where stakeholders can change
variables in the Netlogo model such as, view
the effects of climate change, and then
quickly obtain meaningful results. Those
results would then be transfer over to
Infraworks. So that a fly over the catchment
can be seen and have them see how the results
look at both the catchment scale and on the
local scale and finally send that data onto a

37. 37
projector which will show how the flood
develops on a 3D object. This will give them
a clear view of what the consequences of their
decisions will have on the catchment as well
as allow them to quickly see results, which
will save a lot of time and money.

38. 38
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