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INTEGRATING LIDAR AND FLOOD SIMULATION MODELS IN DETERMINING
EXPOSURE AND VULNERABILITY OF BUILDINGS TO EXTREME RAINFALL-INDUCED
FLOOD HAZARDS
Jojene R. Santillan, Meriam Makinano-Santillan, Linbert C. Cutamora
CSU Phil-LiDAR 1 Project, College of Engineering and Information Technology, Caraga State
University, Ampayon, Butuan City, 8600, Agusan del Norte, Philippines
ABSTRACT
LiDAR-derived Digital Terrain and Surface Models
(DTM/DSM) and flood simulation models were used to
determine exposure and vulnerability of building to various
flood hazard scenarios caused by extreme rainfall events in a
river basin in Mindanao, Philippines. The methodology
consist of (i.) generating a database of buildings from the
DTM and DSM; (ii.) generation of flood depth and hazard
maps through the use of a flood simulation model; and (iii.)
spatial overlay analysis utilizing the building database and
flood maps to determine a building‟s exposure and
vulnerability. This study highlights the importance of
combining high spatial information from LiDAR with
simulation model to generate informative maps showing the
exposure and vulnerability of buildings to flooding.
Index Terms—LiDAR, flood hazard, buildings,
exposure, vulnerability
1. INTRODUCTION
Flooding is one of the most destructive natural disasters. In
the Philippines, flooding due to overflowing of rivers caused
by excessive quantity of rainfall brought by tropical storms
has caused loss of lives and damages to properties as well as
to infrastructures like buildings, roads and bridges [1]. In the
advent of climate change which has caused changes in the
frequency, intensity, spatial extent, duration, and timing of
extreme weather and climate events [2], the need to be more
prepared for flood disasters is becoming more urgent. Flood
simulation models are considered important tools in
simulating and assessing in an advance manner the impacts
of various flood scenarios [3]. Because simulation models
can depict the depth, duration and extent of flooding event,
it allows identification of areas or elements exposed or at
risk to flooding, estimation of the element‟s vulnerability,
and even calculation of possible economic damages [3],
among many other uses. Buildings are the most common
element at risk during a flooding situation. When they get
inundated, residents are forced to evacuate. Hence, it is
crucial to identify in advance the specific buildings that can
be affected by an expected flooding scenario in order to
prevent casualties through evacuation, or to lessen the
impact through conduct of flood mitigation activities. In this
situation, the availability of a building database where each
building is attributed in terms of type (e.g., residential,
commercial, government, educational, etc.), and height
(among many other attributes) can make the required hazard
exposure and vulnerability assessment fast, efficient and
informative.
Figure 1. The Cabadbaran River Basin, Agusan del Norte,
Philippines, shown here in a Digital Surface Model.
In this paper, we present an approach involving the use
of high spatial resolution LiDAR-derived datasets and flood
simulation model to assess the impacts of flooding caused by
various scenarios of extreme rainfall events, with focus on
systematic determination of exposure and vulnerability of
building to these hazards. We implemented the approach to
the Cabadbaran River Basin (CRB) in Agusan del Norte,
Caraga Region, Mindanao, Philippines (Figure 1). With a
total area of 238 km2, this river basin covers an urbanized
portion of Cabadbaran City, with the Cabadbaran River
traversing the city. Many areas in the city were reported to
have been affected by flooding during the onslaught of
Tropical Storms Lingling (Local name: Agaton) and Jangmi
(Seniang) in January and December 2014, respectively [4].
7585978-1-5090-3332-4/16/$31.00 ©2016 IEEE IGARSS 2016
The methods and results presented in this work are an
update of what we have previously shown in an earlier work
[5], where we have discussed building database generation,
and hazard and vulnerability determination. In the present
work, we present a complete discussion of the methodology
which consist of (i.) generating a database of buildings from
the DTM and DSM; (ii.) generation of flood depth and
hazard maps through the use of a flood simulation model;
and (iii.) spatial overlay analysis utilizing the building
database and flood maps to determine a buildings‟ exposure
and vulnerability.
2. METHODS
2.1. Building Database Generation
We used 1-meter resolution LiDAR-derived DTM and DSM
for extracting the building features within the river basin
(Figure 2). Buildings were manually digitized from the
LiDAR DSM using ArcGIS 10.1 software. The extracted
buildings were saved as a GIS Shapefiles. The heights of the
buildings were calculated as the difference between the
average of the base elevations and top elevations for each
footprint. The base and top elevation values were extracted
from the DTM and DSM, respectively. Field surveys,
familiarity with the area, and free online web maps such as
Wikimapia (http://wikimapia.org/) and Google Map
(https://www.google.com.ph/maps) were used as references
to identify the type and name (for non-residential types) of
the buildings. More details about the procedure can be found
in [5].
2.2. Simulation Model Development and Generation of
Flood Depth and Hazard Maps
The simulation model consisted of a hydrologic model of the
river basin, and a two-dimensional (2D) hydraulic model of
the main river and its floodplain. The hydrologic model,
based on the Hydrologic Engineering Center Hydrologic
Modeling System (HEC HMS) Version 3.5, was used to
compute how much volume of water is produced in the river
basin during the occurrence of an extreme rainfall event; the
hydraulic model (using a trial version of Flood Modeller
Pro), was then used to simulate how these volume of water
travels in rivers and in various locations within the river
basin, including how it overflows from the rivers and floods
nearby areas. The reader is referred to [4,6] for more details
on how the flood simulations models were developed,
including the various geospatial datasets used during the
development process as well as its accuracy. For the flood
scenario modeling, the hydrologic model was used to
generate discharge hydrographs corresponding to 3
hypothetical, extreme rainfall events corresponding to return
periods of 25, 50, and 100 years. Each event has 24 hours
duration with accumulated rainfall depths of 248 mm (25-
year), 279 mm (50-year), and 309 mm (100-year). These
rainfall datasets was extracted from Rainfall Intensity
Duration Frequency (RIDF) curves generated by the
Philippine Atmospheric, Geophysical and Astronomical
Services Administration (PAGASA) based on 21 years of
record. The 2D model was used to generate hourly flood
depth and extent for the 3 extreme rainfall events using the
discharge hydrographs computed by the hydrologic model as
inputs. Maximum flood depth maps at 1-m spatial resolution
were then generated from the result of the 2D simulations.
These maps were further transformed into hazard maps by
categorizing the flood depths into 3 levels: low (<0.50 m
depth), medium (0.50 m – 1.50 m depth), and high (>1.5 m
depth).
2.3. Flood Hazard Exposure and Vulnerability
Determination
GIS overlay analysis of the building footprints and flood
hazard maps was conducted to identify which buildings are
exposed to various levels of flood hazards (e.g., low,
medium, high). In addition to this, we also compared the
building heights with the simulated flood depths in
determining their vulnerability using simple criteria. If a
building is located in a location where flood depth is less
than 0.10 m, then it is coded as “Not vulnerable”. If the
flood depth at the building‟s location is 0.1 m to less than
0.25*h (h is building height), then the vulnerability is
“Low”. On the other hand, if the flood depth is > 0.25h and
≤ 0.5h, then the vulnerability is medium. If the flood depth is
> 0.5h, then the vulnerability is high [5].
3. RESULTS AND DISCUSSION
3.1 Buildings Database
A total of 9,086 buildings were extracted from the DSM of
the study area. After attribution, 94.71% (or 8,605) of these
were found to be residential while the remaining 5% belongs
to government, commercial, educational, and other types.
The average height of all buildings was computed at 4.10 m.
3.2 Exposure of Buildings to Flood Hazards
The flood hazard maps generated through model simulation
are shown in Figure 2. As expected, there is an increase of
flooded areas as the rainfall event becomes more extreme
(i.e., higher return periods). Figure 3 summarizes the number
of buildings exposed to different levels of flood hazards
under different extreme rainfall scenarios. An example map
showing the locations of these exposed buildings can be
found in Figure 4. Statistics show that as the rainfall return
period increases (which also means increase in rainfall
intensity and duration), the number of buildings affected by
flooding also increases. Consequently, the number of
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buildings that are not flooded decreases. In all rainfall
scenarios considered, majority of the buildings appears to be
not flooded. For flood-affected buildings, more buildings are
exposed to „low‟ flood hazard levels than those in „medium‟
and „high‟ hazard levels. This result means that majority of
areas within the river basin where buildings are located are
relatively not prone to flooding; and if there is flooding, the
level of hazard is low.
Figure 2. Flood hazard maps of Cabadbaran River Basin for 25-,
50-, & 100-year rainfall return periods.
3.3 Vulnerability of Buildings to Flood Hazards
Figure 5 shows the number of buildings that were
categorized according to their vulnerability to flooding
caused by rainfall events of different return periods. The
locations of these vulnerable buildings are shown in Figures
6. It was observed that more than 40% of the buildings are
not vulnerable to flooding. For flood affected buildings,
more buildings are in „low‟ vulnerabilities, with increasing
number as the rainfall return period increases. The generated
statistics also show that buildings exposed to medium and
high flood hazard levels does necessarily mean they will also
have medium and low vulnerability. Looking at the graphs
(Figures 3 and 5), the total number of buildings under
medium and high hazard exposure are higher than the total
number of buildings in medium and high vulnerabilities.
This implies that even if a building‟s location has medium or
high flood hazard levels, a building‟s vulnerability can be
lesser if its height is much higher than the depth of flooding.
All of these results, however, only used height as basis for
assessing a building‟s vulnerability. The type of building
material and other factors were not considered.
Figure 3. Number of buildings exposed to different levels of flood
hazards under different extreme rainfall scenarios.
Figure 4. Example map of Cabadbaran City proper showing
exposure of buildings to flood hazards.
Figure 5. Number of buildings exposed to different flood
vulnerability levels under different extreme rainfall scenarios.
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Figure 6. Example map of Cabadbaran City proper showing
vulnerability of buildings to flood hazards.
4. CONCLUSIONS
This study highlights the importance of combining high
spatial information from LiDAR DTM and DSM with flood
simulation model to generate informative maps showing the
exposure and vulnerability of buildings to flooding. The
results of this case study made for Cabadbaran River Basin
highlights the use of a building database extracted from
LiDAR data (with each building attributed in terms of type
and height) to generate statistics as well as in creating maps
that can show the spatial distribution of buildings exposed to
low, medium and high hazard levels of flooding caused by
extreme rainfall events with return periods of 25, 50 and 100
years. The height information derived for each building also
allowed fast generation of statistics and maps showing the
building‟s vulnerability to flooding.
Although the vulnerability assessment was purely based
on the building height, the information generated from the
analysis can be very useful in flood disaster preparedness
and mitigation. One practical application would be
identifying those buildings (and informing their occupants)
that are at risk of flooding when a particular rainfall event of
specific return period is expected to occur. Since the maps
and statistics of those buildings exposed and vulnerable to
flooding were already generated according to rainfall return
period, it is already easy to identify those locations and
conduct appropriate measures such as early evacuation to
prevent casualties if an extreme rainfall event is expected to
occur.
5. ACKNOWLEDGEMENT
This work is an output of the Caraga State University (CSU)
Phil-LiDAR 1 project under the “Phil-LiDAR 1. Hazard
Mapping of the Philippines using LiDAR” program funded
by the Department of Science and Technology (DOST). The
LiDAR DTM and DSM used in this work were provided by
the University of the Philippines Disaster Risk and Exposure
for Mitigation (UP DREAM)/Phil-LIDAR 1 Program. We
also thank CH2M Hill for providing us a trial version of
Flood Modeller Pro. We acknowledge the CSU Phil-LiDAR
1 researchers namely Jesiree Serviano, Sherwin Pulido,
Melfred Berdera, Jared Culdora, Arthur Amora, Ronald
Makinano, Jennifer Marqueso, and Almer Cris Estorque who
assisted in the LiDAR data processing, building extraction,
and in the conduct of field surveys necessary for flood
model development.
6. REFERENCES
[1] M.H., Cheng, “Natural disasters highlight gaps in
preparedness,” The Lancet, vol. 374, pp. 1317-1318, 2009.
[2] IPCC, Managing the Risks of Extreme Events and Disasters to
Advance Climate Change Adaptation. A Special Report of
Working Groups I and II of the Intergovernmental Panel on
Climate Change, Cambridge University Press, Cambridge, UK and
New York, USA, 2012.
[3] S.N. Jonkman, M. Bočkarjova, M. Kok and P. Bernardini,
"Integrated hydrodynamic and economic modelling of flood
damage in the Netherlands," Ecological Economics, vol. 66, pp.
77-90, 2008.
[4] J.R. Santillan and M. Makinano-Santillan, "Analyzing the
impacts of tropical storm-induced flooding through numerical
model simulations and geospatial data analysis," in 36th Asian
Conference on Remote Sensing 2015 (ACRS 2015): Fostering
Resilient Growth in Asia, Quezon City, Metro Manila, Philippines,
October 19-23, 2015, vol. 5, pp. 3479-3487, 2015.
[5] J.R. Santillan, M. Makinano-Santillan and L.C. Cutamora, "3D
building GIS database generation and free online web maps and its
application for flood hazard exposure assessment", in 36th Asian
Conference on Remote Sensing 2015 (ACRS 2015): Fostering
Resilient Growth in Asia, Quezon City, Metro Manila, Philippines,
October 19-23, 2015, vol. 3, pp. 2037-2050, 2015.
[6] J.R. Santillan and M. Makinano-Santillan, "Combining
geospatial data and numerical models to map and differentiate
flooding extents caused by two tropical storms in the Philippines,"
paper presented at the 13th Southeast Asian Survey Congress
(SEASC 2015), Singapore, 2015.
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