Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model

Abstract

Climate change can impact coastal areas in different ways, including flooding, storm surges, and beach erosion. Of these, flooding has a major impact on the operation of coastal drainage systems. This paper develops a new flood screening model using a LiDAR based digital elevation model (DEM) to improve the implementation of Victorian's coastal flooding risk assessment and management. Hydrological elevation models are directed towards protection from cloudbursts and applied to rising sea level. The aim is to simulate water flow on the ground and in streams, and the resulting accumulation of water in depressions of the blue spot using DEM. Due to the presence of pipes, watercourses, bridges and channels it was required that the DEM data to be lowered. The reservoirs of rain will prevent seawater from flowing across the stream channel into land. The rain drain will be open during normal sea levels to allow rain water in the river to move and flow in to the sea. Traditionally, geographic information system (GIS) assists with spatial data management, but lacks modelling capability for complex hydrology problems and cannot be relied upon by decision-makers in this sector. Functionality improvements are therefore required to improve the processing or analytical capabilities of GIS in hydrology. This research shows how the spatial data can be primarily processed by GIS adopting the spatial analysis routines associated with hydrology. The objective of this paper is to outline the importance of GIS technology for coastal flood management. Following a definition of the coastal flood, and, short description of its peculiarities and the urgency of its management, this paper describes the use of GIS technology in coastal flood management, its advantages and the consideration for accuracy. This is followed by the information and LiDAR data required for coastal flood management and the application area in coastal flood management. This paper method is presented to conduct a first high-resolution DEM screening to detect the degree and capacities of the sinks in the coastal landscape. When their capacities are established, the rain volumes received during a rainstorm from their coastal catchments are saved as attributes to the pour points. The conclusion emphases the importance of a geographical information system in coastal flood management for efficient data handling and analysis of geographically related data. Local governments at risk of coastal flooding that use the flood screening model can use to determine appropriate land use controls to manage long-term flood risk to human settlements.

Full text

Journal of Civil Engineering and Architecture 15 (2021)
doi: 10.17265/1934-7359/2021.01.001
Finding Areas at Risk from Floods in a Downpour Using
the Lidar-Based Elevation Model
Sultana Nasrin Baby1, Colin Arrowsmith1, Gang-Jun Liu1, David Mitchell1, Nadhir Al-Ansari2 and Nahala Abbas3
1. School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology, RMIT University, GPO Box 2476,
Melbourne 3001, Australia
2. Lulea University of Technology, Lulea 971 87, Sweden
3. School of Engineering & Technology, Central Queensland University, Melbourne, VIC 3000, Australia
Abstract: Climate change can impact coastal areas in different ways, including flooding, storm surges, and beach erosion. Of these,
flooding has a major impact on the operation of coastal drainage systems. This paper develops a new flood screening model using a
LiDAR based digital elevation model (DEM) to improve the implementation of Victorian’s coastal flooding risk assessment and
management. Hydrological elevation models are directed towards protection from cloudbursts and applied to rising sea level. The
aim is to simulate water flow on the ground and in streams, and the resulting accumulation of water in depressions of the blue spot
using DEM. Due to the presence of pipes, watercourses, bridges and channels it was required that the DEM data to be lowered. The
reservoirs of rain will prevent seawater from flowing across the stream channel into land. The rain drain will be open during normal
sea levels to allow rain water in the river to move and flow in to the sea. Traditionally, geographic information system (GIS) assists
with spatial data management, but lacks modelling capability for complex hydrology problems and cannot be relied upon by
decision-makers in this sector. Functionality improvements are therefore required to improve the processing or analytical capabilities
of GIS in hydrology. This research shows how the spatial data can be primarily processed by GIS adopting the spatial analysis
routines associated with hydrology. The objective of this paper is to outline the importance of GIS technology for coastal flood
management. Following a definition of the coastal flood, and, short description of its peculiarities and the urgency of its management,
this paper describes the use of GIS technology in coastal flood management, its advantages and the consideration for accuracy. This
is followed by the information and LiDAR data required for coastal flood management and the application area in coastal flood
management. This paper method is presented to conduct a first high-resolution DEM screening to detect the degree and capacities of
the sinks in the coastal landscape. When their capacities are established, the rain volumes received during a rainstorm from their
coastal catchments are saved as attributes to the pour points. The conclusion emphases the importance of a geographical information
system in coastal flood management for efficient data handling and analysis of geographically related data. Local governments at risk
of coastal flooding that use the flood screening model can use to determine appropriate land use controls to manage long-term flood
risk to human settlements.
Key words: LiDAR, flood-risk, model builder, blue spot model, ESRI, DEM.
1. Introduction
In this research, the procedure for conducting a
pilot study for flood-risk awareness or focused coastal
land use planning is described. To support new land
development decision-making spatial models to
estimate the flood risk for existing infrastructure in
Bass Coast Shire Council (BCSC) were developed.
Corresponding author: Nadhir A. Al-Ansari, professor,
research fields: water resources and environment.
Previous studies focused on different goals in the
same study area. Previous research developed
methods based on geographic information system
(GIS)-embedded hydrological models to generate
missing council GIS datasets.
The main aim of this research is to show where
sinks are located, connected to the storm water system,
and may cause infrastructure near the coast to be
flooded.
Flood-risk awareness or focused planning policy to
be put into practice requires a business workflow,
D
DAVID PUBLISHING

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
2
specific information products, and a spatial database.
Moreover, to develop a comprehensive flood-risk
digital elevation model (DEM), a dataset with an
appropriate scale, with enough spatial detail and
appropriate attribute information is required.
GIS-embedded hydrological models should also be
linked to the database. These matters are discussed to
provide a framework for answering the research
questions listed below:
 How can the current process for drainage
analysis be improved?
 How does a spatial decision support system help
to identify infrastructure at risk from sea level rise
(SLR) induced flooding?
1.1 Background and Purpose
In this research the duty of local governments for
floodplain management and especially for flood risk
reduction in Victoria is outlined. Also, the reliance of
the local governments on the flood mapping
information created by CMAs, and on Melbourne
water flood data is discussed [1]. The BCSC area
faced heavy precipitation in May 2012 and higher than
expected precipitation in August 2012. Phillip Island
received over 90 mm of rain, including 55 mm in one
day, nearly achieving the normal September
precipitation in less than 24 hours [1]. This heavy
downpour caused flooding over the district, including
areas that are usually not prone to flood.
Precipitation is the main source of major floods in
Australia [2, 3]. Floods caused by rainfall are either
river floods or underground floods. Although flood
behaviour varies with the topography, the approximate
geographical extent and time of flooding can be
predicted by utilizing precipitation-runoff models.
Stormwater flash flooding happens during storms and
causes an overflow as it surpasses the limit of the
subsurface stormwater infrastructure [4]. Overland
flash flooding also happens when runoff moves over
the ground towards the closest topographic
discouragement territory, ordinarily in developed or
rural areas secured by impervious surfaces whose
volume can be increased but not quickened. Although
flash flooding occurs over small areas, its damage is
frequently more severe than a riverine flood because
of very little warning time [5]. For instance, in 2005
riverine floods compromised around 20,000 properties
in Melbourne, while stormwater flooding in a similar
area threatened 82,000 properties [5].
In recent years, Melbourne has experienced several
sudden, extreme rainfall events. On 30th Dec. 2016,
the State Emergency Service (Assessment) received
more than 2,500 calls for assistance since the heavy
rain hit. Melbourne’s north, north-east and south-east
suffered the most flood damage [6]. At the height
(peak) of the storm, about one millimeter of rain fell
every minute, causing rivers to burst their banks.
Claire Yeo from the Bureau of Meteorology Severe
Weather Meteorologist reported that rain fell in a short
time, with up to 70 millimetres recorded in the
Dandenong Ranges in half an hour [7].
2. Theory and Analysis Methodology
This research project was designed to develop a
screening method for buildings and roads in flood
prone areas.
This method was selected to assess where
infrastructure with a chance of getting flooded is
located within a pour point area. This is an unfair
categorization of infrastructure’s flood risk based on
its terrain level within the sinks. Sheets of water flow
downhill after heavy rain always finding a stream to
flow into, and continue to engage in the natural water
cycle. Water flows according to the steepest gradient
and may get trapped in a sink. Every sink has a
contributing catchment. As more water flows the
lower sinks fill. Based on 1 m the hydrologically
corrected digital elevation model (DEM), near the
coast, features the class of road and buildings and
easily identifies the sinks and their maximum water
depth when filled to their pour point in heavy rainfall.
To do this using Blue Spot Model (BSM) a map of

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
3
Inverloch was developed which identifies low-lying
areas that have no natural drainage. A blue spot is an
area where there is a relatively high likelihood of
flooding and the consequences are significant. In a
cloudburst (extreme rainfall event), blue spots may fill
and overflow, damaging buildings and roads that lie
within and adjacent to them. BSM tools will support
flood-risk sensitive land use planning at the local level
and allow the usage of new forms of information to
assist in the decision process.
The key aim of the BSM tool is to support local
government in preparing for future cloudbursts. The
following sections present a method for creating a
BSM, the development of a screening method to
assess buildings and roads in high flood risk areas,
and a geoprocessing model. The geoprocessing model
involves analysis, data management, editing, and other
operations that use elevation data to find the locations
of blue spots using a BSM. The study area is in
Inverloch, but the models have international
applicability because the criteria consist of the land
surface, buildings and streets. An underlying
hypothesis is that the DEM produces a very accurate
flow direction surface; this hypothesis has been
improved for DEMs with higher resolution [8].
Drainage flow models will not work in spots where
the water will not flow. Flooding problems in
low-lying regions such as sinks, depressions, or
hollows are very common and come in all sizes and
shapes. Landscapes appearing flat may contain low
depressions that trap rainwater (Fig. 1).
Some residential areas are developed in low-lying
areas where depressions are not noticed under dry
conditions. Stormwater in coastal urban areas collects
from roofs, sidewalks, driveways, footpaths and other
impermeable or hard surfaces by rain runoff. The soil,
organic matter, garbage, garden fertilizers and
driveway oil residues that it carries can contaminate
downstream waterways. The stormwater system in
Victoria is different from the sewage network. It
becomes evident during a downpour that the soil
surface cannot naturally drain, and the stormwater
system does not function effectively. Coastal
buildings, streets, infrastructure, tram or railway
tracks are also vulnerable [9].
Cultivated soil presents some risks in low lying
areas. On agricultural land, there can be a threat to
yields and equipment. Construction in built-up areas
causes house flooding unless buildings are built on
high ground. Even infrastructure not in depressions
may still be at risk as, during heavy rain water flows
towards the discharge points, and adjacent areas may
be flooded.
Morris et al. [10] developed a DEM to provide
overland flow paths and catchment boundaries. GIS
and hydrological modelling can help assess the local
nature of flood risk and identify areas where new
residential
housing
may
be at risk of flooding. Before
(a) (b)
Fig. 1 (a) An orthophoto Inverloch map from 2010 which shows low-lying grasslands and a creek; (b) the same area in 2018
is now a residential development.
Source: Google Earth.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
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the residential development, the drainage was efficient
as some water was able to drain through pastures into
the underground water basins. However, in the BCSC
region, several developments were located on land
that was once swamps or ponds and was typically
farmland. Today, some houses exist in low-lying areas
where water accumulates during an event of extreme
rain. Homeowners living in these low-lying areas that
have been converted from agriculture to residential
housing face the challenge of regular flooding.
Spatial data are fundamental to hydrological
modelling of real-world hydrological forms [11].
Despite the complexity of stormwater management in
urban catchments [12], as discussed site-specific and
catchment-scale runoffs are assessed using the BCSC
existing overland flow path model and catchment
boundaries. Thus, existing catchment boundary and
overland flow path datasets provide the basic
information for implementing flood-risk informed
land use planning. It does this through a
GIS-embedded hydrological model, for which each
land unit has its hydrological situation [13]. In the
remainder of this article, the methods for developing
the BSM-based map using the GIS-embedded
hydrological model are also described. In describing
the overland flow path model, rainfall information is
used at a sample site as a pilot study. The following
section concludes with the investigation results.
The BSM Fill Up Values are underpinned by a
function for classifying basins and make a numerical
calculation of flood risks to buildings in case of heavy
rainfall. For example, two Danish companies, NIRAS
(an international consulting group in Europe) and
COWI (an international consulting group, specialising
in engineering, environmental science and economics,
based in Lyngby, Denmark) have developed BSM
based maps for Denmark [14].
This research presents a comprehensive GIS-based
decision support tool that integrates with BSM for
effective management of coastal flooding. The BSM is
customized and used in the pilot study area. This
model is updated based on the current overland flow,
the catchment and highlights blue spot areas—low
lying areas and sinks where flooding risks are higher
(see Fig. 2).
Fig. 2 shows the results of the assessment of
overland flow in a heavy rainfall event. The houses
shown as blue are the blue spot buildings which will
be flooded first. The model uses ArcGIS
geoprocessing to determine the flooded areas and their
Fig. 2 The BSM based map of the lowland area identified with the Future Coast LiDAR data 2009.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
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neighbouring watersheds. The DEM fill identifies the
amount of rain that would be needed to fill the
depressions, by partitioning the BSM based sink-filling
volume according to the watershed. The impact of the
drainage network on flooding can also be predicted.
It is worth considering here that the model results
are enough to identify critical flood-risk thresholds for
single buildings. In the absence of building attributes,
the best way to do this is to create a worst-case
scenario by calculating the critical level of flooding
for a building based on ground floor level. In fact, this
is true of many houses, storerooms, shops, and
workplace facilities. Though, for all buildings, it is not
true. The actual flood level can be higher than
estimates for building raised on the above ground
level. Equally, the standard for houses with basements
can be lower than expected. The BCSC housing
record includes information of the building but does
not include that information in the housing feature
table. It normally provides no evidence for the
building’s base heights. The integration of building
features into the model will be to improve results.
An additional component of vulnerability is that the
water level for a building depends on the exact
vertical location of the building within the blue spot
area [9]. The other factors are equivalent, a building at
or near the bottom of a blue spot will be flooded
quicker than a higher house on its hillside.
Introducing this work, as discussed, in real life ideal
runoff conditions are rare, but in an extreme rainfall
event, the basic concept of hydrological behaviour
could be relaxed. Ordinary flow levels that can be
accommodated by the drainage networks may not
have the required capacity during peak storms. At the
point when this occurs, precipitation will create
streams that, in part or totally, fill the blue spot.
Notice that the models described here do not reflect
the change of surface runoff by a storm drain and
other infrastructure.
The risk assessment for individual buildings could
be improved if an examination of the permeability of
the surface, whether significant sections of the local
river basins are paved or not, should be made. A
bigger paved surface means a faster outflow. A raster
data set indicating the percentage of the solid
impervious surface for the Inverloch area should be
added to the resource geodata. Measurements of slope
and length of flows within river basins would also be
relevant to determine which buildings would be hit
first in a downpour.
2.1 Methodology for Blue Spot Modelling
This blue spot model (BSM) is implemented with
ArcMap and the ArcGIS Spatial Analyst extension,
four geodatabases including Inputs.gdb,
outputs_bluestop.gdb, outputs_bluestopFillUP.gdb
and resource.gdb and four toolboxes using model
builders (Fig. 3). Conceptually, the BSM has three
main purposes:
(1) It determines blue spots on the Digital Elevation
Model (DEM).
(2) It manages this result and outlines the footprint
of the building so that the data are in the appropriate
format to make a spatial selection.
(3) This selects the buildings situated within or
adjacent to the blue spots on the map.
The main processes in the model builder are as
follows:
 Blue spots are recognized by running the Fill
geoprocessing tool on the DEM.
 The minus tool subtracts values in the true DEM
(Small Sinks Filled) from values in the filled DEM
(All Sinks Filled) on a cell-by-cell basis.
 Con (restrictive assessment) tool evaluates an
expression as true or false for each cell.
 The expression “value > 0” is evaluated by the
cell raster for a Bluespot depth cell, which is true for
any cell in a blue spot. These cells are given an
arbitrary value of 1.
 Group blue spot cells individually into numbered
regions based on fluency. An alternative is set to
define diagonally connected cells as coordinates.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
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Fig. 3 The overall workflow of the blue spot model (based on a tool developed by Ref. [9]).
 The output raster dataset is Bluespot with IDs.
Dissolving the polygons on their grid code attribute
merges the diagonally connected polygons with the
bluespots they belong to. The final goal of this model
is to find and select buildings within or adjacent to
bluespots. The buildings are spatially compared to the
bluespots using a specified relationship. In this case,
the relationship is an intersection. Two features
intersect if they touch, or partially overlap, or if one
contains the other. Therefore, buildings will be
selected if they are adjacent to bluespots, or if they are
partly or completely within bluespots.
2.2 Models of Data Preparation
The toolbox contains the following
two-geoprocessing models. The geodatabases and
toolbox contents are listed in Table 1.
 The BSM identifies structures inside or adjoining
the blue spots. The outcomes appear, with very coarse
dimensions and illustrate which structures are at risk
of a flood hazard in a storm. The model does not
attempt to measure this hazard.
 The Bluespot Fill Up Values model investigates
and determines the BSM volume of each BSM and its
surrounding watershed area (the basin that channels
water to the river). From these results, the model
calculates how much rain it will take to fill the blue
spot entirely. This model takes into consideration
some positioning of flood hazard. The blue spot that
requires less rainfall to overflow represents a higher
risk flood hazard to buildings and infrastructure.
2.2.1 DEM-Based Characterisation of BSM
ArcGIS hydrology tools, the DEM can be analysed
to determine the blue spot regions, calculate their
volume, size and identify areas that contribute to the
flow of water in a cloudburst.
In the pilot study area, it is important to analyse the
DEM beyond the BCSC boundaries and include the
nearby watersheds. To guarantee that all blue spot
regions and watersheds are recognized accurately, the
DEM for Inverloch was extended to include a 0.5
kilometre buffer. The Vicmap Elevation, Future
coastal 1 m DEM & 0.5 m contours was derived from
airborne
LiDAR.
Native Format: DEM-XYZ ASCII

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
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Table 1 The contents of the blue spot model geodatabases.
Name Contents
Layers Visualizing input spatial data.
Inputs.gdb Starting data for models (Inverloch_LiDAR_point, DEM, building, road).
Outputs_the BSM.gdb Empty. Hold outputs to classify the BSM (Blue spots, Buildings Touch BSM, Road Touch BSM).
Outputs_BSM FillUp.gdb Outputs data to classify the BSM fill upvalues.
Resource Data. gdb Extra raster and feature data to explore and analyse.
BSM_Metric.tbx Geoprocessing tools in the Model Builder environment (see Fig. 3).
BSM_Metric_NoBuildings Model versions for input datasets where no footprints are available to create.
ESRI Grid ASCII Contours—ESRI Shapefiles,
MapInfo TAB. The created DEM was inspected to
identify the sinks and low-lying areas utilizing the
cutting tool in the SAGA-GIS and assessing the
impact of pit expulsion calculations on surface
overflow reproduction. A DTM (Digital Topographic
Model) with pits removed is a precondition for
hydrologic analysis. Two pit removal methods, the
carving method and the filling method, are
investigated in this study for three different
geomorphometric areas. The input data are
photogrammetrically measured DEM with a resolution
of 5 × 5 meters. Šamanović et al. [15] argued that
choosing the correct calculation is critical, and
suggested using a DEM without pits, including the
minimum geomorphometric changes. The vertical
precision of the DEM is critical for GIS-based
hydrological modelling. The methods used in this
research enables local organizations to evaluate the
quality of the GIS databases, and use the GIS based
hydrological models to improve flood risk
management [13].
2.2.2 Blue Spot Model (BSM) and Affected
Buildings
The limit of the examination region was constrained
to areas covered by the LiDAR dataset. The DEM for
the examination region was created based on the
LiDAR data utilizing Inverse Distance Weighted
(IDW) interpolation in the ArcGIS Geostatistical
Analyst extension. In IDW only known z values and
distance weights are used to determine the unknown
areas. IDW has the advantage that it is easy to define
and therefore easy to understand the results.
These models examine the DEM using hydrological
tools to discover the BSM. At that point, the location
of the blue spot region is identified in relation to
existing structures and highlights the structures that
are inside or nearby the blue spot region. These
structures are at greater risk of being flooded. The
geoprocessing model includes input data, workflow
tools and runs as a single operation. The model
process involves an input dataset associated to a tool
(yellow color) connected to an output dataset (blue
color) (see Fig. 3). Components input and output
variables because their properties can be accessed and
thus their path names can be changed. Conceptually,
the model functions include the following three main
steps, which are then explained in the following
section:
 Identify the BSM Fill Up Values from the DEM.
 Using the layer of buildings so that the
information is in the best possible structure for a
spatial decision.
 Spatial determination buildings layer on the
guide that exists in or is contiguous to the BSM (i.e.
identifying the blue spots).
2.2.3 Identify the Blue Spot Regions on the Digital
Elevation Model
This stage of the process involves identifying the
blue spot regions on a predetermined DEM. Once this
is done it is possible to spatially identify the structures
that are inside or adjoining the BSM. These structures
are at risk of flooding in a storm.
The blue spot regions are distinguished by running
the Blue Spot Model Fill Up Values geoprocessing
process twice on the DEM. The process is run once to

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
8
Fig. 4 The BSM output for the building(gray) located within a sink (sky colour) [9].
fill sinks under 0.05 meters down, which are thought
to be potential mistakes in the DEM. The output is the
best DEM we can create. Next, the process is run a
second time to fill all sinks to their pour levels. The
output, a filled DEM without any sinks by any means,
is vital for the following tasks.
The minus operation subtracts values for the
genuine DEM (little sinks filled) from qualities in the
filled DEM (all sinks filled) on a cell-by-cell basis.
The outcome is a raster dataset (the BSM is identified
cell by cell) demonstrating the areas and profundities
of the substantial sink, or the BSM.
With raster image analysis there are two types of
cells (BSM cells and non-BSM cells) derived using
the Con (restrictive assessment) tool. This tool
assesses whether this condition is evident or false for
every cell and allots a value to the cell accordingly.
When this “Value > 0” for the raster BSM cell heights,
it is a Blue Spot and it is assigned a value of 1. Cells
that have a “Value < 0” are not Blue Spot. The raster
yield informational is collected using a cell by cell
process.
The BSM cells have been identified, but they have
not been grouped in an intuitively meaningful way. It
is normal to think about a contiguous set of blue spot
cells encompassed by non-blue spot cells as a blue
spot region. A first raster pixel value is allocated in
this cell by cell process, followed by determining the
blue spot value for every cell to create blue spot and
non-blue spot zones. Accordingly, the next stage is to
group the blue spot cells into areas with the same
number dependent on continuity. A choice is made
whether to characterize cells diagonally associated
corner to corner as adjacent.
The blue spot regions, which can be thought of as
raster objects, are converted to a polygon feature class
(the blue spot Polygons). The final output of the
model includes data, which can be displayed and
analysed with other feature classes (Fig. 4).
2.2.4 Using the ArcGIS Dissolve Tool
In the raster-to-polygon conversion that produces
the blue spot polygons dataset, raster cells that are
diagonally associated with the BSM, which ought to
have a place with BSM, are made as isolated
highlights. Dissolving the polygons based on their cell
value consolidates the diagonally connected polygons
with the blue spot region they are connected to.
3. Results of Blue Spot Modelling
The four important datasets are Buildings TouchBS
(buildings touching blue spot) and Roads TouchRS
(Roads
Touching
blue
spot).
Figs.
5
and
6
show
the

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
9
Fig. 5 The BSM output for the Inverloch area.
Fig. 6 Building within the BSM in the Inverloch area—yellow denotes buildings, the blue spot areas are shown in dark blue,
and the light blue are buildings that interest with the modelled blue spot areas.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
10
Fig. 7 Road within the blue spot model in the Inverloch area—red denotes roads, the blue spot areas are shown in dark blue,
and the light blue are roads that intersect with the modelled blue spot areas.
location of the blue spot regions in the Inverloch area
and affected buildings. In Fig. 6 some vertical stripes
are evident in the results from the BSM, which
highlight potential errors in the DEM. The following
section assesses these potential errors to see Fig. 7.
As illustrated in Fig. 6, over 467 buildings lie
within or adjacent to the blue spot regions. From the
attributes in the buildings layer, Inverloch has
approximately 6,165 buildings. This means that
approximately 7.9% of buildings in a cloudburst have
some degree of flood risk. The analysis was done
again based on the DEM correction model vertical
stripes in the results. After cleaning up the LDAR data,
different results were obtained. The updated results
show that 345 buildings lie within or adjacent to the
updated BSM. In other words, 5.6% of the buildings
will experience flood risk in a downpour.
As illustrated in Fig, 7, over 212 roads lie within or
adjacent to the blue spot regions. From the attributes
in the roads layer, there are about 1,034 roads in
Inverloch. This means that approximately 20.5% of
the roads have flood risk in a rainstorm. Analysis has
been repeated based on the correction DEM model
vertical stripes in the results. After cleaning up the
LiDAR data, different results were obtained. The
updated results show that 149 roads (14.4%) lie within
or adjacent to the updated blue spot regions and will
experience flood risk in a downpour.
3.1 Vertical Accuracy Validation Tools for LiDAR
Data
LiDAR ground points were validated at various
levels of the minimum distance around each survey
permanent marks [16]. The methodology adopted in
this study prevents the gridding effect in the final
evaluation.
To understand the degree to which gridding would
impact the vertical accuracy, a direct regular IDW

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
11
interpolation procedure and geo measurement IDW
were utilized. The impact of the geo measurement
IDW and basic IDW in inferred DEMs exactness were
investigated. Moreover, the autocorrelation between
LiDAR ground points and GCPs has been evaluated
utilizing an Average Nearest Neighbour (ANN)
investigation proposed strategies for this study,
successive least separation. The contrast between the
LiDAR measured height and the rise as dictated by the
Permanent Marks (PMs) in the evaluated separation
was around 0.5 m at a 95% certainty level.
The difference between the LiDAR height and the
value in the PMs was around 0.5 m at a 95%
confidence level. Therefore, the LiDAR ground point
dataset can be utilized for flood mapping that does not
require the vertical accuracy to be greater than 0.5 m.
The LiDAR ground point dataset does not contain
enough vertical accuracy for drainage calculations, as
a vertical precision of 10 cm is required [13]. Fig. 8
shows the results of a process for deleting duplicate
points and problematic points, (-999, -0). Clean the
raster of these duplicate and error points to have a
smooth uncovered earth DEM. After utilizing the
IDW interpolation system and erasing blend devices,
another BSM was created with the outcomes shown in
Fig. 7.
3.2 The Blue Spot Model (BSM) Analysis Results
After data analysis, buildings that are in the BSM
attribute table can be examined to determine how
many roads and buildings in the Inverloch area risk of
a flood. As illustrated in Fig. 6 there are many
buildings within the BSM areas spread throughout the
Inverloch area. However, floods may affect other
infrastructure types, as well as buildings and highways.
Using this model, it is possible to add other
infrastructure datasets, such as trails, and railways, to
determine where they are with the blue spot regions.
Identifying the blue spot regions does not assess risk
rates for buildings and does not pose the same risk to
all blue spot areas. How quick the blue spot regions
filling and overflowing in a rainfall depends on its
depth, flood hazards and catchment size, or local
watershed that contributes to it.
3.3 Assessing Flooding Risk to Buildings and Roads
This section discusses how flood hazard risk to
buildings can be assessed, along with how much
precipitation is needed to fill each Blue spot region to
its pour point. These data will help improve the
assessment of flooding risk to structures. This model
has been developed for situations in which building
footprints may be accessible or not accessible. The
model identifies blue spot regions on a DEM and
computes how much precipitation is needed to fill up
a blue spot region in a downpour. This datum
improves the assessment of flood risk for a building
situated in a blue spot region. A building in a blue
spot region that fills up rapidly has a higher level of
flooding risk than a building in blue spot region that
fills up gradually [9].
This is based
on
the
hydrological
assumption
that
Fig. 8 (a) Delete duplicate and problematic points, merge LiDAR and create DEM; (b) the vertical stripes error and (c) the
error-free model.
(a) (b) (c)

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
12
each blue spot region in the landscape has a catchment
area in which this region contributes only to the flow
of that blue spot region. It can be determined how
much rainfall is needed to fill the blue spot region by
calculating the capacity of the blue spot region and the
area of its watershed. “For example, if a blue spot
region’s volume is 500 m3 and its watershed is 10,000
m2, the rainfall needed to fill the blue spot region to its
pour point is 500 m3/10,000 m2 = 0.05 m = 50 mm”
[9]. In fact, not all the rainfall that falls in the
watershed streams into the blue spot region because
ideal run-off situations do not exist. However, the
run-off conditions in a storm are near perfect. The
water balance condition P = I + E + Ao + Au + M
expresses that precipitation (P) is equivalent to the
interception by vegetation (I) plus evapotranspiration
(E) plus overland (Ao) plus surface run-off (Au) plus
storage in soils (M). In this specific situation, a local
store implies blue spot region [9].
For this process, the approach used by Balstrøm and
Crawford [9] was implemented. In a rainstorm,
blockages, dissipation and infiltration of soils were
nominated as zero. The extreme capacity of the
Victoria drainage system in local locations is around
40 millimeters of downpour per day [3]. Focusing on
1 hour of precipitation and if the day-to-day maximum
is 60 minutes, the soil infiltration and sewerage (Au)
value will be set at 40. Surplus runoff (Ao) in the
situation will not be a concern until after the BSM fills
in. “For the fill-up values, the equation can therefore
be streamlined to P = 40 + M or M = P – 40
millimeters for every hour” [9]. If 90 millimetres of
downpour occurs in 60 minutes, the sewer frame will
redirect 40 millimetres, while 50 millimetres will flow
into the BSM—filling it up either slightly or entirely.
If BSM is filled to the point where it pours, the
overflow (Ao) will join the downstream drain, dam,
waterway or ocean [9].
Although this model makes for an improvement
assessment of flooding risk, there are limitations to
this approach as overflow downstream of the blue spot
region is not considered. Nor is the height of the
structures inside the blue spot region. For instance, if a
building is situated close to the base of the blue spot
region, it could be flooded before the blue spot region
fills. Further, underground structures such as
basements are not considered [9].
The model identifies the blue spots in the BSM and
it computes their volumes and watersheds. These data
are useful in calculating the amount of precipitation
expected to fill each blue spot region. Many of the
workflow
model
procedures
are
operations
for
table
Fig. 9 Left map assesses flood risk to buildings Inverloch west side and right map assessing flooding risk to buildings in
Wreck Creek, Inverloch.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
13
manipulations: including joining fields, adding fields
and field values for calculation. The BSM analyses the
amount of water that is needed to fill each blue spot,
thus making it possible to assign relative degrees of
flood risk (see Fig. 9).
The colours in the legend for the fill-up values start
at 40 millimetres to represent the drainage network limit.
If the fill-up area includes values of 0-20 mm, they
will be classified as 40-60 mm when the symbology is
associated. Add field to the BSM touching buildings
as well and measure their values by [Fill-up] + 40 mm.
BCSC experienced large precipitation in August
2012, following previous storms. Phillip Island
experienced over 90 mm rain, including 55 mm in one
day, nearly achieving the normal September
precipitation in less than 24 hours. It was likely that
blue spot regions associated with the top 2-3 risk
categories (colored as red, dark orange, orange in Fig.
9) would fill up. This heavy rain did cause flooding
across the region.
Some buildings in Fig. 9 that appear susceptible to
flooding are not highlighted. While there may be a
blue spot region in those areas, they are not
highlighted as being at risk as the blue spot region fill
up volume is in excess of 140 millimeters rain
balanced. It is very unlikely that these blue spot
regions would flood. The calculations predict the
whole blue spot region would not be filled, yet this is
not generally the situation. Some blue spot regions are
lasting water bodies, for example, Wreck Creek in
Inverloch area.
Further analysis is required to find out how many
buildings are within blue spot regions of different risk
levels. Assuming perfect run-off conditions within the
catchment area and no sewer system to drain the water,
the Fill UP value can be estimated by dividing the
individual sink volume by its catchment. BSM can be
classified
in
the
highest
risk
category
with
the
BSM
Fig. 10 Roads and buildings touching blue spots in Ayr Creek, Inverloch.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
14
buildings level attribute question “Fill Up ≥ 0 and Fill
Up ≤ 20. Spatial building data are selected that
intersect within the selected set of the BSM. The
watersheds layer can be applied to analyse the
relationship between the blue spot regions and the
areas that contribute to their flow. Precipitation affects
the lower parts of infrastructure such as buildings and
roads. Flooding on the road networks should be
avoided because it leads to traffic jams, making roads
unsafe and damaging the road surface (Fig. 10).
Blue spot models can be used to perform accurate
water flow calculations considering the cavities and
dips as well as other surface conditions. These models
produce a “blue spot map” that shows where and how
intensely the road network will flood for a given flood.
These models identify the location of the watercourse
and develop guidelines for how to reduce flood
exposure. The result is a screening method that the
municipal environment department planners, road
authorities and other interests can use. While the BSM
has been incorporated into the computations, the
variation in assessment of future flood risk is difficult
due to climate variation.
This research shows a unique approach to the BSM
to describing the urban overland runoff under a heavy
rainfall scenario in an innovative way. The key
finding from this case study is that a high-resolution
modelling methodology is important. Furthermore, the
distributed data model creates a feasible data schema
for subdividing the scene data under basin from
hydrology recognitions empowering it to fit into
genuine hydrology conditions.
It is also integrated with coastal urban heterogeneity
distribution models, opening an entryway to even
more extensive inclusion of hydro-displaying related
datasets to be included. Also, unlike a “one for
all”-modelling approach, the modified sub-model
group method makes it possible to produce diverse
individual stormwater models depending on different
modified target rainfall events and flooding objects. It
provides a possible modelling approach to adapt to the
dynamic world. Also, multiple hydrological
examinations were connected in the model. Not all
sub-models generated from the entire drainage basin
provide a reliable boundary for hydrology modelling,
the small-scale hydrological changes. Sub models may
achieve improved flooding connectivity between
coastal and mainland areas. The automatic process of
the Blue Spot model with little manual input A
programmed method in BSM with minimal manual
information sources requires reduced analysis time
when developing the input hydrological model.
4. Conclusions
In this research, the methodology for producing a
blue spot map for Inverloch that identifies low-lying
blue spot regions with no natural drainage was
presented. In a cloudburst event, the blue spot regions
may fill up and overflow, damaging buildings and
roads that lie within and adjacent to them. BSM tools
will support flood-risk land use planning at the local
level and allow the usage of new forms of information
to assist in the decision process.
The results presented in this research involved new
applications of geoprocessing to derive the blue spot
regions and their watersheds locally. The fill-up
qualities are determined by partitioning the BSM
volume by the local watersheds. It is then possible to
assess how much floodwater the drainage network can
accommodate. These results, however, need to be
viewed with some caution. In Australia, when storms
are heavy from a specific wind direction over a couple
of days, many low-lying coastal areas are at risk of
getting flooded. Thus, research focuses on which
models are useful for coastal drainage connection to
the main land drainage analysis. This flood screening
model is useful for local government planners to
understand areas that might be threatened due to a
sudden or a long-term coastal flood impact.
Based on that analysis, the research model allowed
assessment of flood-risk thresholds for infrastructure.
However, the best thing that can be done without

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model
15
building features is to create a worst-case scenario by
ensuring that the crucial flood level for a building is at
its base height. Flood levels might be higher than
accepted for structures with high building levels or
structures built above ground. On the other hand, the
dimensions might be lower than expected for
structures with storm cellars. The BCSC property
department has data about whether structures have
cellars, yet those data are excluded in the building
feature table. It does not have building databases on
basements. Including the features of the infrastructure
inside the model will improve the outcome.
Since a hydrological model depends on
characteristics of a given study area, no specific model
of flow direction is universally applicable, which is
the fundamental step in the hydrological models
integrated into a GIS. The implicit assumption is that
the DEM produces a consistent surface of the flow
path and this hypothesis is very high-resolution digital
elevation models. Another component of uncertainty
is that the amount of water input to a building depends
on the structure actual vertical position inside the blue
spot region. Different factors are equivalent; a
building at or near a blue spot region’s low point is
going to be flooded before a building on its high point.
As discussed in the introduction to this study, ideal
conditions for outflow are rare, but in a downpour,
basic hydrological hypotheses change. Normal soil
infiltration capacity is irrelevant, and the drainage
systems will very easily exceed full capacity. When
this happens, the rainfall transforms into rapid
overland flows that fill blue spot regions partially or
completely. BSM deployed in this study does not find
surface runoff diverting by drainage ditches or other
channels. The research also considered some
improvements to the BSM’s Fill up module.
An examination of the permeability of the surface,
whether the large sections of the river basins are lined,
should enhance risk management for individual
building. Faster outflow means more paved surface. It
would be useful to set solid impermeable surface
percentage raster data for the area of analysis at
Inverloch. A study of the slope and length of flows
within the river basins would also be relevant to
determine which buildings in the downpour will be
affected first.
This research assesses flood risks for residential
areas caused by cloudbursts. The focus has been on
developing models to estimate the flood risk for an
existing building or planned new developments.
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