Application of GIS: Queensland Flood Risk Analysis

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GEOM20013 A3 Queensland Flood Hazard and Risk Analysis

Derrick Lim

860296

Robert Asquith

864700

Kyle Wood

742742

Sarah Osborne

833307

Queensland Flood Hazard and Risk Analysis

Abstract The purpose of this report is to generate a flood hazard risk map for the state of Queensland. Queensland is severely affected by flooding on almost an annual basis. Flood risk analysis relies on GIS technologies where variables can be effectively calculated and weighted to understand which areas are most at risk. The methodology of creating a flood risk map occurs in two parts; first mapping the hazard of flooding in Queensland using typological (slope and elevation), lithological and hydrological data as well as climatic patterns and land use. This produces an initial hazard risk map using multi-criterion evaluation (MCE). Next, vulnerabilities of societal and environmental elements were collected, mapped and assigned a weighting of importance. The final hazard risk map comprises both steps with their variables classified according to risk. The results of the final flood risk map illustrate that Greater Brisbane, Gold Coast, Sunshine Coast and Northern Queensland are most at risk of flooding but also the most devastating consequences to social and environmental systems will occur in these areas. This flood hazard risk map can be used to asses and provide recommendations for flood mitigation in the future. The most important areas of flood mitigation to improve are infrastructural, namely investment in water overspill in dams for prevention and technological - implementing warning systems for residents of flood prone areas. National Parks are most prone to flooding in Northern Queensland however these are cyclical natural process that should not have much intervention.

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Queensland Flood Hazard and Risk Analysis

Contents Abstract ................................................................................................................................................... 2 Introduction ............................................................................................................................................ 5 Methodology........................................................................................................................................... 6 Overview ............................................................................................................................................. 6 Data Hierarchy .................................................................................................................................... 7 Data Processing ................................................................................................................................... 8 Hazard Parameters.......................................................................................................................... 8 Risk Elements ................................................................................................................................ 11 Elements at Risk ................................................................................................................................ 15 Computation of Elements at Risk.................................................................................................. 19 Data Weighting ................................................................................................................................. 15 Validation .......................................................................................................................................... 20 Maps ..................................................................................................................................................... 21 Hazard Analysis ................................................................................................................................. 21 Flood Hazard Parameters.............................................................................................................. 21 Flood Hazard Map ......................................................................................................................... 22 Risk Analysis ...................................................................................................................................... 23 Flood Risk Vulnerabilities .............................................................................................................. 23 Flood Risk Map .............................................................................................................................. 31 Flood Hazard Validation .................................................................................................................... 32 Discussion.............................................................................................................................................. 33 Limitations ........................................................................................................................................ 33 Analysis Validation ............................................................................................................................ 33 Response Recommendations............................................................................................................ 33 Conclusion ............................................................................................................................................. 35 References ............................................................................................................................................ 36 Appendix I – Soil Types ......................................................................................................................... 37 Appendix II – Sample Screenshots ........................................................................................................ 39 Appendix III – MODIS Flood Extent Imagery ......................................................................................... 40

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Queensland Flood Hazard and Risk Analysis

Figures and Maps

Figure 1 Relationship of input data in the analysis process.................................................................................... 7 Figure 2 Model Builder sequence for infrastructure ............................................................................................ 19 Figure 3 Model Builder sequence for industry ..................................................................................................... 19 Figure 4 Flood parameters input where (a) Elevation, (b) Slope, (c) Flow direction, (d) Flow accumulation, (e) Rain days more than 10mm, (f) Mean annual NWI, (g) Distance to rivers, (h) Density of Rivers, (i) Soil types, (j) Land use. ............................................................................................................................................................... 21 Figure 5 Total hazard map .................................................................................................................................... 22 Figure 6 Total infrastructure risk map .................................................................................................................. 23 Figure 7 Infrastructure vulnerabilities input where (a) State controlled roads, (b) Railway networks, (c) Railway stations, (d) Age care and retirement facilities, (e) Residential buildings, (f) Residential buildings at extreme hazard ................................................................................................................................................................... 24 Figure 8 Total industry risk map ........................................................................................................................... 25 Figure 9 Industry vulnerabilities input where (a) Commercial buildings, (b) Health and welfare buildings, (c) Emergency services buildings, (d) Grazing fields and pasture areas, (e) Agriculture areas, (f) Mining areas ...... 26 Figure 10 Total environment risk map.................................................................................................................. 27 Figure 11 Environmental conservation areas. Classifications are described by CLUM. ....................................... 28 Figure 12 Total population risk map ..................................................................................................................... 29 Figure 13 Population risk per key urban centres, extracted from Figure 12 ........................................................ 30 Figure 14 Composite flood risk map, including all input hazard parameters and risk vulnerabilities, weighted for influence and value respectively .......................................................................................................................... 31 Figure 15 Validation of flood maps where (a) Historic extent of flood inundation (Bundaberg 2010, Brisbane & Ipswich 1974, Bundaberg 2013, SW QLD 2012, QLD 2011, Logan & Albert Rivers 2017, QLD 2012, Mackenzie River 2017), (b) Floodplain assessment overlay, (c) Equal weighted flood inundation, (d) Pairwise weighted flood inundation, (e) Equal weighted overlay assessment, (f) Pairwise overlay assessment............................... 32

Tables

Table 1 Hazard parameters .................................................................................................................................... 8 Table 2 Hazard parameters, continued .................................................................................................................. 9 Table 3 Hazard parameters, continued ................................................................................................................ 10 Table 4 Risk vulnerabilities ................................................................................................................................... 11 Table 5 Risk vulnerabilities, continued ................................................................................................................. 12 Table 6 Risk vulnerabilities, continued ................................................................................................................. 13 Table 7 Risk vulnerabilities, continued ................................................................................................................. 14 Table 8 Hazard parameter weighting by pairwise comparison of influence to flooding ...................................... 15 Table 9 Infrastructure parameter weighting by pairwise comparison of value ................................................... 16 Table 10 Industry parameter weighting by pairwise comparison of value .......................................................... 16 Table 11 Risk parameter group weighting by pairwise comparison of value ....................................................... 16 Table 12 Accuracy validation summary. Accuracy within each hazard class for both model weightings is determined by the number of cells within their 'correct' location in the Floodplain Assessment Overlay. Accuracy in hazard classes 1 and 2 is determined by the percentage of cells ‘correctly’ outside the overlay. Accuracy in hazard classes 3 and 4 is determined by the percentage of cells ‘correctly’ inside the overlay. ...... 20 Table 13 Reliability validation. Reliability of the model under both equal weighting and pairwise comparison weightings is shown by the per-hazard class composition of all seven selected flood extents. Higher reliability will occur with greater percentages in hazard classes 3 and 4............................................................................. 20

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Queensland Flood Hazard and Risk Analysis

Introduction Flooding is the most common natural disaster, accounting for 43% of all natural disasters from 1994 to 2013, impacting the lives of nearly 2.5 billion worldwide (UN Office for Disaster Risk Reduction, 2015). The state of Queensland is particularly susceptible to flooding, enduring 10 major floods since 2010 to the present. Considering the horrific loss that flooding can cause, much research has gone into minimising potential damage from flooding, or flood risk analysis. Flood risk analysis relies on data interpreted by GIS in order to gain an understanding of the properties which put an area at risk. This report is aimed at creating a flood risk analysis for the state of Queensland, which is a particularly powerful tool with the capability to save an immeasurable amount of lives and resources. A flood risk analysis can be created by understanding which parts of the state have the highest level of flood hazard, using this data to determine which parts of the population, environment, industry and infrastructure are most at risk, then offering proposals to mitigate the potential damage of the most extremely vulnerable areas. To execute a flood risk analysis, it is necessary to establish a multicriteria evaluation (MCE). This can be done with GIS to interpret several datasets into maps regarding which parts of the state are most likely to flood. Data analysed includes; elevation, water flow accumulation, water flow direction, land use type, net-water index, days of rain per year, river density, river proximity, grade of slope, and type of soil. Maps produced with GIS are easy to read and provide insight about the nature of floods. When we compile these maps into a single overall Hazard Map, this becomes a tool for understanding which areas are the top priorities to mitigate flood damage. Flood risk has

benefited from frequently-updated global satellite imagery, recording instances of widespread flooding. NASA’s MODIS system is often configured for this use. Liu et al (2016) and Glas (2017) have both demonstrated the use of MODIS flood images (Glas through Vanneuville’s LATIS system) to create a quantitative-based MCE and standardization methodology.

When we take this data and compare it to the locations which are most vulnerable regarding population, environment, industry, and infrastructure, we can establish which areas require the most urgent attention, or most vulnerable. Using this data, we can create a Vulnerability Map which will indicate which areas would be the most destructive if they were to flood. Given the nature of the data, there is much interpretation involved in deciding how much weight to put on each element regarding the composite Hazard or Vulnerability maps, as well as deciding how to classify qualitative data, such as land use, in a manner that pertains to the risk of flooding. Once determined, the next step is to decide how to lower the risk of flooding in these specific areas. This can be accomplished by returning to the individual maps which contributed to the Hazard and Vulnerability maps, and determining which factors are the most significant contributors to the high risk of flooding and vulnerability in that area. After isolating specific factors, proposals will centre around lowering the risk of these factors. Risk analysis is then an extremely effective tool by uncovering the most susceptible areas, the most valuable areas, and layering them to understand the best methods for preventing flood damage.

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Queensland Flood Hazard and Risk Analysis

Methodology Provided below is a visual overview of the entire methodological approach.

• Climatic patterns

• River desnity contributes the least, so was weighted to 0.0676

Hazard Analysis and Model Validation

Vulnerability Assessment and Risk Assessment

Element definition

• Land use

• HCL 1 has the lowest contribution • HCL 6 has the highest contribution • For example, minimal slope has a greater influence on flooding, therefore is HCL 6

A range of environmental and societal elements at risk (EAR) were considered for flood risk analysis including: • Population • Environment • Industry • Infrastructure

Degrees of likelihood were classified into four hazard classes (HC). • HC 1 has the lowest flooding likelihood • HC 4 has the highest flooding likelihood

EAR and any sub-elements were weighted by value of potential holistioc socioeconomic loss due to flooding. • Population has the greatest loss value,so was weighted to 0.36 • Environmental EAR has the lowest loss value, so was weighted to 0.13

Validation

Parameters and weightings were combined to map the distribution of flooding likelihood.

Risk calculation

Hydrological data

• Slope contributes the most, so was weighted to 0.1328

Hazard calculation

• Lithological data

Ranges of individual parameters were classified into six hazard contribution levels (HCL).

Value weighting

• Topological data

Each parameter as a whole was assigned a weighting representing degree of contribution to flooding likelihood.

Parameter classification

Contributions to flooding were Risk Analysis considered as input parameters including:

Contribution weighting

Parameter definition

Overview The efficacy of our hazard model was validated with weighting and record comparisons. •An equally weighted model was used as a 'baseline' comparison •Governement Flood Overlay and flood extent records were comapred to our model's extent and prediction. HC at each EAR and their value weightings were combined to map overall flood risk across Queensland. Degrees of risk were classified into four risk classes (RC). • RC 1 has the lowest risk from flooding • RC 4 has the highest risk from flooding

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Queensland Flood Hazard and Risk Analysis

Data Hierarchy

Figure 1 Relationship of input data in the analysis process

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Queensland Flood Hazard and Risk Analysis

Data Processing Hazard Parameters Hazard

Elevation

Slope

Flow Direction, Fdirection

Flow Accumulation, Faccumulation

Weighting Value

0.1127

0.1328

0.0352

0.1173

Data

DEM

DEM

DEM

Source

Geoscience Australia http://www.ga.gov.au/scientifictopics/national-locationinformation/digital-elevation-data

Geoscience Australia http://www.ga.gov.au/scientifictopics/national-locationinformation/digital-elevation-data

Geoscience Australia http://www.ga.gov.au/scientific-topics/national-locationinformation/digital-elevation-data

DEM & Flow Direction Geoscience Australia http://www.ga.gov.au/scientific-topics/nationallocation-information/digital-elevation-data

Method

The elevation model is visualized by inserting the DEM into ArcGIS. The unit used is in meters (m), ranging from -34 to 1581m above sea level. It is then classified into different elevation range and then reclassified into hazard classes as shown below.

Slope was derived from the DEM using the Slope Tool, using the unit of percentage. The slope percentage was then reclassified into hazard classes, shown below.

Flow Direction was calculated from the DEM using first the Fill Tool and then the Flow Direction Tool. The eightdirection (D8) flow model output was then reclassified into hazard classes based on its frequency and its ranking.

Elevation (m) < 100 100 – 200 200 – 300 300 – 400 400 – 500 > 500

HCL 6 5 4 3 2 1

This range is adjusted according to (Chen, 2015).

Slope (%) <2 2–4 4–6 6–8 8 – 10 > 10

HCL 6 5 4 3 2 1

These hazard range and values for slope are adapted from (Chen, 2015).

Output Value 1 2 4 8 16 32 64 128

Direction

Freq

E SE S SW W NW N NE

23053956 12511553 27370192 16097591 34451408 14813421 27661520 13409307

Rank (th) 4 8 3 5 1 6 2 7

HCL 4 1 5 3 6 2 5 2

The results show that most streams flow towards West, North and South direction in the Queensland State.

Flow accumulation was calculated by inputting the output of Flow Direction using the Flow Accumulation Tool. The output range of the flow accumulation is then reduced by expressing it in log10 for better visualization using the raster calculator. Then, to obtain a more persistent flow network, the equation Con("flow accum_log">=2,"flow accum_log), adapted from Montgomery, 1994 is then applied by using the raster calculator. The contributing cells of the flow accumulation is then classified and reclassified into hazard classes shown below. Flow Accumulation Value (log10)

HCL

>5 4–5 3.5 – 4 3 – 3.5 2.5 – 3 2 – 2.5

6 5 4 3 2 1

Table 1 Hazard parameters

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Queensland Flood Hazard and Risk Analysis

Hazard

Days of rain, greater than 10mm, RD10 0.1280

Net Water Index (NWI)

Distance to rivers, Rproximity

Rivers density, Rdensity

0.1280

0.0777

0.0676

Data

Rainfall days greater than 10mm, ASCII dataset

Mean annual rainfall, raster Mean annual evapotranspiration, raster

Watercourses, polyline

Watercourses, polyline

Source

BOM http://reg.bom.gov.au/jsp/ncc/c limate_averages/raindays/index .jsp?period=an&product=10mm #maps

BOM http://reg.bom.gov.au/jsp/ncc/climate_averages/ev apotranspiration/index.jsp?maptype=3&period=an http://reg.bom.gov.au/jsp/ncc/climate_averages/rai nfall/index.jsp

Queensland Government Data http://qldspatial.information.qld.gov.au/catalogue/c ustom/search.page?q=%22Watercourse%20identific ation%20map%20-%20watercourse%20%20Queensland%22

Queensland Government Data http://qldspatial.information.qld.gov.au/ca talogue/custom/search.page?q=%22Water course%20identification%20map%20%20watercourse%20-%20Queensland%22

Method

The RD10 ASCII dataset was imported to ArcMap, rasterized to number of days with precipitation greater than 10mm (‘heavy’ rainfall) was input into ArcMap and reclassified into six hazard classes as shown below.

ASCII datasets (, both based on the same 1961-1990 period ) of mean annual rainfall (recorded) and evapotranspiration (area potential, given thermal evaporation and vegetated transpiration) were rasterized, projected to MGA 55 an compared. The ratio of this water input and output is expressed as the Net Water Index. NWI = [precipitation] / [evapotranspiration] The index values were used to create six hazard classes, as shown below. NWI HCL

A Euclidean Distance was performed on the watercourse polylines in ArcMap , set to a maximum distance of 1000m. This was then reclassed into five set bounds distance from a watercourse, representing hazard. After rasterization, all remaining cells (beyond the initial Euclidean distance) were classed into the final sixth class, greater than 1000m.

Line Density tool in ArcMap was used with a search radius of 1km to determine the density of watercourses across Queensland. The result was reclassified into six classes of hazard, based on Chen, 2015.

Weighting Value

Days

HCL

74 - 81 60 -74

6 5

46 – 60 28 - 46 14 -28 4 – 14

4 3 2 1

> 1.0 0.5 – 1.0 0.4 – 0.5 0.3 – 0.4 0.2 – 0.3 0 - 0.2

6 5 4 3 2 1

Distance (m) < 200 200 - 400 400 - 600 600 - 800 800 - 1000 > 1000

HCL 6 5 4 3 2 1

Density (rivers/km2 ) > 3.8 2.3 - 3.8

HCL

1.5 – 2.3 0.8 – 1.5 0.2 – 0.8 < 0.2

4 3 2 1

6 5

Table 2 Hazard parameters, continued

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Queensland Flood Hazard and Risk Analysis

Hazard

Soil Types

Land Use

Weighting Value

0.0932

0.1075

Data

Polygons

Polygons

Source

Australian Soil Resource Information System (ASRIS) http://www.asris.csiro.au/themes/Atlas.html

Queensland Government Data https://data.qld.gov.au/dataset/land-use-mapping-series

Method

Soil types were classified into 6 categories in terms of its water retention capabilities from very high capacity to very low capacity as shown in the table below. The Polygon to Raster Tool was then used to create raster and applying into respective hazard classes.

Land uses as classified by the Queensland Government were organised into the 6 categories shown in the table below. The Polygon to Raster Tool was then used to create raster with the allocated hazard classes.

Soil types Hydrosol/Organosol Vertosol Ferrosois/Dermosol Chromosois/ Kandosol Tenosol/Kurosol/Sodosol/Calcarosol/ Rudosol Podosol

HCL 1 2 3 4 5 6

Land use Forest Grazing/Pastures Agriculture Urban Areas Mining

HCL 1 2 3 4 5

Water

6

These use categories and hazard classes were determined by analyzing and comparing existing flood hazard studies from Chen (2015), Kazakis (2015) and Blistonova (2016). We were careful to note the specific context of each, making conscious decisions of land use susceptibility for the Queensland context.

Table 3 Hazard parameters, continued

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Queensland Flood Hazard and Risk Analysis

Risk Elements Elements at risk

Infrastructure weighting value = 0.21

Weighting Value

0.13

0.14

0.11

Vulnerability

State Controlled Roads

Railway Network

Railway Stations

Data

Line

Line

Points

Source

Department of Transport and Main Roads from Queensland Government Data https://data.qld.gov.au/dataset/state-controlled-roadsqueensland

Queensland Government Data https://data.qld.gov.au/dataset/transport-featuresqueensland-series/resource/87227ac9-2148-4ed0-996f3f7a080f70a2

Queensland Rail from Queensland Government Data https://data.qld.gov.au/dataset/transport-featuresqueensland-series/resource/275ecb3d-0a95-4ab8-83b213a6b3f68ffc

Method

Feature Vertices to Point Tool is used to convert line data to points. The Extract Values to Points Tool was then used to classify each point with hazard values, incorporating the hazard values from the Hazard Map which are in the form of a raster.

Feature vertices to Point Tool is used to convert line data to points. After that, the Extract Values to Points Tool is used to classify each point with a hazard value from the Hazard Map.

Extract Values to Points Tool was used to categorize each point incorporating with the hazard values from the Hazard Map that was in a form of a raster.

Table 4 Risk vulnerabilities

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Queensland Flood Hazard and Risk Analysis

Elements at risk

Infrastructure weighting value = 0.21

Weighting Value

0.24

0.32

Vulnerability

Age Care and Retirement Facilities

Residential Properties at Extreme Hazard

Data

Points

Points

Source

Queensland Digital Cadastral Database (DCDB) from Queensland Government Data https://data.qld.gov.au/dataset/buildings-queensland-series/resource/4427378c-c223412e-840f-fa434ba4228e

Queensland Digital Cadastral Database (DCDB) from Queensland Government Data https://data.qld.gov.au/dataset/buildings-queensland-series/resource/4427378c-c223412e-840f-fa434ba4228e

Method

Age Care and Retirement Facilities is subtracted from the Queensland Government Building Points Data to these 2 interested categories from the attribute table. Then, the Extract Values to Points Tool is applied with the Hazard Map to incorporate with the given hazard values to each point.

Residential building points is subtracted from the Queensland Government Building Points Data, selecting it specifically using the field calculator from the attribute table. The Extract Values to Points Tool is then used to assign each point according to the hazard value from the hazard map. After that, only residential building points that has the attribute of extreme hazard category is only selected for the risk calculations. However, both maps with all residential properties and residential properties at extreme hazard is mapped and is shown in Figure 7 and Figure 8.

Table 5 Risk vulnerabilities, continued

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Queensland Flood Hazard and Risk Analysis

Elements at risk

Industry (weighting value = 0.31)

Weighting Value

0.23

0.17

0.14

0.11

Vulnerability

Emergency Services Building

Agriculture

Grazing Field and Pastures

Mining

Data

Points

Polygon

Polygon

Polygon

Source

DCDB from Queensland Government Data https://data.qld.gov.au/dataset/buildingsqueensland-series/resource/4427378cc223-412e-840f-fa434ba4228e Industries that is categorized under Emergency Services in the DCDB database is selected as listed below. Extract Values to Points Tool is used to classify each point incorporating with the hazard map’s hazard values.

ALUMC from Queensland Government Data https://data.qld.gov.au/dataset/land-usemapping-series

ALUMC from Queensland Government Data https://data.qld.gov.au/dataset/landuse-mapping-series

ALUMC from Queensland Government Data https://data.qld.gov.au/dataset/land-usemapping-series

Agriculture land use is selected and grouped using the data taken from the Queensland Land Use Data which is in the form of polygons. The Extract by Mask tool is then used to incorporate with the hazard values from the hazard map

Grazing Field and Pastures from the land use data is selected and grouped using the data taken from the Queensland Land Use Data which is in the form of polygons. To incorporate the hazard values from the hazard map into these polygons, the Extract by Mask tool is then used.

Mining areas is selected from the Queensland Land Use Data which is in the form of polygons. The Extract by Mask tool is then used to incorporate these polygons with the hazard values assigned to and from the hazard map.

Method

Emergency Services Building included: - Ambulance Stations - SES Coordination Centre - Fire Station - Marine Rescue & Coast Guard Station - Police Station - SES Facility - Water Police Facility

Table 6 Risk vulnerabilities, continued

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Queensland Flood Hazard and Risk Analysis

Elements at risk

Industry weighting value = 0.31

Weighting Value

0.08

0.27

Vulnerability

Commercial

Health and Welfare

Data

Points

Points

Source

DCDB from Queensland Government Data https://data.qld.gov.au/dataset/buildings-queensland-series/resource/4427378c-c223412e-840f-fa434ba4228e

DCDB from Queensland Government Data https://data.qld.gov.au/dataset/buildings-queensland-series/resource/4427378c-c223412e-840f-fa434ba4228e

Method

Commercial industries as listed below is selected and subtracted from the Queensland Government Building Points Data from its attribute table. The Extract Values to Points Tool is then used to align its hazard values with the hazard map.

The Queensland Government Building Points Data is used to select the Health and Welfare industries as shown below from its attribute table using the Field Calculator. Then, the Extract Values to Points Tool is utilized to incorporate with the hazard values of the hazard map.

Commercial Industries included: - Shopping Centre - Tourist Attractions - Tourism Hubs

Health and Welfare industries included: - General Hospitals - Nursing Homes - Medical Centre

Table 7 Risk vulnerabilities, continued

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Queensland Flood Hazard and Risk Analysis

Data Weighting It is recognised that all inputs considered thus far, hazard parameters and vulnerable elements, vary in their independent degree of influence and value respectively. Slope, for example, has the greatest influence on flooding risk, while health and welfare buildings are some of the most valuable to society. To quantify each input’s contribution, a pairwise comparison was used to calculate relative weightings. This allowed consideration of the academic literature to help inform this study, selectively applying appropriate precedent pairwise comparison from disparate analyses. While not all flood hazard and risk studies utilize this method of pairwise AHP weighting, we were able to benefit from those which did, including Chen (2015) and Kazakis (2015). It must be noted that, like the sourced reference studies, our comparisons of parameter weightings involve subjective decisions accounting for local context and research. Thus, different opinions and research will yield different weightings and therefore produce different output hazard models. These differences have been explored in our model validation. Hazard weightings, Table 8, were calculated and applied to the HCL value of each parameter layer when combined in ArcMap. The formula below represents this process. Total Hazard = 0.11 * "Elevation" + 0.13 * "Slope" + 0.04 * "Flow_direction" + 0.12 * Con(IsNull("Flow_accumulation"),0," Flow_accumulation ") + 0.13 * "Raindays_10" + 0.13 * NWI" + 0.08 * "River_proximity" + 0.07 * "River_density" + 0.10 * "Soil_types" + 0.11 * "Land_use"

The output hazard map, Figure 5, represents likelihood of flooding after considering all input parameters and weightings as calculated.

Elevation

Elevation

Slope

Fdirection

Faccumulation

RD10

NWI

Rproximity

Rdensity

Lithology

Land use

Weighting

1.00

0.67

2.00

0.67

0.67

0.67

2.00

2.00

3.00

2.00

0.1127

Slope

1.49

1.00

2.00

0.67

1.00

1.00

2.00

2.00

2.00

2.00

0.1328

Fdirection

0.50

0.50

1.00

0.33

0.33

0.33

0.33

0.33

0.33

0.33

0.0352

Faccumulation

1.49

1.49

3.03

1.00

1.00

1.00

1.00

1.00

1.00

1.00

0.1173

RD10

1.49

1.00

3.03

1.00

1.00

1.00

2.00

2.00

1.00

1.00

0.1280

NWI

1.49

1.00

3.03

1.00

1.00

1.00

2.00

2.00

1.00

1.00

0.1280

Rproximity

0.50

0.50

3.03

1.00

0.50

0.50

1.00

2.00

0.67

0.67

0.0777

Rdensity

0.50

0.50

3.03

1.00

0.50

0.50

0.50

1.00

0.67

0.67

0.0676

Lithology

0.33

0.50

3.03

1.00

1.00

1.00

1.49

1.49

1.00

0.67

0.0932

Land use

0.50

0.50

3.03

1.00

1.00

1.00

1.49

1.49

1.49

1.00

0.1075

Table 8 Hazard parameter weighting by pairwise comparison of influence to flooding

The vulnerability of elements at risk of flooding was calculated with additional pairwise comparisons, first considering a selection of various components of infrastructure (Table 9) and industry (Table 10). The element groups of population, environment, industry and infrastructure were then comparede (Table 11). The equations below incorporate all the applied relevant weightings on hazards, elements at risk and vulnerabilities to generate the total risk map, hence, establishing the flood risk map.

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Queensland Flood Hazard and Risk Analysis

đ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒ = 1 Ă— Population_ Density đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸ = 1 Ă— Environmental_ Conservation đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź = (0.23 Ă— Emergency_Services_Buildings) + (0.17 Ă— Agriculture) + (0.14 Ă— Grazing) + (0.11 Ă— Mining) + (0.08 Ă— Commercial) + (0.27 Ă— Health_and_Welfare) đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź = (0.14 Ă— Rail_Network) + (0.11 Ă— Rail_Stations) + (0.19 Ă— State_Controlled_Roads) + (0.24 Ă— Agecare_Retirement_Facilities) + ďż˝0.32 Ă— Residential_BuildingsExtreme_Hazard ďż˝ đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡đ?‘‡ đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘… = (đ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ťđ??ť) Ă— (0.36 Ă— đ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒđ?‘ƒ) Ă— (0.13 Ă— đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸đ??¸) Ă— (0.31 Ă— đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź) Ă— (0.21 Ă— đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?)

As applied in ArcMap using the Raster Calculator tool: Total Risk = “Hazard_Map�*(0.36*Pop + 0.13*Con(IsNull(‘’Environment’’),0, ‘’Environment’’) + 0.31*Con(IsNull(‘’Industry’’),0, ‘’Industry’’) + 0.21*Con(IsNull(‘’Infrastructure’’),0, ‘’Infrastructure�))

Railway networks

Railway stations

State controlled roads

Retirement and Aged care

Residential properties

Weighting

Railway networks

1.00

2.00

0.50

0.50

0.50

0.1433

Railway stations

0.50

1.00

0.50

0.50

0.50

0.1078

State controlled roads

2.00

2.00

1.00

0.50

0.50

0.1867

Retirement and Aged care

2.00

2.00

2.00

1.00

0.50

0.2422

Residential properties

2.00

2.00

2.00

2.00

1.00

0.3200

Table 9 Infrastructure parameter weighting by pairwise comparison of value Emergency services

Agriculture

Grazing

Mining

Commercial

Health & welfare

Weighting

1.00

2.00

2.00

2.00

2.00

1.00

0.2333

Agriculture

0.50

1.00

2.00

2.00

2.00

0.50

0.1702

Grazing

0.50

0.50

1.00

2.00

2.00

0.50

0.1365

Mining

0.50

0.50

0.50

1.00

2.00

0.50

0.1086

Commercial

0.50

0.50

0.50

0.50

1.00

0.50

0.0847

Health & welfare

2.00

2.00

2.00

2.00

2.00

1.00

0.2667

Emergency services

Table 10 Industry parameter weighting by pairwise comparison of value Population

Environment

Industry

Infrastructure

Weighting

Population

1.00

2.00

2.00

2.00

0.3611

Environment

0.50

1.00

0.50

0.50

0.1250

Industry

2.00

2.00

1.00

1.00

0.3056

Infrastructure

1.00

1.00

1.00

1.00

0.2083

Table 11 Risk parameter group weighting by pairwise comparison of value

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Queensland Flood Hazard and Risk Analysis

Elements at Risk Population The estimated resident population of Queensland in 2017 is 4,928,457 people and a population density of 0.03 people per hectare (.ID the population experts, 2017). The distribution of people is concentrated in the urban areas of the state on the eastern coast line. Brisbane, Gold Coast, Moreton Bay and Sunshine Coast have the largest population per Local Government Area (LGA) compared to the rest of the state (Queensland Government, 2017). The largest growth areas in terms of population is concentrated in South East Queensland, and notable levels of growth are occurring in regional towns in the north - Cairns, Toowoomba and Townsville. As of June 2016, 39 LGA’s in regional Queensland had estimated resident populations fewer than 1000 persons (Queensland Government, 2017). Environment Queensland is a biodiversity hotspot. Covering 172.8 million hectares, Queensland has a mainland coastline of 6900km and 1165 offshore islands and cays (Queensland Government, 2016). Home to many diverse plant and animal species which are found in a variety of different ecosystems such as desert, rainforests and coral reefs. Similarly, Queensland’s climate is broad with areas of temperate, wet and dry tropics as well as semi-arid, arid climatic zones. Current pressures on terrestrial ecosystems are land clearing and fragmentation for pasteurisation and urban development. There are 13 bioregions to represent the primary level of biodiversity classification in Queensland. They range from grasslands to temperate rainforest to arid desert zones. As of 2015 there were 1383 regional ecosystems, about 80% of the state – 9% are in protected areas. The main vegetation groups in Queensland are Acacia and Eucalyptus forests which as of 2013 had less than 60% remnant native vegetation (Queensland Government, 2016). Pressure applied to native biodiversity is in the form of invasive non-native species of plant and animals, most evident in South East Queensland where heavy fragmentation has resulted in only 13.7% of intact remnant patches of 1000 hectares or more remaining. Terrestrial ecosystems are essential for providing assets such as food and fuel for both humans and the species that live within them and regulate them. Freshwater wetland ecosystems work to mitigate floods and provide potable water for humans, industry and agriculture. As of 2015, 94% of pre-European settlement freshwater wetlands remain intact, however, only 8% are under protection from the state (Queensland Government, 2016). Freshwater ecosystems are essential in reducing the impact of flooding on urban areas within Queensland. Queensland’s climate is strongly influenced by seasonal variations. Those that influence the extent of flooding are the annual summer monsoon season and the El Nino Southern Oscillation phenomenon which changes from the El Nino phase to the La Nina phase around every 10 years. El Nino is associated with below average rainfall throughout winter, spring and summer whereas the La Nina is linked to above average occurrence of tropical cyclones, summer rainfall, and floods (Queensland Government, 2016). Furthermore, warmer than average sea surface temperatures also have a great impact on Queensland’s weather systems as they are responsible for flood producing rainfall and damaging wind and storm surges affecting the east coast of Queensland. Currently, sea levels are rising putting cities and coastal towns at risk of flooding.

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Queensland Flood Hazard and Risk Analysis

Industry Agriculture accounts for 2.5% of Queensland economy and was the base industry upon which Queensland was founded. Employing 57000 people, more than half of agriculture output is produced in the Darling Downs-Maranoa, Queensland Outback, Fitzroy and Wide Bay regions (Queensland Treasury, 2017). Most agriculture is produced for export. $9.1 billion of rural commodities was exported in 2014-15, over half went to China and Japan (Queensland Treasury, 2017). Agriculture is particularly impacted by flooding compared to the other major industries. Queensland’s resource sector accounts for about 7.4% of its total economy. Exported resources include natural gas, hard coking coal, minerals, semi-soft/PIC coal and thermal coal. More than 90% of Queensland’s saleable coal is exported overseas to China, Japan, India, Korea and Taiwan (Queensland Treasury, 2017). As a result, Queensland is the world’s largest seaborn exporter of metallurgical coal which exceeded 220 million tonnes in 2014-15 (Queensland Treasury, 2017). Construction is the third largest industry employer, employing almost 217000 people and makes up 11% of the economy. The surge in resources investment and the coal and LNG booms have resulted in the engineering construction industry rising significantly from 2004-2014 (Queensland Treasury, 2017). Queensland has the second largest tourism industry in Australia after New South Wales. Tourism makes up 24.6% of the national total and employs around 130900 people. Brisbane is the most popular tourist destination where 39% of international visitors stayed in 2015 (Queensland Treasury, 2017). Infrastructure The two fundamental drivers for infrastructure in Queensland are economic activity and population growth. Population growth will result in an increased demand for essential services such as education, water and transport. South Eastern Queensland has the fastest growing population and is home to Queensland’s largest cities and towns therefore it has different needs for infrastructure compared to its regional counterparts. There is a general shift from employment in the resource sector to services in Queensland. The largest employment sector is health care and social assistance, combined with the states ageing population will need an urgent increase in nursing homes, hospitals and emergency services as well as accommodating residential growth with public transport, roads, digital and residential infrastructure. Most of this will occur in South Eastern Queensland, particularly Brisbane and Gold and Sunshine Coasts as it holds 67% of the state’s population (Queensland Government, 2016). Consequently, South Eastern Queensland is most vulnerable to flooding. Every year the Queensland government spends millions of dollars repairing infrastructure after flood damage has occurred. After the January 2011 flood in Brisbane, it cost $440 million to repair the cities infrastructure, assets, transport, waterways, parks and community areas. $156 million was spent on roads and related infrastructure, $40 million for disaster related clean up, $19 million dollars for drain networks repairs, $6 million for wharves, jetties and pontoons and $4.5 million for pools libraries (Brisbane City Council, 2012). Transport networks are always severely affected after flood damage. Between 2010 and 2013 it cost $64 billion to repair 8741 kilometres of road, 1733 bridges and culverts - more than 25% of the total road network in Queensland (Queensland Government, 2016).

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Queensland Flood Hazard and Risk Analysis

Computation of Elements at Risk The applied hazard values for each vulnerability and each element led to the formulation of a series of comprehensive risk maps (Figure 6 to Figure 14). Furthermore, the competency of these risk maps can allow the Queensland State Government and other important stakeholders to analyze the different elements at risk more empirically. To ensure that the Raster Calculator operates properly in its final calculation with the added layers, vulnerability layers in the form of polygons and points were converted into a raster using the Polygon to Raster and the Points to Raster Tool. Moreover, for each raster layer that consists of NoData values in its respective cells is then set to a value of 0 using the Raster Calculator. However, it was noted that some of the vulnerability layers have empty cells. Therefore, the Mosaic to New Raster Tool is used to merge the existing Queensland State boundary dataset with these problem layers, both in the form of a raster. Thus, reclassifying the NoData cells from the output rasters with the value 0. Conversely, as there were many vulnerability datasets found with many empty cells, the Model Builder was used to building geoprocessing these workflows, hence allowing the multiple merging processes to running simultaneously and rapidly (see Figure 2 and Figure 3).

Figure 2 Model Builder sequence for infrastructure

Figure 3 Model Builder sequence for industry

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Queensland Flood Hazard and Risk Analysis

Validation Due to the myriad choices available in risk analysis, must be performed to assess the accuracy and reliability of our model and its output. Accuracy has been tested with a comparison to the Queensland Government’s Floodplain Assessment Overlay, and reliability through comparison to known historic flood extents. Furthermore, noting the subjective nature of many decisions made, additional comparisons were made between our model with equal-weighting of inputs and our chosen weightings. The mapped results of these comparisons can be seen in Figure 15. Appendix II shows raw calculations. Because the comparison Overlay is binary, and our model comprises four classes of hazard, validation has been made by taking HC 1 and 2 as ‘less likely’ to flood and HC 3 and 4 as ‘more likely’ to flood. Assuming HC 1 and 2 would therefore not be included in the Overlay and HC 3 and 4 would be, accuracy per hazard class of our pairwise weighted model compared to an equal weighted model is shown below. Hazard class 1 2 3 4

Equal weighting 77.2% 65.1% 34.9% 39.3%

Pairwise comparison weighting 82.2% 67.5% 38.1% 59.9%

Table 12 Accuracy validation summary. Accuracy within each hazard class for both model weightings is determined by the number of cells within their 'correct' location in the Floodplain Assessment Overlay. Accuracy in hazard classes 1 and 2 is determined by the percentage of cells ‘correctly’ outside the overlay. Accuracy in hazard classes 3 and 4 is determined by the percentage of cells ‘correctly’ inside the overlay.

To validate model reliability, selected flooding extents were compared with our model to determine what HC would have been assigned to areas of known flooding. The results are summarized below. Hazard class 1 2 3 4

Equal weighting 10.0% 37.2% 34.4% 14.0%

Pairwise comparison weighting 3.3% 33.1% 42.5% 21.2%

Table 13 Reliability validation. Reliability of the model under both equal weighting and pairwise comparison weightings is shown by the per-hazard class composition of all seven selected flood extents. Higher reliability will occur with greater percentages in hazard classes 3 and 4.

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Queensland Flood Hazard and Risk Analysis

Maps Hazard Analysis Flood Hazard Parameters

a

b

c

d

e

f

g

h

Figure 4 Flood parameters

input where (a) Elevation, (b) Slope, (c) Flow direction, (d) Flow accumulation, (e) Rain days more than 10mm, (f) Mean annual NWI, (g) Distance to rivers, (h) Density of Rivers, (i) Soil types, (j) Land use.

i

j

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Queensland Flood Hazard and Risk Analysis

Flood Hazard Map

Figure 5 Total hazard map

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Queensland Flood Hazard and Risk Analysis

Risk Analysis Flood Risk Vulnerabilities

Figure 6 Total infrastructure risk map

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Queensland Flood Hazard and Risk Analysis

a

b

c

d

Figure 7 Infrastructure vulnerabilities input where (a) State controlled roads, (b) Railway networks, (c) Railway stations, (d) Age care and retirement facilities, (e) Residential buildings, (f) Residential buildings at extreme hazard e

f

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Queensland Flood Hazard and Risk Analysis

Figure 8 Total industry risk map

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Queensland Flood Hazard and Risk Analysis

a

b

c

d

Figure 9 Industry vulnerabilities input where (a) Commercial buildings, (b) Health and welfare buildings, (c) Emergency services buildings, (d) Grazing fields and pasture areas, (e) Agriculture areas, (f) Mining areas e

f

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Queensland Flood Hazard and Risk Analysis

Figure 10 Total environment risk map

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Queensland Flood Hazard and Risk Analysis

Figure 11 Environmental conservation areas. Classifications are described by CLUM.

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Queensland Flood Hazard and Risk Analysis

Figure 12 Total population risk map

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Queensland Flood Hazard and Risk Analysis

Figure 13 Population risk per key urban centres, extracted from Figure 12

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Queensland Flood Hazard and Risk Analysis

Flood Risk Map

Figure 14 Composite flood risk map, including all input hazard parameters and risk vulnerabilities, weighted for influence and value respectively

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Queensland Flood Hazard and Risk Analysis

Flood Hazard Validation

a

b

c

d

Figure 15 Validation of flood maps where (a) Historic extent of flood inundation (Bundaberg 2010, Brisbane & Ipswich 1974, Bundaberg 2013, SW QLD 2012, QLD 2011, Logan & Albert Rivers 2017, QLD 2012, Mackenzie River 2017), (b) Floodplain assessment overlay, (c) Equal weighted flood inundation, (d) Pairwise weighted flood inundation, (e) Equal weighted overlay assessment, (f) Pairwise overlay assessment e

f

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Queensland Flood Hazard and Risk Analysis

Discussion Limitations Several limitations must be acknowledged when using GIS before considering the results generated. For example, all the data presented in the hazard and risk maps are static however they are constantly changing in real time. A balance between input data quality and processing constraints was required, particularly regarding the DEM due to the large scale of our study area. Therefore, DEM data were processed with a resolution of 3 arcseconds, approximately 90m. This choice directed all other input data and spatial processing, limiting our output analysis to 90m cell sizes. As a result, data representation at a smaller scale was distorted, causing inaccuracy and incompleteness of the data. Selecting a higher resolution would provide a more accurate analysis over the scale of the study area, although 90m cell size was the optimal balance between accuracy and processing speed over state level. Furthermore, the symbols used in our maps neglect the exact size and types of buildings (see Figure 2 to 6), not considering of the risks associated with different building types in terms of individual value. Conversely, working with SA2 demographics resulted in drastic differences in terms of population density. The combination of rural and urban SA2 areas does not provide useful data regarding analyzing flood risk at a statewide scale. In addition, other factors such as age groups that are more vulnerable to flooding could be included to create more impactful results. In conclusion, the incorporation of more variables into the datasets of hazards, elements at risk, and vulnerabilities such as schools and factories will produce a more thorough analysis of flood risk.

Analysis Validation Validation with both Floodplain Assessment Overlay and flood extent comparisons showed a general improvement of both accuracy and reliability in our model through a pairwise comparison. While further modifications could be made to increase accuracy and reliability, a greater number of sources of comparison would also help the validation process.

Response Recommendations Accounting for the data from our maps, the most dynamic hazards contributing to our Composite Weighted Flood Risk Map include the slope, elevation, NWI, and rainfall. The highest risk levels can be found along the coast, namely in South Eastern Queensland and the northern part of Queensland. The highest risk scores can be found in these areas not only because of their exposure to hazards but also because of the valuable features they contain. In the greater Brisbane area, there are more than one million people, around 67% of the state (Queensland Government, 2016). This high density of people factors significantly, as population was given a 0.36 weighting in our Composite Weighted Flood Risk Map. Naturally, with a large population comes lots of infrastructure, which also produces a high embedded value of vulnerability. As a result of this cumulation of vulnerable features paired with a high hazard score, we have chosen to focus on the greater Brisbane area for a portion of our recommendations. Additionally, the northern part of Queensland shares a similar hazard level Page 33 of 40

Queensland Flood Hazard and Risk Analysis

because of the many environmental protection areas it contains which was attributed to a high risk level. As Greater Brisbane is most at risk of flooding according to our weighted maps, the most effective ways of minimising flood risk damage are infrastructural investment and the spread of warnings and information to the population. Infrastructure changes that will reduce the flood hazard can be classified in three parts; existing water stores, defensive measures and quickly and easily rebuilding programs. Firstly, to mitigate flood hazard it is important to review current storage capacities of existing storages, operational strategies and assess whether new stores are necessary (Queensland Government, 2017). Dedicated flood mitigation compartments in existing dams, ‘dry’ dams or storm water detention basins and levees are the best preventive measure available with these techniques already implemented in Wivenhoe and Somerset Dams in South East Queensland. The next most important component of minimizing flood damage is to spread information to as much of the population as possible. Information about how to react to a flood, when areas are at higher risk of flooding, and what everyone can do to cause as little damage as possible can be extremely effective. Fortunately, the Queensland government has already set up a very welldesigned and detailed website providing all this information for the public at Queensland Government (Queensland Government, 2018). Beyond this, Queensland has set up other useful tools to share knowledge about flood risk, such as their FloodWise Property Report (Brisbane City Council, 2017), in which residents can enter their address and receive a report on what level of risk their property is at of flooding. Finally, urban drainage systems are extremely important to mitigating flood damage and need to be as capable as possible to handle high volumes of storm water. The Brisbane city council has developed a tool to receive complaints from residents regarding the storm water drains (Brisbane City Council, 2017), whether they are damaged or a resident thinks one should be added in a specific space. This website should also be promoted in the hopes that residents of Brisbane can help to keep the city’s storm water drains at peak performance. Therefore, an emphasis should be placed on promoting these websites and spreading awareness about the information they contain to as much of the population as possible. As for the northern part of Queensland, the myriad of National Parks and preserved land should be left to flood occasionally as a natural process uninhibited. However, it is important to keep visitors and campers informed about the hazards involved with entering this area. Particularly when a park may be at high risk of flooding, it would be safest to not allow visitors in. Spreading information about flooding in the area through signposts, brochures, and informed staff members will help to keep everyone who enters this area as safe as possible, while allowing natural processes to continue.

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Queensland Flood Hazard and Risk Analysis

Conclusion Flooding is a frequent natural disaster in Queensland that has detrimental consequences to the infrastructure, environment, homes and lives of people affected. The areas at risk of flooding must be communicated to the public effectively. The most proficient way of communication is a flood hazard risk map which has comprehensively analysed, sourced and weighted all variables; societal, infrastructural and environmental associated with the risk of flooding. This resource can be used to identify areas most at risk, and asses and provide recommendations to regions with the most consequential outcomes to flooding. Most effective recommendations include; investment in preventative infrastructure and developing warning devices and action plans to remove the public to safety in time. Overall, the created flood hazard risk map in this report is invaluable regarding public safety and protecting state wide assets.

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Queensland Flood Hazard and Risk Analysis

References .ID the population experts. (2017). Queensland about the profile areas. Retrieved from Australia Community Profile. Australian Government. (2008). THE CANE TOAD (BUFO MARINUS). Department of the Environment, Water, Heritage and the Arts . environment.gov.au. Australian Government. (2014). The evolution of Australian towns. Department of Infrastructure, Regional Development in Cities. Australian Government. Australian Government, Bureau of Meteorology . (2018). Average annual & monthly days of rain. Commonwealth of Australia. Australian Soil Resource Information System. (2013). Atlas of Australian Soils. CSIRO. Blistanova, M., Zeleňáková, M., Blistan, P., & Ferencz, V. (2016). Assessment of flood vulnerability in Bodva river basin, Slovakia. Acta Montanistica Slovaca, 21(1). Brisbane City Council. (2012). Brisbane City Council 12-month Flood Recovery Report. Brisbane: Brisbane City Council. Brisbane City Council. (2017). Flood Wise Property Reports. Retrieved from Bisbane City Council: https://www.brisbane.qld.gov.au/planning-building/planning-guidelines-tools/online-tools/floodwise-propertyreports Brisbane City Council. (2017). Report it: Stormwater drains. Retrieved from Bisbane City Council: https://www.brisbane.qld.gov.au/about-council/contact-council/report-it-stormwater-drains Chen, Y., Liu, R., Barrett, D., Gao, L., Zhou, M., Renzullo, L., & Emelyanova, I. (2015, December 15). A spatial assessment framework for evaluating flood risk under extreme climates. Science of the Total Environement, 538, 512-523 . Geosicence Australia. (2017). National Elevation Data Framework (NEDF). Canberra: Australian Government. Glas, H. J.-W. (2017). AGIS-based tool for flood damage assessment and delineation of a methodology for future riskassessment: case study for Annotto Bay, Jamaica. Natural Hazards, 88(3), 1867-1891. Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region, Greece. Science of the Total Environment, 538, 555-563. Montgomery, D. (1994). Road surface drainage, channel initiation, and slope instability. Water Resources Research, 30(6), 1925-1932. Queensland Government. (2016). Queensland State of the Environment 2015 in Brief. Department of Environment and Heritage Protection. Brisbane: State of Queensland. Queensland Government. (2016). State Infrastructure Plan Part A: Strategy. Department of Infrastructure, Local Government and Planning. Brisbane: State of Queensland. Queensland Government. (2017). Population growth highlights and trends, Queensland, 2017 edition. Queensland Treasury. Brisbane: Queensland Government Statistician’s Office. Queensland Government. (2017). Queensland bulk water opportunities statement. Department of Energy and Water Supply. State of Queensland. Queensland Government. (2018). Prepare for your decisions to affect others. Retrieved from If Its Flooded, Forget It: http://floodwatersafety.initiatives.qld.gov.au/ Queensland Government. (2018). Queensland Spatial Catalogue . The State of Queensland. Queensland Treasury. (2017, June 19). Queensland Economy. Retrieved from Queensland Treasury, Queensland Government: https://www.treasury.qld.gov.au/economy-and-budget/queensland-economy/ UN Office for Disaster Risk Reduction. (2015). The human cost of natural disasters 2015: a global perspective. Centre for Research on the Epidemiology of Disasters. Reliefweb.

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Appendix I – Soil Types (1=Very High Capacity, 2=High Capacity, 3=High/Moderate Capacity, 4= Moderate Capacity, 5=Low Capacity, 6=Very Low Capacity)

I= Hydrosol

1

J= Hydrosol

1

Jw= Hydrosol

1

Io= Hydrosol

1

Jb= Hydrosol

1

Mc= Hydrosol 1 MF= Hydrosol 1 NV= Hydrosol 1 NX= Hydrosol 1 NY= Hydrosol 1 NZ= Hydrosol 1 QM= Hydrosol 1 SV= Hydrosol

1

Z= Organosol

1

CC= Vertosol

2

In= Vertosol

2

Kb= Vertosol

2

Kd= Vertosol

2

Kf= Vertosol

2

Kh= Vertosol

2

NN= Vertosol

2

Md= Ferrosol

3

Mo= Ferrosol

3

TM= Hydrosol 1 CB= Vertosol

2

II= Vertosol

2

Ka= Vertosol

2

Kc= Vertosol

2

Ke= Vertosol

2

Kg= Vertosol

2

MM= Vertosol 2 OO= Vertosol

2

Mg= Ferrosol

3

Mp= Ferrosol Ab= Chromosol

3 4

CM= Kandosol 4 EE= Kandosol 4 Fb= Kandosol

4

Fp= Kandosol

4

FF= Dermosol 4

Mh= Dermosol 4 Mi= Dermosol 4 MJ= Dermosol 4 Mj= Dermosol 4 Mk= Dermosol 4 MK= Kandosol 4 Ml= Dermosol 4

Gb= Dermosol 4

ML= Kandosol 4 Mm= Dermosol 4

Gh= Dermosol 4

MN= Kandosol 4

G= Dermosol

4

GG= Dermosol 4 Gj= Dermosol

4

H= Dermosol

4

Gz= Dermosol 4 Hc= Dermosol 4 HF= Chromosol 4 Ib= Dermosol

4

Ii= Dermosol

4

Ja= Kandosol

4

If= Dermosol

4

Ij= Dermosol

4

LM= Dermosol 4 LN= Dermosol 4 M= Dermosol

4

Ma= Kandosol 4 MB= Kandosol 4 Mb= Kandosol 4 MD= Dermosol 4 ME= Dermosol 4 Me= Dermosol 4 Mf= Dermosol 4 MG= Dermosol 4

Mn= Dermosol 4 MO= Kandosol 4 MP= Kandosol 4 MQ= Kandosol 4 Mq= Kandosol 4 MR= Kandosol 4 Mr= Kandosol 4 MS= Kandosol 4 Ms= Kandosol 4 MT= Kandosol 4 Mt= Kandosol 4 Mu= Kandosol 4 MV= Kandosol 4 MW= Dermosol 4 Mw= Kandosol 4 MX= Dermosol 4 Mx= Kandosol 4 MY= Kandosol 4 My= Kandosol 4 MZ= Kandosol 4 Mz= Kandosol 4 O= Chromosol 4

Oa= Chromosol

4

Pa= Chromosol

4

Ob= Chromosol

4

Pb= Chromosol

4

Pc= Chromosol 4 Pj= Chromosol 4 Pk= Chromosol 4 Ps= Chromosol 4 Q= Chromosol 4 Qa=Chromosol 4 Qb= Chromosol 4 Qj= Chromosol 4 Qr= Chromosol 4 Qs= Chromosol Ra= Chromosol

4 4

Rb= Chromosol

4

Rd= Chromosol

4

Rh= Chromosol

4

Rc= Chromosol

4

Rf= Chromosol 4 Rg= Chromosol 4 Rs= Chromosol

4

S= Chromosol 4 Sc= Chromosol 4 Page 37 of 40

Queensland Flood Hazard and Risk Analysis

Sd= Chromosol

4

Se= Chromosol 4 Sp= Chromosol 4 SQ= Chromosol

4

Tf= Chromosol 4 Ua= Chromosol 4 Uc= Chromosol Vc= Chromosol Wb= Chromosol Wc= Chromosol WM= Dermosol

4 4 4 4 4

Xc= Chromosol 4 A= Rudosol

5

AA= Tenosol

5

Ac= Sodosol

5

AD= Tenosol

5

Aa= Kurosol

5

AB= Tenosol

5

AC= Tenosol

5

AY= Tenosol

5

B= Rudosol

5

BC= Rudosol

5

BE= Tenosol

5

AZ= Tenosol

5

BB= Calcarosol 5 BD= Tenosol

5

BF= Calcarosol 5 BG= Calcarosol 5 BV= Rudosol

5

BY= Rudosol

5

BX= Rudosol

5

Bz= Rudosol

5

Cd= Tenosol

5

D= Tenosol DD= Calcarosol

5

F= Tenosol

5

Fc= Tenosol

5

Fq= Tenosol

5

FV= Tenosol

5

Ca= Tenosol

5

Cz= Tenosol

5

5

E= Tenosol

5

Fa= Tenosol

5

Fd= Calcarosol 5 Fu= Tenosol

5

Fx= Tenosol

5

Fz= Rudosol

5

Ge= Tenosol

5

Gg= Tenosol

5

HG= Sodosol

5

JJ= Tenosol

5

JV= Tenosol

5

JY= Tenosol

5

KK= Tenosol

5

Fy=Calcarosol 5 Gd= Tenosol

5

Gf= Tenosol

5

Gk= Tenosol

5

HH= Sodosol

5

JK= Tenosol

5

JX= Tenosol

5

JZ= Tenosol

5

KL= Tenosol

5

La= Calcarosol 5 Lb= Calcarosol 5 Ld= Calcarosol 5

Lg= Calcarosol 5

Td= Kurosol

5

LK= Tenosol

5

Ub= Sodosol

5

Na= Sodosol

5

Ui= Sodosol

5

Nc= Sodosol

5

Vb= Sodosol

5

Oc= Sodosol

5

Wa= Kurosol

5

Og= Sodosol

5

Wf= Kurosol

5

P= Sodosol

5

Xd= Sodosol

5

Pf= Kurosol

5

Yb= Sodosol

5

Pl= Kurosol

5

Qc= Sodosol

5

Re= Sodosol

5

Rp= Sodosol

5

Rr= Sodosol

5

Rz= Sodosol

5

Si= Sodosol

5

Sk= Sodosol

5

Ta= Kurosol

5

Tc= Kurosol

5

Lh= Calcarosol 5 LL= Tenosol

5

Nb= Sodosol

5

Nd= Sodosol

5

Od= Sodosol

5

Ok= Sodosol

5

Pd= Kurosol

5

Ph= Sodosol

5

Pu= Kurosol

5

Qd= Sodosol

5

Ro= Sodosol

5

Rq= Sodosol

5

Rt= Sodosol

5

Sh= Sodosol

5

Sj= Sodosol

5

Sl= Sodosol

5

Tb= Sodosol

5

Ti= Sodosol

5

Uf= Sodosol

5

Va= Sodosol

5

Vd= Sodosol

5

Wd= Kurosol

5

X= Sodosol

5

Ya= Sodosol

5

Cb= Podosol

6

Page 38 of 40

Queensland Flood Hazard and Risk Analysis

Appendix II – Sample Screenshots

Calculations in Microsoft Excel of model validation.

Calculations in Microsoft Excel of pairwise comparison weighting.

Page 39 of 40

Queensland Flood Hazard and Risk Analysis

Appendix III – MODIS Flood Extent Imagery

Page 40 of 40

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