Defining flood risk management strategies: A systems approach

Journal Pre-proof Defining flood risk management strategies: A systems approach Thanh Mai, Shahbaz Mushtaq, Kate Reardon-Smith, Paul Webb, Roger Stone, Jarrod Kath, Duc-Anh An-Vo PII:

S2212-4209(19)30729-0

DOI:

https://doi.org/10.1016/j.ijdrr.2020.101550

Reference:

IJDRR 101550

To appear in:

International Journal of Disaster Risk Reduction

Received Date: 6 June 2019 Revised Date:

4 October 2019

Accepted Date: 26 February 2020

Please cite this article as: T. Mai, S. Mushtaq, K. Reardon-Smith, P. Webb, R. Stone, J. Kath, D.-A. AnVo, Defining flood risk management strategies: A systems approach, International Journal of Disaster Risk Reduction (2020), doi: https://doi.org/10.1016/j.ijdrr.2020.101550. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Defining Flood Risk Management Strategies: A Systems Approach

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1

Abstract:

2

Recent years have seen a growing recognition of and attention to strengthening

3

community flood resilience as a key leverage for achieving sustainable flood risk

4

management. The literature on the success of focusing on ex-post responses to flood events

5

shows poor outcomes in mitigating flood risks across the board. Here, we employ systems

6

thinking to conceptualise the dynamics underlying interactions among endogenous

7

characteristics of the flood-prone community, and apply this, as a case study example, to the

8

regional township of Roma in inland Queensland, Australia—a location that has experienced

9

significant flooding in recent decades. Our results show that current flood mitigation policies

10

are likely to be maladaptive due to unintended consequences that ultimately undermine the

11

effectiveness of the interventions in the longer term. Based on our analysis, we conclude that

12

integrated flood risk management, effected through well-targeted proactive investments—

13

such as investment in infrastructure; technology advances; capacity improvements; shifts in

14

systems, management practices and behaviour—and risk transfer (insurance) options,

15

provides opportunities for reducing future relief and recovery costs and increasing fiscal

16

stability and the long term well-being of communities in the face of increasing climate

17

variability and flood risk under climate change. Conceptualisation and visualisation of the

18

flood risk system, as exemplified in this paper, provides insights which might help to guide

19

more cost-effective and sustainable flood risk mitigation investment in flood-prone

20

communities.

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Keywords: dynamic complexity, feedback loops, flood risk management, flood-prone community, insurance

2

1

1. Introduction

2

Floods are a common extreme natural event, caused by a variety of external factors

3

(e.g. heavy rainfall, river overflows, tidal surges) (Shah et al., 2018) and represent significant

4

risk to human settlements throughout the world (Kundzewicz et al., 2018). Climate change,

5

along with associated increases in climate variability, is likely to drive more frequent and

6

severe flood events in many locations (Cai et al., 2015; Morrison et al., 2018; Kundzewicz et

7

al, 2019). During the last five decades, the number of globally reported flood events has

8

steadily increased (Tanoue et al., 2016), affecting increasingly large numbers of people and

9

causing significant economic loss and damage to affected environments (López-Marrero and

10

Tschakert, 2011; Kundzewicz et al., 2014; Tanoue et al., 2016). Approaches to Food Risk

11

Management (FRM) have often focused on the adoption of resistance-based strategies, which

12

attempt to control flood threats through large investments in infrastructure and influence

13

flood behaviour though laws and regulations (Morrison et al., 2018). However, despite

14

international efforts and technological advancements in FRM, flood losses continue to rise

15

(Johnson and McGuinness, 2016; Tanoue et al., 2016), raising questions as to the efficacy of

16

current approaches.

17

Studies have shown that flood risk is a multifaceted problem that produces a complex

18

web of impacts that affect many sectors of the economy (Armah et al., 2010; Akmalah and

19

Grigg, 2011; Barendrecht et al., 2017; Mavhura, 2019). Vulnerability to flooding is not solely

20

a function of exposure to external hazards such as climatic extremes. Rather, it is a result of

21

dynamic interactions involving multiple interdependent endogenous (e.g. physical, socio-

22

economic, hydrological) factors of flood-prone communities (Mavhura, 2019) that increase

23

an individual’s or a community’s susceptibility to impact as a result of that hazard. In

24

addition, managing flood risk—the potential for harmful consequences due to the combined

25

effect of both flooding hazard and vulnerability—often involves a diverse array of

26

stakeholders, each with different management objectives that potentially lead to unforeseen

27

conflicts of interest and/or consequences arising from the formulation and implementation of

28

FRM policies and strategies. For instance, homeowners and businesses may be primarily

29

concerned with engineered solutions such as flood levees to prevent inundation of buildings;

30

land developers may focus more on aspects of land use planning; and insurers and

31

government agencies may prefer market-based solutions which compensate for financial

32

losses (Deegan, 2005). Interactions between all the aforementioned factors mean that flood

33

risk systems are inherently dynamic and complex.

3

1

Despite a growing sense of the dynamics and complexity of flood risk systems,

2

research, government policy and measures designed to mitigate flood risk have primarily

3

focused on ex post responses to flood events (Surminski and Thieken, 2017), although there

4

are exceptions (e.g. the ‘Room for the River’ program in the Netherlands (Zevernbergen et

5

al., 2015) and the ‘EU Floods Directive’ (Adamson, 2018)). Reactive or crisis-driven

6

approaches, which persist in most countries, often emphasise certain components of the

7

system and fall short in addressing the greater complexity in the system associated with

8

multiple pressure and feedback loops which may obscure the underlying drivers (Ismail et al.,

9

2017). In many cases, structural engineering solutions (e.g. flood levees) play a key role in

10

FRM (Schelfaut et al., 2011; Tanoue et al., 2016). Alongside these, non-structural solutions

11

have also been instigated through land use modification (Egli, 2002; White and Richards,

12

2007; Dawson et al., 2011) and early warning systems to enhance flood preparedness (Collins

13

and Kapucu, 2008; Richards et al., 2008; Sweta, 2014; Girons Lopez et al., 2017). However,

14

despite the validity of these approaches, there is growing consensus that none of these

15

solutions succeeds in isolation (Hartman, 2011; Di Baldassarre et al., 2013a, b) and that a

16

diversity of integrated context relevant solutions is needed to enhance flood resilience

17

(Dieperink et al., 2018).

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To address these shortcomings, we contend that the study of FRM would benefit from

19

the application of systems thinking which may provide insight into the complex set of

20

dynamic interacting flood drivers and inhibitors operating, as well as their potential impact on

21

flood vulnerability in affected communities. This fresh approach employs a set of ‘synergistic

22

analytic skills’ used to help describe the interactions and feedback mechanisms within

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dynamically complex systems that produce outcomes; to predict their behaviour; and to

24

formulate interventions to achieve desired (and avoid unintended) results (Sterman, 2000;

25

Maani and Cavana, 2007; Kelly et al., 2013). Systems thinking has been widely applied in

26

many fields to manage multifaceted or ‘wicked’ problems (Mai and Smith, 2015; Mai et al.,

27

2019). Indeed, this approach has previously been applied to flood management. For example,

28

Mavhura (2019) applied systems thinking to examine vulnerability to flood disasters in a

29

rural district in Zimbabwe, Africa. The dynamic relationships between flood risk factors are

30

also described in the socio-hydrology literature. For instance, Simonovic (2011), Di

31

Baldassarre et al. (2013 a,b) and Viglione et al. (2014) used dynamic mathematical models to

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conceptualise human-flood system interactions. These studies highlight the importance of

33

using a systems approach to understand and inform FRM. However, they fall short of

34

explaining the root causes of observable patterns of flood behaviour, which is needed if we 4

1

are to better anticipate potential future flood behaviour and the consequences of FRM

2

policies.

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There is increasing recognition that, in order to manage flood risk, we need to take an

4

integrated approach that is cognisant of the overall socio-ecological system in question

5

(Driessen et al., 2016, 2018; Dieperink et al., 2018); hence, this paper employs systems

6

thinking to conceptualise the dynamics underlying interactions between the multiple

7

endogenous factors of a flood-prone community. The objectives of the paper are to:

8

•

develop a conceptual model (systems model) of a flood risk system;

9

•

use this systems model to assess the potential consequences of current FRM policy;

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and •

suggest improvements to the current flood policy for achieving sustainable FRM.

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We use a case study approach, focusing on the regional town of Roma—a community

14

in inland Queensland, Australia which has experienced frequent and intense flooding and

15

associated property damage (URBIS, 2014) and where flood risk management remains a

16

challenging task. The systems approach applied in this study, along with the key

17

recommendations, is likely to be relevant to similar communities across Australia, and

18

elsewhere around the world. However, as Dieperink et al. (2018) contend, different flood risk

19

contexts require different mixes of solutions to build flood resilience; hence, FRM strategies

20

need to be responsive to “what works, where, when, and how”.

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2. Research methods

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2.1. Case study description

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Roma is a large rural town of almost 7,000 inhabitants (ABS, 2016) in the Maranoa

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district of inland Queensland, Australia (Fig.1). Situated on the banks of Bungil Creek, a

26

tributary of the Condamine-Balonne River system, the town has a history of significant flood

27

events, with 18 moderate and eight major flood events over the last 50 years (URBIS, 2014).

28

Recent major flooding in 2010–2011 and 2012 had wide reaching consequences for the

29

community (URBIS, 2014). In 2010, the local Bungil Creek overflowed its banks several

30

times, peaking at 7.0 and 8.1 m in February and March, respectively. In April 2011, the town

31

was again inundated when, after heavy rainfall, Bungil Creek peaked at 7.65 m, forcing the

32

evacuation of hundreds of properties. In February 2012, Roma experienced its worst recorded

5

1

flood event, with record river levels of 8.40 m and 444 homes inundated—twice as many as

2

in the preceding events (URBIS, 2014).

3

4 5 6

Figure 1 Location and stages of levee construction in the township of Roma, Queensland, Australia

7 8

The local government agency, Maranoa Regional Council, responded to these events by

9

instigating a flood study, which recommended a two-stage flood mitigation response

10

(Maranoa Regional Council, 2019a). Stage 1 consisted of clearing vegetation along the creek

11

bank and constructing a flood levee (Fig. 1) designed to protect the 480 most vulnerable

12

properties from above-floor flooding. Stage 2 involved further mitigation measures, including

13

a diversion channel and extension of the flood levee to protect an additional 51 properties.

14

The Council also established a Local Disaster Management Plan, a Local Disaster

15

Management Group and Local Disaster Coordination Centre (Maranoa Regional Council

16

(2019b). Flood preparation information was also made available on the Maranoa Regional

17

Council website, based on the Queensland Government ‘Get Ready’ emergency planning 6

1

guidelines for households (Queensland Government, 2019a) including advice that property

2

owners seek insurance advice. However, access to affordable insurance for Roma households

3

became a significant issue after the 2010–2012 flood events, when most of the local

4

insurance companies withdrew their flood risk products, while the remaining (mostly

5

international) insurance companies significantly increased their premiums to accommodate

6

the high risk of flooding (URBIS, 2014).

7

2.2. Formulating a conceptual model for FRM

8

In this study, Causal Loop Diagrams (CLDs) were used to develop a conceptual model

9

for FRM, allowing the flood risk system to be mapped and providing a simple visualisation of

10

the system which was relatively easy for stakeholders to review. A CLD consists of a set of

11

nodes representing (i) variables (words or phrases) in the system; (ii) relationships between

12

the variables depicted as arrows indicating the direction (polarity) of each relationship; and

13

(iii) any time lag (or delay) between variable responses where impact within the system is not

14

immediate but takes place in the longer term. Pairs of interacting variables within a CLD are

15

linked by arrows to form either reinforcing (R) or balancing feedback loops (B). By

16

incorporating feedback loops within the system, the CLD creates a concise story about a

17

particular problem or issue.

18

The formulation of a CLD for flood risk, specific to the case study situation, went

19

through four key stages. Firstly, we identified key flood drivers and impacts (i.e. variables)

20

from a comprehensive review of the literature on natural disasters, including research

21

publications, government documents, and federal disaster mitigation policies. We next used

22

these variables to develop a preliminary CLD by creating potential links, with polarities, and

23

factoring in time delays (where relevant) between the variables. The key elements of the

24

preliminary CLD were then shared with a range of stakeholders (e.g. local government

25

personnel, local businesses, householders, construction companies) for comment to produce a

26

working CLD. Finally, the working CLD was reviewed; and errors or inconsistencies

27

identified in the model were corrected.

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2.3. System archetypes

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CLDs have the capacity to provide new insights into the emergent properties of

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dynamic complex systems such as flood risk and vulnerability. This conceptual tool can also

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provide a solid foundation for identifying leverage points for actions (i.e. intervention

32

strategies) that may have a lasting impact on the system (Maani and Cavana, 2007). Leverage

7

1

points are not intuitive (Meadows, 1999), but may become more apparent once system

2

archetypes (SAs) are identified. SAs are generic models that explain archetypal behaviours

3

common to a variety of systems (Sterman, 2000). For example, fixes that fail is a system

4

archetype that represents situations in which unintended and often harmful consequences

5

follow well-intentioned but essentially short-term interventions (Maani and Cavana, 2007).

6

SAs also encompass management principles that can be employed to modify the

7

structure of the system to increase the likelihood of permanently eliminating problematic

8

patterns of behaviour (Mai and Smith, 2015). For instance, the management principle

9

underpinning the fixes that fail SA is to shift the focus onto fundamental causes and solutions

10

that address these. If a quick fix is necessary, it can be used to gain time while working

11

towards longer-term sustainable solutions (Sterman, 2000). Identified SAs for this study were

12

used to determine relevant leverage points. We also used the management principles of these

13

SAs to suggest systemic intervention strategies to mitigate flood risk, and thereby flood risk

14

vulnerability, in the case study community.

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3. Results

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3.1. A conceptual model for flood risk in Roma

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The final conceptual model for flood risk in Roma is shown in Fig.2. There are ten

19

feedback loops embedded in the model, including five reinforcing (R1 to R5) and five

20

balancing (B1 to B5) loops. The reinforcing loop R1 represents the process leading to

21

increasing potential harm associated with flooding. Loop R2 represents a potential worsening

22

of the flood risk situation. Loops R3 and R4 present consequences of flood relief, while loop

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R5 demonstrates the process that fuels flood losses due to the moral hazard associated with

24

the flood levee constructed in response to recent flooding. The five balancing loops in the

25

model work against flood losses through different limiting factors. These include property

26

development (B1), flood relief (B2), levee investment (B3), flood insurance (B4), and

27

integrated flood levee–insurance feedbacks (B5). These loops are briefly described in the

28

following section.

8

Flood premium

Access to insurance Frequency and intensity of flood events

B5 R2 B4

Required insurance

Flood levee investment B3

Community resilience

Flood losses

R1

Public demand for mitigation B1

R3 B2 Dependance on flood relief

Property development in Roma R4

Flood relief

1 2 3 4 5 6

R5 Relative attractiveness of Roma

Living costs

Figure 2 A system dynamics model for flood risks in Roma, Queensland. A solid line indicates that the two variables change in the same direction, while a dotted line means they move in the opposite way. Double bars (||) indicate a time delay in response between the two variables. Loop identifiers show either reinforcing (R) or balancing (B) loops, where R loops are positive feedbacks that amplify changes, and B loops are negative feedbacks that confer stability.

7 8

3.2. Description of the conceptual model of flood risk

9

•

Community resilience (R1)

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There is growing recognition that future flood-related damage can only be prevented

11

by strengthening community flood resilience (i.e. the ability to reduce, prevent and cope with

12

flood risk; Schelfaut et al., 2011; Kousky and Shabman, 2015; Surminski and Thieken, 2017),

13

as highlighted by the Sendai Framework for disaster risk reduction 2015–2030 (UNISDR,

14

2010). The mutual reinforcing interaction between community flood resilience and flood

15

losses can be portrayed by loop R1 in Fig.3. That is, increased flood damage reduces

16

community flood resilience and reduced community resilience results in increased flood

17

losses.

18

9

Frequency and intensity of flood events

Flood losses

R1

Community resilience

1 2

Figure 3 Reinforcing feedback between community resilience and flood losses.

3 4

Community resilience can be measured according to the livelihood capital (physical,

5

natural, economic, human and social) of the community (Schelfaut et al., 2011; Keating et al.,

6

2014; Mavhura, 2019); as such, building capital across all these dimensions can positively

7

contribute to the overall of community flood resilience (Schelfaut et al., 2011). This study

8

focuses on physical engineering (flood levee) and financial (insurance) solutions that have

9

been used to improve flood resilience in the case study community.

10

•

Potential harm of flooding (R2)

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The reinforcing loop (R2) shows the response of private insurance bodies, without

12

government actions, in dealing with flood risk. This loop contains the variables of Flood

13

losses, Flood premium, Access to insurance, and Community resilience (Fig. 4)

14 15 Flood premium

Access to insurance

R2

Community resilience

R1

Frequency and intensity of flood events

Flood losses

16 17 18

Figure 4 Potential harm of flooding

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With the prospect of growing and widespread flood-related damage in the region, the cost

21

of insurance is a significant expense and many households are effectively denied access to

22

insurance. Even where flood insurance is available, the premiums may be high; for example, 10

1

following the 2010–2012 flood events, premiums in Roma rose by up to 1500 per cent

2

(Moore, 2013) resulting in policy premiums too expensive for the general community.

3

Reduced access to flood insurance reduces community resilience (Kousky and Shabman,

4

2015). This vicious cycle not only creates a burden for the insurance sector but also increases

5

the vulnerability to flood losses of local communities.

6

•

Property development (B1)

7

Loop B1 demonstrates a natural equilibrium level of residential development in the

8

Roma township. This loop includes three variables—Relative attractiveness of Roma,

9

Property development in Roma, and Flood losses (Fig.5).

10

Flood premium

Access to insurance

R2

Community resilience

R1

Frequency and intensity of flood events

Flood losses

B1 Property development in Roma 11

Relative attractiveness of Roma

12 13

Figure 5 Flood risk and property development (‘Property development in Roma’ refers to the number and value of properties in the town)

14 15

As mentioned in Section 2.1, Roma is a thriving community—popularly considered

16

‘the gateway to the outback’—which supports a diversity of industries (i.e. agriculture,

17

tourism, hospitality, and energy resource extraction). These industries generate a wide range

18

of employment opportunities, attracting migrants seeking a better livelihood to the region.

19

The increase in both migrants and tourists has driven property development in Roma.

20

However, given the town’s history of serious flooding, such property development brings

21

with it increasing vulnerability if effective action is not taken by both government and other

22

stakeholder organisations to mitigate ongoing flood risk. Unless this happens, future flooding

11

1

might work to reduce the relative attractiveness of the region, which in turn will ultimately

2

reduce the population in the region.

3

•

Flood relief (B2)

4

Loop B1 above represents the situation in which no action is taken by government or

5

private institutions either before or after a flood event. However, this rarely happens in real

6

life, as the primary purpose of government and relevant private institutions is to provide for

7

the security of its citizens (Deegan, 2005; Cigler, 2017). The role of emergency support for

8

communities experiencing a flood event is illustrated by the loop B2 in Fig. 6.

9 Flood premium

Access to insurance

R2

Community resilience

Frequency and intensity of flood events

R1

Flood losses

B1 B2

Property development in Roma

Relative attractiveness of Roma

Flood relief

10 11

Figure 6 Alleviating the impacts of flooding through flood relief

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In the case of Roma, during the devastating floods in 2010, 2011 and 2012, the local

14

community received significant support from outside the community (e.g. State Emergency

15

Services, Lifeline and others). Although the objective of this assistance was not to prevent

16

future flood damage, it provided relief for local people, especially for those who were unable

17

to meet their immediate essential needs (i.e. food, essential clothing, medical supplies or

18

temporary accommodation). This type of external relief contributed to the reduction of

19

further potential losses in the region (Wenger, 2015).

20

• Flood levee (B3)

12

1

Structural engineering solutions (e.g. flood levees) have often played a key role in

2

managing flood risk (Schelfaut et al., 2011; Tanoue et al., 2016). The relationship between

3

levee investment and flood losses can be depicted by loop B3 (Fig.4). This balancing loop

4

contains the variables of Flood losses, Public demand for mitigation, and Flood levee

5

investment.

6 Flood premium

Access to insurance

R2

Frequency and intensity of flood events

Flood levee investment B3

Community resilience

R1

Flood losses Public demand for mitigation

B2

Property development in Roma

B1 Relative attractiveness of Roma

Flood relief

7 8

Figure 7 Mitigating the impacts of flood through the flood levee investment

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Extreme flood events and their wide-reaching consequences for the township of Roma

11

led to increasing public demand for mitigation capacity. As a consequence, government and

12

the private sector have undertaken mitigation programs to reduce flood risk in the region. Part

13

of this is represented by the flood levee (see Fig 1), consisting of a 5.2 km earthen wall

14

between the town and Bungil Creek (Stage 1), completed in early 2015, and additional levee

15

extension and drainage works (Stage 2), recently completed in 2019 (Maranoa Regional

16

Council, 2019c; Queensland Government, 2019c). The development of the flood levee

17

improves capacity to control flooding in Roma township, mitigating flood risk for the local

18

community (Wenger, 2015); this is expected, in turn, to reduce future flood-related losses for

19

this community (Queensland Government, 2019c).

20

•

Flood insurance (B4)

13

1

Loop B4 illustrates the flood insurance mechanism that homeowners and businesses

2

commonly use to protect their property and assets. The primary purpose of this financial

3

mechanism is to transfer uncertain flood risk in exchange for payment of a certain premium

4

(Kousky and Shabman, 2015). This balancing loop includes the variables of Flood losses,

5

Public demand for mitigation, Required insurance, Access to insurance, and Community

6

resilience (Fig. 8).

7 Flood premium

Access to insurance

R2

Frequency and intensity of flood events

Required insurance

Flood levee investment

B4 B3 Community resilience

R1

Flood losses Public demand for mitigation

B2

Property development in Roma

B1 Relative attractiveness of Roma

Flood relief

8 9 10

Figure 8 Mitigation with the flood insurance

11 12

As previously described, flood losses often lead to increasing public demand for

13

mitigation capacity. This forces homeowners and businesses to take out appropriate insurance

14

and to ensure their building structure meets the required building code. As a result, an

15

increased number of residents will be insured. Increasing numbers of residents with access to

16

insurance means increased levels of risk transfer and enhanced community resilience, as such

17

measures effectively reduce flood losses.

18

•

Flood relief trap (R3 & R4)

19

Reinforcing loops R3 and R4 are concerned with the effects of flood relief. Loop R3

20

contains the variables of Flood losses, Flood relief, Dependence on flood relief, and

14

1

Community resilience, while loop R4 includes the variables of Flood losses, Flood relief,

2

Living costs, Relative attractiveness of Roma, and Property development in Roma (Fig. 9).

3 Flood premium

Access to insurance

R2

Frequency and intensity of flood events

Required insurance

Flood levee investment

B4 B3 Community resilience

R1

Flood losses Public demand for mitigation B1

R3 B2

Dependance on flood relief

Property development in Roma R4

Flood relief

4

Relative attractiveness of Roma

Living costs

5 6

Figure 9 Consequences of flood relief

7 8

It is generally accepted that flood relief can sometimes introduce a perverse incentive as

9

it diminishes community awareness of the full extent of flood losses, particularly where aid is

10

external to the system (e.g. Pryce and Chen, 2011). In the case of Roma, the increased

11

frequency and intensity of flood events has attracted much attention (and support) from both

12

government and the private sector. Receiving support from outside could create a false sense

13

of security that might lead the local community to become reliant on relief programs. This

14

may reduce the capacity of the community to withstand future flood events and thus

15

potentially increase flood losses (represented by loop R3).

16

Receiving support from outside the community could also result in a reduced cost of

17

living in the region. This might make the region more attractive, leading to increased

18

settlement or property development in the region, again potentially increasing future flood

19

losses (represented by loop R4).

20

•

Levee trap (R5)

15

1

Loop R5 describes the potential harm that a flood-prone community might face due to the

2

installation of a flood levee. This loop contains the variables of Flood damage, Public

3

attention for mitigation, Flood levee, Relative attractiveness of Roma, and Property

4

development in Roma (Fig. 10). Flood premium

Access to insurance Frequency and intensity of flood events

R2

Required insurance

Flood levee investment

B4 B3 Community resilience

R1

Flood losses Public demand for mitigation

R3 B2

Dependance on flood relief

B1 Property development in Roma R4

Flood relief

5

R5 Relative attractiveness of Roma

Living costs

6 7

Figure 10 Consequence of flood levee

8 9

As mentioned earlier, flood losses frequently result in public pressure for flood mitigation

10

and hence engineering solutions such as levee construction. However, investing in such flood

11

protection measures may also provide people with a false sense of security that contributes to

12

further settlement in the region. On the other hand, increased levee construction also

13

increases the volume of water that can be held in the river system. If levees are breached, it is

14

likely there will then be greater flood-related losses (Wenger, 2015).

15

•

Integrated flood levee and insurance (B5)

16

Balancing loop B5 describes the integration of structural engineering and insurance

17

solutions in mitigating flood losses. This loop comprises the variable of Flood losses, Public

18

demand for mitigation, Flood levee investment, Flood premium, Access to insurance, and

19

community resilience (Fig. 11).

20

16

1 Flood premium

Access to insurance

R2

B5

Frequency and intensity of flood events

Required insurance

Flood levee investment

B4 B3 Community resilience

R1

Flood losses

Public demand for mitigation B1

R3

B2

Dependance on flood relief

R4

Flood relief

2 3

Property development in Roma

R5 Relative attractiveness of Roma

Living costs

Figure 11 Mitigation through integration of flood levee and insurance solutions

4 5

Frequent flooding in the Roma region has resulted in high insurance premiums for

6

households. Given the high flood risk in the region, the insurance industry stopped offering

7

policies in areas they deemed to be too highly exposed. Due to this, the local council, with

8

state and federal funding, undertook a flood risk mitigation program, constructing a flood

9

levee to protect the town. The construction of the flood levee was designed to reduce asset

10

losses in future flood events and has resulted in significantly reduced insurance premiums.

11

For example, prior to construction of the flood levee, a householder in Roma might pay more

12

than $3300 p.a. in household insurance premiums; this figure was expected to fall to about

13

$1300 p.a. following completion of the flood levee work (Madigan, 2012), a reduction of

14

approximately 70%. Reduced premiums should increase access to insurance, which in turn

15

will increase community resilience (Kousky and Shabman, 2015) and reduce flood-related

16

losses.

17

3.3. System Archetype and intervention strategies

18

The first archetype that can be seen within the conceptual model for flood risk is fixes

19

that fail. As mentioned in Section 2.3, this archetype represents situations where the

20

managerial response to a problem is a quick fix. This fix works in the short-term (balancing

21

effect); however, it may also have unintended and potentially harmful consequences that may 17

1

exacerbate the original problem (reinforcing effect), in which case the system can revert to

2

the original or a worse condition after a period of delay (Senge, 2006).

3

In the case of FRM in Roma, the problem is flood loss due to the frequency and

4

intensity of flood events. The ‘quick’ fix applied by the government and certain interest

5

groups was to deliver flood relief (i.e. financial and other forms of aid) to mitigate losses

6

(represented by loop B2 in Fig.2). There is, however, potential for several unintended

7

consequences associated with this action (Fig.12 (a)). By receiving support from outside,

8

individuals and, indeed, the community may come to rely on this assistance, leading to a

9

diminished capacity within the community to withstand extreme flood events and leaving it

10

prone to ongoing future harm (represented by loop R3 in Fig.2). Receiving outside support

11

might also result in reduced costs of living, in turn increasing the relative attractiveness of the

12

region. This might drive property development in the region, which will in turn increase

13

future flood-related losses (represented by loop R4 in Fig.2). Obviously, the quick fix of

14

implementing flood relief can help address the damage and losses in the short-term; however,

15

the consequences of this action, after a delay, is that the risk of damage persists and might

16

become greater than before (Fig. 12(c)).

17

Flood losses

Public demand for mitigation.

Flood relief

B2

Flood losses.

B3

Levee investment.

R3 Dependence on flood relief

Community resilience

R5 Property development in Roma

R4 Relative attractiveness of Roma

18

Living cost

Relative attractiveness of Roma.

Property development in Roma.

(b)

(a)

19 20

Flood losses

Flood relief or Levee construction

Quick fixes

Time

18

1

(c)

2 3 4

Figure 12 Structure (a and b) and behaviour (c) of the fixes that fail archetype for FRM: (a) flood relief and (b) flood levee measure. In Fig.12 (c), the solid line indicates flood relief or levee construction, while the dashed line designates flood losses.

5

Another fixes that fail archetype is associated with levee construction by government

6

and/or the private sector to protect at-risk household, business and community assets. This is

7

particularly problematic in the case of increasing risk of extreme events (e.g. with climate

8

change), which could lead to increased magnitude of flooding which exceeds the design

9

capacity of the levee (represented by loop B3 in Fig.2), potentially increasing the level of

10

residual risk over time (Zischg et al., 2018). An intended consequence associated with levee

11

construction is to increase the height a river can reach before it overflows its banks, although

12

Zischg (2018) points to the dynamic and adaptive behaviour of flood flows (with potentially

13

altered flood risk pathways) in response to floodplain development, which might mediate

14

this. Should this occur, however, such a fix may also increases the probability of disastrous

15

flooding, increasing the level of risk to the community (Di Baldassarre et al., 2013a)—

16

especially where there has been a ‘rebound’ effect and additional urban development due to

17

an increased perception of safety following the levee bank construction (Zischg et al., 2018)

18

and/or diminished collective flood memory (Viglione et al., 2014)—and consequent losses

19

under extreme circumstances (represented by loop R5 in Fig.2). Clearly, while the relatively

20

quick fix of levee construction can reduce losses associated with medium–high flood events,

21

over time the consequence of this action may result in more extensive damage caused by

22

extreme flood events (Fig. 12c).

23 24

The second system archetype seen in the conceptual model is limits to growth. This

25

archetype represents situations where improvements in performance or growth are limited

26

and cannot go on forever (Maani and Cavana 2007). In the case of FRM in Roma, potential

27

flood losses were, for a time, being fuelled by high flood insurance premiums, meaning

28

reduced levels of risk transfer as fewer and fewer homeowners and businesses were taking

29

out insurance (represented by loop R2 in Fig. 2). However, this reinforcing loop will

30

eventually be constrained by several limiting factors, especially levee constructions

31

(represented by loop B3 in Fig.2) that will control flood risk growth. Therefore, the limits to

32

growth archetype can be represented by a reinforcing loop that is growing flood risk (risk

33

transfer) and a balancing loop that is constraining growing flood risk (risk management) (Fig.

34

12(a)). The limits to growth archetype means that constraints on access to affordable 19

1

insurance for Roma households produced increasing risk associated with flooding over time

2

as such limits are approached. In this case the limits are the levee investment capacity of local

3

government. The behaviour of this system archetype can be demonstrated in Figure 13 (b).

4 Levee investment capacity Risk transfer

Assess to insurance

R2 Risk transfer

Risk management

Flood losses

Flood premium

5

B3

Flood levee investment

Risk management Public demand for mitigation

(a) Levee investment capacity Flood losses

Levee construction

Time

(b) 6 7 8

Figure 13 Structure (a) and behaviour (b) of limits to growth archetype for levee investment within Roma, where the solid line indicates flood losses, the dotted line represents levee construction and the dashed line designates levee investment capacity.

9

4. Discussion

10

Floods are an increasingly common and costly natural disaster, imposing significant

11

stresses on societies and environments. Despite global efforts to mitigate flood risks,

12

solutions so far have seldom been long-lasting. This is likely the result of what Hartmann

13

(2011) refers to as ‘clumsy’ floodplain planning and management, based on a lack of

14

comprehensive understanding of the complex nature of flood risk. Policies and measures

15

designed to mitigate such risks have primarily focused on addressing separate parts of the

16

flood risk system, while neglecting its interconnected nature. This situation creates potential

17

for unintended consequences and policy resistance, which is the tendency for policy

18

interventions to be delayed, diluted or defeated by the response of the system to interventions

19

(Sterman, 2000). To effectively manage flood risk, policy-makers need to develop a deeper

20

1

understanding of the interconnectedness of the drivers of flood risk and of how these

2

connections influence patterns of flood risk behaviour in flood prone communities.

3

Using a systems thinking approach, we constructed a conceptual model that represents

4

an endogenous complex system for flood risk. The model provides the integration of the

5

mental models that were shared and discussed by relevant stakeholders, supported by

6

evidence from peer-reviewed literature. It explains the sources of dynamism and complexity

7

that have given rise to the predicament in managing flood risks in the case study. This

8

dynamically complex systems model shows that relationships between the key flood risk

9

drivers and inhibitors have complex cause-effect relationships generated through multiple

10

feedback loops. The conceptual model, and especially the process of its development, will

11

likely help stakeholders to better understand the long-term effects and complexities involved

12

in FRM in Roma and to develop an appreciation and understanding of alternative mental

13

models. The model was found to be particularly useful in illustrating and building an

14

understanding of the ‘bigger picture’ of FRM and of how factors affecting the system are not

15

isolated and independent, but dynamically inter-linked. The model can therefore be used as a

16

platform for dialogue, communication, collaboration and decision making between

17

stakeholders in the region. This makes it a potentially powerful tool for policy makers and

18

practitioners, who share the responsibility of managing flood risks. This is essential for the

19

development and acceptability of FRM strategies and policies in flood prone communities

20

(Head, 2014; Osberghaus, 2015), such as Roma, where a shared vision and coordination

21

between stakeholders has been identified as one of the key leverage points for ensuring

22

sustainable FRM.

23

The model also enables the visualisation of feedback loops that play a significant role

24

in systems analysis by helping to identify drivers and inhibitors of sustainable FRM.

25

Importantly, it helps to identify leverage points in the system that can assist managers to

26

address the root causes of problems, rather than just treating the symptoms. Analysing the

27

flood risk system in this way will better enable managers to devise appropriate intervention

28

strategies that can help in achieving a sustainable FRM strategies in the region. Specifically,

29

the fixes that fail system archetype, so-called maladaptation, identified in this case study

30

reveals that current FRM policies that foster adaptation in the short-term may insidiously

31

affect the long-term vulnerability and/or adaptive capacity of flood prone communities to

32

climate change. The main problem related to unintended consequences of such FRM policies

33

is due to an apparent focus on disaster relief and, in the case of Roma, engineering solutions,

34

without considering the impact of these within system as a whole. Further, the critical role 21

1

played by private industry (e.g. insurance and financial sector) in building the community’s

2

ability to withstand extreme flood events needs to be taken into account. Without the

3

development of holistic integrated solutions, the consequences will be not only fail to

4

mitigate flood damage but also create a cycle that intensifies future flood-related damage.

5

The lesson that can be learned from the fixes that fail archetype is that flood risk

6

management should focus on the long-term, with quick fixes only used to gain the time

7

needed to implement longer-term solutions to the problem (Senge 2006). In other words, both

8

government and private sectors must develop collaborative integrated approaches to

9

implement measures that avoid those unintended consequences with potential to exacerbate

10

risk. Future FRM policy will need to expand the focus to include fundamental solutions,

11

developing options to enhance the overall resilience of flood-prone communities. Although

12

the effects of such solutions may take longer to become evident, they will likely have far

13

more sustainable outcomes.

14

It is important to point out that, in terms of the limits to growth system archetype,

15

leverage lies in the balancing loop; thus, in order to change the behaviour of the system, we

16

must identify and change those factors which currently limit the system (Senge, 2006). The

17

management principle of this system archetype in the case of Roma is to address factors

18

constraining growth in the flood risk system. Phaup and Kirschner (2010) argue that public

19

policies for addressing flood risk need to be consistent with long-term objectives and

20

developed in advance of the loss events. Well-targeted proactive (ex-ante adaptation)

21

investments aimed at flood risk management—through investment in infrastructure,

22

technology advances, capacity improvements, shifts in systems, management practices and

23

behaviour together with risk transfer options—would be expected to reduce future relief and

24

recovery costs and increase fiscal stability and long term well-being under increased flood

25

risk.

26

More specifically, a program of proactive targeted investment in the case of Roma

27

would act to keep insurance premiums in check by minimising flood risks. This is because

28

flood risk mitigation (e.g. levee construction) and flood insurance strategies are effectively

29

inseparable. While the Roma flood levee enhances the insurability of risk, insurance policy

30

premiums may ultimately enforce further risk reduction measures, such as extending the

31

levee or alternative measure such as relocation of vulnerable assets (e.g. Okada et al., 2014)

32

or redesigning open space (e.g. Du et al., 2019), over time. The integration of these strategies

33

can create positive synergies that will increase the community’s flood resilience (see Fig.11).

34

Decision makers must therefore recognize the vital role that integration of flood risk 22

1

management (i.e. mitigation) and flood risk transfer (i.e. insurance) plays in creating positive

2

synergies to accelerate an at-risk community’s flood resilience.

3 4

5. Conclusion

5

Globally, floods can be extremely damaging natural disasters. However, in spite of

6

significant advances in knowledge and investment, flood losses continue to rise. While some

7

European countries have made significant progress in thinking more holistically about flood

8

risk and putting strategies in place to manage this accordingly, many countries continue to

9

manage flood risk through reactive crisis-driven approaches. Such unilateral approaches limit

10

our ability to comprehend the interconnected nature of the multiple components of flood risk,

11

many of which are often latent, or hidden and remote from the actual flood-affected

12

communities. Our study about vulnerability to flooding in the regional town of Roma,

13

Australia, has shown that flood risk systems comprise multiple interdependent components

14

that are interwoven into a complex system; hence, change in one part may affect other parts,

15

impacting the whole system.

16

A systems thinking tool, particularly CLD, can help to unlock insights into the complex

17

nature of flood risk through visualising the dynamics underlying the system structure. It

18

allows examination of both the dominant feedback loops currently influencing flood risk and

19

the subdominant or latent feedback loops that are likely to influence flood risk in the future,

20

and makes visible the emergence of unintended consequences of policy decisions. This

21

illustrates the distinct advantage of the systems approach over the ‘silo’ approaches adopted

22

in earlier studies of flood risk. The CLD can also be used as a framework for decision making

23

and capacity development for policy makers and practitioners who share responsibility for

24

delivery of sustainable FRM. As such, the CLD serves as a sound and valuable foundation for

25

identifying high priority areas that would support decision makers in devising appropriate

26

long-term intervention strategies to proactively and effectively manage flood risk.

27 28

Acknowledgements

29

This research was supported by the Queensland State Government Drought and Climate

30

Adaptation (DCAP) program (grant number DCAP 05)

31 32

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