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Daniel Henstra, Jason Thistlethwaite (2017)Climate Change, Floods, and Municipal Risk Sharing in Canada
Background: Floods are the most common and most expensive natural hazard, and they are expected to become more frequent as the climate changes. This article presents research that used re/insurance catastrophe models to estimate the influence of climate change on flood-related losses. The geographic focus of the study was the Canadian Maritimes—specifically Halifax, Nova Scotia—and it sought to determine how municipal risks due to rainfall-driven riverine floods could change as a result of climate change. Results: Findings show that annual flood losses could increase by up to 300% under a business-as-usual climate scenario by the end of the century (i.e., no mitigation or adaptation), even without accounting for changes to the built environment that could increase exposure (e.g., no population or economic growth). Conclusions: Increasing flood risk demands an open discussion about how much risk is acceptable to the community and what controls on further growth of exposure are necessary. Moreover, projected increases in flood losses put into question long-term insurability in the Halifax area, and highlight a broader problem facing manyother areas in Canada as well. Keywords: Flood, Catastrophe losses, Risk management, Climate change, Public policy, Insurance Background dissemination, and their use in the public domain is rare Catastrophe models are computer-assisted calculations (Sampson et al. 2014). that estimate financial losses resulting from natural haz- However, the information generated by catastrophe ard events. Created primarily for insurance purposes, ca- models is a potentially valuable input for public policy. tastrophe models quantify expected losses due to claims First, by using catastrophe models to identify areas that of policyholders affected by a particular hazard, such as are particularly prone to flood damage, insurers generate a flood or earthquake. The information generated by ca- damage and loss information that could be used to im- tastrophe models is valuable to insurers for many rea- prove maps of at-risk communities (Surminski and Thie- sons, including understanding their exposure to perils, ken 2017). Such maps could enable governments to informing risk-based premiums, and detecting areas that prioritize investments in flood mitigation and encourage are uninsurable due to their high level of risk (Botzen homeowners to purchase flood insurance. Second, catas- and van den Bergh 2008; Lloyds 2014). Private sector trophe models generate loss estimates resulting from catastrophe models are proprietary in nature, so access both frequent and rare floods, which could offer govern- is restricted to insurers who are willing and able to in- ments a basis to weigh the costs and benefits of flood vest in data and technology. As a result, they are often mitigation investments (e.g., structural protections along unavailable for public research studies and mass rivers), regulate land use to reduce property exposure, and determine ways to share flood risk among govern- ments, private stakeholders and homeowners. * Correspondence: email@example.com Similar to global trends, flooding is Canada’s largest Department of Political Science, Faculty of Arts, University of Waterloo, contributor to disaster losses, estimated to account for Waterloo, ON N2L 3G1, Canada 78% of federal disaster assistance costs (United Nations Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 2 of 13 st International Strategy for Disaster Risk Reduction frequency of heavy precipitation…will increase in the 21 (UNISDR), 2011; Parliamentary Budget Officer (PBO), century over many areas of the globe”, particularly in high 2016). Flood-related losses are influenced by several fac- latitude regions like Canada. Greater precipitation will tors, including population growth and economic develop- cause “increases in local flooding in some catchments or ment in flood-prone areas, a reduction of permeable regions” (Intergovernmental Panel on Climate Change surfaces (e.g., wetlands) and the impacts of climate change (IPCC), 2012, p. 13). The impact that climate change has (Kundzewicz et al. 2014). Climate change is a source of had on historical flood-related losses (i.e., direct economic uncertainty for flood risk management, because physical damages) is less clear. Socioeconomic development, rather components of the hydrological cycle are subject to than climate change, has been considered the primary change (e.g., extreme rainfall is expected to become more contributor to increasing global natural hazard losses frequent). This changing flood regime demands accurate (Bouwer 2013), and increasing exposure of people and and up-to-date information on flood risk (Alexander et al. property in flood-prone areas is considered to be the pri- 2016), but existing flood maps in Canada are outdated, mary driver of growing flood losses in recent decades typically focus on a single type of flood hazard (e.g., river- (Kundzewicz et al. 2014). ine) and provide no information to estimate economic Changes in the frequency and intensity of rainfall over consequences (MMM Group 2014; Stevens and Hanschka the next century will likely create more opportunities for 2014). However, catastrophe models that examine riverine flood damages to occur in some regions (Rosenzweig et al. and surface water flood risk have improved in recent years 2002;Bouwer 2013;Kundzewicz etal. 2014). Quantifying due in part to the introduction in 2015 of residential flood future flood risk is difficult, however, because there are insurance as an optional coverage in select provinces many factors that influence flood losses, including uncer- (Calamai and Minano 2017; Meckbach 2016). tainties surrounding emissions pathways (Bouwer 2013). This study aims to contribute to a growing body of litera- Flood risk is the product of exposure (i.e., assets likely to ture on the quantification of flood risk under the current be affected by flooding), the frequency of occurrence (i.e., and future climate (Bouwer et al. 2010; Feyen et al. 2009; how often flooding impacts an area), and vulnerability Kundzewicz et al. 2014). For the purposes of this research, (i.e., susceptibility to suffering damages) (de Moel et al. we adopted a relatively narrow definition of “flood risk” to 2009). Climate change influences flood risk in that the mean potential direct economic losses associated with probability of flooding events changes in response to flooding. Re/insurance catastrophe models made available greenhouse gas emissions. For example, one of the key im- to the researchers were used to (1) quantify flood risk (i.e., pacts associated with different emissions scenarios is direct economic losses) caused by rainfall-driven riverine changes in the return period of extreme precipitation flooding in residential areas, and (2) determine how the in- events (e.g., “a 1-in-20-year annual maximum daily pre- formation generated can be used to inform Canadian pub- cipitation event is likely to become a 1-in-5-year to st lic policy in the face of a changing climate. The models 1-in-15 event by the end of the 21 century in many re- were tested in Halifax Regional Municipality, Nova gions”, including Eastern Canada) (Intergovernmental Scotia—a mid-sized coastal city that has recently experi- Panel on Climate Change (IPCC), 2012). enced riverine floods due to heavy rainfall (CBC 2014). Perhaps due to the complexity in quantifying flood risk, The paper begins by providing an overview of catas- there is a relatively small body of literature that explores trophe models and Canada’s current disaster manage- the impact of climate change on flood losses (Bouwer ment policy landscape. It then describes the study 2013). Bouwer (2013), for instance, states that “there are methodology, including why the study area was selected, only few studies that have translated such changes in ex- the procedure used to map present flood risk and how treme weather to economic impacts, and very little quanti- future flood-related losses were estimated. The third sec- fication is usually given of how large the impact from tion presents the results, with a focus on how climate climate change on extreme weather losses potentially is” change could influence flood-related losses. The paper (p. 916). Moreover, existing studies in developed nations closes with a discussion of the relevance of the results focus primarily on European countries and the United for Canadian disaster management policy and for inter- States, with few studies from Canadian coastal regions national scholarship on flood risk in a changing climate. (Bouwer 2013;Kundzewiczetal. 2014; Lemmen et al. 2016). Efforts that quantify flood risk are beneficial particu- Climate change, catastrophe models and Canada’s larly for determining what is at risk, estimating increases in disaster management policy losses over time, and analysing how policymakers and Climate change impacts regions across the globe in differ- decision-makers can address these growing risks (e.g., find- ent ways, particularly in how it affects the hydrological ing a balance between risk reduction costs and benefits). cycle. The Intergovernmental Panel on Climate Change Many of the factors that influence flood risk are found (IPCC) (2012), p. 13 states that “it is likely that the in catastrophe (CAT) models (Bouwer 2013), including Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 3 of 13 exposure, hazard and flood probability reflected as a encompassing approximately 74% of the 400,000 people stochastic event set (Sampson et al. 2014). The stochastic who reside in HRM (Statistics Canada 2016). event set is composed of thousands of flood event simula- HRM has a relatively mild climate compared to other tions that are informed by observational data but also cap- parts of Atlantic Canada, with a mean annual temperature ture events that exceed the magnitude of historical data to of 7.5 °C and mean annual precipitation of 1468 mm (En- capture tail-end risks (i.e., rarely occurring floods that have vironment and Climate Change Canada (ECCC), 2018). potentially catastrophic consequences) (Sly and Ma 2013). The city experiences few extreme hot and cold days, due The event set offers a “database of extreme precipitation to moderation by the Gulf Stream, but it faces occasional events over the catchment(s) that drive fluvial or pluvial hydrological risks due to hurricanes, Nor’easter storms, risk” (Sampson et al. 2014, p. 2306). CAT models also con- and rainfall-driven riverine flooding. This research focused tain location-specific information, such as the siting and on rainfall-driven riverine flooding, since it had recently characteristics of property assets, and can therefore esti- affected neighbourhoods in HRM and many other com- mate damage due to certain flood depths (depth-damage munities in the Canadian Maritimes (Harding 2017; functions) to determine which assets are most vulnerable Weeks 2017). Riverine flooding can occur when extreme to flood-related losses (Lloyds 2014; Sampson et al. 2014). rainfall or ice jams cause rivers to reach their capacity and However, research coupling climate change and CAT overflow onto surrounding land. Since studies in Nova model data is sparse, meaning there is an opportunity to Scotia have focused primarily on coastal flood impacts (in- make better use of this technology to inform public policy cluding sea level rise) (see Leys 2009;Lemmen etal. and investments aimed at reducing flood risk. 2016), this study offered an opportunity to develop new Canada and 186 other countries have adopted the knowledge about flood risks on Canada’s East coast. In Sendai Framework on Disaster Risk Reduction (Henstra addition, HRM staff had recently identified a need to up- and Thistlethwaite 2017b). The framework identifies four date their riverine flood maps and floodplain regula- “priorities for action”, which include using risk assess- tions—HRM restricts residential development in the ments to better understand disaster risk; strengthening 20-year floodplain but allows flood-proofed development governance to manage disaster risk; investing in disaster in the 100-year floodplain (Environment and Climate reduction and resilience; and enhancing disaster prepared- Change Canada (ECCC), 2013;Berman 2016;Irish 2016). ness for effective response in order to “build back better” In the event of a disaster, governments provide com- (United Nations International Strategy for Disaster Risk pensation funds to those affected. The availability of reli- Reduction (UNISDR), 2015). Although these principles able climate change scenarios for this part of Canada are not new—they build on a solid foundation of research also supported its selection as a test site for projecting that dates back more than 70 years (e.g., White 1945;Bur- the influence of climate change on flood loss estimates ton et al. 1978; White et al. 2001)—they have experienced in the near and long-term future. Finally, the study area a renewed emphasis as the costs of natural disasters have covered a reasonably small catchment area, which risen dramatically in recent years. allowed the researchers to maximize the likelihood that Despite Canada’s commitment under the Sendai Frame- changes in rainfall conditions caused by climate change work to adopt risk assessment as the basis for disaster risk would produce relatively uniform responses in riverine reduction, many Canadian jurisdictions lack up-to-date flood conditions. The next section describes the study’s flood risk information (MMM Group 2014). Although the research design and methods. Government of Canada has a renewed effort underway to improve flood mapping (Natural Resources Canada, Methods 2017), a lack of information has impeded efforts to pre- Mapping present flood risk vent or reduce flood consequences on people and prop- The research was divided into two steps, each employing erty. The primary objective of this research was to use a distinct methodology. The first step focused on mapping CAT models to estimate the influence of climate change the spatial distribution of present flood risk in the study on flood risk in a Canadian municipality, in order to in- area. The second step quantified financial losses resulting form flood risk management decisions. from various flood events under high-emission and low-emission climate change scenarios. Models produced Study area for re/insurance purposes were used in this study to quan- The study area consisted of the urban core of Halifax tify present and future flood risk resulting from direct Regional Municipality (HRM), Nova Scotia, Canada—a losses on residential properties. geographically large municipality with spatially distrib- The goal of the first part of this research was to use uted populated areas, particularly along the coastline an existing 2D hydrodynamic flood model to map the (Fig. 1). In comparison to other parts of the municipal- location of residential properties at risk of riverine ity, the urban core has the highest population density, flooding (i.e., exposure to flooding), and determine Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 4 of 13 Fig. 1 Study area which properties had a higher susceptibility to dam- model outputs were of specific interest to this re- ages than the rest (i.e., higher vulnerability). For ex- search: (1) flood hazard extents, which depict the ample, not all properties susceptible to a 100-year likely inundation zone for floods of different probabil- flood would be affected equally; those located in areas ities; and (2) annual damage ratios (ADR), which as- where the depth and velocity of flood waters is highest sign values between 0 and 1 to individual grid cells would suffer the greater damages. To this end, the re- that demarcate the susceptibility to physical flood searchers partnered with JBA Risk Management, a damage. Building footprints and building characteris- firm with international expertise in natural hazard tics were retrieved from HRM to pinpoint the location modelling, to gain access to data outputs generated by of residential properties in the study area. Through a its proprietary 2D hydrodynamic flood model. This unique data-sharing agreement, this was the first study model has been used by the Canadian insurance in- to use the model for research purposes. dustry to quantify the number of properties vulnerable Based on the data available, residential properties were to flooding (Parliamentary Budget Officer (PBO), considered at-risk of flooding if they were: 2016)and JBA’s datasets assist insurers in pricing pre- miums for individual properties (JBA Risk Manage- classified by HRM as single-dwelling residential ment 2018). JBA’s model maps flood-prone areas in buildings; and Canada by incorporating federal government data on within the 1500-year riverine flood zone (lowest the location of major and minor rivers, historical probability flood event where spatial data was streamflow, and water levels, as well as other topo- available); and graphical and environmental inputs (Hunter et al. within areas with an ADR value above 0, meaning the 2007;Lambetal. 2009; Faulkner et al. 2016). Two property would suffer at least some flood damages. Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 5 of 13 Since the criteria were all spatially-explicit, a geo- calculated in this study are therefore reflective of insured graphic information system (GIS) was used to map and losses for that 56% and do not incorporate policy limits quantify the number of residential buildings at risk of and deductibles. flooding. The spatial analysis did not, however, incorpor- G-CAT® can model the influence of many factors related ate specific property characteristics that could reduce to riverine risk (e.g., snowmelt), but for this study, G-CAT® flood damages due to lack of available data, including was used solely to model rainfall-driven riverine flooding. property-level flood protections (e.g., backwater valves). The decision to focus on rainfall-driven riverine losses was a To create an additional subgrouping of high-risk prop- result of the availability of climate change data that was used erties, a supplemental spatial analysis was conducted. to adjust G-CAT® to reflect future climate change. More This spatial analysis associated each building footprint specifically, climate-adjusted intensity-duration-frequency with an ADR value to then classify the buildings based (IDF) curves at specific gauge locations across Canada have on their vulnerability to flood damages. Buildings that been developed in recent years (see Simonovic et al. 2016; intersected with multiple ADR grid cells were given the Sandink et al. 2016). IDF curves capture the intensity of maximum damage ratio. The ADR dataset is reflective rainfall (i.e., how much rain falls over a period of time), its of “the expected annualized cost of flood damage at a duration (i.e., how long this rain period lasts), and its fre- specific location, expressed as a proportion of the sum quency (i.e., how often this type of event occurs) (Auld insured” (JBA Risk Management 2017). In this study, 2012). These IDF curves are available publicly through an “high risk” properties were those that were inundated by online portal that allows researchers to select temporal pe- the 100-year flood (1% probability of occurring on any riods, emissions scenarios, and climate change models at given year) or more frequent events, and they had a sta- various rain gauge sites across Canada. tistically higher ADR than the rest of the properties at Climate-adjusted IDF data were retrieved at a specific risk (i.e., the top 25% of the dataset) (see Appendix 1 for rain gauge site in the study area, for early-, mid-, and further details). This qualitative classification scheme late-century scenarios under a 2 °C and 4 °C global shows which properties in the study area, relative to temperature increase (see Appendix 2 for details). Given other at-risk properties, have a higher annual probability that G-CAT®‘s flood event catalogue is defined by daily of being flooded, and are more likely to suffer flood streamflow estimates, the 24-h IDF curves served as in- damages. Finally, the areas where high risk properties puts for adjusting G-CAT® to reflect future climate concentrate where identified. change. It was assumed that precipitation changes in the study area would have a direct impact on rivers, since Modelling present and future flood-related losses rivers often have similar responses to similar precipita- The goal of the second part of this research was to quan- tion patterns (United States Geological Survey (USGS), tify present and future losses incurred from flood damages 2016). This assumption allows for changing the fre- to residential property, and how changes in losses influ- quency of occurrence of flood events in the entire prob- enced the average annual flood losses (AAL). AAL is a ability curve (including low probability and high value that is equivalent to a “pure premium” and thus can probability events) to reflect increases in the volume be used as a basis for calculating risk-based flood insur- and intensity of precipitation under climate change. ance premiums available to consumers, whereby a higher Thesizeofthe studyareaisbest suitedfor this type of AAL is likely to result in higher market premiums (Sly approach, where the relationship between a change in and Ma 2013; Lloyds 2014; Ermolieva et al. 2017). AAL is precipitation and flood is most evident. All loss esti- calculated “as the sum product of the mean loss and the mates presented associated with single flood return pe- annual likelihood of occurrence (i.e., the event rate) for riods (retrieved from an exceedance probability [EP] each event in the [catastrophe model] event set” (Grossi curve) and AAL, including those for future climate and TeHennepe 2008). For this research, AAL serves as change, reflect the existing built infrastructure and do an indicative figure for detecting overall changes in the not account for additional risks caused by socioeco- costs of flooding over time. nomicchanges,futurebuild up in flood-proneareas, A second model, G-CAT® Canada Flood Model, was and indirect losses. used for this part of the research (Guy Carpenter 2015). G-CAT® is built using JBA’s hazard maps, flood defence data, and a 10,000-year stochastic event set, but it also Results incorporates insurance policyholder information from Present flood risk various Canadian insurers to calculate the financial ex- Findings show that of the 54,000 single-dwelling resi- posure of insured assets (Contant 2015). In the HRM dential buildings within the study area, 11% are at risk study area, insurance policy-level exposure information of suffering damages due to riverine flooding. Approxi- was available for about 56% of the population. The losses mately 1300 residential properties (2.4% of total) can be Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 6 of 13 considered high risk based on this study’s classification Future flood risk scheme. Changes in flood losses due to climate change were cal- By mapping the location of these at-risk properties, it culated using the G-CAT® model under early-, mid- and was possible to identify certain zones with a high dens- late-century scenarios. Results show that, depending on ity of residential properties exposed to flood hazard the climate change trajectory, there are significant ma- across the study site. The highest concentration of terial differences between how flood losses change under flood exposure occurs along waterbodies and channels, low emissions (2 °C) and high emissions (4 °C) scenarios with damage also affecting low-lying areas adjacent to (Fig. 3). For example, due to changes in rainfall intensity these features. Researchers calculated the number of expected in the study area, losses that would result from high risk properties within postal code areas to identify a 100-year EP flood would increase from CAD $7 mil- where high risk properties concentrate across the study lion to CAD $67 million in a 4 °C world by the end of area (Fig. 2). the century (Fig. 4). By contrast, losses for the same The results from the G-CAT® model estimate that a flood event in a 2 °C world would incur $10 million in moderate probability riverine flood (100-year EP) caused damages by the end of the century. The differences be- by heavy rainfall under the current climate would result tween the two emissions scenarios stem from differences in CAD $7 million in losses, while CAD $79 million in in rainfall intensity expected to occur during a 24-h losses from residential property damages alone emerge period. The 100-year rainfall (24-h duration) event from a low probability flood (500-year EP). The current under historical conditions produced 6.3 mm of rain per annual expected loss (i.e., AAL) is CAD $543,000, hour; but this rate changes to 6.49 mm/hour under a whereby losses from events with an average recurrence 2 °C world and 7.69 mm/hour under 4 °C world. interval of 100-years or greater are primarily influencing Average annual losses are projected to increase by 300% this value. More frequent flood events (e.g., 5-year EP) by the end of the century under a 4 °C climate change do not cause residential flood losses and thus do not scenario (Fig. 5). Floods that have a high probability of contribute to the AAL. This finding is consistent with occurring each year (e.g. 5-year EP) show little or no HRM’s planning regulations that prevent residential de- change in flood losses depending on the climate scenario. velopment in the 20-year floodplain. Meanwhile, larger floods that have a comparatively lower Fig. 2 Spatial distribution of flood risk in the study area. Map insets a and b show the location of residential properties at risk of flooding in parts of HRM Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 7 of 13 Fig. 3 Changes in flood losses for all modelled flood events. Uncertainty in model results is represented using the 25th and 75th percentiles of loss around the EP curve (illustrated using dotted lines), computed on a per event basis (see Cleveland 2015; Foote et al. 2017) probability of occurrence each year (e.g. 100-year EP) present flood management is effective at handling fre- show significant increases in flood losses under future quently occurring floods and any climatic changes asso- climate scenarios. When aggregating these flood events ciated with these events. However, flood losses increase together to calculate the AAL, the losses associated disproportionately in relation to comparatively lower with events that have 10-year to 1000-year EP are pri- probability-high impact events. In their analysis of the marily driving the increase in AAL. This suggests that distribution of claims under the U.S. National Flood Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 8 of 13 Fig. 4 Changes in flood losses for the 100-year EP flood Insurance Program, for example, Cooke et al. 2014 acceptable to the community; and (2) controls on further found that “the tail of flood insurance claims seems to growth of exposure in these areas if the risk is deemed be getting fatter over the time period in our data. This unacceptable. Second, the research findings support would indicate the extremes are getting even more Canada’s commitment to the Sendai Framework on Dis- extreme.” aster Risk Reduction and flood risk reduction efforts. Third, projected increases in flood losses put into ques- Discussion and conclusions tion long-term insurability in the Halifax area, and high- This section discusses the relevance of research findings light a broader problem facing many other areas in in relation to Canadian flood risk management policy Canada as well (Thistlethwaite 2016). and concludes with implications for the broader inter- International literature explains that the “contribution national literature. from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the con- Implications for Canadian flood risk management policy tribution from anthropogenic climate change” by There are three main lessons for Canadian flood risk mid-century (Bouwer 2013, p. 927). The assessment pre- management policy. First, there is a need for frank and sented here estimates that average annual losses could inclusive discussion about (1) how much flood risk is increase by 137% by mid-century and 300% by Fig. 5 Percentage change in average annual flood losses Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 9 of 13 late-century due to climate change alone. This suggests more expensive in the future, and (2) private insurance that HRM and other municipalities facing similar risks could become inaccessible to a growing number of will need to reduce property exposure by preventing the homeowners due to their increasing risk levels. As gov- growth of exposed assets in flood-prone areas. Strict ernments in Canada looks to shift financial liabilities land use regulations on floodplains represent the main away from publicly-funded disaster assistance programs avenue for preventing this growth of exposure. In Nova (Henstra and Thistlethwaite 2017a), governments may Scotia, municipalities like HRM prevent residential de- need to create mechanisms to address insurability gaps velopment in the 20-year EP floodplain, but allow flood- in high risk zones in partnership with insurers. By man- proofed residential development in the 100-year EP aging and controlling the growth of flood risks, govern- floodplain (Nova Scotia 2013). The results of this study ments can ensure that insurance remains affordable support more strict land use regulations that mitigate and available to most Canadian residential property risk from events that may be rare today but could be- owners. come more frequent in the future (e.g., the 500-year EP event) and to enhance current land use regulations (e.g., Relevance for international scholarship and future prevent any new residential developments on the research 100-year EP floodplain). These measures can limit new This research contributes to the growing body of litera- development in areas already vulnerable to flooding and ture that focuses on quantifying flood risks across glo- subject to growing climate change risk during the life- bal regions. Research findings show that future flood span of the property. risks could increase in HRM, and this is consistent with The results of this research support Canada’s adoption what is expected in European flood-prone regions as of the Sendai Framework on Disaster Risk Reduction. reported by Feyen et al. (2009), Bouwer et al. (2010), Te Historically, Canada has used a primarily “structural” Linde et al. (2011), and Bouwer (2013). Although this flood management approach, whereby engineered solu- study does not incorporate socioeconomic change, it tions (e.g., building dikes and levees) have been built to does present comparable findings. For example, in the reduce risks, and governments were responsible for HRM study area, climate change could cause increases compensating victims and rebuilding to pre-disaster in AAL between 37%–137% by mid-century, while in conditions in the aftermath of a disaster. The findings of the Rhine basin in the Netherlands, AAL is projected to this research show that flood risk could increase sub- increase from 43 to 160% by 2030 due to climate stantially over time as a result of climate change, which change alone (te Linde et al. 2011). justifies the continued use of risk reduction measures for Although this study follows Bouwer’s(2013) “frame- dealing with existing high-risk properties. This can be work for quantitative modeling of economic damages accomplished, in part, by ensuring that homes that are from extreme weather”, it is necessary to emphasize that rebuilt after suffering flood damage incorporate protec- this study is a first assessment for quantifying flood risks tions, such as elevated foundations and water-resistant in the Canadian Maritimes, and there are some limita- materials, to mitigate future flood damage (Kovacs and tions. One key gap is that the methodology used does Sandink 2013). The provincial governments of both not incorporate projected socioeconomic changes (e.g., Nova Scotia and Alberta have offered to buy homes that population growth) and adaptation scenarios to quantify were severely been damaged by floods in recent years how various adaptation solutions could be most effective (Cryderman 2013; Pace 2016). This strategy is best for reducing existing risks. Studies that compare adapta- approached through an intergovernmental effort that is tion options (e.g., structural vs. nature-based options; not currently in effect, because municipalities have re- changes in land use) can help identify best strategies for ported difficulty in being able to buy out properties on reducing risks and promoting the realization of adapta- their own (Deacoff 2015). tion projects that have a positive return on investment Increases in AAL reveal that insurance availability and (Lemmen et al. 2016). Second, there is a potential need affordability could be compromised in the future. It is to create guidelines on how to conduct climate estimated that there are currently 5–10% of residential adjusted-flood risk assessments at the local scale (e.g., properties in Canada that are not able to purchase flood data and models available, components to analyze) in insurance or be offered affordable premiums given their order to facilitate access to this information to govern- high-risk levels (Calamai and Minano 2017). The results ments and ensuring that model outputs can be com- of this research show that current flood risks can pared between jurisdictions. Finally, future studies increase over time, meaning that residential properties should also capture other aspects that contribute to will be more frequently impacted by damaging flood flood risk (e.g., public assets) as well as social vulnerabil- events. This can have two implications on private insur- ity indicators since this analysis solely focused on resi- ance markets: (1) risk-based premiums could become dential property loss. Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 10 of 13 Appendix 1 Fig. 6 Classification of at risk and high risk residential properties. Brewer and Pickle (2002) and Albano et al. (2017) served as guidance for this classification Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 11 of 13 Appendix 2 Table 1 Sample 24-h precipitation conditions used for adjusting the G-CAT® model Description Time Period Climate scenario Duration Rainfall Intensity (24-h event), mm/hr Historical conditions 1955–2009 2-yrs 2.81 1955–2009 5-yrs 3.74 1955–2009 10-yrs 4.36 1955–2009 25-yrs 5.14 1955–2009 50-yrs 5.72 1955–2009 100-yrs 6.3 Future Climate 2015–2045 RCP 2.6 2-yrs 3.12 (early-century) 2015–2045 RCP 2.6 5-yrs 4.06 (2015–2045) 2015–2045 RCP 2.6 10-yrs 4.69 2015–2045 RCP 2.6 25-yrs 5.46 2015–2045 RCP 2.6 50-yrs 6.05 2015–2045 RCP 2.6 100-yrs 6.64 2015–2045 RCP 8.5 2-yrs 3.15 2015–2045 RCP 8.5 5-yrs 4.1 2015–2045 RCP 8.5 10-yrs 4.72 2015–2045 RCP 8.5 25-yrs 5.5 2015–2045 RCP 8.5 50-yrs 6.08 2015–2045 RCP 8.5 100-yrs 6.68 Future Climate 2035–2065 RCP 2.6 2-yrs 3.18 (mid-century) 2035–2065 RCP 2.6 5-yrs 4.11 (2035–2065) 2035–2065 RCP 2.6 10-yrs 4.69 2035–2065 RCP 2.6 25-yrs 5.47 2035–2065 RCP 2.6 50-yrs 6.05 2035–2065 RCP 2.6 100-yrs 6.62 2035–2065 RCP 8.5 2-yrs 3.33 2035–2065 RCP 8.5 5-yrs 4.34 2035–2065 RCP 8.5 10-yrs 5.01 2035–2065 RCP 8.5 25-yrs 5.86 2035–2065 RCP 8.5 50-yrs 6.49 2035–2065 RCP 8.5 100-yrs 7.12 Future Climate 2065–2095 RCP 2.6 2-yrs 3.21 (late century) 2065–2095 RCP 2.6 5-yrs 4.07 (2065–2095) 2065–2095 RCP 2.6 10-yrs 4.68 2065–2095 RCP 2.6 25-yrs 5.41 2065–2095 RCP 2.6 50-yrs 5.95 2065–2095 RCP 2.6 100-yrs 6.49 2065–2095 RCP 8.5 2-yrs 3.49 2065–2095 RCP 8.5 5-yrs 4.62 2065–2095 RCP 8.5 10-yrs 5.36 2065–2095 RCP 8.5 25-yrs 6.31 2065–2095 RCP 8.5 50-yrs 7.01 2065–2095 RCP 8.5 100-yrs 7.69 Retrieved from IDF_CC tool. Shearwater meteorological station (ID: 8205091), located at 44.64 N, 63.51 W; elevation of 53 m. All precipitation estimates were generated using an ensemble of 22 Global Circulation Models as made available by the IDF_CC tool (Simonovic et al. 2016). Accessed January 2016 Thistlethwaite et al. Geoenvironmental Disasters (2018) 5:8 Page 12 of 13 Acknowledgements Brewer, C.A., and L. Pickle. 2002. Evaluation of methods for classifying We thank Guy Carpenter & Company and JBA Risk Management for sharing epidemiological data on choropleth maps in series. Annals of the Association their models and guiding this project. We thank Halifax Regional Municipality of American Geographers 92: 662–681. for providing feedback on the results of this research. GIS flood datasets Burton, I., R.W. Kates, and G.F. White. 1978. The environment as hazard, 240. provided by JBA Risk Management. Copyright JBA Risk Management Limited London, UK: Oxford University Press. 2013-2017. 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Geoenvironmental Disasters – Springer Journals
Published: Dec 1, 2018
Keywords: Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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