Does planned retreat matter? Investigating land use

Does planned retreat matter? Investigating land use change under the impacts of flooding induced by sea level rise Jie Song, Xinyu Fu, Ruoniu Wang, Zhong-Ren Peng & Zongni Gu

Mitigation and Adaptation Strategies for Global Change An International Journal Devoted to Scientific, Engineering, Socio-Economic and Policy Responses to Environmental Change ISSN 1381-2386 Mitig Adapt Strateg Glob Change DOI 10.1007/s11027-017-9756-x

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Author's personal copy Mitig Adapt Strateg Glob Change DOI 10.1007/s11027-017-9756-x O R I G I N A L A RT I C L E

Does planned retreat matter? Investigating land use change under the impacts of flooding induced by sea level rise Jie Song 1,2 & Xinyu Fu 3 & Ruoniu Wang 4 & Zhong-Ren Peng 3 & Zongni Gu 3

Received: 30 October 2016 / Accepted: 19 July 2017 # Springer Science+Business Media B.V. 2017

Abstract Coastal regions worldwide are during the process of rapid urban expansion. However, expanded urban settlements in land-sea interfaces have been faced with unprecedented threats from climate change related hazards. Adaptation to coastal hazards has received increasing attention from city managers and planners. Adaptation and land management practices are largely informed by remote sensing and land change modeling. This paper establishes a framework that integrates land change analysis, coastal flooding, and sea level rise adaptation. Multilayer perceptron neural network, similarity learning, and binary logistic regression were applied to analyze spatiotemporal changes of residential, commercial, and other built-up areas in Bay County, Florida, USA. The prediction maps of 2030 were produced by three models under four policy scenarios that included the population relocation strategy. Validation results reveal that three models return overall acceptable accuracies but generate distinct landscape patterns. Predictions indicate that planned retreat of residents can greatly reduce urban vulnerability to sea level rise induced flooding. While managed realignment of the coast brings large benefits, the paper recommends different mixes of adaptation strategies for different parts of the globe, and advocates the application of reflective land use planning to foster a more disaster resilient coastal community.

* Zhong-Ren Peng [email protected]

1

College of Architecture and Urban Planning, Chongqing University, No. 174, Shazheng Street, Chongqing 40030, China

2

The Shimberg Center for Housing Studies, College of Design, Construction, and Planning, University of Florida, P.O. Box 115703, Gainesville, FL 32611, USA

3

Department of Urban and Regional Planning, University of Florida, P.O. Box 115701, Gainesville, FL 32611-5701, USA

4

College of Architecture, Construction and Planning, The University of Texas at San Antonio, P.O. Box 115706, Gainesville, FL, USA

Author's personal copy Mitig Adapt Strateg Glob Change

Keywords Land use change . Sea level rise . Population relocation . Urban growth . Flooding . Multilayer perceptron . SimWeight . Logistic regression . Land use planning

1 Introduction Coastal regions have undergone a spike of rapid urbanization resulting in substantial anthropogenic pressures on land-sea ecosystems. Meanwhile, urban expansion confronts residents with the adverse consequences of climate change. Climate change is likely to induce various coastal hazards, among which flooding, sea level rise (SLR), and storm surge impose enormous threat on coastal communities (Klijn et al. 2015). Moreover, climate change also increases the vulnerability of coastal cities and small communities by intensifying marine and riverine flood (Satterthwaite 2008; Thieken et al. 2016). Hence, heightened risks have urged international communities to develop climate change adaptation plans (Bierbaum et al. 2013; Measham et al. 2011). While studies at global and regional levels concerning SLR impacts and human-environment responses are prospering, there is a necessity to decipher the relationship between these two facets—at the local level (Cooper et al. 2004)—which, to our knowledge, is scarce in the literature. Disentangling this relationship provides insights into interpreting the on-the-ground consequences of globally changing processes at a granular level. Human response to SLR impacts largely depends on how the society allocates urban facility systems and in what ways policy makers orient future city trajectories (Song et al. 2017). This process is commonly supported by land use planning practice around the world. The evolvement of land use planning is increasingly informed by remote sensing, land change simulation, and other data-intensive techniques (Verburg et al. 2004; Wu et al. 2014). And one of the fundamental approaches to investigate coastal land change is to explore the spatiotemporal variations of land covers and project their trajectory into the future—in relation to climate and non-climate drivers (Hansen 2010). To address the gap between SLR adaptation and urban dynamics, this paper aims to develop a land change framework designed to integrate climatic change processes and societal responses (adaptation strategies and urban simulations). The objectives are to (1) validate and compare three land change models—which employs distinct algorithms—in capturing past urban growth patterns and (2) assess urban exposure to SLR-induced flooding under distinct policy scenarios.

2 Literature review 2.1 Sea level rise impacts and coping strategies Rising sea water alters coastal human-environment systems in a complex way. SLR has an apparent effect on the interaction of built and natural environment in coastal areas: wetlands, oceanfront regions (beaches and cliffs), human related land uses, and water bodies (Table 1). Wetlands are the forefronts and may diminish because of SLR (Kirwan and Megonigal 2013). Salt marshes and mangroves are sensitive to their water environments which are essentially linked to

Author's personal copy Mitig Adapt Strateg Glob Change Table 1 Potential impacts of sea level rise on the evolvement of the components of a coastal natural and human system Component

Climatic and non-climatic Interacting forces related to sea level rise components

Wetlands (marsh and mangrove)

The duration of inundation period, sediment budget, nutrients, and organic mass

Beaches, barrier islands, Erosion, submergence, and soft cliffs and sediment movement

Fresh water bodies

Human settlements

Human exploitation for other land uses like agriculture and aquaculture Human modifications as hard defensive structures Human modifications Wetland migration Human modifications as hard defensive structures Human overexploitation

Loss potential

High

High

High Salinization (groundwater issues), higher water table (sea water intrusion) Low for developed Higher water table (drainage The migration and countries losses of wetlands, issues), inundation, marine, groundwater pollution, Very high for developing and riverine floods countries and small beach deterioration, The fertilization of islands and self-modification agricultural areas

the equilibrium of organic and inorganic masses. This balance is fragile in the face of SLR, owing to a prolonged period of inundation and an inadequate sediment budget. It may be broken and lead to wetland losses and the formation of open waters. However, it is evident that the overall mass of wetlands can be retained with respect to relative sea level rise—if marshes may migrate landwards (Kirwan and Megonigal 2013). In this sense, the impact of SLR wetlands is less imminent than the other interacting elements, which are related to human colonization of wetlands. Like wetlands, beaches, barrier islands, and soft cliffs are the components of another subsystem that SLR threatens directly. These components have such issues as tidal erosion, subsidizing trend, and sediment losses (FitzGerald et al. 2008). These land-sea interfaces are likely to disappear, if their space to migrate inland is limited by other non-climate factors— dikes, levels, or various hard defensive structures—and coastal squeeze has been widely seen in developed countries across the Europe (Pontee 2013). Seawater invades the fresh water system as well. Salt water intrusion leads to the salinization of groundwater and higher water table (Nicholls 2011; Nicholls et al. 2011). It is inevitable even if seawalls prevent the overtopping of waves; the water can still penetrate through the porous rocks underneath protective structures (Pulido-Leboeuf 2004). The overexploitation of groundwater further exacerbates the inconsistency of water tables between the ocean and urbanized areas. Human settlement is another delicate component along coasts. Steady degradation of natural subsystems accelerates the urban exposure to various outcomes of SLR: inundation (Snoussi et al. 2008), flooding (FitzGerald et al. 2008), and the shrinking of arable lands (Dasgupta et al. 2009). Moreover, coastal urban expansion is faced by counteracting effects from other components: wetland migration, the instability of fresh water supply, and beach transformation.


Coastal nations have attempted to defeat rising water by engineering work, but such actions would increasingly expose coastal communities to SLR—because dikes and seawalls squeeze the ability of wetlands and beaches to adapt to salty water. Consequently, at a certain point developing countries will have to abandon their vulnerable settlements and retreat landward (Nicholls et al. 2011), despite the immense costs and efforts that are put to address SLR and control shoreline regions. Having fully acknowledged SLR threats, governments of different levels, research groups, and coastal residents have taken diverse actions to combat SLR’s negative consequences. For better understanding of global efforts on SLR adaptation measures (Table 2), we reviewed literature on this topic and governmental reports from Europe (Kabat et al. 2009; Nicholls and Mimura 1998; Stive et al. 2011; Tol et al. 2008; VanKoningsveld et al. 2008), South and Southeast Asia (Cochrane et al. 2017; Karim and Mimura 2008; Shaw et al. 2013; Siddiqui 2017; Smajgl et al. 2015), Africa (Frihy and El-Sayed 2013; Musa et al. 2016; UNPD 2011), Oceania (Abel et al. 2011; Cooper and Lemckert 2012; Evans et al. 2014; Hurlimann et al. 2014), and small island countries (Barnett 2001; Nicholls and Mimura 1998; Yamamoto and Esteban 2010). For the purpose of readability, these authors and sources are acknowledged once at this point without further repetition in the following paragraphs. South and Southeast Asia has been one of the more vulnerable regions to SLR, given its exposure, income level, and adaptive capacity to disasters. Numerous developing countries have operationalized accommodation strategies when deploying traditional defensive work along the coast. Nevertheless, their local communities are unprepared to implement the plans and policies designed at the national and regional level—due to a scarcity of resources and information. For instance, Bangladesh has a country-level adaptation program of actions and is granted assistance from the international community (e.g., Adaptive Crop Agriculture). It also has relatively mature programs in alleviating disaster related impacts and population displacement. However, the overall coverage of levees and dikes is low, and local residents count on temporary shelters when a disastrous flood occurs. Additionally, SLR has triggered a massive population migration into inland metropolises, but the receiving cities’ ability to accommodate these Benvironment refugees^ is unsatisfactory. Similar inconsistency is seen in Vietnam. This nation has developed several countrywide SLR adaptation plans, but it remains a question of how governments bring these plans into effect in those local municipalities that lack sufficient knowledge and funds. The country also enhances climate change-tolerant agriculture, yet the occupancy of wetlands and beaches may disturb the overall sustainability of the nation’s coastal system—which in turn has a potential to collapse with more severe SLR (Table 1). And such systematic breakdown would lead to an expedited progress toward forced abandonment of a great portion of coastal territories in low-income countries. European countries, particularly those adjacent to North Sea, are pioneers in adapting to increasing sea levels. The Netherlands represent a forefront where multiple strategies have been adopted in response to marine hazards, especially coastal flooding. Its first Water Management White Paper dates back to 1968—with a primary focus on flood defense and fresh water supply. The White Paper has undertaken several amendments since the entry of the twenty-first century, and embraced the prevention of sand loss, beach nourishment, and other aspects of flood mitigation. Along with the Flood Defense Act, the Dutch strategies responding to rising seawater becomes a prototype that has a long history of sustaining land reclamation from the ocean. In other words, the Dutch society has predominantly preferred hard protection structures over the other strategic alternatives. In recent years, though, this country has managed to synthesize accommodative measures into its coastal management system—

Region

Country/locations

Adaptation measures to sea level rise Typical measures

Selected adaptation policies, programs, and institutions

Bangladesh

XX

XX

X

Adaptive Crop Agriculture Including Innovative Farming Practices, National Adaptation Programs of Actions, National Strategy on the Management of Disaster and Climate Induced Displacement, and Bangladesh Climate Change Strategy and Action Plan

Vietnam

XX

XX

X

Europe

Netherlands

XXX

XXX

X

Africa

Egypt

XXX

XXX

X

Accommodation (flooding shelters, insurances, adaptive crops) Protection (levees) Managed retreat (personal retreat to nearby cities and metropolises) Accommodation (resilient agriculture, aquaculture, alternative means for livelihood) Protection (dikes and sluice gates) Accommodation (Bworking with nature,^ Broom for river^) Protection (dikes and seawalls) Accommodation (resilient agriculture) Protection (seawalls and levees)

Oceania

Australia

X

X

XXX

X

XXX

Asia

Island Countries Maldives, Tuvalu, XX and so on

XXX = strong; XX = medium; X = weak

Pilot relocation projects; insurance packed with mortgage Accommodation (multilevel adaptive management, Bmanagement as experiment^) Managed retreat (planned migration to inland countries)

National Program on Responding to Climate Change, Mekong Delta Plan, Climate Change Adaptation Master Plan, and Mekong Delta Water Resources Plan

Coastal Policy White Papers, Water Management White Papers, Flood Defense Act, and Delta Committee’s recommendations Egypt’s Strategy for adaptation to Climate and Disaster Risk Reduction, Environment Impact Assessment Mandate, Strategic Impact Assessment Mandate, and BLiving With the Sea^ (United Nation Development Program) A-line Seawall Program (Gold Coastal City Council), New South Wales Retreat Policy, Land use Ordinances, Coastal Councils, and Regional Coastal Boards South pacific Regional Environmental Program

Author's personal copy

Accommodation Protection Managed retreat

Mitig Adapt Strateg Glob Change

Table 2 A description of adaptation strategies and policies to sea level rise in vulnerable countries or localities


Bworking with nature^ and Broom for rivers^—when decision-makers realize that the likelihood of the failure of dike systems may soar with intensified flooding risks. However, the abandonment of coastal infrastructure is still the least preferable option in the Netherlands and similar high-income countries in Europe. Like countries in South and Southeast Asia, Egypt gains support from the United Nations (Blive With the Sea^) and other international organizations. It enforces a national strategy that specifies concrete countermeasures and a near-to-middle-term budget for the actions. The countrywide document also allocates funds for local-level relocation, when frequent floods make coastline areas inhabitable. Australia is another country which began to protect urban development in hazardous areas several decades ago. Seawall programs of different scales were widespread in this nation, and the A-line Program conducted in Gold Coastal City Council represents a successful protective structure. Recent years have also seen the increasing use of beach nourishment and planned relocation—alternative pathways to adapt to rising sea. Retreat Policies guides pilot relocation projects, and local-level institutions (e.g., Coastal Councils and Regional Coastal Boards) shoulder a higher responsibility of coastward development. New Zealand, a neighbor to Australia, applies a similar roadmap, implementing minimum floor level, coastal set-back, and other measures that allow room for SLR. Unfortunately, the fate of small island countries is not optimistic. Historic records of tide gauge stations suggest that higher sea levels would submerge Maldives, Kiribati, Tuvalu, and other island countries—featured by atolls. As such, their sovereignty as an independent country would become challenged. However, the Dutch solution, sea dykes, may be cost inefficient in that associated expenditure amounts to a large segment of the GDP of island countries. And hardening the coastline destroys the prospect of tourism related industry—a leading sector in the majority of these nations. Instead, working with the sea and managed retreat are the more viable options than pure engineered structures along the coast. Regionallevel collaboration is growing fast and results in various innovative plans such as Bmultilevel adaptive management.^ Furthermore, a few vulnerable countries have passed decrees that allocate public funds to purchase land from elsewhere—when the entire nation becomes submerged during the next century. Proactively planning future retreat seems the only feasible option, given the projected acceleration of SLR. The examination of worldwide adaptation strategies indicates that planned retreat is moved from research to agenda. However, the current preliminary analysis is inadequate, and sufficiently depicting such a global trend is another crucial domain but beyond the scope of this study. The addressing of such urgency is being formulated in another paper.

2.2 Land use change analysis in coastal regions Land use change analysis is a powerful means to illustrate coastal landscape evolution, and the technique is employed by many studies to assess socio-ecological forces behind urban development. Land use simulation involves an integration of different modeling phases and techniques. SLEUTH, an integrated Cellular Automaton (CA)-based modeling environment, is one of the early-stage tools in forecasting urban growth (Santé et al. 2010). Recent development in computer science enables the integration of different approaches. A sequential procedure is to (1) evaluate transition potential based on historical maps by logistic regression, colony and simulated annealing algorithm, and support vector machines (Zheng et al. 2015), (2) calculate land demand by Markov Chain (MC) process (Guan et al. 2011), and (3) assign


urbanization cells spatially by a CA model. The coupling of MC and CA is highly suggested because it could generate reliable results (Guan et al. 2011). Since the twenty-first century, research on land change determinants has gained increasing popularity as land use policies could be better informed if their contextual factors are well understood. Among various techniques, logistic regression has been in widespread use due to its simplicity and effectiveness. The family of logistic regression includes globally non-spatial models (Hu and Lo 2007), spatially explicit auto-logistic and expansion models (Deng and Srinivasan 2016; Liao and Wei 2012; Nakakaawa et al. 2011), and localized models such as Geographically Weighted Regression (GWR) (Shafizadeh-Moghadam and Helbich 2015). For instance, Hu and Lo (2007) used a global logistic regression to analyze the urban growth of Atlanta, USA, and found that population density, distance to major urban infrastructures and activity centers, and the densities of neighborhood attributes were important explanatory variables. Such findings were echoed by similar studies (Vermeiren et al. 2012). Nevertheless, global models were criticized because the presence of spatial autocorrelation violates the independent assumption among dependent variables (Dendoncker et al. 2007). Therefore, weighted logistic regression, which includes a spatial-lag component of dependent variables, was developed to overcome this issue. Deng and Srinivasan (2016) used this method to model urban growth in Beijing, China, and found that it outperformed orthodoxy logistic method. Recently, GWR and spatial expansion model became popular because they further incorporated local variations of parameters (Luo and Wei 2009; Shafizadeh-Moghadam and Helbich 2015). However, logistic regression suffers from some crucial shortcomings. First, modeling only binary land changes—urban or non-urban—fails to address complex land use classifications. Second, it neglects initial state of a land cell during calibration (Lin et al. 2014). Multinomial logit models were introduced accordingly and widely applied in arable land decline (Lin et al. 2014; Xu et al. 2013) and urbanization process (Hao et al. 2015; Zhao and Peng 2012). In these models, three or more land transitions were considered, and therefore, driving factors could be interpreted in response to each transition. Nevertheless, the combination of land change and coastal hazards is much less discussed in the current literature. While SLR is a global issue and large-scale analysis is booming, the need for localized assessment of SLR impacts and adaptation is warranted (Cooper and Pilkey 2004; Fenster and Dolan 1993; Pilkey et al. 1993). Such efforts are in dearth yet will be a focal point in this field as the relative SLR varies from location to location, and a generalizable framework to optimize local regions’ responses to SLR is imperative. To our best knowledge, this work is a few of the early inquiries that aim to localize SLR consequences, and to develop a procedure preparing vulnerable areas worldwide for future climate conditions—when SLR is inevitable and planned retreat is a viable, cost efficient option.

3 Study region Bay County coastal areas, located in Florida, United States of America (USA) were selected as a study region for this work (Fig. 1). The study region comprises three counties—Bay Country, Walton County, and Washington County, of which the first is the largest in land area and the most vulnerable to SLR due to its topographic and socioeconomic characteristics. Bay County is characteristic of a deltaic form, a topography that is more jeopardized by relative SLR than other coastal landforms (Nicholls and Mimura 1998). It has been hit by seventeen hurricanes since 1877


Fig. 1 The maps showing the study area

(Hurricanecity 2015). Because of inadequate land management in the past, urban growth in Panama City, the largest city in the county, appears in low-lying areas (Bay County Online 2016). Bay County’s economy overwhelmingly hinges on tourism, and the county’s leading industries are services and real estate. At the Metropolitan Statistical Area of Panama City, taxable sales in tourism and recreation in July, 2016, approximately doubled the number 11 years ago (Bureau of Economic and Business Research 2016), but over-reliance upon a few similar industry sectors amplifies the county’s vulnerability to acute disasters related to SLR. Bay County has over 50% of small businesses, and a large portion of its local firms are without chained enterprises in other locations. The overall business vulnerability of Bay County would increase rapidly with SLR (Song et al. 2016). According to U.S. Census Bureau (2015), total population of Bay County is 168,852, and over 50,000 people reside in two primary coastal cities—Panama City and Panama City Beach—where shoreline erosion, seawater intrusion, sediment loss, and frequent flooding are major SLR-derivative issues.

4 Data and methods Figure 2 displays an overall research workflow comprised of three steps: (1) model calibration (past land use change modeling), (2) model validation (the comparison of three models), and (3) model predictions (examination of SLR-induced flooding impacts). The purpose of calibration and validation is to justify three transition potential models and interpret crucial driving factors. For the land change models, this research employed three widely used approaches: Multilayer Perceptron neural network (MLP), Similarity Weighted Instance-based Learning (SimWeight), and binary logistic regression.


Fig. 2 Overall research workflow

4.1 Data We used the data sets of land cover maps and sea level rise scenarios. We also collected socioeconomic data to examine their relationships with urbanization progress for historical periods. Each of these datasets and their processing procedures are introduced with details in the following sections.

4.1.1 Land use maps Land use maps of 1995, 2004, and 2013 for the study region were collected from the Florida Geographic Data Library (FGDL). Original land use maps were reclassified into residential, commercial and services, industrial, urban other (institutional and transportation-related), vacant land, and water bodies. Water bodies were assumed to be unchanged and excluded from the analysis.


4.1.2 Driving variables Twenty-two variables were initially selected based on literature review and data availability (Table 3). These variables were next pretested by the Cramer’s statistics. The Cramer’s statistics is a chi-square test that compares spatial distributions of each land class and potential variables. It is recommended that variables with a Cramer’s coefficient of 0.15 or less should be discarded, and a high value of more than 0.40 indicates a good predictive potential (Clark Labs 2016).

4.1.3 Sea level rise datasets and the generation of flooding maps Direct impacts of SLR upon coastal regions may be marginal. Thus, a SLR-induced flooding map was developed to exemplify the results of rising sea levels. This was accomplished by a hurricane model that was developed by Hsu (2014). Generally, several steps were followed to consider the effects of SLR and change in Sea Surface Temperature (SST) on flooding intensification. First, rise in SST increased the central pressure of hurricanes (Knutson and Tuleya 2004). The enhanced

Table 3 Initial selection of potential explanatory variables Name

Description

Data sources

SOILPH SOILERODE

The relative acidity or alkalinity of soil An erodibility factor that quantifies the vulnerability of soil particles to water erosion Percent rise of slope Euclidean distance to major roads, 1995 Euclidean distance to aviation facilities, 1995 Euclidean distance to coastal lines Euclidean distance to residential areas, 1995 Euclidean distance to commercial and service areas, 1995 Euclidean distance to industrial areas, 1995 Euclidean distance to institutional areas and transportation facilities, 1995 Euclidean distance to water bodies, 1995 Euclidean distance to parks and recreational facilities, 2015 Euclidean distance to cultural centers and library facilities, 2015 Euclidean distance to healthcare facilities, 2014 Euclidean distance to community centers, 2010 Euclidean distance to public and private schools, 2012 The number of residential pixels in the Moore neighborhood, 1995 The number of commercial pixels in the Moore neighborhood, 1995 The number of industrial pixels in the Moore neighborhood, 1995 The number of institutional pixels in the Moore neighborhood, 1995 The number of vacant pixels in the Moore neighborhood, 1995 Total population per acre, 1990

Geospatial Data Gateway

SLOPE DISTRD95 DISTAP95 DISTCOAST DISTRE95 DISTCM95 DISTIND95 DISTOTHER95 DISTWATER95 DISTPARK15 DISTCULTURE15 DISTHEALTH14 DISTCENTER10 DISTSCHOOL12 NR95 NCM95 NIND95 NOTHER95 NVAC95 POP90

Florida Geographic Data Library

U.S. Census


pressure and other parameters were applied to calculate surge heights through a Surge Response Function (SRF) (Irish et al. 2009). Second, in 2030, a hurricane was simulated to strike Bay County at a place where it resulted in a disastrous storm surge and 500-year flooding. Local SLR data were considered to increase surge heights through an adjustment function (Udoh 2012). The worst SLR scenario projected by the Intergovernmental Panel on Climate Change (IPCC 2007) was applied to amplify hurricane consequences. Specifically, local sea level and SST was expected to increase by 0.2 m and 1.23 °C, according to the A1F1 scenario that represents the highest level of greenhouse gas emission in 2030. Next, different surge heights were calculated by the SRF and adjustment function in a number of stations defined along the coastline. SRF zones were delineated in every station, and the surge value was constant in each zone. Eventually, surge values were intersected with the Digital Elevation Model to identify flooding areas (Knutson and Tuleya 2004).

4.2 Transitional potential models MLP is an artificial neural network model recently used in LULCC modeling (Mozumder et al. 2016). As a machine learning method, MLP constructs a multivariate function between dependent and explanatory variables (Clark Labs 2016). More specifically, it establishes multiple neutrons between input layers (independent variables) and output layers (transitional and persistent maps). It then creates a net of connections among neutrons and runs different combinations of weights for input layers using the multivariate function. MLP uses 50% of a random sample from each transition for training the function, and the rest 50% data are used as a test set. This technique excels in dealing nonlinearity between explanatory factors and transitions and produces highly accurate estimations. Yet, machine learning is a Bblack-box^ model so that modelers can hardly interpret explanatory variables. SimWeight is a non-parametric machine learning approach that is based on a modified K nearest-neighborhood algorithm. Each transition is divided into two classes: change and persistence. The procedure is to calculate weighted distances of an assessed pixel to its surrounding cells with known classes in the variable space (Sangermano et al. 2010). For each assessed pixel, its membership for change is determined by Eq. 1: ! 1 c ∑i¼1 1− 1 1 þ e di ðc ≤K Þ ð1Þ Membershipchange ¼ K where K denotes the number of nearest pixels with known classes, c is the number of change pixels, and d is the distance to a changed pixel i within K nearest cells. A high value implies that a pixel has a greater transition potential. Overall, SimWeight is distribution free and only requires one user defined parameter—K. In practice, modelers test different K values and select the optimum one according to modeling accuracy. It is suggested that K be set as 1/10 of a sample size, and a sample of 1000 to 2000 pixels is sufficient for training purpose (Clark Labs 2016). Logistic regression is a statistical technique that is different from machine learning methods. The change probability is estimated through Eq. 2: Pic ¼

expðU ic Þ 1 þ expðU ic Þ

ð2Þ


where Uic denotes a suitability function, as shown in Eq. 3: U ic ¼ β 1 Pi1 þ β 2 Ai2 þ β3 N i3 þ β 4 SEi4 þ ε

ð3Þ

Pi1, Ai2, Ni3, and SEi4 are the vectors of physical conditions, accessibility factors, neighborhood conditions, and socioeconomic attributes associated with a cell i, β1, β2, β3, and β4 are the corresponding vectors of linear coefficients, and ε denotes the vector of unobserved components which is assumed to follow a Gumbel probability density function that is independent and identically distributed. Coefficients are calibrated using the maximum likelihood technique. Logistic regression is particularly useful in quantifying the relationships between land change and explanatory variables.

4.3 Model calibration 4.3.1 Land change analysis Land changes from 1995 to 2004 were analyzed and calibrated, and corresponding changes from 2004 to 2013 were modeled and contrasted. However, only major land transitions with 500 ha or more were modeled. The land change analysis was performed using the Land Change Modeler (LCM). Model results showed that there were eight primary transitions that were grouped into five sub models (Table 4).

4.3.2 Land change demands This research employed the MC model to predict land demands. The first-order MC assumes that current state of a process only relies on its previous state. Two historic land use maps in 1995 and 2004 were overlaid to obtain a Markov matrix, which showed estimated changes for each land use category. This matrix was then converted into a basic MC likelihood matrix (base matrix hereafter) based on which future land demands were predicted (Takada et al. 2010). The predictions are described by Eq. 4: 0

. 1

B λ1 B AM ¼ H B ⋮ @ 0

1 t

… ⋱

0 ⋮.

⋯

1

ðλn Þ

C C −1 CH A

ð4Þ

t

where AM is the annual change matrix, t is the number of simulation years, H is the eigenvector of the base matrix, and λi is the i-th eigenvalue of the base matrix.

4.3.3 Modeling process Three models were implemented in the LCM, and the outcome maps were probability surfaces where values range from zero to one for each transition. In MLP, a recommended sample size of 10,000 was used for major transitions such as vacant to residential areas. For minor transitions, a sample size of 2000 was applied. Each sub model that grouped similar transitions was presumably

Author's personal copy Mitig Adapt Strateg Glob Change Table 4 Major land transitions and sub models used by MLP in the LCM from 1995 to 2004 Transitions

Residential

Commercial and services

Industrial

Urban other

Vacant land

Vacant to residential Residential gain Commercial to residential Vacant to commercial Commercial gain Vacant to industrial Industrial gain Vacant to urban other Other urban gain Residential to vacant Vacant gain Commercial to vacant Other to vacant

influenced by the same driving factors. In SimWeight, different K values (50, 100, 200, 250, 350, 450) were tested with a sample size of 2000, and a K value of 50 was determined after the sensitivity test. For logistic regression, a forward regression procedure was conducted to exclude insignificant variables for each transition in Statistical Package for the Social Science (SPSS). Significant variables were next applied in the LCM. To mitigate the spatial dependency, 50% pixels were tested for each transition using the stratified sampling technique.

4.4 Model validation Kappa coefficient has been extensively applied in LULCC practices, and it is believed to be more credible than percent agreement measures (Michalski et al. 2008; Zheng et al. 2015). Therefore, it was utilized in this study to assess overall model performance. Based on a contingency table, the kappa index can be determined by the following equation (Zheng et al. 2015). K¼

j j ∑i¼1 Pij −∑i¼1 PiS * PSi j 1−∑i¼1 PiS * PSi

ð5Þ

where Pij is the frequency of pixels of i-th category in a modeled map falling in the j-th category of the actual map, PiS is the frequency of pixels of i-th category in the simulated map, and PSi denotes the proportion of pixels of i-th category in the actual map. It is suggested that a kappa of 0.6 or greater reflects a high level of agreement between simulations and reality (Landis and Koch 1977). However, the kappa coefficient was recently criticized for its limited ability to (1) differentiate disagreement, quantity, and allocation and (2) compare with a naïve model (Bradley et al. 2016). Therefore, another validation tool, SimiVal, was also applied to overcome these shortcomings. SimiVal provides a comprehensive assessment in terms of quantity allocation and landscape structure. The SimiVal tool requires three input layers: observed maps at t1 and t2, and a simulated map at t2 developed based on land changes from t0 to t1. Similarity statistics were generated using linear regressions that associate modeled spatial patterns with perfect, random, and systematical biased cases. Spatial metrics between modeled and actual maps were also examined in terms of spatial autocorrelation and landscape patterns. This tool was run in the R project with the source code offered by Bradley et al. (2016). The Relative Operating Characteristic (ROC) curve was additionally used to validate the probabilistic results generated by logistic regression. Past studies have suggested that good logistic regression models often have a ROC value of 0.7 or higher (Zheng et al. 2015).


4.5 Predictions of 2030 Four scenarios were developed to account for the variability in urbanization rates and SLR adaptation strategies. Urbanization rate controlled future land demands for residential, commercial, and urban other areas. Two rates were considered in predictions. The first (G1) was a business-as-usual rate that was consistent with historical trends. The second (G2) was to increase conversion rates from vacant to residential, commercial, and urban other areas by 50% (Geneletti 2013). Increased land demands were assumed to accommodate economic development and population growth. Such modifications were realized by adjusting the MC matrix for transitions from vacant to urban areas. Adaptation strategies were demonstrated through growth incentives/constraints. The first (P1) represented a Bdo-nothing^ policy whereby no developmental constraints were implemented. The second policy (P2) corresponded to a planned retreat strategy in which residents in predicted flooding zones would be moved to less vulnerable areas. Several maps of incentives/constraints were prepared for this policy. For transitions from vacant to residential, commercial, and urban other areas, maps contained three classes: prohibited (areas falling into flooding polygons), neutral (areas outside prohibited and incentive zones), and incentive (areas with high transitional probabilities of 0.5 or above according to the results of transition potential models). Based on previous studies, 0, 1, and 2 were assigned to three classes respectively (Zheng et al. 2015). For the residential to vacant transition, the map included only two classes: prohibited (areas outside flooding polygons) and incentive (areas within flooding polygons). Finally, land use predictions in 2030 were developed following the same process as in model calibration. A total of 12 maps were produced corresponding to three models (MLP, SimWeight, and Logistic) and four scenarios (G1P1, G1P2, G2P1, and G2P2).

5 Results 5.1 Model calibration 5.1.1 MC matrix from 1995 to 2004 Table 5 serves as a base MC matrix for predicting land demands in 2013. The table shows that major contributors to residential growth were commercial and vacant areas. In contrast, considerable industrial areas were converted into vacant, transportation, institutional, and other land uses. Noticeably, a sizable portion of built-up areas changed into forest, farmland, and other vacant lands from 1995 to 2004.

5.1.2 Land demand prediction in 2013 The historical trend of land changes indicates that (1) residential and urban other areas increase at the expense of vacant lands, (2) commercial areas remain approximately unchanged, and (3) industrial lands shrink sharply (Table 6). This pattern is largely captured by the simulated land areas in 2013, but the prediction overestimates growth rates for some land use categories. Such errors remind us of extrapolating future land demands with caution, and scenario-based land demand predictions may be used to minimize errors.

Author's personal copy Mitig Adapt Strateg Glob Change Table 5 Base Markov Chain matrix from 1995 to 2004 From 1995 to 2004

Residential Commercial and services Industrial Urban other Vacant Water bodies

Residential


Industrial

Urban other

Vacant

Water bodies

0.78 0.16

0.03 0.58

0.00 0.02

0.02 0.09

0.16 0.15

na

0.01 0.02 0.03 na

0.05 0.02 0.00

0.38 0.02 0.00

0.15 0.69 0.01

0.38 0.25 0.95

5.1.3 Analysis of driving forces from 1995 to 2004 As a whole, soil pH and erodibility factor, distance to water bodies, and the number of neighboring vacant pixels are significantly associated with the spatial patterns of land use classes (Table 7). Land value per acre is discarded due to its low Cramer’s coefficients. Individual values suggest that residential land use is significantly associated with distance to built-up areas, the number of neighboring residential pixels, and population density per acre. However, for commercial, industrial, and other urban land uses, very few variables have a high Cramer’s value (0.4 or above). This may be partly because these land uses are relatively small compared to residential and vacant lands. Table 8 shows the estimated coefficients of variables for different land use types from 1995 to 2004. Overall, variables may contribute differently to distinct transitions. For vacant to residential transition, the distance to roads with a negative regression coefficient (−0.00008) indicates a higher probability of residential growth in a vacant area nearer to major roads. This is also true for other accessibility related factors. In other words, higher accessibility to existing infrastructure contributes to a higher probability of residential development in vacant areas. Interestingly, distance to coastlines (0.00006) is positively associated with the likelihood of residential growth, which indicates that new developments are oriented inland. Additionally, both vacant areas with a high level of population density and proximity to current settlements have a larger chance of converting into housing units. For commercial to residential transition, the positive coefficient of distance to roads (0.00009) suggests a higher likelihood of conversion into residential use for a commercial pixel farther away from major roads. The variables such as distance to airports, institutional areas, and population density have a similar effect. Moreover, commercial cells surrounded by many residential and vacant pixels are likely to change. In other words, separate commercial units are prone to convert Table 6 Historical land development and predicted land demands in 2013 (in hectares) Year

Residential


Industrial

Urban other

Vacant

1995 2004 2013

15,760.00 19,742.25 20,776.25 23,522.25

3748.50 3662.00 3742.50 3346.75

1176.25 1096.25 756.50 1584.25

4349.75 5248.75 8121.25 5288.25

249,348.50 243,966.25 242,669.75 239,974.00

Actual Simulated

Author's personal copy Mitig Adapt Strateg Glob Change Table 7 Cramer’s coefficients of potential explanatory variables Variables

Soil pH Soil erodibility factor Slope Distance to roads Distance to airports Distance to coastal lines Distance to residential areas Distance to commercial and service areas Distance to industrial areas Distance to institutional areas and transportation facilities Distance to water bodies Distance to parks and recreational facilities Distance to cultural centers and library facilities Distance to healthcare facilities Distance to community centers Distance to public and private schools The number of residential pixels in the Moore neighborhood, 1995 The number of commercial pixels in the Moore neighborhood, 1995 The number of industrial pixels in the Moore neighborhood The number of institutional pixels in the Moore neighborhood The number of vacant pixels in the Moore neighborhood Total population per acre Land value per acre

Overall Cramer’s V

Individual Cramer’s V for each land use class Re

Com

Ind

Ou

Va

0.4459 0.4366 0.2752 0.3075 0.2178 0.1132 0.3726 0.3508

0.1878 0.1706 0.1706 0.2134 0.1666 0.1785 0.4472 0.3503

0.0769 0.0569 0.0569 0.1457 0.1475 0.1246 0.1714 0.3182

0.1853 0.0242 0.0538 0.0549 0.0537 0.0393 0.0528 0.0554

0.0787 0.0732 0.0548 0.1174 0.2392 0.0835 0.0838 0.1341

0.8467 0.8469 0.5117 0.5353 0.3631 0.1310 0.6089 0.5530

0.3435 0.3340

0.1957 0.1985

0.0962 0.1260

0.2014 0.0657

0.0741 0.2346

0.6207 0.5708

0.4321 0.2923

0.2497 0.2745

0.1080 0.1694

0.0501 0.0588

0.0854 0.0741

0.8278 0.5045

0.2146

0.2888

0.2863

0.0510

0.1391

0.2578

0.2164 0.1969 0.1963 0.3221

0.3074 0.3061 0.3109 0.7034

0.2978 0.2387 0.2400 0.1287

0.0595 0.0505 0.0648 0.0087

0.1408 0.1025 0.1511 0.0510

0.1946 0.1875 0.1257 0.1697

0.2562

0.0956

0.5532

0.0309

0.0875

0.0866

0.1624

0.0030

0.0291

0.3554

0.0628

0.0146

0.2255

0.0350

0.0428

0.0259

0.4975

0.0221

0.4381

0.2082

0.1257

0.0424

0.1784

0.9206

0.2137 0.0404

0.4561 0.0587

0.1208 0.0646

0.0030 0.0028

0.0452 0.0133

0.1305 0.0703

Variables of a value of 0.15 or less should be discarded Italicized numbers mean the Cramer's value is greater than 0.4, and the bolded number suggests a Cramer's value of 0.15 or greater Re residential, Com commercial and services, Ind industrial, Ou urban other, Va vacant

into residential use. This inference is further strengthened by the regression results of vacant to commercial transition. The negative regression coefficients of distance to roads (−0.00022), cultural facilities (−0.00007), healthcare facilities (−0.00008), and community centers (−0.00009) indicate higher chances of converting into commerce for a vacant pixel nearer to urban facilities. Such patterns can also be seen in the transition of vacant to urban other areas (institutions and transportation facilities). Regarding vacant to industrial transition, physical conditions become crucial factors. Areas with higher soil pH, erodibility factor, and slope are more likely to be developed into industrial lands. Furthermore, industrial developments tend to occur in less populated places that are far away from water bodies, parks, and hospitals. Industrial growth is more likely to appear in shoreline areas, as indicated by the regression coefficient of distance to coastlines (−0.00013). However, public health concerns may arise in that shoreline industrial facilities are exposed to

−1.00300

−5.01700 0.11216 0.00900 19.93% 22.70% 0.7972

−0.00006 −1.88034

−0.94633 1.27348 0.95885 30.35% 49.40% 0.9136

−0.00011

0.00038

0.00026 −0.00044 0.00016 0.00008 0.00015 −0.00020

0.00009 0.00007 −0.00010 −0.00216

Com-Re

−0.00008 0.00007 0.00006 −0.00156 −0.00020

0.38562

Va-Re

7.66300 40.67% 56.80% 0.9441

−6.79900

0.00020 −0.00007 −0.00008 −0.00009 0.00020

−0.00120 −0.00070

−0.00022 −0.00006

Va-Com

−1.10451 2.72900 26.30% 56.20% 0.9398

−1.58200 −4.67700

0.00015 −0.00013 −0.00032

0.00075 0.00010

−0.00013 −0.00140 0.00019 −0.00057

0.95800 5.03029 0.51900

Va-Ind

−2.89227

−1.36922 −0.29439 1.91900 11.12% 16.30% 0.7370

−3.05840 −2.37580 −9.66983 1.23887 8.45400 22.90% 45.70% 0.8937

0.00002 0.00002

0.67061

−0.00016 0.00006 −0.00087 0.00018 0.00019 −0.00013

0.00011 −0.00008

−3.43355 −0.11808 0.44065 15.85% 19.50% 0.7498

0.00007 0.00007

0.00014

−0.00036

−3.19915 −0.14296 1.30100 9.61% 13.80% 0.7150

0.00009

−0.00006

−0.00042 0.00012

0.00033 −0.00009 −0.00012

0.17586 0.00016

0.06780 −0.00009

−0.00030 0.00007 −0.00005

−0.15869

−0.17125

Ou-Va

−0.21778

Com-Va

Re-Va

Va-Ou

Re residential, Com commercial and services, Ind industrial, Ou other urban, Va vacant land

For instance, Va-Re represents the land transition of vacant to residential areas, where 1 denotes that original vacant pixels changed into residential ones, and 0 represents the pixel remained unchanged. All variables are significant at 0.05 level

Soil pH Soil erodibility factor Slope Distance to roads Distance to airports Distance to coastal lines Distance to residential areas Distance to commercial and service areas Distance to industrial areas Distance to institutional areas and transportation facilities Distance to water bodies Distance to parks and recreational facilities Distance to cultural centers and library facilities Distance to healthcare facilities Distance to community centers Distance to public and private schools The number of residential pixels in the Moore neighborhood The number of commercial pixels in the Moore neighborhood The number of industrial pixels in the Moore neighborhood The number of institutional pixels in the Moore neighborhood The number of vacant pixels in the Moore neighborhood Total population per acre Constant Pseudo R square Cox and Snell R square Roc value

Variables

Table 8 Estimated variables of binary logistic regressions

Author's personal copy

Mitig Adapt Strateg Glob Change


potential leakage of hazardous materials resulted from storm surge, floods, and other coastal disasters. As for urban to vacant transitions, built-up areas with low accessibility to infrastructure tend to convert into agricultural, forest, and other vacant lands.

5.2 Model validation 5.2.1 Change potential analysis Change potential maps are temporal probabilities of changes from one land use category to another. Hence, a first step to assess model performance was to visually contrast transition potential maps developed through the abovementioned methods: MLP, SimWeight, and logistic regression. The assessment is illustrated by transitions from vacant to urban areas (Fig. 3). Change probabilities were binned into five categories: 0, 0–0.25, 0.25–0.5, 0.5–0.75, and 0.75– 1 (Mozumder et al. 2016). Take vacant to residential transition for example. Overall, all models largely capture the locations of change. Pixels in the vicinity of major roads and existing urban areas have higher chances of developing into residential areas. However, three models generate different quantities of change. Logistic regression develops very limited number of pixels with high change probabilities. On the contrary, SimWeight and MLP assign substantially more areas as high change potential than logistic regression. Furthermore, SimWeight assigns more weights to pixels nearer to built-up areas than MLP due to its nearest-neighborhood algorithm (Mozumder et al. 2016). These findings are also reflected in other transitions (Fig. 3).

5.2.2 Kappa coefficients An average kappa value of 0.66 suggests a high agreement between simulated and actual maps (Fig. 4). The model fit is sufficient for residential, industrial, and vacant areas. Individually, logistic regression obtains the highest agreement for residential areas. MLP performs better than the other alternatives for commercial, industrial, and other urban areas. Kappa values, however, fail to measure landscape structures and other crucial validation aspects. Therefore, another assessment will be introduced in the next section using the SimiVal tool.

5.2.3 Non-spatial and landscape metrics The regression coefficients of MLP, SimWeight, and logistic regression are −0.04 (P value: 0.5), −0.03 (P value: 0.62), and −0.01 (P value: 0.89), respectively, indicating an extremely weak association with the metrics of random case (Fig. 5a–c). In other words, all models exhibit credible capacity to capture the changes of observed maps. Figure 5d depicts the overall performance for three models in a similarity space, and logistic regression produced a map that was the most similar to observed patterns. Table 9 further displays quantity and spatial metrics for three models. The values of PaAve and PaVar of three models are approximately the same as those of observed case, indicating three models’ capacity in capturing actual landscape patterns. SimWeight predicts a higher level of compactness and spatial autocorrelation than logistic and MLP. This finding further justifies our observations in transition potential maps (Fig. 3). With regard to quantity statistics, all models predict substantially more change pixels than the observed case. For instance, there are 13,695 observed pixels in vacant to residential transition (T3:1), whereas three models almost double this quantity of change.


Fig. 3 a–l Comparison among major transition potential maps from 2004 to 2013 developed by three models. Va to Re vacant to residential, Va to Com vacant to commercial, Va to Ind vacant to industrial, Va to Other vacant to urban other areas

5.3 SLR-induced flooding Figure 6 depicts potentially inundated areas owing to a 500-year flood—which would be enhanced by a moderate SLR of 0.2 m. A vast territory adjacent to the west, north, and east bays may be submerged, with the total flooded area amounting to 3100 ha—a bulky segment of which is built up zones. If a more extreme SLR up to 0.9-m is considered, as many scientists projected, the total inundation areas would increase more than tenfold, topping 46,000 ha.

5.4 Model prediction Figure 7 shows land use predictions of 2030 by three models under four scenarios: G1P1, G1P2, G2P1, and G2P2 (see Section 4.5). Built-up areas are expected to grow near existing settlements in coastal regions, as indicated in G1P1 and G2P1 scenarios. In other words,


Fig. 4 Comparison of actual and simulated maps in 2013. Specifically, Kno denotes kappa for no information for quantity and location. Klocation is an index measuring how well the model performs in terms of cell-by-cell comparison between simulated and actual maps. Kstandard is the overall kappa coefficient

coastline areas continue to attract new developments if there is no policy intervention. In contrast, with the population relocation strategy (P2) residents within flooding areas are largely displaced, and newly urbanized areas extend in the northern and western hinterland by 2030. This scenario is further illustrated by Fig. 8 that shows urban exposure to the 500-year flooding at a larger geographical scale. Vulnerable residents would be confronted with inundation by 2030 if local governments do not take adaptation measures. On the contrary, G1P2 and G2P2 scenarios relocate the vast majority of current residents at risk and prohibit new developments in flooding prone areas. Table 10 displays the inundated areas of three land uses in 2030. With higher growth rates (G1P1 and G2P1 scenarios), residential areas increase by an average of 500 ha in flooding prone zones. This reveals that coastal communities’ exposure to flooding would rise if they are unprotected and unprepared. Conversely, managed population displacement can markedly mitigate such exposure by relocating potentially inundated households (G1P2 and G2P2 scenarios). Additionally, three models differ in terms of the quantity of relocated urban areas. While all vulnerable residents are relocated by MLP, approximately 15% citizens remain in the flooding zone according to the logistic regression. This discrepancy may result from different algorithms applied in three models. SimWeight and logistic approaches prefer areas in the vicinity of existing settlements over distant hinterland. These results suggest that the population relocation has great potential in mitigating regional flooding vulnerability from the modeling perspective. However, it is equally crucial to understand obstacles, motivations, and ways for promoting the retreat option.


Fig. 5 Overall validation metrics for three models using linear regression. a logistic regression, b MLP, c SimWeight, and d similarity space

6 Discussions 6.1 Recommendations for global adaptation strategies Adapting to changing coasts worldwide generally translates into shoreline management practices. There exists a diversity of adaptation pathways that have been developed and are globally transferable; these include such strategies as real adaptation options (Nicholls et al. 2013) and five principles for coastal governance (Abel et al. 2011). These approaches are largely identical in nature. Essentially, the key of sustainable adaptation is to accommodate geomorphological changes of coasts and leave space for nature, while flexibility may be allowed in different coastal regions. Dynamic adaptation pathways and systematic monitoring are appropriate for deltaic regions, particularly for developed countries. Coastward migration due to labor force needs seems inevitable in the coming years. Large parts of densely populated estuaries are home to metropolises that drive coastward migration, a tendency likely continuing into several decades. Hence, a synthesis of both flood defense systems and managed realignments of protective lines is advocated in deltaic cities. While hard structures continue to upgrade to address the storminess of higher intensities, attempts should also be made to identify the beneficiaries of the protection and

Author's personal copy Mitig Adapt Strateg Glob Change Table 9 The quantity and spatial metrics for the observed, random, and three simulations

Total quantity Nop PaAve PaVar Moran’s I Geary’s C Allocation distance T1:1 T1:2 T1:3 T2:1 T2:2 T2:3 T3:1 T3:2 T3:3

Observed case

Random case

Logistic

MLP

SimWeight

38,423 4312 0.05906 0.00036 0.52955 0.46589 100 68,560 1631 8411 467 36,482 2464 13,695 11,755 977,376

38,423 33,224 0.07854 0.00003 0.00097 0.99468 – 75,901 1325 1376 656 38,056 701 17,170 17,195 968,461

58,397 3216 0.05998 0.00035 0.55247 0.44322 121.44 65,886 0 12,716 2307 29,741 7365 25,505 10,504 966,817

58,266 3475 0.05761 0.00037 0.58017 0.41565 120.75 65,875 0 12,727 2216 29,990 7297 25,553 10,473 966,800

58,311 2958 0.05883 0.00036 0.62076 0.37439 118.00 65,894 0 12,708 2266 29,823 7324 25,494 10,519 966,813

Total quantity total number of changed pixels involved in all transitions, Nop the number of patches, PaAve the ratio of average perimeter to area, PaVar the ratio of variance of perimeter to area, Moran’s I and Geary’s C measurements for spatial autocorrelation, Allocation distance a measurement for identifying distance of predicted pixels to the observed change instances, T transitions among different land use classes, 1 residential areas, 2 other urban areas, 3 vacant areas and minor water bodies

redistribute—fairly—the risks of possible defense failure. In other words, beneficiaries, rather than the whole society, have to bear the costs of sea walls; further, they may shoulder a larger risk of economic losses due to flood overtopping. Secondly, adequate space along coasts should be allowed for wetland migration, and may serve as the reservoirs of sediment which is a necessity of marsh movement. Lastly, large-scale monitoring of coast morphological variations is imperative. The monitoring may help to detect the deteriorating parts of defensive lines and hot spots of salt water erosion. While planned retreat is theoretically effective in the long run, its public opposition in developed deltaic areas is expected, and the implementation of small pilot projects may be planned to examine its feasibility. For small islands such as Maldives, Kiribati, and Tuvalu, managed retreat and significant readjustment of protection lines appear the only option. Flood overtopping risks in low-lying island countries are considerably higher than in other locations, resulting in a much larger investment cost for sustaining defensive projects. Unfortunately, the cost is unaffordable for numerous island states, because it may amount to a significant portion of annual national income that relies heavily on tourism; yet tourism is largely hampered by sea walls that damage natural features of beaches. Hence, it is recommended that progressive abandonment of hard structures is to be expected and well-planned. Regional and national efforts may initiate the formulation of abandonment plans, specifying accommodation space for sea water intrusion and migrating wetlands and making policies to facilitate planned retreat of vulnerable dwellers. Local governments should be granted sufficient authority and resources to deploy shoreline management projects and relocate hazardous urban developments. However, large-scale relocation practice due to SLR is uncommon and still requires extensive research and experiments. Hence, a key avenue to successful retreat—and coastal management in general—is essentially resilient shoreline management, as outlined in the next section.


Fig. 6 The flood depth of a 500-year return period under the 0.2-m sea level rise in 2030

6.2 Operationalizing resilient shoreline management Moving from the proposed agenda of planned retreat into action requires phenomenal efforts. Many studies have found that adaptation planning is still in a nascent stage and its progress is very slow due to several major constraints (Bedsworth and Hanak 2010; Carmin et al. 2012). Primary obstacles are the lack of information (i.e., uncertainty of SLR and its future impacts), institutional incapability (i.e., the absence of constitutional power), and resource constraints (i.e., insufficient financial and technical capacity). Such hardships are particularly true for planned retreat since it often counteracts the economic growth desired by local governments. As adaptation decisions are always made locally, the most appropriate option could vary significantly from one community to another. Therefore, the selection of proper strategies requires in-depth studies of local risk and vulnerability to SLR. Moreover, localities should conduct sound cost-benefit analysis for adaptation alternatives, which are currently lacking in existing planning documents in the US (Fu et al. 2016). We thereby propose several general principles of the building of institutional capacities and land use planning to facilitate a SLR adaptive community in coastal lowlands. Framing a resilient institutional system is a nexus connecting policies and research outcomes with concrete SLR adaptation practices through governments at different levels. The lynchpin of springy hazard governance is a horizontal, vertical network of well-coordinated, learning active institutions. Abel et al. (2011), among others (Dovers 2009; Glavovic et al. 2010), characterizes such a system with three crucial elements: multilevel governance with the proper allocation of authority and resources, a high tolerance towards uncertainties in policy making, and the distribution of benefits and latent risks among the beneficiaries of coastward development. Moreover, adaptive capacities of institutions are linked to five principles: the skills and performance of each actors; management capacities; hieratical networking capacities; a well-organized regulatory framework that stipulates relevant laws, policies, and regulations; and the paradigm shift of societal norms and values (Storbjörk and Hedrén 2011).


Fig. 7 Land use predictions in 2030 under different scenarios by three models. G1 business-as-usual rate of urban growth, G2 G1 rate multiplied by 1.5, P1 baseline policy, P2 planned retreat policy

In addition, planned retreat strategies can be boosted through responsive land use planning and development management. Land use ordinance is an efficient means to turn the blueprints of hazard governance into actions. In fact, land use planning and management has been advocated by many scholars for its effectiveness in hazard mitigation (Abel et al. 2011; Burby et al. 2000; Frazier et al. 2010). It facilitates SLR adaptation as well. Centerpieces to the facilitation are adaptive land use plans, the evaluation of SLR potentials, and development regulations. Improvement of current land use schemes are (1) to allow the adjustment of existing urban growth boundaries and land use codes (Frazier et al. 2010) and (2) to address the issues of relocating important facilities and lifeline infrastructure. The assessment of hazards, including SLR potentials, is critical in identifying risk-prone areas. The inclusion of the climatic uncertainty into land


Fig. 8 Urban exposure to sea level rise induced flooding in 2030 under four scenarios. G1 – business-as-usual rate of urban growth; G2 – G1 rate multiplied by 1.5; P1 – baseline policy; P2 – planned retreat policy

use codes is the key to the success of SLR adaptation, and local governments need determination to integrate such uncertainties into land use policies. In this regard, room should be allowed for the revisions of land use codes—according to updated projections of SLR. Putting together, land use plans and hazard assessment offer the technical footing for development management, a means to constrain new urban projects in risky areas and encourage urban growth in less flood-prone areas. Transferable development rights and development approval grants are two empirical tools: the former being the transfer of development rights and the compensation for relocation costs, and the

Author's personal copy Mitig Adapt Strateg Glob Change Table 10 Inundated land uses in 2030 under different policy scenarios by three models (in hectares) Scenarios

G1P1 G1P2 G2P1 G2P2

Residential

Commercial

Urban other

MLP

SimWeight

Logistic

MLP

SimWeight

Logistic

MLP

SimWeight

Logistic

4446 0 4990 0

5000 112 5586 112

4687 664 5135 664

805 666 923 666

900 707 1032 707

929 599 1153 599

1886 1473 2115 1473

2104 1408 2442 1429

2333 1490 2597 1490

latter being an agreement to remove new structures once sea level is rising to a certain distance from the properties. Lastly, land use planning and management is more efficient when implemented in an incentive-based way than in a coercive or sanction-based approach. Innovative financing solutions would also move the wheels of proposed strategies into actions and ensure that the transition is economically sound. Expediting planned relocation through financial ways is twofold. First, the purchase behaviors towards hazardous properties are intrinsically associated with insurance premiums. Individuals’ willingness to possess scenic yet risky coastal properties diminishes with an increasing rate of insurance—which can be partially tied to hazard resilient building standards—and with higher mortgage rates. Amplified difficulties of gaining insurance and mortgage deter people’s willingness to pay for a risky property. However, to achieve such a goal needs negotiation among governments, insurers, and banks. Second, financing planned relocation of existing development is a burning issue, particularly in fund-raising processes. This procedure involves two crucial questions of who pays for the land and property acquisition in risky regions, and who is responsible for the infrastructure and facility deployment in receiving areas. Historically, public funds pay for land acquisition, but they inadequately cover all the vulnerable structures. New fund-raising mechanism must be developed in order to transfer risks and costs from the society to the beneficiaries of coastal amenities. A few examples are to Bbuy back and rent dwellings^ and Bnew repurchase programs for risk-prone assets^ (Rulleau and Rey-Valette 2017). These projects were recently implemented, and their outcomes are open to evaluation. Planned retreat is further complicated because relocation has psychological implications. Local residents may be emotionally attained to their places, exhibiting a high appreciation to their neighborhoods, social ties, and beaches, and they are reluctant to relocate to unfamiliar areas (Evans et al. 2014). Such psychological hints are manifest when people make individual decisions. Hence, one countermeasure is to encourage community-scale—rather than individual-level— decision making for an area prone to SLR (Glavovic et al. 2010). Another pathway is to apply the principle of Bcatastrophes as opportunities^ (Abel et al. 2011), which pushes relocation and restricts rebuilding in hazardous locations immediate after a disaster. However, in-depth studies concerning psychological implications of planned retreat are in paucity yet of great necessity and calls for multidisciplinary collaboration—from natural scientists, lawyers, and psychologists. Still, city managers and planners need tools to protect existing vulnerable areas from SLRinduced hazards. Building codes, zoning regulations are traditional ways to control development intensity and building standards. Recent prosperity of emerging technologies reflects a tendency to rely on diverse and large-volume data for decision making. Such devices as drones and unmanned aviation vehicles have already been deployed along shoreline to generate real-time information on weather conditions and sea level variations (Cochrane et al. 2017). These tools can be a value supplement to existing information and early warning systems.


7 Conclusions This work validated and contrasted three transition potential models using the land cover change maps of 1985, 2004, and 2013 in Bay County, Florida. Average kappa value was above 0.65, indicating that MLP, SimWeight, and logistic regression all performing well in capturing overall patterns of historical land changes. Yet, three models did generate distinct landscape metrics. MLP assigned high built-up probabilities to hinterland, whereas SimWeight produced a more spatially correlated landscape that clustered near existing settlements than the other two alternatives. Logistic regression was the most conservative in considering potential urban areas. Most importantly, such spatial variations may mirror different urban growth patterns, i.e., sprawl and compact development. Given complex urban dynamics in coastal areas, the multi-platform method may be more reliable than traditional approaches. Scenario-based predictions have been justified in numerous land change studies (Halmy et al. 2015; Thapa and Murayama 2012). This study employed four policy scenarios and detected interesting coastal urban dynamics in 2030. Under the policy fostering coastal developments, new residential areas likely extend from, or infill within, current built-up regions. Additionally, many inland housing units in the north may disappear in two decades. Conversely, the population relocation strategy would relocate the majority of residents that are potentially inundated by SLR-induced flooding. The study of multiple policy scenarios has particular contributions. First, different growth rates help understand the impacts of increased anthropogenic pressures upon coastal landscape due to economic development and population growth. Second, the reduction of residents’ vulnerability to flooding can be visualized before and after adaptation measures are implemented. Third, this study offers a sound approach for integrating land change analysis and planned retreat policies. Its framework is beneficial to local planners and coastal managers in making adaptation decisions for rising sea levels. In addition to methodological contributions, this paper concludes several points that benefit worldwide efforts in combating SLR.

& & &

For highly developed deltaic areas, sea walls and other hard structures will be a primary adaptation option into the following decades, but this should be accompanied by Bsoft^ strategies which allow space for water and nature. Given excessive defense costs and uncertain magnitude of future SLR, small island countries need to abandon a large segment of current defensive lines and plan for the retreat of coastal citizens. The lynchpins of sustainable shoreline management include (1) multilevel and resilient institution system, (2) responsive land use planning, (3) innovative financing for adaptation measures, (4) a better understanding of households’ complex sentiments towards SLR coping strategies, and (5) the integration of advanced technologies.

However, this work has several limitations. First, it only simulated planned retreat and neglected a few other adaptation options. Population relocation is a complex process that involves multiple factors (for instance, land value and tax revenue) and different stakeholders (residents, governments, and developers). Second, the developed model has a high level of uncertainties concerning future SLR and urban growth directions. Third, a global analysis of the applicability of the retreat-based modeling is missing, and this lack weakens methodological generalizability. These issues deserve further investigation and can be promising extensions of current research.

Author's personal copy Mitig Adapt Strateg Glob Change Acknowledgements This study was supported by the Florida Sea Grant, Grant No. R/GOM-RP-2, BA Parameterized Climate Change Projection Model for Hurricane Flooding, Wave Action, Economic Damages, and Population Dynamics.^ This work was also funded by the University of Florida Graduate School Dissertation Award.

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