Environmental Management DOI 10.1007/s00267-015-0446-8

Modeling Future Land Use Scenarios in South Korea: Applying the IPCC Special Report on Emissions Scenarios and the SLEUTH Model on a Local Scale Haejin Han • YunSeop Hwang • Sung Ryong Ha Byung Sik Kim



Received: 29 July 2013 / Accepted: 2 January 2015 Ó Springer Science+Business Media New York 2015

Abstract This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change. We quantified the population-driven demand for urban land use for each qualitative storyline and allocated the urban demand in geographic space using the SLEUTH model. The model results demonstrate the possible land use/land cover change

Electronic supplementary material The online version of this article (doi:10.1007/s00267-015-0446-8) contains supplementary material, which is available to authorized users. H. Han (&) Division of Water Research, Korea Environment Institute, Bidg. B, 370 Sicheong-daero, Sejong 339-007, Republic of Korea e-mail: [email protected] Y. Hwang Department of International Business and Trade, Kyung Hee University, Seoul, Republic of Korea e-mail: [email protected] S. R. Ha Department of Urban Engineering, Chungbuk National University, Cheongju, Republic of Korea e-mail: [email protected] B. S. Kim Department of Urban & Environmental Disaster Prevention Engineering, School of Disaster Prevention, Kangwon National University, Chuncheon, Republic of Korea e-mail: [email protected]

scenarios for the years from 2000 to 2070 by examining the broad narrative of each SRES within the context of a local setting, such as the Kyoungan River Basin, constructing narratives of local development shifts and modeling a set of ‘best guess’ approximations of the future land use conditions in the study area. This study found substantial differences in demands and patterns of land use changes among the scenarios, indicating compact development patterns under the SRES B1 compared to the rapid and dispersed development under the SRES A2. Keywords SLEUTH  Land use scenario  IPCC SRES  Climate change  Land use modeling

Introduction Human activities cause land use and land cover changes, and simultaneously affect the local, regional, and global climate through urbanization, the removal and reintroduction of forests, agricultural development, and desertification (Foley et al. 2005; Rosenthal et al. 2007). Anthropogenic changes in land use and land cover have large effects on climate by changing the surface water and energy budgets (Kalnay and Cai 2003) and in turn, humaninduced climate change can affect land use and land cover through the anthropogenic adaptation of land use practice and the natural adaptation of vegetation structure and distribution (Cao and Woodward 1998). These anthropogenic changes in land use and climate as well as their interactions have led to a variety of environmental problems from local to regional scales such as deterioration of water resources (Wilby et al. 2006) and quality (Hall et al. 1999), loss of biodiversity (Opdam and Wascher 2004; Pounds and Puschendorf 2004), and

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impaired human health (Patz et al. 2005; Ebi and McGregor 2008). Thus, an integrated and quantitative approach to impact assessment is required, in which an internally consistent set of fine-resolution climate and land use scenarios drive process-based models of vulnerable systems to assess the likely impacts and develop feasible adaptation strategies in a local or regional context (Bierwagen et al. 2010; Worrall et al. 2003). While a number of studies have developed fine-resolution climate scenarios dynamically or statistically downscaled global climate models (Flint and Flint 2012; Salathe et al. 2007), few studies have provided quantitative scenarios of future land use with fine-resolution consistent with an interpretation of the IPCC Special Report on Emissions Scenarios (SRES) storylines (Nakicenovic et al. 2000) for impact and adaptation analyses. Most land use scenarios consistent with the SRES scenarios were developed at the global scale using integrated assessment models (Strengers et al. 2004; Gillingham et al. 2008) and at the continental scale (Rounsevell et al. 2006; Fekete-Farkas et al. 2005) with a low resolution of *50 and *16 km, respectively. A few studies using cellular automata-based (CA-based models) have recently produced fine-resolution land use scenarios to apply regional air quality assessment (Solecki and Oliveri 2004), flood risk mitigation (Barredo and Engelen 2010), and coastal adaptation planning (Hansen 2010) in light of climate change. However, these CA-based studies are limited in being unable to address multilevel driving forces, which are considered to be important for the development of land use-change scenarios (Reginster and Rounsevell 2006). For instance, Solecki and Oliveri (2004) assumed the relative magnitude of land conversion for each SRES scenario compared with the current trend projection based on a review of the existing literature, not accounting for quantitative scenarios of socioeconomic drivers of urban demand in the model. Notwithstanding the limitation of coarse scale-scenarios, Reginster and Rounsevell (2006) developed and applied the multi-step methodology of the scenario-construction in which macro drivers of urban demand and micro drivers of spatial patterns were taken into consideration at the European level. The overall goal of this study is to construct quantitative, spatially explicit fine-resolution scenarios of future land use at the local scale that are consistent with the assumptions used when developing climate-change scenarios such that our land use scenarios can be integrated with local assessments of climate change effects on environmental endpoints. To achieve our goal, we have three research questions: (1) What are the principal drivers of the demand and spatial patterns of urban land uses that are specific to our study site? (2) How can we define the qualitative description of alternative futures of the identified drivers consistent with the

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global SRES narratives in a local context? (3) How can we translate the downscaled qualitative description into quantitative scenarios of urban and other land uses using a CAbased model available to produce fine-resolution data? The specific tasks of this study include: (1) developing a fine-resolution (30 m 9 30 m) land cover modeling system based on the SLEUTH model (Clarke et al. 1997), (2) downscaling the IPCC SRES A2 and B1 as a narrative that can be translated into alternative scenarios of future land use using the multistep procedure, and (3) developing three alternative future land use scenarios (i.e., SRES A2 and B1 and BAU) for the years from 2000 to 2070 for the Kyoungan watershed and its vicinity, an area covering 1,540 km2, which is located near Seoul, South Korea (Fig. 1). While the scope of this research covers multiple land use types, we pay special attention to urbanization and the associated land use changes because of its potential impacts on the sustainability of the wider environment (Reginster and Rounsevell 2006). In the Republic of Korea, urban land use has been the most important competitor for land due to its increasing land rent and high reliance on agricultural imports (Anderson and Strutt 2012).

Method Overview of the Approach The fine-scale (30 m 9 30 m) land use scenarios were developed for each decade through 2070 for the Kyoungan watershed and its vicinity, which is an area of over 1,540 km2 (30.6 km 9 50.5 km) located in the middle of Kyunggi province and the southeastern part of Seoul in South Korea (Fig. 1). The Kyoungan watershed is one of the conservation-focused watersheds draining directly into the Paldang Reservoir, from which approximately 20 million residents of Seoul and the surrounding areas receive drinking water. Thus, there is a critical need to assess risks associated with the combined impacts of emerging climate change and land use change and to develop effective longterm plans to protect water resources. For development and application of local land use scenarios to the integrated assessment, we have chosen the meta narrative description of IPCC SRES global storylines describing the trends of socio-economic, environmental, and institutional variables that drive the future local pattern and magnitude of land use change (Nakicenovic et al. 2000). The SRES were constructed along two axes: the degree of globalization (A) versus regionalization (B) and the degree of economic (1) versus environmental (2) development. We selected SRES A2 and B1, which are associated with more and less rapid land use conversion, respectively, and a BAU scenario as the reference.

Environmental Management Fig. 1 Study area (see Online Resource Table S6 for municipality names)

The methodology of this study is largely based on two steps: (1) interpretation of two storylines (SRES A2 and B1) to develop qualitative descriptions of the potential drivers of change that might affect future urban land use in the study area and (2) quantitative assessment of future urban demand as a function of changes in the relevant drivers for each scenario and of the corresponding urban and other land use patterns through the application of the SLEUTH model to allocate the urban demand in the study area (Fig. 2). Unlike urban scenarios, we do not apply the multi-step procedure into construction of other land use scenarios due to limitation of the SLEUTH model. However, we instead developed the scenarios by extending the influence of the urban growth into other land categories based on spatial and temporal autocorrelation of the land use transition process and modified exclusion/attraction layers that reflect different land use policies (e.g., agricultural, forest, and environmental policies) and spatial patterns within the SLEUTH model.

Qualitative Interpretation We downscaled and translated the broad and qualitative narrative of each storyline to the local scale for urban

driving forces in qualitative terms through a multi-step process. The first step was to identify the main driving factors that are responsible for the demand and pattern of urban development for the study area. Numerous urban economists have identified two major driving forces for urban demand: population growth, reflecting demographic characteristics, and the demand for housing (Bierwagen et al. 2010) and economic growth (Reginster and Rounsevell 2006). For the driving forces of urban patterns, we assumed that the three pattern drivers of urban development include: (1) policies for urban planning, environment, energy, and transportation (2) transport patterns, including accessibility of the transport network, transport innovation, and the quality of the infrastructure, and (3) the lifestyle preferences of the population based on expert judgment and a review of the literature. In the second step, the interpretation commences with a qualitative description of the range and role of the identified urban change drivers that might affect land uses of the study area future based on the SRES A2 and B1 narratives. Because the BAU scenario is a projection of current trends in land use development based on the best-fit parameters derived from calibration during 1975–2000, we do not present the qualitative urban scenario interpretation for the BAU scenario here.

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Environmental Management Fig. 2 Modeling framework for the development of future land use scenarios

Quantitative Assessment Urban Demand Model To quantify the demand for urban land use for each qualitative storyline, we developed a regression model using independent variables that reflect the key drivers of population and economic growth identified previously. Data on the county-level population for years from 1975 to 2000 (KOSIS 2012) and the province-level GRDP for years from 1975 to 2000 at 5-year intervals were obtained from the Korean Statistical Information Service (KOSIS) database (KOSIS 2013).The dependent variable, the area of urban land use, was calculated from the land cover data, which were obtained for the years from 1975 to 1995 from the Water Resources Management Information System (WAMIS 2012) and for 2000 from the Environmental Geographic Information System (EGIS 2012). The future urban land use demands were calculated for the SRES A2 and B1 and the BAU scenario. We obtained the existing and projected national population and GDP for every 5 years from 2000 to 2070 for the SRES A2 and B1 and the BAU scenarios from the Center for International Earth Science Information Network (CIESIN) (Gaffin et al. 2004) and the KOSIS (2012), respectively. We subsequently downscaled the national population and GDP to the

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provincial level as a function of the respective baseline values. Since fertility rates have been below the replacement level of 2.1 and together with the aging population in the Republic of Korea, the forecasted population for the BAU scenario will slightly increase until 2020 and then continuously decline to 2070 (Fig. 3). In contrast, under the A2 scenario based on the IIASA slow demographic transition projection (high fertility and high mortality rates) (Lutz 1996), the population will steadily increase by approximately 1.6-fold during 2000–2070. Under the B1 scenario based on the IIASA rapid transition projection (low fertility and low mortality rates) (Lutz 1996), the population will fist peak at 2035, then decline until 2050, and continue to increase until 2070. The discontinuity in the population trend slopes of the B1 scenario at 2050 was due to the limitations of the downscaling method such as differences in 2050 population growth rates between countries and the corresponding regions (Gaffin et al. 2004). Spatial Allocation Model: SLEUTH We used the SLEUTH model to allocate the urban demands in geographic space due to its advantages in translating the storylines into modeled land use through manipulation of the model’s input parameters, data layers,

Environmental Management Fig. 3 Exclusion layers used for the a BAU, b A2, and c B1 scenarios. Figure legend represents levels of exclusion/ attraction

and source codes (Solecki and Oliveri 2004; Norman et al. 2009; Onsted and Chowdhury 2014; Feng et al. 2012). SLEUTH is an acronym based on the inputs required to use the model: slope, land use, exclusion/attraction, urban extent, transportation, and hillshade (USGS 2014). SLEUTH is a probabilistic CA-based urban growth model (UGM) coupled with the Land Cover Deltatron Model (LCDM) that simulates land cover change as a result of urbanization (Clarke et al. 1997). The LCDM assumes that urbanization drives further land use change. This model uses CA-based rules, historically measured land cover transition probabilities and local topography to create deltatrons, which is an artificial agent of change that has life in change space. These deltatrons enforce spatial and temporal auto-correlation in the land cover transition process [For further details, see Candau and Clarke (2000)]. For this study, we used a new version of the SLEUTH model, SLEUTH-3r, which is more computationally efficient than the original version of the model (Jantz et al. 2010). The spatial dynamics of the SLEUTH model are determined by four growth rules: spontaneous new growth, new spreading center growth, edge growth, and roadinfluenced growth (Solecki and Oliveri 2004). These growth rules are applied sequentially during each growth cycle and are controlled through the interaction of five growth coefficients (diffusion, breed, spread, slope, and road gravity), each of which may have a value from 0 to 100, with higher values producing a stronger influence. During the growth cycle, these growth coefficients can be altered by a ‘self-modification’ function (Clarke et al.

1997), which is intended to more realistically simulate different growth rates over time. To simulate accelerated growth (boom cycle), the growth coefficients are multiplied by a user-defined boom factor that is greater than one when the rate of growth exceeds a specified critical threshold. In contrast, to simulate depressed growth (bust cycle), the growth coefficients are multiplied by a userdefined bust factor that is less than one when the rate of growth falls below a specified critical threshold. Without self-modification, SLEUTH will simulate a linear growth rate until the availability of developable land diminishes. We utilized self-modification when calibrating and creating scenarios. The implementation of the SLEUTH model for this study was performed in four steps: data input preparation, model calibration, validation, and prediction. Data Input Preparation All input files were first rasterized at a 30-m resolution to the spatial extent of the study area (1,041 9 1,700 cells) and then converted into grayscale GIF format, which is a requirement of the model. The slope layers were derived from a 30-m national digital elevation model (DEM) (NGII 2012), using ArcGIS 9.3 Spatial Analyst. Land uses for two time points are required if land use modeling by LCDM is desired. Land use data (defining the Class 1 Anderson categories of urban, agricultural, forest, wetland, water, and barren land) for the years 1975–1995 at 5-year intervals and for 2000 and 2007 were obtained from WAMIS and EGIS, respectively (Online Resource Fig. S1).

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For calibration, the SLEUTH-3r model requires inputs of the historic urban extent and transportation network for at least two time points. We used six urban extent (Online Resource Fig. S2) and transportation maps (KTD 2011, Online Resource Fig. S3) for the years from 1975 to 2000 at 5-year intervals so that a more dynamic model of growth and the influence of transportation on urbanization could be presented. The roads were then weighted according to their relative urban attractiveness. In this study, a national expressway was given a value of 100; national and urban highways were given a value of 75; provincial roads were given a value of 50; special/metropolitan city roads and other roads were given a value of 25; and non-road cells had a value of 0. The exclusion/attraction layer allows the user to implement growth attractors and constraints on the model. This layer contains probabilities of exclusion or attraction: areas that should be partially or completely excluded from development have values more than 50, areas that are neutral for development have a value of 50, and areas that will attract development have values less than 50. The exclusion layer used in the calibration was developed considering physical and legislative resistance to urban development. The excluded areas due to physical constraints included water bodies (e.g., lakes, rivers, streams). We identified the boundaries of the excluded areas based on the land use map for 2000. In addition, the areas excluded because of legislative constraints included protection areas designated by various laws and orders derived from the environmental conservation value assessment map (ECVAM) for 2000 (MOE 2011). The ECVAM is an overlay map that classifies the national land of Korea into five grades, ranging from Class 1, representing the most protected land, to Class 5, representing the most developable land (Park et al. 2012), based on a comprehensive evaluation of 56 legal conservation zones and 11 environmental factors. Out of the 56 legal conservation zones used by the ECVAM, only 19 zone types regulated by eight different laws were located within the study areas (Table 1). After overlapping the 12 legal conservation zones, we reclassified the one-to-five scale from the ECVAM to the zero-to-100 scale of the SLEUTH model: classes 1, 2, 3, 4, and 5 were given a value of 100 (completely excluded), 70, 50, 30, and zero, respectively (Fig. 4). Calibration and Validation We calibrated SLEUTH using the ‘‘brute force’’ calibration method through three sequential calibration phases (coarse, fine, and final) with 30-m resolution data to refine a best-fit set of the five parameter values (Silva and Clarke 2002), which can accurately reproduce urban growth from 1975 to 2000 within the study area. The results of each calibration step

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were evaluated using the fit statistics calculated during the model runs, leading to a narrower range of the best-fit set. The choice of appropriate goodness-of-fit measures is important; however, there is no consensus regarding which goodness-of-fit measure or set of measures should be used (Jantz et al. 2010). Because the inclusion of multiple metrics complicates the interpretation of the model’s behavior (Draper 1995), we focused on two metrics: the pixel fractional difference (PFD) and cluster fractional difference (CFD). The PFD and CFD, which SLEUTH-3r calculates directly, compare the numbers of urban pixels and urban clusters, respectively, in the modeled outputs to those in the control maps (Jantz et al. 2010). We selected parameter sets that were able to match both the PFD and CFD within ±10 %. SLEUTH is stochastic model based on the Monte Carlo method to generate multiple simulations of growth for each unique parameter set, so the PFD and CFD that SLEUTH-3r calculated are averaged over the Monte Carlo trials. After the best-fit parameters were identified for the study area, the model was initialized in 2000 and run in predict mode to 2007 with 100 Monte Carlo trials. This process produced two prediction maps that describe the likelihood and character of land cover change. One is a map of the most probable forecasted class for each simulation year with the land class present most often over the 100 Monte Carlo iterations. The other map is a map of uncertainty that is associated with the land cover forecasts, which is calculated by counting the number of times each class was at a given location over all Monte Carlo iterations (Candau et al. 2000). We performed validation based on the three-map comparison method put forth by Pontius et al. (2008). The validation method overlaid the following three maps: the reference map of 2000, the reference map of 2007, and the most probable prediction map for 2007, which allows one to distinguish the pixels that are corrected due to persistence versus the pixels that are corrected due to change. From this validation, the percent of the map can be classified into the following five categories: (1) Misses (error due to observed change predicted as persistence), (2) Hits (correct due to observed change predicted as change), (3) Wrong Hits (error due to observed change predicted as change to the wrong gaining category), (4) False Alarms (error due to observed persistence predicted as change), and (5) Correct Rejections (correct due to observed persistence predicted as persistence). Scenario Development For the BAU scenario, we initialized with the latest urban extent map of the year 2000 and used the final values of the best-fit growth coefficient derived through the calibration process. We used the same exclusion/attraction layer that were used for calibration, assuming no change in spatial drivers that would influence

Environmental Management Table 1 List of land use policies applied in the study area (modified levels of protection of exclusion/attraction layers for each scenario) Categories

Natural resource protection

Conservation area

Calibration and BAU

SRES A1

SRES B2

Class

Value

Class

Value

Class

Value

1

100

4

30

1

100

1

100

3

50

1

100

Scenery area Productive green area

2 2

70 70

5 5

0 0

1 1

100 100

Green area

3

50

5

0

2

70

Green belt for preservation Ecological conservation area

Green buffer zone

Acts

Act on planning and use of National territory

Act on urban parks and greenbelt

Scenery green

2

70

5

0

1

100

2

70

5

0

1

100

Urban nature park

3

50

5

0

2

70

Children’s playground

4

30

5

0

3

50

Neighborhood park

4

30

5

0

3

50

Sports park

4

30

5

0

3

50

Wildlife and plant conservation area

Wildlife and Plant Conservation Act

1

100

1

100

1

100

Park and nature preservation area

Natural Park Act

1

100

1

100

1

100

Water resource protection

Riparian buffer zone Water source protection areas

Four Major River Act Water supply and waterworks installation Act

1 1

100 100

1 1

100 100

1 1

100 100

Forest protection

Conservation forest with common interests

Management of Mountainous District Act

1

100

5

0

1

100

2

70

4

30

1

100

3

50

5

0

2

70

3

50

5

0

2

70

Farmland protection

Conservation forest with forestry goods Agricultural promotion areas Land consolidation areas

Farmland Act

Fig. 4 Future population projections within the study sites from 2010 to 2070 for the three scenarios studied: BAU, A2, and B1 [Source Center for International Earth Science Information Network (CIESIN) (Gaffin et al. 2004)]

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urban patterns in the future. For the two scenarios, we translated the qualitative urban scenario interpretations for the SRES A2 and B1 into the SLEUTH modeling by (1) applying different sets of growth parameters to SLEUTH with a self-modification function to reflect different rates of growth over time and to match the amount of urban demand estimated from the developed urban demand model, (2) modifying exclusion/attraction layers that reflect different land use policies and spatial patterns, and (3) using different weights for the transportation layers to reflect the road-influenced growth for each scenario. The amount of land use conversion every 5 years from 2000 to 2070 was forced by the total amount of urban development estimated by the urban demand model in ‘‘Urban demand model’’ section using the self-modification functionality.

Results

environmental and energy policies, and places a greater emphasis on low carbon emissions. Thus, restrictive spatial planning with high levels of regulation leads to compact forms of cities and increased edge growth around the Seoul metropolitan area, increasing the rate of infilling and decreasing spontaneous growth and new spreading centers; the ultimate results include less pressure on other land uses, such as protected areas, and reduced construction costs for road networks (i.e., no new roads will be built). In addition, as a result of the low-carbon-targeted societal development and rapid increases in fuel prices and the information-oriented economy, citizens tend to settle in the traditional large urban centers, i.e., the Seoul metropolitan area, with a larger supply of jobs, services, and information. Thus, automobile transport miles are reduced and the use of public transportation increased, encouraging growth along railroad corridors. In addition, spatial planning options are implemented to preserve urban green spaces and increase active re-greening and afforestation.

Qualitative Interpretation SRES A2

Quantitative Assessment

Because the A2 scenario is characterized by a strong orientation toward family and community values, a high fertility rate results in a corresponding steady increase in population (Table 2). Due to high population and economic growth, the urban areas greatly increase. In addition, as a result of self-reliance and high local orientation, which lead to an increase in the demand for family housing and local development, small- and medium-sized cities expand most rapidly outward from the Seoul metropolitan area, leading suburbanization, and counter-urbanization. In addition, there is minimal infilling and a minimal increase in existing urban and suburban densities. However, because there is little public concern for environmental issues under the A2 scenario, it is expected that the current levels of spatial planning and environmental policy become weak, leading to a high amount of conversion of land from green space to urban land use. High energy consumption and longer transport distances between residential, commercial, and industrial areas cause an increase in per capita automobile vehicle miles, which promotes road corridor growth and growth associated with new suburban areas.

Urban Demand Model

SRES B1 Compared to the A2 scenario, there is lower requirement for urban areas as a result of the low population growth caused by a lower fertility rate and slow income growth rate (Table 2). As an environmentally/equity-oriented scenario, the central government under the B2 scenario is strong, with a high level of regulation especially for

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In this study, based on linear regression analyses, we found that population is a statistically significant predictor of temporal variation in urban growth (Online Resource Table S1). The regression results show a strong exponential relationship between the area of urban land and population (R2 = 0.9881) (Fig. S4). Note that in this study, urban demand was only estimated until the population reached its maximum value because SLEUTH cannot simulate the conversion of urban land to non-urban land, although a small amount of urban abandonment and re-greening can take place in reality. The maximum values of urban demand for the BAU and B1 scenarios are 246 km2 in 2020 and 301 km2 in 2070 (Online Resource Table S2). The continuous increasing pattern and high growth rate of population under the A2 scenario results in the continuous and largest increase in urban demand from 134 km2 in 2000 to 487 km2 in 2070. SLEUTH Model Calibration and Validation Based on the evaluation of fit statistics of the multiple calibrations, we were able to achieve the area match within 1 % (PFD = -0.01) and urban cluster match within 12 % (CFD = -0.12) when comparing the mapped and modeled estimates of urban areas and clusters for 2000. The final best-fit parameters were: diffusion = 77, breed = 1, spread = 100, slope resistance = 25, and road

Environmental Management Table 2 Outline of the driving forces of urban demand and patterns for the SRESs A2, B1, and BAU Driving factors

Population

Downscaled interpretation of the spatial drivers for each scenario A2

B1

BAU

High fertility rate due to high orientation toward local communities and family

Low fertility and mortality rates due to economic growth, high female education, and labor-force participation

Very low fertility rates falling below replacement levels until 2050 due to the high opportunity costs of child bearing resulted from the changes in individual incentives such as increased returns to higher education and the expanding opportunities for individual fulfillment possibly by new economic and political freedoms

Highest population growth among the three scenarios for this study

Lower population growth compared to A2 scenario

However, after 2050, the strong pronatalist policies (such as doubling of paid maternal leave, and improved child benefits) combined with increased immigration will increase the number of birth, particularly of second and third children

Sustainable development

High economic growth although not as rapid as A2

High domestic population movement from urban to peri-/sub-urban (counterurbanization) Societal, economic and political frameworks

Local-oriented heterogeneous world High per capita income

Moderate economic growth

High emphasis on environmental protection

High-paying jobs in newly established urban/suburban centers

High-paying jobs and services in

Strong government with restrictive spatial planning

Political framework focused on economic growth

Greater emphasis on environmental protection

Relaxed spatial planning constraints

Effective energy and resource usage, low carbon emissions

Increased urban land supply

Strong government with restrictive spatial planning High subsidies and incentives for environmental protection and energy savings

Lifestyle preference

Energy intensive lifestyle

Less energy- and material-intensive lifestyle (dematerialization)

Cultural pluralism

Resource-friendly lifestyle based on clean and light technologies

Less energy- intensive lifestyle

Less emphasis on economic, social and cultural interactions between regions Transport pattern

Increase in per capita automobile vehicle miles High reliance on fossil fuel

Decrease in per capita automobile vehicle miles Public transport-oriented traveling

High investment in transport infrastructure (improve local roads)

High taxes on fossil fuel usage and congestion tolls

gravity = 25. The high diffusion and spread values and low breed parameter values demonstrate that the study area experienced urbanization outward of the existing urban centers, which contributed to compact urban growth during

Moderate decrease in per capita automobile High taxes on fossil fuel usage and congestion tolls

1975–2000. In addition, the low values of the slope and road gravity parameters indicate that topography and roads are not critical factors for urban sprawl across the study area.

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We conducted validation based on the three-map comparison method. Fig. S5 in the Online Resource shows the distribution of two types of agreement and three types of disagreement. While the largest portion of agreement is persistence simulated correctly (gray pixels in Fig. S5), the largest component of disagreement is change simulated as persistence (blue pixels in Fig. S5). The overall figure of merit is 44, which means the amount of correctly predicted change is smaller than the sum of the various types of errors (Table S3). Producer’s accuracy and user’s accuracy are 50 and 66, respectively, which reflects the fact that the simulated change is 24 percent less than the observed change. Simulation to 2070 For the SRES A2 and B1, we simulated future land use patterns through modification of the transportation and exclusion/attraction layers and by applying different future growth rates defined from the qualitative urban scenario interpretation of the SRES (Table 3). First, we assumed no change in the growth parameters derived from the calibration for the BAU scenario. Similarly, for both the A2 and B1 scenarios, we utilized the same values for the slope and road growth coefficients, assuming that the resistance of development to slope and the influence of transportation on urban development do not vary by scenario. However, other growth parameters, such as diffusion, breed, and spread, were modified to reflect the spatial patterns qualitatively interpreted from the SRES (Online Resource Table S4). For the SRES A2, we used a diffusion coefficient at the maximum level (i.e., 100), increased the breed coefficient from 1.5 to 80, and decreased the spread coefficient from 100 to 30 to simulate dispersed urban sprawl and exacerbate the

pressure of development on non-urban land surrounding the urban areas. In contrast, because the environment-oriented SRES B1 used smart-growth policies, we used a spread coefficient at the maximum level to promote edge growth and decreased dispersion and breed coefficients for minimal spontaneous growth. In addition, we used selfmodification so that the corresponding amount and rate of growth estimated by the urban demand model occur. Table S4 in Online Resource shows the values of the critical thresholds and multiplier for the bust-and-boom cycles in each scenario. For the BAU scenario, we used a lower value of the bust multiplier, 0.85, than in the other scenarios (0.95 for SRESs A2 and B1) so that the system would rapidly enter a bust cycle beginning with the first simulation year, causing the total amount of urban growth to level off after only 20 years (Online Resource Fig. S6). For the A2 scenario, we used two different sets of selfmodification coefficients for the years before and after 2050 because the rates of annual urban growth calculated by the urban demand model should decline until 2050 and then increase until 2060 (Table S4). Second, alternative scenarios were implemented by developing and using exclusion/attraction layers that reflect different land use policies embedded in each scenario. For the BAU scenario, we used the same exclusion/attraction layers that were developed for the calibration, assuming no change in land use policies (Table 3). Currently, the study site has strong spatial planning regulations, but such restrictions are expected to weaken under the A2 scenario. Thus, we assumed the continued protection of existing acts regarding land associated with water and natural resources but the partial protection of acts regarding restricted development area and conservation forest for commons

Table 3 Set of growth scenarios and corresponding SLEUTH model adjustments for each scenario Quantitative implementation

BAU

SRES A2

SRES B1

SLEUTH growth parameters

Used the best-fit growth parameters (see Table S4)

Increased breed coefficients to promote new growth

Decreased diffusion and breed coefficients to reduce spontaneous growth Increased spread coefficient to increase edge growth

Exclusion/ attraction layer

No change in the exclusion layer used for calibration

Increased diffusion coefficient to promote dispersed urban patterns Continued protection of existing acts for lands associated with water resources and natural values listed in Table 1 Partial protection of acts for development-restricted areas and conservation forest for commons

Continued protection of all existing acts listed in Table 1 Areas around existing cities assumed to be more likely to become urbanized (distance \450 m)

No protection of acts for farmland, natural monument protection areas, or conservation forest for forestry goods Transportation layer

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No change

All roads given more weight

All roads given less weight

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and no protection for acts regarding farmland, natural monument protection areas, or conservation forest for forestry goods (Table 1). In addition, to encourage the development of urban growth away from existing urban areas, we added a 450-m buffer zone around the existing urban cities to the final exclusion/attraction layer and assigned a value of 80 to those features to indicate strong resistance to development. For the SRES B1, which is focused on a high level of biodiversity conservation, keeping with all current restrictive policies for development, we applied a higher protection for the core areas and buffer zones of the Korean Peninsula Ecological Network (KPEN) developed by the Ministry of Environment to intensively protect areas with high biodiversity. The weights of the exclusion/attraction layers for the core and buffer zones were given values of 80 and 60, respectively. To encourage development in or near existing cities, we added 450-m buffers around the existing cities and assigned these buffers a value of 30. Figure 4 shows the final exclusion/attraction layer used for the scenarios. Finally, using the most recent layer of transportation that we could obtain, which was for 2009, we modified the weights of various types of roads, as listed in Online Resource Table S5. We used increased weights of all road types for the A2 scenario to promote road corridor growth and decreased weights of all road types for the B1 scenario to limit road-influenced growth.

Comparison of the Future Land Use Maps The SLEUTH model visualizes different extents and patterns of urban growth and the corresponding other land use changes across the three scenarios: BAU (Online Resource Fig. S7), SRES A2 (Online Resource Fig. S8), and SRES B1 (Online Resource Fig. S9). Note that the SLEUTH simulation for the BAU scenario only proceeded until the year 2020, when the predetermined number of urban cells from the urban demand model was reached (Fig. S6). In addition, Fig. S10 shows the corresponding uncertainty map for each scenario based on Monte Carlo Simulation, showing SRES A2 scenarios show higher uncertainty than those of the BAU and B1 scenarios. Land change rates were calculated for 10-year intervals based on the sum of all conversion for all types of land uses occurring within the interval (Online Resource Fig. S11). The highest rates were most commonly associated with the SRES A2 and declined from 9.7 % of the landscape changing for the 2000–2010 interval to 2.5 % for the 2040–2050 interval but then increased again to 9.38 % for the 2060–2070 interval. In contrast, the SRES B1 had a continuous decrease in the rates of land use change from 12.95 % for the first interval to 1.12 % for the last interval.

Regarding changes in individual major land use classes (urban, agriculture, and forest) across the study area during the study period, the A2 scenario showed the largest increases in urban area, covering 31.2 % of the landscape in 2070 compared to 8.4 % of the landscape in 2000, at the expense of the agricultural land, which continuously decreases from 23.3 % in 2000 to 2.8 % in 2070. In addition, the projected percent of landscape in forest areas under the A2 gradually increases from 55.9 % in 2000–66.7 % in 2050 but then slightly declines to 63.9 % due to high urbanization (Table 4). Compared to the A2 scenario, the environmentally oriented B2 scenario exhibited a slower rate of urbanization and experienced the largest increases in forest area, from 55.9 to 70.2 % of the landscape, which arises from the replacement of agricultural land and abandoned rangeland with forests as well as the reforestation strategies embedded in spatial planning policy, and therefore is consistent with the environmentally oriented B1 scenario. The B1 scenario also experienced a large decline in agricultural land from 23.3 to 7.4 % of the landscape. Aside from the aggregated dynamics of land use change for the entire study area, we also analyzed the rates of land use changes at the municipal scale. The highest rates of land use change between 2000 and 2050 in all scenarios occurred on the southwestern side of the study site, including the YI-C (30 % for BAU, 39 % for A2, 43 % for B1), followed by YI-B (27 % for BAU, 35 % for A2, 39 % for B1), HS (22 % for BAU, 36 % for A2, 35 % for B1), and PT (21 % for BAU, 33 % for A2, 35 % for B1) (Online Resource Table S6). Land use changes occurring in YI-C and YI-B were mostly derived from urbanization (52 and 71 % of total land use change rates under the B1 scenario, respectively), whereas changes in the southern parts (i.e., HS and PT) were primarily due to re-forestation (72 and 68 % for the B1 scenario, respectively). Low rates of change occurred in the northern half of the study area, which is either already highly urbanized near the Seoul metropolitan area, including KN, KD, and SP, or limited due to physical conditions (mountainous area) or restrictive spatial planning, such as in YJ, YP, HN, and KJ. The BAU scenario, with a more stringent set of policies targeted toward limited growth and natural resource protection, showed highly constrained growth over the entire region, with most of the growth occurring in and around existing urban centers similar to that in the B1 scenario (Fig. 5c). We found that the B1 scenario is more similar to the present day than it is to the A2 scenario. In addition, we found less conversion of land use change in all scenarios in the northeastern part of the study area, where physical conditions, such as high slopes, limit the potential of land use changes. In terms of patterns of reforestation, although the total area of forest between the A2 and B1 scenarios

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Environmental Management Table 4 Area at various time points expressed as a percent of the landscape in three major land use classes (urban, agriculture, and forest) for 2000, 2020, 2050, and 2070 for the three scenarios (%)

Historical data (2000)

BAU (2020)a

A2 2020

Urban

B1 2050

2070

2020

2050

2070 19.4

8.4

15.3

16.0

19.6

31.2

16.8

19.1

Agriculture

23.3

19.3

16.5

10.0

2.8

18.8

9.3

7.4

Forest

55.9

61.7

60.8

66.7

63.9

61.7

68.1

70.2

a

Because land use change under the BAU scenario does not change after 2020, the values for 2050, and 2070 are same as 2020

were similar in 2050, a substantially greater portion of the landscape was occupied by larger patches of forest in the B1 scenario than in the A2 scenario, which is consistent with the SRES assumptions.

Discussion In this paper, we have translated the alternative pathways of extreme SRES storylines (A2 and B1) as well as the BAU scenario in terms of the drivers of urban demand (i.e., population) and patterns (i.e., policy framework, lifestyle, and transport patterns) to quantifiable elements to be modeled in the SLEUTH model. SLEUTH Model This paper demonstrates that new version of SLEUTH implemented the two levels-drivers such as demand drivers and spatial pattern drivers identified from a statistical approach and urban theories to construct fine-resolution scenarios of land uses. Although the SLEUTH coupled with the Deltatron module can simulate not only the patterns of urban growth but also of other land cover changes, a CA-based Deltatron cannot explicitly incorporate the demands of other land use categories into the simulation. The validation errors are summarized in Table S3, illustrating the sources of errors. The overall figure of merit is only 44 %, showing the SLEUTH model has some limitations in accurately simulating both patterns and quantity of land use changes. False Alarms are less than Misses, which implies that SLEUTH simulated less change than the observed change (Pontius et al. 2011) (Table S3). Land Use Scenarios The A2 scenario showed the largest increase in urban area at the expense of agricultural land, which continuously decreases during 2000–2070 (Fig S11) and the projected percent of landscape in forest areas under the A2 gradually

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increases during 2000–2050 but then slightly declines until 2070 due to high urbanization (Table 4). Compared to the A2 scenario, the environmentally oriented B2 scenario exhibited a slower rate of urbanization and experienced the largest increase in forest area. This arises from the replacement of agricultural land and abandoned rangeland with forests as well as the reforestation strategies embedded in spatial planning policy, and therefore is consistent with the environmentally oriented B1 scenario. Despite the different directions of future land use change, a number of previous studies have drawn similar conclusions concerning plausible future land use change scenarios under the SRES scenario at the local and regional scales (Verburg and Overmars 2009; Rounsevell and Reay 2009). Regarding A2, for instance, they concluded there will be high per capita land use conversion due to higher demand of bigger houses, longer transport distances between homes and work places, and weaker spatial planning (Hansen 2010; Solecki and Oliveri 2004; Reginster and Rounsevell 2006; Bierwagen et al. 2010). For B1, they expect a decrease in per capita land use conversion and minimum urban sprawl because of more stringent spatial planning rules, high fuel prices, and attractions to medium-sized cities with frequent and fast public transport connections to the larger cities. Regardless of the SRES storyline used, a number of previous studies reported large declines in agricultural land, primarily due to the relative low increase in demand decoupled from the economic and population growth and increases in agricultural production based on the farreaching assumption about the role of agro-technological development (Busch 2006; Rounsevell et al. 2005; Oh et al. 2011). In addition, we found that the A2 and B1 scenarios showed distinct spatial patterns of urban development and other land uses across the study area. The A2 scenario showed higher dispersed urban patterns with more urban patches and a smaller mean patch size than the SRES B1 (see the bottom map of Fig. 5a), where urbanization is currently relatively low due to various restrictive spatial planning processes. In contrast, urbanization under the B1 scenario was associated with existing urban centers and

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Fig. 5 Comparison of the land use patterns for the entire study area (upper) and enlarged view of southeastern part of the study area (lower) under the three scenarios for 2050: a A2, b B1, and c BAU. Black areas represent the baseline extent of urban land in the year

2000; red areas are the newly urbanized area in the year 2050; green areas are the newly forested area in the year 2050 and gray areas are other land uses (agriculture, forest, range, barren, wetland, and water) in the year 2050

showed compact urban patterns and the clustering of larger patches of urban area than in the A2 scenario, produced by filling gaps and holes (Fig. 5b). Although the quantity of change in urban area is similar between the A2 and B1 scenarios in 2050 (Table 4), the spatial patterns differ greatly, reflecting alternative urban development processes: counter-urbanization (A2) and periurbanization (B1) (Fig. 5). This result demonstrates the

importance of the development of spatially explicit land use scenarios with a fine resolution downscaled from the SRES storylines because the aggregated trends shown in Table 4 tend to mask the potentially large consequences of alternative spatial patterns at the local scale when assessing their impacts on the environment and developing appropriate spatial planning policies (Rounsevell et al. 2006).

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Limitations and Uncertainty The development of scenarios always entails a number of limitations and uncertainties in terms of the technical and conceptual aspects (Rounsevell et al. 2006). Although scenarios do not constitute a prediction of the future, it is important that users of scenarios are aware of the different sources of uncertainties and limitations that can be derived from the Story-and-Simulation methodology. The uncertainties and limitations of the current study include (1) the subjective nature of qualitative interpretation, (2) one-way coupling between the SLEUTH modeling and drivers of land use, (3) errors within the SLEUTH and statistical modeling, and (4) the availability and quality of the empirical data at the appropriate scale. In this study, a multi-step process to translate the SRES scenarios into SLEUTH modeling was performed based on our subjective judgments, not only in the interpretation of the SRESs in the local context but also in the identification of specific growth parameters from these interpretations (Table S4). For instance, numerous different urban growth parameters in the SLEUTH model for the corresponding scenarios among the developers likely occurred and contributed to the discrepancies in the pattern and rate of land use change. However, a number of studies reported similar outcomes (i.e., diffuse patterns in the A2 scenario and concentrated patterns in the B1 scenario) that arose from different assumptions or subjective judgments, which suggests a certain degree of coherence in future land use changes (Petrov et al. 2009; Solecki and Oliveri 2004). To improve the quality and acceptance of the subjective interpretation of storylines within the local context, further work must be performed based on the insights and feedback from local partners in a stakeholder process. The urban demand model and SLEUTH model were loosely coupled, which involves a one-way interaction between the two models (i.e., one directional flow of data from the urban demand model to SLEUTH model), with no capacity for feedback mechanisms between the macro factors and urban growth. Although the relationship can be improved through a spatial transformation of macro factors for use as inputs in the urban growth model, this method is challenging at the local scale because of the limited availability of fine-resolution macro data (Wu et al. 2010). The SLEUTH model used here, like other land use models, contains uncertainties and limitations in the values of model input parameters and the uncertainties of the model process formulation (Rounsevell et al. 2006). The specific uncertainties of the SLEUTH model include (1) the inability to simulate conversion from urban to non-urban uses, (2) no capacity for the consideration of macro-factor effects in the Deltatron module, and (3) no change in exclusion/attraction during the simulation period. Although

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we used the recently improved version of the SLEUTH model, SLEUTH-3r (Jantz et al. 2010), such limitations and uncertainties remain that require further study. Solecki and Oliveri (2004) developed and used dynamic exclusion layers by modifying the original SLEUTH source code. In this regard, we plan to perform additional code modifications to reduce the defined uncertainties and limitations. Finally, the accuracy of most land use scenario exercises is heavily dependent on the quality of the observed maps of current and past land use distributions, on which the critical processes of scenario development, such as the calibration of the model, and statistical downscaling exercises were based. Because SLEUTH-3r is extremely sensitive to the accuracy of the input maps, there may be serious implications of using inaccurate inputs on the developed scenarios and the results of subsequent integrated analyses that uses such scenarios. Thus, we should give careful consideration about the propagation of errors and uncertainties from scenarios to subsequent modeling analyses (Rounsevell et al. 2006).

Conclusion Our study has demonstrated that it is possible to develop quantitative, spatially explicit, fine-resolution scenarios of future land use at the local scale through a multi-step process using the SLEUTH model based on the different global and qualitative SRES. First, we downscaled and interpreted the two extreme SRES: the economically oriented A2 and environmentally oriented B1 scenarios, as well as a reference BAU scenario in terms of the drivers of urban demand and spatial patterns. We subsequently transformed the interpreted future trends in these drivers to quantitative growth parameters and inputs for use in the SLEUTH model. Most previous SLEUTH modeling studies have estimated the total amount of urban development based on an extrapolation of inert historic growth trends derived from calibration without considering the influence of macro driving forces, such as socioeconomic factors (Jantz et al. 2010; Solecki and Oliveri 2004). However, we adopted the population-drive demand into the SLEUTH model by establishing an exponential relationship between the amount of urban growth and population based on various regression analyses. Overall, our scenarios suggest that urban areas would occupy 15.3 % (BAU), 31.2 % (A2), and 19.4 % (B1) of the study area in 2070 (Table 4). In addition, the economically oriented A2 scenario displays more diffuse patterns compared to the environmentally oriented B1 scenario. An evident added value of the SLEUTH model within this framework is its ability to develop interactive land use scenarios that consider various socioeconomic development

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pathways and policies, to visualize and quantify outcomes spatially, and thus to identify vulnerable areas to future threats. The resulting spatially explicit maps of land use changes can be analyzed for integrated impacts on the environment and society with climate change and be used to develop proactive and smart land use planning and environmental management options. We are aware that a number of improvements are needed to develop a more reasonable set of land use change scenarios in our future studies, including the incorporation of macro factors into non-urban land use simulation, dynamic interaction between the demand model and land use modeling, and improvements to the SLEUTH model. Acknowledgments We acknowledge the support provided by the Korean Environment Institute (RE2011-05) and two Grants from National Research Foundation of Korea founded by the Korean Government (NRF-2011-0028914 & NRF-2013S1A5B6043772). Conflict of interests The authors declare that they have no conflict of interest.

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Modeling future land use scenarios in South Korea: applying the IPCC special report on emissions scenarios and the SLEUTH model on a local scale.

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a ...
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