Effects of land-use-change scenarios on terrestrial carbon stocks in South Korea

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<ul><li><p>ORIGINAL PAPER</p><p>Effects of land-use-change scenarios on terrestrialcarbon stocks in South Korea</p><p>Dong Kun Lee Chan Park Dana Tomlin</p><p>Received: 1 June 2011 / Revised: 7 October 2013 / Accepted: 23 October 2013</p><p> International Consortium of Landscape and Ecological Engineering and Springer Japan 2013</p><p>Abstract The amount of carbon stored in soil and vege-</p><p>tation varies according to land use. Land-use changes</p><p>(LUCs) affect those carbon stocks. Changes in carbon</p><p>stocks also affect greenhouse gas emissions. Predicting</p><p>LUCs is therefore necessary to establish quantitative targets</p><p>for carbon dioxide (CO2) reduction. This study attempts to</p><p>model LUCs and the associated changes in carbon stocks</p><p>for South Korea between 2005 and 2030. It examines four</p><p>LUC scenarios suggested by the Intergovernmental Panel</p><p>on Climate Change. Each scenario is assessed in terms of its</p><p>effect on South Korean carbon stocks. Under all four sce-</p><p>narios, afforestation leads to carbon sequestration with an</p><p>average net uptake of 22.431.5 MtC. The scenario yielding</p><p>the highest sequestration rate increase (from 12.4 to 14.1</p><p>MtC/year) results in levels of sequestration equal to 8.3 %</p><p>of South Koreas 2005 CO2 emissions. This is equivalent to</p><p>a value of 304 million dollars in the European Union carbon</p><p>market. Clear differences among the scenarios tested sug-</p><p>gest that land use must be regarded as an important factor in</p><p>any plan for future carbon sequestration.</p><p>Keywords Biomass Carbon sequestration Climate change Low carbon society (LCS) Soil carbon</p><p>Introduction</p><p>Over the past several decades, it has become widely rec-</p><p>ognized and accepted that the observed increases in the</p><p>concentration of greenhouse gases (GHGs) have raised</p><p>global temperatures and altered climate patterns [Inter-</p><p>governmental Panel on Climate Change (IPCC) 2007].</p><p>Between 1989 and 1998, the net removal of carbon from the</p><p>atmosphere by terrestrial ecosystems has been estimated to</p><p>have averaged 2.3 GtC/year (IPCC 2000). Throughout the</p><p>1990s, however, anthropogenic releases from the carbon</p><p>pools of terrestrial ecosystems due to land-use changes</p><p>(LUCs) have been estimated at 1.6 GtC/year (IPCC 2000).</p><p>The combined result of these natural and anthropogenic</p><p>processes corresponds to the net removal of carbon from the</p><p>atmosphere by terrestrial ecosystems (Schlamadinger et al.</p><p>2007). Given the significance of emissions/removals and</p><p>the potential to influence both through policy measures, it</p><p>will be important to continue to include the Land use, Land-</p><p>Use Change, and Forestry (LULUCF) guidelines in future</p><p>international climate-change agreements (Parrotta 2002;</p><p>Schlamadinger et al. 2007).</p><p>LUCs not only affect GHG emissions but also carbon</p><p>stocks associated with soil and vegetation (Feddema et al.</p><p>2005; Schulp et al. 2008), as different land uses are associated</p><p>with different soils and differing amounts of vegetation</p><p>Electronic supplementary material The online version of thisarticle (doi:10.1007/s11355-013-0235-6) contains supplementarymaterial, which is available to authorized users.</p><p>D. K. Lee</p><p>Landscape Architecture, College of Agriculture &amp; Life Science,</p><p>Seoul National University, San 56-1 Sillim-Dong, Gwanak-Gu,</p><p>Seoul 151-743, Korea</p><p>e-mail: dklee7@snu.ac.kr</p><p>C. Park (&amp;)Center for Social and Environmental Systems Research,</p><p>National Institute for Environmental Studies, 16-2 Onogawa,</p><p>Tsukuba, Ibaraki 305-8506, Korea</p><p>e-mail: momo7@snu.ac.kr</p><p>D. Tomlin</p><p>Landscape Architecture, School of Design, The University of</p><p>Pennsylvania, 3451 Walnut street, Philadelphia, PA 19104, USA</p><p>e-mail: tomlin.dana@verizon.net</p><p>123</p><p>Landscape Ecol Eng</p><p>DOI 10.1007/s11355-013-0235-6</p></li><li><p>(Jeong et al. 1998; Arrouays et al. 2001; Bellamy et al. 2005;</p><p>Schulp et al. 2008). Soil carbon stocks in built-up areas and</p><p>croplands are generally lower than those in forestland</p><p>(Lamlom and Savidge 2003; Schulp et al. 2008). The con-</p><p>version of forestland to other land-use types has been found to</p><p>decrease carbon stocks, whereas conversion of other land-use</p><p>types to forestland usually leads to increased carbon stocks</p><p>[Korea Forest Research Institute (KFRI) 1996; Lettens et al.</p><p>2005]. Significantly, forests also store large amounts of car-</p><p>bon in vegetation (KFRI 1996; Jeong et al. 1998; Lamlom</p><p>and Savidge 2003; Schulp et al. 2008). Among the factors that</p><p>will influence future soil carbon stocks, LUCs are expected to</p><p>have the largest impact (Smith et al. 2005a).</p><p>LUCs occur as the result of complex socioeconomic</p><p>activity and physical factors (Verburg et al. 2002; Yim and</p><p>Choi 2002; Cho 2008), and future LUC is always uncertain.</p><p>To reduce this uncertainty, cellular automata are often used</p><p>in spatially explicit experiments that simulate LUC as a</p><p>function of user-specified relationships among neighboring</p><p>land uses (Verburg et al. 2002). Despite the recent progress</p><p>in integrating the biophysical and socioeconomic drivers of</p><p>LUC (Verburg et al. 2002; Yim and Choi 2002; Cho 2008),</p><p>however, the prediction of future land use remains quite</p><p>difficult (Rounsevell et al. 2006). One tool that has proven</p><p>helpful in this regard is scenario analysis (Alcamo 2001;</p><p>Rounsevell et al. 2006). Recently, a number of studies have</p><p>synthetically assessed relationships between LUC and car-</p><p>bon-stock change (e.g., Smith et al. 2005b; Zaehle et al.</p><p>2007; Schulp et al. 2008). Notwithstanding an increase in</p><p>attention to carbon-sink management in climate-change</p><p>negotiations and in academic studies, the role of future LUC</p><p>in carbon stocks is uncertain in South Korea (Ahn and Han</p><p>2004). These uncertainties can be attributed to gaps in our</p><p>understanding of both future LUCs and difficulties in</p><p>quantifying the response of carbon sequestration to LUCs.</p><p>The aim of this study was to model the effects of LUCs on</p><p>carbon stocks in South Korea between 2005 and 2030. LUCs</p><p>are analyzed using specific LUC scenarios. Carbon-stock</p><p>changes are analyzed using a set of high-resolution map.</p><p>Methodology</p><p>The overall approach to this effort is diagrammed in Fig. 1.</p><p>LUC modeling and its scenarios are discussed in more</p><p>detail in LUC scenarios, LUC modeling. The carbon-</p><p>stock calculation method is described in Calculation of</p><p>carbon stock changes.</p><p>LUC scenarios</p><p>Land use has changed significantly throughout South Korea</p><p>in recent years, and this trend is likely to continue in the</p><p>future. However, it is not easy to predict future LUC, so</p><p>several possible scenarios were explored. LUC scenarios</p><p>are created based on the four storylines presented in IPCCs</p><p>Special Report on Emission Scenarios. The scenarios are</p><p>reviewed by many socioeconomic experts in South Korea.</p><p>The four LUC scenarios employed are respectively called</p><p>A1, A2, B1, and B2. The A1 and 2 scenarios assume future</p><p>oriented more toward economic development than envi-</p><p>ronment protection; B1 and 2 scenarios do just the opposite.</p><p>In the A and B1 scenarios, international cooperation is</p><p>emphasized over regionalism; in the A and B2 scenarios,</p><p>the opposite is true. In the A1 scenario, cropland and forest</p><p>are the most likely to be urbanized. Regulations protecting</p><p>greenbelt areas are lifted to accommodate economic growth</p><p>and the expansion of private property, and the control of</p><p>cropland development is relaxed due improvements in</p><p>agricultural productivity. In the A2 scenario, urbanization</p><p>progresses at the same level, but grassland and forest are</p><p>more likely to change into cropland because of an increased</p><p>need for agricultural products due to a crisis in food secu-</p><p>rity. Forest area decreases as it changes into grassland,</p><p>urbanization, and cropland, whereas greenbelt and agri-</p><p>cultural development regions are kept at present-day levels.</p><p>In the B1 scenario, urbanization progresses at a lower level,</p><p>and as interest in the natural environment increases, more</p><p>cropland and grassland areas are changed into forest as a</p><p>result of the Afforestation and Reforestation Clean Devel-</p><p>opment Mechanisms (A/R CDM) project that has been</p><p>adapted by IPCC to reduce actual carbon dioxide (CO2) in</p><p>the atmosphere. As the need to grow bioenergy resources</p><p>increases, grassland and forest areas are changed to crop-</p><p>land, and developmental controls protecting greenbelt and</p><p>cropland areas are kept at present-day levels. In the B2</p><p>scenario, as there is little new urbanization, land use</p><p>changes into forest occur mainly on marginal cropland</p><p>areas, where it is hard to grow agricultural products, and on</p><p>grassland areas such as golf courses. Additionally, devel-</p><p>opmental controls protecting cropland areas are kept in</p><p>place for the reasons of food security (Table 1).</p><p>LUC modeling</p><p>Land-change modeler (LCM) is used to predict future LUCs.</p><p>Land-use transition probabilities have been established by</p><p>analyzing correlations between past land usage and sus-</p><p>pected driving factors. Legal issues and policies, gross</p><p>regional domestic product (GRDP), and scenario-specific</p><p>populations were considered as driving factors. Topographic</p><p>elevation, slope, distance from roads, and distance from</p><p>built-up areas were also used. The multilayer perceptron</p><p>(MLP) neural network method is a primary method for</p><p>generating land-use transition probabilities. To analyze past</p><p>changes in land use, the Korean Ministry of Environments</p><p>Landscape Ecol Eng</p><p>123</p></li><li><p>Level 1 land-cover maps of 1989 and 1999 were converted</p><p>from their original 30 9 30-m resolution to a coarser</p><p>100 9 100-m resolution in order to encompass the entire</p><p>country, creating files of manageable size. The probabilities</p><p>of particular LUC in the future were calculated by applying a</p><p>Markov technique. Resulting transition matrixes were then</p><p>modified according to the level of urbanization and</p><p>economic growth associated with each scenario. The prob-</p><p>ability of LUC was also constrained in legislatively protected</p><p>conservation areas, such as greenbelts, national parks, the</p><p>Demilitarized Zone, the Baekdudaegan, wetlands, conser-</p><p>vation forests, waterfronts, agricultural development</p><p>regions, forest resource conservation areas, water source</p><p>conservation areas, and wild animal and plant protection</p><p>Fig. 1 Carbon-stock change calculation</p><p>Landscape Ecol Eng</p><p>123</p></li><li><p>areas. These transition rules were ultimately applied to</p><p>existing land-use patterns by employing a cellular automaton</p><p>technique, which simulates LUC incrementally by repeat-</p><p>edly updating discrete locations according to site conditions,</p><p>with an immediate vicinity (Yim and Choi 2002; Fang et al.</p><p>2005). This method of LUC modeling had demonstrated</p><p>good performance in previous regional studies of South</p><p>Korea (e.g., Yim and Choi 2002; Lee and Park 2009).</p><p>Calculation of carbon-stock changes</p><p>To calculate the effects of future LUC on carbon stocks, an</p><p>LUC and a carbon-stock calculation method were coupled</p><p>(Schulp et al. 2008). Such coupling is difficult due to the</p><p>different spatial and temporal resolutions of available LUC</p><p>and carbon-stock calculations (Smith et al. 2005a). Most</p><p>assessments that address the effect of LUC on carbon stock</p><p>strongly simplify the dynamics of LUC (Smith et al. 2005a;</p><p>Zaehle et al. 2007; Schulp et al. 2008). Changes in carbon</p><p>stock were calculated by considering changes in biomass</p><p>associated with LUC and natural growth. Carbon-stock</p><p>changes are calculated for each land use or LUC as follows:</p><p>Soil stock change soil carbon stock 2030 soil carbon stock 2005 </p><p>Biomass stock change stem volume incrementation afforestation deforestation decomposition of litter</p><p>Changes in carbon stock soil stock change biomass stock change</p><p>Soil carbon stocks start to change to a new equilibrium</p><p>after LUC. This is a rapid process that occurs immediately</p><p>Table 1 Land-use-change (LUC) scenarios and story lines</p><p>LUC</p><p>scenarios</p><p>Storylines</p><p>A1 scenario More cropland and forest is easy to change into urban</p><p>use than the other scenarios and are most likely to be</p><p>urbanized</p><p>Regulations protecting greenbelt areas are lifted to</p><p>accommodate economic growth and expansion of</p><p>private property</p><p>Control of cropland development is relaxed due to</p><p>improvements in agricultural productivity</p><p>A2 scenario Urbanization progresses at the same level</p><p>More grassland and forest are more likely to change</p><p>into cropland than other scenarios (food security)</p><p>Forest areas decrease as they change into grassland,</p><p>urban areas, and cropland</p><p>Greenbelt and agricultural development regions are</p><p>kept at present level</p><p>B1 scenario Urbanization progresses at a lower level</p><p>More cropland and grassland areas are changed into</p><p>forest (A/R CDM)</p><p>Grassland and forest are changed into cropland</p><p>(bioenergy)</p><p>Developmental controls preserved greenbelts, and</p><p>cropland areas are kept at present level</p><p>B2 scenario Little new urbanization</p><p>Land use changes into forest occur mainly on marginal</p><p>cropland areas</p><p>Developmental regulations for protecting cropland</p><p>areas are kept (food security)</p><p>A/R CDM Afforestation and Reforestation Clean Development</p><p>Mechanisms</p><p>Table 2 Soil carbon stock by land-use type</p><p>Forest Paddy</p><p>field</p><p>Upland</p><p>field</p><p>Grassland Other</p><p>Soil carbon (tC/</p><p>ha)</p><p>67.9 60.5 45.9 45.9 11.5</p><p>Lee et al. (2001)</p><p>Table 3 Annual growth rate of stem volume</p><p>Coniferous</p><p>forest</p><p>Deciduous</p><p>forest</p><p>Mixed</p><p>forest</p><p>Annual growth rate of stem</p><p>volume (m3/ha)</p><p>5.68 3.45 4.56</p><p>Korea Forest Research Institute (KFRI) (2006)</p><p>Fig. 2 Estimation method of total biomass [Korea Forest ResearchInstitute (KFRI) 2006]</p><p>Table 4 Coefficient for carbon-stock calculation by vegetation type</p><p>Stem density</p><p>(ton/m3)</p><p>Biomass</p><p>expansion factor</p><p>Carbon fraction of</p><p>biomass</p><p>Coniferous</p><p>forest</p><p>0.47 1.651 0.5</p><p>Deciduous</p><p>forest</p><p>0.80 1.720 0.5</p><p>Mixed forest 0.635 1.685 0.5</p><p>Korea Forest Research Institute (KFRI) (2006)</p><p>Landscape Ecol Eng</p><p>123</p></li><li><p>after conversion, and it levels off when soil carbon stocks</p><p>approach equilibrium (Freibauer et al. 2004; KFRI 2006).</p><p>It is expected that equilibrium is reached approximately</p><p>20 years after conversion (KFRI 2006). Carbon</p><p>accumulation can in fact differ according to the various</p><p>characteristics of soils (Lal 2004; Smith et al. 2008). The</p><p>change in soil carbon stock as a consequence of land-use</p><p>conversion depends on the differential sizes of the soil C</p><p>pool between land-use types at equilibrium (Zaehle et al.</p><p>2007). To estimate carbon accumulation in soil, the</p><p>methods of Lee et al. (2001) were used with five types of</p><p>land use: forest, paddy field, upland field, grassland, and</p><p>others (Table 2). Land-cover maps were converted into five</p><p>land-use categories: soil under forest area, 67.9 tC/ha;</p><p>paddy field, 60.5 tC/ha\; upland field, 45.9 tC/ha; grassland,</p><p>45.9 tC/ha; and other areas, 11.5 tC/ha (Lee et al. 2001).</p><p>All soil carbon-stock changes consider the upper 30 cm of</p><p>the soil (Lee et al. 2001; Schulp et al. 2008).</p><p>Carbon accumulation in vegetation was calculated by</p><p>considering stem-volume incrementation, afforestation,</p><p>deforestation, and decomposition of litter between 2005</p><p>and 2030. Stem-volume incrementation is considered in</p><p>Fig. 3 Land-use change</p><p>Fig. 4 Result of land-use change in A1 scenario (see text for definition)</p><p>Landscape Ecol Eng</p><p>123</p></li><li><p>areas designated as forest-land-remaining-forest-land,</p><p>afforestation, and deforestation areas. Tre...</p></li></ul>

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