DOI QR코드

DOI QR Code

Analysis of Land Use Change Using RCP-Based Dyna-CLUE Model in the Hwangguji River Watershed

RCP 시나리오 기반 Dyna-CLUE 모형을 이용한 황구지천 유역의 토지이용변화 분석

  • Kim, Jihye (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Park, Jihoon (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Song, Inhong (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Song, Jung-Hun (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Jun, Sang Min (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Kang, Moon Seong (Department of Rural Systems Engineering, Seoul National University, Research Institute for Agricultural and Life Sciences, Seoul National University)
  • 김지혜 (서울대학교 생태조경.지역시스템공학부) ;
  • 박지훈 (서울대학교 생태조경.지역시스템공학부) ;
  • 송인홍 (서울대학교 농업생명과학연구원) ;
  • 송정헌 (서울대학교 생태조경.지역시스템공학부) ;
  • 전상민 (서울대학교 생태조경.지역시스템공학부) ;
  • 강문성 (서울대학교 조경.지역시스템공학부, 서울대학교 농업생명과학연구원)
  • Received : 2015.01.28
  • Accepted : 2015.06.10
  • Published : 2015.06.30

Abstract

The objective of this study was to predict land use change based on the land use change scenarios for the Hwangguji river watershed, South Korea. The land use change scenario was derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The CLUE (conversion of land use and its effects) model was used to simulate the land use change. The CLUE is the modeling framework to simulate land use change considering empirically quantified relations between land use types and socioeconomic and biophysical driving factors through dynamical modeling. The Hwangguji river watershed, South Korea was selected as study area. Future land use changes in 2040, 2070, and 2100 were analyzed relative to baseline (2010) under the RCP4.5 and 8.5 scenarios. Binary logistic regressions were carried out to identify the relation between land uses and its driving factors. CN (Curve number) and impervious area based on the RCP4.5 and 8.5 scenarios were calculated and analyzed using the results of future land use changes. The land use change simulation of the RCP4.5 scenario resulted that the area of urban was forecast to increase by 12% and the area of forest was estimated to decrease by 16% between 2010 and 2100. The land use change simulation of the RCP8.5 scenario resulted that the area of urban was forecast to increase by 16% and the area of forest was estimated to decrease by 18% between 2010 and 2100. The values of Kappa and multiple resolution procedure were calculated as 0.61 and 74.03%. CN (III) and impervious area were increased by 0-1 and 0-8% from 2010 to 2100, respectively. The study findings may provide a useful tool for estimating the future land use change, which is an important factor for the future extreme flood.

Keywords

References

  1. Clarke, K.C., Hoppen, S., and Gaydos, L., 1997, A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area, Environment and Planning B 24: 247-261. https://doi.org/10.1068/b240247
  2. Costanza, R., 1989, Model goodness of fit: a multiple resolution procedure, Ecological Modelling 47: 199-215. https://doi.org/10.1016/0304-3800(89)90001-X
  3. Cuo, L., Beyene, T.K., Voisin, N., Su, F., Lettenmaier, D.P., Alberti, M., and Richey, J.E., 2011, Effects of mid-twenty-first century climate and land cover change on the hydrology of the Puget Sound basin, Washington, Hydrological Processes 25: 1729-1753. https://doi.org/10.1002/hyp.7932
  4. Erdogan, N., Nurlu, E., and Erdem, U., 2011, Modelling land use changes in Karaburun by using CLUE-s, ITU A/Z 8(2): 91-102.
  5. Hagen, A., 2002, Multi-method assessment of map similarity, 5th AGILE Conference on Geographic Information Science.
  6. Hurtt, G.C., Chini, L.P., Frolking, S., Betts, R.A., Feddema, J., Fischer, G., Fisk, J.P., Hibbard, K., Houghton, R.A., Janetos, A., Jones, C.D., Kindermann, G., Kinoshita, T., Goldewijk, K.K., Riahi, K., Shevliakova, E., Smith, S. Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D.P., and Wang, Y.P., 2011, Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Climate Change 109: 117-161. https://doi.org/10.1007/s10584-011-0153-2
  7. Intergovernmental Panel on Climate Change (IPCC), 2000, IPCC special report emissions scenarios.
  8. Kim, W.G. and Ryu, T.S., 2011, Strategy of flood control capacity enhancement on existing multipurpose dams to the effect of climate change, Journal of the Korean Professional Engineers Association 44(2): 23-28.
  9. Mitsova, D., Shuster, W., and Wang, X., 2011, A cellular automata model of land cover change to integrate urban growth with open space conservation, Landscape and Urban Planning 99: 141-153. https://doi.org/10.1016/j.landurbplan.2010.10.001
  10. Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emori, S., Erda, L., Hibbard, K., Jones, R., Kainuma, M., Kelleher, J., Lamarque, J.F., Manning, M., Matthews, B., Meehl, J., Meyer, L., Mitchell, J., Nakicenovic, N., O'Neill, B., Pichs, R., Riahi, K., Rose, S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T., van Ypersele, J.P., and Zurek, M., 2008, Towards new scenarios for analysis of emissions, climate change, impacts, and response strategies, IPCC expert meeting report.
  11. National Institute of Meteorological Research (NIMR), 2011, Climate change scenario report for IPCC AR5, 17-46.
  12. Oh, Y.-G., 2011, An assessment of green house gas emissions in cropland and forest considering land-use change affected by climate change, Ph.D. diss., Seoul National University.
  13. Oh, Y.-G., Yoo, S.-H., Lee, S.-H., and Choi, J.-Y., 2011, Prediction of paddy field change based on climate change scenarios using the CLUE model, Paddy Water Environ 9:309-323. https://doi.org/10.1007/s10333-010-0244-0
  14. Pontius, R.G. Jr. and Schneider, L.C., 2001, Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA, Agriculture, Ecosystems and Environment, 85: 239-248. https://doi.org/10.1016/S0167-8809(01)00187-6
  15. Veldkamp, A. and Fresco, L.O., 1996, CLUE: a conceptual model to study the conversion of land use and its effects, Ecological Modelling 85: 253-270. https://doi.org/10.1016/0304-3800(94)00151-0
  16. Verburg, P.H. and Overmars, K.P., 2009, Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model, Landscape Ecology 24: 1167-1181. https://doi.org/10.1007/s10980-009-9355-7
  17. Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and Mastura, S.S.A, 2002, Modeling the spatial dynamics of regional land use: the CLUE-S model, Environmental Management 30(3): 391-405. https://doi.org/10.1007/s00267-002-2630-x
  18. Wolfram, S., 1984, Cellular automata as models of complexity, Nature, 311(5985): 419-424. https://doi.org/10.1038/311419a0
  19. Yim, C.-H. and Choi, D.-S., 2002, Predicting micro land use dynamics: a cellular automata modelling approach, Journal of Korea Planners Association 37(4): 229-239.

Cited by

  1. Estimating Changes in Habitat Quality through Land-Use Predictions: Case Study of Roe Deer (Capreolus pygargus tianschanicus) in Jeju Island vol.12, pp.23, 2015, https://doi.org/10.3390/su122310123