DOI QR코드

DOI QR Code

Predicting Landslide Damaged Area According to Climate Change Scenarios

기후변화 시나리오를 적용한 산사태 피해면적 변화 예측

  • Song Eu (Landslide Team, National Institute of Forest Science)
  • 유송 (국립산림과학원 산사태연구팀)
  • Received : 2023.12.07
  • Accepted : 2023.12.22
  • Published : 2023.12.30

Abstract

Due to climate changes, landslide hazards in the Republic of Korea (hereafter South Korea) continuously increase. To establish the effective landslide mitigation strategies, such as erosion control works, landslide hazard estimation in the long-term perspective should be proceeded considering the influence of climate changes. In this study, we examined the change in landslide-damaged areas in South Korea responding to climate change scenarios using the multivariate regression method. Data on landslide-damaged areas and rainfall from 1981-2010 were used as a training dataset. Sev en indices were deriv ed from rainfall data as the model's input data, corresponding to rainfall indices provided from two SSP scenarios for South Korea: SSP1-2.6 and SSP5-8.5. Prior to the multivariate regression analysis, we conducted the VIF test and the dimension analysis of regression model using PCA. Based on the result of PCA, we developed a regression model for landslide damaged area estimation with two principal components, which cov ered about 93% of total v ariance. With climate change scenarios, we simulated landslide-damaged areas in 2030-2100 using the regression model. As a result, the landslide-damaged area will be enlarged more than the double of current annual mean landslide damaged area of 1981-2010; It infers that landslide mitigation strategies should be reinforced considering the future climate condition.

기후변화로 인해 우리나라의 산사태 피해는 지속적으로 증가하고 있다. 사방사업 등 산사태 피해저감을 효과적으로 수립하기 위해서는 기후변화 영향을 고려하여 장기간의 산사태 위험도를 추정할 필요가 있다. 이 연구에서는 다변량 회귀분석을 통해 기후변화에 따른 산사태 피해면적의 변화를 예측하였다. 1980-2010 년의 산사태 피해면적과 강우관측자료를 학습자료로 적용하여 다변량 회귀모형을 구축하였다. 이때 강우관측자료를 통해 SSP 시나리오에서는 제공하는 7가지 강우인자를 추출하였다. 이후 분산팽창지수로 다중공선성을 검정하고 주성분 분석을 통해 차원을 축소하여 2개의 주성분을 독립변인으로 하여 산사태 피해면적 추정 모형을 도출하였다. 기후변화 시나리오를 활용하여 2030-2100년까지의 산사태 피해면적 변화를 추정한 결과, 산사태 피해면적은 1981년-2010년의 연평균 산사태 면적의 최대 2배 이상으로 증가하는 것으로 나타났다. 이 연구의 결과는 미래 기후변화를 고려한 산사태 피해저감 대책 수립 및 보강의 필요성을 제시하는 기초자료로 활용 가능할 것으로 보인다.

Keywords

References

  1. Cha, S., C. -H. Lim, M. Hong, J. Lim, and W. -K. Lee, 2023: Landslide Vulnerability Assessment Based on Climate Change Scenarios Using the Maximum Entropy (MaxEnt) Model. Journal of Climate Change Research 14(2), 145-156. (in Korean with English abstract) https://doi.org/10.15531/KSCCR.2023.14.2.145
  2. Crozier, M. J., 2010: Deciphering the Effect of Climate Change on Landslide Activity: A Review. Geomorphology 124(3-4), 260-267. https://doi.org/10.1016/j.geomorph.2010.04.009
  3. Jakob, M., and T. Owen, 2021: Projected Effects of Climate Change on Shallow Landslides, North Shore Mountains, Vancouver, Canada. Geomorphology 393, 107921. https://doi.org/10.1016/j.geomorph.2021.107921
  4. Jones, R. N., 2000: Managing Uncertainty in Climate Change Projections - Issues for Impact Assessment. Climatic Change 45, 403-419. https://doi.org/10.1023/A:1005551626280
  5. Jun, B. H., and N. G. Kim, 2010: Classification of Landslide Occurrence using Statistic Analysis of Rainfall Data. Crisisonomy 6(3), 103-112. (in Korean with English abstract)
  6. Kay, A. L., H. N. Davies, V. A. Bell, and R. G. Jones, 2009: Comparison of Uncertainty Sources for Climate change impacts: flood frequency in England. Climatic Change 92, 41-63. https://doi.org/10.1007/s10584-008-9471-4
  7. Kim, H., J.-H. Lee, H.-J. Park, and J.-H. Heo, 2021: Assessment of Temporal Probability for Rainfall-induced Landslides Based on Nonstationary Extreme Value Analysis. Engineering Geology 294, 106372. https://doi.org/10.1016/j.enggeo.2021.106372
  8. Kim, H. G., D. K. Lee, C. Park, S. Kil, Y. Son, and J. H. Park, 2015: Evaluating Landslide Hazards Using RCP 4.5 and 8.5 Scenarios. Environmental Earth Sciences 73, 1385-1400. https://doi.org/10.1007/s12665-014-3775-7
  9. Kim, J., K. -O. Bu, J. Choi, and Y. Byeon, 2018: Climate Change in the Past 100 Years of Korean Peninsula, National Institute of Meteorological Sciences, 31pp.
  10. Kirschbaum, D., S. Kapnick, T. Stanley, and S. Pascale, 2020: Changes in Extreme Precipitation and Landslides over High Mountain Asia. Geophysical Research Letters 47(4), e2019 GL085347. https://doi.org/10.1029/2019GL085347
  11. Lim, C. -H., and H. -J. Kim, 2022: Can Forest-Related Adaptive Capacity Reduce Landslide Risk Attributable to Climate Change?-Case of Republic of Korea. Forests 13, 49. https://doi.org/10.3390/f13010049
  12. Lin, Q., S. Steger, M. Pittore, J. Zhang, L. Wang, T. Jiang, and Y. Wang, 2022: Evaluation of Potential Changes in Landslide Susceptibility and Landslide Occurrence Frequency in China under Climate Change. Science of the total environment 850, 158049. https://doi.org/10.1016/j.scitotenv.2022.158049
  13. Menard, S., 2002: Applied Logistic Regression Analysis (Sage University Papers Series on Quantitative Application in the Social Sciences, 07-106.). Sage Publications, 128pp.
  14. Nash, J. E. and J. V. Sutcliffe, 1970: River Flow Forecasting Through Conceptual Models. Part 1: A Discussion of Principles. Journal of Hydrology 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  15. National Institute of Forest Science, 2021: Forest Disaster White Paper 2020. National Institute of Forest Science, 177pp.
  16. Park, S. -J., and D. -K. Lee, 2021: Predicting Susceptibility to Landslides under Climate Change Impacts in Metropolitan Areas of South Korea Using Machine Learning. Geomatics, Natural Hazards and Risk 12, 2462-2476. https://doi.org/10.1080/19475705.2021.1963328
  17. Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, 2011: Scikit-learn: Machine Learning in Python. the Journal of machine Learning research 12, 2825-2830.
  18. Related Ministries 2021: (2020) Abnormal Climate Report. Korea Meteorological Administration, 167pp.
  19. Shim, S., J. Kim, H. M. Sung, J.-H. Lee, S.-H. Kwon, M.-A. Sun, J.-C. Ha, Y.-H. Byun, and Y.-H. Kim, 2021: Future Changes in Extreme Temperature and Precipitation over East Asia under SSP Scenarios Journal of Climate Change Research, 12(2), 143-162. https://doi.org/10.15531/KSCCR.2021.12.2.143 (in Korean with English abstract)
  20. Sobie, S. R., 2020: Future Changes in Precipitationcaused Landslide Frequency in British Columbia. Climatic Change 162, 465-484. https://doi.org/10.1007/s10584-020-02788-1