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쓰나미에 의한 유목의 생성과 퇴적패턴의 수치모의실험

Numerical Experiment of Driftwood Generation and Deposition Patterns by Tsunami

  • 강태운 (한국교통대학교 건설환경도시교통공학부) ;
  • 장창래 (한국교통대학교 건설환경도시교통공학부) ;
  • 이남주 (경성대학교 건설환경도시공학부) ;
  • 이원호 (한국교통대학교 건설환경도시교통공학부)
  • Kang, Tae Un (Department of Civil Engineering, Korea National University of Transportation) ;
  • Jang, Chang-Lae (Department of Civil Engineering, Korea National University of Transportation) ;
  • Lee, Nam Joo (Department of Civil Engineering, Kyungsung University) ;
  • Lee, Won Ho (Department of Civil Engineering, Korea National University of Transportation)
  • 투고 : 2021.10.12
  • 심사 : 2021.11.18
  • 발행 : 2021.12.31

초록

본 연구는 유목의 생성 및 퇴적과 쓰나미 흐름을 수치모형을 활용하여 실험하였다. 이를 위해 2차원 수심적분흐름모형과 유목동역학모형을 사용하였다. 연구지역은 일본 센다이(Sendai)해안가로서 관측자료(Inagaki et al. 2012)를 이용하여 시뮬레이션 결과와 유목의 퇴적패턴을 비교검증하였다. 본 연구를 위해 흐름의 항력으로 인해 유목이 발생하는 단순화된 모형이 개발되었다. 또한 유목 발생량을 고려하기 위해 Google Earth를 활용하여 연구지역의 퇴적된 유목 수를 추정하였으며 그 결과, 해안숲의 30만 그루의 나무으로부터 13000개 이상의 유목이 발생하여 내륙으로 이송되는 수치모의를 수행하였다. 이 수치실험 결과는 Inagaki et al. (2012)의 관측데이터와 유사하였다. 또한, 유목의 발생과 퇴적 패턴의 재현성은 종방향 퇴적패턴에서 높은 상관성을 나타냈다. 추후에는 목재의 크기, 경계 조건, 격자 크기와 같은 유목 매개변수에 대한 민감도 분석을 구축하여 유목의 이동패턴을 분석할 필요가 있을 것으로 판단된다. 이러한 모델링은 물의 흐름과 유목에 따른 재해예측에 유용한 방법론이 될 것으로 기대된다.

We studied driftwood behaviors including generation and deposition in a tsunami using a numerical simulation. We used an integrated two-dimensional numerical model, which included a driftwood dynamics model. The study area was Sendai, Japan. Observation data collected by Inagaki et al. (2012) were used to verify the simulation results by comparing them with driftwood deposition patterns. A simplified model was developed to consider the threshold of driftwood generation by the drag force of water flows. To consider the volume of driftwood generated, we estimated the total wood number in the study area using Google Earth. Therefore, we simulated more than 13,000 pieces of driftwood that were generated and transported inland from approximately 300,000 trees that were growing in the forest. The final distribution of the driftwood was similar to the observation data. The reproducibility of the generation and deposition patterns of driftwood showed good agreement in terms of longitudinal deposition pattern. In the future, a sensitivity analysis on driftwood parameters, such as the size of the wood, boundary conditions, and grid size, will be implemented to predict the travel patterns of driftwood. Such modeling will be a useful methodology for disaster prediction based on water flow and driftwood.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원 수생태계 건강성 확보 기술개발사업의 지원을 받아 연구되었습니다. (2020003050002)

참고문헌

  1. Choi, G., Kim, K. and Park, Y. 2003. Changes in water depth and velocity by debris around piers. J. Korea Water Resour. Assoc. 36(2): 273-284. (in Korean). https://doi.org/10.3741/JKWRA.2003.36.2.273
  2. Fritz, H.M., Phillips, D.A., Okayasu, A., Shimozono, T., Liu, H., Mohammed, F., Skanavis, V., Synolakis, C.E. and Takahashi, T. 2012. The 2011 Japan tsunami current velocity measurements from survivor videos at Kesennuma Bay using LiDAR. Geophysical research letters 39: L00G23, doi:10.1029/2011GL050686.
  3. Global Map Japan. 2021. Available Online: http://cyberjapandata.gsi.go.jp on Sep. 30th, 2021.
  4. Gotoh, H., Okayasu, A., Watanabe, Y. 2013. Computational wave dynamics. Advanced series on ocean engineering. World Scientific, Singapore, 87.
  5. Google Earth. 2021. Available Online: https://www.google.com/maps on Sep. 30th, 2021.
  6. Inagaki, K., Nakaza, E., Iribe, T. and Watanabe, Y. 2012. Distribution of the pine trees drowned by Tohoku Tsunami along Sendai coastal area. Journal of Japan Society of Civil Engineers Ser. B3 (Coastal Engineering), 68(2): I_120-125. (in Japanese). https://doi.org/10.2208/jscejoe.68.I_120
  7. International River Interface Cooperative (iRIC). 2021. Available Online: http://i-ric.org/en on Sep. 30th, 2021.
  8. Kang, T. 2018. Studies on morphodynamics in shallow rivers with effects of vegetation and large wood using computational models, Doctoral dissertation., Hokkaido University.
  9. Kang, T. and Jang, C-L. 2020. An Experiment on flow simulation depending on opening configuration of weir using a numerical model. Journal of Ecology and Resilient Infrastructure 7(3): 218-226, https://doi.org/10.17820/eri.2020.7.3.218. (in Korean)
  10. Kang, T. and Kimura, I. 2018. Computational modeling for large wood dynamics with root wad and anisotropic bed friction in shallow flows. Advances in Water Resources 121: 419-431. https://doi.org/10.1016/j.advwatres.2018.09.006
  11. Kang, T., Kimura, I. and Shimizu, Y. 2018. Study on advection and deposition of driftwood affected by root in shallow flows. Journal of Japan Society of Civil Engineers Ser., B1 (Hydraulic Engineering), 74(4): I_757-I_762. https://doi.org/10.2208/jscejhe.74.i_757
  12. Kang, T., Kimura, I. and Shimizu, Y. 2020. Numerical simulation of large wood deposition patterns and responses of bed morphology in a braided river using large wood dynamics model. Earth Surface Processes and Landforms 45: 962-977, DOI: 10.1002/esp.4789.
  13. Kang, T., Kimura, I. and Onda, S. 2021. Application of computational modeling for large wood dynamics with collisions on moveable channel beds. Advances in Water Resources 152:103912, doi.org/10.1016/j.advwatres.2021.103912.
  14. Kimura, I. 2018. NaysCUBE solver manual (updated). Available Online: http://i-ric.org/en on September 30th, 2021.
  15. Kimura, I. and Kitanozo, K. 2020. Effects of the driftwood Richardson number and applicability of a 3D-2D model to heavy wood jamming around obstacles. Environmental Fluid Mechanics 20: 503-525. https://doi.org/10.1007/s10652-019-09709-6
  16. Kimura, I., Kang, T., and Kato, K. 2020. Computation on submerged large wood behavior around a driftwood trapping facility. Journal of Japan Society of Civil Engineers (JSCE) Ser. B1 (Hydraulic Engineering), 76(2): I_967-I_972. (in Japanese) https://doi.org/10.2208/jscejhe.76.2_I_967
  17. Lee, K, Jang, C-L. 2018. Numerical investigation of space effects of serial spur dikes on flow and bed changes by using Nays2D. J. Korea Water Resour. Assoc. 49(3): 241-252, doi: 10.3741/JKWRA.2016.49.3.241. (in Korean)
  18. Ministry of Land, Infrastructure and Transport. 2016. Korea building code. (in Korean)
  19. National Disaster Management Institute (NDMI). 2012. Tsunami Hazard Mapping through Characteristic Analysis of Inundation (III). (in Korean)
  20. Oishi, Y., Imamura, F., and Sugawara, D. 2015. Near-field tsunami inundation forecast using the parallel TUNAMI-N2 model: Application to the 2011 Tohoku-Oki earthquake combined with source inversions. Geophysical research letters 42: 1083-1091, doi:10.1002/2014GL062577.
  21. Oishi, Y., Imamura, F., Sugawara, D. and Furumura, T. 2016. Investigation of reliable tsunami inundation model in urban areas using a supercomputer. Journal of Japan Society of Civil Engineers (JSCE) Ser. B1 (Hydraulic Engineering), 72(2): I_409-I_414. (in Japanese) https://doi.org/10.2208/jscejhe.72.I_409
  22. Ruiz-Villanueva, V., Blade, E., Sanchez-Juny, M., Marti-Cardona, B., Diez-Herrero, A., and Bodoque, J.M. 2014. Two-dimensional numerical modeling of wood transport. Journal of Hydroinformatics 16(5): 1077-1096. https://doi.org/10.2166/hydro.2014.026
  23. Ruiz-Villanueva, V., Gamberini, C., Blade, E., Stoffel, M., Bertoldi, W. 2020. Numerical Modeling of Instream Wood Transport, Deposition, and Accumulation in Braided Morphologies Under Unsteady Conditions: Sensitivity and High-Resolution Quantitative Model Validation. Water Resources Research doi.org/10.1029/2019WR026221.
  24. Shimizu, Y., Inoue, T., Suzuki, E., Kawamura, S., Iwasaki, T., Hamaki, M., Omura, K., Kakegawa, E. and Yoshida, T. 2015. Nays2D Flood Solver Manual. Available Online: http://i-ric.org/en (accessed in 2021)
  25. Shimizu, Y., Takebayashi, H., Inoue, T., Hamaki, M., Iwasaki, T. and Nabi, M. 2012. Nays2DH Solver Manual. Available online: http://i-ric.org/en (accessed in 2021)