DOI QR코드

DOI QR Code

Mapping of Inundation Vulnerability Using Geomorphic Characteristics of Flood-damaged Farmlands - A Case Study of Jinju City -

침수피해 정보를 이용한 농경지의 지형학적 침수취약지도 작성 - 진주시를 사례로 -

  • Kim, Soo-Jin (Graduate School, Gyeongsang Nat'l University) ;
  • Suh, Kyo (Dept. of Landscape Architecture and Rural System Engineering, Seoul Nat'l University) ;
  • Kim, Sang-Min (Dept. of Agricultural Environment, National Academy of Agricultural Science(NAAS)) ;
  • Lee, Kyung-Do (Dept. of Agricultural Engineering (Insti. of Agric. & Life Sci.), Gyeongsang National University) ;
  • Jang, Min-Won (Dept. of Agricultural Environment, National Academy of Agricultural Science(NAAS))
  • 김수진 (경상대학교 대학원) ;
  • 서교 (서울대학교 조경.지역시스템공학부 (농업생명과학연구원)) ;
  • 김상민 (경상대학교 지역환경기반공학과 (농업생명과학연구원)) ;
  • 이경도 (국립농업과학원 농업환경부) ;
  • 장민원 (경상대학교 지역환경기반공학과 (농업생명과학연구원))
  • Received : 2013.08.22
  • Accepted : 2013.09.11
  • Published : 2013.09.30

Abstract

The objective of this study was to make a map of farmland vulnerability to flood inundation based on morphologic characteristics from the flood-damaged areas. Vulnerability mapping based on the records of flood damages has been conducted in four successive steps; data preparation and preprocessing, identification of morphologic criteria, calculation of inundation vulnerability index using a fuzzy membership function, and evaluation of inundation vulnerability. At the first step, three primary digital data at 30-m resolution were produced as follows: digital elevation model, hill slopes map, and distance from water body map. Secondly zonal statistics were conducted from such three raster data to identify geomorphic features in common. Thirdly inundation vulnerability index was defined as the value of 0 to 1 by applying a fuzzy linear membership function to the accumulation of raster data reclassified as 1 for cells satisfying each geomorphic condition. Lastly inundation vulnerability was suggested to be divided into five stages by 0.25 interval i.e. extremely vulnerable, highly vulnerable, normally vulnerable, less vulnerable, and resilient. For a case study of the Jinju, farmlands of $138.6km^2$, about 18% of the whole area of Jinju, were classified as vulnerable to inundation, and about $6.6km^2$ of farmlands with elevation of below 19 m at sea water level, slope of below 3.5 degrees, and within 115 m distance from water body were exposed to extremely vulnerable to inundation. Comparatively Geumsan-myeon and Sabong-myeon were revealed as the most vulnerable to farmland inundation in the Jinju.

Keywords

References

  1. 강정은, 이명진, 2012, 퍼지모형과 GIS를 활용한 기후변화 홍수취약성 평가 -서울시 사례를 중심으로-, 한국지리정보학회지, 15(3), 119-136.
  2. 구신회, 김성삼, 박영진, 최재원, 2011, 정밀지형자료와 과거 침수피해정보를 활용한 침수흔적도 구축 정확도 개선, 한국지형공간정보학회지, 19(4), 91-99.
  3. 김성삼, 정길섭, 이준우, 박영진, 김의명, 2012, 자연 재해 관리를 위한 침수취약등급도 구축방안, 2012 한국지형공간정보학회 추계학술대회, 167-168.
  4. 김철, 김석규, 2003, GIS를 이용한 홍수취약지역 예측, 대한토목학회논문집, 23(3B), 175-181.
  5. 농림수산식품부, 2008, 농업재해대책 업무편람.
  6. 박원창, 김감래, 지종덕, 2010, 지적정보를 이용한 침수흔적도의 관리 및 활용방안, 한국지적정보학회지, 12(1), 1-12.
  7. 박종덕, 구자용, 2011, 모바일 GIS를 이용한 홍수 위험 경보 서비스 구현, 대한지리학회지, 46(6), 738-750.
  8. 박태선, 김광묵, 윤양수, 이승복, 2005, 홍수피해특성 분석 및 홍수피해지표 개발에 관한 연구, 국토연구원.
  9. 박현미, 김의명, 2012, 침수취약등급도 제작을 위한 지형저지대 생성기법, 2012 한국지형공간정보학회추계학술대회, 105-106.
  10. 소방방재청, 2012, 2011 재해연보.
  11. 안상진, 전계원, 곽현구, 2003, 홍수피해로 인한 침수면적 산정에 관한 사례연구, 대한토목학회논문집, 23(3B), 183-189.
  12. 이근상, 2012, GIS와 지적도를 이용한 전주천 홍수 위험지역 평가, 한국지적정보학회지, 14(2), 1-14.
  13. 이한세, 2004, 상습침수지역의 문제점 및 개선방안, 국토연구, 273, 16-23.
  14. 지홍기, 2006, 2006 남강 홍수피해 조사분석, 한국수자원학회지, 39(8), 46-60.
  15. 최예환, 2000, 농경지 침수방지 대책, 환경연구, 17, 226-250.
  16. 홍순희, 이종국, 정태천, 한상현, 2004, 상습침수지역의 호우침수재해예측서비스, 한국기상학회 2004년도 가을 학술대회, 218-219.
  17. 황유정, 2006, 홍수에 의한 침수 취약지역 예측에 관한 연구, 한국지역지리학회지, 12(1), 172-178.
  18. Hamilton, R. M, 2000, Science and Technology for Natural Disaster Reduction, Natural Hazards Review, 1(1), 56-60. https://doi.org/10.1061/(ASCE)1527-6988(2000)1:1(56)
  19. Wilhelmi, O. V. and Wilhite, D. A., 2002, Assessing Vulnerability to Agricultural Drought: A Nebraska Case Study, Natural Hazards, 25, 37-58. https://doi.org/10.1023/A:1013388814894

Cited by

  1. Estimating Real-time Inundation Vulnerability Index at Point-unit Farmland Scale using Fuzzy set vol.20, pp.2, 2014, https://doi.org/10.7851/ksrp.2014.20.2.001
  2. Investigating flood susceptible areas in inaccessible regions using remote sensing and geographic information systems vol.189, pp.3, 2017, https://doi.org/10.1007/s10661-017-5811-z
  3. Flood Mapping Using Multi-Source Remotely Sensed Data and Logistic Regression in the Heterogeneous Mountainous Regions in North Korea vol.10, pp.7, 2018, https://doi.org/10.3390/rs10071036