Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Flood analysis for agriculture area using SWMM model: case study on Sindae drainage basin

  • Inhyeok Song (Department of Agricultural and Rural Engineering, Chungnam National University) ;
  • Hyunuk An (Department of Agricultural and Rural Engineering, Chungnam National University) ;
  • Mikyoung Choi (Department of Agricultural and Rural Engineering, Chungnam National University) ;
  • Heesung Lim (Department of Rural Research Institute, Korea Rural Community Corporation)
  • Received : 2023.10.16
  • Accepted : 2023.11.08
  • Published : 2023.12.01
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Abstract

Globally, abnormal climate phenomena have led to an increase in rainfall intensity, consequently causing a rise in flooding-related damages. Agricultural areas, in particular, experience significant annual losses every year due to a lack of research on flooding in these regions. This study presents a comprehensive analysis of the flood event that occurred on July 16, 2017, in the agricultural area situated in Sindaedong, Heungdeok-gu, Cheongju-si. To achieve this, the EPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) was employed to generate runoff data by rainfall information. The produced runoff data facilitated the identification of flood occurrence points, and the analysis results exhibited a strong correlation with inundation trace maps provided by the Ministry of the Interior and Safety (MOIS). The detailed output of the SWMM model enabled the extraction of time-specific runoff information at each inundation point, allowing for a detailed understanding of the inundation status in the agricultural area over different time frames. This research underscores the significance of utilizing the SWMM model to simulate inundation in agricultural areas, thereby validating the efficacy of flood alerts and risk management plans. In particular, the integration of rainfall data and the SWMM model in flood prediction methodologies is expected to enhance the formulation of preventative measures and response strategies against flood damages in agricultural areas.

Keywords

Acknowledgement

본 연구는 2023년도 농림축산식품부의 재원으로 농림식품기술기획평가원의 지원을 받아 수행된 연구임(322082-3, 농업용수 이용량 절감을 위한 스마트 농업용수 계측 모니터링 및 자동 관개 기술 개발).

References

  1. An H, Jeong A, Kim Y, Noh J. 2018. Development of 2D inundation model based on adaptive cut cell mesh (K-Flood). Journal of Korea Water Resources Association 51:853-862. [in Korean] https://doi.org/10.3741/JKWRA.2018.51.10.853
  2. An SS, Bang NK, Lee JS, Bang SS, Nam WH, Kim HJ. 2022. Simplified analysis of agricultural water network model using SWMM: A case study of Mandae Reservoir. Journal of the Korean Society of Agricultural Engineers 64:27-37. [in Korean]
  3. Cheongju City. 2017. Flood damage white paper. Cheongju City, Cheongju, Korea.
  4. Choi DG, Choi J. 2015. Mapping inundation areas using SWMM. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 33:335-342. [in Korean] https://doi.org/10.7848/KSGPC.2015.33.5.335
  5. Choi J, Jeon J, Kim T, Kim B. 2021. Comparison of inundation patterns of urban inundation model and flood tracking model based on inundation traces. Journal of the Korean Society of Agricultural Engineers 54:71-80. [in Korean]
  6. Chow VT. 1959. Open-channel hydraulics. McGraw-Hill Book Company, New York, USA.
  7. Jang HI. 2009. A study on the causes and types of inland waterlogging in agricultural lands of Chungnam. Master's thesis, Kongju National Univ., Kongju, Korea. [in Korean]
  8. Joo JH, Kim HB. 2022. Rainfall frequency and urban flood damage: Focused on capacity level of districts in Seoul, Korea. Journal of the Korean Regional Development Association 34:121-138. [in Korean] https://doi.org/10.22885/KRDA.2022.34.3.121
  9. Kim H, Lee JH, Park HJ, Heo JH. 2021. Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis. Engineering Geology 294:106372.
  10. KMA (Korea Meteorological Administration). 2017. ASOS Cheongju Weather Observation Station precipitation data. Accessed in https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36 on 5 July 2023. [in Korean]
  11. KMA (Korea Meteorological Administration). 2020. Climate anomaly report. KMA, Daejeon, Korea. [in Korean]
  12. Lee SS, Yang IJ, Lee MH, Noh SJ. 2022. Improving policies for urban inundation management to cope with the climate crisis. Korea Environment Institute, Sejong, Korea. [in Korean]
  13. Lim HS. 2023. Development of real-time forecasting model for urban flooding based on machine learning. Ph.D. dissertation, Chungnam National Univ., Daejeon, Korea. [in Korean]
  14. MAFRA (Ministry of Agriculture, Food and Rural Affairs). 2023. Ministry of Agriculture, Food and Rural Affairs' farm map service. Accessed in https://agis.epis.or.kr/ASD/main/intro.do on 5 July 2023. [in Korean]
  15. MOIS (Ministry of the Interior and Safety). 2017. Flood trace map. Accessed in https://www.bigdata-map.kr/search/55903992 on 5 July 2023. [in Korean]
  16. MOIS (Ministry of the Interior and Safety). 2021. Flood trace map. Accessed in https://safemap.go.kr/opna/data/dataView.do?objtId=212 on 5 July 2023. [in Korean]
  17. Moon C. 2006. Operational status of disaster preventing city plan in Korea. The Korea Spatial Planning Review 51:151-168. [in Korean]
  18. Yeo MH, Nguyen VTV, Kim YS, Kpodonu TA. 2022. An integrated extreme rainfall modeling tool (SDExtreme) for climate change impacts and adaptation. Water Resources Management 36:3153-3179.