한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제36권1호
  • /
  • Pages.117-128
  • /
  • 2003
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

비점원오염모델을 이용한 오염총량모의시스템의 개발 및 적용

Development and Application of Total Maximum Daily Loads Simulation System Using Nonpoint Source Pollution Model

  • 강문성 (서울대학교 농업생명과학연구원) ;
  • 박승우 (서울대학교 농업생명과학대학 생물자원공학부)
  • 발행 : 2003.02.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 소유역에서의 오염총량을 추정하기 위하여 위성영상 카테고리분류 인공신경망 모형과 지리정보시스템 기반의 오염총량모의시스템(Total maximum daily Loads simulation System, TOLOS)을 개발하였으며, 발안유역의 HP#6 소유역을 시험유역으로 선정하여 유역 수문·수질 모니터링을 수행하였고, 시험유역의 도형 자료를 구축하여 TOLOS의 적용성을 평가하였다. TOLOS의 오염총량추정 모듈인 SWAT 모형은 논에서의 지표배수량을 고려하여 구성하였다. TOLOS을 이용하여 일별 측정 자료인 유출량, 유사량, 그리고 영양물질에 대하여 SWAT 모형의 보정과 검정을 실시하였으며, 그 결과 적용 가능성이 있는 것으로 나타났다.

The objectives of this study are to develop the total maximum daily loads simulation system, TOLOS that is capable of estimating annual nonpoint source pollution from small watersheds, to monitor the hydrology and water quality of the Balkan HP#6 watershed, and to validate TOLOS with the field data. TOLOS consists of three subsystems: the input data processor based on a geographic information system, the models, and the post processor. Land use pattern at the tested watershed was classified from the Landsat TM data using the artificial neutral network model that adopts an error back propagation algorithm. Paddy field components were added to SWAT model to simulate water balance at irrigated paddy blocks. SWAT model parameters were obtained from the GIS data base, and additional parameters calibrated with field data. TOLOS was then tested with ungauged conditions. The simulated runoff was reasonably good as compared with the observed data. And simulated water quality parameters appear to be reasonably comparable to the field data.

키워드

참고문헌

  1. 강문성, 박승우 (2001). '인공신경망 이론을 이용한 소유역에서의 장기유출해석' 한국농공학회지, 제43권, 제2호, pp. 69-77
  2. 권순국 (1998). '우리 나라 비점원 수질오염 관리의 문제점과 개선방안' 대한환경농학회지, 제20권, 제1호, pp. 1497-1510
  3. 김지훈, 홍성구, 권순국 (1998). '지리정보시스템을 이용한 SWAT모형의 적용' 한국농공학회지, 제40권, 제4호, pp. 67-76
  4. 농림부 (2000). '농업생태환경 모니터링 및 종합적 환경관리시스템 개발 사업'
  5. 윤춘경, 전지홍 (2001). '오염총량제와 BASINS에 의한 유역오염부하 산정 검토' 한국농촌환경연구회, 서울대 농업개발연구소, 비점원오염에 관한 한. 일 국제SYMPOSIUM, pp. 28-36
  6. 환경부 (1999a). '오염총량관리계획수립지침' 환경부고시, 제1999-143호
  7. 환경부 (2000). '비점오염원 관리 요령'
  8. 환경부 (2000). '팔당상수원 비점오염원 최적관리사업'
  9. Diluzio, M., Srinivasan, R., and Arnold, J. (2001). 'ArcView Interface for SWAT2000' Texas Agricultural Experiment Station, Temple, Texas
  10. Jackson, T. J., Ragan, R. M., and Fitch, W. N. (1977). 'Test of Landsat-Based Urban Hydrologic Modeling' J. Water Resour. Plann. Manage. Div., ASCE 103 (WR1), pp. 141-158
  11. Jensen, J. (1996). 'Introductory Digital Image Processing : A Remote Sensing Perspective' Englewood Cliffs, New Jersey, Prentice- Hall
  12. Maidment, David R. et al. (1992). 'Handbook of Hydrology: Chap. 24 Remote Sensing' McGraw-Hill
  13. Neitsch, S. L., Arnold, J. G., Kiniry, J. G., and Williams, J. R. (2001) 'Soil and Water Assesment tol, Theoretical Documentation,' Version 2000, USDA ARS, Temple, Texax
  14. Novotny, V. and Olem, H. (1993). 'Water Quality, Prevention, Identification, and Management of Diffuse Pollution', Van Nostrand Reinhold, New York
  15. Paola, J. D. and Schowengerdt, R. A.( 1995). 'A Detailed Comparison of Backpropagation Neural Network and Maximum-Likelihood Classifiers for Urban Land Use Classification' IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 33, No. 4, pp: 981-996 https://doi.org/10.1109/36.406684
  16. USEPA (1992) 'Compendium of Watershed Models for TMDL Development', EPA 841-R-94-002
  17. USEPA (1998). 'Report of the Federal Advisory Committee on the Total Maximum Daily Load (TMDL) Program'
  18. USEPA (1999). 'Protocol for Developing Nutrient TMDLs' EPA 841-B-99-007
  19. USEPA (2001) 'Better Assessment Science Interating Point and Nonpoint Source User's Mannual'

피인용 문헌

  1. Simulation on stream flow and nutrient loadings in Gucheng Lake, Low Yangtze River Basin, based on SWAT model vol.208, pp.1-2, 2009, https://doi.org/10.1016/j.quaint.2008.12.018
  2. Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network vol.53, 2014, https://doi.org/10.1016/j.measurement.2014.03.044
  3. Applying SWAT for TMDL programs to a small watershed containing rice paddy fields vol.79, pp.1, 2006, https://doi.org/10.1016/j.agwat.2005.02.015
  4. Development of agricultural reservoir water supply simulation system vol.20, pp.2, 2014, https://doi.org/10.7851/ksrp.2014.20.2.103
  5. Auto-calibration for the SWAT Model Hydrological Parameters Using Multi-objective Optimization Method vol.51, pp.1, 2009, https://doi.org/10.5389/KSAE.2009.51.1.001
  6. Application and Effectiveness Analysis of SWAT Filter Strip in Golji Watershed vol.33, pp.1, 2014, https://doi.org/10.5338/KJEA.2014.33.1.30
  7. Estimating design floods based on the critical storm duration for small watersheds vol.7, pp.3, 2013, https://doi.org/10.1016/j.jher.2013.01.003
  8. Development of a Component-Based Modeling Framework for Agricultural Water-Resource Management vol.8, pp.8, 2016, https://doi.org/10.3390/w8080351
  9. Simulating the Effects of Agricultural Management on Water Quality Dynamics in Rice Paddies for Sustainable Rice Production—Model Development and Validation vol.9, pp.11, 2017, https://doi.org/10.3390/w9110869
  10. Overview of Remote Sensing and GIS Uses in Watershed and TMDL Analyses vol.24, pp.4, 2019, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001742