DOI QR코드

DOI QR Code

Classification and Performance Evaluation Methods of an Algal Bloom Model

적조모형의 분류 및 성능평가 기법

  • Cho, Hong-Yeon (Coastal Engineering Research Division, Korea Institute of Ocean Science and Technology) ;
  • Cho, Beom Jun (Coastal Engineering Research Division, Korea Institute of Ocean Science and Technology)
  • 조홍연 (한국해양과학기술원 연안공학연구본부) ;
  • 조범준 (한국해양과학기술원 연안공학연구본부)
  • Received : 2014.11.26
  • Accepted : 2014.12.29
  • Published : 2014.12.31

Abstract

A number of algal bloom models (red-tide models) have been developed and applied to simulate the redtide growth and decline patterns as the interest on the phytoplankton blooms has been continuously increased. The quantitative error analysis of the model is of great importance because the accurate prediction of the red-tide occurrence and transport pattern can be used to setup the effective mitigations and counter-measures on the coastal ecosystem, aquaculture and fisheries damages. The word "red-tide model" is widely used without any clear definitions and references. It makes the comparative evaluation of the ecological models difficult and confusable. It is highly required to do the performance test of the red-tide models based on the suitable classification and appropriate error analysis because model structures are different even though the same/similar words (e.g., red-tide, algal bloom, phytoplankton growth, ecological or ecosystem models) are used. Thus, the references on the model classification are suggested and the advantage and disadvantage of the models are also suggested. The processes and methods on the performance test (quantitative error analysis) are recommend to the practical use of the red-tide model in the coastal seas. It is suggested in each stage of the modeling procedures, such as verification, calibration, validation, and application steps. These suggested references and methods can be attributed to the effective/efficient marine policy decision and the coastal ecosystem management plan setup considering the red-tide and/or ecological models uncertainty.

적조에 관한 관심이 고조되면서 적조모형 개발에 관한 연구가 활발하게 지속적으로 추진되어 다양한 적조모형이 개발 적용되고 있는 실정이다. 적조는 연안생태환경 및 양식어업에 직접적인 피해를 주며, 모형을 이용한 적조 발생 및 이동양상의 정확한 예측결과는 사전 피해저감 대책수립 및 관리에 활용할 수 있기 때문에 모형의 예측 성능평가도 중요하다. 그러나 적조모형은 적조생물을 포함하는 생태모형 또는 환경생태모형이라는 유사한 용어가 명확한 구분 없이 사용되고 있어 혼동을 유발하고 있다. 각각의 모형은 유사한 용어를 사용한다 할지라도 모형의 개발 목표, 구조와 예측성능에서 차이가 나기 때문에 적절한 기준에 근거한 분류와 오차분석에 근거한 모형의 성능평가가 요구된다. 따라서 본 연구에서는 다양한 적조 모형의 구조 및 요소모형을 고려한 분류기준을 제시하고, 분류된 적조모형의 구조를 고려한 장 단점 및 한계를 제시하고, 모형 활용과정에서 실질적으로 가장 중요한 정량적인 성능평가(오차평가) 기법을 제안한다. 모형의 성능평가는 모형의 보정 및 검정과정으로 분류하여 제시하였다. 제시된 기준이나 기법은 모형의 불확실성을 고려한 정책결정 및 대책수립, 환경관리에 기여할 것으로 판단된다.

Keywords

References

  1. Baretta, J.W., W. Ebenhoh, and P. Ruardij. 1995. The European regional seas ecosystem model, a complex marine ecosystem model, Netherlands Journal of Sea Research, 33(3/4): 233-246. https://doi.org/10.1016/0077-7579(95)90047-0
  2. Beck, M.B. 1983. Uncertainty, system identification, and the prediction of water quality, 3-68, Uncertainty and Forecasting of Water Quality (Editors : Beck, M.B. and van Straten, G.)
  3. Blauw, A.N., H.F.J. Los, M. Bokhorst, and P.L.A. Erftemeijer. 2009. GEM: a general ecological model for estuaries and coastal waters, Hydrobiologia, 618: 175-198. https://doi.org/10.1007/s10750-008-9575-x
  4. Box, G.E.P., G.M. Jenkins, and G.C. Reinsel. 2008. Time Series Analysis, Forecasting and Control, Fourth Edition, Sec. 1.3, John Wiley & Sons.
  5. Bowie, G.L., W.B. Mills, D.B. Porcella, C.L. Campbell, J.R. Pagenkopf, G.L. Rupp, K.M. Johnson, P.W.H. Chan, S.A. Gherini, C.E. Chamberlin, and B.O. Barnwell. 1985. Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling, Second Edition, Environmental Research Lab., EPA/600/3-85/040. US. EPA.
  6. Castellani, M., R. Rosland, A. Uritzberea, and O. Fiksen. 2013. A mass-balance pelagic ecosystem model with size-structured behaviourally adaptive zooplankton and fish, Ecological Modelling, 251: 54-63. https://doi.org/10.1016/j.ecolmodel.2012.12.007
  7. Cho, B.J., H.Y. Cho, and S. Kim, 2014. 8. Outlier detection and treatment for the conversion of chemical oxygen demand to total organic carbon, Korean Society of Coastal and Ocean Engineers, 26(4): (in Korean) https://doi.org/10.9765/KSCOE.2014.26.4.207
  8. Dowd, M. 2006. A sequential Monte Carlo approach for marine ecological prediction, Environmetrics, 17: 435-455. https://doi.org/10.1002/env.780
  9. Edwards, A.M., and J. Brindley. 1996. Oscillatory behaviour in a three-component plankton population model, Dynamics and Stability of Systems, 11(4): 347-370. https://doi.org/10.1080/02681119608806231
  10. Eykhoff, P. 1974. System Identification, Parameter and State Estimation, Chap. 1, John Wiley & Sons.
  11. Franks, P.J.S. 2002. NPZ models of plankton dynamics: Their construction, coupling to physics, and application, Review, J. of Oceanography, 58: 379-387. https://doi.org/10.1023/A:1015874028196
  12. Glover, D.M., W.J. Jenkins, and S.C. Doney. 2011. Modeling Methods for Marine Science, Cambridge University Press.
  13. Hai, D-N, N-N. Lam, and J.W. Dippner. 2010. Development of Phaeocystis globosa blooms in the upwelling water of the South Central coast of Viet Nam, J. of Marine Systems, 83(3-4): 253-262. https://doi.org/10.1016/j.jmarsys.2010.04.015
  14. Jorgensen, S.E., and G. Bendoricchio. 2001. Fundamentals of Ecological Modeling, Third Edition, Chap. 2, Elsevier.
  15. Kawamiya, M., M. Kishi, Y. Yamanake, and N. Suginohara. 1995. An ecological-physical coupled model applied to Station Para, J. of Oceanography, 51: 635-664. https://doi.org/10.1007/BF02235457
  16. Kishi, M.J., S. Ito, B. Megrey, K.A. Rose, and F.E. Werner. 2011. A review of the NEMURO and NEMURO.FISH models and their application to marine ecosystem investigations, Review, J. of Oceanography, 67:3-16. https://doi.org/10.1007/s10872-011-0009-4
  17. Lee, D.I., J-M. Choi, Y-G. Lee, M-O. Lee, W-C. Lee, and J-K. Kim. 2008. Coastal environmental assessment and management by ecological simulation in Yeoja Bay, Korea, Estuarine, Coastal and Shelf Science, 80: 495-508. https://doi.org/10.1016/j.ecss.2008.08.022
  18. Lenes, J.M., J.J. Wlash, and B.P. Darrow. 2013. Simulating cell death in the termination of Karenia brevis blooms: implications for predicting aerosol toxity vectors to humans, Marine Ecology Progress Series, 493: 71-81. https://doi.org/10.3354/meps10515
  19. Li, H. and J. Wu. 2006. Uncertainty analysis in ecological studies: An overview, 45-66, Scaling and Uncertainty Analysis in Ecology: Methods and Applications (Editors: Wu, J., Jones, K.B., Li, H. and Loucks, O.L.). Springer.
  20. Mandal, S., S. Ray, and P.B. Ghosh. 2012. Modeling nutrient (dissolved inorganic nitrogen) and plankton dynamics at Sagar Island of Hooghly-Malta estuarine system, West Bengal, India, Natural Resource Modeling, 25(4): 629-652. https://doi.org/10.1111/j.1939-7445.2011.00116.x
  21. Mann, K.H., and J.R.N. Lazier. 2006. Dynamics of Marine Ecosystems, Biological-Physical Interactions in the Oceans, Third Edition, Blackwell Pub.
  22. Martin, J.L., and S.C. McCutcheon. 1999. Hydrodynamics and Transport for Water Quality Modeling, Part IV, Lewis Publishers.
  23. National Fisheries Research and Development Institute, 2002. Handbook of Marine Harmful Algal Blooms in Korean Waters. (in Korean)
  24. Robson, B.J., and D.P. Hamilton. 2004. Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia, Ecological Modeling, 174: 203-222 https://doi.org/10.1016/j.ecolmodel.2004.01.006
  25. Son, J.W., Y.C., Park, and H.J. Lee. 2003. Characteristics of Total Organic Carbon and Chemical Oxygen demand in the Coastal Waters of Korea, The Sea, J. of the Korean Society of Oceanography, 8(3):317-326 (in Korean).
  26. Sun, K., Z. Qiu, Y. He, and B. Yin. 2014. Nutrient-controlled growth of Skeletonema costatum: an applied model, Chinese Journal of Oceanology and Limnology, 32(3): 608-625. https://doi.org/10.1007/s00343-014-3201-8
  27. Velo-Suarez, L., B. Reguera, S. Gonzalez-Gil, M. Lunven, P. Lazure, E. Nezan, and P. Gentien. 2010. Application of a 3D Lagrangian model to explain the decline of a Dinophysis acuminita bloom in the Bay of Biscay, J. of Marine Systems, 83(3-4): 242-252. https://doi.org/10.1016/j.jmarsys.2010.05.011