IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p13999-d955296.html
   My bibliography  Save this article

A Study on Identifying Priority Management Areas and Implementing Best Management Practice for Effective Management of Nonpoint Source Pollution in a Rural Watershed, Korea

Author

Listed:
  • Jinsun Kim

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Jiyeon Choi

    (Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)

  • Minji Park

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Joong-Hyuk Min

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Jong Mun Lee

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Jimin Lee

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Eun Hye Na

    (National Institute of Environmental Research, Incheon 22689, Korea)

  • Heeseon Jang

    (National Institute of Environmental Research, Incheon 22689, Korea)

Abstract

It is difficult to accurately identify and manage the paths of nonpoint source (NPS) pollution in rural watersheds because their discharge patterns vary depending on season, region, and agricultural characteristics. In this study, flow and water quality during rainfall events were monitored in Songya watershed, an impaired, rural area in South Korea. A method of identifying priority management areas was proposed through scientific objectification and quantification of key factors controlling NPS, such as land use, agricultural type, and load. For the load calculation, a watershed model was developed using Hydrological Simulation Program Fortran (HSPF). Three priority management areas—Mulhan Stream, Osan Stream, and the upstream area of Songya Stream—were selected. Using the developed model, constructed wetlands with the capacity of 1000 m 3 were applied at the lower reach of each priority management subbasin and the impacts on NPS pollution reduction were tested. The simulated results showed that BOD and TP concentrations at the outlet of Songya watershed were lowered by 9.2% and 6.0%, respectively. It is expected that the method proposed in this study for identifying priority management areas and implementing best management practice in agricultural watersheds can be applied to similar areas which struggled with NPS pollution.

Suggested Citation

  • Jinsun Kim & Jiyeon Choi & Minji Park & Joong-Hyuk Min & Jong Mun Lee & Jimin Lee & Eun Hye Na & Heeseon Jang, 2022. "A Study on Identifying Priority Management Areas and Implementing Best Management Practice for Effective Management of Nonpoint Source Pollution in a Rural Watershed, Korea," Sustainability, MDPI, vol. 14(21), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13999-:d:955296
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/13999/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/13999/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maugis, C. & Celeux, G. & Martin-Magniette, M.-L., 2009. "Variable selection in model-based clustering: A general variable role modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3872-3882, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maugis, C. & Celeux, G. & Martin-Magniette, M.-L., 2011. "Variable selection in model-based discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1374-1387, November.
    2. Rendon Aguirre, Janeth Carolina, 2017. "Clustering Big Data by Extreme Kurtosis Projections," DES - Working Papers. Statistics and Econometrics. WS 24522, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
    4. Anzanello, Michel J. & Fogliatto, Flavio S., 2011. "Selecting the best clustering variables for grouping mass-customized products involving workers' learning," International Journal of Production Economics, Elsevier, vol. 130(2), pages 268-276, April.
    5. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
    6. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
    7. Nema Dean & Rebecca Nugent, 2013. "Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 339-357, September.
    8. Cristina Tortora & Paul D. McNicholas & Ryan P. Browne, 2016. "A mixture of generalized hyperbolic factor analyzers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 423-440, December.
    9. C. Biernacki & J. Jacques & C. Keribin, 2023. "A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 332-381, July.
    10. Matthieu Marbac & Mohammed Sedki & Tienne Patin, 2020. "Variable Selection for Mixed Data Clustering: Application in Human Population Genomics," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 124-142, April.
    11. Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
    12. Cappozzo, Andrea & Greselin, Francesca & Murphy, Thomas Brendan, 2021. "Robust variable selection for model-based learning in presence of adulteration," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    13. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
    14. Léna CAREL & Pierre ALQUIER, 2017. "Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm," Working Papers 2017-38, Center for Research in Economics and Statistics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13999-:d:955296. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.