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A Three-Dimensional Coupled Hydrodynamic-Ecological Modeling to Assess the Planktonic Biomass in a Subalpine Lake

Author

Listed:
  • Wen-Cheng Liu

    (Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 360302, Taiwan)

  • Hong-Ming Liu

    (Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 360302, Taiwan)

  • Rita Sau-Wai Yam

    (Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan)

Abstract

In this study, a coupled three-dimensional hydrodynamic-ecological model was developed to comprehensively understand the interaction between the hydrodynamics and ecological status of a lake. The coupled model was utilized to explore the hydrodynamics, water quality, and ecological status in an ecologically rich subalpine lake (i.e., Tsuei-Feng Lake (TFL), located in north-central Taiwan). The measured data of water depth, water temperature, water quality, and planktonic biomass were gathered to validate the coupled model. The simulated results with a three-dimensional hydrodynamic and water quality-ecological model reasonably reproduced the variations in observed water depth, water temperature, water quality, and phytoplankton and zooplankton biomass. Sensitivity analysis was implemented to determine the most influential parameter affecting the planktonic biomass. The results of sensitivity analysis indicated that the predation rate on phytoplankton (PRP) significantly affects the phytoplankton biomass, while the basal metabolism rate of zooplankton (BMZ) importantly affects the zooplankton biomass. Furthermore, inflow discharge was the most important environmental factor dominating the phytoplankton and zooplankton biomass of TFL. This implies that the runoff in the catchment area caused by rainfall and the heavy rainfall induced by climate change may affect the planktonic biomass of the lake.

Suggested Citation

  • Wen-Cheng Liu & Hong-Ming Liu & Rita Sau-Wai Yam, 2021. "A Three-Dimensional Coupled Hydrodynamic-Ecological Modeling to Assess the Planktonic Biomass in a Subalpine Lake," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12377-:d:675441
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    1. Kerimoglu, Onur & Jacquet, Stéphan & Vinçon-Leite, Brigitte & Lemaire, Bruno J. & Rimet, Frédéric & Soulignac, Frédéric & Trévisan, Dominique & Anneville, Orlane, 2017. "Modelling the plankton groups of the deep, peri-alpine Lake Bourget," Ecological Modelling, Elsevier, vol. 359(C), pages 415-433.
    2. Gao, Hailong & Shi, Qianyun & Qian, Xin, 2017. "A multi-species modelling approach to select appropriate submerged macrophyte species for ecological restoration in Gonghu Bay, Lake Taihu, China," Ecological Modelling, Elsevier, vol. 360(C), pages 179-188.
    3. Li, Yuzhao & Liu, Yong & Zhao, Lei & Hastings, Alan & Guo, Huaicheng, 2015. "Exploring change of internal nutrients cycling in a shallow lake: A dynamic nutrient driven phytoplankton model," Ecological Modelling, Elsevier, vol. 313(C), pages 137-148.
    4. Gal, G. & Hipsey, M.R. & Parparov, A. & Wagner, U. & Makler, V. & Zohary, T., 2009. "Implementation of ecological modeling as an effective management and investigation tool: Lake Kinneret as a case study," Ecological Modelling, Elsevier, vol. 220(13), pages 1697-1718.
    5. Dou, Ming & Ma, Xiaokuan & Zhang, Yan & Zhang, Yongyong & Shi, Yaxin, 2019. "Modeling the interaction of light and nutrients as factors driving lake eutrophication," Ecological Modelling, Elsevier, vol. 400(C), pages 41-52.
    6. Fenocchi, Andrea & Rogora, Michela & Morabito, Giuseppe & Marchetto, Aldo & Sibilla, Stefano & Dresti, Claudia, 2019. "Applicability of a one-dimensional coupled ecological-hydrodynamic numerical model to future projections in a very deep large lake (Lake Maggiore, Northern Italy/Southern Switzerland)," Ecological Modelling, Elsevier, vol. 392(C), pages 38-51.
    7. Snortheim, Craig A. & Hanson, Paul C. & McMahon, Katherine D. & Read, Jordan S. & Carey, Cayelan C. & Dugan, Hilary A., 2017. "Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake," Ecological Modelling, Elsevier, vol. 343(C), pages 39-53.
    8. Khangaonkar, Tarang & Nugraha, Adi & Premathilake, Lakshitha & Keister, Julie & Borde, Amy, 2021. "Projections of algae, eelgrass, and zooplankton ecological interactions in the inner Salish Sea – for future climate, and altered oceanic states," Ecological Modelling, Elsevier, vol. 441(C).
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    1. Theng, Vouchlay & Sith, Ratino & Uk, Sovannara & Yoshimura, Chihiro, 2023. "Phytoplankton productivity in a tropical lake-floodplain system revealed by a process-based primary production model," Ecological Modelling, Elsevier, vol. 479(C).

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