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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-332
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-332
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Jul 2019

Submitted as: research article | 04 Jul 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Improving lake mixing process simulations in the Community Land Model by using K profile parameterization

Qunhui Zhang1, Jiming Jin1,2, Xiaochun Wang3, Phaedra Budy4,2, Nick Barrett2, and Sarah E. Null2 Qunhui Zhang et al.
  • 1College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, Shaanxi 712100, China
  • 2Department of Watershed Sciences, Utah State University, Logan, Utah 84322, USA
  • 3JIFRESSE, University of California, Los Angeles, 90095, USA
  • 4US Geological Survey, Utah Cooperative Fish & Wildlife Research Unit, Logan, Utah 84322, USA

Abstract. We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM assumes that mixing is driven primarily by wind, and it produces large biases in thermal process simulations. We improved the CLM lake model by using KPP, where vertical mixing was driven by winds and surface thermal forcing, the latter representing the net heat flux in the lake boundary layer. We chose an Arctic Alaskan lake to evaluate this improved lake model. Results demonstrated that KPP could reproduce the observed lake mixing and significantly improved lake temperature simulations when compared to the original mixing scheme in CLM. Our newly improved model better represents the transition between stratification and turnover due to surface thermal forcing combined with high winds. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.

Qunhui Zhang et al.
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Short summary
We improved lake mixing process simulations by applying the vertical mixing scheme KPP in CLM. The current mixing scheme in CLM is driven mainly by winds, while KPP considers not only winds but surface thermal forcing as well. Modeling results indicate the WST and lake temperature profile simulations using KPP are greatly improved when compared to the original mixing scheme in CLM. The improved lake model should be useful for reliable lake process predictions and better ecosystem services.
We improved lake mixing process simulations by applying the vertical mixing scheme KPP in CLM....
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