Preprints
https://doi.org/10.5194/hess-2020-671
https://doi.org/10.5194/hess-2020-671

  13 Jan 2021

13 Jan 2021

Review status: this preprint is currently under review for the journal HESS.

Plant Hydraulic Transport Controls Transpiration Response to Soil Water Stress

Brandon P. Sloan1,2, Sally E. Thompson3, and Xue Feng1,2 Brandon P. Sloan et al.
  • 1Department of Civil Environmental and Geo-Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455
  • 2Saint Anthony Falls Laboratory, University of Minnesota - Twin Cities, Minneapolis, MN 55455
  • 3Department of Civil, Environmental and Mining Engineering, University of Western Australia, Perth, Australia

Abstract. Plant transpiration downregulation in the presence of soil water stress is a critical mechanism for predicting global water, carbon, and energy cycles. Currently, many terrestrial biosphere models (TBMs) represent this mechanism with an empirical correction function (β) of soil moisture – a convenient approach that can produce large prediction uncertainties. To reduce this uncertainty, TBMs have increasingly incorporated physically-based Plant Hydraulic Models (PHMs). However, PHMs introduce additional parameter uncertainty and computational demands. Therefore, understanding why and when PHM and β predictions diverge would usefully inform model selection within TBMs. Here, we use a minimalist PHM to demonstrate that coupling the effects of soil water stress and atmospheric moisture demand leads to a spectrum of transpiration response controlled by soil-plant hydraulic transport (conductance). Within this transport-limitation spectrum, β emerges as an end-member scenario of PHMs with infinite conductance, completely decoupling the effects of soil water stress and atmospheric moisture demand on transpiration. As a result, PHM and β transpiration predictions diverge most when conductance is low (transport-limited), atmospheric moisture demand variation is high, and soil moisture is moderately available to plants. We apply these minimalist model results to land surface modeling of an Ameriflux site. At this transport-limited site, a PHM downregulation scheme outperforms the β scheme due to its sensitivity to variations in atmospheric moisture demand. Based on this observation, we develop a new dynamic β that varies with atmospheric moisture demand – an approach that balances realism with parsimony and overcomes existing biases within β schemes.

Brandon P. Sloan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-671', Anonymous Referee #1, 14 Jan 2021
    • AC1: 'Reply to RC1', Brandon Sloan, 22 Mar 2021
  • RC2: 'Review on hess-2020-671', Anonymous Referee #2, 15 Jan 2021
    • AC2: 'Reply to RC2', Brandon Sloan, 22 Mar 2021
  • RC3: 'Comment on hess-2020-671', Stefano Manzoni, 27 Jan 2021
    • AC3: 'Reply to RC3', Brandon Sloan, 22 Mar 2021

Brandon P. Sloan et al.

Brandon P. Sloan et al.

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Short summary
Plants affect the global water and carbon cycles by modifying their water use and carbon intake in response to soil moisture. Global climate models represent this response with either simple empirical models or complex physical models. We reveal that the latter improves predictions in plants with large flow resistance; however, adding dependence on atmospheric moisture demand to the former matches performance of the latter, leading to a new tool for improving carbon and water cycle predictions.