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<p>Plants can alter their physiology through modifying root growth and architecture to adapt to water-limited environments. However, current climate models do not fully incorporate these physiological regulatory processes, leading to uncertainties and inaccuracies in climate projections. Here, we integrate a novel root water uptake scheme into the Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM), enabling dynamic root water uptake through hydrotropic growth to replenish plant water storage. We conduct global offline simulations during 1981–2014 using three meteorological forcing datasets and assess the model performance in simulating soil moisture (SM), gross primary productivity (GPP), latent heat flux (LE), and total runoff (RF). Our findings reveal that the dynamic root scheme significantly enhances SM and RF simulations across diverse geographical regions, while also improving GPP and LE estimates in tropical forests such as the Amazon. Moreover, incorporating the dynamic root process into BCC_AVIM benefits longstanding challenges in land-surface/climate modelling such as the underestimation of transpiration-to-evapotranspiration ratio and rain use efficiency under water stress. However, uncertainties persist, stemming from reference data, meteorological forcing, and parameter constraints. We recommend parameter optimization using time series of root traits as a foundational step toward enhancing the robustness and portability of the dynamic root scheme. Additionally, incorporating optimal stomatal conductance and plant hydraulic schemes into future models is essential to refine the representation of the hydrological cycle between land surface and atmosphere.</p>

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Implementation and evaluation of a dynamic root water uptake scheme in the Beijing climate center atmosphere-vegetation interaction model

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • Luyao Yang,
  • Jianduo Li,
  • Yanwu Zhang,
  • Ping Zhao,
  • Weiping Li,
  • Tongwen Wu,
  • Guo Zhang

摘要

Plants can alter their physiology through modifying root growth and architecture to adapt to water-limited environments. However, current climate models do not fully incorporate these physiological regulatory processes, leading to uncertainties and inaccuracies in climate projections. Here, we integrate a novel root water uptake scheme into the Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM), enabling dynamic root water uptake through hydrotropic growth to replenish plant water storage. We conduct global offline simulations during 1981–2014 using three meteorological forcing datasets and assess the model performance in simulating soil moisture (SM), gross primary productivity (GPP), latent heat flux (LE), and total runoff (RF). Our findings reveal that the dynamic root scheme significantly enhances SM and RF simulations across diverse geographical regions, while also improving GPP and LE estimates in tropical forests such as the Amazon. Moreover, incorporating the dynamic root process into BCC_AVIM benefits longstanding challenges in land-surface/climate modelling such as the underestimation of transpiration-to-evapotranspiration ratio and rain use efficiency under water stress. However, uncertainties persist, stemming from reference data, meteorological forcing, and parameter constraints. We recommend parameter optimization using time series of root traits as a foundational step toward enhancing the robustness and portability of the dynamic root scheme. Additionally, incorporating optimal stomatal conductance and plant hydraulic schemes into future models is essential to refine the representation of the hydrological cycle between land surface and atmosphere.