A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity
A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity
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DOI：
10.1016/j.agrformet.2019.05.005
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发表时间：
2019-08
影响因子：
6.2
通讯作者：
Shaoqing Liu;G. Ng
中科院分区：
文献类型：
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作者：
Shaoqing Liu;G. Ng
最近的研究已经开始将空间变异的植物性状纳入生态水文学模型，但时间性状变异性的研究仍然不足。由于其影响生态系统功能的潜力，将压力引起的时间性状变异表示到模型中应该是研究的重点。我们提出了一种新的数据模型集成方法来识别植物性状的时间变异性并生成随机时间模型参数化。数据条件随机参数化是在 CLM 4.5 模型中开发的，利用全局性状数据作为先验信息，并在沙漠灌木丛地点进行了测试。综合实验表明，该框架成功地揭示了随时间变化的特征值。通过现场生态水文观测，我们发现常见阔叶常绿灌木的比叶面积 (SLA) 具有时间动态性，并且与季节性可用水量显着相关。我们基于数据调节的 SLA 估计和土壤湿度构建了一个回归模型，并用它来生成 40 年事后模拟的随机 SLA 参数。与标准静态参数相比，时间随机 SLA 参数可带来更高的生产率和用水效率。我们的时间随机方法可以帮助评估胁迫引起的性状可塑性，从而扩展我们对稀疏空间植物性状数据库的理解，并提高我们模拟全球变化下碳和水通量的能力。
Recent studies have begun to incorporate spatially variable plant traits into ecohydrological models, but temporal trait variability remains under-studied. Because of its potential to influence ecosystem function, representing stress-induced temporal trait variability into models should be a research priority. We present a new data-model integration approach to identify temporal variability in plant traits and generate stochastic-in-time model parameterizations. The data-conditioned stochastic parameterization was developed within the CLM 4.5 model utilizing global trait data as prior information and tested for a desert shrubland site. A synthetic experiment demonstrated that the framework successfully uncovered time-varying trait values. Using in-situ ecohydrological observations, we found the specific leaf area (SLA) for a common broadleaf-evergreen-shrub to be temporally dynamic and significantly correlated with seasonal water availability. We constructed a regression model based on the data-conditioned SLA estimates and soil wetness and used it to generate stochastic SLA parameters for a 40-year hindcast simulation. The stochastic-in-time SLA parameters resulted in greater productivity and water use efficiency than a standard static parameter. Our stochastic-in-time method can help evaluate stress-induced trait plasticity that extends our understanding beyond sparse spatial plant trait database and improve our ability to simulate carbon and water fluxes under global change.