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Upgrading plant-functional-types with plant trait variability in ecohydrological models: A stochastic parameterization approach

在生态水文模型中利用植物性状变异升级植物功能类型：随机参数化方法

基本信息

批准号：
1724781
负责人：
Gene-Hua Ng
金额：
$ 35.14万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724781&HistoricalAwards=false
关键词：
Upgrading plant functional types trait

项目摘要

Ecohydrological models that incorporate carbon and water cycles can predict related changes in vegetation growth and soil water reservoirs in response to climate and land-use perturbations. They are thus critical for helping us prepare for ecosystem and water resource vulnerabilities. Such models also play an important role in determining feedbacks from vegetation and soils that can accelerate or slow climate change. However, much of the uncertainty in these models arises because of how they currently represent plants. These models simplify the enormous diversity of plants into a tractable number of ?plant-functional-types? (PFTs), each of which have uniform, fixed sets of parameters applied to them. Although PFTs group together related species with similar characteristics, recently compiled global plant data reveal that certain plant traits can vary just as much within these pre-specified PFT groups as between distinct groups. Because these plant properties can affect plant uptake of water and CO2, this study will incorporate plant trait variability into ecohydrological models in order to improve future predictions about our changing ecosystems, water resources, and climate. The current paradigm of fixed-parameter PFTs in ecohydrological models needs upgrading to align with new findings in ecology on plant trait variability. The proposed work offers a new stochastic approach that simulates plant trait plasticity; these are adaptations that occur over time and space in response to complex environmental drivers and give rise to variability within PFTs. A spatiotemporally stochastic PFT parameterization will be developed based on global plant trait data that capture distributions of intra-PFT variability. Importantly, the parameterization will be further conditioned on spatiotemporal biotic and abiotic data to fill gaps in the plant trait datasets and rigorously account for uncertainties in applying sparse global data to these models. This approach marks a novel departure from recent modeling efforts that incorporate trait variability as random parameters fixed in space and time or as deterministic inputs that fail to address uncertainties. The stochastic PFT parameterization will be first developed for a desert shrubland setting, which critically needs a new model representation that can capture temperature and moisture acclimation by its plants. Simulations with the new model will reveal relationships in desert shrublands between plant traits and environmental variables such as climate and soil type. Plant trait variability is ubiquitous among all PFTs; the stochastic parameterization approach generated in this study will thus benefit ecohydrological modeling globally.

包含碳和水循环的生态水文模型可以预测植被生长和土壤水库对气候和土地利用扰动的相关变化。因此，它们对于帮助我们为生态系统和水资源脆弱性做好准备至关重要。这种模式在确定来自植被和土壤的反馈方面也发挥着重要作用，这些反馈可以加速或减缓气候变化。然而，这些模型中的许多不确定性源于它们目前如何表示植物。这些模型将植物的巨大多样性简化为可处理的“植物功能类型”。（pft），每一个都有统一的、固定的参数集应用于它们。尽管PFT将具有相似特征的相关物种组合在一起，但最近汇编的全球植物数据显示，在这些预先指定的PFT群体中，某些植物性状的差异与不同群体之间的差异一样大。由于这些植物特性会影响植物对水和二氧化碳的吸收，本研究将把植物性状变异纳入生态水文模型，以改善未来对生态系统、水资源和气候变化的预测。当前生态水文模型中固定参数PFTs的范式需要升级，以适应生态学中关于植物性状变异的新发现。提出了一种新的模拟植物性状可塑性的随机方法；这些适应是随着时间和空间的变化而发生的，是对复杂的环境驱动因素的响应，并导致pft内部的变异性。基于捕获PFT内部变异分布的全球植物性状数据，将开发时空随机PFT参数化。重要的是，参数化将进一步以时空生物和非生物数据为条件，以填补植物性状数据集的空白，并严格考虑将稀疏全局数据应用于这些模型的不确定性。这种方法标志着对最近的建模工作的一种新颖的背离，这些建模工作将特征变异性作为固定在空间和时间中的随机参数或作为无法解决不确定性的确定性输入。随机PFT参数化将首先针对荒漠灌木环境进行开发，该环境迫切需要一种新的模型表示，能够捕捉其植物对温度和湿度的适应。利用新模型进行的模拟将揭示荒漠灌木地植物性状与气候和土壤类型等环境变量之间的关系。植物性状变异在所有pft中普遍存在；因此，本研究中产生的随机参数化方法将有利于全球生态水文建模。