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

• 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; Upgrading; plant; functional; types; trait

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.

结合碳和水周期的生态水文模型可以预测植被生长和土壤水库的相关变化，以应对气候和土地利用扰动。 因此，它们对于帮助我们为生态系统和水资源脆弱性做准备至关重要。 这样的模型在确定植被和土壤的反馈方面也起着重要作用，这些反馈可以加速或缓慢气候变化。 但是，由于它们目前代表植物的方式，这些模型中的许多不确定性都是出现的。 这些模型将植物的巨大多样性简化为植物功能类型的典型数量？ （PFTS），每个都有均匀的固定参数集。 尽管PFTS将相关的物种组合在一起，但最近编译的全球植物数据表明，某些植物特征在这些预先指定的PFT组中可能与不同组之间的植物特征差异。 由于这些植物特性会影响植物的水和二氧化碳的吸收，因此本研究将纳入植物特征的变异性中，以改善有关我们不断变化的生态系统，水资源和气候的未来预测。 生态水文模型中固定参数PFT的当前范式需要升级，以与植物特征变异性生态学的新发现保持一致。 拟议的工作提供了一种新的随机方法，可以模拟植物特质可塑性。这些是对复杂的环境驱动因素响应的时间和空间进行的适应，并在PFT内引起可变性。 将基于捕获PFT内变异性分布的全球植物性状数据开发时空随机PFT参数化。 重要的是，该参数化将进一步以时空生物和非生物数据为条件，以填补植物特征数据集中的空白，并严格地解释了将稀疏全局数据应用于这些模型的不确定性。 这种方法标志着与最近的建模工作的新颖性，该工作将特征变异性纳入了时空和时间或无法解决不确定性的确定性输入中的随机参数。 随机PFT参数化将首先用于沙漠灌木丛设置，该设置非常需要新的模型表示，该模型表示可以捕获其植物的温度和水分适应。 使用新模型的模拟将揭示植物特征与环境变量（例如气候和土壤类型）之间的沙漠灌木丛的关系。 在所有PFT中，植物特征的变异性无处不在；因此，本研究中产生的随机参数化方法将使全球生态水文模型受益。