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.

结合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因气候和土地利用扰动而发生的相关变化。因此，它们对于帮助我们应对生态系统和水资源脆弱性至关重要。这些模型在确定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，这些模型的大部分不确定性是由于它们目前代表植物的方式而产生的。这些模型将植物的巨大多样性简化为易于处理的“植物功能类型”。（PFT），每个都应用了统一的、固定的参数集。尽管 PFT 将具有相似特征的相关物种归为一类，但最近编制的全球植物数据表明，某些植物性状在这些预先指定的 PFT 组内与不同组之间的差异一样大。由于这些植物特性会影响植物对水和二氧化碳的吸收，因此本研究将把植物性状变异纳入生态水文学模型，以改进未来对不断变化的生态系统、水资源和气候的预测。当前生态水文模型中固定参数 PFT 的范式需要升级，以符合生态学关于植物性状变异的新发现。拟议的工作提供了一种模拟植物性状可塑性的新随机方法；这些是随着时间和空间发生的适应复杂环境驱动因素的变化，并导致 PFT 内的变异性。将根据捕获 PFT 内变异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，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中的空白，并严格考虑将稀疏全局数据应用于这些模型时的不确定性。这种方法标志着与最近的建模工作的全新区别，最近的建模工作将特征变异性纳入空间和时间中固定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 参数化将首先针对沙漠灌木丛环境开发，该环境迫切需要一种新的模型表示来捕获植物对温度和湿度的适应情况。新模型的模拟将揭示沙漠灌木丛中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。