Knowledge Resource Center for Ecological Environment in Arid Area
| 项目编号 | 1724781 |
| Upgrading plant-functional-types with plant trait variability in ecohydrological models: A stochastic parameterization approach | |
| Gene-Hua Ng | |
| 主持机构 | University of Minnesota-Twin Cities |
| 开始日期 | 2017-09-01 |
| 结束日期 | 2021-08-31 |
| 资助经费 | 351353(USD) |
| 项目类别 | Standard Grant |
| 资助机构 | US-NSF（美国国家科学基金会） |
| 项目所属计划 | Hydrologic Sciences |
| 语种 | 英语 |
| 国家 | 美国 |
| 英文简介 | 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. |
| 来源学科分类 | Geosciences |
| URL | https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724781 |
| 资源类型 | 项目 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/342788 |
| 推荐引用方式 GB/T 7714 | Gene-Hua Ng.Upgrading plant-functional-types with plant trait variability in ecohydrological models: A stochastic parameterization approach.2017. |
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