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| CAREER: Elucidating Large-Scale Spatial Patterns of Ecosystem Traits with Data Assimilation | |
| 项目编号 | 1942133 |
| Alexandra Konings | |
| 项目主持机构 | Stanford University |
| 开始日期 | 2020-02-01 |
| 结束日期 | 01/31/2025 |
| 英文摘要 | Computer models are used to make global predictions about the state of life on earth and the atmosphere that surrounds it. These models make important predictions about global climate and the links to plant and microbial life on earth. Many of these models rely on simple relationships about plants on earth and their connections to the atmosphere. This CAREER award will explore new ways of developing these very important relationships and the factors (changes in soil, light, water, and more) that result in their differences. Because there is not enough information about spatial variations in vegetation and soil types, most models assume that vegetation response types vary only based on land cover types. Past research suggests that other well-known properties affect vegetation sensitivities, for e.g. how dry a particular location is, or how much clay the soil has. This CAREER award will use a new modelling framework together with satellite data to derive a map of optimal plant parameters around the world, and to test these relationships even in regions where field measurements are scarce. The research will also determine whether using these relationships can improve model predictions of how much carbon dioxide ecosystems absorb. The results of this award will improve predictions of how ecosystems respond to climatic changes by enabling more accurate predictions of carbon dioxide uptake, plant growth, and soil decomposition. Additionally, this award includes several educational components for high school students (teacher training) through undergraduates (including redesign of the material for a class, and undergraduate research experience) to post-collegiate (creating a workshop on mathematical techniques for incorporating observations into models). Large scale models of terrestrial ecosystems are one of the dominant sources of uncertainty in predictions of climate change. They have remained uncertain despite decades of effort to increase the sophistication of process representations. However, much less attention has been paid to parameter optimization. Ecosystem model parameters are assigned solely based on a handful of plant functional types, without accounting for the enormous variety of plant behavior across the globe. This project will test a new pathway for forming alternatives to plant functional types: using data assimilation. The proposed work will use the CARbon DAta MOdel fraMework (CARDAMOM), which combines a simple ecosystem model, remote sensing data, and Markov Chain Monte Carlo simulations to determine ecosystem parameters that result in the most realistic fluxes and carbon pools in each pixel across the globe. The resulting parameter maps cannot be used directly in other models but will be used to test so-called environmental filtering relationships to predict ecosystem parameter variability based on other factors whose spatial variation is well known (e.g. climate, soil type, etc). This award will test whether assimilating remote sensing data in CARDAMOM can be used to derive environmental filtering relationships across the globe using approaches similar to those from recent in situ analyses, but without relying on the quality and quantity of in situ measurements (particularly problematic in traditionally under-sampled regions like the tropics). It will also create and demonstrate the value of such relationships for heterotrophic respiration, whose spatial variability cannot be constrained by in situ measurements alone. The educational components of the project include development of several instructional modules on topics related to ecosystem processes and climate change for middle and high school biology, chemistry, and physics teachers. The project will also be used to support a bi-annual workshop on data assimilation with CARDAMOM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. |
| 资助机构 | US-NSF |
| 项目经费 | $264,242.00 |
| 项目类型 | Continuing Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/210999 |
| 推荐引用方式 GB/T 7714 | Alexandra Konings.CAREER: Elucidating Large-Scale Spatial Patterns of Ecosystem Traits with Data Assimilation.2020. |
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