CAREER: Elucidating Large-Scale Spatial Patterns of Ecosystem Traits with Data Assimilation

职业：通过数据同化阐明生态系统特征的大规模空间模式

• 批准号: 1942133
• 负责人: Alexandra Konings
• 金额: $ 66.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942133&HistoricalAwards=false
• 关键词: CAREER; Elucidating; Large; Scale; Spatial

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.

计算机模型用于对地球及其周围大气的生命状况进行全球预测。这些模型对全球气候以及地球上植物和微生物生命的联系做出了重要预测。其中许多模型依赖于地球上植物及其与大气的联系的简单关系。该职业奖将探索发展这些非常重要的关系的新方法以及导致它们差异的因素（土壤、光照、水等的变化）。由于没有足够的关于植被和土壤类型空间变化的信息，大多数模型假设植被响应类型仅根据土地覆盖类型而变化。过去的研究表明，其他众所周知的特性也会影响植被敏感性，例如特定地点的干燥程度，或土壤的粘土含量。该职业奖将使用新的建模框架和卫星数据来得出世界各地最佳工厂参数的地图，并在现场测量稀缺的地区测试这些关系。该研究还将确定利用这些关系是否可以改进对二氧化碳生态系统吸收量的模型预测。该奖项的结果将通过更准确地预测二氧化碳吸收、植物生长和土壤分解来改进对生态系统如何应对气候变化的预测。此外，该奖项还包括针对高中生（教师培训）、本科生（包括重新设计课程材料和本科生研究经验）到大学后学生（创建一个关于将观察结果纳入模型的数学技术的研讨会）的多个教育内容。 陆地生态系统的大规模模型是气候变化预测中不确定性的主要来源之一。尽管几十年来努力提高过程表示的复杂性，但它们仍然不确定。然而，对参数优化的关注却少得多。生态系统模型参数仅根据少数植物功能类型进行分配，而没有考虑全球范围内植物行为的巨大差异。该项目将测试一种形成植物功能类型替代方案的新途径：使用数据同化。拟议的工作将使用碳数据模型框架（CARDAMOM），它结合了简单的生态系统模型、遥感数据和马尔可夫链蒙特卡罗模拟，以确定生态系统参数，从而在全球每个像素中产生最真实的通量和碳库。所得参数图不能直接用于其他模型，但将用于测试所谓的环境过滤关系，以根据空间变化众所周知的其他因素（例如气候、土壤类型等）预测生态系统参数变异性。该奖项将测试是否可以使用与近期原位分析类似的方法，利用豆蔻中的同化遥感数据来推导出全球范围内的环境过滤关系，但不依赖于原位测量的质量和数量（在热带等传统采样不足的地区尤其成问题）。它还将创建并证明异养呼吸的这种关系的价值，其空间变异性不能仅通过现场测量来限制。 该项目的教育部分包括为初中和高中生物、化学和物理教师开发几个与生态系统过程和气候变化相关主题的教学模块。该项目还将用于支持两年一度的 CARDAMOM 数据同化研讨会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Canopy Height and Climate Dryness Parsimoniously Explain Spatial Variation of Unstressed Stomatal Conductance

• DOI: 10.1029/2022gl099339
• 发表时间: 2022-07
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Yanlan Liu;Olivia Flournoy;Quan Zhang;K. Novick;R. Koster;A. Konings

Diagnosing evapotranspiration responses to water deficit across biomes using deep learning

使用深度学习诊断跨生物群落缺水的蒸散响应

• DOI: 10.1111/nph.19197
• 发表时间: 2023
• 期刊: New Phytologist
• 影响因子: 9.4
• 作者: Giardina, Francesco;Gentine, Pierre;Konings, Alexandra G.;Seneviratne, Sonia I.;Stocker, Benjamin D.
• 通讯作者: Stocker, Benjamin D.

Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution

• DOI: 10.1029/2023wr035481
• 发表时间: 2023-11
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: N. Holtzman;Yujie Wang;Jeffrey D. Wood;Christian Frankenberg;A. Konings

Intra‐Specific Variability in Plant Hydraulic Parameters Inferred From Model Inversion of Sap Flux Data

• DOI: 10.1029/2021jg006777
• 发表时间: 2022-05
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: Yaojie Lu;Brandon P. Sloan;S. Thompson;A. Konings;G. Bohrer;A. Matheny;Xue Feng

Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake

水资源压力主导 21 世纪热带土地碳吸收

• DOI: 10.1029/2023gb007702
• 发表时间: 2023
• 期刊: Global Biogeochemical Cycles
• 影响因子: 5.2
• 作者: Levine, Paul A.;Bloom, A. Anthony;Bowman, Kevin W.;Reager, John T.;Worden, John R.;Liu, Junjie;Parazoo, Nicholas C.;Meyer, Victoria;Konings, Alexandra G.;Longo, Marcos
• 通讯作者: Longo, Marcos