EAGER-NEON: Detecting Disturbance and Ecosystem Response in Continental Observatory Networks

目前陆生植物和微生物为社会提供的最重要的生态系统服务之一是清除人类排放到大气中的二氧化碳（CO2）的约四分之一。 然而，这种所谓的“碳汇”模式在全球和国家一级的未来仍不确定。 一些证据表明，最近的干扰可能是由于美国西部的干旱，导致陆地生态系统突然成为大气中的碳源，而不是碳汇。 随着我们进入碳责任时代，我们必须能够更准确地说明整个北美的生态系统碳生产和消费。 在这项研究中，将首次综合计算机模拟与NSF氖观测网络新获得的数据，以评估氖数据，并更好地了解这种大规模的环境扰动如何影响非洲大陆的碳平衡。 该项目还将涉及博士后和研究生水平的培训，以及积极的学习经验，这将扩大本科生在科学中代表性不足的群体的参与发展。该项目的最终目标是确定可能的干扰机制，导致整个美国大陆尺度的生态系统从净碳汇到净碳源的转变。 在这个项目中，我们将使用卫星数据来识别与不同的生态系统干扰机制（如虫害，干旱，火灾）相关的空间和时间指纹，并使用这些指纹来模拟与陆地植被模型中的不同干扰机制相关的随机和机械过程。 新获得的氖观测数据将被用来对这些陆地植被模拟进行基准测试，模拟将被用来优化观测网络以进行干扰探测。 该项目将从两个方面推进生态系统科学。 首先，将开发用于优化从全球到大陆到区域尺度的分层观测网络的新算法。 第二，将改进陆面模型中的扰动动力学，并将模拟更大空间尺度的扰动。

One of the most vital ecosystem services currently provided to society by terrestrial plants and microorganisms is the removal of approximately one-quarter of the human-generated carbon dioixide (CO2) emitted to the atmosphere. However, the future of this so-called "carbon sink" pattern at the global and national level remains uncertain. Some evidence suggests that recent disturbance possibly due to drought in the Western US has caused terrestrial ecosystems to suddenly become sources of carbon to the atmosphere, rather than carbon sinks. As we enter an era of carbon accountability it is imperative that we are able to more accurately account for ecosystem carbon production and consumption across North America. For this study, computer simulation in conjunction with newly acquired data from NSF's NEON observing network will be synthesized, for the first time, to evaluate the NEON data, and better understand how such large-scale environmental disturbance affects the carbon balance of the continent. The project will also involve training at the postdoctoral and graduate student levels, as well as development of active learning experiences that will broaden the participation of undergraduates from underrepresented groups in science.The ultimate goal of this project is to identify possible disturbance mechanisms that have caused a continental scale shift in ecosystems across the US from being a net carbon sink to a net carbon source. In this project we will use satellite data to identify spatial and temporal fingerprints associated with different ecosystem disturbance mechanisms (e.g. insect infestation, drought, fire) and use these fingerprints to simulate the random and mechanistic processes associated with different disturbance mechanisms in land vegetation models. Newly acquired NEON observation data will be used to benchmark these land vegetation simulations and simulations will be used to optimize the observation network for disturbance detections. This project will advance ecosystem science in two ways. First, new algorithms for the optimization of hierarchical observation networks from global to continental to regional scales will be developed. Second, disturbance dynamics in land surface models will be refined, and disturbance over larger spatial scales will be simulated.