Collaborative Proposal: ABI Innovation: Model-data synthesis and forecasting across the upper Midwest: Partitioning uncertainty and environmental heterogeneity in ecosystem carbon

合作提案：ABI 创新：中西部上游地区的模型数据综合和预测：划分生态系统碳的不确定性和环境异质性

• 批准号: 1062547
• 负责人: Kenton McHenry
• 金额: $ 66.67万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062547&HistoricalAwards=false
• 关键词: Collaborative; Proposal; ABI; Innovation; Model

The University of Illinois at Urbana-Champaign and the University of Wisconsin - Madison are awarded collaborative grants to develop an integrated ecological bioinformatics toolbox dubbed the Predictive Ecosystem Analyzer (PEcAn) which consists of: 1) a scientific workflow system to manage the immense amounts of publicly-available environmental data and 2) a Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models. This project is motivated by the fact that many of the most pressing questions about global change are not necessarily limited by the need to collect new data as much as by our ability to synthesize existing data. This project seeks to improve this ability by developing a framework for integrating multiple data sources in a sensible manner. PEcAn is initially being developed around the Ecosystem Demography model (ED), one of the few terrestrial biosphere models capable of integrating a large suite of observational data at different spatial and temporal scales, but is designed to interface with a wide class of ecosystem models. The output of the data assimilation system will be a regional-scale high-resolution estimate of both the terrestrial carbon cycle and plant biodiversity based on the best available data and with a robust accounting of the uncertainties involved. The workflow system will allow ecosystem modeling to be more reproducible, automated, and transparent in terms of operations applied to data, and thus ultimately more comprehensible to both peers and the public. It will reduce the redundancy of effort among modeling groups, facilitate collaboration, and make models more accessible the rest of the research community. As a test bed for the development and application of these ecological bioinformatics tools, the project will focus on the temperate/boreal transition zone in northern Wisconsin, a region that is expected to show large climate change responses and is arguably the most data-rich region in the country. The tools developed here will enable us to partition carbon flux and pool variability in space and time and to attribute the regional-scale responses to specific biotic and abiotic drivers. The data-assimilation framework will partition different sources of uncertainty, which will enable a better understanding of which are limiting our inference, and provide a more complete propagation of uncertainty into model forecasts. ED will then be used to forecast regional-scale dynamics under decadal to centennial scale climate change scenarios. This approach will allow us to assess for the first time how much our uncertainty about the current state of the ecosystem impacts our ability to anticipate the future. The tools developed in this project will not only find broad use in the ecological community but will also have direct relevance to important policy and management debates about climate change mitigation and carbon credit markets. Specifically, it will allow a repeatable, scientifically defensible, and temporally up-to-date analysis of the state of the carbon cycle base on a broad synthesis of the best available data. Within the scientific community, these tools will be broadly applicable to numeous ecosystem models and facilitate the use and evaluation of predictive models by non-modelers. The tools developed here are also well-positioned to synthesize the large volumes of information coming out of a number of NSF-supported research networks, such as the LTER network and NEON. To encourage use and development, we will make open-source code, documentation, and tutorials available on the project website, pecanproject.org. To further disseminate these tools and methods, this project also has a strong education component consisting of three elements: 1) the development of a graduate seminar on eco-informatics that will be offered in both face-to-face and online formats, 2) the participation of the PIs in two existing summer courses, one of which is offered at a tribal college located within our study region, and 3) direct training of students and postdocs directly involved with the project.

伊利诺伊大学伊利诺伊大学乌尔巴纳 - 坎佩尼大学和威斯康星大学 - 麦迪逊大学获得合作赠款，以开发一种综合的生态生物信息信息箱，称为预测生态系统分析仪（PECAN），该工具箱由：1）组成：1）一个科学工作流程，以管理在公共环境数据中的巨大范围，以管理综合数据和2）贝尔斯的信息。最先进的生态系统模型。该项目的激励是因为许多关于全球变化的最紧迫问题不一定受到收集新数据的限制，而不是我们合成现有数据的能力。该项目旨在通过开发一个以明智的方式建立多个数据源的框架来提高这种能力。最初是围绕生态系统人口统计模型（ED）开发的，这是少数能够在不同的空间和时间尺度上整合大量观察数据的陆地生物圈模型之一，但旨在与广泛的生态系统模型接口。数据同化系统的输出将是基于最佳可用数据的区域规模的高分辨率估计值，以及对所涉及的不确定性的强大核算。 工作流程系统将使生态系统建模在应用于数据的操作方面更可重复，自动化和透明，因此最终对同行和公众更加理解。 它将减少建模小组之间的努力冗余，促进协作，并使模型在研究界的其他成员中更容易访问。作为这些生态生物信息学工具开发和应用的测试床，该项目将重点放在威斯康星州北部的温带/北方过渡区，该地区有望显示出较大的气候变化反应，并且可以说是该国数据最丰富的地区。这里开发的工具将使我们能够在时空的碳通量和池变异性分开，并将区域尺度响应归因于特定的生物和非生物驱动器。数据辅助框架将把不同的不确定性来源分开，这将使人们能够更好地理解哪些不确定性限制了我们的推论，并将不确定性更完整地传播到模型预测中。然后，ED将用于预测在十年级的气候变化情景下的区域尺度动力学。 这种方法将使我们第一次评估我们对生态系统当前状态的不确定性影响我们预期未来的能力。该项目中开发的工具不仅将在生态界找到广泛的使用，而且还将与重要的政策和管理辩论有关的有关缓解气候变化和碳信贷市场的辩论具有直接相关性。具体而言，它将允许对最佳可用数据的广泛合成碳循环基础的状态进行可重复的，科学的辩护和时间最新的分析。在科学界，这些工具将广泛适用于数字生态系统模型，并促进非模型的使用和评估。此处开发的工具还具有良好的位置，可以合成许多NSF支持的研究网络（例如LTER Network和Neon）的大量信息。为了鼓励使用和开发，我们将在项目网站pecanproject.org上提供开源代码，文档和教程。为了进一步传播这些工具和方法，该项目还具有强大的教育组成部分：1）开发有关生态信息学的研究生研讨会，将以面对面和在线格式提供，2）PIS在两个现有的夏季课程中参与了两个现有的夏季课程，其中一个在我们的研究区域内提供了一个直接的培训，并直接培训学生，并在3个专业的培训中进行培训。