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合成和预测：划分生态系统碳的不确定性和环境异质性

基本信息

批准号：
1062204
负责人：
Ankur Desai
金额：
$ 10.39万
依托单位：
University of Wisconsin-Madison
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=1062204&HistoricalAwards=false
关键词：
Collaborative Proposal ABI Innovation Model

项目摘要

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.

伊利诺伊大学厄巴纳-香槟分校和威斯康星大学麦迪逊分校获得合作资助，开发一个名为“预测生态系统分析器”(PEcAn) 的综合生态生物信息学工具箱，该工具箱包括：1) 一个科学的工作流程系统，用于管理大量的生态生物信息学工具箱。公开可用的环境数据和 2) 贝叶斯数据同化系统，用于在最先进的生态系统模型中综合这些信息。该项目的动机是，有关全球变化的许多最紧迫的问题不一定受到收集新数据的需要的限制，而是受到我们综合现有数据的能力的限制。该项目旨在通过开发一个以合理的方式集成多个数据源的框架来提高这种能力。 PEcAn 最初是围绕生态系统人口学模型 (ED) 开发的，该模型是少数能够整合不同空间和时间尺度的大量观测数据的陆地生物圈模型之一，但旨在与各种生态系统模型相连接。数据同化系统的输出将是基于现有最佳数据并对所涉及的不确定性进行强有力的核算，对陆地碳循环和植物生物多样性进行区域规模的高分辨率估计。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加可重复、自动化和透明，从而最终更容易被同行和公众所理解。它将减少建模小组之间的工作冗余，促进协作，并使模型更容易为研究界的其他成员所使用。作为这些生态生物信息学工具开发和应用的试验台，该项目将重点关注威斯康星州北部的温带/寒带过渡区，该地区预计将表现出巨大的气候变化响应，并且可以说是数据最丰富的地区在国内。这里开发的工具将使我们能够划分碳通量和池在空间和时间上的变化，并将区域规模的反应归因于特定的生物和非生物驱动因素。数据同化框架将划分不同的不确定性来源，这将使我们能够更好地理解哪些因素限制了我们的推断，并将不确定性更完整地传播到模型预测中。然后，ED 将用于预测十年至百年尺度气候变化情景下的区域尺度动态。这种方法将使我们首次评估我们对生态系统当前状态的不确定性对我们预测未来的能力有多大影响。该项目开发的工具不仅将在生态界得到广泛应用，还将与有关减缓气候变化和碳信用市场的重要政策和管理辩论直接相关。具体来说，它将允许在广泛综合最佳可用数据的基础上，对碳循环状态进行可重复的、有科学依据的、最新的分析。在科学界，这些工具将广泛适用于众多生态系统模型，并促进非建模人员对预测模型的使用和评估。这里开发的工具也可以很好地合成来自许多 NSF 支持的研究网络（例如 LTER 网络和 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内容，由三个要素组成：1）开发生态信息学研究生研讨会，以面对面和在线形式提供，2） PI 参与现有的两门暑期课程，其中一门是在我们研究区域内的一所部落学院提供的，以及 3）对直接参与该项目的学生和博士后进行直接培训。