MCA Pilot PUI: Data Intensive Research Training (DIRT) in forecasting soil respiration at core terrestrial NEON sites

MCA 试点 PUI：预测陆地 NEON 核心站点土壤呼吸的数据密集型研究培训 (DIRT)

• 批准号: 2321958
• 负责人: John Zobitz
• 金额: $ 18.44万
• 依托单位: Augsburg University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321958&HistoricalAwards=false
• 关键词: MCA; Pilot; PUI; Data; Intensive

In this NSF Mid-Career Advancement (MCA) award, the PI will expand his knowledge and skills in advanced ecosystem informatics to a “big data” environmental data science problem. The science of ecological forecasting makes predictions about ecosystems in response to environmental change. Ecological forecasts in turn, aid in development of nature-based solutions for Earth's changing climate. Some forecasts have a large degree of uncertainty, such as those for terrestrial carbon cycling. Gains and losses of CO2 from the soil, known as soil efflux, are an important source of this uncertainty because they depend on climate inputs with a high degree of variability, such as temperature and precipitation. This research will develop open-access real-time forecasts of soil CO2 effluxes using mathematical and computational approaches. Forecasts will be developed across 47 long-term research sites in the continental United States that are part of the NSF-supported National Ecological Observatory Network (NEON). The principal investigator (PI), in partnership with a mentor from the NSF-supported Ecological Forecasting Initiative, will further develop a tool to address a community forecasting challenge for soil effluxes, leading to its broader adoption in the ecological community. The skills gained through this project will provide a long-term and sustainable research trajectory for the PI, and will help him develop new undergraduate research and training experiences. Real-time forecasts of components of the terrestrial carbon cycle (i.e. gross primary productivity and net ecosystem carbon exchange) are important for understanding and developing nature-based solutions in a changing climate. Despite the importance of soil carbon storage to climate mitigation, standardized prediction of terrestrial soil carbon efflux across seasonal and interannual timescales lags behind other terrestrial carbon forecasts with increased model forecast uncertainty from a (current) lack of structural representation of soil processes. This project will develop forecasts for soil carbon efflux across seasonal and interannual timescales at all NEON terrestrial sites. Project deliverables include incorporation of a suite of soil carbon models (spanning a range of model complexity) into an integrated informatics toolbox for ecosystem modeling (the Predictive Ecosystem Analyzer or PEcAn) to generate soil efflux forecasts. Modeled effluxes will be validated against existing measurements or databases (e.g. Soil Respiration Database or the Continuous Soil Respiration Database) where appropriate. This project will directly connect NEON data and ecological forecasting techniques with the broader ecological community. Developed methods and tools will be open-access for the scientific community,notably through an open-source R package and by incorporation of models into the PEcAn ecological informatics workflow.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.

在这个 NSF 职业中期发展 (MCA) 奖项中，PI 将把他在高级生态系统信息学方面的知识和技能扩展到“大数据”环境数据科学问题。生态预测科学对生态系统做出预测，以应对环境变化。生态预测反过来有助于针对地球气候变化制定基于自然的解决方案，一些预测具有很大程度的不确定性，例如土壤中二氧化碳的增加和损失。土壤流出量是这种不确定性的一个重要来源，因为它们依赖于高度可变的气候输入，例如温度和降水。这项研究将利用数学和计算方法开发土壤二氧化碳流出量的开放实时预测。首席研究员 (PI) 与来自美国国家科学基金会 (NSF) 的导师合作，将在美国大陆 47 个长期研究地点进行预测，这些研究地点是 NSF 支持的国家生态观测站网络 (NEON) 的一部分。 NSF 支持的生态预测计划将进一步开发一种工具来解决土壤流出量的社区预测挑战，从而使其在生态界得到更广泛的采用。通过该项目获得的技能将为该项目提供长期且可持续的研究轨迹。首席研究员，并将帮助他开发新的本科生研究和培训经验。实时预测陆地碳循环的组成部分（即总初级生产力和净生态系统碳交换）对于理解和开发不断变化的基于自然的解决方案非常重要。尽管土壤碳储存对减缓气候变化很重要，但跨季节和年际时间尺度的陆地土壤碳流出的标准化预测仍落后于其他陆地碳预测，因为（当前）缺乏土壤过程的结构表征，因此增加了模型预测的不确定性。项目将对所有 NEON 陆地站点跨季节和年际时间尺度的土壤碳流出进行预测 项目可交付成果包括纳入一套土壤碳模型（涵盖一系列范围）。模型复杂性）集成到生态系统建模综合信息学工具箱（预测生态系统分析器或 PEcAn）中，以生成土壤流出量预测，将在适当的情况下根据现有测量或数据库（例如土壤呼吸数据库或连续土壤呼吸数据库）进行验证。该项目将直接将 NEON 数据和生态预测技术与更广泛的生态界联系起来，开发的方法和工具将向科学界开放，特别是通过该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。