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中期职业发展（MCA）奖，PI将扩大他的知识和技能，在先进的生态系统信息学的“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。反过来，生态预测有助于为地球气候变化制定基于自然的解决方案。有些预测有很大程度的不确定性，如陆地碳循环预测。土壤中CO2的增加和损失，即土壤排放，是这种不确定性的一个重要来源，因为它们取决于具有高度可变性的气候输入，如温度和降水。这项研究将使用数学和计算方法开发土壤CO2排放的开放式实时预测。预测将在美国大陆的47个长期研究地点进行，这些地点是NSF支持的国家生态观测网络（氖）的一部分。首席研究员（PI）与NSF支持的生态预测倡议的导师合作，将进一步开发一种工具，以解决社区对土壤流出物的预测挑战，从而使其在生态社区中得到更广泛的采用。通过该项目获得的技能将为PI提供长期和可持续的研究轨迹，并将帮助他开发新的本科研究和培训经验。对陆地碳循环的组成部分（即总初级生产力和生态系统净碳交换）进行实时预测，对于了解和制定气候变化中基于自然的解决方案至关重要。尽管土壤碳储量对气候减缓的重要性，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落后于其他陆地碳预测，因为（当前）缺乏土壤过程的结构表示，模型预测的不确定性增加。该项目将在所有氖陆地站点制定跨季节和年际时间尺度的土壤碳排放预测。项目交付成果包括将一套土壤碳模型（涵盖一系列模型复杂性）纳入用于生态系统建模的综合信息学工具箱（预测生态系统分析仪或PEcAn），以生成土壤流出预测。适当时，将根据现有测量值或数据库（例如土壤呼吸数据库或连续土壤呼吸数据库）对模拟流出物进行验证。该项目将直接将氖数据和生态预测技术与更广泛的生态社区联系起来。所开发的方法和工具将向科学界开放获取，特别是通过开源R包以及将模型纳入PEcAn生态信息学工作流程。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。