MCA: Improving understanding of controls over spatial heterogeneity in dryland soil carbon pools in the age of big data

MCA：提高大数据时代对旱地土壤碳库空间异质性控制的理解

• 批准号: 2219027
• 负责人: Heather Throop
• 金额: $ 49.58万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219027&HistoricalAwards=false
• 关键词: MCA; Improving; understanding; controls; over

Soils may alter the trajectory of climate change because of their potential to store or release large amounts of carbon, thus altering the concentration of atmospheric carbon dioxide. However, understanding and predicting current and future soil carbon dynamics requires the capability to accurately describe spatial patterns of soil carbon and forecast changes via reliable models. At present, patterns and controls over soil carbon cycle processes are poorly resolved in dryland (arid and semi-arid) ecosystems, which cover nearly half of Earth's terrestrial surface and store one third of global soil carbon. The ‘big data’ revolution has dramatically increased data available to address ecological problems such soil carbon dynamics. However, effective use of big data requires sophisticated data handling skills and use of emerging analytical tools such as machine learning and application of these tools to process modeling. This project will advance the investigator’s research skills in big data handling and will enhance their ability to mentor students in modern approaches to data-intensive ecological problems. Machine learning and process modeling will be used to increase understanding of patterns and controls over spatial heterogeneity in dryland soil carbon. This information is critical for scientifically based evaluation of dryland management strategies of soil carbon storage. This project will explore patterns and mechanistic controls over spatial heterogeneity in dryland soil organic carbon pools. Exploring patterns in two contrasting dryland settings, a semi-arid grassland with well-documented long-term management and vegetation change and a poorly characterized hyper-arid system, will provide deeper understanding of the relationships between environmental variables and soil organic carbon across drylands. Coupling this exploration of soil organic carbon spatial patterns with process modeling will enhance understanding of the mechanistic drivers of soil organic carbon heterogeneity. Spatial downscaling of a deep learning enhanced earth system modeling approach will provide insight into the fine scale mechanisms that drive soil organic carbon heterogeneity and how they respond to environmental change. This mid-career advancement grant will enable the primary investigator to: develop skills for handling and analyzing large and complex data sets; use machine learning approaches to describe spatial patterns of heterogeneity in soil organic carbon pools in two contrasting dryland field sites where the primary investigator has extensive prior experience and data, and; apply a deep learning enhanced earth system model to a dryland site and use this model to explore mechanistic drivers of carbon cycling. This project will build mutually beneficial partnerships between the primary investigator and two research partners, and an engineer with expertise in machine learning and remote sensing and an expert in ecological process models and deep learning enhanced earth system modeling.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.

土壤可能会改变气候变化的轨迹，因为它们有可能存储或释放大量碳，从而改变了大气二氧化碳的浓度。但是，理解和预测当前和未来的土壤碳动态需要能够准确描述土壤碳的空间模式，并通过可靠的模型进行预测变化。目前，在旱地（干旱和半干旱）生态系统中，对土壤碳循环过程的模式和控制效果很差，这些生态系统覆盖了地球陆地表面的几乎一半，并存储了全球土壤碳的三分之一。 “大数据”革命大大增加了可用的数据来解决生态问题，例如土壤碳动态。但是，有效使用大数据需要复杂的数据处理能力，并使用新兴的分析工具，例如机器学习和这些工具的应用来处理建模。该项目将提高研究者在大数据处理方面的研究技能，并将以现代化的数据密集型生态问题的方式增强其精神学生的能力。机器学习和过程建模将用于增加对旱地土壤碳中空间异质性的模式和控制的理解。该信息对于基于科学的旱地管理策略的科学评估至关重要。该项目将探索对旱地土壤有机碳池空间异质性的模式和机械控制。在两个对比的旱地环境中探索模式，这是一个具有据可查的长期管理和植被变化的半干旱草地，以及较差的超模型系统，将为环境变量与整个旱地的土壤有机碳之间的关系提供更深入的了解。将这种对土壤有机碳空间模式的探索与过程建模相结合，将增强对土壤有机碳异质性机械驱动因素的理解。深度学习增强的地球系统建模方法的空间降尺度将洞悉驱动土壤有机碳异质性及其对环境变化的响应的精细机制。这项职业中期晋升赠款将使主要研究人员能够：开发处理和分析大型和复杂数据集的技能；使用机器学习方法来描述在两个对比的旱地现场的土壤有机碳池中异质性的空间模式，在这些对比的旱地现场，主要研究者具有丰富的先前经验和数据，并且；将深度学习增强的地球系统模型应用于旱地站点，并使用此模型探索碳循环的机械驱动器。该项目将在主要研究者和两个研究合作伙伴之间建立互惠互利的合作伙伴关系，并具有机器学习和遥远敏感性专家的工程师，并且是生态过程模型的专家和深度学习增强的增强了地球系统建模。该奖项反映了NSF的法定任务，并通过评估基金会的知识级别和广泛的影响来评估NSF的法定任务。