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

土壤可能改变气候变化的轨迹，因为它们具有储存或释放大量碳的潜力，从而改变大气中二氧化碳的浓度。然而，理解和预测当前和未来土壤碳动态需要准确描述土壤碳空间格局并通过可靠的模型预测变化的能力。目前，在干旱（干旱和半干旱）生态系统中，土壤碳循环过程的模式和控制还没有得到很好的解决，而干旱（干旱和半干旱）生态系统覆盖了地球陆地表面的近一半，储存了全球土壤碳的三分之一。“大数据”革命极大地增加了可用于解决土壤碳动态等生态问题的数据。然而，有效利用大数据需要复杂的数据处理技能和新兴分析工具的使用，如机器学习和应用这些工具进行流程建模。该项目将提高研究者在大数据处理方面的研究技能，并将提高他们指导学生使用现代方法解决数据密集型生态问题的能力。机器学习和过程建模将用于增加对旱地土壤碳空间异质性的模式和控制的理解。这些信息对于科学评价旱地土壤碳储量管理策略至关重要。本项目将探索旱地土壤有机碳库空间异质性的模式和机制控制。在两种截然不同的旱地环境中探索模式，一种是具有充分记录的长期管理和植被变化的半干旱草地，另一种是特征不明显的超干旱系统，将有助于更深入地了解旱地环境变量与土壤有机碳之间的关系。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制驱动因素的理解。深度学习增强地球系统建模方法的空间降尺度将提供对驱动土壤有机碳异质性及其如何响应环境变化的精细尺度机制的见解。这项中期职业晋升补助金将使主要研究者能够：培养处理和分析大型复杂数据集的技能；使用机器学习方法来描述两个不同旱地土壤有机碳库异质性的空间模式，其中主要研究者具有丰富的经验和数据；将深度学习增强的地球系统模型应用于旱地，并使用该模型探索碳循环的机制驱动因素。该项目将在主要研究者和两个研究伙伴之间建立互利的伙伴关系，以及在机器学习和遥感方面具有专业知识的工程师和生态过程模型和深度学习增强地球系统建模方面的专家。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.