CAREER: Scaling Complexities in Soil Biogeochemistry

这项NSF职业奖的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。大气中过多的碳(C)会导致地球变暖，而土壤中的C会改善水分保持和养分的可获得性。大气中的碳是通过燃烧化石燃料产生的，也是土壤中微生物分解植物物质的副产品。因此，土壤中的碳与大气中的碳直接相关。地球系统模型(计算机模拟)很难预测土壤碳在接下来的100年里是增加还是减少。一些模拟预测土壤碳的大幅增加，这使得研究人员希望土壤将有助于减少持续的二氧化碳排放。相比之下，其他模拟预测土壤将释放更多的碳，导致一个反馈循环，即使二氧化碳排放量大幅减少，也会导致大气中碳的增加。NSF这个职业项目的研究将通过在模拟中添加关于碳如何在土壤中移动的细节，以及通过收集更多数据来帮助校准和验证模拟，来提高对C循环和全球变暖的预测性理解。该项目还将开发和推广有用的术语，以便更准确地描述要在计算机模拟中建模的详细过程，并为研究生和本科生提供研究培训。该项目将通过一个新的多尺度建模框架将传统的土壤碳模型与过程丰富的理解联系起来，从而扩大对土壤碳动态的预测性理解。传统的土壤C模型将土壤C分成池，然后C以与池中的C成正比的速率离开池。虽然这捕捉到了培养研究中观察到的动态，但对于给定的土壤类型，参数化仍然很难先验地预测，依赖于土壤有机碳储量和培养呼吸速率来进行参数化和验证。通过用非线性模型更明确地表示微生物动力学和矿物-有机相互作用等过程，并通过数值实验和数学分析将这些过程显式模型与传统的线性土壤C模型联系起来，这项工作将能够结合非传统的测量来为传统的模型参数化提供信息。在该项目中，土壤测量将与美国政府机构持有的储存库中的主要开放数据档案互联，以支持对土壤碳动态的更广泛的预测性理解。该项目还将通过以数据为中心的实践社区，带头开发具有民主化治理结构的共享语言(本体)，将国际上各土壤项目的研究人员联系起来。为研究生和本科生提供的培训机会将与研究和外联活动结合起来。通过将已建立的和新的对土壤碳动态的理解与丰富的土壤数据相结合，该项目的结果将帮助决策者更好地管理与碳储量和排放相关的活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF CAREER award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Too much carbon (C) in the atmosphere causes the Earth to warm, while C in the soil improves water retention and nutrient availability. C in the atmosphere is produced through burning fossil fuels and also as a byproduct of decomposition of plant matter by microorganisms in soil. As a result, soil C is directly connected to that in the atmosphere. Earth system models (computer simulations) struggle to predict whether soil C will increase or decrease over the next 100 years. Some simulations predict huge increases in soil C, leading researchers to hope that soils will help to mitigate continued CO2 emissions. By contrast, other simulations predict that soils will release more C, leading to a feedback loop that will cause atmospheric C to increase even if CO2 emissions are greatly reduced. Research in this NSF CAREER project will improve predictive understanding of the C cycle and global warming by adding details to the simulations about how C moves through soil, as well as by pulling together more data to help calibrate and validate simulations. The project will also develop and promote a useful terminology to more accurately describe the detailed processes to be modeled in computer simulations, as well as provide research training for graduate and undergraduate students. This project will expand predictive understanding of soil C dynamics by connecting traditional soil C models to a process-rich understanding via a new multi-scale modeling framework. Traditional soil C models divide soil C into pools; C then leaves a pool at a rate proportional to the C in that pool. While this captures the observed dynamics in incubation studies, the parameterization remains difficult to predict, a priori, for a given soil type, relying on soil organic C stocks and incubated respiration rates for both parameterization and validation. By representing processes like microbial dynamics and mineral-organic interactions more explicitly in non-linear models, and linking these process-explicit models to the traditional linear soil C models via numerical experiments and mathematical analysis, this work will be able to incorporate non-traditional measurements to inform traditional model parameterizations. In this project, soil measurements will be interconnected from primary open data archives in repositories and held by U.S. government agencies to support an expanded predictive understanding of soil carbon dynamics. This project will also spearhead development of shared language (ontology) with a democratized governance structure via data-centered communities of practice linking researchers across soil projects internationally. Training opportunities for graduate and undergraduate students will be integrated with the research and outreach activities. By linking established and new understanding of soil C dynamics with the rich legacy of soil data, results from this project will help policy makers to better manage activities related to C stocks and emissions.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.