Collaborative Research: MSA: Upscaling soil organic carbon measurements at the continental scale: Evaluating emergent ecosystem properties using multivariate quantitative methods

合作研究：MSA：扩大大陆尺度土壤有机碳测量：使用多元定量方法评估新兴生态系统特性

基本信息

批准号：
2106138
负责人：
Samantha Weintraub-Leff
金额：
$ 4.74万
依托单位：
Battelle Memorial Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106138&HistoricalAwards=false
关键词：
Collaborative Research MSA Upscaling soil

项目摘要

Increase in atmospheric carbon dioxide (CO2) is a major cause of global climate change. One of the most effective nature-based solutions to this challenge lies right under our feet - the soil. Globally, soil contains more carbon than in the Earth's atmosphere and vegetation combined. National and international initiatives are in place to increase soil organic carbon (SOC) content and storage capacity to combat climate change. The multifaceted benefits of SOC storage can also ensure food and nutritional security for the Earth's human population and help meet many of the United Nations Sustainable Development goals. However, it is not clear how long soil can provide these ecosystem services to our global community. This is partly because SOC data available from various sources and predictions based on computer models don’t agree with each other. This project aims to provide a robust estimate of SOC for the conterminous United States (CONUS), which can help identify potential reasons for inconsistency across different models and ultimately facilitate policy-makers in making informed decisions about climate change. It will also offer research training opportunities for students as well as workshops and training courses for teachers. For the U.S., there is a unique opportunity to use spatial clustering approaches to reduce uncertainties in SOC dynamics and constrain models at the continental scale by upscaling site-based measurements across the National Ecological Observatory Network (NEON). Emergent ecosystem properties will be evaluated by using multivariate quantitative methods to extrapolate or interpolate point-scale SOC measurements from a spatial constellation of NEON terrestrial sites to CONUS. Data collected across NEON terrestrial sites will be coupled with an array of multivariate geographic clustering algorithms (k-means clustering, ensemble clustering) and machine-learning (convolutional neural network, artificial neural network) approaches. These quantitative analyses will also enable uncertainty quantification of spatial representativeness of SOC and help identify potential future relocatable (or mobile) sites for additional ground-truth measurements of variables related to terrestrial C cycle processes. Existing NEON biogeochemistry, microbial, hydrology, sensor, and remote sensing data products will be leveraged to produce quantitative SOC regional maps for CONUS using similar combinations of climatic, ecological, environmental, geochemical, and microbial variables. The algorithms developed with NEON data will be validated with other point-scale data like SoDaH (SOils DAta Harmonization database) and ISNC (International Soil Carbon Network). The spatial mismatch of derived representativeness-based SOC regional maps for CONUS will be evaluated with existing gridded databases: SoilGrids, Harmonized World Soil Database (HWSD), Northern Circumpolar Soil Carbon Database (NCSCD), and gridded U.S. Soil Survey Geographic Database (gSSURGO).EON-based SOC regional maps for CONUS will also be integrated with downscaled historical SOC predictions from participating models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The robust (and scalable) estimate of SOC for CONUS will enable the diagnosis of terrestrial C cycle processes using historical CMIP6 model runs. Broader impacts will involve training opportunities at the undergraduate and graduate levels, and workhops and training courses to teach data analysis workflow methods.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.

大气二氧化碳（CO2）的增加是全球气候变化的主要原因。针对这一挑战的最有效的基于自然的解决方案之一就在我们的脚下 - 土壤。在全球范围内，土壤含有比地球大气层和植被合并更多的碳。国家和国际倡议已建立，以提高土壤有机碳（SOC）含量和储存能力以打击气候变化。 SOC存储的多方面好处还可以确保地球人口的粮食和营养安全，并帮助实现联合国许多可持续发展目标。但是，尚不清楚土壤可以为我们的全球社区提供这些生态系统服务多长时间。这部分是因为从计算机模型的各种来源可获得的SOC数据相互依存。该项目旨在为Conterminines（CONUS）提供对SOC的强大估计，该估计可以帮助确定各个模型之间的潜在原因，并最终支持政策制定者对气候变化做出明智的决定。它还将为学生提供研究培训机会，并为教师提供研讨会和培训课程。对于美国而言，有一个独特的机会使用空间聚类方法来通过在国家生态天文台（NEON）上进行基于基于站点的测量值来减少SOC动力学和约束模型中的不确定性。将通过使用多元定量方法来评估新兴生态系统特性，以推断或插入插入点尺度的SOC测量，从霓虹灯地点的空间星座到CONUS。跨霓虹灯陆地部位收集的数据将与一系列多变量地理聚类算法（K-均值聚类，集合聚类）和机器学习（卷积神经元网络，人工神经元网络）方法相结合。这些定量分析还将实现SOC空间代表性的不确定性量化，并有助于确定潜在的未来重定位（或移动）位点，以实现与陆地C周期过程相关的变量的其他基础真相测量。现有的霓虹灯生物地球化学，微生物，水文学，传感器和遥感数据产品将被利用，以使用气候，生态，环境，地球化学和微生物变量的类似组合来生成圆锥的定量SOC区域图。用霓虹灯数据开发的算法将通过其他点级数据（例如Sodah（土壤数据协调数据库）和ISNC（国际土壤碳网络）验证。将通过现有的基础数据库评估基于代表性的SOC区域地图的空间不匹配：土壤晶格，统一的世界土壤数据库（HWSD），北部圆极土壤碳碳数据库（NCSCD），并与基于美国的土壤数据库（GENE SOC INCORITION）一起进行。耦合模型比较项目第6阶段（CMIP6）的组合。 Conus的SOC的强大（和可扩展）估计将使用历史CMIP6模型运行来诊断陆地C周期过程。更广泛的影响将涉及本科和研究生级别的培训机会，以及教授数据分析工作流程方法的工作室和培训课程。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和更广泛的影响来审查标准，以评估的评估被认为是宝贵的支持。