Collaborative Research: Network Cluster: Using Big Data approaches to assess ecohydrological resilience across scales

合作研究：网络集群：使用大数据方法评估跨尺度的生态水文恢复力

• 批准号: 2012188
• 负责人: Adrian Harpold
• 金额: $ 61.95万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012188&HistoricalAwards=false
• 关键词: Collaborative; Research; Network; Cluster; Using

The part of the Earth surface that sustains life (the Critical Zone) consists of bedrock, soil, water, air, and living things that have been interacting for billions of years. The Critical Zone can absorb many disturbances while still supporting life on Earth, but the age of humans or the “Anthropocene” has put unprecedented pressure on the Critical Zone. When long-term disturbances like climate change are combined with short-term disturbances like fires or flooding, ecosystems can be pushed past a breaking point where important ecosystem services shut down. This project seeks to understand and predict Critical Zone resilience in the face of multiple natural and human disturbances. The research will combine river flow and chemistry data from throughout the U.S. to shed light on how quickly ecosystems recover from local disturbances and to what degree long-term change is altering the structure of the Critical Zone. The team of researchers will combine ecological approaches with data science methods to analyze large quantities of information from thousands of locations. Existing data from public and private organizations across the U.S. will be compiled into a publicly available data base to compare ecosystem recovery times and trajectories. The general patterns observed in the “big data” analysis will be tested by in-depth field studies at four sites experiencing multiple disturbances such as wildfire, acidification, and extreme storms. This project will help Earth science researchers and educators across the U.S. develop a deeper understanding of how the Earth system maintains itself and how humans can avoid eroding the ecosystem services that sustain us. To achieve this goal, all data and approaches will be shared publicly and the research team will lead an innovative outreach and education program. This comprehensive program will educate 7-12 grade teachers to bring cutting-edge ecology and data science to schools across America. To enhance diversity in Earth science and data science, a field camp will be co-designed and implemented in collaboration with historically black colleges and universities. While observatory-based Critical Zone research produces important findings on catchment-scale processes, the global scale of disturbance in the Anthropocene transcends the bounds of a single site or funding cycle, posing a challenge for traditional investigative approaches. This spatial and temporal mismatch significantly limits the predictive power of individual site studies in the context of regional- to continental-scale environmental change. To advance network-scale syntheses and integrate across scales, this project will apply an iterative “pattern to process” and “process to pattern” approach to investigate how Critical Zone structure controls water, carbon, nutrients, and response to overlapping disturbances in the context of multi-dimensional resilience. In this context, the overarching hypothesis is that Critical Zone structure (i.e. configuration of biological, chemical, and physical characteristics) controls the timing, direction, and intensity of linkages among multiple responses and that these linkages regulate ecosystem resilience and resistance to climate and land cover disturbance. To test this overarching hypothesis, (1) existing ecohydrological data will be compiled from across the continental U.S. into a multi-dimensional Critical Zone database, (2) advanced statistical analysis will be performed using complex-systems tools on “big data” to identify state changes in ecological function and ecosystem services, (3) process-hypotheses will be refined based on these data-driven approaches, and (4) in-depth process investigations will be performed at four high-vulnerability focal sites in the northeast and southwest. The database and complex-systems approaches will be shared to empower the Critical Zone community to transition into a phase of data-driven hypothesis generation and cross-site research. To broaden the participation of underrepresented groups in Critical Zone and data science the project will educate and empower a diverse new generation of STEM thinkers from middle and high school to the graduate college level. A comprehensive grade 7-12 education program will reach hundreds of students from high-need Vermont schools (historically marginalized economic, racial or disability groupings) by “educating the educators” in Critical Zone and data science. Furthermore, in collaboration with HBCUs, outdoor education on Critical Zone and data science will be designed and implemented for a diverse group of undergraduate students. This project is jointly funded by the Critical Zone Collaborative Network Program, the Hydrologic Sciences Program in the NSF Division of Earth Sciences, and the Established Program to Stimulate Competitive Research (EPSCoR).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.

维持生命（关键区域）的地球表面的一部分是基岩，土壤，水，空气和活物已经相互作用数十亿年的东西。关键区域可以吸收许多灾难，同时仍能维持地球上的生命，但是人类或“人类世”的年龄给临界区带来了前所未有的压力。当像气候变化之类的长期骚乱与诸如火灾或洪水等短期骚乱相结合时，可以将生态系统推向关闭重要的生态系统服务关闭的突破点。面对多种自然和人类灾难，该项目试图理解和预测关键区域的弹性。这项研究将结合整个美国的河流流量和化学数据，以阐明生态系统从当地灾害中恢复的速度以及长期变化正在改变关键区域的结构。研究人员团队将结合生态方法与数据科学方法，以分析数千个位置的大量信息。来自美国各地公共和私人组织的现有数据将被编译成公开可用的数据库，以比较生态系统恢复时间和轨迹。在“大数据”分析中观察到的一般模式将通过在野火，酸化和极端风暴等多种灾难的四个地点进行深入的现场研究来测试。该项目将帮助美国各地的地球科学研究人员和教育者对地球系统如何维护自己以及人类如何避免侵蚀维持我们的生态系统服务有更深入的了解。为了实现这一目标，所有数据和方法都将公开共享，研究团队将领导创新的外展和教育计划。这项全面的计划将教育7 - 12年级的教师，将尖端的生态和数据科学带到美国的学校。为了增强地球科学和数据科学的多样性，将与历史悠久的黑人学院和大学合作共同设计和实施野外训练营。尽管基于观察的关键区域研究产生了有关集水规模过程的重要发现，但人类世的全球灾难规模超越了单个站点或融资周期的界限，对传统的调查方法提出了挑战。这种空间和暂时的不匹配显着限制了在区域至连续尺度的环境变化的背景下，各个场地研究的预测能力。为了推进网络规模的合成并跨尺度整合，该项目将应用一种迭代的“模式来处理”和“过程”方法，以研究关键区域结构如何控制水，碳，养分以及对多维弹性的背景下对重叠灾害的反应。在这种情况下，总体假设是关键区域结构（即生物学，化学和物理特征的构型）控制多个响应之间联系的时机，方向和强度，并且这些链接调节生态系统的抵抗力以及对气候和土地覆盖灾难的抵抗力。为了检验这一总体假设，（1）现有的生态数据将从美国各地汇编为多维关键区域数据库，（2）使用“大数据”上的复杂系统工具进行高级统计分析，以确定生态功能和生态系统服务中的状态变化，（3）基于这些过程的研究，（3）在这些过程中，（3）在这些过程中，（3）逐步研究了（4）。东北和西南的四个高胶合焦点地点。数据库和复杂系统方法将被共享，以使关键区域社区能够过渡到数据驱动的假设生成和跨站点研究的阶段。为了扩大代表性不足的团体在关键区域和数据科学中的参与，该项目将教育并赋予潜水员新一代的STEM思想家从中学和高中到研究生学院的水平。一项全面的7-12年级教育计划将通过“教育教育工作者”和数据科学的“教育教育者”，从而吸引数百名佛蒙特学学校（历史使经济，种族或残疾分组）的学生接触。此外，将与HBCUS合作，为关键区域和数据科学的户外教育设计和实施，为一群的本科生组成。该项目由关键区协作网络计划，NSF地球科学部的水文科学计划以及刺激竞争性研究的既定计划（EPSCOR）共同资助。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛影响来评估NSF的法定任务，并被认为是诚实的支持。