Collaborative Research: From Surface Dynamics to Strata: Quantifying the Signals of Surface Processes in Space and Time

合作研究：从地表动力学到地层：量化时空地表过程的信号

• 批准号: 1854490
• 负责人: Anjali Fernandes
• 金额: $ 13.35万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854490&HistoricalAwards=false
• 关键词: Collaborative; Research; Surface; Dynamics; Strata

River deltas are complex landforms which transport water and sediment from continents to oceans. They are extremely sensitive to environmental changes, e.g. sea-level rise, sediment and water supply, and therefore communities that rely on the resources of these dynamic and sensitive landscapes are at risk. The past dynamics of deltas are partially preserved in their sedimentary records, and these records represent valuable templates for testing predictive models of future change. To understand these systems, the researchers propose to create a predictive framework that enables them to relate the arrangement of channels and floodplains on the surface of deltas to the record of past dynamics preserved in their sediments. Much like river deltas, the earth science field also needs to be responsive to changing demographics in the scientific community through strategic distribution of resources and opportunities. This project provides support for underrepresented minority lead scientists, early career investigators, and inclusive research experiences for students, allowing the researchers to leverage the knowledge and experience that comes from a diverse research team. The framework of support proposed here could be a valuable template for creating a more diverse and inclusive scientific community, particularly in earth science, which continues to have the lowest (9%) rates of ethnic and racial diversity of all other STEM fields.Records of past environmental states contained within the sedimentary deposits of river deltas, while amongst the most complete on Earth, are extremely challenging to decipher. The premise of the proposed research is that the sedimentary record of deltaic systems is mediated by a combination of their autogenic dynamics (e.g. avulsion and lateral migration rates) and allogenic forcings (e.g. climate, tectonics, sea level). The researchers propose to develop an integrated statistical framework to characterize deltaic transport systems and their sedimentary records across a range of spatial and temporal scales. To construct this framework, they will iterate between data from physical and numerical experiments, and field data-sets, providing quantitative constraints on scale-dependent dynamics and their contribution to filtering signals stored in the subsurface sedimentary record. This information is crucial for (1) developing and constraining delta evolution models, (2) inferring paleo-environmental conditions from the stratigraphic record, and (3) aiding the management of natural resources (e.g. coastal aquifers, hydrocarbon reservoirs, etc.) by improving predictions of subsurface reservoir properties over a range of scales. This project is jointly funded by the Geomorphology and Land-Use Dynamics Program 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.

河流三角洲是复杂的地貌，将水和沉积物从大陆输送到海洋。他们对环境变化极其敏感，例如海平面上升、沉积物和供水，因此依赖这些动态和敏感景观资源的社区面临风险。三角洲过去的动态部分保存在沉积记录中，这些记录代表了测试未来变化预测模型的宝贵模板。为了了解这些系统，研究人员建议创建一个预测框架，使他们能够将三角洲表面的河道和洪泛区的排列与沉积物中保存的过去动态记录联系起来。就像河流三角洲一样，地球科学领域也需要通过资源和机会的战略分配来应对科学界人口结构的变化。该项目为代表性不足的少数族裔首席科学家、早期职业研究人员提供支持，并为学生提供包容性研究经验，使研究人员能够利用来自多元化研究团队的知识和经验。这里提出的支持框架可以成为创建更加多元化和包容性的科学界的宝贵模板，特别是在地球科学领域，在所有其他 STEM 领域中，该领域的民族和种族多样性比例仍然最低（9%）。河流三角洲沉积物中过去的环境状态虽然是地球上最完整的，但破译起来却极具挑战性。 本研究的前提是三角洲系统的沉积记录是由其自生动力学（例如撕脱和侧向迁移速率）和同种异体强迫（例如气候、构造、海平面）的组合介导的。 研究人员建议开发一个综合统计框架来描述三角洲运输系统及其在一系列空间和时间尺度上的沉积记录。为了构建这个框架，他们将在物理和数值实验的数据以及现场数据集之间进行迭代，为尺度相关的动力学及其对过滤存储在地下沉积记录中的信号的贡献提供定量约束。这些信息对于（1）开发和约束三角洲演化模型，（2）从地层记录推断古环境条件，以及（3）通过以下方式帮助管理自然资源（例如沿海含水层、碳氢化合物储层等）至关重要。改进对一定范围内的地下储层特性的预测。该项目由地貌和土地利用动态计划以及刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。