Collaborative Research: Separating the Climate and Weather of River Channels: Characterizing Dynamics of Coarse-Grained River Channel Response to Perturbations Across Scales

合作研究：分离河道的气候和天气：表征粗粒度河道对跨尺度扰动响应的动态

• 批准号: 2220504
• 负责人: Claire Masteller
• 金额: $ 31.39万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220504&HistoricalAwards=false
• 关键词: Collaborative; Research; Separating; Climate; Weather

Mountain rivers play a distinctive role as the primary agents of distributing sediment and nutrients from mountains to lowlands, as critical freshwater source areas for the Western United States, and as vital aquatic habitats. However, mountain rivers are highly susceptible to the compound hazards presented by global climate change and shifting precipitation patterns. Increasing rainfall intensity can result in more frequent flooding and landsliding while increasing drought severity enhances the threat of wildfire, vegetation loss, and extreme erosion risks. This project aims to develop a physical model to assess the sensitivity of mountain river channels to these shifts in climate and their associated hazards. This research will help to identify where river channels are susceptible to significant change and guide management and engineering practices for mitigation and sustainable restoration. Project results will directly support underserved and minority high school and undergraduates with hands-on engaging learning and research STEM experiences at project Universities.This research aims to develop an understanding of how mountain river channel geometry dynamically responds to flooding and other watershed perturbations. The research is organized around the central question of separating river response due to a perturbation from the inherent natural variability present within watersheds. Separating signals of change from variability involves three components: a physics-based model for the expected river conditions under natural forcing, quantification of the inherent natural variability within the river channel system across the riverbed and reach scale, and a physical description with quantified adjustment times for river response to perturbations across these scales. This research will accomplish all three components by: (1) leveraging high resolution lidar topography and sediment transport data to establish a baseline level of variability and the dependence on the scale of variability on climatic and geologic factors; (2) developing a physical link between hydraulic perturbations, river planform instability, and the threshold processes underpinning sediment transport through high resolution laboratory experiments; and (3) pilot a signal-to-noise framework at sites where documented perturbations have both destabilized the system or appear to have been buffered by river processes. Expected results will aid researchers and engineers in determining which river systems are vulnerable to erosion due changing climate and landscapes, and provide a foundation for treating rivers dynamically within the next-generation of river flood hazard forecasting models.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.

山区河流发挥着独特的作用，作为主要的代理人分配沉积物和营养物质从山区到低地，作为美国西部的重要淡水来源地区，并作为重要的水生栖息地。然而，山区河流非常容易受到全球气候变化和降水模式变化带来的复合危害的影响。 降雨强度的增加可能导致更频繁的洪水和滑坡，而干旱的严重程度增加了野火，植被损失和极端侵蚀风险的威胁。该项目旨在开发一个物理模型，以评估山区河流对气候变化及其相关危害的敏感性。这项研究将有助于确定哪些河道容易发生重大变化，并指导管理和工程实践，以减轻和可持续恢复。项目成果将直接支持服务不足和少数民族高中和本科生在项目大学的动手参与学习和研究STEM经验。这项研究旨在了解山区河流河道几何形状如何动态响应洪水和其他流域扰动。该研究是围绕中心问题的分离河流的响应，由于干扰的固有的自然变异存在于流域内。从变化信号分离涉及三个组成部分：一个物理为基础的模型，在自然强迫下的预期河流条件，量化的固有的自然变化在整个河床和河段尺度的河道系统内，和一个物理描述与量化的调整时间，河流对这些尺度上的扰动的反应。这项研究将通过以下方式完成所有三个组成部分：（1）利用高分辨率激光雷达地形和泥沙输运数据，确定变化的基线水平以及变化规模对气候和地质因素的依赖性;（2）通过高分辨率实验室实验，在水力扰动、河流平面形状不稳定性和支持泥沙输运的阈值过程之间建立物理联系;以及（3）在记录的扰动既破坏了系统稳定性又似乎被河流过程缓冲的地点试行信噪比框架。预期结果将帮助研究人员和工程师确定哪些河流系统容易受到气候和景观变化造成的侵蚀，并为在下一代河流洪水灾害预测模型中动态处理河流提供基础。该奖项反映了NSF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。