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.

山区河流作为将沉积物和养分从山区分配到低地的主要媒介，发挥着独特的作用，是美国西部重要的淡水源区，也是重要的水生栖息地。然而，山区河流极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洪水和山体滑坡更加频繁，而干旱的严重程度则增加了野火、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来评估山区河道对这些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，并指导缓解和可持续恢复的管理和工程实践。项目成果将直接支持服务水平低下和少数族裔的高中和本科生在项目大学中亲身参与学习和研究 STEM 经验。这项研究旨在加深对山区河道几何形状如何动态响应洪水和其他流域扰动的了解。这项研究是围绕由于流域内固有的自然变化的扰动而分离河流响应的中心问题进行的。将变化信号与变异性分开涉及三个组成部分：自然强迫下预期河流条件的基于物理的模型、跨河床和河段规模的河道系统内固有自然变异性的量化、以及河流对这些尺度上的扰动响应的量化调整时间的物理描述。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沉积物传输数据来建立变异性的基线水平以及变异性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建立水力扰动、河流平面不稳定性和支撑沉积物迁移的阈值过程之间的物理联系； (3) 在记录的扰动破坏系统稳定或似乎已被河流过程缓冲的地点试点信噪比框架。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因气候和景观变化而受到侵蚀，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。