Quantifying the coevolution of bedload transport and bed topography in mountain rivers: field and flume experiments using smartrocks

量化山区河流底质输送和河床地形的协同演化：使用 smartrocks 进行现场和水槽实验

• 批准号: 1053508
• 负责人: Joel Johnson
• 金额: $ 32.23万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053508&HistoricalAwards=false
• 关键词: Quantifying; coevolution; bedload; transport; bed

Quantifying the coevolution of bedload transport and bed topography in mountain rivers: Field and flume experiments using smartrocksThe bottom of most steep mountain rivers is composed of coarse sediment (gravel to boulders), and the morphology (slope, width, depth, sediment size distribution, surface roughness) of a given channel develops over time due to size-dependent sediment transport and sorting. Conversely, transport rates of both water and sediment are controlled (in part) by channel morphology. The overall goal of this project is to better understand key feedbacks among sediment size, sediment transport rate and channel bed topography, in order to more accurately predict all these factors in natural rivers. A field experiment in river bed evolution will be conducted in which part of a mountain river channel (Reynolds Creek Experimental Watershed, Idaho) is modified using construction equipment. Starting from this artificially smoothed and straightened channel reach, the coevolution of bed topography and bedload transport rate will be measured over time. Complementary experiments will be conducted in laboratory flumes (artificial rivers in which variables can be precisely controlled). Hypotheses will be evaluated using unique data sets collected in both the field and laboratory by applying new and developing technologies. Methods include custom "smartrocks" (gravel and cobbles embedded with accelerometers) and clast tracking using radio frequency identification (RFID) tags. Channel bed morphology will be measured over length scales from centimeters to kilometers by combining airborne and ground-based laser topography surveys (LiDAR).Understanding river bed topography and bedload transport has many practical applications with broad significance to society. Mountain rivers are economically important (for example, cattle ranching takes place along one of the field sites at Reynolds Creek). Predicting how a river will respond to a flood of a given size depends on understanding channel stability, which in turn depends on the channel feedbacks being studied. River restoration efforts require a stronger scientific grounding to be able to engineer channels that will be stable over a given range of floods. Dam removal planning depends strongly on predicting channel feedbacks that occur while river discharge, sediment transport rate and channel morphology are all rapidly changing. In many rivers, transported sediment is also regulated as a pollutant, and better predictions of transport rates are critical for scientifically informed land-management decisions. Finally, the bed topographies of engineered and natural river channels form specific ecological niches, such as for spawning salmon that are sensitive to sediment sizes on the channel bottom.

量化山区河流中河床输送和河床地形的协同演化：使用 smartrocks 进行现场和水槽实验大多数陡峭的山区河流的底部由粗沉积物（砾石到巨石）组成，并且形态（坡度、宽度、深度、沉积物尺寸分布、由于尺寸依赖的沉积物运输和分类，给定通道的表面粗糙度随着时间的推移而变化。 相反，水和沉积物的输送速率（部分）由河道形态控制。 该项目的总体目标是更好地了解沉积物尺寸、沉积物输送速率和河床地形之间的关键反馈，以便更准确地预测自然河流中的所有这些因素。 将进行河床演变的现场实验，其中使用建筑设备对山区河道的一部分（爱达荷州雷诺兹溪实验流域）进行改造。 从这个人工平滑和拉直的河道范围开始，随着时间的推移，将测量河床地形和河床输送速率的协同演化。补充实验将在实验室水槽（可以精确控制变量的人工河流）中进行。 将通过应用新技术和开发技术，使用在现场和实验室收集的独特数据集来评估假设。 方法包括定制“智能岩石”（嵌入加速度计的砾石和鹅卵石）和使用射频识别 (RFID) 标签进行碎屑跟踪。通过结合机载和地面激光地形测量（LiDAR），可以在从厘米到公里的长度范围内测量河床形态。了解河床地形和床质输送具有许多实际应用，对社会具有广泛意义。 山区河流具有重要的经济意义（例如，雷诺兹溪 (Reynolds Creek) 的一处田地沿线有养牛场）。 预测河流将如何应对给定规模的洪水取决于对河道稳定性的了解，而河道稳定性又取决于所研究的河道反馈。 河流恢复工作需要更强大的科学基础，才能设计出在特定洪水范围内保持稳定的渠道。 大坝拆除规划在很大程度上取决于对河流流量、泥沙输送速率和河道形态快速变化时发生的河道反馈的预测。 在许多河流中，输送的沉积物也作为污染物进行监管，更好地预测输送速率对于科学地做出明智的土地管理决策至关重要。最后，人工河道和天然河道的河床地形形成了特定的生态位，例如对河道底部沉积物尺寸敏感的产卵鲑鱼。