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.

定量的推移质运输和河床地形在山区河流的共同演变：现场和水槽实验，使用smartrocksThe底部最陡峭的山区河流是由粗泥沙（砾石巨石），和形态（坡度，宽度，深度，泥沙粒径分布，表面粗糙度）的一个给定的通道随着时间的推移，由于尺寸相关的泥沙运输和分选。 相反，水和沉积物的输运速率（部分）由通道形态控制。 该项目的总体目标是更好地了解泥沙粒径、输沙率和河床地形之间的关键反馈，以便更准确地预测天然河流中的所有这些因素。 将进行河床演变的现场实验，其中部分山区河道（雷诺兹溪实验流域，爱达荷州）使用施工设备进行修改。 从这个人工平整和拉直的河道河段开始，将测量河床地形和推移质输沙率随时间的共同演变。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。 通过应用新的和正在开发的技术，将使用在现场和实验室收集的独特数据集对假设进行评估。 方法包括定制的“smartrocks”（嵌入加速计的砾石和鹅卵石）和使用射频识别（RFID）标签的碎屑跟踪。通过机载和地基激光地形测量（LiDAR）相结合，可以测量从厘米到千米的河床形态。了解河床地形和推移质输移具有广泛的社会意义。 山区河流在经济上很重要（例如，雷诺兹溪的一个牧场沿着有养牛场）。 预测河流如何应对给定规模的洪水取决于对河道稳定性的理解，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，以便能够设计在给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预测河道反馈，而河流流量，泥沙输移率和河道形态都在迅速变化。 在许多河流中，输移的沉积物也作为污染物受到管制，更好地预测输移率对于科学知情的土地管理决策至关重要。最后，工程和天然河道的河床地形形成了特定的生态位，例如对产卵鲑鱼来说，它们对河道底部的沉积物大小很敏感。