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

山地河流中河床输运和河床地形的共同演化量化：使用智能岩石的野外和水槽实验大多数陡峭的山地河流的底部由粗泥沙（砾石到巨石）组成，由于泥沙输运和分选的大小依赖，特定河道的形态（坡度、宽度、深度、泥沙大小分布、表面粗糙度）随着时间的推移而发展。相反，水和泥沙的输运速率（部分地）受河道形态的控制。该项目的总体目标是更好地了解泥沙大小、输沙速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所有这些因素。将进行河床演变的实地试验，其中部分山区河道（爱达荷州雷诺兹河实验流域）使用施工设备进行改造。从这段人为理顺和拉直的河道河段开始，将随时间测量河床地形和河床输运率的共同演化。补充实验将在实验室水槽（可精确控制变量的人工河流）中进行。通过应用新的和发展中的技术，使用在现场和实验室收集的独特数据集来评估假设。方法包括定制的“智能岩石”（砾石和鹅卵石中嵌入加速度计）和使用射频识别（RFID）标签的碎屑跟踪。通过结合机载和地面激光地形测量（LiDAR），将测量从厘米到公里的长度尺度的河床形态。了解河床地形和河床输运有许多实际应用，对社会具有广泛意义。山间河流在经济上具有重要意义（例如，雷诺溪的一个牧场就在附近）。预测一条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，而这又取决于所研究的河道反馈。河流修复工作需要更强大的科学基础，以便能够设计出在特定洪水范围内稳定的河道。大坝拆除计划在很大程度上依赖于预测河道反馈，而这些反馈发生在河流流量、输沙速率和河道形态都在快速变化的情况下。在许多河流中，输运的泥沙也作为一种污染物受到管制，更好地预测输运率对于科学地作出土地管理决策至关重要。最后，工程河道和天然河道的河床地形形成了特定的生态位，例如产卵的鲑鱼对河道底部的沉积物大小很敏感。

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.