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

山区河流推移质输运和河床地形共同演变的量化：使用Smartrock的野外和水槽试验大多数陡峭的山区河流的底部是由粗泥沙(从砾石到巨石)组成的，由于泥沙的输送和分选，给定河道的形态(坡度、宽度、深度、泥沙粒度分布、表面粗糙度)随着时间的推移而发展。相反，水和泥沙的输移速率(部分)受河道形态的控制。该项目的总体目标是更好地了解泥沙尺寸、泥沙输移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(爱达荷州雷诺溪实验分水岭)将使用建筑设备进行改造。从这一人工平整的河段开始，随着时间的推移，将测量河床地形和推移质输沙率的共同演变。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。将利用实地和实验室收集的独特数据集，通过应用新的和正在开发的技术，对假设进行评估。这些方法包括定制的“智能小车”(嵌入加速计的砾石和鹅卵石)和使用射频识别(RFID)标签的碎石追踪。结合机载和陆基激光地形测量(LiDAR)，将测量从厘米到公里的长度尺度上的河床形态，了解河床地形和推移质具有许多实际应用，具有广泛的社会意义。山区河流在经济上很重要(例如，沿着雷诺斯溪的一个田野地点进行养牛场)。预测一条河流将如何应对给定大小的洪水，取决于对河道稳定性的了解，而稳定性又取决于正在研究的河道反馈。河流修复工作需要更强大的科学基础，才能设计出在给定洪水范围内保持稳定的河道。除坝规划在很大程度上依赖于预测河道反馈，而河道流量、输沙率和河道形态都在迅速变化。在许多河流中，输送的沉积物也作为污染物受到监管，更好地预测输沙率对于科学地做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.