Fluid dynamics across the interface in gravel-bed rivers; quantification and numerical modelling of flow in the hyporheic zone

水在自然河流中的流动方式是最复杂的现象之一，难以在环境中进行准确的建模和预测。对于发生在河床表面以下的流动（在一个被称为“隐流”区的区域），在构成河底的鹅卵石和石头之间的空间，情况更是如此。精确模拟这些水流的努力一直受到阻碍，因为迄今为止，从鹅卵石之间的微小空间测量水流速度被证明是一个无法解决的问题。但为什么这要让科学家担心呢？首先，溪流生态学家现在认识到，暗流带是多种物种的重要栖息地。河床上方的水流进入地下的方式在很大程度上决定了为这个栖息地提供多少氧气和营养物质。其次，渔业管理人员早就明白，鲑鱼卵在河床孵化的可能性将取决于向它们产卵的沙砾沉积物持续供应含氧水。第三，河流系统中的污染物（如重金属）经常附着在一种叫做胶体的微观颗粒上，这种颗粒往往会沿着水流路径移动。因此，了解水流如何在河床内运动将对确定污染物的行为大有帮助。因此，有许多非常重要的环境问题需要对河床内的水如何流动进行详细的预测，但没有办法准确地测量或建模。本提案采用开创性的新方法，试图应对这一挑战。第一项任务是精确测量床层内的流量，这一重大问题将通过一种新的微型piv（颗粒成像测速）技术来解决。该系统借鉴了为医疗应用而开发的技术，采用了一个小型内窥镜数码相机，可以放置在实验河床内。通过在水流中植入微小的反射颗粒，并提供高强度的激光照明，内窥镜摄像机可以记录它们如何在河床卵石之间的小缝隙中移动。使用一个特殊的处理器，这些数字图像可以转换成数字数据，准确地记录水流如何流经河床，然后进入河床。这样的测量在以前是不可能的。该项目的第二阶段是利用这个独特的数据集所带来的新认识来开发和测试一个3-D数值模型，该模型可以精确预测河水在河床表面上下的流动情况。这将使用一个特殊修改的计算流体动力学（CFD）模型来实现。这些模型代表了最先进的技术，然而地下水流的问题已经被证明对它们在这种环境中的应用来说问题太大了。然而，我们的团队设计了一种方法，通过这种方法，鹅卵石可以被“冲掉”，并且可以预测鹅卵石周围和鹅卵石之间相邻间隙的流动。测量和建模方法的进步将在这个项目中使用，这代表了真正的突破，将解开从最具挑战性的自然环境中获取有用数据的固有问题。同时，可以广泛应用的数值模型的发展将确保这种新的理解可以应用和适应各种现实世界的环境挑战。

The way in which water flows within a natural river is one of the most complex phenomenon to model and predict accurately in the environment. This is even more so for the flow that occur just beneath the surface of the river bed (in a region termed the 'hyporheic' zone), between the spaces of pebbles and stones that make up the bottom of a river. Efforts to accurately model these flows have been hampered by the fact that obtaining measurements of water velocity from the tiny spaces between pebbles has so far proved an irresolvable problem. But why should this worry scientists? Firstly, stream ecologists now recognise that the hyporheic zone is an important habitat for a diverse range of species. The way flow from above the bed makes its way into the subsurface largely dictates how much oxygen and nutrients are supplied to this habitat. Secondly, fisheries managers have long understood that the probability of salmon eggs laid in river beds hatching will be dependent on a continuous supply of oxygenated water to the grevelly sediments in which they are laid. Thirdly, pollutants in river systems (such as heavy metals) often become attached to microscopic particles called colloids, which tend to follow flow pathways. An understanding of how flow moves within a river bed will thus go a long way to establishing pollutant behaviour. There are thus a broad range of highly important environmental issues that require detailed predictions of how water moves within a river bed, yet there is no way of measuring or modelling this accurately. Using pioneering new approaches this proposal seeks to meet this challenge. The first task is to accurately measure flow within the bed, this significant problem will be overcome using a new micro-PIV (particle imaging velocimetry) technique. This system borrows technology developed for medical applications by employing a small endoscopic digital camera which can be placed within an experimental river bed. By seeding the flow with tiny reflective particles, and providing high intensity illumination from a laser, the endoscopic camera can record how they move within the small gaps found between pebbles in the river bed. Using a special processor, these digital images can be turned into numerical data that accurately records how flow moves across and then into the river bed. Such measurements have never been possible before. The second phase of the project is to use the new understanding made possible by this unique dataset to develop and test a 3-D numerical model that can precisely predict how water will flow above and below the surface of a river bed. This will be achieved using a specially modified computational fluid dynamics (CFD) model. Such models represent the state-of-the-art, yet the issue of subsurface flow has proved too problematic for them to be applied in such environments. However, our team has devised a method whereby the pebbles can be 'blanked out' and the flow predicted around them and into adjacent gaps between pebbles. The advances in measurement and modelling approach that will be used in this project represent real breakthroughs that will unlock the inherent problem of gaining useful data from one of the most challenging of natural environments. Meanwhile, the development of a numerical model that can be widely applied will ensure that this new understanding can be applied and adapted to meet a variety of real world environmental challenges.