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Fine Sediment Deposition and Filtration Under Losing and Gaining Flow Conditions: A Particle Tracking Model Approach

机译:损失下的细沉积物沉积和过滤,流动条件:粒子跟踪模型方法

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Fine particle deposition within riverbeds plays a major role in riverine ecology and biogeochemistry by altering hyporheic exchange flux. Moreover, it is ubiquitous within streams and rivers across all flow stages. However, the dynamics of fine particle deposition are still not completely understood in rivers, and continuum models like the advection dispersion equation require modifications to represent the processes accurately. To enhance understanding of fine particle dynamics, we developed a novel numerical particle tracking model that simulates fine particle deposition as a stochastic process under losing, neutral, and gaining streamflow conditions. These flow conditions generate three different velocity profiles by combining the free surface and groundwater flows. In addition, a novel aspect of our model is the storage of filtered particles to estimate concentration fields within the bed. Our simulated results are qualitatively compared with previous laboratory flume experimental results of kaolinite deposition under similar conditions. The model indicates that fine particle deposition patterns and residence time functions depend heavily on the exchange flux between stream and groundwater, as well as bed filtration properties as the deposition of particles occurs at greater depths in the losing stream condition than in the neutral and gaining cases. Therefore, the spatial pattern of particle deposition is a direct result of pore water velocity profiles, while the concentration depends on filtration dynamics within the bed.
机译:通过改变多余交换通量,河床内的细粒子沉积在河流生态和生物地球化学中起着重要作用。此外,在所有流动阶段的溪流和河流内普遍存在。然而,在河流中仍然没有完全理解细粒子沉积的动态,并且平流分散方程的连续模型需要修改以准确地代表过程。为了增强对细粒子动力学的理解,我们开发了一种新型数值粒子跟踪模型,其模拟细粒沉积作为失去,中性和获得流流条件下的随机过程。这些流动条件通过组合自由表面和地下水流动产生三种不同的速度分布。此外,我们模型的新颖方面是储存过滤的颗粒以估计床内的浓度田地。我们的模拟结果与类似条件下的高岭石沉积的先前实验室水槽实验结果进行了定性。该模型表明细颗粒沉积图案和停留时间函数在很大程度上取决于流和地下水之间的交换通量,以及床过滤性能,因为颗粒的沉积发生在失去流条件的更大深度中,而不是中性和增长的情况。因此,颗粒沉积的空间模式是孔隙水速度谱的直接结果,而浓度取决于床内的过滤动力学。

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