本文报道了一个三维水动力模型的实现 预测浊度和颗粒物通过饮用水库的去向。这项工作 基于自然的浊度降低策略 通过水库，可以经济高效地改善水质。 墨尔本受保护的集水区向300多万人供应消毒但未经过滤的水 人这片水域被誉为世界上最宜人、最安全的水域之一，但 配水系统中的泥沙含量高于过滤水。 西尔万水库是墨尔本供水系统的重要组成部分。平均数 水库中的滞留时间约为3个月。然而，考虑到复杂性 储层的性质、短路的可能性很高，以及短路对浊度的影响 出口处的水平基本未知。 使用两种不同的数值模型来模拟水库中的水流: DYRESM，一维流体动力学模型；还有ELCOM-CAEDYM，一个三维的 流体力学和水质模型。建模的主要目标 是为了确定出口处测得的浊度的主要来源，以便 管理和运营工作可以适当集中。模型有一个 高数据要求及其发展要求安装温度传感器 测量流入流和高分辨率热敏电阻链和气象 储液罐中的传感器。还进行了广泛的现场试验，以跟踪 并对模型进行验证。 收集的数据表明，水库中的循环受两个因素控制: A. 强烈的日内波信号，导致水体出现较大的垂直偏移 柱后水平运输；还有起飞深度，这决定了分层。 模型显示，最快的流入流出行程时间约为3小时 稀释约100倍，表明尽管发生了一些混合，但 一部分流入的水到达出口时几乎没有滞留。模型还表明，出口处的浊度主要由入口浊度决定，而非 现场产生的浊度是由缓慢沉降的颗粒组成的。这个 风暴期间产生的沉降速度更快的颗粒物会在夜间造成浊度峰值 但不影响出口浊度水平。这意味着 从集水区向Silvan集水可以根据浊度进行审查 限制并允许产量增加。 对各种管理场景进行了建模，最终结果是减少 出口处的浊度，并优化滞留时间。结果将用于优化 水库的运行和重点进一步的调查工作。该模型将用于 评估未来管理和运营场景的影响。包括表格，数字。

This paper reports on the implementation of a three-dimensional hydrodynamic model to predict the fate of turbidity and particulates through a drinking water reservoir. This work offers the potential to develop turbidity reduction strategies based on the nature of flows through the reservoir enabling cost-effective improvements in aesthetic water quality. Melbourne's protected catchments supply disinfected but unfiltered water to over 3 million people. This water has a reputation as being one of the world's most pleasant and safe, but has higher sediment loads in the distribution system than filtered water. Silvan Reservoir is an essential part of Melbourne's water supply system. The average detention time in the reservoir is approximately 3 months. However, given the complex nature of the reservoir, short-circuiting is highly likely and the effects of this on turbidity levels at the outlets has been largely unknown. Two different numerical models were used to simulate water flow in the reservoir: DYRESM, a one-dimensional hydrodynamics model; and, ELCOM-CAEDYM, a three-dimensional hydrodynamics and water quality model. The main objective of the modelling was to determine the main source of the turbidity measured at the outlets so that management and operational efforts can be appropriately focused. The models have a high data requirement and their development required the installation of temperature gauging on inflow streams and high-resolution thermistor chains and meteorological sensors in the reservoir. An extensive field experiment was also undertaken to track the inflowing water paths and to validate the models. The data collected indicated that circulation in the reservoir is controlled by two things: a strong daily internal wave signal, which causes large vertical excursions of the water column followed by horizontal transport; and, the off-take depths, which set the stratification. The models have shown that the fastest inflow-outflow travel time is approximately 3 hours with a dilution of about 100 times indicating that although some mixing is occurring, a portion of the inflow water is reaching the outlet with very little to no detention. The models also show that turbidity at the outlets is dominated by the inlet turbidity rather than the in-situ generation and that this turbidity is made up of slow settling particles. The faster settling particles, which are generated during storm events, cause turbidity spikes at the inlets but do not affect the outlet turbidity levels. This implies that the strategy for harvesting water from the catchments into Silvan could be reviewed based on turbidity limits and may allow for an increase in yield. Various management scenarios were modelled with the ultimate outcome being to reduce turbidity at the outlets and optimize detention times. The results will be used to optimize operation of the reservoir and focus further investigation works. The model will be used to assess the implications of future management and operational scenarios as they arise. Includes table, figures.