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首页> 外文期刊>Water resources research >Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions
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Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions

机译：在不同放电条件下预测Delta通道网络中的水和沉积物分区

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摘要

Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel network. Herein, multiple nodal relations that predict flow partitioning at individual bifurcations, utilizing various hydraulic and channel planform parameters, are tested against field data collected from the Selenga River delta, Russia. The data set includes 2.5 months of time-continuous, synoptic measurements of water and sediment discharge partitioning covering a flood hydrograph. Results show that width, sinuosity, and bifurcation angle are the best remotely sensed, while cross-sectional area and flow depth are the best field measured nodal relation variables to predict flow partitioning. These nodal relations are incorporated into a graph model, thus developing a generalized framework that predicts partitioning of water discharge and total, suspended, and bedload sediment discharge in deltas. Results from the model tested well against field data produced for the Wax Lake, Selenga, and Lena River deltas. When solely using remotely sensed variables, the generalized framework is especially suitable for modeling applications in large-scale delta systems, where data and field accessibility are limited.
机译：渠道分岔控制了斯特拓水和沉积物的分布，这些材料的路由有助于沿海地区的土地建设。虽然存在很少的实用方法，以提供分配信道网络内的多个分岔处的流动分配准确预测。这里，利用各种液压和信道平面参数预测各个分叉在各个分叉处的多个节点关系，用于从俄罗斯Selenga River Delta收集的现场数据进行测试。数据集包括水和沉积物排放分区的2.5个月的时间连续，沉积物排放分区。结果表明，宽度，索引和分叉角度是最佳的偏远感测，而横截面积和流动深度是最佳场测量的节点关系变量，以预测流动分配。这些节点关系被纳入图形模型，从而开发了一种推广的框架，其预测Δ中的排水和总，悬浮和床单沉积物排放的划分。模型的结果对蜡湖，Selenga和Lena River Deltas生产的现场数据进行了测试。当仅使用远程感测变量时，广义框架特别适用于在大型DELTA系统中建模应用，其中数据和现场可访问性受到限制。

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