A reduced-complexity model for river delta formation – Part 2: Assessment of the flow routing scheme
A reduced-complexity model for river delta formation – Part 2: Assessment of the flow routing scheme
复制标题
河流三角洲形成的复杂性降低模型 - 第 2 部分：流量演算方案评估
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Passalacqua
中科院分区：
文献类型：
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作者：
M. Liang;N. Geleynse;D. Edmonds;P. Passalacqua
摘要。在一篇相关论文（梁等人，2015年）中，我们引入了一个用于河流三角洲形成的低复杂度模型（RCM），该模型是使用基于地块的“加权随机游走”方法来模拟水和泥沙通量路径而开发的。这个模型（称为DeltaRCM）由一个作为水动力组件的水流路径方案（称为FlowRCM）和一组作为地貌动力组件的泥沙输运规则组成。在这项工作中，我们通过一系列水动力测试来评估FlowRCM的性能，将模型输出与Delft3D和理论预测进行比较。这些测试旨在揭示FlowRCM解析流场特征的能力，这些特征在河道过程层面对于三角洲动力学至关重要。特别是，我们关注（1）回水剖面，（2）河口沙坝周围的水流，（3）通过单个分汊的水流，以及（4）通过分流河道网络的水流。我们表明，虽然简单的规则无法重现所有精细尺度的水流结构，但FlowRCM能够捕捉到对于三角洲过程至关重要的流场特征，例如分汊和决口、河道与淹没岛屿之间的通量分配，以及导致河口沙坝生长的河口通量分布的不稳定性。最后，我们讨论了FlowRCM的优势和局限性，并确定了最适合它的环境。
Abstract. In a companion paper (Liang et al., 2015) we introduced a reduced-complexity model (RCM) for river delta formation, developed using a parcel-based "weighted random walk" method for routing water and sediment flux. This model (referred to as DeltaRCM) consists of a flow routing scheme as the hydrodynamic component (referred to as FlowRCM) and a set of sediment transport rules as the morphodynamic component. In this work, we assess the performance of FlowRCM via a series of hydrodynamic tests by comparing the model outputs to Delft3D and theoretical predictions. These tests are designed to reveal the capability of FlowRCM to resolve flow field features that are critical to delta dynamics at the level of channel processes. In particular, we focus on (1) backwater profile, (2) flow around a mouth bar, (3) flow through a single bifurcation, and (4) flow through a distributary channel network. We show that while the simple rules are not able to reproduce all fine-scale flow structures, FlowRCM captures flow field features that are essential to deltaic processes such as bifurcations and avulsions, the partitioning of flux between channels and inundated islands, and the instability of flux distribution at channel mouths which is responsible for mouth-bar growth. Finally, we discuss advantages and limitations of FlowRCM and identify environments most suitable for it.
影响因子：
3.3
作者：
Nicholas A
通讯作者：
Nicholas A