耦合泥沙连通性与单位泥沙图预测输沙:方法发展与流域尺度应用

IF 2.9 3区 地球科学 Q1 Environmental Science
Nabil Al-Aamery, James F. Fox, Tyler Mahoney, Arlex Marin-Ramirez
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引用次数: 0

摘要

类似于单位水线法的单位泥沙图方法,在过去50年中很少被应用,可能是由于泥沙核标度的限制。我们假设空间明确的泥沙连通性模型可以与单位泥沙图理论相结合,以估计整个流域的泥沙源区和动员时间,并估计水文事件的泥沙通量。我们使用沉积物连通性的概率和1小时单位沉积物图的对数正态参数化来制定模型。采用高性能计算集群辅助的两阶段校准程序,对美国肯塔基州三级流域的沉积物输运应用进行了模拟。结果充分证明了该方法的有效性,其中Nash-Sutcliffe效率在第一阶段和第二阶段分别高达0.87和0.84,模型验证阶段分别高达0.88。连通性概率的结果显示了传输事件之间和内部的变异性,高流量孤立事件的连通性为7.5%。对数正态分布有效地估计了泥沙图的上升边缘和下降边缘。建模结果的后处理显示了沉积物连通性概率的重要性,因为模拟忽略了它会产生不充分的结果。浅层人工神经网络模型的后处理表明,在事件尺度上,泥沙连通性和地表径流共同控制着产沙量。结果表明,每小时时间步长能够捕捉瞬时网络中沉积物连通性的开始和峰值连通性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting Sediment Transport by Coupling Sediment Connectivity With the Unit Sediment Graph: Method Development and Watershed-Scale Application

Predicting Sediment Transport by Coupling Sediment Connectivity With the Unit Sediment Graph: Method Development and Watershed-Scale Application

The unit sediment graph approach, analogous to the unit hydrograph method, was rarely applied in the past 50 years, presumably due to limitations from scaling the sediment kernel. We hypothesised that spatially explicit sediment connectivity modelling might be combined with unit sediment graph theory to estimate sediment source zones and time of mobilisation across the watershed and estimate sediment flux for hydrologic events. We formulated the model using the probability of sediment connectivity with log-normal parameterisation of the 1-h unit sediment graph. Simulations were carried out for a sediment transport application in a third-order watershed in Kentucky, USA, using a two-stage calibration procedure assisted by a high-performance computing cluster. Results showed sufficient evidence for the efficacy of the approach, including Nash-Sutcliffe Efficiency as high as 0.87 and 0.84 in Stages 1 and 2, respectively, of calibration and 0.88 for model validation. Results of the probability of connectivity showed variability across and within transport events, and 7.5% connectivity for the high flow isolated event. The log-normal distribution effectively estimated the rising limb and the falling limb of the sediment graphs. Post-processing of modelling results showed the importance of the probability of sediment connectivity, as simulations omitting it produced inadequate results. Post-processing with a shallow artificial neural network model showed that both sediment connectivity and surface runoff control sediment yield at the event scale. Results showed the ability of the hourly time step to capture the onset of sediment connectivity and peak connectivity across the ephemeral network.

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来源期刊
Hydrological Processes
Hydrological Processes 环境科学-水资源
CiteScore
6.00
自引率
12.50%
发文量
313
审稿时长
2-4 weeks
期刊介绍: Hydrological Processes is an international journal that publishes original scientific papers advancing understanding of the mechanisms underlying the movement and storage of water in the environment, and the interaction of water with geological, biogeochemical, atmospheric and ecological systems. Not all papers related to water resources are appropriate for submission to this journal; rather we seek papers that clearly articulate the role(s) of hydrological processes.
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