Continental Scale Heterogeneous Channel Flow Routing Strategy for Operational Forecasting Models

Continental Scale Heterogeneous Channel Flow Routing Strategy for Operational Forecasting Models
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业务预测模型的大陆尺度异质河道流量路由策略

DOI:
10.1111/1752-1688.12847
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发表时间:
2021
期刊:
JAWRA Journal of the American Water Resources Association
影响因子:
--
通讯作者:
Mandli, Kyle
Mandli, Kyle
中科院分区:
--
文献类型:
--
作者:
Meselhe, Ehab;Lamjiri, Maryam A.;Flint, Kelly;Matus, Sean;White, Eric D.;Mandli, Kyle

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业务预测模型对公众的好处是多方面的。它们支持水资源管理决策,为减轻天气和洪水相关灾害的影响提供机会,并可能挽救生命和财产。河道流量演算是这些模型的关键组成部分,并影响其预测洪水深度、持续时间和范围的能力。业务预报环境中的大陆规模河道流量路由遇到广泛的水力特征。部署计算要求高的方法,如动态波,应在时间和空间上限制在惯性项显著的条件下(通常在低梯度环境和回水效应突出的情况下);否则,高效和鲁棒的方法,例如,运动波、马斯京根-康吉波或扩散波应是默认值。这里提出的异构路由方法提供了一个框架,以评估摩擦,惯性和压力之间的平衡,并从战略上触发适当的波近似。这里推荐的策略是根据环境水力条件激活适当的波浪近似值,并在这些近似值之间平滑过渡。这一战略,如果成功实施,将达到业务预测模型的性能指标,即计算效率,准确性和最大限度地减少计算不稳定性之间的平衡。
The benefits of operational forecasting models to the general public are numerous. They support water management decisions, provide the opportunity to mitigate the impacts of weather‐ and flood‐related disasters and potentially save lives and properties. Channel flow routing is a key component of these models and affects their ability to forecast flood depth, duration, and extent. Continental scale channel flow routing within the operational forecasting environments encounters a broad spectrum of hydraulic characteristics. Deploying computationally demanding approaches, such as the dynamic wave, should be limited in time and space to conditions where the inertia terms are significant (typically in low‐gradient environments and whenever backwater effects are prominent); otherwise, efficient and robust methods, e.g., Kinematic, Muskingum‐Cunge or diffusive waves should be the default. The heterogeneous routing approach presented here provides a framework to evaluate the balance between friction, inertia, and pressure and strategically triggers the appropriate wave approximation. The strategy recommended here is to activate the appropriate wave approximation based on the ambient hydraulic conditions, and smoothly transitions among these approximations. This strategy, if successfully implemented, would strike a balance among the performance metrics of operational forecasting models, namely, computational efficiency, accuracy, and minimization of computational instabilities.
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