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Continental Scale Heterogeneous Channel Flow Routing Strategy for Operational Forecasting Models
Author(s) -
Meselhe Ehab,
Lamjiri Maryam A.,
Flint Kelly,
Matus Sean,
White Eric D.,
Mandli Kyle
Publication year - 2021
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12847
Subject(s) - computer science , flow routing , routing (electronic design automation) , channel (broadcasting) , inertia , flow (mathematics) , flood myth , scale (ratio) , operations research , environmental science , engineering , mathematics , telecommunications , computer network , physics , geometry , geotechnical engineering , classical mechanics , philosophy , theology , quantum mechanics
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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