Fast approach for unsteady flow routing in complex river networks based on performance graphs

We present a new model for unsteady flow routing through dendritic and looped river networks based on performance graphs. The model builds upon the application of Hydraulic Performance Graph (HPG) to unsteady flow routing introduced by Gonzalez-Castro (2000) and adopts the Volume Performance Graph (VPG) introduced by Hoy and Schmidt (2006). The HPG of a channel reach graphically summarizes the dynamic relation between the flow through and the stages at the ends of the reach under gradually varied flow (GVF) conditions, while the VPG summarizes the corresponding storage. Both, the HPG and VPG are unique to a channel reach with a given geometry and roughness, and can be computed decoupled from unsteady boundary conditions by solving the GVF equation for all feasible conditions in the reach. Hence, in the proposed approach, the performance graphs can be used for different boundary conditions without the need to recompute them. Previous models based on the performance graph concept were formulated for routing through single channels or channels in series. The new approach expands on the use of HPG/VPGs and adds the use of rating performance graphs for unsteady flow routing in dentritic and looped networks. We exemplify the applicability of the proposed model to subcritical unsteady flow routing through a looped network and contrast its simulation results with those from the well-known unsteady HEC-RAS model. Our results show that the present extension of application of the HPG/VPGs appears to inherit the robustness of the HPG routing approach in Gonzalez-Castro (2000). The unsteady flow model based on performance graphs presented here can be extended to include supercritical flows.Keywords: Looped network, Hydraulic routing, Modeling, Simulation, Unsteady flow, Dendritic network, River hydraulics, Flooding, Flow routingKeywords: Looped network, Hydraulic routing, Modeling, Simulation, Unsteady flow, Dendritic network, River hydraulics, Flooding, Flow routin

A New Coupled Optimization‐Hydraulic Routing Model for Real‐Time Operation of Regulated River Systems

This paper presents the River Simulation and Optimization Coupled Model (RSOCM) for the optimal operation of regulated river systems in real-time conditions. This model couples a highly robust and numerically efficient hydraulic routing approach with the well-known multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed model overcomes the lack of robustness of current models for unsteady flow routing. The lack of robustness of current unsteady flow models (e.g., numerical instability) constitutes a strong limitation for the development and implementation of real-time strategies in complex hydraulic systems that are intended to fulfill multiple objectives (e.g., minimization of flooding, optimal water allocation at specified diversion points). The real-time control of such complex systems may require thousands of computations of hydraulic routing for each operation interval. The optimization objectives supported by the RSOCM model include the optimal water allocation at specified diversion points and minimization of flooding. To demonstrate the proof of concept of the RSOCM model, it was applied to a hypothetical river system. As frame of comparison for the RSOCM model, the MODSIM-DSS model (Colorado State University, 1995) was used in this paper. The results suggest that the RSOCM model is a robust tool that can be potentially used for the optimal operation of multi-objective regulated river systems in real-time conditions

Dynamic Framework for Intelligent Control of River Flooding: Case Study

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control

Attitudes towards vaccines and intention to vaccinate against COVID-19: a cross-sectional analysis - implications for public health communications in Australia

Objective To examine SARS-CoV-2 vaccine confidence, attitudes and intentions in Australian adults as part of the iCARE Study. Design and setting Cross-sectional online survey conducted when free COVID-19 vaccinations first became available in Australia in February 2021. Participants Total of 1166 Australians from general population aged 18-90 years (mean 52, SD of 19). Main outcome measures Primary outcome: responses to question € If a vaccine for COVID-19 were available today, what is the likelihood that you would get vaccinated?'. Secondary outcome: analyses of putative drivers of uptake, including vaccine confidence, socioeconomic status and sources of trust, derived from multiple survey questions. Results Seventy-eight per cent reported being likely to receive a SARS-CoV-2 vaccine. Higher SARS-CoV-2 vaccine intentions were associated with: increasing age (OR: 2.01 (95% CI 1.77 to 2.77)), being male (1.37 (95% CI 1.08 to 1.72)), residing in least disadvantaged area quintile (2.27 (95% CI 1.53 to 3.37)) and a self-perceived high risk of getting COVID-19 (1.52 (95% CI 1.08 to 2.14)). However, 72% did not believe they were at a high risk of getting COVID-19. Findings regarding vaccines in general were similar except there were no sex differences. For both the SARS-CoV-2 vaccine and vaccines in general, there were no differences in intentions to vaccinate as a function of education level, perceived income level and rurality. Knowing that the vaccine is safe and effective and that getting vaccinated will protect others, trusting the company that made it and vaccination recommended by a doctor were reported to influence a large proportion of the study cohort to uptake the SARS-CoV-2 vaccine. Seventy-eight per cent reported the intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing, etc) postvaccine. Conclusions Most Australians are likely to receive a SARS-CoV-2 vaccine. Key influencing factors identified (eg, knowing vaccine is safe and effective, and doctor's recommendation to get vaccinated) can inform public health messaging to enhance vaccination rates