Modeling complex flow structures and drag around a submerged plant of varied posture

Although vegetation is present in many rivers, the bulk of past work concerned with modeling the influence of vegetation on flow has considered vegetation to be morphologically simple and has generally neglected the complexity of natural plants. Here we report on a combined flume and numerical model experiment which incorporates time-averaged plant posture, collected through terrestrial laser scanning, into a computational fluid dynamics model to predict flow around a submerged riparian plant. For three depth-limited flow conditions (Reynolds number = 65,000–110,000), plant dynamics were recorded through high-definition video imagery, and the numerical model was validated against flow velocities collected with an acoustic Doppler velocimeter. The plant morphology shows an 18% reduction in plant height and a 14% increase in plant length, compressing and reducing the volumetric canopy morphology as the Reynolds number increases. Plant shear layer turbulence is dominated by Kelvin-Helmholtz type vortices generated through shear instability, the frequency of which is estimated to be between 0.20 and 0.30 Hz, increasing with Reynolds number. These results demonstrate the significant effect that the complex morphology of natural plants has on in-stream drag, and allow a physically determined, species-dependent drag coefficient to be calculated. Given the importance of vegetation in river corridor management, the approach developed here demonstrates the necessity to account for plant motion when calculating vegetative resistance

Patch-scale representation of vegetation within hydraulic models

Submerged aquatic vegetation affects flow, sediment and ecological processes within rivers. Quantifying these effects is key to effective river management. Despite a wealth of research into vegetated flows, the detailed flow characteristics around real plants in natural channels are still poorly understood. Here we present a new methodology for representing vegetation patches within computational fluid dynamics (CFD) models of vegetated channels. Vegetation is represented using a Mass Flux Scaling Algorithm (MFSA) and drag term within the Reynolds-Averaged Navier-Stokes Equations, which account for the mass and momentum effects of the vegetation respectively. The model is applied using three different grid resolutions (0.2, 0.1 & 0.05 m) using time-averaged solution methods and compared to field data. The results show that the model reproduces the complex spatial flow heterogeneity within the channel and that increasing the resolution leads to enhanced model accuracy. Future applications of the model to the prediction of channel roughness, sedimentation and key eco-hydraulic variables are presented, likely to be valuable for informing effective river management

Supplementary information files for An open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions

Supplementary information files for article An open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions Abstract Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low-likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.</p

Data and Code from Modelling flow-induced reconfiguration of variable rigidity aquatic vegetation

Data and model from the paper 'Modelling flow-induced reconfiguration of variable rigidity aquatic vegetation' published in the Journal of Hydraulic ResearchDOI:10.1080/00221686.2020.1866693Larger datasets not included here are available on request.</div

Temporal variability and within‐plant heterogeneity in blade biomechanics regulate flow‐seagrass interactions of Zostera marina

Seagrasses are marine flowering plants that have important roles in the ecological and physical processes of many coastal areas. Seagrass modelling to date has mostly assumed that seagrasses have uniform biomechanical traits in space and time. In this study we compare the biomechanical traits of Zostera marina blades collected in late summer and spring from a lagoon in southern Denmark. Then, we describe how biomechanics vary depending on (i) seasonality, (ii) storage in laboratory conditions with high nutrient levels, (iii) blade rank and (iv) position along blades. The data collected with these direct measurements are fed into a numerical structural model that simulates seagrass response to an idealized flow and accounts for plant non uniformity. The model is used to assess the effects of temporal variability and within-plant heterogeneity in blade biomechanics on flow-seagrass interactions. Results show that seagrass biomechanics are affected considerably by seasonality and laboratory storage. This biomechanical variability has a key role in defining flow-seagrass interactions, enhancing light availability in summer and reducing potential drag force in spring. Significant within-plant heterogeneity associated with both blade rank and along-blade position is reported. Compared to temporal variability, within-plant heterogeneity has a secondary role in determining flow seagrass interactions; however, blade rank is associated with a consistent reduction in the drag force. The results presented improve the understanding of flow-seagrass interactions by clarifying the importance of variations in seagrass blade biomechanical traits and their origin

Modelling flow-induced reconfiguration of variable rigidity aquatic vegetation

Aquatic vegetation is an important component of coastal and riverine environments and plays a significant role in shaping their evolution. The extent and nature of eco-hydraulic interaction depends upon the geometric and biophysical properties of the vegetation which affect the drag force and vegetation reconfiguration. Such vegetation properties commonly vary along each stem. However, this variability has not received significant attention in previous models. Here, we present a biomechanical model, based upon local parameterisation of stem properties which can represent variable rigidity stems. The model is validated for straight and curved beams before being applied to experimental data using surrogates with variable thickness and Young’s modulus. Finally, the model is applied to saltmarsh vegetation data. The results for saltmarsh vegetation show that using stem-averaged properties may result in errors in predicted drag force of up to 26% and highlights the need to consider the reconfiguration of variable rigidity stems

The influence of three‐dimensional topography on turbulent flow structures over dunes in unidirectional flows

Dunes are the most prevalent bedform present in sand-bedded rivers and their morphology typically comprises multiple scales of three-dimensional topography. However, our understanding of flow over dunes is predicated largely on two-dimensional models, a condition which is rare in nature. Here, we present results of Large Eddy Simulations over a static, three-dimensional dune field, using a two- and three- dimensional topographic realisation, to investigate the interaction between bed topography and turbulent flow structures. We show that flow over two-dimensional bedforms increases the velocity over the stoss slope and reduces the size of the leeside separation zone as compared to 3D topography. Flow over three-dimensional bedforms generates twice as many vortices as over two-dimensional bedforms, and these vortices are longer, wider and taller than flow over their two-dimensional counterparts. Turbulence is dominated by hairpin-shaped vortices and Kelvin-Helmholtz instabilities that interact with the bed in the brink point region of the dune crest and down the lee slope, and generate high shear stresses for long durations. These results are used to propose a new conceptual model showing the differences between flow over two- and three-dimensional bedforms. The findings highlight how the size, morphology and stacking of coherent flow structures into larger flow superstructures may be critical in sediment entrainment, and may dictate the relationship between event duration and magnitude that drives sediment impulses at the bed. This will ultimately lead to an increased in the three-dimensionality of bedform morphology

Revisiting the Gage–Bidwell Law of Dilution in relation to the effectiveness of swimming pool filtration and the risk to swimming pool users from <i>Cryptosporidium</i>

The transfer of water from a swimming pool to the treatment location is key in determining the effectiveness of water treatment by filtration in removing turbidity and managing the risk from particulate material, including microbial pathogens, such as Cryptosporidium spp. A key recommendation for pool operators when dealing with an accidental faecal release (the likely main source of high Cryptosporidium oocyst concentrations in pools) is that the pool water should be filtered for at least six turnover cycles prior to use. This paper briefly outlines the theoretical basis of what has become known as the Gage–Bidwell Law of Dilution, which provides a basis for this recommendation, and extends the idea to account for the impact of filter efficiency. The Gage–Bidwell Law reveals that for each pool turnover 63% of the water resident in the pool at the start of the turnover period will have been recirculated. Building on this, we demonstrate that both filter efficiency and water-turnover time are important in determining filtration effectiveness and can be combined through a single parameter we term ‘particle-turnover’. We consider the implications of the Gage–Bidwell Law (as referred to in the original 1926 paper) for the dynamics of the ‘dirt’ content of pool water, whether in terms of a specific particle size range (e.g., Cryptosporidium oocysts) or turbidity

Saltmarsh flow and vegetation data

Data for the paper:Marjoribanks, T. I., Lague, D., Hardy, R. J., Boothroyd, R. J., Leroux, J., Mony, C., & Puijalon, S. ( 2019).Flexural rigidity and shoot reconfiguration determine wake length behind saltmarsh vegetation patches. Journal of Geophysical Research: Earth Surface, 124. </div

An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions

Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low-likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.</p