Flow-vegetation interactions at the plant-scale: the importance of volumetric canopy morphology on flow field dynamics

Vegetation is abundant in rivers, and has a significant influence on their hydraulic, geomorphological, and ecological functioning. However, past modelling of the influence of vegetation has generally neglected the complexity of natural plants. This thesis develops a novel numerical representation of flow through and around floodplain and riparian vegetation, focusing on flow-vegetation interactions at the plant-scale. The plant volumetric canopy morphology, which comprises the distribution of vegetal elements over the three-dimensional plant structure, is accurately captured at the millimetre scale spatial resolution using Terrestrial Laser Scanning (TLS), and incorporated into a Computational Fluid Dynamics (CFD) model used to predict flow. Numerical modelling, with vegetation conceptualised as a porous blockage, is used to improve the process-understanding of flow-vegetation interactions. Model predictions are validated against flume experiments, with plant motion dynamics investigated, and analysis extended to consider turbulent flow structures and the plant drag response. Results demonstrate the spatially heterogeneous velocity fields associated with plant volumetric canopy morphology. The presence of leaves, in addition to the posture and aspect of the plant, significantly modifies flow field dynamics. New insights into flow-vegetation interactions include the control of plant porosity, influencing ‘bleed-flow’ through the plant body. As the porosity of the plant reduces, and bleed-flow is prevented, the volume of flow acceleration increases by up to ~150%, with more sub-canopy flow diverted beneath the impermeable plant blockage. Species-dependent drag coefficients are quantified; these are shown to be dynamic as the plant reconfigures, differing from the commonly assigned value of unity, and for the species’ investigated in this thesis range between 0.95 and 2.92. The newly quantified drag coefficients are used to re-evaluate vegetative flow resistance, and the physically-determined Manning’s n values calculated are highly applicable to conveyance estimators and industry standard hydraulic models used in the management of the river corridor

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

Deriving planform morphology and vegetation coverage from remote sensing to support river management applications

With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, Geographical Information Systems, GIS), the use of remotely sensed information for monitoring riverine hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support flood risk and river management applications. Focusing on a reach of the Po River (Italy), satellite imagery from Landsat 5, 7 and 8 for the period 1988-2018 were analyzed in Google Earth Engine (GEE) to investigate changes in river planform morphology and vegetation dynamics associated with transient hydrology. An improved understanding of these correlations can help in managing sediment transport and riparian vegetation to reduce flood risk, where biogeomorphic processes are commonly overlooked in flood risk mapping. In the study, two established indices were analyzed: the Modified Normalized Difference Water Index (MNDWI) for monitoring changes in the wetted river planform morphology, inferring information about sediment dynamics, and the Normalized Difference Vegetation Index (NDVI) for evaluating changes in vegetation coverage. Results suggest that planform changes are highly localized with most parts of the reach remaining stable. Using the wetted channel occurrence as a measure of planform stability, almost two-thirds of the wetted channel extent (total area = 86.4 km2) had an occurrence frequency > 90% (indicating stability). A loss of planform complexity coincided with the position of former secondary channels, or zones where the active river channel had narrowed. Time series analysis of vegetation dynamics showed that NDVI maxima were recorded in May/June and coincided with the first peak in the hydrological regime (occurring in late spring and associated with snowmelt). Seasonal variation in vegetation coverage is potentially important for local hydrodynamics, influencing flood risk. We suggest that remotely sensed information can provide river scientists with new insights to support the management of highly anthropized watercourses

‘InfraRivChange’: A Web Application to Monitor River Migration at Sites of Critical Bridge Infrastructure in the Philippines

No abstract available

Commensurate lattice distortion in the layered titanium oxypnictides Na2_{2}Ti2Pn2_{2}Pn_{2}O (Pn=Pn = As, Sb) determined by X-ray diffraction

We report single crystal X-ray diffraction measurements on Na$_2$Ti$_{2}Pn_{2}$O ($Pn$ = As, Sb) which reveal a charge superstructure that appears below the density wave transitions previously observed in bulk data. From symmetry-constrained structure refinements we establish that the associated distortion mode can be described by two propagation vectors, ${\bf q}_{1} = (1/2, 0, l)$ and ${\bf q}_{2} = (0, 1/2, l)$, with $l=0$ (Sb) or $l = 1/2$ (As), and primarily involves in-plane displacements of the Ti atoms perpendicular to the Ti--O bonds. The results provide direct evidence for phonon-assisted charge density wave order in Na$_2$Ti$_{2}Pn_{2}$O and identify a proximate ordered phase that could compete with superconductivity in doped BaTi$_{2}$Sb$_{2}$O

Policy Brief: Making Space for Philippine Rivers

No abstract available

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

Cloud‐based computing, access to big geospatial data, and virtualization, whereby users are freed from computational hardware and data management logistics, could revolutionize remote sensing applications in fluvial geomorphology. Analysis of multitemporal, multispectral satellite imagery has provided fundamental geomorphic insight into the planimetric form and dynamics of large river systems, but information derived from these applications has largely been used to test existing concepts in fluvial geomorphology, rather than for generating new concepts or theories. Traditional approaches (i.e., desktop computing) have restricted the spatial scales and temporal resolutions of planimetric river channel change analyses. Google Earth Engine (GEE), a cloud‐based computing platform for planetary‐scale geospatial analyses, offers the opportunity to relieve these spatiotemporal restrictions. We summarize the big geospatial data flows available to fluvial geomorphologists within the GEE data catalog, focus on approaches to look beyond mapping wet channel extents and instead map the wider riverscape (i.e., water, sediment, vegetation) and its dynamics, and explore the unprecedented spatiotemporal scales over which GEE analyses can be applied. We share a demonstration workflow to extract active river channel masks from a section of the Cagayan River (Luzon, Philippines) then quantify centerline migration rates from multitemporal data. By enabling fluvial geomorphologists to take their algorithms to petabytes worth of data, GEE is transformative in enabling deterministic science at scales defined by the user and determined by the phenomena of interest. Equally as important, GEE offers a mechanism for promoting a cultural shift toward open science, through the democratization of access and sharing of reproducible code

Our Sun. IV. The Standard Model and Helioseismology: Consequences of Uncertainties in Input Physics and in Observed Solar Parameters

Helioseismology provides a powerful tool to explore the deep interior of the Sun: for example, the adiabatic sound speed can be inferred with an accuracy of a few parts in 10,000. This has become a serious challenge to theoretical models of the Sun. Therefore, we have undertaken a self-consistent, systematic study of sources of uncertainties in the standard solar model, which must be understood before the helioseismic observations can be used as constraints on theory. We find that the largest uncertainty in the sound speed in the solar interior, namely, 3 parts in 1000, arises from uncertainties in the observed photospheric abundances of the elements; uncertainties of 1 part in 1000 arise from (1) the 4% uncertainty in the OPAL opacities, (2) the 5% uncertainty in the basic pp nuclear reaction rate, (3) the 15% uncertainty in the diffusion constants for the gravitational settling of helium, and (4) the 50% uncertainties in diffusion constants for the heavier elements. (Other investigators have shown that similar uncertainties arise from uncertainties in the interior equation of state and in rotation-induced turbulent mixing.) The predicted pre-main-sequence solar lithium depletion is a factor of order 20 (an order of magnitude larger than that predicted by earlier models that neglected gravitational settling and used older opacities), and is uncertain by a factor of 2. The predicted neutrino capture rate is uncertain by 30% for the Cl-37 experiment and by 3% for the Ga-71 experiments (not including uncertainties in the capture cross sections), while the B-8 neutrino flux is uncertain by 30%.Comment: LaTeX, 38 pages (including 8 figures); ApJ, in press. Added figures/color figurea available at http://www.cita.utoronto.ca/~boothroy/sun4.htm