This paper reports a novel method for the incorporation of complex plant morphologies into a computational fluid dynamics (CFD) model, allowing the numerical prediction of flows around individual plants. The morphological complexity, which comprises the vertical and lateral distribution of individual branches and leaves is captured through terrestrial laser scanning (TLS) and is maintained in the numerical prediction of flow fields. This is achieved where the post‐processed, voxelized plant representation is incorporated into a CFD scheme through a mass flux scaling algorithm (MFSA). Flow around Prunus laurocerasus has been modelled under foliated and defoliated states following the removal of leaves. The complex plant morphologies are shown to produce spatially heterogeneous downstream velocity fields, with velocity profiles that deviate significantly from the idealized inflected shape. Rapid transition between the high velocity free stream zone and the zone of reduced velocity in the plant wake indicate shearing of flow, with the point of reattachment extending up to seven plant lengths downstream. The presence of leaves significantly modifies the flow field response, with development of a second, more pronounced wake structure around the dense foliage. This approach provides a full flow numerical description of the pressure field, enabling the vegetative drag force to be quantified. For the example given here, drag force is an order of magnitude greater for the foliated state. The methodology outlined here demonstrates the importance of accurately representing complex plant morphology in hydraulic models, and allows drag forces and coefficients to be calculated for specific plant species. Copyright © 2015 John Wiley & Sons, Ltd.

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. This article is categorized under: Science of Water

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.

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.

Urban flooding is a key global challenge which is expected to become exacerbated under global change due to more intense rainfall and flashier runoff regimes over increasingly urban landscapes. Consequently, many cities are rethinking their approach to flood risk management by using green infrastructure (GI) solutions to reverse the legacy of hard engineering flood management approaches. The aim of GI is to attenuate, restore, and recreate a more natural flood response, bringing hydrological responses closer to pre‐urbanized conditions. However, GI effectiveness is often difficult to determine, and depends on both the magnitude of storm events and the spatial scale of GI infrastructure. Monitoring of the successes and failures of GI schemes is not routinely conducted. Thus, it can be difficult to determine whether GI provides a sustainable solution to manage urban flooding. This article provides an international perspective on the current use of GI for urban flood mitigation and the solutions it offers in light of current and future challenges. An increasing body of literature further suggests that GI can be optimized alongside gray infrastructure to provide a holistic solution that delivers multiple co‐benefits to the environment and society, while increasing flood resilience. GI will have to work synergistically with existing and upgraded gray infrastructure if urban flood risk is to be managed in a futureproof manner. Here, we discuss a series of priorities and challenges that must be overcome to enable integration of GI into existing stormwater management frameworks that effectively manage flood risk. This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water Science of Water > Water Extremes