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Improved understanding of the connection between riparian vegetation and
channel change requires evaluating how fine-scale interactions among
stems, water, and sediment affect larger scale flow and sediment
transport fields. We propose a spatially explicit model that resolves
patch-scale (submeter) patterns of hydraulic roughness over the reach
sca...
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Dredging has a significant effect on sediment transportation, water quality and flow conditions in a river channel. However, often the effects of dredging are not studied carefully in advance. One reason for this is the lack of suitable study approach. Studies on river dynamics require high quality geometric models of riverbed, banks and floodplain...
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... Vegetation plays a critical role in the structure and function of riparian systems as well as the broader rangeland water cycle (Wilcox et al. 2017). Above ground, plants provide surface roughness to redistribute flow patterns and facilitate deposition and soil building (Manners et al. 2013). Many riparian plants have adaptations to withstand stream or overland flows, such as cordlike rhizomes, fibrous root masses, coarse The National Wetland Plant List (Lichvar et al. 2012(Lichvar et al. , 2016USACE 2018) characterizes plant species by the frequency in which they are found in wetlands leaves, and strong flexible crowns (Winward 2000). ...
Water scarcity and climatic variability shape human settlement patterns and wildlife distribution and abundance on arid and semi-arid rangelands. Riparian areas-the transition between water and land-are rare but disproportionately important habitats covering just a fraction of the land surface (commonly < 2% in the western U.S.). Riparian areas provide critical habitat for fish and other aquatic species, while also supporting the vast majority (70-80%) of terrestrial wildlife during some portion of their life cycle. Diverse riparian types serve as vital sources of water and late summer productivity as surrounding uplands dry during seasonal drought. The health and function of rangeland riparian systems are closely tied to hydrology, geomorphology, and ecology. Riparian areas have attracted intense human use resulting in their widespread degradation. Conservation actions, including improved livestock grazing management and restoration, can help maintain and enhance riparian resilience to drought, wildfire, and flooding. This chapter provides readers with an introduction to the importance of riparian areas in rangelands, their nature and ecology, functions for wildlife, and prevailing management and restoration approaches.
... The simplest approach is to represent multiple stems/foliage as an extra sub-grid component, which contributes an additional drag force. This approach has been used for static vegetation models [42,82] as well as for dynamic vegetation models, where the foliage can either be treated as a static component attached to the bending stem [31,46] or as a dynamic term that adjusts with the velocity to account for variation in flow-leaf interaction [62,73,86]. In the above examples, methods such as terrestrial laser scanning (TLS) and image analysis as well as stem sampling are used to help define the morphological characteristics. ...
... They demonstrated that foliage can increase the drag by an order of magnitude and that the inclusion of both complex morphology and foliage produced flow structure not simulated by rigid cylinders. Manners et al. [42] applied a multi-scale approach, whereby stem-resolving simulations using finescale TLS data were used to calculate roughness values for reach-scale simulations. While these approaches represent significant advances in the representation of complex morphology, there remains a significant gap in the representation of reconfiguration of morphologies beyond single stems (Fig. 1). ...
Vegetation is a common occurrence within natural rivers and coastal systems. It can have a profound effect on flow and sediment dynamics which means it may be utilised as a nature-based solution or may require management to prevent adverse effects. Understanding the complex interaction between flow and vegetation is essential to effective management. This two-way interaction is a function of both the flow and vegetation properties. There are a broad range of methods that have been used to represent flow-vegetation interactions within numerical models, ranging from the rigid cylinder approximation to flexible and complex morphologies. Here, I review recent methods employed to represent flow-vegetation interactions and identify key future research directions. In particular, this chapter focuses on the role of vegetation characteristics including morphology and biomechanics on determining flow and sediment dynamics. I conclude that there is a need to both improve our understanding of the impact of such vegetation characteristics, and improve the representation of these impacts, within reach-scale models used for river and coastal management.KeywordsEco-hydrodynamicsFlow-vegetation interactionsNumerical modellingComputational fluid dynamicsAquatic ecology
... The importance of wider reaches increases during major floods, where greater topographic variability on the floodplain provides a broader range of hydraulic conditions during overbank flows to facilitate seed interception and large-scale recruitment processes (e.g., Scott et al., 1996;Florsheim and Mount, 2002;Cooper et al., 2003;Van Appledorn et al., 2021;Tranmer et al., 2022a). For example, new recruitment sites can be created in secondary channels on the floodplain that erode or accrete during floods and in depositional sites in the lee of vegetation on higher surfaces (e.g., Gurnell and Petts, 2002;Stella et al., 2011;Manners et al., 2013;Gurnell, 2014;Bywater-Reyes et al., 2017;Tranmer et al., 2020a;Sumaiya et al., 2021). Given their reliance on major floods, wider reaches may exhibit large recruitment events at the multi-decadal to centurial timescale. ...
Native riparian forests provide the physical structure, nutrient inputs, and habitat elements necessary to support ecosystem function for many aquatic and terrestrial species. However, regeneration of these important ecotones is being negatively impacted by anthropogenic and climatic pressures, driving managers to consider creative ways to increase native forest recruitment. Decision-making tools currently have limited capacity to assist native forest management and restoration since most cannot identify which recruitment mechanisms fail or where those failures occur on the floodplain. In response, we reconfigured a validated spatially-distributed riparian forest recruitment model to identify the limiting recruitment mechanisms and delineate their spatial extent on the floodplain to guide and prioritize management and restoration efforts. Here we focus on native Salicaceae species, whose seed dispersal mechanism is primarily driven by hydrochory.
Results indicate that the success of individual recruitment parameters in semi-confined river systems changes with both hydrologic condition and relative floodplain width (Wfp∗ = Wfloodplain/Wchannel). Overall, the disturbance mechanism (shear stress), required to create bare-soil conditions, was the most significant limitation to native forest recruitment in the system. Greater potential riparian recruitment occurs in wet hydrologic years than in average hydrologic years owing to greater floodplain inundation, but inundation alone is a poor success metric since recruitment requires an appropriate sequence of geomorphic disturbance, seed delivery, and water availability over the growing season. Consequently, narrow floodplains (Wfp∗< ∼3) have greater recruitment efficiency, potential recruitment per available floodplain area, than wide floodplains (Wfp∗> ∼3) but wide floodplains provide greater total potential recruitment area during wet hydrologic conditions. Model output maps that delineated individual and combined failure mechanisms during average and wet hydrologic years in the managed South Fork Boise River, USA were used to identify areas on the floodplain for potential restoration actions.
... For field-based measurements, the vegetation height can be determined using a handheld hypsometer (Gillihan, 2013), using conventional forest inventory methods with poles or trigonometric transformations of distance and angle measurements (Sexton, Bax, Siqueira, Swenson, & Hensley, 2009), and using terrestrial laser scanning (TLS) (Bywater-Reyes, Diehl, & Wilcox, 2018). Similarly, vegetation density can be determined by counting the number of stems and measuring the diameter for a specific area (Gillihan, 2013), using TLS (Manners, Schmidt, & Wheaton, 2013), and using a parallel photographic method (Delai, Kiss, & Nagy, 2018). LAI is determined directly in the field using active laser sensors (e.g., TLS) (Antonarakis, Richards, Brasington, & Muller, 2010) or passive optical sensors (Gillihan, 2013). ...
Riparian vegetation provides many noteworthy functions in river and floodplain systems, including its influence on hydrodynamic processes. Traditional methods for predicting hydrodynamic characteristics in the presence of vegetation involve the application of static Manning's roughness, which does not directly account for vegetation characteristics and neglects changes in roughness due to local water depth and velocity. The objectives of this study were to (1) implement numerical routines for simulating vegetation‐induced hydraulic roughness in a two‐dimensional (2D) hydrodynamic model; (2) evaluate the performance of two vegetation roughness approaches; and (3) compare vegetation parameters and hydrodynamic model results based on field‐based and remote sensing acquisition methods. Two roughness algorithms were coupled to an existing 2D hydraulic solver, which requires vegetation parameters to calculate spatially distributed roughness coefficients. Vegetation parameters were determined by field survey and using airborne light detection and ranging (LiDAR) data for San Joaquin River, California, USA. Water surface elevations modeled using vegetation‐based roughness approaches produced an acceptable overall performance, but the results were sensitive to the vegetation parameterization method (field based vs. LiDAR). Spatial variations in roughness and hydraulic conditions (water depth and velocity) were observed based on vegetation species and discharges for vegetation‐based approaches. The proposed approach accounts for the complexities of the physical environment instead of relying on traditional roughness as model inputs. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the area having spatial variation of vegetation (e.g., species and density). However, additional research is needed to quantify model performance with respect to spatially distributed water depth and velocity and parameterization of vegetation characteristics.
... Outside of fluvial research there is an increasing awareness of the potential of remote sensing methods to help drive the scalability of functional traits, especially in relation to physical traits such as plant height, leaf area index, phenology, and biomass (Abelleira Martínez et al., 2016;Aguirre-Gutiérrez et al., 2021), yet considerable limitations remain due to the uncertainty in relating spectral and physical properties to functional traits (Houborg et al., 2015). Upscaling localised high resolution data is possible however, for example from TLS (Terrestrial Laser Scanning) 195 to large scale ALS (Airborne Laser Scanning) data (Manners et al. (2013). Advances in UAV (Uncrewed Aerial Vehicle) remote sensing can create an important link between these two scales of data collection. ...
... such complex branching. However, methods pioneered by Manners et al. (2013) may help to overcome this by relating vertical 690 profiles from TLS and ALS data to enable upscaling to larger extents. ...
Vegetation plays a critical role in the modulation of fluvial process and morphological evolution. However, adequately capturing the spatial variability and complexity of vegetation characteristics remains a challenge. Currently, most of the research seeking to address these issues takes place at either the individual plant scale or via larger scale bulk classifications, with the former seeking to characterise vegetation-flow interactions and the latter identifying spatial variation in vegetation types. Herein, we devise a method which extracts functional vegetation traits using UAV laser scanning and multispectral imagery, and upscale these to reach scale guild classifications. Simultaneous monitoring of morphological change is undertaken to identify eco-geomorphic links between different guilds and the geomorphic response of the system in the context of long-term decadal changes. Identification of four guilds from quantitative structural modelling based on analysis of terrestrial and UAV based laser scanning and two further guilds from image analysis was achieved. These were upscaled to reach-scale guild classifications with an overall accuracy of 80 % and links to magnitudes of geomorphic activity explored. We show that different vegetation guilds have a role in influencing morphological change through the stabilisation of banks, but that limits on this influence are evident in the prior long-term analysis. This research reveals that remote sensing offers a solution to the difficulty of scaling traits-based approaches for eco-geomorphic research, and that these methods may be applied to larger areas using airborne laser scanning and satellite imagery datasets.
... Muller (1997) (Brodu and Lague, 2012) and be subsequently analysed to reveal internal structures as well as vertical profiles which can be used for flow modelling and roughness estimates (e.g. Manners, Schmidt and Wheaton, 2013;Jalonen et al., 2015;Vasilopoulos, 2017). ...
... The above summarises how there have been great advances in our understanding of plant scale influences of flow and large-scale classifications of vegetation from remote sensing, yet there seems to be few attempts to cross these scales to apply local vegetation modelling to large scale datasets. Manners, Schmidt and Wheaton (2013) used their local scale TLS data to inform a larger scale model of vegetation frontal areas from ALS, and in forestry linking TLS to ALS has been used to verify large scale modelling methods (Lindberg et al., 2012;Brede et al., 2019), but fluvial applications are still less common. Advances in remote sensing technology and new approaches to classifying vegetation by looking beyond traditional hydrology methods may enable the improved understanding of our eco-geomorphic interactions. ...
... Manning's n values for different species across various flow scenarios (Antonarakis et al., 2009), investigate spatially variable flow dynamics at differing depths due to submerged riparian vegetation (see Figure 2-5, (Manners, Schmidt and Wheaton, 2013)), and provide a link between vegetation roughness and subsequent trailing bar morphology (Bywater-Reyes, Wilcox and Diehl, 2017). Identifying and quantifying areas of vegetation at the fine scale is important for applying drag coefficients, with Brodu and Lague (2012) successfully classifying TLS scans whilst Jalonen et al. (2015) identified and calculated woody area from voxel models. ...
The importance of vegetation within the fluvial domain is well established, influencing both flow and morphology, and has long been recognised as a key component of the river corridor. Despite this, adequately capturing the spatial and structural variability of vegetation for us to understand the eco-geomorphic feedbacks occurring at a range of scales remains a challenge. Currently, the focus of this research takes place at either the individual plant scale, looking into vegetation-flow interactions, or at larger scales, attempting to spatially discretise vegetation for bulk roughness metrics. Subsequently, hydrodynamic models are typically based around these bulk roughness values which exclude vegetation structure. The aim of this research is to attempt to bridge this gap and link the different scales of analysis to improve our understanding of eco-geomorphic interactions. This is achieved by: (1) Examining current remote sensing methods that may be used for fluvial research, (2) Developing a novel UAV based remote sensing system to collect plant scale data for reach scale analysis, (3) Extracting trait-based metrics for individual plants and upscaling these to reach scale extents, (4) Implementing these traits-based parameters in to a 2D hydrodynamic model. At present, the main trade offs in remote sensing centre around scale and resolution, whereby capturing larger areas reduces the detail of the phenomena being studied. Structure from Motion (SfM) photogrammetry has helped to bridge this gap yet fails to reconstruct topography in vegetated reaches and cannot resolve vegetation structure. These drawbacks have herein been overcome with the introduction of UAV based laser scanning techniques, capable of accurately capturing topography in vegetated reaches as well as resolving vegetation structure. This data can be used to extract traits-based vegetation metrics, identify individual guilds within a river corridor, and be scaled to spatially discretise vegetation structure at reach scales. Guilds are then evaluated against monitored morphological change to investigate eco-geomorphic feedbacks. These vegetation metrics and classifications are subsequently used to parameterise a 2D hydrodynamic model, showing the impact that vegetation discretisation methods have on model outputs. This research has developed methods for obtaining reach scale data on vegetation structure to better inform our understanding of eco-geomorphic feedbacks. The robustness and scalability of these methods presents future avenues of research, both within the fluvial domain and for other environmental research applications, where eco-geomorphic feedbacks have a major influence in shaping the Earth’s surface.
... The amount increases with the inundation duration and the area of inundation [11]. Further, models predict that the inundation depth increase the floodplain roughness and with that might increase sedimentation on the floodplain [12]. The accumulated nutrients can have a positive effect on the productivity of the floodplain vegetation [13]. ...
... Sediment retention is a complex phenomenon that depends on the flood and different biogeomorphic processes in the floodplain [18,19]. While deposition of coarse sediment is mostly influenced by the topography of the floodplain, the vegetation type and structure influencing fluvial processes and sediment transport [20,21] are most relevant for sedimentation of finer grain sizes [12,18,22]. Communities of herbaceous vegetation were more efficient in accumulating fine sediment compared to shrublands and floodplain forests [23], and reed beds caused more nitrogen and phosphorus deposition than grass and woodlands [13]. ...
... With our in situ measurements, we improve the understanding of sediment and nutrient retention in floodplains by providing insights on the vegetation structure besides the floodplain topography and simultaneously disentangling sedimentation on and underneath the vegetation. Our findings will help at the small scale to improve existing model approaches to predict sediment and nutrient retention on floodplains [12,20,50,82]. Notably, we found that more biomass increases sediment and nutrient retention on the vegetation. ...
Sediment and nutrient retention are essential ecosystem functions that floodplains provide and that improve river water quality. During floods, the floodplain vegetation retains sediment, which settles on plant surfaces and the soil underneath plants. Both sedimentation processes require that flow velocity is reduced, which may be caused by the topographic features and the vegetation structure of the floodplain. However, the relative importance of these two drivers and their key components have rarely been both quantified. In addition to topographic factors, we expect vegetation height and density, mean leaf size and pubescence, as well as species diversity of the floodplain vegetation to increase the floodplain’s capacity for sedimentation. To test this, we measured sediment and nutrients (carbon, nitrogen and phosphorus) both on the vegetation itself and on sediment traps underneath the vegetation after a flood at 24 sites along the River Mulde (Germany). Additionally, we measured biotic and topographic predictor variables. Sedimentation on the vegetation surface was positively driven by plant biomass and the height variation of the vegetation, and decreased with the hydrological distance (total R ² = 0.56). Sedimentation underneath the vegetation was not driven by any vegetation characteristics but decreased with hydrological distance (total R ² = 0.42). Carbon, nitrogen and phosphorus content in the sediment on the traps increased with the total amount of sediment (total R ² = 0.64, 0.62 and 0.84, respectively), while C, N and P on the vegetation additionally increased with hydrological distance (total R ² = 0.80, 0.79 and 0.92, respectively). This offers the potential to promote sediment and especially nutrient retention via vegetation management, such as adapted mowing. The pronounced signal of the hydrological distance to the river emphasises the importance of a laterally connected floodplain with abandoned meanders and morphological depressions. Our study improves our understanding of the locations where floodplain management has its most significant impact on sediment and nutrient retention to increase water purification processes.
... However, different efforts have been made to link vegetation characteristics to the surface roughness [25,26], but very few of them validated the findings through hydrodynamic modeling [1,27]. Proper configuration of vegetation characteristics in the hydrodynamic model reduces the percentage error in the predicted output and improves the overall performance [28]. ...
Aquatic vegetation modifies the in-stream roughness and hence influences the magnitude and distribution of flow parameters in the main channel and the flood plain. Incorporating influences of vegetation in flow modeling is, therefore, extremely important for deciding management measures. Here, we present a modified form of a two-dimensional depth-averaged shallow water model coupled with rigid emergent vegetations. The explicit second-order accurate TVD McCormack predictor–corrector finite difference method with operator splitting technique is used to solve the governing equations in MATLAB. The equations are transformed into boundary-fitted coordinates to handle the geometrical complexities in the flow domain. The TVD approach is robust and gives accurate results free from numerical oscillations. The Manning’s n value from the randomly distributed vegetation is calculated by assigning the stems at the subgrids and later incorporated within the hydrodynamic simulations. The model is calibrated and validated with the experimental results performed in a rectangular channel with different vegetation densities. The coupled model is further applied in a converging mixed flow channel, a U-type channel, and a natural braided channel with a series of spurs. Results indicate that the vegetation modifies the specific energy and the secondary current strength of the flow. The computed outputs are compared with the published experimental work and the field measured data. The model performance is evaluated from two statistical indexes and found satisfactory.
... The influence of uncertainty of the Manning's roughness coefficient on model output variance is generally comparable to discharge, representing 20-40% of the variability in predicted stage. Increasingly sophisticated methods are being developed to more accurately constrain landcover-specific roughness values [61,62]. Yet, many have questioned the applicability of spatially distributed roughness parameterization to floodplain mapping models [25]. ...
As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.
... leaf area index, stem or crown diameter, vegetation height). LiDAR technology has several advantages in this case: it measures structural attributes directly and can account for complex, multilayered structures (Manners et al., 2013;Jalonen et al., 2015). Hydrodynamic modeling is often combined with classification-derived mapping, with separate modeling of hydrodynamic properties of each vegetation class (Straatsma and Baptist, 2008;Zahidi et al., 2018). ...
Riparian vegetation is a central component of the hydrosystem. As such, it is often subject to management practices that aim to influence its ecological, hydraulic or hydrological functions. Remote sensing has the potential to improve knowledge and management of riparian vegetation by providing cost-effective and spatially continuous data over wide extents. The objectives of this review were twofold: to provide an overview of the use of remote sensing in riparian vegetation studies and to discuss the transferability of remote sensing tools from scientists to managers. We systematically reviewed the scientific literature (428 articles) to identify the objectives and remote sensing data used to characterize riparian vegetation. Overall, results highlight a strong relationship between the tools used, the features of riparian vegetation extracted and the mapping extent. Very high-resolution data are rarely used for rivers longer than 100 km, especially when mapping species composition. Multi-temporality is central in remote sensing riparian studies, but authors use only aerial photographs and relatively coarse resolution satellite images for diachronic analyses. Some remote sensing approaches have reached an operational level and are now used for management purposes. Overall, new opportunities will arise with the increased availability of very high-resolution data in understudied or data-scarce regions, for large extents and as time series. To transfer remote sensing approaches to riparian managers, we suggest mutualizing achievements by producting open-access and robust tools. These tools will then have to be adapted to each specific project, in collaboration with managers.
