Utility of remotely operated underwater vehicle in flood inundation mapping for dam failure: A case study of Lake Tuscaloosa Dam

A remotely operated underwater vehicle (ROV) is a tethered underwater mobile device that can conduct a bathymetric survey cost-efficiently. Assessment of the utility of ROV-based bathymetric surveys in flood inundation mapping remains limited. This study aims to examine the utility of ROV-based bathymetric surveys in high-resolution flood inundation mapping for a hypothetical case study of the Lake Tuscaloosa Dam breach in the state of Alabama, USA. This study conducted the sensitivity test of flood inundation mapping to the river channel depth (ROV-based vs. digital elevation model [DEM]-based) and initial flow condition (e.g., wet vs. dry), via six different simulations of the parallelized diffusion hydrodynamic model (pDHM). This study found that the ROV-based pDHM runs had higher maximum water depths over the flood-inundated areas, ranging from +73% to +166% of the simulated depths of the DEM-based pDHM runs. However, the impact of initial streamflow condition on the maximum depths was limited. This study also found that the pDHM runs with a dry initial flow condition delayed the time to reach the maximum depth after the dam breach by 2 h relative to the pDHM runs with a wet initial streamflow condition. This study suggests that ROV-based bathymetry surveys improve flood inundation mapping by emphasizing the influence of river channel depth, initial streamflow conditions, and bathymetry, thereby bolstering community resilience to a potential human-made hazard such as dam failure.     

EVALUATING SHALLOW‐WATER BATHYMETRY FROM THROUGH‐WATER TERRESTRIAL LASER SCANNING UNDER A RANGE OF HYDRAULIC AND PHYSICAL WATER QUALITY CONDITIONS

The fine‐scale structure of the water–sediment boundary in fluvial environments is dynamic and complex, influencing near‐bed flows, sediment transport and instream ecology. However, accurate high‐resolution surveying of marginally or partially inundated areas of river channels is problematic. Previous work has shown that terrestrial laser scanning (TLS) through relatively shallow‐water columns using standard green‐wavelength equipment introduces errors of <5 mm in a static, clear water column. This paper presents seven laboratory and field tests of through‐water TLS under variable flow velocities, depths, suspended sediment concentrations, water colour levels and scan ranges. Flow velocity decreased point accuracy only for supercritical flows, whereas point density decreased as a function of both water depth and suspended sediment concentration. A similar point return threshold was observed for water colour variations with no grains in suspension. Conversely, point precision and accuracy were a function of suspended sediment concentration alone (a threshold of 0.11 g L^?1 was observed). Field tests showed larger errors (<10 mm) and lower point precisions. A clear‐water depth‐penetration limit of 0.68 m was identified. Fluvial bathymetry acquired from through‐water TLS is presented for a gravel/boulder bed reach. Despite observed limits, these experiments demonstrate that our approach provides centimetre‐resolution bathymetry and sub‐aerial survey in an integrated dataset without the need for the following: (i) additional financial resources; (ii) concurrent depth measurements; or (iii) extra field effort for bathymetry acquisition, thereby enabling regular surveys to characterize the fine‐scale structure of channel beds and to constrain the geomorphic effect of individual flood events. Copyright © 2013 John Wiley & Sons, Ltd.     

MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES

Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R² = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R² of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R² value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam–Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6‐LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud‐free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three‐layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.     

The effect of a local mesh refinement on hydraulic modelling of river meanders

Large-scale river models are generally discretized by relatively large mesh cells resulting in bathymetry discretization errors and numerical effects. These hydraulic models are generally calibrated by altering the bed roughness to compensate for these errors and effects. Consequently, the calibrated roughness values are mesh-dependent while generally local mesh refinements are executed after model calibration to study the effects of river interventions. This study shows both the errors caused by bathymetry discretization and numerical effects for locally refined meshes. First, schematised river meanders with a flat bed in the transverse flow direction are analysed to isolate the induced numerical effects by the mesh. Afterwards, a case study is considered to verify if similar mesh influences are found in natural river meanders. Curvilinear, triangular and hybrid (combination of curvilinear and triangular cells) meshes are used with different resolutions. The analysis shows that in the schematised river meanders lower depth-averaged flow velocities and larger water depths are simulated with coarser meshes. In the case study, substantial differences in hydrodynamics between the meshes are obtained suggesting that the bathymetry discretization is more influential than the numerical effects. Finally, it was found that triangular meshes, and rivers with narrow meander bends, are most sensitive to mesh resolution. Especially in these cases, it is desirable to refine the mesh at the desired locations before model calibration.     

Application of GIS modelling to quantify fish habitats in lakes

Quantitative measures of fish habitat in lakes and impoundments have seldom been proposed. The availability of Geographic Information Systems (GIS), however, now provides a means for researchers to assess fish habitat on a whole-lake scale. GIS modelling was used in this study to quantify the impacts of stratification on the amount and distribution of brown trout ( Salmo trutta ) habitat in a large reservoir in Victoria, Australia. Species-specific temperature and dissolved oxygen tolerance ranges for brown trout were applied to the hydrological profile and lake bathymetry. The model predicted that brown trout habitat was reduced by 82% in the summer, and the model was validated with position data from acoustically tagged adult brown trout. This study demonstrated that a quantifiable assessment of fish habitat on a whole-lake spatial scale can be made. It also could be applied to a range of fish species, with applications in ecosystem monitoring and fisheries management.     

Combining remote sensing with physical flow laws to estimate river channel geometry

A reliable representation of river terrains is essential to river research. Field surveys of river channel geometry are time‐consuming, costly, and logistically constrained and thus would encounter difficulties to achieve sufficient spatial coverage, resolution, and frequency of resurvey. This paper aims to demonstrate an efficient approach to building a river terrain model (RTM), the emphasis being on how to combine bathymetry and topography derived from satellite images captured at different flow stages. A method for calculating the difference between high and low stages (DHLS) based on the uniform‐flow theory is proposed. Calculations are carried out for a 13‐km long meandering section of the gravel‐bed Goulais River in Canada, which features pools and riffles, alternating point bars, and midchannel bars. A RTM for this complex section has been successfully produced. It consists of three data components: bathymetry at low stage, topography at high stage, and DHLS. The results capture realistic characteristics, including thalweg shift, steep outer banks, and gradual inner banks. They also show realistic longitudinal and lateral locations of pools and riffles. To illustrate potential applications of RTM, this paper has computed extreme bed shear stresses at bankfull discharge through hydrodynamics simulations of depth‐averaged flow in the river section and further estimated bed‐sediment grain‐size distributions. The estimates compare well with field measurements. The DHLS can vary significantly along a river channel. The proposed method for determining it is not site‐specific, and hence applicable to other rivers. The novelty of the methodology discussed lies in combining remote sensing techniques with physical flow laws.     

Numerical Modelling of Braided Rivers with Structure‐from‐Motion‐Derived Terrain Models

The development of three‐dimensional reconstructions of channel morphology has historically been limited by the high costs of geospatial data collection and software modelling. Advances in image processing, sensor technology and portable remote‐sensing platforms, however, now offer the opportunity to derive survey quality terrain models at significantly reduced cost and without traditional deployment and logistical constraints. There is a pressing need to establish whether new geospatial technologies such as structure‐from‐motion photogrammetry can be used to deliver topographic data products that are suitable for higher‐dimensional hydrodynamic modelling. To address this question, we evaluate the results of simulations using Delft3D that were designed to model distributed, depth‐averaged flows in a wide, shallow, gravel‐bed braided river. The topography for these simulations was derived from digital elevation models (DEMs) generated using structure‐from‐motion and optical bathymetric mapping of two linked reaches of the Ahuriri River, New Zealand. The DEM quality achieved vertical surface errors of 0.10 m in non‐vegetated areas and 0.20 m in inundated areas. Simulations with 1.5 m and 2.5 m resolution grids for low‐flow, medium‐flow and high‐flow conditions were calibrated and tested against field real‐time kinematic‐global navigation satellite system observations. Results revealed that modelled depth errors were comparable to the DEM uncertainty, while simulated and observed inundation patterns achieve a maximum of 81% agreement. Given the complexity of the braided network and shallow flow depths, these simulations provide a powerful demonstration of the suitability of these terrain models for hydrodynamic applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of Discharge and End Depth in Trapezoidal Channel by Support Vector Machines

This paper presents the results of an application of support vector machines based modelling technique (radial based kernel and polynomial kernel) to determine discharge and end-depth of a free overfall occurring over a smooth trapezoidal channel with positive, horizontal or zero and negative bottom slopes. The data used in this study are taken from the earlier published work reported in the literature (Ahmad 2001). The results of the study indicate that the radial based function and polynomial kernels support vector machines modelling technique can be used effectively for predicting the discharge and the end depth for a trapezoidal shaped channel with different slopes as compared to the empirical relations suggested by Ahmad (2001); Gupta et al. (1993) and a back propagation neural network technique. The predicted values of both discharge and end depth compared well to the results obtained by using empirical relations derived in previous studies as well as with a back propagation neural network model. In case of discharge prediction, correlation coefficient was more than 0.995 with all three different slopes, while it was more than 0.996 in predicting the end depth using radial based kernel of support vector machines algorithm. Thus, suggesting the application and usefulness of this technique in predicting the discharge as well as end depth in the trapezoidal shaped channel as an alternative to the empirical relations and neural network algorithm. Further, a smaller computational time is an added advantage of using support vector machines in comparison to the neural network classifier, as observed in the present study.     

Testing a novel sonar-based approach for measuring water depth and monitoring sediment storage in beaver ponds

Widely available ‘fish-finder’ echo-sounding devices are beginning to be used in bathymetric studies to estimate geomorphic change. To date, however, there have been no applications in shallow and complex wetlands, where changes in sediment storage are notoriously dynamic in time and difficult to describe accurately in space. Therefore, in this study, we tested the performance of an ‘off-the-shelf’ fish-finder for mapping bathymetry in a shallow beaver pond. We tested fish-finder sonar depth readings against a traditional-sounding lead-line method across 21 paired Sampling Points with a minimum depth of 0.31 m and a mean of 0.65 m. Spatial accuracy of the unit was also tested against a differentially corrected Global Navigation Satellite System (GNSS) receiver. Measured depths to pond bottom from the fish-finder were on average within 5%, although significantly 0.015 m (SD = 0.034) less than those obtained by the lead-line method. Spatial accuracy, however, varied greatly compared to the corrected GNSS receiver readings, with a mean discrepancy of 2.7 m (SD = 1.5) but up to 6.2 m. Given the close match of depth readings between the two methods, we conclude that sonar is a suitable, cost-effective, and less-intrusive method than existing techniques, even in moderately vegetated shallow waterbodies. Methods do need to be adopted to account for poor spatial precision with ‘off-the-shelf’ fish-finder models, but this can be rectified with survey design or using a secondary GNSS. Application of this technology will allow rapid one-off surveys or repeated monitoring of depth, bedform and sediment accumulation in otherwise hard-to-access or disturbance-sensitive wetlands, such as beaver ponds and water treatment or flow attenuation wetlands.     

Kayak drifter surface velocity observation for 2D hydraulic model validation

Advances in remote sensing, informatics, software, and microprocessors enable meter‐resolution two‐dimensional (2D) hydrodynamic models that produce nearly a census of ecohydraulic conditions over long river segments with 10⁵ to 10⁸ computational elements. It is difficult to test statistical and spatial model performance at such scope using fixed‐point velocity measurements, because field methods are so expensive, laborious, slow, and restricted by safety factors. This study evaluated low‐cost water surface particle tracking by kayak with real‐time kinematic GPS for 2D model validation using 7.2 km of the lower Yuba River in California. Observed flows were between 15 to 140 m³/s, which were in‐channel up to and including bankfull conditions. The coefficients of determination between 5,780 observations and 2D model predictions were 0.79 and 0.80 for velocity magnitude and direction, respectively. When surface speed was downscaled and compared to modelled depth‐averaged velocity, median unsigned difference was 15.5%. Standard hydrological model performance metrics affirmed satisfactory validation. Surface tracking provided the novel benefit of enabling validation of velocity direction, and that testing found satisfactory performance using all metrics. Having 10 to 1,000 times more data enables robust statistical testing and spatial analysis of both speed and direction, which outweighs the loss of depth‐averaged data. Both fixed‐point and kayak particle tracking methods are useful tools to help evaluate 2D model performance.     

Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data

Urmia Lake as a most vital water bodies in Iran, has been shrinking since the late twentieth century and its area has dramatically decreased. To develop and apply any plans to survive the lake, qualitative and quantitative analysis and any modeling, deriving physical information such as volume, area and their changes are very crucial. The objectives of this study were therefore, intended firstly, to study the bathymetry of Urmia Lake with a more satisfactory approach using Landsat- LDCM satellite image and in situ measurement data. The polynomial model was developed to predict the water depth in Urmia Lake area. This model was developed with the input series of reflectance values from blue, green, red and NIR bands in the Landsat- LDCM satellite imagery for Urmia Lake taken on 12 April 2013 of the sampling sites from actual depth measured were taken on the same date. Also, using a large archive of Landsat imagery (TM, ETM+ and LDCM), a counter of equivalent elevation were established for deriving the bathymetry of desiccated areas by mapping the edges of the lake and finally assembled with bathymetry derived from polynomial model. In-situ depth measurements were used to evaluate resultant derived bathymetric map. This comparison shows reasonable agreement between the Landsat-derived depths and those measured in the field with RMSE of 0.27 cm and R² = 0.91. The maximum and mean depths measured were 4.9 and 11 m respectively. The maximum depth measured was located at the upper part of the lake. As well as, developed multi-regression equation used for deriving another bathymetry map using Landsat- LDCM satellite image taken on Sep. 2015 for salt deposition monitoring. Results indicates that about 64 cm salt deposition has occurred during the last two years. Secondly, to make stage curves of lake, multi-temporal changes of water body have been derived from Landsat, MODIS and AVHRR satellite images sets since 1972. In this regard, the area of Urmia Lake at different level was estimated base on object oriented and pixel base classification using 78 satellite images. Finally, stage curve (volume- area- level relations) was derived from bathymetry map.     

多波束测深方法对坝下泄洪消能建筑物冲刷破坏的检测

为了无损检测坝下泄洪消能建筑物的冲刷破坏程度,将多波束测深方法从海洋测绘引进到水电站检测,结合水下机器人视频验证,对某水电站的坝下泄洪消能建筑物中的冲刷破坏情况进行检测,以查明水泥混凝土护坦的冲刷破坏程度以及边墙的淘刷深度。通过检测的过程及资料分析,确定了多波速测深系统无信号屏蔽的适用条件,对冲刷、磨损、淘空等精细检测特征进行了总结。结果表明,多波束测深系统对冲刷磨损情况检测效果良好,可以清晰判断冲刷磨损范围及程度,对混凝土错台现象反应明显;对冲刷淘空情况检测效果较好,对冲坑部位水下地形绘制精确,对淘空区域的范围及深度判断较好。     

Improvement of pre- and post-processing environments of the dynamic two-dimensional reservoir model CE-QUAL-W2 based on GIS.

An Environmental Information System (EIS) coupled with a Geographic Information System (GIS) and water quality models is developed to improve the pre- and post-data processing function of CE-QUAL-W2. Since the accuracy of the geometric data in terms of a diverse water body has a great effect on the water quality variables such as the velocity, kinetic reactions, the horizontal and vertical momentum, to prepare the bathymetry information has been considered a difficult issue for modellers who intend to use the model. For identifying Cross Section and Profile Information (CSPI), which precisely contains hydraulic features and geographical configuration of a waterway, the automated CSPI extraction program has been developed using Avenue Language of the PC Arc/view package. The program consists of three major steps: (1) getting the digital depth map of a waterway using GIS techniques; (2) creating a CSPI data set of segments in each branch using the program for CE-QUAL-W2 bathymetry input; (3) selecting the optimal set of bathymetry input by which the calculated water volume meets the observed volume of the water body. Through those approaches, it is clear that the model simulation results in terms of water quality as well as reservoir hydraulics rely upon the accuracy of bathymetry information.     

Application of airborne multispectral digital imagery to quantify riverine habitats at different base flows

We employed an integrated system of airborne remote sensing and ground surveys for regional mapping of instream habitats under variable flows over a 70 km section of the Lower Yakima River in southern Washington, USA. Airborne multispectral digital imagery was obtained in conjunction with field survey measurements and used to quantify the spatial extent, condition and temporal changes of selected river habitat characteristics under two different flows (14 and 28 m³ s^?1). Under each flow, geomorphic measures were quantified (e.g. channel complexity, number and size of habitats). Water depth and velocity were also classified for instream habitats, and temperature and turbidity were recorded. Remote sensing classification accuracies for islands, exposed rocks and water surfaces were greater than 99%, while more detailed depth/flow classifications were less accurate (68% and 72%, for the high and low flows, respectively). While high turbidity (>4 Nephelometric Turbidity Units (NTU)), shadows and bidirectional reflectance factor reduced classification accuracies, the overall effect of these factors was minimal. Under the low‐flow situation, off‐channel habitats were less abundant, more isolated and had shallower depths and warmer temperatures. Our analysis suggests that airborne multispectral imagery, coupled with appropriate ground truth data, can be a viable method for regional mapping of diverse riverine habitats under variable flows. We concluded from this analysis that the higher flow situation provided substantially better habitat than currently exists in the Lower Yakima River. Copyright © 2002 John Wiley & Sons, Ltd.     

Experimental and Numerical Investigation of Flow Over Ogee Spillway

Ogee type spillway is one of the most preferred sluice types due to its functional suitability and high safety factor. It is used for controlling the flow rates and water levels in reservoirs, such as lowering the water level in emergency situations, maintaining normal river functions and discharging excess water. The main aim of this study is to investigate the flow over ogee type spillway by performing experiments in an open channel flume in the laboratory and simulating with numerical model. The numerical model having the same dimensions with the physical model is modeled with two different programs of ANSYS-Fluent and OpenFOAM. The flow depths of the models were measured at four points, H₁, H₂, H₃, H₄. In the numerical analysis, two different turbulence models, K-ε and K-ω SST turbulence model were used in order to investigate the accuracy of the turbulence models in the open channel. According to the results, R² values, obtained from ANSYS- Fluent for the each measurement points where H₁, H₂, H₃, H₄, are 0.9776, 0.9859, 0.9701, 0.9916 and obtained from OpenFOAM for the each measurement points 0.9920, 0.9687, 0.9855, 0.9926 respectively. The findings show that the numerical tools have been sufficiently developed to simulate flow depths and water surface profiles.

     

Using satellite data to extract volume–area–elevation relationships for Urmia Lake,Iran

Urmia Lake in the northwest of Iran is the second largest hyper-saline lake worldwide. During the past two decades, a significant water level decline has occurred in the lake. The existing estimations for the lake water balance are widely variable because the lake bathymetry is unknown. The main focus of this study is to extract the volume–area–elevation (V–A–L) characteristics of Urmia Lake utilizing remote sensing data and analytical models. V–A–L equations of the lake were determined using radar altimetry data and their concurrent satellite-derived surface data. Next, two approximate models, a power model (PM) and a truncated pyramid model (TPM), were parameterized for Urmia Lake and checked for accuracy. Results revealed that in comparison with the satellite-derived reference volume–elevation equation, the PM slightly over-predicts the volume of Urmia Lake while the TPM under-estimates the lake storage. Variations of the lake area and volume between 1965 and 2011 were examined using the developed V–A–L equations. Results indicated that the lake area and volume have declined from the historical maximum values by 2200 km² and 33 km³, respectively. To restore Urmia Lake to a level to maintain ecological benefits, 13.2 km³ of water is required. This study demonstrates the use of remote sensing data of different types to derive V–A–L equations of lakes. Substituting satellite-derived V–A–L equations for common empirical formulas leads to more accurate estimations of a lake water balance, which in turn, provides insight to water managers for properly assessing and allocating water resources to downstream ecosystems.     

Spectrally based bathymetric mapping of a dynamic,sand‐bedded channel: Niobrara River,Nebraska, USA

Methods for spectrally based mapping of river bathymetry have been developed and tested in clear‐flowing, gravel‐bed channels, with limited application to turbid, sand‐bed rivers. This study used hyperspectral images and field surveys from the dynamic, sandy Niobrara River to evaluate three depth retrieval methods. The first regression‐based approach, optimal band ratio analysis (OBRA), paired in situ depth measurements with image pixel values to estimate depth. The second approach used ground‐based field spectra to calibrate an OBRA relationship. The third technique, image‐to‐depth quantile transformation (IDQT), estimated depth by linking the cumulative distribution function (CDF) of depth to the CDF of an image‐derived variable. OBRA yielded the lowest depth retrieval mean error (0.005 m) and highest observed versus predicted R² (0.817). Although misalignment between field and image data did not compromise the performance of OBRA in this study, poor georeferencing could limit regression‐based approaches such as OBRA in dynamic, sand‐bedded rivers. Field spectroscopy‐based depth maps exhibited a mean error with a slight shallow bias (0.068 m) but provided reliable estimates for most of the study reach. IDQT had a strong deep bias but provided informative relative depth maps. Overprediction of depth by IDQT highlights the need for an unbiased sampling strategy to define the depth CDF. Although each of the techniques we tested demonstrated potential to provide accurate depth estimates in sand‐bed rivers, each method also was subject to certain constraints and limitations.     

Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management

In this paper, we assess the capabilities of an unmanned/uninhabited aerial vehicle (UAV) to characterize the channel morphology and hydraulic habitat of a 1‐km reach of the Elbow River, Alberta, Canada, with the goal of identifying the advantages and challenges of this technology for river research and management. Using a small quadcopter UAV to acquire overlapping images and softcopy photogrammetry, we constructed a 5‐cm resolution orthomosaic image and digital elevation model (DEM). The orthomosaic was used to map the distribution of geomorphic and aquatic habitat features, including bathymetry, grain sizes, undercut banks, forested channel margins, and large wood. The DEM was used to initialize and run River2D, a two‐dimensional hydrodynamic model, and resulting depth and velocity distributions were combined with the mapped physical habitat features to produce refined estimates of available habitat in terms of weighted usable area. Based on 297 checkpoints, the vertical root‐mean‐squared error of the DEM was 8.8 cm in exposed areas and 11.9 cm in submerged areas following correction of the DEM for overprediction of elevations as a result of the refractive effects of water. Overall, we find several advantages of UAV‐based imagery including low cost, high efficiency, operational flexibility, high vertical accuracy, and centimetre‐scale resolution. We also identify some challenges, including vegetation obstructions of the ground surface, turbidity, which can limit bathymetry extraction, and an immature regulatory landscape, which may slow the adoption of this technology for operational measurements. However, by enabling dynamic linkages between geomorphic processes and aquatic habitat to be established, we believe that the advantages of UAVs make them ideally suited to river research and management. Copyright © 2014 John Wiley & Sons, Ltd.     

Distribution of Diatoms in Lake Superior Sediments

Four maps showing semiquantitative abundance patterns of diatoms throughout Lake Superior were made by examination of 170 cores at sediment depths of 0–1, 10, 20, and 50 cm. These maps show a decrease in diatom abundance with sediment depth and an absence of diatoms in most open-lake sediments. Diatoms in surficial sediments are most abundant in Jive regions in Lake Superior: Thunder Bay, Keweenaw Bay, the Thunder Bay and northshore troughs, and a region to the southwest of Isle Royale. Only three of these five regions still show relatively abundant diatoms at the 50-cm sediment depth. Diatom abundance in the sediments is positively correlated with sedimentation rates, water depth, and proximity to shore. Nearshore deep-water troughs act as depositional traps for diatoms in western Lake Superior and Keweenaw Bay. Later transport of diatoms by waves and currents generally erases any relationship between diatom abundance in the sediments and diatom biomass in the overlying water. Diatom abundances at 10- and 20-cm depths correlate positively with sedimentation rates, suggesting that biogenous and terrigenous components are eroded, transported, and redeposited as a mixture, rather than being hydraulically separated. Diatoms are abundant at 0–1 cm in regions of both low and high sedimentation rates.