Search Results (6)

Identifying low-head dams (LHDs) and creating an inventory is a priority, as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs and they are not assigned a hazard classification, there is no official inventory of LHDs; a multi-agency taskforce is creating one now by identifying LHDs using Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the creation of the national inventory. We implemented a machine learning approach to use a high-resolution remote sensing data with a Convolutional Neural Network (CNN) architecture. The model achieved 76% accuracy in identifying LHDs (true positives) and 95% accuracy identifying Non-low-head-dams (true negatives) on the validation set. We deployed the trained model for the National Hydrologic Geospatial Fabric (Hydrofabric) flowlines in the Provo River watershed. The results showed a high number of false positives and low accuracy due to the mismatch between Hydrofabric flowlines and actual waterways. We recommend improving the accuracies of the Hydrofabric waterway tracing algorithms to increase the percentage of correctly classified LHDs. Full article

With the increasing availability of low head dam inventories for the United States, the next challenge is discovering how to determine what dams pose the greatest risk to public safety, preferably before a death occurs. Submerged hydraulic jumps create the dangerous current that drowns roughly 50 recreationists each year, and high tailwater is a key element in its formation. Using a simplified approach based on the Manning equation, flat downstream slopes can be a predictor of high tailwater. Stream slopes at low head dams in Colorado, Idaho, Indiana, Maryland, New Mexico, North Carolina, and Pennsylvania were collected from the NHDPlus HR, and dams with recorded fatalities were compared to stream slopes at low head dams with no recorded fatalities. Using the Mann–Whitney U test, there was not enough evidence to reject the null hypothesis that there is no statistically significant difference between the two populations. Until more fatality data are compiled and more low head dam locations are verified, individual testing of dams is recommended to establish each respective flow range that is likely to pose a risk to public safety. Full article

For fishes, swimming performance is an important predictor of habitat use and a critical measure for the design of effective fish passage systems. Few studies have examined burst and prolonged types of swimming performance among several co-occurring species, and swimming performance in many fish communities is undocumented. In this study, we characterize both burst (c-start velocity) and prolonged speed (critical swim speed) across a poorly documented, co-occurring group of stream fishes within the Great Basin of the western USA. We documented the variation in swim speed associated with species, habitat, and body size. Body size had an overwhelming effect on both burst speed and prolonged speed, whereas habitat use and species identity were not significant predictors. Among species, there is no evidence of a trade-off between burst swim speed and prolonged swim speed. Lack of a trade-off in performance between burst swim speed and prolonged swim speed among species may be due to unexpectedly high prolonged swim speeds exhibited by species that used substrate-bracing behaviors. Incorporating body size and variation in behavior, such as substrate-bracing behaviors, into fish passage models will likely be sufficient to ensure the passage of all species without the need to account for species-specific swimming abilities. However, these results characterize the swimming performance for threatened and common fish species such that other comparisons can be made and species-specific studies can access accurate data. Full article

Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management. Full article

Computer simulation of reservoir sediment management strategies is becoming more important as worldwide water supply shrinks due to sediment deposition, while population growth continues. We identified the physical processes underlying each of the several alternatives available to transport incoming or deposited sediments downstream into receiving waters and the governing equations that describe each process. The purpose of this paper is to understand how physical characteristics of reservoir sediment management can be simulated with available computer codes. We described commonly available computer codes and their abilities to solve the appropriate equations in one, two, or three dimensions. The results revealed that one dimensional models are most appropriate for long-term simulations of the evolving reservoir bottom profile, while two or three dimensional codes are more appropriate for simulating density currents and detailed lateral movement of sediments, such as during local pressure flushing near reservoir outlets. We conclude that existing codes can successfully simulate sediment management, but because each code has limitations, they require seasoned judgment in their choice, application, and interpretation. Incorporating sediment prediction and management correctly into the planning, design, and operational phases of dam projects is essential for ensuring that the benefits of reservoir storage are sustained over the long term. The implications of our key findings are that sediment management strategies can be successfully simulated and that such simulations should be performed for our aging dams and newly proposed projects. Full article

Culverts can provide a significant barrier to fish passage by fragmenting fish habitats and impeding the passage success of small-bodied fish. Geographical connectivity is critical to the maintenance of diverse fish assemblages. Culverts with high cross-sectional velocity can cause population fragmentation by impeding passage of small, freshwater fish. Behavioral responses of small fish to high velocities can differ among functional groups, and swimming behavior of many species is not well known. We tested effects of substrate type on swimming behavior in two small, freshwater fish species—southern leatherside chub (Lepidomeda aliciae, a midwater species), and longnose dace (Rhinichthys cataractae, a benthic species)—across three substrate treatments: (1) a bare flume, (2) large flow obstacles, and (3) a natural cobble substrate. Both longnose dace and southern leatherside chub used paths of low velocity and swam in the near-substrate boundary area. Fish in the bare flume and large obstacle treatments swam along the corners of the flume in a straight swim path, whereas fish in the natural substrate treatment used all parts of the flume bed. There was no relationship between passage success of fish and substrate type, fish species, or their interaction. In contrast, substrate type, fish species, and their interaction were significant predictors of passage time. Southern leatherside chub passed through the test section about two to four times faster than longnose dace. Both species took longer to pass through the large flow obstacle treatment compared to the bare flume or natural substrate. The natural substrate created a complex velocity profile with areas of low velocity throughout the entire flume, in contrast to the other two treatments. Our data suggest natural substrates can improve the passage of small fish in high-velocity culverts for both benthic and midwater functional groups. Full article