Bathymetric surveying to gather information about depths and underwater terrain is increasingly important to the sciences of hydrology and geomorphology. Submerged terrain change detection, water level, and reservoir storage monitoring demand extensive bathymetric data. Despite often being scarce or unavailable, this information is fundamental to hydrodynamic modeling for imposing boundary conditions and building computational domains. In this manuscript, a novel, low-cost, rapid, and accurate method is developed to measure submerged topography, as an alternative to conventional approaches that require significant economic investments and human power. The method integrates two types of Unmanned Aerial Systems (UAS) sampling techniques. The first couples a small UAS (sUAS) to an echosounder attached to a miniaturized boat for surveying submerged topography in deeper water within the range of accuracy. The second uses Structure from Motion (SfM) photogrammetry to cover shallower water areas no detected by the echosounder where the bed is visible from the sUAS. The refraction of light passing through air–water interface is considered for improving the bathymetric results. A zonal adaptive sampling algorithm is developed and applied to the echosounder data to densify measurements where the standard deviation of clustered points is high. This method is tested at a small reservoir in the U.S. southern plains. Ground Control Points (GCPs) and checkpoints surveyed with a total station are used for properly georeferencing of the SfM photogrammetry and assessment of the UAS imagery accuracy. An independent validation procedure providing a number of skill and error metrics is conducted using ground-truth data collected with a leveling rod at co-located reservoir points. Assessment of the results shows a strong correlation between the echosounder, SfM measurements and the field observations. The final product is a hybrid bathymetric survey resulting from the merging of SfM photogrammetry and echosoundings within an adaptive sampling framework.

Lateral flow separation occurs in rivers where banks exhibit strong curvature. In canyon‐bound rivers, lateral recirculation zones are the principal storage of fine‐sediment deposits. A parallelized, three‐dimensional, turbulence‐resolving model was developed to study the flow structures along lateral separation zones located in two pools along the Colorado River in Marble Canyon. The model employs the detached eddy simulation (DES) technique, which resolves turbulence structures larger than the grid spacing in the interior of the flow. The DES‐3D model is validated using Acoustic Doppler Current Profiler flow measurements taken during the 2008 controlled flood release from Glen Canyon Dam. A point‐to‐point validation using a number of skill metrics, often employed in hydrological research, is proposed here for fluvial modeling. The validation results show predictive capabilities of the DES model. The model reproduces the pattern and magnitude of the velocity in the lateral recirculation zone, including the size and position of the primary and secondary eddy cells, and return current. The lateral recirculation zone is open, having continuous import of fluid upstream of the point of reattachment and export by the recirculation return current downstream of the point of separation. Differences in magnitude and direction of near‐bed and near‐surface velocity vectors are found, resulting in an inward vertical spiral. Interaction between the recirculation return current and the main flow is dynamic, with large temporal changes in flow direction and magnitude. Turbulence structures with a predominately vertical axis of vorticity are observed in the shear layer becoming three‐dimensional without preferred orientation downstream.

This research examines the mass failure and seepage erosion of sandbars due to rapid fluctuations in river stage using a full-scale laboratory model. Hydroelectric dams operated to provide electricity at peak demand produce rapid river stage fluctuations. During decreasing river stage, the groundwater table becomes higher than the river stage, increasing pore water pressures and exfiltrating groundwater. This can cause seepage erosion and mass failures in the banks and bars. In the Colorado River in the Marble and Grand Canyons, maximal downramp and upramp rates have been imposed on the Glen Canyon Dam operations. Our experiments research the efficacy of these discharge ramp rate restrictions to reduce sandbar erosion. The laboratory model consists of a two-dimensional sandbar face (8 m long, 2.5 m high and 0.5 m wide). Multiple experiments were conducted in a range of slopes, varying from 12 to 26 . An analysis of historical and current ramp rates at 47 locations along the river provided the basis of laboratory downramp rates in the range from 0.1 to 0.6 m h À1 . Results show that bank stability is reached at a slope of approximately 14 . The erosion of intermediate slopes (18 -22 ) is controlled by seepage erosion, whereas the erosion of steep slopes (26 ) is governed by mass failures. Erosion rates per diurnal cycle do not depend on ramp rates, but they increase with sandbar steepness. Therefore, steep sandbar faces would rapidly erode by mass failure and seepage erosion to shallower stable slopes in the absence of other erosion processes, regardless of dam discharge ramp rates. Our experiments only address seepage erosion and mass failure; increasing the daily magnitude and/or duration of peak discharge may increase the erosion of bars by turbulent sediment transport. were implemented.Minimum releases were constrained to 226.5 m 3 s À1 by day and 141.5 m 3 s À1 by night, maximum releases to 708 m 3 s À1 and maximum daily fluctuations within 24 h to 226.5 m 3 s À1 . Discharge ramp rates were confined to 113 and 42.5 m 3 s À1 h À1 along the rising and falling limbs, respectively (US Department Figure 2. Downstream view of 30.8 RM sandbar, site code 9. The newly deposited sandbar slope shown is 26 . This figure is available in colour online at wileyonlinelibrary.com/journal/rra.Figure 3. (a) Box plot and whisker diagrams of downramp rates records during historical dam operation criteria, year 1988 to 1991. (b) Box plot and whisker diagrams of downramp rates records during MLFF dam operation criteria, year 1997 to 2009. Data outside of the inner fence and outer fence are represented by dots that are overlapping.

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m2 to 102 km2) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m3⁄m3. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products.