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Freshwater mussel populations have been declining at an alarming rate around the world. Herein, we investigate whether changing flow conditions, as they affect juvenile freshwater mussel settling, could be a potential causative factor for this decline in the Dan River, North Carolina, USA. We deployed two uplooking velocity sensors on the riverbed between May and November 2019: one where mussels reside and another where they do not. From this data, we calculated shear velocity, which is a measure of the turbulence that acts to lift particles into suspension and acts against particle settling. We determined that a shear velocity less than 0.66 cm/s would be required to settle relatively large and dense juvenile mussels onto the riverbed; however, the lowest shear velocity we measured was 0.9 cm/s. Additionally, we estimated that juvenile freshwater mussels as large as 280-510 µm could always be suspended and not be able to settle onto the riverbed at these two locations. Therefore, the flow during May-November 2019 was high enough to prohibit recruitment of juvenile freshwater mussels at the sensor locations. Furthermore, we have identified that the magnitude of the lowest flows has increased since 2000, which may be exacerbating the decline in freshwater mussels.

For over 10 days, sediment from a highly turbid spring, affected by activities for the Mountain Valley Pipeline (MVP), entered into Sinking Creek, a tributary of the New River. This presentation will describe what is known about the incident, to what extent the impact on Sinking Creek can be assessed with available information, and what is unknown that limits a full impact assessment. This presentation will mostly focus on quantifying the transport and fate of sediment delivered to Sinking Creek between January 27th and February 6th prior to sediment control efforts. This presentation will also highlight what is not known and what limits a full impact assessment.

This study evaluates the laser in situ scattering and transmissometry (LISST) instrument LISST-SL2, a laser diffraction instrument for suspended sediment sampling in rivers, with concurrent physical measurements of suspended sediment concentration (SSC) and particle size distribution (PSD) as well as velocity measurements by an acoustic Doppler current profiler (ADCP). We collected 136 LISST-SL2 samples along with 61 physical samples for SSC measurement, of which 24 physical samples included PSD measurement during 2018-2020 from 11 sites in Washington state and Virginia. An effective density is required to convert the measured volumetric SSC by the LISST-SL2 into a reported mass SSC, and by default the LISST-SL2 assumes a value of 2.65 g/mL. From our data set, we computed effective densities (mass SSC/volumetric SSC) that ranged from 0.5 to 5.4 g/mL, with a best-fit value of 2.05 g/mL. Additionally, the LISST-SL2 was not able to measure the finest sediment sizes in suspension, which affects the resulting PSD. Therefore, we propose some adjustments of the LISST-SL2 data with a supporting physical sample to account for these effective density and PSD issues. When doing so, we were able to reduce the root-mean square relative error (RMSRE) to 18% from 117% for SSC, and to 26% from 78% for PSD. LISST-SL2 velocities were generally higher than ADCP velocities with a 21% RMSRE. Our results and guidance will allow for more accurate sampling by the LISST-SL2, which has potential for studying spatial and temporal variation of suspended sediment characteristics in rivers.

The distinction between a “stream” and “river” is imprecise and vague despite the popular usage of the terms across disciplines for describing flowing waterbodies. Based on an analysis of named flowing waterbodies in the continental United States, we suggest a bank-to-bank channel width of 15 m as a working threshold in defining smaller “streams” from larger “rivers.”.

Excess fine sediment (<2 mm) deposition on gravel streambeds can degrade habitat quality for stream biota. Two measures of fine sediment deposition include embeddedness and silt cover (<62.5 mu m). Embeddedness measures fine sediment in interstitial pore spaces, whereas silt cover, primarily deposited during low flows, measures fine sediment draped on the streambed's surface. Here, we demonstrate that a baseline level of embeddedness and a maximum value of silt cover can be predicted from bankfull shear velocity, which can be estimated from river channel and streamflow characteristics, independently of knowing the sediment supply. We derive an equation for bankfull shear velocity that only requires knowing bankfull flow, channel width, and channel slope, which can be readily obtained in the United States from freely available, remotely sensed data. We apply this methodology to data collected at 30 sites in the Piedmont region of Virginia and North Carolina. This work is an important step in developing statistical models of stream ecosystems in which geophysical variables can predict embeddedness and silt cover, which commonly limit biotic assemblages.

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the costeffectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of nearchannel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

An increase in heavy precipitation associated with climate change has exacerbated flooding in the Eastern U.S. To assess regional flood risk with changing climatic conditions, we demonstrate the application of a novel hydrologic modeling framework that integrates climate projections with a coupled Noah-MP land surface model and a two-dimensional HEC-RAS hydrodynamic model. We employ this framework along a 41 km reach of the Susquehanna River near Harrisburg, Pennsylvania, where recent flood damages exceeded $2 billion (2011 Irene and Lee floods). Historical and future 30-year and 100-year peak-discharge estimates were compared to assess how flood risk might be altered due to climate change. Results indicate that precipitation increases from climate change do not always lead to increases in flood risk, because interplay of hydrological components in the watershed, which are considered by Noah-MP, largely controls flooding severity. However, climate change is expected to increase the severity of extreme events; if a 50-year flood (the recurrence interval of Tropical Storm Lee) occurred toward the end of the 21st century in the worst-case emission scenario, then flood volume would increase by 40% and flood extent by 15%, due to an increase in soil moisture from a wetter overall climate.

Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance o

Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance o

River networks modify material transfer from land to ocean. Understanding the factors regulating this function for different gaseous, dissolved, and particulate constituents is critical to quantify the local and global effects of climate and land use change. We propose the River Network Saturation (RNS) concept as a generalization of how river network regulation of material fluxes declines with increasing flows due to imbalances between supply and demand at network scales. River networks have a tendency to become saturated (supply >> demand) under higher flow conditions because supplies increase faster than sink processes. However, the flow thresholds under which saturation occurs depends on a variety of factors, including the inherent process rate for a given constituent and the abundance of lentic waters such as lakes, ponds, reservoirs, and fluvial wetlands within the river network. As supply increases, saturation at network scales is initially limited by previously unmet demand in downstream aquatic ecosystems. The RNS concept describes a general tendency of river network function that can be used to compare the fate of different constituents among river networks. New approaches using nested in situ high-frequency sensors and spatially extensive synoptic techniques offer the potential to test the RNS concept in different settings. Better understanding of when and where river networks saturate for different constituents will allow for the extrapolation of aquatic function to broader spatial scales and therefore provide information on the influence of river function on continental element cycles and help identify policy priorities.

River networks modify material transfer from land to ocean. Understanding the factors regulating this function for different gaseous, dissolved, and particulate constituents is critical to quantify the local and global effects of climate and land use change. We propose the River Network Saturation (RNS) concept as a generalization of how river network regulation of material fluxes declines with increasing flows due to imbalances between supply and demand at network scales. River networks have a tendency to become saturated (supply >> demand) under higher flow conditions because supplies increase faster than sink processes. However, the flow thresholds under which saturation occurs depends on a variety of factors, including the inherent process rate for a given constituent and the abundance of lentic waters such as lakes, ponds, reservoirs, and fluvial wetlands within the river network. As supply increases, saturation at network scales is initially limited by previously unmet demand in downstream aquatic ecosystems. The RNS concept describes a general tendency of river network function that can be used to compare the fate of different constituents among river networks. New approaches using nested in situ high-frequency sensors and spatially extensive synoptic techniques offer the potential to test the RNS concept in different settings. Better understanding of when and where river networks saturate for different constituents will allow for the extrapolation of aquatic function to broader spatial scales and therefore provide information on the influence of river function on continental element cycles and help identify policy priorities.