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This dataset reports data from Nghiem et al. (2024), "Testing floc settling velocity models in rivers and freshwater wetlands." Please refer to "readme.xlsx" for a description of each data file. The original sediment grain size distribution data for each sample can be found online on the NASA Delta-X repository. 

Abstract. Flocculation controls mud sedimentation and organic carbon burial rates by increasing mud settling velocity. However, calibration and validation of floc settling velocity models in freshwater are lacking. We used a camera, in situ laser diffraction particle sizing, and suspended sediment concentration–depth profiles to measure flocs in Wax Lake Delta, Louisiana. We developed a new workflow that combines our multiple floc data sources to distinguish between flocs and unflocculated sediment and measure floc attributes that were previously difficult to constrain. Sediment finer than ∼10 to 55 µm was flocculated with median floc diameter of 30 to 90 µm, bulk solid fraction of 0.05 to 0.3, fractal dimension of ∼2.1, and floc settling velocity of ∼0.1 to 1 mm s−1, with little variation along water depth. Results are consistent with a semi-empirical model indicating that sediment concentration and mineralogy, organics, water chemistry, and, above all, turbulence control floc settling velocity. Effective primary particle diameter is ∼2 µm, about 2 to 6 times smaller than the median primary particle diameter, and is better described using a fractal theory. Flow through the floc increases settling velocity by an average factor of 2 and up to a factor of 7 and can be described by a modified permeability model that accounts for the effect of many primary particle sizes on flow paths. These findings help explain discrepancies between observations and an explicit settling model based on Stokes' law that depends on floc diameter, permeability, and fractal properties. 

Abstract. Coastal marsh survival relies on the ability to increase elevation and offset sea level rise. It is therefore important to realistically model sediment fluxes between marshes, tidal channels, and bays as sediment availability controls accretion. Traditionally, numerical models have been calibrated and validated using in situ measurements at a few locations within the domain of interest. These datasets typically provide temporal information but lack spatial variability. This paper explores the potential of coupling numerical models with high-resolution remote sensing imagery. Products from three sensors from the NASA Delta-X airborne mission are used. Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides vertical water level change on the marshland and was used to adjust the bathymetry and calibrate water fluxes over the marsh. AirSWOT yields water surface elevation within bays, lakes, and channels, and was used to calibrate the Chezy bottom friction coefficient. Finally, imagery from AVIRIS-NG provides maps of total suspended solids (TSS) concentration that were used to calibrate sediment parameters of settling velocity and critical shear stress for erosion. Three numerical models were developed at different locations along coastal Louisiana using Delft3D. The coupling enabled a spatial evaluation of model performance that was not possible using simple point measurements. Overall, the study shows that calibration of numerical models and their general performance will greatly benefit from remote sensing. 

This document describes geomorphic relative age mapping and radiocarbon (14C) measurements used to construct floodplain age models for three locations within the Yukon River Watershed: Huslia, Alaska (65.700 N, 156.387 W), Alakanuk, Alaska (62.685 N, 164.644 W), and Beaver, Alaska (66.362 N, 147.398 W). We describe the field sampling protocols, geomorphic mapping of cross-cutting relationships (aided by digital elevation models (DEMs) and high-resolution satellite imagery), 14C and optically stimulated luminescence (OSL) lab analyses, Markov Chain Monte Carlo (MCMC) interpolation through the geomorphic–radiogenic age constraints, and the resulting floodplain terrain age models. 

The carbon stored in permafrost deposits represents the single largest soil carbon reservoir on Earth. Concerns about the instability and dynamics of this carbon reservoir during permafrost thaw associated with polar amplification of climate warming contribute a large part of the uncertainty in forecasting future climate. We have been studying the carbon dynamics of permafrost deposits contained in the floodplains of large Arctic rivers. Across Arctic floodplains, accelerating bank erosion can liberate permafrost organic carbon (OC) as carbon dioxide (CO2) or methane (CH4), and/or redeposit it in fluvial units. These different fates have very different implications for climate feedback. Determining OC stocks and their dynamics in Arctic floodplain cutbanks and point bars, as well as the OC load in fluvial transport, is essential to better understand the recycling and export of permafrost carbon. As part of a National Science Foundation (NSF) funded project to better understand the effects of erosion in the Yukon River Basin, floodplain sediments were collected between June and September 2022 at two locations underlain by discontinuous permafrost within the Yukon River Basin in Alaska: Beaver (65.700° North (N), 156.387° West (W)) and Huslia (66.362° N, 147.398° W). This dataset mainly reports OC contents for collected subsurface sediments in floodplains measured by elemental analyzer. The coupled mercury content can be found in Isabel et al., 2024 (https://doi.org/10.18739/A2RF5KH5J). 

This dataset includes field measurements of above-ground biomass made between May and October, 2023 in three locations within the Yukon River Watershed: Huslia, Alaska(AK) (65.700 N, 156.387W), Beaver, AK (66.362 N, 147.398W), and Alakanuk, AK (62.685N, 164.644W). We measured a total of 11,335 trees, distributed in 190 field plots (approximately 10 meter (m) x 10 m). We apply allometric scaling relations to convert measurements of tree diameter to kilograms of dry biomass. We then link these filed measurements of above-ground biomass density to the mean forest canopy height (MCH), derived from airborne Light Detection and Ranging (LiDAR) data. We derive empirical regressions linking MCH to above-ground biomass in each of the field sites, and then apply these empirical relationships to the LiDAR datasets to obtain maps of above-ground biomass density. This dataset includes both the field observations (coordinates, tree type, and tree diameter of the 11,335 inventoried trees) and the processed above-ground biomass maps (georeferenced TIFF files, with a spatial resolution of 10 m). 

The file "riverfloc_datacompilation.csv" contains the data in csv format. The file "metadata.txt" contains the metadata describing the data in the csv file. This version corrects an error in which the ionic strength and relative charge density (variables 48 and 50) were underestimated by a factor of 1000.