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PIE LTER extensometer measurements of marsh bank sediment deformation, soil creep in West Creek, Rowley, MA.

Muddy banks of marsh channels experience soil creep – a viscous-like slow deformation resulting in a net downslope transport. Here we present the first field evidence of soil creep in a mesotidal salt marsh using high precision measurements of soil deformation taken with a vibrating-wire extensometer over two years.

PIE LTER, Year 2013-2018, remote sensing derived sediment concentration maps, movies, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges at Plum Island Sound, Massachusetts.

PIE LTER, Year 2013-2018, remote sensing (Landsat8 OLI sensors and Sentinel-2A/2B) derived sediment concentration maps, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges for Plum Island Sound estuary, Massachusetts.

PIE LTER trapping capacity of suspended cohesive sediments of subdomains at Plum Island Sound, Massachusetts (numerical simulations).

Trapping capacity of suspended cohesive sediments of subdomains at Plum Island Sound, Massachusetts (from numerical simulations) are predicted using 2D Delft3D FLOW/MOR model and vegetation module during different stages of tidal height and direction.

PIE LTER, Year 2017-2018, locations, date, sediment concentration and spectral reflectance measurement methods of 40 water samples at Plum Island Sound and deep ocean, Massachusetts.

PIE LTER, Year 2017-2018, locations, date, sediment concentration and spectral reflectance measurement methods of 40 water samples at Plum Island Sound and deep ocean, Massachusetts

openOpenMar 2016View details →

Sierra Ladrones Study Basin (SLSB) Sediment Micro Climate Research in Watersheds at the Sevilleta National Wildlife Refuge, New Mexico (1992-1995)

To support the hydrology studies in the Sierra Ladrones Study Basin, a network of moisture potential sensors and temperature sensors wereinstalled in the stream-channel sediments and adjacent soils atvarious locations up thru the watersheds in 1992. Two rain gaugeswere also added up through the watershed gradient to complement therain gauge on the weather station (Met43) at the base of the watershedto provide a better measure of moisture inputs to these watersheds.This file contains data for 1992 to 1994.

openCustomJan 2012View details →

Sediment Characteristics of Hog and Metompkin Islands of the Virginia Coast Reserve 2010

For complete information regarding sampling and analytical methods, and for contextualization of the data and variables, please see C. Wolner's 2011 thesis, available on the VCR LTER website.

openCustomSep 2012View details →

Sediment characteristics in experimental seagrass meadows in Virginia, 2012

Using a large-scale restoration (>1700 ha) in the Virginia coastal bays as a model system, we evaluated the role of seagrass, Zostera Â marina, restoration in carbon storage in sediments of shallow coastal ecosystems. Sediments of replicate seagrass meadows representing different age treatments (as time since seeding: 0, 4, and 10 years), were analyzed for % carbon, % nitrogen, bulk density, and organic matter content at 1-cm increments to a depth of 10 cm.

openCustomSep 2011View details →

Sediment characteristics in experimental seagrass meadows in Virginia, 2011

Using a large-scale restoration (>1700 ha) in the Virginia coastal bays as a model system, we evaluated the role of seagrass, Zostera Â marina, restoration in carbon storage in sediments of shallow coastal ecosystems. Sediments of replicate seagrass meadows representing different age treatments (as time since seeding: 0, 4, and 10 years), were analyzed for % carbon, % nitrogen, bulk density, organic matter content, and ^210Pb for dating at 1-cm increments to a depth of 10 cm.

openCustomJun 2014View details →

Biophysical Controls on Sediment Suspension in South Bay, VA 2013-2014

Research was conducted in South Bay, Virginia, a shallow (1-2 m mean depth) subtidal coastal bay on the Delmarva Peninsula. Hydrodynamic measurements including current velocity, wave characteristics, shear stress, as well as biological samples, light and SSC data were recorded for 1-3 weeks over 2 consecutive years during the spring, summer, fall and winter seasons. See: Ross Timmerman 2014. Biophysical Controls on Sediment Suspension in a Shallow Coastal Bay. M.S. Thesis, University of Virginia. Data tables are included for sediment characteristics, wave height, wave vector measurements and light.

openCustomJun 2016View details →

Sediment grain size in seagrass restoration plots in the Virginia coastal bays, 2010-2016

This dataset contains particle size distributions for sediment samples collected from Z. marina restoration plots in Hog Island Bay and South Bay, VA. Samples were collected every three years starting in 2010 from 64 sites in Hog Island Bay (58 restored seagrass sites, 6 bare sites), and 12 sites in South Bay (6 restored seagrass sites, 6 bare sites). Bare sites in South Bay were no longer sampled after 2013, due to colonization of the sites by seagrass. Restored sites were seeded between 2001-2008; plot-level particle size distributions were combined based on the age of the plots during each sampling year.

openCustomJun 2014View details →

Lead 210 profiles in sediment cores in South Bay, Virginia

This dataset includes 210Pb (dpm g) and bulk carbon (mg/ml) data from four 20-cm sediment cores collected in South Bay, Virginia. Two of the cores were collected in 2011, one from within the restored Zostera marina (eelgrass) meadow and one from a nearby bare site. The other two cores were collected in 2014 from a different meadow site and a different bare site. Both meadow cores were collected within original restoration seed plots. The SB-5 bare core was collected north of the restored meadow. The 2-bare core was collected west of the meadow, near Man and Boy Channel. 210Pb and carbon profiles from the meadow sites were used to establish meadow carbon accumulation rates in 2011 and 2014, respectively. Bare areas in this system appear to be non-depositional. Excess 210Pb was not observed in the profiles from the two bare cores.

openCustomJul 2014View details →

South Bay, VA, seagrass sediment and autotroph source stable isotope compositions, 2014

Abstract text from Oreska et al. (2017): Non-seagrass sources account for 50% of the sediment organic carbon (SOC) in many seagrass beds, a fraction that may derive from external organic matter (OM) advected into the meadow and trapped by the seagrass canopy or produced in situ. If allochthonous carbon fluxes are responsible for the non-seagrass SOC in a given seagrass bed, this fraction should decrease with distance from the meadow perimeter. Identifying the spatial origin of SOC is important for closing seagrass carbon budgets and "blue carbon" offset-credit accounting, but studies have yet to quantify and map seagrass SOC stocks by carbon source. We measured sediment d13C, d15N, and d34S throughout a large (6 km2), restored Zostera marina (eelgrass) meadow and applied Bayesian mixing models to quantify total SOC contributions from possible autotroph sources, Z. marina, Spartina alterniflora, and benthic microalgae (BMA). Z. marina accounted for <40% of total meadow SOC, but we did not find evidence for outwelling from the fringing S. alterniflora salt-marsh or OM advection from bare subtidal areas. S. alterniflora SOC contributions averaged 10% at sites both inside and outside of the meadow. The BMA fraction accounted for 51% of total meadow SOC and was highest at sites furthest from the bare subtidal-meadow edge, indicative of in situ production.

openCustomAug 2012View details →

Sediment Grain Size Transects on the coast of Virginia, August 2012

Sediment grain size distributions were determined for five sediment sampling transects in four of the coastal bays in Northampton, Co, VA.

opencc-by-4.0May 2020View details →

Macrofossils in sediments from Genggahai Lake on the Tibetan Plateau

The data include the remains of Macrofossils from the genghai sedimentary core since nearly 16 Ka. Based on the selection and identification of submerged plants, Cladocera and diatom fossils in the sediments, the changes of the main aquatic communities in the lake were analyzed.

opencc-by-4.0May 2020View details →

Model data repository of "How sediment thickness influences subduction dynamics and seismicity"

This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness Tsed of 4 km on a cluster using executables.

opencc-by-4.0Jul 2020View details →

Dataset in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters

3000fullhisy: raw data to reproduce Fig. 2 ppsdspec.npz: all spectrogram as shown in Fig. 3a AllVelMods: All velocity model shown in Fig. 3b ac.out.final.npz: auto-correlation image in Fig. 3c DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d

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Nitrogen enrichment stimulates wetland plant responses whereas salt amendments alter sediment microbial communities and biogeochemical responses

Freshwater wetlands of the temperate north are exposed to a range of pollutants that may alter their function, including nitrogen (N)-rich agricultural and urban runoff, seawater intrusion, and road salt contamination, though it is largely unknown how these drivers of change interact with the vegetation to affect wetland carbon (C) fluxes and microbial communities. We implemented a full factorial mesocosm (378.5 L tanks) experiment investigating C-related responses to three common wetland plants of eastern North America (Phragmites australis, Spartina pectinata, Typha latifolia), and four water quality treatments (fresh water control, N, road salt, sea salt). During the 2017 growing season, we quantified carbon dioxide (CO2) and methane (CH4) fluxes, above- and below-ground biomass, root porosity, light penetration, pore water chemistry (NH4+, NO3-, SO4-², Cl-, DOC), soil C mineralization, as well as sediment microbial communities via 16S rRNA gene sequencing. Relative to freshwater controls, N enrichment stimulated plant biomass, which in turn increased CO2 uptake and reduced light penetration, especially in Spartina stands. Root porosity was not affected by water quality, but was positively correlated with CH4 emissions, suggesting that plants can be important conduits for CH4 from anoxic sediment to the atmosphere. Sediment microbial composition was largely unaffected by N addition, whereas salt amendments induced structural shifts, reduced sediment community diversity, and reduced C mineralization rates, presumably due to osmotic stress. Methane emissions were suppressed by sea salt, but not road salt, providing evidence for the additional chemical control (SO4-2 availability) on this microbial-mediated process. Thus, N may have stimulated plant activity while salting treatments preferentially enriched specific microbial populations. Together our findings underpin the utility of combining plant and microbial responses, and highlight the need for more integrative studies to predict the consequences of a changing environment on freshwater wetlands.

opencc-zeroJun 2020View details →

opencc-by-4.0Sep 2020View details →

Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"

Global seafloor density prediction results using the random forest regressor machine learning algorithm.  Dataset S1. Seafloor density measurements.  Columns are labeled with a header and include associated drilling project and measurement type for each sample.  File format: CSV text file Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution.  Units are g/cm^3.  File format: netCDF (.nc) Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution.  Units are g/cm^3.  File format: netCDF (.nc)

opencc-by-4.0Aug 2020View details →

U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru — Datasets

Title: U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru — Datasets Version: 2.0 Date of Release: August 18, 2020 Last Update: September 16, 2020 Identifier: 10.5281/zenodo.4031644 Permalink: <a href="https://doi.org/10.5281/zenodo.4031644">https://doi.org/10.5281/zenodo.4031644</a> Associated publication: Chen, C.Y., McGee, D., Woods, A., Pérez, L., Hatfield, R.G., Edwards, R.L., Cheng, H., Valero-Garcés, B.L., Lehmann, S.B., Stoner, J.S., Schwalb, A., Tal, I., Seltzer, G.O., Tapia, P.M., Abbott, M.B., and Rodbell, R.T. (2020) U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru. Quaternary Science Reviews. 244: 106422 doi: <a href="https://doi.org/10.1016/j.quascirev.2020.106422">10.1016/j.quascirev.2020.106422</a> Link to publication: <a href="http://doi.org/10.1016/j.quascirev.2020.106422">https://doi.org/10.1016/j.quascirev.2020.106422</a> Suggested citation: Please reference the associated publication above when using any datasets or materials in this repository. Contact information: Christine Y. Chen, cychen@caltech.edu OR cychen.earth@gmail.com Dates of data collection and generation: February 2016 to July 2019 --------------- VERSION NOTES (v2.0) This online data repository was updated in order to correct errors in the U-Th data tables (Tables A1, A2, and A3) and subsequently update the age-depth model. The corrections impacted the files named 'CYChen et al. (2020) QSR - U-Th Data Tables for PLJ-1 and 1996 cores - v2.xlsx' and 'CYChen et al. (2020) QSR - Input Your Own Depths for Age-Depth Model - v2.xlsx'. No changes have been made to the original versions of other datasets. However, all components of this data repository have been given the "v2" identifier to be consistent with the versioning scheme applied to this data repository as a whole by Zenodo. --------------- DESCRIPTION OF DATA This data repository contains the following datasets and supplementary materials. Specific documentation and, where applicable, instructions of use are included in the readme section of each file. We refer the user to the original manuscript (see above) and the text of the Supplemental Materials published alongside this manuscript for additional general information regarding the collection and generation of these data. SUPPLEMENTARY TEXT AND FIGURES <ul> <li>CYChen et al. (2020) QSR - Supplementary Materials.pdf</li> </ul> DATA TABLES FOR PLJ-1 and 1996 CORE <ul> <li>CYChen et al. (2020) QSR - U-Th Data Tables for PLJ-1 and 1996 cores - v2.xlsx:  U-Th data tables for the PLJ-1 and 1996 core (with all digits preserved where possible, for reproducibility and data longevity)</li> <li>CYChen et al. (2020) QSR - Other Data for PLJ-1 core - v2.xlsx: sample locations and corresponding elemental concentrations, carbon coulometry, and color reflectance data</li> <li>CYChen et al. (2020) QSR - Ostracode Analysis for PLJ-1 core - v2.xlsx: ostracode paleoecological data from the PLJ-1 core</li> </ul> MODELING URANIUM-THORIUM ISOTOPIC EVOLUTION OF LAKE SEDIMENTS <ul> <li>CYChen et al. (2020) QSR - Modeling Spreadsheet for Uranium-Thorium Isotopic Evolution of Lake Sediments - v2.xlsx: spreadsheet for simulating the uranium-thorium isotopic evolution of a sample that has undergone open system behavior with respect to uranium and/or a sample of mixed composition (an impure carbonate)</li> </ul> AGE-DEPTH MODEL <ul> <li>CYChen et al. (2020) QSR - Input Your Own Depths for Age-Depth Model - v2.xlsx: spreadsheet for extracting the radiometric-based age-depth model generated by Bacon by splice depth</li> </ul>