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110 results for “river sediment”
Water column and sediment porewater nutrient concentrations from lagoon, river, and ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Several water types (lagoon, river, ocean) and surface sediment porewater samples from the coastal Beaufort Sea system were sampled seasonally to investigate temporal and spatial shifts in nutrient dynamics. Surface and bottom water samples were collected in April, June, July, and August and analyzed for ammonium, nitrate + nitrite, orthophosphate, and silica.
Physical Characteristics and Stratigraphy of Deep Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of piston-coring deep (around 1m) soil cores from Shark Slough sites, including FCE LTER site SRS3 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Radiometric Characteristics of Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of radiometric dating of soil cores from Shark Slough sites, including FCE LTER sites SRS3 and SRS4 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Physical and Chemical Characteristics of Soil Sediments from the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, August 2004 - ongoing
These data represent the results of annual soil sampling and analysis from all 17 FCE LTER transect locations from Year 2004 thru ongoing. Surface soils from 0-10 cm have been homogenized and analyzed from Sawgrass and mangrove sites and Florida Bay sites. Soils and sediments were analyzed for a suite of physical/chemical variables, including bulk density, organic matter content, extractable iron, AVS and CRS sulfur, and various forms of extractable phosphorus. These analyses are completed to document the differences in soil structure among transect sites, and to provide a baseline dataset against which long-term changes in the physical/chemical properties of the soils can be detected.
Percentage of Carbon and Nitrogen of Soil Sediments from the Shark River Slough, Taylor Slough and Florida Bay within Everglades National Park (FCE LTER), Florida, USA, August 2008 - ongoing
These data represent the results of CHN Analysis from annual soil sampling from all 17 FCE LTER transect locations from Year 2008 thru ongoing. In 2017, the samples were collected before Hurricane Irma (2017a) and post Hurricane Irma (2017b). Surface soils from 0-10 cm been homogenized and analyzed from Sawgrass and mangrove sites and Florida Bay sites. Soils and sediments were analyzed for percentage by weight of total Carbon and Nitrogen with a PerkinElmer CHNS /O 2400 Series II Elemental Analyzer. These analyses are completed to document the differences in soil composition among transect sites, and to provide a baseline dataset against which long-term changes in the properties of the soils can be detected.
Coupling between Sediment and Water Column Populations of Ammonia Oxidizing Thaumarchaeota in the Duplin River near Sapelo Island, Georgia
Populations of nitrifying organisms in the water column at Marsh Landing display a midsummer peak in the abundance of ammonia oxidizing Archaea (AOA) at the site, coinciding with a peak in nitrite concentration. Marsh Landing is at the mouth of the Duplin River, a dead-end tidal channel that drains an extensive area of salt marsh. While the lower Duplin River at Marsh Landing exchanges tidally with Doboy Sound and thus South Atlantic Bight (SAB) coastal waters, water in its upper reaches has a residence time of weeks. The work reported here had two goals: 1) test the hypothesis that the surrounding salt marsh is the source of nitrifiers seen in water samples taken at Marsh Landing; and 2) compare the seasonal dynamics of nitrifiers in surficial sediments with those in the water column. We sampled 6 stations along the ~20 km length of the Duplin River. We collected surface water samples (~0.20 m) at low- to mid-tide, monthly from April-December 2014. Sediment samples (top 1 cm) were collected at the same time from unvegetated creek bank at 2 locations on the Duplin River and from 4 locations spanning the creek bank-to-upland gradient of the saltmarsh accessible from the Teal Boardwalk. The abundance of ammonia oxidizing Archaea, Marine Group 1 Archaea (Thaumarchaeota), ammonia oxidizing Betaproteobacteria (AOB), Bacteria and Nitrospina, a nitrite oxidizing bacterium, were determined by quantitative PCR (qPCR) of DNA extracted from the samples. This data set contains the abundance estimates from April to December 2014 for sediment and water column samples, with corresponding water quality measurements (temperature, salinity and nitrogenous nutrient concentrations).
PIE LTER marsh sediment porewater nutrient concentrations from Spartina sp. and Typha sp. sites along the Parker River and Rowley River, MA.
Marsh sediment porewater nutrient concentrations [NH4+, NO3-, DOC, TDN, H2S] and salinity are reported from Spartina sp. and Typha sp. sites along the Parker and Rowley Rivers, MA. Porewater peeper poles are used for collection and the poles are located in the vicinity of the marsh water table sites for the Railroad, Typha, Shad and Nelson sites.
Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef
<p>Dataset includes in-situ (n = 144,912) and laboratory-dispersed (n = 64) particle size measurements collected using laser diffractometry from nine rivers discharging along 800 km of Great Barrier Reef, Queensland Australia coastline. Two field campaigns (24 February to 5 March 2021 and 24<sup>th</sup> to 31<sup>st</sup> of April 2021) were undertaken to collect vertical profiles of in-situ particle size, water velocity, turbidity, and salinity. Water samples were collected for analysis of laboratory-dispersed particle size and suspended-sediment concentration. Water samples were collected using US-P61 or Van-Dorn samplers deployed alongside a LISST 200x laser diffractometer and an EXO2 YSI multiparameter water quality sonde. Total depth and water velocity were measured using a Nortek Signature ADCP and Teledyne RiverRay ADCP during the first and second field campaigns, respectively. During both campaigns, measurements were undertaken during relatively high discharge events when discharge exceeded the 90<sup>th</sup> percentile of 2020/2021 gauged wet season flows.</p> <p>Data are provided in three csv files. "In_situ_data.csv" contains in situ measurements of particle size, turbidity, salinity, and depth along with estimates of shear rate. Shear rate is estimated from theory and measurements of ADCP-measured total depth and depth-averaged flow (see equation 2 of Livsey et al., 2022). "Lab_data_this_study.csv" contains particle size measurements of suspended-sediment following laboratory dispersion along with coeval measurements of in-situ particle size, turbidity, salinity, and shear rate averaged over the filling time of the US-P61 sampler. "Lab_data_DES_WQI.csv" contains laboratory dispersed particle size measurements collected by the Department of Environment and Science Water Quality Investigation Unit of Queensland Australia (Turner et al., 2013) and compared to data in "Lab_data_this_study.csv" in Livsey et al (2022). </p> <p>Further details of the data collection effort and interpretation of the data are published in Livsey et al (2022) at https://doi.org/10.1029/2021JC017988. </p> <p>Additional data from the 24 February to 5 March 2021 field campaign, funded by CSIRO Oceans and Atmosphere, are available from Crosswell et al (2022) at https://doi.org/10.25919/2vbh-cx08.</p> <p>References:</p> <p>Crosswell, Joey; Carlin, Geoff; Daniel, Livsey; Hillyer, Katie; Steven, Andy (2022): FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia. v1. CSIRO. Data Collection. 10.25919/2vbh-cx08</p> <p>Livsey, D. L., Crosswell, J. R., Turner, R. R., Steven, A. D. L., & Grace, P. R. (2022) Flocculation of riverine sediment draining to the Great Barrier Reef, implications for monitoring and modelling of sediment dispersal across continental shelves. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2021JC017988</p> <p>Turner. R, Huggins. R, Wallace. R, Smith. R, Vardy. S, Warne. M St. J. (2013). Total suspended solids, nutrient, and pesticide loads (2010-2011) for rivers that discharge to the Great Barrier Reef Great Barrier Reef Catchment Loads Monitoring 2010-2011 Department of Science, Information Technology, Innovation and the Arts, Brisbane.</p> <p> </p> <p> </p>
Water quality data (River sediment, Nitrogen and Phosphorus loads) for Africa
<p>Output data on African water quality and scripts for preprint - "One third of African rivers fail to meet the 'good ambient water quality' nutrient targets" at <a href="https://dx.doi.org/10.2139/ssrn.4829742">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4829742</a> . Please check the readme file for data description. The data includes river flow, sediment load, nitrogen and Phosphorus loads for Africa at daily and yearly time scale. This work is currently under review in Ecological Indicators journal. </p>
River Sediment Database (RivSed)
<p>The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 <em>Geophysical Research Letters</em>). </p> <p>The paper associated with RivSed: <strong>Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. <em>Environmental Research Letters. </em><a href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8">https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8</a></strong></p> <p> </p> <p> </p> <p><strong>Files:</strong></p> <p>1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. </p> <p>2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column.</p> <p>3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp).</p> <p>4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp).</p> <p>5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths.</p> <p>6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv)</p> <p>7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv)</p> <p>8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.</p> <p> </p>
Benthic chlorophyll concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll and gross oxygenic photosynthesis were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll were measured in the bulk surface centimeter depth of sediment. Gross oxygenic photosynthesis rates were integrated over 100 um resolution measurements below the sediment water interface using oxygen microelectrodes. Several relatively pristine sites on Sapelo Island (Moses Hammock, Dean Creek and Marine Institute) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration and photosynthesis rate across bank and marsh zones with higher photosynthetic biomass and activity in the spring season. Nutrient inputs to these study sites are also suggested as a control on gross oxygenic photosynthesis rates as evidenced by a relationship between photosynthesis rate and land-use.
Benthic chlorophyll concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from June and August 2002
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll and gross oxygenic photosynthesis were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll were measured in the bulk surface centimeter depth of sediment. Gross oxygenic photosynthesis rates were integrated over 100 um resolution measurements below the sediment water interface using oxygen microelectrodes. Several relatively pristine sites on Sapelo Island (Moses Hammock, Dean Creek and Marine Institute) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration and photosynthesis rate across bank and marsh zones with higher photosynthetic biomass and activity in the spring season. Nutrient inputs to these study sites are also suggested as a control on gross oxygenic photosynthesis rates as evidenced by a relationship between photosynthesis rate and land-use.
Benthic chlorophyll, density, porosity, and organic content concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.
Benthic chlorophyll, density, porosity, and organic content concentrations in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from June and August 2002
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.
Porewater nutrients, dissolved organics, redox species, and gasses in estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from Fall 2000 through Fall 2002
Seasonal patterns of estuarine creek-bank sediment porewater nutrients, dissolved organics, redox species and gasses were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Porewater ammonium, nitrate+nitrite, phosphate, silica, dissolved organic carbon, nitrogen and phosphorus, reduced iron and manganese, chloride, sulfate, hydrogen sulfide, pH, dissolved inorganic carbon, methane and nitrogen gas were measured at centimeter scale resolution depth profiles with the use of porewater equilibration meters. Several relatively pristine sites on Sapelo Island (Moses Hammock, Dean Creek and Marine Institute), a site slightly impacted by development (White Oak Creek) and a presumably heavily impacted site (Dover Bluff) show different levels and patterns of several porewater constituents.
Bulk carbon, nitrogen, porosity, chlorophyll a and phaeopigments in sediments of the Parker and Rowley Rivers, Newbury and Rowley, Massachusetts, PIE LTER.
Measurements of bulk sediment carbon, nitrogen, chlorophyll a, phaeopigments and porosity 1993 - 2017 at a variety of sites in the Parker and Rowley Rivers, Newbury and Rowley Massachusetts.
Geochemical sediment fingerprinting dataset from Oroua river catchment, New Zealand
<p>Geochemical dataset collected to determine key source contributions to overbank sediment deposition for specific particle size fractions as described in "Vale, S., Smith, H., Matthews, A., & Boyte, S. (2020). Determining sediment source contributions to overbank deposits within stopbanks in the Oroua River, New Zealand, using sediment fingerprinting. <i>Journal of Hydrology (New Zealand)</i>, <i>59</i>(2), 147-172." </p>
Floodplain Sediment Storage Times in a Simulated Meandering River
<p>This dataset contains four files which enable another researcher to bypass the most computationally intensive parts of the simulations (in MATLAB) associated with the publication of my dissertation thesis. These files result as the analysis of the Simulation of the Long-Term Evolution of a Meandering River (https://doi.org/10.5281/zenodo.5651840).</p> <p>The first of these files contain the storage time and age distributions of simulated sediments from the upstream reach of the simulated river, captured after analyzing the upstream 16 mini reaches of the floodplain.</p> <p>The second file contains the same distributions from two reaches after analyzing 36 mini reaches (the downstream reach values can be found by subtraction of the data in these two files)</p> <p>The third file contains other useful parameters tracked throughout the simulation</p> <p>The fourth file contains the X & Y coordinates which mark the boundaries of each mini reach</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p>
Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"
<p>Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research.</p> <p>This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.</p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>
River Sediment Database-Amazon (RivSed-Amazon)
<p>The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018. </p> <p>The methods used to produce this database were initially developed in the following publications:</p> <ul> <li>Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. <em>Environmental Research Letters. </em><a href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8">https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8</a> <strong>and </strong></li> <li>Gardner et al. (2020). The color of rivers. Geophysical Research Letters. <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946</a></li> </ul> <p>The publication associated with RivSed-Amazon is in review.</p> <p><strong>Files:</strong></p> <p>1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository. </p> <p>2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of SSC and associated data that is joinable to SWORD based on the ""reach_id".</p> <p>3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp).</p> <p>4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp).</p> <p>5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv).</p> <p>6) The final training data used to build the xgboost machine learning model (train_v1.1.csv).</p> <p>7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time.</p> <p>8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.</p> <p> </p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.