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270 results for “Water Depth”
Year 2017, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2017 water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound
Year 2018, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2018, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2023.
Four YSI EXO2 water quality sondes were deployed from May 2023 to October 2023 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2024.
Four YSI EXO2 water quality sondes were deployed from April 2024 to October 2024 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2024. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2025.
Four YSI EXO2 water quality sondes were deployed from May 2025 to November 2025 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2025. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2000. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2000,15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2001. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2001,15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2002. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2002, 15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023
<p>The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023.</p> <p>Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis. </p> <p>Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)
<p>Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas > 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>".</p>
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model
<p>Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. </p> <p>An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a> </p> <p><strong>Dataset:</strong> A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution.<em> "NH-SWE_dataset_MAP.png"</em> shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. </p> <p><strong>Files: </strong>The dataset is provided in two different formats:</p> <ol> <li>Individual <em>.csv</em> files for each station in the NH-SWE dataset at <em>"NH_SWE_dataset_vector_files.zip"</em></li> <li>Full-dataset <em>.csv </em>matrices with dates as rows and NH-SWE stations as columns at <em>"NH_SWE_dataset_matrix_files.zip"</em></li> </ol> <p><strong>Metadata:<em> </em></strong><em>"NH_SWE_METADATA.csv"</em> Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the <em>"README_fileformats.txt"</em> file. </p> <p><strong>ΔSNOW model parameter regionalisation: </strong>The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in<em><strong> </strong>"DeltaSNOW_parameter_regionalisation.zip"</em>. The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the <em>"README_regionalisation.txt"</em> file. </p> <p><strong>Data use: </strong>Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See <em>"README_datausage.txt"</em></p> <p><strong>Version history:</strong><br>v1: Initial upload. The ΔSNOW model regionalisation was missing.<br>v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.</p> <p><strong>Reported errors:</strong><br>The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). <br>The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).<br>Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. <br>Japan country code is "JA" in the metadata, although the official country code should be JP. </p>
Water depth observed in front of lake-terminating glaciers in Patagonia
<p>This is the dataset of water depth observed in front of O'Higgins, Upsala, Viedma, and Tyndall glaciers in southern Patagonia.</p> <p> </p> <p>Data format:</p> <p>Latitude [deg], Longitude [deg], Depth [m]</p>
AquaMatch Secchi Disk Depth Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Secchi Disk Depth Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat Secchi Disk Depth dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 10, 1970, to July 1, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Res
Methane ebullition and diffusion rates, turbulence, water temperature, and water depth data from Falling Creek Reservoir (Virginia, USA) in the ice-free period during 2016-2019
This dataset includes weekly and subweekly methane ebullition and diffusion rates collected from March through November in 2016, 2017, 2018, and 2019 in Falling Creek Reservoir (FCR), a drinking water reservoir owned and managed by the Western Virginia Water Authority and located in Vinton, Virginia, USA. In 2016, ebullition rates were measured at four near-shore sites along a longitudinal gradient in FCR that included four replicate locations within each site, for a total of 16 sites in the reservoir. In 2017, methane emission rates and five different potential environmental driver variables (water temperature, wind speed, pressure, turbulence, primary production) were measured at five longitudinal transects that included four replicate sites per transect, for a total of 20 sites in the reservoir. In 2018, ebullition was measured at the same five longitudinal transects but with two replicate sites per transect, for a total of 10 sites. In 2019, ebullition was only measured at the furthest upstream transect monitored in 2017-2018, with four replicate sites within that transect. The dataset consists of four tables: 1) weekly methane ebullition and diffusion rates and the water depths at each of the 24 unique monitoring sites; 2) 10-minute resolution sediment-water interface and surface water temperatures from 16 sites in 2017; 3) weekly sediment-water interface and surface turbulence at the four transects in 2017; and 4) geographical coordinates of each of the 24 sites in the reservoir.
Soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature data from Toolik Field Station, Toolik Lake, Alaska for 2008.
Weather data file for Arctic Tundra LTER site at Toolik Lake. Only the sensors that are measured every 10 minutes and averaged every three hours are include, i.e. soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature.
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