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Dataset Title:  Sensor measurements for dissolved inorganic carbon from the Sage Lot Pond salt
marsh tidal creek in Waquoit Bay, MA from July to December 2015
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_768607)
Range: time = 2015-07-07T20:15:00Z to 2015-12-18T15:15:00Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4363001e+9, 1.4504517e+9;
    String axis "T";
    String bcodmo_name "date";
    String description "Date and time (UTC) formatted to ISO8601 standard. Format: yyyy-mm-ddTHH:MM";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  TEMPERATURE {
    Float32 _FillValue NaN;
    Float32 actual_range 4.85, 30.61;
    String bcodmo_name "temperature";
    String description "Water temperature";
    String long_name "TEMPERATURE";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  SALINITY {
    Float32 _FillValue NaN;
    Float32 actual_range 14.47, 32.65;
    String bcodmo_name "sal";
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "Water salinity";
    String long_name "Sea Water Practical Salinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "practical salinity scale";
  }
  CHANOSDIC {
    Float32 _FillValue NaN;
    Float32 actual_range 1246.12, 2482.21;
    String bcodmo_name "DIC";
    String description "Dissolved inorganic carbon";
    String long_name "CHANOSDIC";
    String units "micromoles per kilogram (umol/kg)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Samples were collected from the Sage Lot Pond salt marsh tidal creek in
Waquoit Bay, MA at approx. 41.5546N, 70.5071W.
 
In situ, high-frequency sensors for DIC and salinity were deployed at the
mouth of the tidal creek in Sage Lot Pond at the latitude and longitude listed
in the location above. DIC was measured with an autonomous sensor called
CHANnelized Optical Sensor (CHANOS). An EXO2 Multiparameter Sonde (YSI Inc.,
Yellow Springs, OH) was submerged in the tidal creek to measure temperature
and salinity. The YSI EXO2 recorded at intervals ranging from 2 min to 8 min.
Reported YSI EXO2 sensor accuracy specifications are: 1% of the reading for
salinity and 0.05 \\u00b0C for temperature. CHANOS was placed on a platform
atop the marsh adjacent to the creek with the inlet pumping from the creek at
the same depth and within 30 cm of the EXO2 Sonde. This setup avoided any
interference by CHANOS on water flow in the creek. There is no significant
concentration difference with depth in the creek (data not shown). In order to
prevent fouling, sample water was filtered by a 100\\u03bcm plastic disc filter
(Keller Products, Acton, MA) followed by a copper mesh filter. CHANOS was
powered by two 12 V batteries that were charged with two 250W solar panels
(Renogy, Ontario, CA).
 
CHANOS uses spectrophotometric principles to measure DIC and pH using two
independent channels (Wang et al., 2015). Briefly, CHANOS consisted of syringe
pumps for delivery of reagents, junction boxes containing valves, thermistors,
and optical and fluidic components for DIC and pH analysis, and an electronics
housing, as well as reagent bags for storage of CRM, hydrochloric acid,
reference solution, and pH- sensitive indicator solution. For this study, only
[DIC] measurements were used. The DIC channel uses an improved
spectrophotometric method described in detail in Wang et al. (2013) whereby a
countercurrent flow configuration between acidified seawater and a pH-
sensitive indicator solution in a tube-in-tube design achieves fast,
continuous CO2 equilibration across highly CO2-permeable Teflon AF 2400
tubing. After CO2 exchange in the countercurrent flow cell, the indicator
solution is directed into an optical cell for detection. Each measurement
cycle is ~15min. The system achieved a precision of ~ \\u00b1 2.5 \\u03bcmol
kg-1 and an accuracy of ~ \\u00b1 5.0 \\u03bcmol kg-1 during coastal deployments
(Wang et al., 2015).";
    String awards_0_award_nid "765031";
    String awards_0_award_number "OCE-1459521";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459521";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"Salt marsh continuous DIC 
   measured by CHANnelized Optical Sensor (CHANOS) 
  PI: Zhaohui Aleck Wang (WHOI) 
  Co-PIs: Kevin Kroeger & Meagan Gonneea (USGS) 
  Version date: 23-May-2019";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2019-05-24T19:54:34Z";
    String date_modified "2019-05-29T15:35:39Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.768607.1";
    String history 
"2022-10-07T15:09:06Z (local files)
2022-10-07T15:09:06Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_768607.das";
    String infoUrl "https://www.bco-dmo.org/dataset/768607";
    String institution "BCO-DMO";
    String instruments_0_acronym "YSI EXO";
    String instruments_0_dataset_instrument_nid "768617";
    String instruments_0_description "Comprehensive multi-parameter, water-quality monitoring sondes designed for long-term monitoring, profiling and spot sampling. The EXO sondes are split into several categories: EXO1 Sonde, EXO2 Sonde, EXO3 Sonde. Each category has a slightly different design purpose with the the EXO2 and EXO3 containing more sensor ports than the EXO1. Data are collected using up to four user-replaceable sensors and an integral pressure transducer. Users communicate with the sonde via a field cable to an EXO Handheld, via Bluetooth wireless connection to a PC, or a USB connection to a PC. Typical parameter specifications for relevant sensors include dissolved oxygen with ranges of 0-50 mg/l, with a resolution of +/- 0.1 mg/l, an accuracy of 1 percent of reading for values between 0-20 mg/l and an accuracy of +/- 5 percent of reading for values 20-50 mg/l. Temp ranges are from-5 to +50 degC, with an accuracy of +/- 0.001 degC. Conductivity has a range of 0-200 mS/cm, with an accuracy of +/-0.5 percent of reading + 0.001 mS/cm and a resolution of 0.0001 - 0.01 mS/cm.";
    String instruments_0_instrument_name "YSI EXO multiparameter water quality sondes";
    String instruments_0_instrument_nid "768595";
    String instruments_0_supplied_name "EXO2 Multiparameter Sonde (YSI Inc., Yellow Springs, OH)";
    String instruments_1_acronym "CHANOS";
    String instruments_1_dataset_instrument_nid "768619";
    String instruments_1_description 
"CHANOS uses spectrophotometric principles to measure DIC and pH using two independent channels (Wang et al., 2015). Briefly, CHANOS consisted of syringe pumps for delivery of reagents, junction boxes containing valves, thermistors, and optical and fluidic components for DIC and pH analysis, and an electronics housing, as well as reagent bags for storage of CRM, hydrochloric acid, reference solution, and pH- sensitive indicator solution. 

Refer to Wang et al. (2015) doi: 10.1021/es504893n";
    String instruments_1_instrument_name "CHANnelized Optical Sensor";
    String instruments_1_instrument_nid "768618";
    String instruments_1_supplied_name "CHANnelized Optical Sensor";
    String keywords "bco, bco-dmo, biological, chanosdic, chemical, data, dataset, date, density, dmo, earth, Earth Science > Oceans > Salinity/Density > Salinity, erddap, iso, management, ocean, oceanography, oceans, office, practical, preliminary, salinity, science, sea, sea_water_practical_salinity, seawater, temperature, time, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/768607/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/768607";
    String param_mapping "{'768607': {'ISO_DateTime_UTC': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/768607/parameters";
    String people_0_affiliation "Woods Hole Oceanographic Institution";
    String people_0_affiliation_acronym "WHOI";
    String people_0_person_name "Zhaohui Aleck Wang";
    String people_0_person_nid "51347";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "United States Geological Survey";
    String people_1_affiliation_acronym "USGS";
    String people_1_person_name "Meagan Gonneea";
    String people_1_person_nid "768545";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "United States Geological Survey";
    String people_2_affiliation_acronym "USGS";
    String people_2_person_name "Kevin Kroeger";
    String people_2_person_nid "768544";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Shannon Rauch";
    String people_3_person_nid "51498";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Salt Marsh Paradox";
    String projects_0_acronym "Salt Marsh Paradox";
    String projects_0_description 
"NSF Award Abstract:
Carbon production in vegetated coastal systems such as marshes is among the highest in the biosphere. Resolving carbon production from marshes and assessing their impacts on coastal carbon cycling are critical to determining the long-term impacts of global change such as ocean acidification and eutrophication. In this project, researchers will use new methods to improve the assessment of carbon production from salt marshes. The overarching goals are to understand the role of coastal wetlands in altering carbonate chemistry, alkalinity, and carbon budgets of the coastal ocean, as well as their capacity to buffer against anthropogenically driven chemical changes, such as ocean acidification. This project will involve training for undergraduate, graduate, and postdoctoral researchers, and will provide educational opportunities for students from a local Native American tribe.

Tidal water, after exchange with intertidal salt marshes, contains higher total alkalinity (TA), higher carbon dioxide, but lower pH. These highly productive, vegetated wetlands are deemed to export both alkalinity and dissolved inorganic carbon (DIC) to the ocean. This creates an apppartent paradox in that salt marshes are both an acidifying and alkalizing source to the ocean. Limited studies suggest that marsh DIC and alkalinity export fluxes may be a significant player in regional and global carbon budgets, but the current estimates are still far too uncertain to be conclusive. Unfortunately, tidal marsh ecosystems have dramatically diminished in the recent past, and are likely to diminish further due to sea level rise, land development, eutrophication, and other anthropogenic pressures. To assess the potential impacts of this future change, it is imperative to understand its current status and accurately evaluate its significatce to other parts of the carbon cycle. Similarly, little is know about the distinct sources of DIC and alkalinity being exported from marshes via tidal exchange, although aerobic and various anaerobic respiration processes have been indicated. In this study, researchers will undertake an in-depth study using new methods to vastly improve export fluxes from intertidal salt marshes through tidal exchange over minutes to annual scales, characterize and evaluate the compostiion (carbonate versus non-carbonate alkalinity) of marsh exported TA, the role and significance of the DOC pool in altering carbonate chemistry and export fluxes, identify sources of DIC being exported in tidal water, and investigate how marsh export of TA and DIC impacts carbonate chemistry and the carbon and alkalinity budgets in coastal waters.";
    String projects_0_end_date "2019-02";
    String projects_0_geolocation "Sage Lot Pond salt marsh tidal creek in Waquoit Bay, MA at approx. 41.5546N, -70.5071W";
    String projects_0_name "Collaborative Research: The Paradox of Salt Marshes as a Source of Alkalinity and Low pH, High Carbon Dioxide Water to the Ocean: A First In-depth Study of A Diminishing Source";
    String projects_0_project_nid "765032";
    String projects_0_start_date "2015-03";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Sensor measurements for dissolved inorganic carbon from the Sage Lot Pond salt marsh tidal creek in Waquoit Bay, MA from July to December 2015. DIC was measured with an autonomous sensor called CHANnelized Optical Sensor (CHANOS).";
    String time_coverage_end "2015-12-18T15:15:00Z";
    String time_coverage_start "2015-07-07T20:15:00Z";
    String title "Sensor measurements for dissolved inorganic carbon from the Sage Lot Pond salt marsh tidal creek in Waquoit Bay, MA from July to December 2015";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
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