Attribute_Accuracy_Report:
No formal attribute accuracy tests were conducted for the PAR sensors. All other continuous monitoring sensors were bench tested for accuracy in the laboratory at the beginning and end of the field season; salinity sensors were also field checked periodically. At the beginning of the field season, data from temperature sensors logging in air were compared to a National Institute of Standards and Technology (NIST) Traceable Digital Thermometer (Fisher brand) co-deployed on the bench. Logged temperature readings that were within manufacturer’s accuracy specification of temperature measured by the NIST Traceable Thermometer were deemed acceptable. Note that draft shields were not consistently used for air temperature sensors; use of draft shields could have protected the air temperature sensor from unknown influences such as direct exposure to sunlight and wind gusts. Water level sensors were tested in the laboratory for accuracy by comparing logged readings in air to the barometers used for barometric compensation; logged pressure readings that were within manufacturer’s accuracy specification of the barometer’s logged pressure in air were deemed acceptable. Salinity sensors were checked with a purchased calibration standard (Ricca 50.000 millisiemens per centimeter, catalog number 2248-1) and re-calibrated as needed. These procedures were repeated at the end of the field season. If sensor accuracy was found to have drifted over the field season, data corrections would be applied as described in the Process Steps portion of this metadata record.
Each logged parameter set was queried for maximum and minimum values to be sure logged data fell within expected ranges for the environmental conditions. Data were plotted to look for any obvious instrument errors (data jumps or gaps or noisy data). Erroneous data in the logged data files may be the result of one of several sources: vandalism of an instrument, removal of an instrument for maintenance, corrupt data file due to malfunctioning instrument, or wells with insufficient water level to produce reliable data. These data were removed from the final data report.
Gaps in the continuous data record for a given parameter may have been the result of one of several sources: sensor not deployed during that time period, vandalism of an instrument, removal of an instrument for maintenance, corrupt data file due to malfunctioning instrument, or wells with insufficient water level to produce reliable data.
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Horizontal positions were determined with a Trimble Real-Time Kinematic (RTK) GPS with Positional Dilution of Precision (PDOP) and Horizontal Dilution of Precision (HDOP) values less than 1.000. Horizontal accuracy is estimated to be +/- 5 centimeters. No formal positional accuracy tests were conducted.
Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report:
Water level loggers were deployed in tidal salt marsh creeks and in wells on the salt marsh surface; sensor depth data from these instruments were converted to water elevation relative to NAVD88 using land surface elevations measured by Trimble Real-Time Kinematic (RTK) GPS in December 2016. Details on this calculation are described in the process steps. Vertical Dilution of Precision (VDOP) for RTK GPS measurements were less than 1.000; vertical accuracy of this method is +/- 5 cm. No formal positional accuracy tests were conducted.
Process_Step:
Process_Description:
In 2016 and 2017, PVC wells were installed at each site by pushing the well section into the sediment by hand or lightly tapping the top of the well with a mallet. The tip of the slotted screened well section was pushed about 30 cm below the land surface; the threaded portion (connecting the slotted length with the solid length) was below the sediment surface. At least 1 meter of well casing remained above the sediment surface to ensure that the groundwater well was not over-topped with surface water during the high tide. A vent hole was drilled near the top of the solid well casing. Wells at these locations were destroyed by ice over the winter (between 2016 and 2017). The Stony Brook wells were reinstalled in 2017 using the protocol previously described. Exact well heights and sensor deployment lengths are summarized in a separate entity and attribute section. The process date represents the latest process date.
Process_Date: 2017
Process_Step:
Process_Description:
Additional sensors were co-deployed at the same sites as the groundwater level sensors including: dual channel (soil and air) temperature loggers, a barometer, and a photosynthetically active radiation (PAR) sensor. Soil temperature sensors were attached to a wooden stick and pushed by hand so that the bottom of the temperature sensor was 10 cm below the land surface. The air temperature sensor was attached the the groundwater well. The barometer was deployed in air near to the well location on a nearby tree limb or on a post with the PAR sensor. The PAR sensor was deployed above the height of the nearest vegetation in a location that would not be shaded by vegetation or other structures. Deployments took place during 2016 and 2017, with the process date representing the latest process date.
Process_Date: 2017
Process_Step:
Process_Description:
Water level loggers were deployed in creeks near to the tidal restriction restoration by attaching the logger to a post driven into the creek bottom. Manual measurements of logger deployment heights were used in conjunction with RTK elevation measurements of the creek bottom to convert logged sensor depth to water elevation (further details given in process steps following). Deployments took place during 2016 and 2017, with the process date representing the latest process date.
Process_Date: 2017
Process_Step:
Process_Description:
Data loggers were deployed in April 2016 and periodically downloaded in the field using instrument manufacturers' software (HOBOPro, WinSitu, EcoView); data were inspected for general accuracy. Creek sensor deployment heights were measured before removing the sensor for maintenance and after re-deploying the sensor to account for sensor movement and/or infilling or scouring around the creek post. Dates of site visits are listed below, with the process date representing the latest process date.
Bass Creek: 4/21/2016; 5/19/2016; 6/30/2016; 8/30/2016; 11/9/2016; 12/21/2016
Quivett Creek: 4/1/2016; 5/19/2016; 6/29/2016; 8/30/2016; 10/28/2016; 12/21/2016
State Game Farm: 4/8/2016; 5/19/2016; 6/27/2016; 9/7/2016; 11/9/2016; 12/22/2016
Stony Brook: 4/8/2016; 5/16/2016; 6/29/2016; 8/30/2016; 10/28/2016; 12/20/2016; 04/24/2017; 05/03/2017; 05/18/2017; 06/14/2017; 07/11/2017; 08/16/2017; 09/11/2017
11/14/2017
Process_Date: 20171114
Process_Step:
Process_Description:
Pressure data from non-vented water level sensors were barometrically corrected in manufacturers’ software (HOBOPro or WinSitu BaroMerge); corrected pressure was converted to sensor depth assuming a brackish water density (HOBOPro: 1.010 grams per cubic centimeter; WinSitu: 1.012 grams per cubic centimeter). Barometric pressure and density corrections were processed throughout the deployment period and checked while finalizing the data report; the process date represents the latest date of data processing.
Process_Date: 2019
Process_Step:
Process_Description:
Data from all continuous monitoring sensors were exported from manufacturers' software as a .csv file and compiled using MATLAB. Corrections for sensor drift, and water elevation calculations were performed in MATLAB as described in the following process steps. Data resulting from erroneous measurements (malfunctioning sensors or insufficient water within a well) were excluded from the final dataset. Data were processed throughout the deployment period and checked while finalizing the data report; the process date represents the latest date of data processing.
Process_Date: 2019
Process_Step:
Process_Description:
Salinity sensors were periodically cleaned and checked for fouling and calibration drift in the field. Fouling was determined as the difference between a sensor reading in solution prior to cleaning and after cleaning. Calibration drift was determined as the difference between the prepared value of a calibration solution and cleaned sensor reading in calibration solution. If the absolute value of the sum of the fouling and drift are beyond the manufacturer's accuracy specification, then a correction factor (calculated from these fouling and drift assessments) would be applied to the data (Wagner and others 2006). A correction factor was only necessary for Stony Brook well salinity data between April 05, 2017 and September 11, 2017. A correction factor data (-15.74% for Stony Brook Natural; -4.068% for Stony Brook Restored) was applied linearly to well salinity data over this time period. Field checks and data processing occurred throughout the deployment period and checked while finalizing the data report; the process date represents the latest date of data processing.
Citation used:
Wagner, R.J., Boulger, R.W., Jr., Oblinger, C.J., and Smith, B.A., 2006, Guidelines and standard procedures for continuous water-quality monitors—Station operation, record computation, and data reporting: U.S. Geological Survey Techniques and Methods 1-D3, 51 p.
Process_Date: 2019
Process_Step:
Process_Description:
Creek water level (WL_NAVD88) was calculated in MATLAB using the following measured parameters (all measurements are in meters):
a: depth of sensor as calculated in manufacturer's software
b: land surface elevation (NAVD88) at deployment location; measured by RTK
c: deployment post height above land surface; measured periodically in the field using a meter stick
d: measured distance from the top of the deployment post to the deployed sensor; measured periodically in the field using a meter stick
e: length of deployed sensor
Creek water elevation is calculated as:
WL_NAVD88 = a +(b+c)-d-e
Field measured deployment heights for specific time periods are summarized in a txt file (tab-delimited, filename: Logger_DeploymentHeightSummary_2016_2017) included with this data release. Calculations were processed throughout the deployment period; the process date represents the latest date of data processing.
Process_Date: 2019
Process_Step:
Process_Description:
Well water elevation (WL_NAVD88) and depth below land surface (dbs) were calculated in MATLAB using the following parameters (units for all measurements are in meters):
a: depth of sensor as calculated in manufacturer's software
b: land surface elevation (NAVD88) at deployment location; measured by RTK
c: deployment well height above land surface; measured periodically in the field using a meter stick
d: total length of the deployment cable and water level sensor; measured in the field using a meter stick
Well water elevation is calculated as:
WL_NAVD88 = a +b+c-d
Well water depth below land surface is calculated as:
dbs = a+c-d
Field measured deployment heights for specific time periods are summarized in a txt file (tab-delimited, filename: Logger_DeploymentHeightSummary_2016_2017) included with this data release. Calculations were processed throughout the deployment period; the process date represents the latest date of data processing.
Process_Date: 2019
Process_Step:
Process_Description:
Added keywords section with USGS persistent identifier as theme keyword.
Process_Date: 20200806
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Person: VeeAnn A. Cross
Contact_Position: Marine Geologist
Contact_Address:
Address_Type: Mailing and Physical
Address: 384 Woods Hole Road
City: Woods Hole
State_or_Province: MA
Postal_Code: 02543-1598
Contact_Voice_Telephone: 508-548-8700 x2251
Contact_Facsimile_Telephone: 508-457-2310
Contact_Electronic_Mail_Address: vatnipp@usgs.gov
