Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
  • Problem Description
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

Continuous water-quality monitoring was conducted in five salt marsh ponds (mean water depths < 1m) located within SMIIL from April 2023–June 2024. Water quality sensors were mounted approximately 6 inches above the bottom of the pond to a fixed pole. Each pond was equipped with internally logging HOBO sensors (Onset Computer Corp.) that measured pressure, conductivity/salinity, dissolved oxygen (DO), and pH. Temperature was also recorded by each of the sensors, and temperature data provided here reflects the median of the four sensors in each pond. An additional HOBO pressure sensor, deployed at a central location within SMIIL, recorded air temperature and barometric pressure. During maintenance trips every 4-16 weeks, sensors were retrieved, cleaned, recalibrated (DO and pH only) and redeployed in their pond for a total of five deployments. Data files from each sensor were downloaded during each maintenance visit. Discrete surface-water samples for DO, dissolved inorganic carbon (DIC), and total alkalinity (TA) measurements were also collected during maintenance visits and used to validate DO and pH time series data (see discrete sample dataset, also in this project).

All deployments were merged into a continuous time series and retimed to 10-minute bins to resolve offsets and irregular sampling times. Quality control procedures were based on protocols adapted from the U.S. Integrated Ocean Observing System (IOOS) Quality Assurance/Quality Control of Real-Time Oceanographic Data (QARTOD) recommendations (IOOS, 2018; Palevsky et al., 2023; Chua et al. related dataset in this project). All data was processed through a combination of manual and automated quality checks.

As the first quality control check, the depth time series for each pond was manually inspected with “human-in-the-loop” (HITL) annotations to identify and document bad data. The initial depth time series was calculated using two HOBO water level loggers: one measuring the pressure in each pond, and the other measuring the barometric pressure in the area. The difference between the two sensors was used to calculate sensor depth in the HOBOware software package. Points flagged for review were compared to service trip logs in order to diagnose the reason for bad data (e.g., periods where sensors were not fully submerged in seawater). Known bad flagged points from these observations were removed from the analysis for all parameters.

Next, data for all parameters was subject to automated tests (Table 1), including 1) a gross range test to check if data for a specific parameter were within reasonable ranges relative to each sensor’s output ability and plausible ranges for the parameter in question, 2) a spike test to evaluate if the absolute value of a measurement exceeded a threshold relative to the points around it, and 3) a stuck value test to ensure the logger was recording real fluctuations in its specified measurement. Thresholds per parameter were kept consistent across all ponds. For time points where any individual parameter was flagged by one of these quality control tests, data from that time point were removed from the analysis for all parameters. Automated tests for dissolved oxygen were performed on intermediate data in units of μmol/L rather than the final reported units of μmol/kg.

Table 1. Thresholds for the initial automated quality control tests for collected data. Data points exceeding these thresholds were flagged and removed prior to subsequent processing.

Because all four HOBO data loggers measured temperature, each logger’s temperature was evaluated independently in the quality control process. The finalized temperature time series presented here represents the median of the four individual sensors’ temperature measurements per time point.

Salinity in Practical Salinity Units (PSU) was calculated from conductivity and temperature using the Gibbs Seawater Toolbox (GSW, McDougall and Barker 2011). Onset HOBO Conductivity data loggers are not capable of internal calibration, so data were compared with nearby channel station salinity, water level, and temperature data (Gull Platform, ~1 km away, see Chua et al. OWP related dataset) for salinity calibration and validation. At high tides, when channel and pond water levels were at their highest, temperature data from all five ponds were similar to that in the channel, consistent with the expectation that channel water entered the ponds during high tides. However, the preliminary salinity data from the HOBO loggers in the ponds were offset from the channel salinity data during this period, indicating calibration offsets in the HOBO Conductivity measurements. In order to correct for these calibration offsets, we identified high tide periods when channel water entered the ponds based on water depth and temperature data and determined calibration offsets for the pond salinity measurements by correcting the pond salinity to match that of the channel during these high tide periods. Salinity data reported here for the Ring, Shark, and Long Reach ponds were corrected using this approach. For the final two ponds (White and Drum), the HOBO Conductivity sensors deployed at these sites malfunctioned and did not report any usable data. Salinity data reported for White and Drum ponds are the median calibrated salinity values from the three ponds that did not experience sensor malfunctions.

HOBO DO loggers were calibrated every service trip before deployment using two points: a 100% saturation open air point and a 0% point using sodium sulfite (Na₂SO₃) dissolved in deionized water. HOBO pH loggers were calibrated every service trip before deployment using three-point calibration standards: pH 4.0, pH 7.0, and pH 10.0.  Calibration accuracy for both DO and pH was validated based on discrete samples collected at the beginning and end of each deployment (see related dataset).  For comparison, pH, DIC, and TA data were converted to hydrogen ion space ([H+])  using the CO2SYSv3 MATLAB package (Lewis & Wallace, 1998; Sharp, 2023; Van Heuven et al., 2011).  While discrete sample data was useful for validation of the pH sensors, because of uncertainty in the discrete measurements, no further action was taken in calibration. 

Preliminary DO concentration in micromoles per liter provided by the HOBO logger was recalculated using the calibrated salinity (described above), temperature, and depth variables input into the Aanderaa Data Instruments AS (2017) compensation equations. We calculated the density (rho) of the seawater using the calibrated salinity, temperature and depth variables in the GSW to convert the DO time series into micromoles per kilogram. DO percent saturation was calculated by normalizing the recalculated DO concentration to the equilibrium saturation DO concentration (DO_sat = DO_measured / DO_{eq sat}​ ×100), where DO_sat was calculated using the final quality-controlled temperature and salinity (Garcia & Gordon, 1992).

- Imported "Drum_finaldataset.xlsx", "LongReach_finaldataset.xlsx", "RingEast_finaldataset.xlsx", "Shark_finaldataset.xlsx", "White_finaldataset.xlsx" into the BCO-DMO system
- Concatenated all files
- Converted date time to ISO 8601 format
- Used the files names to create a "site" parameter with the study sites listed
- Rounded values as requested by submitter -- Depth: 4 decimal places, Salinity: 3 decimal places, DOconc: 4 decimal places, DOsat: 4 decimal places, and pH: 4 decimal places
- Exported file as "988873_v1_salt_pond_monitoring.csv"

Salt marshes are highly productive, dynamic coastal environments that experience large fluctuations in biogeochemical parameters such as dissolved oxygen and pH in response to both natural forcings and anthropogenic impacts. At present, we have a limited understanding of the magnitude of biogeochemical variability in coastal habitats, hindering our ability to predict how they will respond in the future to episodic events and long-term change. This incomplete picture owes to a lack of sustained water quality measurements in coastal and estuarine systems worldwide.

This project investigates biogeochemical cycling in the Seven Mile Island Innovation Laboratory (SMIIL), a network of tidal marshes and channels in coastal New Jersey that is a site of historic and contemporary dredging and a testbed for marsh restoration techniques such as beneficial use of dredged sediment. It leverages multiple data sets of continuous, high-frequency (10-minute) measurements of physical and biogeochemical parameters, including water depth, temperature, salinity, dissolved oxygen, pH, chlorophyll a, and turbidity, as well as precipitation, wind speed, wind direction, temperature, relative humidity, and atmospheric pressure data. Two long-term biogeochemical sensor data sets, comprising a three-year time series at three sites in the main marsh channel (from June 2021–June 2024) and 1-2 year time series at six distinct salt ponds (August 2022–June 2024), were collected. Additionally, short-term (~months long) dredging and sediment placement monitoring data were collected at five different locations in 2022 and 2023. Data collection was designed to capture a variety of natural (diel, tidal, seasonal, storm-related) and human-created (sediment dredging and placement) conditions. Factory-calibrated biogeochemical sensors were field calibrated and maintained every 4 to 12 weeks. Discrete samples for dissolved oxygen, total alkalinity, and dissolved inorganic carbon were collected at the start and end of each sensor deployment for additional sensor calibration and validation. Altogether, our high-quality, multiyear dataset provides critical insights into the inherent variability of biogeochemical conditions in temperate salt marshes on diel and seasonal timescales, as well as how they may respond to transient events (e.g., storms, dredging activities) and over the longer term.