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Experimental sitesExperimental designs varied across the 39 research sites with plot size ranging from 0.04 ha to 80 ha. The size of the plot drainage areas varied accordingly from 0.02 to 56 ha. The number of site-years of available data ranging from 2 to 17 with a mean of 7 years. There were diverse soil types, five soil textural classes and soil organic carbon ranging from 0.1% to 3.7%. Corn (Zea mays) and soybean (Glycine max) were the predominant crops grown, but 23 site-years had popcorn (Zea mays everta), wheat (Triticum aestivum), forage, oats (Avena sativa), or sugar beets (Beta vulgaris).CD was practised at the greatest number (19) of sites (Fig. 2) across seven states in the Midwest and North Carolina. The research sites extended from 35.8° to 46.4° N and 76.7° to 96.9° W. The majority of sites (30) were on private farm (cooperator) fields through a lease or collaborative arrangement, with the remaining 9sites on university-owned and managed research farms. The USDA soil drainage class for the dominant soil type at each site ranged from somewhat poorly drained to very poorly drained11. The subsurface drainage of all sites consisted of 102 mm-diameter perforated corrugated tubing except MN_Clay sites (76 mm diameter tubing) and included both CD and free drainage (FD) treatments. Tile depth ranged from 0.61 m to 1.22 m, and tile spacing varied from 6 m to 36 m with median 13.7 m. All sites had similar drain spacings across treatments except IA_di4 and IA_Washington. These two sites varied tile spacing and/or tile depth. IA_di4 tile spacing differed with 27 m and 36 m for FD and CD plots, respectively. While at IA_Washington, tile spacing was 12 m in the shallower drainage treatment compared to 18 m spacing in the conventional drainage treatment. Seven sites had replicated drainage treatments with an average drainage area of 1.1 ha. Sites that did not include replications were larger farm fields with an average drainage area of 10.5 ha, except one university research field with a drainage area of 1.8 ha.Fig. 2Availability of key variables published in the Transforming Drainage data. Number of site-years shown (a) by drainage water conservation practices, and (b) by year measurement occurred.Full size imageDWR research was conducted at seven sites across the Midwest. Individual research site locations ranged from 39° to 46° N and 83° to 96° W. The treatments at the sites included DWR utilizing controlled drainage with sub-irrigation or controlled drainage with on-surface drip irrigation. In addition, there was a comparison treatment of FD with no irrigation. The three Ohio sites included wetland monitoring in addition to drainage water recycling as part of the Wetland Reservoir Subirrigation System (WRSIS) project12.Eight SB sites were monitored as part of this project, seven in Iowa and one in Minnesota. One of the Iowa sites included the first SB installed in the US4. Five sites categorized as ‘Other’ included monitored drainage practices slightly different from the previously described categories. The IN_Tippecanoe site was a wetland with future drainage water recycling planned but not implemented during this period. MN_Clay1 was a conventionally drained farm, MN_Clay3 was an undrained farm with only surface drainage, MN_Redwood2 was an undrained prairie area and ND_Richland had controlled drainage and a sub-irrigated area utilizing a sump pump lift station for water management.Data collected at each siteThe data describes crop and field management, soil physical characteristics, water quality and quantity time series, drainage system design and specific practice variables for the 39 research sites. Weather data, primarily precipitation and air temperature, were also available for each site. However, other data collected varied since the measurement protocols were not coordinated before research was initiated at many sites. Cumulatively, more than 90 in-field variables were measured across all sites to characterize the performance of these alternative agricultural water management strategies. Water quality and quantity time series (drain flow, water table depth, nitrate-N concentration, and precipitation) were considered essential data for temporal robustness and accuracy regarding the hydrological response.Precipitation (39 sites) and drain flow or discharge (36 sites) were the most reported variables, followed by nitrate-N concentration (32 sites) and load (30 sites) (Fig. 2). Other common water quality variables are summarized in Fig. 3. In addition, soil moisture time series collected at varying depths were reported for 16 sites.Fig. 3Type of water quality data in the Transforming Drainage data. Number of site-years per variable shown with type of drainage practice denoted by colour. Ortho P, Total N, and Total P are defined by whether the sample was filtered prior to analysis to remove suspended (solid) content from the aqueous fraction.Full size imageIn addition to the water quantity and quality variables that provide a direct measure of treatment impact to water sustainability, other variables including crop yield, crop and field management and soil characteristic data are important for evaluating inter-site variability. For example, differences in nutrient application with fertilizer and nutrient removal through crop uptake will influence the water quality impact of different treatments. Soil texture (reported for 21 sites), crop yield (29 sites), tillage (27 sites) and fertilizer application (31 sites) were considered most essential of these site characteristic variables13. Along with crop yield, sites reported additional variables that assisted in quantifying plant water, nutrient and carbon uptake, including grain moisture content (13 sites), final plant population (end of season plant density; 9 sites), grain total N (8 sites) and grain biomass (6 sites). Whole plant, vegetative and cob biomass, and whole plant, vegetative, cob and grain N and C contents, forage biomass and leaf area index were reported for five or fewer sites.Sixteen sites reported soil organic carbon and total N, in addition to basic soil texture information. In addition, 31 other soil parameters were reported for a subset of sites; the most common are summarized in Table 1. Soil organic matter, infiltration, lime index, sodium concentration or amount, sodium absorption ratio, neutralizable acid and salinity were reported for five or fewer sites.Table 1 Most reported soil variables and number of sites.Full size tableSummary of measurement methodsMost experiments were not coordinated when the data collection project was initiated; hence research data collected, length of experimentation, years of available data, and protocols varied. Methods for each research site are provided in the data to document differences in measurement schedule, sample size, sample collection frequency, and equipment precision. Here, we summarize methods for determining drain flow, nitrate-N concentration and load, water table, soil properties and weather data due to the variability across sites within these key metrics. Crop yield is not summarized here despite its importance as a metric due to more consistent methods typically used across sites. Inter-site sampling methods for water measurements varied more than methodology for measuring other parameters. This variability is due to differing infrastructure at each site that required different measurement methods and the financial resources available for monitoring.Drain flow measurement and reportingDrain flow or discharge data were reported for 36 sites, including 19 CD, eight SB, six DWR and three with other practices (e.g., wetland). For all CD, three DWR and two wetland sites, drain flow was reported in mm/day (drainage discharge normalized by the drainage area). For all other sites, volumetric drainage discharge was reported in m3/day. Two of the sites (MN_Clay3 and MN_Redwood2) were undrained control sites that did not report drain flow or discharge. A third site (MO_Shelby) focused on the agronomic impact of subsurface drainage practices and did not monitor drain flow.Drain flow was measured hourly or sub-hourly at more than 80% of the sites, followed by aggregation to daily flow measurements. Subsurface drainage flow rates were determined as a function of the water head measured using pressure transducers installed inside drainage control structures or at the drain outlet for approximately two-thirds of the sites. The water head was measured upstream of V-notch or rectangular weirs and empirical equations that depend on the weir dimensions were used to determine drain flow, which was measured and recorded hourly or sub-hourly. For IN_Tippecanoe drain flow was estimated as a function of water head using an empirical rating curve. At three sites, drain flow was measured using inline flow meters and recorded by data loggers. The advantage of this method is that flow could be recorded in either direction, valuable for sites experiencing backflow in the drainage system due to high downstream water levels14. At ND_Richland, drainage was collected at a sump where a current sensor was used to measure pumping frequency to calculate drainage flow15. For an additional three sites, drainage discharge was measured using a depth-velocity meter installed at the outlet of the drainage pipe or a drainage ditch. The drainage discharge was calculated as the product of the flow velocity and the area of flowing water. Only one site (MN_Redwood3) had manual measurements of drain flow that were collected two to three times per week.Measured drain flow data exhibited variable frequency and duration gaps due to instrumentation malfunctioning, particularly with the automated monitoring systems that provide near-continuous data. Missing data and their non-uniform distribution created problems in statistical analyses when comparing aggregated drain flow and loads from different locations. A systematic approach was used to infill missing drain flow data utilizing variables available at all sites (precipitation, temperature, drain flow) and replicate plots where available. The method consisted of the following three phases and completed in progression, when applicable.Phase 1, fill in zero flow.
During most winters in the northern states, the soil is frozen to the depth of the tile, and no subsurface drain flow is expected. Such periods were identified based on expert judgment by researchers at each site, relying on soil and air temperature information and local knowledge of the drainage system’s response to these conditions. If no drainage measurements were available due to frozen soil, the corresponding gaps in the data record were infilled with zero.

Phase 2, predict using replicate plots.
Regression-based estimation was used to infill missing data at three sites which had replicated plots or adjacent fields with available data. Due to the seasonal nature of subsurface drainage from croplands, individual linear regression models were developed for each season: winter (Jan, Feb, Mar), spring (Apr, May, Jun), summer (Jul, Aug, Sep) and autumn (Oct, Nov, Dec). Regression r2 values ranged from 0.66 to 0.94 based on the site and season, although mean across-site values were similar: winter (0.80), spring (0.82), summer (0.80), and autumn (0.83).

Phase 3, populate based on precipitation and drain flow from the preceding day.
The remaining missing daily drain flow data at 11 sites were filled as described below, based on the assumption that drain flow occurs on a given day only if (a) precipitation occurred on that day or (b) the drain continued to flow from the day before.

a.

For days with precipitation, a two-day moving average was calculated to account for the time lag between rainfall and resulting drain flow. A linear regression model was fitted to non-zero drain flow and two-day moving average precipitation for each season, with the model’s intercept fixed to zero. We used these models to predict the missing drain flow data for days with non-zero precipitation. The predicted drain flow values were limited to the drainage system’s capacity by replacing predictions greater than the site’s drainage coefficient (depth of water the drainage system could remove within 24 hours) with the coefficient’s value.

b.

For days with zero precipitation, missing drain flow was calculated from the previous day’s observed flow using the following first-order recession equation

$${Q}_{i}={Q}_{i-1}{e}^{k}$$where Q is daily drain flow, k is the average recession coefficient of falling limbs calculated as a linear slope of ln(Q), and i indicates day. The recession coefficient was calculated as a linear slope between the peak and inflection point of log-transformed daily drain flow data. The coefficient was calculated for all falling limbs of drain flow data, and the average seasonal values were calculated as their arithmetic mean.
The regression model between on-site precipitation and peak flow and recession equation were only applied to the original (pre-gap-filled) drain flow data. Predictions were not made when the number of missing drainage days exceeded 152 (5 months) within a calendar year; therefore, approx. 18% of the drain flow data remain missing. Both the original and filled data are included in the published data.
Nitrate-N concentration and load measurement and reportingNitrate-N (NO3) concentrations were reported for 32 sites, including 15 CD, eight SB, six DWR, and three sites with other practices (e.g., wetland). The three sites not reporting drain flow (MN_Clay3 and MN_Redwood2, MO_Shelby) did not report NO3 concentrations. Two sites (MO_Knox1 and MO_Knox3) provided NO3 load along with discharge in place of reporting the concentration of individual water samples. Two sites (OH_Hardin2 and OH_Henry) did not report NO3 concentrations or load due to limited water sample collection at these sites.Six sites collected flow-proportional samples, in which a sample is collected every time a given volume of water passes through the drainage system. The flow-proportional sampling methods at the sites varied. At NC_Washington, a portion of flow was diverted continuously into a composite sample which was collected fortnightly (or more frequently under high flows). At IA_di4, a proportional sample was collected each time the drainage system was pumped. At MN_Redwood1, flow proportional samples were collected during storm and baseflow conditions. These samples were not composited but rather kept discrete. Seven sites used automated samplers to collect time-proportional samples. Five of these sites composited samples daily, while one site (IN_Randolph) collected samples hourly, then combined samples into approximately weekly composites. One site (IN_Tippecanoe) collected weekly grab samples prior to 2016 but then switched to automated, time-proportional sampling composited weekly in March 2016. Sites that used automated samplers typically switched to manual sampling (every two days to weekly frequency) in winter to protect automated samplers from freezing. Twelve sites collected weekly grab samples, another collected samples 2–3 times per week. One site collected biweekly grab samples, and four sites collected grab samples approximately monthly. Regardless of the collection method, all samples were either frozen or refrigerated (4–5 °C) upon return to the laboratory until analysis.The sampling strategy primarily affects the frequency and compositing strategy of the water samples. Automated samplers permit more complex sampling strategies, such as flow-proportional or sub-daily sampling. However, the disadvantages of this method are the high initial expense of sampling equipment and the propensity for equipment malfunction at below-freezing air temperatures. The potential for equipment failure prompted sites using automated samplers to switch to a manual sampling in winter while drains remained flowing. Manual sampling frequency varied among sites due to differences in site accessibility or personnel availability. Both automated and manual water samples were often composited following collection, and sample compositing frequency ranged from daily to biweekly. Although sample collection frequency and compositing strategy affect the uncertainty of loading measurements, a collection frequency between 3 to 17 days is generally sufficient to reach ± 10% accuracy for annual nitrate load estimation for tile-drained landscapes in the Midwest16.For nitrate-N analysis, 12 sites reported a cadmium reduction followed by a sulfanilamide reaction (equivalent to EPA 353.2). However, there was a slight methodological variation depending on the equipment, either Lachat QuikChem 8000 Flow-Injection Analyzer or SEAL AQ2 Discrete Analyzer. The resulting nitrate-N concentrations calculated via cadmium reduction were directly comparable regardless of the instrument used. At one site, SD_Clay, ion chromatography (EPA 300.1) was used to measure nitrate-N in 2015 but was subsequently switched to a cadmium reduction method. Samples at the seven IA sites were analysed by second-derivative spectrophotometry17.Daily nitrate loads were calculated by multiplying nitrate concentration by drain flow and were therefore available for 32 sites for which both values were reported. Load calculation methods differed slightly in terms of determining the volume of water associated with each concentration. Typically, linear interpolation was used to determine the daily nitrate concentration at sites which collected “grab” water samples following precipitation events or on a schedule spanning two days or more. One variation used assumed the measured concentration was representative of adjacent days (prior and post), hence no interpolation was done. One site (OH_Delaware) used a midpoint approach to determine the time interval in which measured concentrations were associated with, while another site (IA_di4) assumed measured concentrations represented all water drained before the sample was collected.Water table measurement and reportingThe water table was measured at 16 sites including nine CD sites, three SB sites, three DWR sites and one wetland site. Documenting water table fluctuation is key to experimental and modelling research investigating crop production systems on artificially drained soils. In a tile-drained field, the water table is used as an input parameter in estimates of drain flow, evapotranspiration, and soil hydraulic conductivity14,18,19. In controlled drainage, the water table is used to determine CD effectiveness and guide water management in the field for different crop stages. For DWR practice, the water table, particularly the midpoint water table, is used to evaluate sub-irrigation performance, such as uniformity and efficiency20. In a saturated buffer field, the water table is the most important factor used to indicate a field’s saturation status21.The field water table was typically measured at the midpoint between the subsurface drains. Some field studies also measured the water table at two locations, one near the drain tile and the other at the midpoint between two drain tiles. The water table was commonly measured and recorded hourly or sub-hourly using pressure transducers installed inside 1.5–2.5 m deep wells of perforated PVC pipes. The water table depth was calculated using the measured water pressure above the transducer, and the in-situ water temperature and barometric pressure measured in a nearby field and periodically adjusted with manually measured water tables. If there were any discrepancies, all previous water table depth data were moved up or down correspondingly.Differences across sites spanned the type of pressure transducers used, depth of measurement (1.5 to 2.4 m), data collection frequency (0.17 hr (10 min) to 6 hr), location of the measurement, and the length of the screened section of the pipe. The selection of the transducer type was due to individual choice and cost, while differences in the water table depth measurements were affected by the soil types and drain depth. The frequency of data collection was based on data logger capacity, water table variations, and the purpose of the measurements. The length of the perforated (“screened”) section of the pipe, in which the transducer was installed, also varied. For a typical tile-drained field, the pipe was screened beginning 0.3 m below the soil surface while for a saturated buffer, the pipe was screened beginning at the soil surface16. The data collection frequency for the saturated buffer area was every 6 hr since the water table variations were minimal across time. Within the field experiments, data were collected every hour at 10 sites and every 0.17 hr (10 min) at two sites.Soil physicochemical variable measurement and reportingPotentially important soil physical and chemical properties that might affect or be affected by soil drainage were collected from 19 experiments across six states. Data included 17 total variables, continuous and categorical. Soil physical variables included bulk density, hydraulic conductivity (saturated), moisture (water) content, temperature, texture, and soil water retention data (used to form a water retention curve). The remaining 11 variables were chemical properties. The five most common chemical variables characterized were nitrate, total nitrogen, soil organic carbon, pH and cation exchange capacity with several sites using similar methods22.There was large variability of soil sampling depth among the studies and within specific variables at a site, and in a few cases ( More

As NDBC publish their historical and real time in situ wave and meteorological data in multiple online locations, USACE developed a methodology to combine these data sources and develop a unique USACE QCC Measurement Archive that is fully self-describing. This required merging the manually quality controlled data that is stored on the NDBC website with the lower quality netCDF data with metadata files for the same stations that are stored at NCEI. The NOAA DODS source was not included as those data are exact copies of what is found within the NDBC historical station pages.As mentioned, the NDBC website historical station pages contain the cleanest data that has been subjected to manual QA/QC by NDBC Mission Control data analysts. Data collected during service periods (when the buoys were physically on board ships for maintenance) were removed during the manual QA/QC, and are typically not present within the NDBC website data. However this data source contains no metadata other than date and time. This lack of metadata allows for the erroneous inclusion of unidentifiable data from historical time periods where the moored buoys were adrift (inaccurate wave readings, wind, temperature etc.). Additionally, although NDBC switched to a redundant meteorological sensor paradigm during the last decade, only single variable values are available per time stamp per station on the NDBC website. This is because NDBC toggles the release of primary and secondary sensor data to ensure that the highest quality data are published. However, the NDBC website contains no associated metadata indicating when these data release switches occur and hence instrumentation usage is indeterminable. Users often need these sensor details, for example wind sensor height above sea level to extrapolate wind speed at additional heights above the moored buoy. The NDBC website also does not store uncorrected non-directional spectral energy estimates (({c}_{11}^{m})).Conversely, the NDBC netCDF data stored at NCEI includes metadata such as time-stamped GPS positions, instrumentation metadata, data quality flags1, and data release flags (indicating which data were released to the real time stream). These GPS positions allow for the identification of data that was collected while NDBC moored buoys were adrift. For ease of data source identification, these NDBC netCDF files stored at NCEI will be referred to as NCEI netCDF data below. However, readers should remember that these are all NDBC data, with time-paired values that are collected from the same, unique sensor.This NCEI netCDF data source also includes both the primary and secondary redundant meteorological sensor outputs, with metadata, as well as uncorrected non-directional spectral energy estimates (({c}_{11}^{m})). These primary and secondary sensor variables are only found within these NCEI netCDF datasets. However, since 2011, these netCDF data are pulled from the NDBC real-time data stream, which is only subjected to automated QA/QC protocols that flag but do not remove suspect data1. Prior to 2011, the NDBC data were stored in an encoded Trusted Data Format (TDF), but these data were converted into netCDF format in early 2020.Of note is that the NCEI netCDF structures differ for data stored before and after the 2011 switch to netCDF file usage. Throughout the historical netCDF dataset, the netCDF file structures contain non-uniform netCDF formats that are dependent on the data collected during file-specific time periods. Additionally, the pre-2011 netCDF files contain a nominal, fixed deployment position that is repeated for each date/time stamp within the datasets. Furthermore, these pre-2011 netCDF files contain erroneous spectral wave frequency bands that are not included in the NDBC website datasets (and do not match any wave instrumentation frequencies that NDBC has historically deployed). Both formats include instrumentation metadata that are not only inconsistent throughout the years, but within individual netCDF file’s group attributes.Therefore, to mitigate these identified data source issues10, the USACE QCC Measurement Archive process utilized a methodology (Fig. 1) that combines each dataset’s advantages to develop a best available historical NDBC measurement dataset. For example, the GPS data included within the post-2011 NCEI netCDF files were used to detect data that fell outside a reasonable radius of the moored buoy. Conversely, the NDBC website data were used to isolate which primary or secondary sensor data were released to the public – achieved by matching the individual NDBC variable values to the equivalent primary or secondary NCEI netCDF values, therefore identifying the correct netCDF metadata. Additional outlier QA/QC variable checks, station and metadata verification (provided by literature reviews and historical NDBC buoy deployment log books) allowed for the development of a best available, self-described USACE QCC Measurement Archive.Fig. 1Flowchart of the USACE QCC Measurement Archive methodology. This flowchart outlines input data sources, station and metadata verification, selected ‘best’ data sets and output netCDF files.Full size imageThe USACE QCC Measurement Archive methodology process consists of two phases. The first phase of the project processes the historical data, while a second phase annually appends newly available data to the historical database. The data archive routine involves a six step process (Fig. 1) for each buoy station: (1) download, (2) concatenation, (3) metadata verification, (4) comparison, geographical QA/QC and metadata attachment, (5) best dataset selection, and (6) netCDF data file creation. Finally these netCDF files are uploaded to the buoy section of the USACE CHL Data server.These steps were automated using scripts developed in R software11. Where necessary, each script was subset to handle the particular idiosyncrasies10 of the NDBC and NCEI netCDF data archives. To process all of the historical NDBC data (1970–2021), steps two to five in phase one required ~ 400k cpu hours at the Department of Defense (DOD) Supercomputing Resource Center.The following steps outline the methodologies utilized within this USACE QCC Measurement Archive development. For more detailed information, please see the USACE QCC Measurement Archive Standard Operating Procedure (SOP) document that is stored in the Archive GitHub (https://github.com/CandiceH-CHL/USACE_QCC_measurement_archive.git).

1.

Step 1: Download. Historical NDBC data for all NDBC stations are downloaded from the NDBC website and the NCEI archives. Source-specific archive download links are listed in the USACE QCC Measurement Archive SOP. Data from the storage specific files types (detailed below) are extracted for concatenation in step 2.
The NDBC website stores data in zipped yearly and monthly files as standard meteorological (stdmet), spectral wave density (swden), spectral wave (alpha1) direction (swdir), spectral wave (alpha2) direction (swdir2), spectral wave (r1) direction (swr1), and spectral wave (r2) direction data (swr2). These files require unzipping. Included within the NDBC stdmet datasets are collected meteorological and bulk wave data in the following structure: wind direction (°), wind speed (m/s), wind gusts (m/s), significant wave height (m), dominant wave period (seconds), average wave period (seconds), mean wave direction (°), air pressure at sea level (hPa), air temperature (°C), water temperature (°C), dew point temperature (°C), visibility (miles) and tide (ft). Visibility and tide are no longer collected by NDBC, and are disregarded.
The NCEI website stores monthly NDBC files per year in netCDF format. All available data and metadata are extracted from these netCDF files. These files contain the same NDBC data as listed above, but also include additional wave spectral parameters such as uncorrected spectral energy wave data (({c}_{11}^{m})), spectral wave co- and quad-spectra, and four wave data quality assurance parameters that are produced by the NDBC wave processing procedure12.
The NCEI netCDF file formats differ significantly before and after January 2011. After January 2011, these netCDF structures varied throughout the years as NDBC buoy structures and netCDF creation procedures changed. Each format requires format-specific code to extract the data from the variable fields.
For example, the pre-2011 netCDF files consistently contain all variables directly within the main file directory. However, the post-2011 netCDF files are structured by ‘payload’, with subset sensor fields (e.g. ‘anemomenter_1’), which in turn have their own subset variable fields (e.g. wind_speed, ‘wind_direction’) with associated quality control and release flags. Therefore users have to navigate through the payload and sensor subfields to discover the variable data with their associated metadata.
Importantly, these ‘payload’ fields do not always refer to the on-board computer system that serves the sensor suites, e.g. NDBC’s Automated Reporting Environmental System13 (ARES), but also delineate between sensor suites with available primary and secondary sensor data (e.g. ‘payload_1’, ‘payload_2’). Conversely these primary and secondary sensor data (e.g. ‘air_temperature_1’ and ‘air_temperature_2’) may be subset within a single ‘payload’. Of note is that these multiple payloads often contain duplicated data.
These ‘payload’ fields are also important when extracting data captured by NDBC Self-Contained Ocean Observations Payloads (SCOOP), as these netCDF files resemble the physical structure of the buoy stations with their modular sensor assembly. For example, the NCEI netCDF July 2020 data file for station 41009 includes 5 payload subsections. ‘payload_1’ contains an ‘anemometer_1’ sensor suite, which contains subset wind variables and data flags; ‘barometer_1’, with subset air pressure variables and flags; and a ‘gps_1’ sensor suites, with subset lat, lon variables, etc. ‘payload_2’ contains a second ‘anemometer_1’, ‘barometer_1’, ‘gps_1’, ‘air_temperature_sensor_1’, and ‘humidity_sensor_1’ suites. Payload 3 contains a single ‘gps_1’ fields (lat and lon variables with flags), while payloads 4 and 5 house ‘wave_sensor_1’ and ‘ocean_temperature_sensor_1’ sensor suites respectively, both with their own ‘gps_1’ data. In this example, ‘payload_1’ represents an R.M. Young sensor, while ‘payload_2’ is listed as a MetPak Weather Station instrument in the netCDF sensor suite attributes.
NDBC is in the process of redesigning these netCDF file formats to be more user friendly. However, they do not plan to reformat their archive datasets. For more details on the NDBC and NCEI netCDF file formats and code extraction descriptions, please see the USACE QCC Measurement Archive SOP within the Archive GitHub.

2.

Step 2: Concatenation. This step merges each yearly and monthly data files to produce a single time series of concatenated stdmet data, and time series files for each individual spectral wave variable. The concatenated stdmet data format mirrors the NDBC website data formats. To handle the NDBC data, this step allows for the management of differing yearly file formats and spectral frequencies; the concatenation of multiple date and time columns into one field; and the removal of redundant date, time and tide columns in stdmet data. This step allocates the spectral data into the standard NDBC 38 frequencies (old wave sensors), and 47 frequencies (new wave sensors). Finally, this step converts the NDBC r1 and r2 values to their correct units (NDBC r1 and r2 data are scaled by 100 to reduce storage requirements, so these data should be multiplied by 0.01).
To handle the NCEI data, this step allows for the concatenation of stdmet data to create a dataset that matches the NDBC website data nomenclature. This step also removes data that were flagged as erroneous by automated NDBC QA/QC protocols. As unit standards vary between the NCEI and NDBC website archives, this step converts the NCEI netCDF pressure units to match the NDBC units (Pa to hPa), and converts the air, water and dew point temperatures from Kelvin to degree Celsius to match NDBC data. This step also performs outlier QA/QC, where it removes zero (‘0‘) wind gust values when no wind speed values are present; direction values greater than 360 °; obvious variable outliers; and duplicated netCDF data points that are ~5–10 seconds apart. To handle the erroneous netCDF spectral frequency data, the code advances through the spectral data and matches the available spectral frequency data to the appropriate 38 frequencies (old wave sensors) or 47 frequencies (new wave sensors).

3.

Step 3: Verify metadata. This step is applied solely to the NCEI netCDF data files to validate the netCDF metadata with NDBC-sourced, buoy specific metadata spreadsheets. These metadata spreadsheets were constructed from the NDBC database and original NDBC service technician log books, and provide accurate station and sensor information. Scripts verify or insert missing hull type, payload and mooring type; and verify or insert missing instrument processing systems (for wave data only), instrumentation names and sensor deployment heights. If none are available, metadata fields are augmented with pre-set hull-specific instrumentation specifications that were sourced from online references (for hull-specific instrumentation specifications, please see the USACE QCC Measurement Archive SOP).

4.

Step 4: Compare, geographically QA/QC and attach metadata. Compare: Although these data originate from the same sensor, storage protocols resulted in different time stamps for each within their various archives. This step compares the NDBC and NCEI sourced data by matching the datasets by nearest date and time (to the minute), after which geographical data are appended to the NDBC datasets.
As the NDBC data is manually QA/QC’d and does not contain data collected during buoy maintenance operations, these data were considered as a date/time reference to quality control the fixed positions of the pre-2011 netCDF datasets. In other words, if data were present within the NCEI dataset, but not within the NDBC dataset, those NCEI data records were removed.
Of interest are the datasets within the NCEI netCDF files that pre-date any data published on the NDBC website. These data are likely from sensor and processing tests conducted during deployments that were intentionally not released to the public. These early data are included in the USACE QCC Measurement Archive but have quality control (QC) flags that rate them as unreliable. For more information on these earlier datasets, please reference the technical note on utilizing NDBC data, ERDC/CHL CHETN-I-10010.
Geographically QA/QC: Each dataset is filtered to remove GPS positions and associated data that are not within a one (1) degree radius (~60 nautical miles) of the NDBC station watch circles (the surface area through which a buoy can travel while tethered to specific location by a mooring). This radius allows for fluctuations in NDBC deployment locations over the decades, as tests showed that radii of less than one degree significantly removed viable data (see Fig. 2 in the Technical Validation section). Users may wish to further filter their specific datasets to remove additional data points that are outside their target deployment locations; a task now easily achievable with the fully-described, verified metadata included within this USACE QCC Measurement Archive13.
Two methods are used to geographically QA/QC these data: 1) a sorted table of value occurrences to find the most common latitude and longitude positions (using the assumption that the buoy held its correct station for the majority of its life cycle); 2) a manual confirmation and insertion of the primary station locations that were sourced from NDBC buoy specific metadata spreadsheets. This manual step was relevant for buoys that did not consistently hold their stations due to high vandalism rates or strong currents.
Assign metadata: Once the data are geographically QA/QC’d, this step assigns verified metadata (from step 3) to the NDBC stdmet datasets as follows. Station-specific hull type, water depth, payload and mooring type are appended to the NDBC stdmet datasets from the NDBC-sourced, buoy specific metadata spreadsheets. These NDBC Buoy Metadata Spreadsheets and the verified NCEI netCDF metadata are then used to assign the correct primary or secondary sensor designation, which includes metadata such as instrument processing systems (for waves) and instrumentation information (names, deployment heights etc.), to the NDBC stdmet datasets by matching the time paired NDBC variable values with the exact NCEI values.

5.

Step 5: Create best dataset. This step selects a combination of the geographically QA/QC datasets that were created in step 4 above. These best available, self-describing datasets (Fig. 1) include:

NDBC website wind direction, wind speed, wind gust, air pressure at sea level, air temperature, sea surface temperature, significant wave height, dominant and peak periods, mean wave direction, spectral c11, alpha1, alpha2, r1, r2, with their now fully-described, verified metadata.

NCEI netCDF spectral ({c}_{11}^{m}). These data are retained within the USACE QCC Measurement Archive to allow for bulk wave parameter re-calculations without the influences of NDBC shore-side processing protocols.

Verified station metadata obtained from the NDBC Buoy Metadata Spreadsheets.

NCEI netCDF data for the above variables that pre-date the NDBC datasets (where applicable).

6.

Step 6: Create netCDF data files. This step creates monthly netCDF NDBC data files that collate all of the best available data variables that were selected in step 5 above. For easy access by the USACE and user community, these month-long netCDF data files are stored on the USACE CHL Data Server and are updated annually. A static copy of the historical data (1970–2021) is located within the USACE Knowledge Core Library Datasets13. More

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