Dataset Listing

Reynolds Creek - Nutrient Fluxes, Climate, Flux Tower - Eddy Covariance (2014-2018)

Partitioned Carbon and Energy Fluxes Within the Reynolds Creek Critical Zone Observatory

Variables:  eddy covariance measurements

Date Range:  (2014-2018. approximate date range)

Dataset Creators/Authors:  Fellows, A.W.; Flerchinger, G.N.; Seyfried, M.S.; Lohse, K.A.

Contact:  Gerald N. Flerchinger, USDA Agricultural Research Service, Northwest Watershed Research Center, Boise Idaho

Field Area:   Reynolds Creek Experimental Watershed

Description
Keywords & XML
Citation
  • Description

    The data are from the Reynolds Creek Critical Zone Observatory (RC CZO) Climate Gradient Core Network. A network of core observation sites along an elevation/climate gradient was established in 2014 as part of the RC CZO to intensively monitor long-term carbon fluxes and soil carbon dynamics. Data include observations from four eddy covariance (EC) towers and nearby meteorological stations. Core network sites include a Wyoming big sagebrush site, a low sagebrush site, a post-fire mountain big sagebrush site, and an undisturbed mountain big sagebrush site. These sites are designated as wbsec, losec, 138h08ec, and mbsec. All sites are located near long-term meteorological stations. Nancys is within 140 m of wbsec, Lower Sheep is within 500 m of losec, Upper Sheep (138j10) is located within 70 m of 138h08ec, and Reynolds Mountain is located within 800 m of mbsec. Data from the EC towers were used to fill gaps in the meteorological station records.

    Sites are located in the USDA - ARS Reynolds Creek Experimental Watershed. Specifically, site locations are Nancys (43.167545 N 116.713205 W), Lower Sheep (43.1438884 N 116.735561 W), Upper Sheep (43.120711 N 116.723086 W) and Reynolds Mountain (43.064483 N 116.74862 W), all in WGS84.

    Details about the measured variables are available in Comments/README
    Comments
    Short and long wave radiation, air temperature and humidity were collected at all EC sites every 30 minutes using a four-component net radiometer (CNR-1, Kipp & Zonen, Delft, The Netherlands), and a temperature/humidity probe (HMP155C, Vaisala, Helsinki, Finland). Ground heat flux was measured with six heat flux sensors (HFT3, REBS, Seattle, WA) installed 0.08-m deep within the soil and three sets of self-averaging thermocouples installed at 0.02 and 0.06-m deep. Measured soil moisture was used to compute volumetric heat capacity of the soil above the heat flux plates. Soil moisture was measured hourly near the post-fire sagebrush site using two sets of time domain reflectometry probes installed at 0.10 m and every 30 minutes at the remaining sites using Hydra-probe II soil moisture sensors (Stevens Water Monitoring System, Inc., Portland, OR) installed at 0.05 m. Observations at meteorological stations near the EC towers include air temperature, humidity, wind speed and direction, solar radiation. Data were collected at 15-minute intervals and processed at 30-minute intervals. Dual-gauge systems especially designed for the windy and snow-dominated conditions prevalent in the area were used to measure precipitation; precipitation data were processed hourly. EC systems consisted of a three-dimensional sonic anemometer (Model CSAT3, Campbell Scientific, Inc., Logan UT) and an open path infrared gas analyzer (IRGA; Model LI-7500a, LI-COR, Inc., Lincoln, NE) sampled at 10 Hz. Systems were mounted to towers between 1.7 to 2.5 m above the plant canopy; heights were 2.05, 2.09, 3.5, and 2.5 m above the ground surface for the Wyoming big sagebrush, low sagebrush, post-fire sagebrush, and mountain big sagebrush site, respectively. Turbulent fluxes were calculated at 30 min intervals using software developed by LI-COR Biosciences (EddyPro® version 5.2.1; Lincoln, Nebraska USA; https://www.licor.com). Processing options selected in the software included: removing spikes and outlier values in the 10 Hz data with the software’s standard settings; rotating measured wind speeds to account for non-horizontal stream lines using the double rotation method; block averaging to determine the deviations in vertical wind speed and scalar gas concentrations; adjusting for the time-lag between the measured wind speed and scalar gas concentrations by maximizing the covariance between the two time series; compensating for the impact of density fluctuations on gas concentrations using Webb et al. (1980); and adjusting fluxes for high-pass and low-pass filtering effects (Moncrieff et al., 2004, 1997). Missing flux observations were caused by instrument malfunction, non-turbulent conditions, or filtering based on data quality. Non-turbulent conditions were identified as periods with a friction velocity below 0.2 m s−1. Net ecosystem exchange (NEE) was filled and partitioned into ecosystem respiration (Reco) and gross primary production (GPP; g C m−2 s−1) using REddyProc software developed for R (Reichstein et al., 2005; http://www.bgc-jena.mpg.de; version 0.6-0). Missing water fluxes were filled by regression with potential ET for the surrounding 14-day period according to Flerchinger et al. (2014). Daily summaries of gap-filled and partitioned fluxes are provided.
  • Keywords

    Reynolds Creek, eddy covariance, water flux, carbon flux, energy flux, climate

    XML Metadata

    criticalzone.org/national/data/xml-metadata-test/6486/

    XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.

  • Citation for This Dataset

    Fellows, Aaron W.; Flerchinger, Gerald N.; Seyfried, Mark S.; and Lohse, Kathleen. (2017). Data for Partitioned Carbon and Energy Fluxes Within the Reynolds Creek Critical Zone Observatory [Data set]. Retrieved from https://doi.org/10.18122/B2TD7V

    Citation for This Webpage

    Fellows, A.W.; Flerchinger, G.N.; Seyfried, M.S.; Lohse, K.A. (2018). "CZO Dataset: Reynolds Creek - Nutrient Fluxes, Climate, Flux Tower (2014-2018) - Eddy Covariance." Retrieved 18 Apr 2019, from http://criticalzone.org/national/data/dataset/6486/

Data

Reynolds Creek Experimental Watershed - Partitioned Carbon and Energy Fluxes

(k/4/)   Data Level 2,   DOI: 10.18122/B2TD7V

Data Use Policy
Data Sharing Policy
  • Data Use Policy

    DRAFT v.0.4.0

    1. Use our data freely. All CZO Data Products* except those labelled Private** are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you give acknowledgement as described below. Non-CZO data products — like those produced by USGS or NOAA — have their own use policies, which should be followed.

    2. Give proper citation and acknowledgement. Publications, models and data products that make use of these datasets must include proper citation and acknowledgement. Most importantly, provide a citation in a similar way as a journal article (i.e. author, title, year of publication, name of CZO “publisher”, edition or version, and URL or DOI access information. See http://www.datacite.org/whycitedata). Also include at least a brief acknowledgement such as: “Data were provided by the NSF-supported Southern Sierra Critical Zone Observatory” (replace with the appropriate observatory name).

    3. Let us know how you will use the data. The dataset creators would appreciate hearing of any plans to use the dataset. Consider consultation or collaboration with dataset creators.

    *CZO Data Products.  Defined as a data collected with any monetary or logistical support from a CZO.

    **Private. Most private data will be released to the public within 1-2 years, with some exceptionally challenging datasets up to 4 years. To inquire about potential earlier use, please contact us.

  • Data Sharing Policy

    DRAFT v.0.2.5

    All CZO investigators and collaborators who receive material or logistical support from a CZO agree to:

    1. Share data privately within 1 year. CZO investigators and collaborators agree to provide CZO Data Products* — including data files and metadata for raw, quality controlled and/or derived data — to CZO data managers within one year of collection of samples, in situ or experimental data. By default, data values will be held in a Private CZO Repository**, but metadata will be made public and will provide full attribution to the Dataset Creators†.

    2. Release data to public within 2 years. CZO Dataset Creators will be encouraged after one year to release data for public access. Dataset Creators may chose to publish or release data sooner.

    3. Request, in writing, data privacy up to 4 years. CZO PIs will review short written applications to extend data privacy beyond 2 years and up to 4 years from time of collection. Extensions beyond 3 years should not be the norm, and will be granted only for compelling cases.

    4. Consult with creators of private CZO datasets prior to use. In order to enable the collaborative vision of the CZO program, data in private CZO repositories will be available to other investigators and collaborators within that CZO. Releasing or publishing any derivative of such private data without explicit consent from the dataset creators will be considered a serious scientific ethics violation.

    * CZO Data Products. Defined as data collected with any monetary or logistical support from a CZO. Logistical support includes the use of any CZO sensors, sampling infrastructure, equipment, vehicles, or labor from a supported investigator, student or staff person. CZO Data Products can acknowledge multiple additional sources of support.

    ** Private CZO Repository. Defined as a password-protected directory on each CZO’s data server. Files will be accessible by all investigators and collaborators within the given CZO and logins will be maintained by that local CZO’s data managers. Although data values will not be accessible by the public or ingested into any central data system (i.e. CUAHSI HIS), metadata will be fully discoverable by the public. This provides the dual benefit of giving attribution and credit to dataset creators and the CZO in general, while maintaining protection of intellectual property while publications are pending.

    † Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at http://www.datacite.org/whycitedata.