• Sherri L. Johnson - Principal Investigator
  US Forest Service ;Pacific NW Research Station ;3200 SW Jefferson Way, Corvallis, OR, 97331, USA
  Phone: 541-758-7771
  Email: sherri.johnson2@usda.gov, sherri.johnson@oregonstate.edu
  URL: https://www.fs.fed.us/research/people/profile.php?alias=sherrijohnson
• Julia A. Jones - Principal Investigator
  Oregon State University;Department of Geosciences; Wilkinson Hall 104, Corvallis, OR, 97331-5506, USA
  Phone: (541) 737-1224
  Email: Julia.Jones@oregonstate.edu, geojulia@comcast.net
  URL: http://ceoas.oregonstate.edu/profile/jones/
  ORCID: http://orcid.org/0000-0001-9429-8925
• Matthew G Betts - Principal Investigator
  Department of Forest Ecosystems and Society; 201E Richardson Hall; College of Forestry; Oregon State University, Corvallis, OR, 97331
  Phone: (541) 737-3841
  Email: matt.betts@oregonstate.edu
  URL: http://www.fsl.orst.edu/flel/index.htm
• Michael P. Nelson - Principal Investigator
  Department of Forest Ecosystems and Society; 201K Richarson Hall; College of Forestry; Oregon State University, Corvallis, OR, 97331
  Phone: 541-737-9221
  Email: mpnelson@oregonstate.edu
  URL: http://www.michaelpnelson.com
  ORCID: http://orcid.org/0000-0001-6917-4752
• David Bell - Principal Investigator
  Email: david.bell@usda.gov, david.bell@oregonstate.edu
  URL: https://lemma.forestry.oregonstate.edu/about/david-bell

• The H.J. Andrews Experimental Forest is a living laboratory that provides unparalleled opportunities for the study of forest and stream ecosystems in the central Cascade Range of Oregon. Since 1980, as a part of the National Science Foundation Long Term Ecological Research (NSF-LTER) program, the Andrews Experimental Forest has become a leader in the analysis of forest and stream ecosystem dynamics.
• Long-term field experiments and measurement programs have focused on climate dynamics, streamflow, water quality, and vegetation succession. Currently researchers are working to develop concepts and tools needed to predict effects of natural disturbance, land use, and climate change on ecosystem structure, function, and species composition.
• The Andrews Experimental Forest is administered cooperatively by the USDA Forest Service Pacific Northwest Research Station, Oregon State University and the Willamette National Forest. Funding for the research program comes from the National Science Foundation (NSF), US Forest Service Pacific Northwest Research Station, Oregon State University, and other sources.

Data were provided by the HJ Andrews Experimental Forest research program, funded by the National Science Foundation's Long-Term Ecological Research Program (DEB 2025755), US Forest Service Pacific Northwest Research Station, and Oregon State University. National Science Foundation: DEB1440409

• Long-Term Ecological Research
  The Andrews Forest is situated in the western Cascade Range of Oregon, and covers the entire 15,800-acre (6400-ha) drainage basin of Lookout Creek. Elevation ranges from 1350 to 5340 feet (410 to 1630 m). Broadly representative of the rugged mountainous landscape of the Pacific Northwest, the Andrews Forest contains excellent examples of the region's conifer forests and associated wildlife and stream ecosystems. These forests are among the tallest and most productive in the world, with tree heights of often greater than 250 ft (75 m). Streams are steep, cold and clean, providing habitat for numerous aquatic organisms.

• Temperature sensors (Onset Hobo U22-001 (accuracy 0.2 C)) were programmed to record instantaneous temperatures at 30 min intervals and placed in protective flow through housings and secured into the streams. Data were downloaded at a minimum of twice per year.

• After sensors were downloaded, high resolution data were put through a series of programs for quality control and for filling missing values before generating the hourly averages. A major concern of quality control was to detect when the sensors were not in the stream and recording air temperatures. When data anomalies or gaps in data were found, data were filled using the regression relationships with other sensors from this project and from stream temperature data from stream gages. Regressions were calculated using the best fit with other sensors during periods of time when the full data were available.
• After the averaging and filling, data were also visually evaluated for outliers. If outliers were found, estimates were calculated and inserted.

• Evaluation of the high resolution temperatures (15 and 20 minute intervals) were conducted and questionable data flagged. Before calculation of hourly averages, flagged data were removed and missing values calculated using regression relationships with other temperature data from Andrews Forest for that period.
• Script name: WaTeR.py
• This script was designed to flag, clean, average by hour, and fill data from air temperature sensors deployed at the HJ Andrews.
• Datasets: The script currently creates folders: flagged, cleaned, reference, and filled. These contain:
• flagged original data with flags
• cleaned data with the flags removed
• reference cleaned data averaged into hourly timesteps
• filled cleaned data which has been filled using other files in the reference folder based on their respective regressions
• summary daily values with min, max and mean
• Requires: Python, SciPy and NumPy
• Note: This program was written for data loggers started during June (daylight savings time). Since ONSET uses the computer clock for time stamps, raw times are in PDT not PST. Hourly averaging changes times to PST and matches reference file format (where the hour represents the average of temperatures in the preceding hour). If original logger start dates are not in PDT, there is a line of code (currently 313) which can be turned on.
• Settings: This may be run for a file or folder. The file or folder should be found in the same directory as the script. Input the file or folder name (e.g. INPUTFOLDER="Folder") and comment out the unused line (e.g. #INPUTFILE). Input files should contain the site name as this name is retained through processing. Date limits should be specified under the '#Date limits' heading. These form the bookends in which the program will attempt to fill gaps using the available reference files. Reference files are stored together in a folder (e.g. REFERENCE_DIR = "RS data for PC sites") and are labeled with *reformatted* to distinguish them from reference files which have not been modified to match the required input format. All sites are added as they are run, so this should be run twice if they are not already included in the reference folder and you want them as reference files for sites run in the same batch. Site files will not be used if they have *cleaned*, *filled* or *flagged* in their file name to avoid using processed data. Reference files must be in the correct format and include *reformatted* in their filename or they will not be used. These labels are consistent with the output files from this script as well as the script to convert reference data downloaded from the Andrews website (convert_reference_data.py).
• Description: This script serves to flag, prune, average (by hour), and fill air temperature data as detailed below.
• Step 1: Flagging (Original time steps); Output file – (input file name)_flagged_00-0000.csv, where 00-0000 is the month-year of the last data point
• Flagging identifies for each line (date/time) entry:
• Step 2: Pruning (Original time step); Output file – (input file name)_cleaned_00-000.csv
• Pruning removes lines containing extreme, air_past, air, jump and nodata.
• Step 3: Averaging (Hourly time step); Output file – (input file name)_00-0000_reformatted.csv
• Averaging uses only values remaining after pruning. The number of values used to calculate the average is included as a new column. Notes: The command for saving this output file includes the path for the reference folder, if that folder is changed, it should also be changed in this section (or a new folder will be made with the files but they will not be used for filling). Averaging follows the convention used for Andrews weather stations where the hour represents the average of temperatures in the preceding hour. The output is in PST (while all previous outputs are in PDT, matching the raw input).
• Step 4: Filling (Hourly time step); Output file – (input file name)_filled_00-000.csv
• The script uses cleaned data and compares remaining entries to reference files (see Settings). This is done as a linear regression of the cleaned data with each reference file, the output includes the R2 which can be found in the text file corresponding to the input file name. Prior to filling, the script creates placeholder hours bounded by the date range specified under “Date limits” which is the range in which filling is attempted. These values are set at 1000 degrees. The script aims to fill missing (1000 degree) data by moving sequentially through the reference data in order of fit (R2). The linear regression equation is used to modify the reference value for that data point and it replaces the 1000 degree placeholder. The reference file used in the temperature value filling is listed in a neighboring column. If all reference files are examined and no data is found to replace the missing value placeholder, the placeholder is retained, thus 1000 degrees should be treated as "no data".
• Step 5: Max, min, mean (Daily time step); Output file – (input file name)_daily_00-0000.csv
• The script ignores 1000 degree data and calculates daily max, min and mean temperature values from the filled dataset. The number of records (hours) used in the calculation is listed in the column 'count.'
• Figures: Data points over time with flagged (and cleaned) data shown in red. Located within the "flagged" folder as .pdfs.

• Andrews Phenology sites
• Sampling frequency: 30 minutes

• Six streams phenology sites were selected to evaluate the variability of springtime aquatic insect emergence and phenology (SA025) across the Andrews Forest. Stream temperature sensors were installed in these six streams and temperatures measured year round so that calculations of themal accumulation and degree days could occur.
• At low elevation, a gaged stream through old growth forest and a stream through a young forest were selected. At high elevation, a similar pair of gaged streams were selected. Prior information on insect community composition and seasonal emergence (SA022) was used to select these sites.
• Two additional sites were selected; one was a cold water stream whose hydrology and thermal regime is greatly impacted by a headwater spring. The final site was located near the cold water spring and in a stream that goes dry in the later summer.

• HJA Phenology Emergence Site 1 (WS01 stream)
  W -122.25750000, E -122.25750000, N 44.20700000, S 44.20700000
  Altitude: 457 to 457 meter
• HJA Phenology Emergence Site 2 (WS02 stream)
  W -122.24930000, E -122.24930000, N 44.21450000, S 44.21450000
  Altitude: 487 to 487 meter
• HJA Phenology Emergence Site 3 (WS08 stream)
  W -122.17070000, E -122.17070000, N 44.26640000, S 44.26640000
  Altitude: 959 to 959 meter
• HJA Phenology Emergence Site 4 (WS07 stream)
  W -122.17560000, E -122.17560000, N 44.26510000, S 44.26510000
  Altitude: 940 to 940 meter
• HJA Phenology Emergence Site 5 (Cold Creek)
  W -122.12400000, E -122.12400000, N 44.22950000, S 44.22950000
  Altitude: 1000 to 1000 meter
• HJA Phenology Emergence Site 6 (South Creek)
  W -122.12050000, E -122.12050000, N 44.22720000, S 44.22720000
  Altitude: 1000 to 1000 meter
• HJA Phenology Sites
  W -122.26083000, E -122.11159208, N 44.28199677, S 44.20198189
  Altitude: 1314 to 1314 meter
• Andrews Experimental Forest (HJA)
  W -122.26172200, E -122.10084700, N 44.28196400, S 44.19770400
  Altitude: 1631 to 1631 meter

Object name: HT00601.csv

Records: 12228

Attributes: 13

Temporal coverage: 2009-01-01 to 2014-07-31

File size: 717619 byte

Checksum (MD5): 6601535a8367730dacd13af5a2a50873

Format: headers=1, recordDelimiter=\r\n, fieldDelimiter=,, quoteCharacter=", orientation=column

Object name: HT00602.csv

Records: 293466

Attributes: 9

Temporal coverage: 2009-01-01 to 2014-07-31

File size: 16146408 byte

Checksum (MD5): 03c1f3bac211c93804c0cd7138d24748

Format: headers=1, recordDelimiter=\r\n, fieldDelimiter=,, quoteCharacter=", orientation=column