BOREAS HYD-04 Standard Snow Course Data Summary The BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. Table of Contents * 1. Data Set Overview * 2. Investigator(s) * 3. Theory of Measurements * 4. Equipment * 5. Data Acquisition Methods * 6. Observations * 7. Data Description * 8. Data Organization * 9. Data Manipulations * 10. Errors * 11. Notes * 12. Application of the Data Set * 13. Future Modifications and Plans * 14. Software * 15. Data Access * 16. Output Products and Availability * 17. References * 18. Glossary of Terms * 19. List of Acronyms * 20. Document Information 1. Data Set Overview 1.1 Data Set Identification BOREAS HYD-04 Standard Snow Course Data 1.2 Data Set Introduction Snow surveys are made at regular intervals at designated stations or locations throughout the winter to determine the depth, vertically integrated density, and water equivalent. A snow course is a permanently marked traverse where snow surveys are conducted. Snow surveys stratified by landscape features, specifically, terrain, land use and vegetative cover, provide the best estimate of areal snow water equivalent (SWE). Because snow depth has been found to be generally more highly variable than snow density, the ratio of density to depth samples may be substantially reduced from 1:1 while retaining statistically valid estimates of the areal SWE. Snow survey data are the usual base of comparison for estimates or measurements of snow on the ground. Snow surveys were conducted at a special network of snow courses to provide estimates of mean snow depth, density and water equivalent over the BOReal Ecosystem-Atmosphere Study (BOREAS) study areas. 1.3 Objective/Purpose These data were collected to provide a seasonal time series of snow depth and SWE in the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) to supplement biweekly regional snow course measurements. These data will provide background information and reference data for detailed hydrological and remote sensing projects (Goodison et al., 1987). 1.4 Summary of Parameters Biweekly (approximately) SWE, depth, density, and standard deviation of depth. 1.5 Discussion Four snow courses were located in and near the White Gull River watershed in the SSA and were situated in four different land cover types (i.e., old jack pine, old black spruce, young jack pine and a regenerating or open area). An additional course was located in an old aspen stand near Namekus Lake in Prince Albert National Park (PANP). Three snow courses were located in the Sapochi River watershed in the NSA. These courses were situated in three different land cover types (i.e., mixed jack pine and poplar, old black spruce, and a fen). The snow courses were done on or near the first and fifteenth of each month throughout the winter beginning with the first snow cover. Data set one contains measurements from 15-Nov-1993 to 15-Apr-1994 for the SSA sites, and from 20-Nov-1993 to 16-Apr-1994 for the NSA sites. Data set two contains measurements from 08-Nov-1994 to 05-May-1995 for the SSA sites, and from 12-Nov-1994 to 29-Apr-1995 for the NSA sites. Subsequent data sets contain similar measurements for approximately the same time periods. All reference coordinates are taken from 1:50,000-scale topographical sheets. 1.6 Related Data Sets HYD-04 Areal Snow Course Data 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. Barry Goodison Chief, Climate Processes and Earth Observation Division Climate Research Branch 2.2 Title of Investigation Determination of snow cover variations in the boreal forest using passive microwave radiometry. 2.3 Contact Information Contact 1 ------------ John R. Metcalfe Downsview, ON (416) 739-4354 (416) 739-5700 (fax) john.metcalfe@ec.gc.ca Contact 2 ------------ Dr. B.E. Goodison Downsview, ON (416) 739-4345 (416) 739-5700 (fax) barry.goodison@ec.gc.ca Contact 3 ------------ David Knapp NASA Goddard Space Flight Center Greenbelt, MD (301) 286-1424 (301) 286-0239 (fax) David.Knapp@gsfc.nasa.gov 3. Theory of Measurements The conventional course is a selected line of marked sampling points along which depth and density measurements are made. The length of the course and the distance between sampling points vary depending on site conditions and the uniformity of the snow cover. Basic snow sampling equipment consists of a graduated tube with a cutter fixed to its lower end to permit easy penetration of the snow and a spring balance (reading directly in water equivalent units) to weigh the tube and its contents. The density of the snow is determined by dividing the water equivalent by the depth of the snow. 4. Equipment 4.1 Sensor/Instrument Description The large-diameter Eastern Snow Conference (ESC)-30 metric snow sampler with a cutter area of 30 cm2 was used for these measurements. This sampler consists of a single tube, approximately a meter in length, and is constructed of plastic, with a stainless steel cutter. A complete description of this sampler along with plans, specifications and an assessment of errors and accuracy can be found in Farnes et al., 1982. The ESC-30 sampler is the current standard sampler used by the Atmospheric Environment Service (AES) for measurement in shallow snowpack regions. The following is a complete list of equipment supplied to the surveyors: 1 ESC-30 snow sampler, 1 spring balance for ESC-30, 1 cradle, 1 measuring stick/ruler (cm), field book or snow survey form and pencil. 4.1.1 Collection Environment These measurements were collected in the winter at various locations in the NSA and SSA. 4.1.2 Source/Platform Measurements are made at a fixed location or station. The site is selected to provide consistent results over time. The site is accessible by foot, skis and/or vehicle. Individual sampling points are located remote from ground irregularities such as boulders or logs. 4.1.3 Source/Platform Mission Objectives The objective of collecting this data was to provide a seasonal time series of snow cover. 4.1.4 Key Variables Snow depth, density, and water equivalent. 4.1.5 Principles of Operation The snow sampler is lowered vertically into the snowpack with a steady thrust downward. A small amount of twisting aids in driving the tube and cutting thin ice layers; however, considerable force and driving of the sampler may be required to penetrate hard layers of ground ice. Penetration to extract a soil plug helps to prevent the loss of the snow core from the tube; a trace of soil or litter in the cutter indicates that no loss has occurred. Observation of the length of the snow core permits a quick assessment of whether a complete core is obtained; the depth of snow is measured when the sampler is inserted in the snowpack. The sample is weighed in the tube and the combined weight (in water equivalent units) is read directly with the spring balance. The tare weight of the tube is subtracted to obtain the SWE. The density of the snow is determined by dividing the water equivalent by the depth of the snow. A graduated meter stick is used in obtaining ancillary snow depth measurements. More detailed information on suggested snow survey procedures is available in Snow survey and water supply forecasting (U.S. Soil Conservation Service, 1972), Snow surveying (Atmospheric Environment Service, 1973), and Guide to Hydrological Practices (World Meteorological Organization, 1974). 4.1.6 Sensor/Instrument Measurement Geometry The ESC snow sampler extracts a snow core with a surface area of 30 cm2. 4.1.7 Manufacturer of Sensor/Instrument The ESC-30 snow sampler and spring balance are manufactured and calibrated according to Atmospheric Environment Service (AES) specifications. The ESC-30 snow sampler was made under contract for AES. There are no commercial manufacturers of this instrument. Please see specifications on how to make the snow tube in Farnes et al., 1982. 4.2 Calibration The spring balance is used to weigh snow samples. The balance is held in a free position, and the snow sampler tube containing the snow sample is suspended from the balance in a special cradle. The inner and outer tubes of the balance are made of anodized aluminum. The anchorages for the spring in the outer and inner tubes are designed to permit free, unrestricted flexing of the spring over its entire effective length. The balance has a ring at the top for hanging the balance and a hook at the other end from which the snow sampler cradle is hung. The inner tube has a scale engraved into it, which reads from 0 at the bottom to 125 at the top. The scale indicates the SWE in centimeters. 4.2.1 Specifications Calibration is performed by the manufacturer using a set of known weights to adjust the magnification of the spring. For more information see AES spring balance calibration procedure, February 1987, issue 2. 4.2.1.1 Tolerance A balance must read within ?0.3 cm of the actual weight at all levels up to 60.5 cm and ?0.4 cm at levels above 60.5 cm. 4.2.2 Frequency of Calibration Spring balances are checked annually before snow surveys begin. A pail and water (1 liter) can be used to check the accuracy of the balance. First, use a pail of water to register a positive reading on the balance, then add 1 liter of water to the pail. The difference between the two scale readings should equal 37.8 ?0.3 cm. 4.2.3 Other Calibration Information The spring balance should be checked from time to time during the season. Consistent repeat readings (i.e., tare) should be observed, with no indication of appreciable sticking of the spring in the balance. This ensures that the balance is not giving a false reading because the spring is not moving freely. 5. Data Acquisition Methods The following guidelines for completing the snow surveys were provided to the survey teams: 1) Locate the first survey point. Note: The order in which the survey points are done is not important; however, the number used to indicate the measurement site, (i.e., T-bar post) should remain constant throughout the measurement season. 2) Allow the snow sampler to cool to ambient temperature before starting the survey; e.g., it can be kept in the back of a pickup truck or car trunk. The tare or dry weight of the sampler should be recorded at each sample site. 3) Insert snow tube vertically into the snow and turn it clockwise until the cutting teeth just meet the ground surface. If ice layers in the snow are encountered, use a light sawing motion to cut through the ice layer before continuing down through the snow pack. Applying excessive force will collapse the underlying snow and result in a poor sample. 4) Read the depth of snow at the sample point from the outside scale on the snow sampler to the nearest 0.5 cm. 5) Push the tube sufficiently into the surface soil litter, (i.e., moss/leaves) to obtain a soil plug that will hold the snow sample in the tube as the tube and contents are extracted from the snow pack. 6) Once the tube is removed from the snow, remove the soil/litter plug from the end of the tube, (a knife is a useful aid for removing the plug and cleaning the cutter teeth). The sample tube and snow contents are then placed on the cradle, which is suspended from the balance, and the weight of the tube and snow is recorded to the nearest 0.1 cm. It is useful to hang the balance from a solid support such as a ski pole or tree branch if available. 7) Note the length of the snow core (minus the soil plug) as a check on the representativeness of the sample. 8) Empty the snow sampler by pouring the snow out of the top of the sample tube. 9) Proceed to the next sample point, (i.e., site #2), which is 100 m from the first sample location. Record the depth of snow to the nearest 0.5 cm at 10 equally spaced locations along the transect between these sites using the snow stick/ruler. 10) Repeat procedures 1 through 8 at sites #2 through #5. 11) Record any unusual snow conditions, (e.g. ice layers or crusts) as well as general weather conditions. 6. Observations Observations include a general description of weather conditions during the survey and also some references to the snowpack state (i.e., melting). 6.1 Data Notes Pay attention to specific references to missing observations or estimated observations. In particular, the fen site in the NSA was difficult to access in the fall and spring, resulting in some estimation of snow cover using nearby courses. 6.2 Field Notes Copies of field notes are available from the primary contact given in Section 2.3. 7. Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage The centers of the snow courses are approximately located at the following coordinates. These coordinates were determined from 1:50,000 scale maps. The latitudes and longitudes were converted to the North American Datum of 1983 (NAD83). NAD83 SITE BOREAS_X BOREAS_Y LONGITUDE LATITUDE LANDCOVER/VEGETATION ----- ---------- ---------- ---------- ---------- -------------------- NTS 776.362 616.140 98.50308W 55.90621N mature black spruce (NSA) NIY 418.972 336.623 104.61854W 53.85286N young jack pine (SSA) NTJ 769.335 618.054 98.60838W 55.93431N mixed jack pine & aspen (NSA) PAA 323.261 328.311 106.07970W 53.84815N mature aspen (SSA) NIB 421.547 325.673 104.59484W 53.75285N mature black spruce (SSA) NTF 780.986 618.841 98.42247W 55.92261N fen (NSA) NIO 421.563 325.853 104.59434W 53.75445N open regenerating (SSA) NIM 421.752 325.815 104.59154W 53.75395N mature jack pine (SSA) 7.1.2 Spatial Coverage Map Not applicable. 7.1.3 Spatial Resolution These data are point source measurements at the given locations. 7.1.4 Projection Not applicable. 7.1.5 Grid Description Not applicable. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage Measurements were taken on or near the first and fifteenth of each month from approximately mid-November to spring melt during the period from November 1993 to May 1996. 7.2.2 Temporal Coverage Map None. 7.2.3 Temporal Resolution These data were made approximately every 2 weeks. 7.3 Data Characteristics Data characteristics are defined in the companion data definition file (h04stsnd.def). 7.4 Sample Data Record Sample data format shown in the companion data definition file (h04stsnd.def). 8. Data Organization 8.1 Data Granularity All of the Standard Snow Course Data are contained in one dataset. 8.2 Data Format(s) The data files contain numerical and character fields of varying length separated by commas. The character fields are enclosed with a single apostrophe marks. There are no spaces between the fields. Sample data records are shown in the companion data definition files (h04stsnd.def). 9. Data Manipulations 9.1 Formulae 9.1.1 Derivation Techniques and Algorithm density = (swe/(depth*10))*1000 9.2 Data Processing Sequence 9.2.1 Processing Steps None given. 9.2.2 Processing Changes None given. 9.3 Calculations 9.3.1 Special Corrections/Adjustments None. 9.3.2 Calculated Variables Snow density 9.4 Graphs and Plots None. 10. Errors 10.1 Sources of Error Errors in the data collected by the snow survey teams may arise from several sources: 1) Warm temperatures which can cause snow to stick in the tube. 2) Changes to scale precision caused by air temperature fluctuations. 3) Scale readings by different observers or during windy conditions which can yield erratic scale readings. 10.2 Quality Assessment The quality of the data collected by the survey teams is thought to range from good to excellent; generally it is considered to be excellent. Generally the lowest level of data quality exists when the snowpack is shallowest; that is, early in the season and late in the melt phase. No formal quality assurance is done; however, a coarse check is routinely made after the completion of each survey. Missing values are represented by -999.0 and are inserted when the data are not available. 10.2.1 Data Validation by Source None given. 10.2.2 Confidence Level/Accuracy Judgment This data set represents the best estimate of snow depth and SWE by land cover/vegetation type for the immediate vicinity of the snow course on the date of the survey. Care should be exercised when extrapolating the measurements in time and/or to a much larger geographic area. 10.2.3 Measurement Error for Parameters Given by standard error (e.g., depth). 10.2.4 Additional Quality Assessments None. 10.2.5 Data Verification by Data Center The data were spot checked by BORIS to make sure that no conversion errors occurred during loading. 11. Notes 11.1 Limitations of the Data This data set represents the best estimate of snow depth and SWE by land cover/vegetation type for the immediate vicinity of the snow course on the date of the survey. Care should be exercised when extrapolating the measurements in time and/or to a much larger geographic area. 11.2 Known Problems with the Data None given. 11.3 Usage Guidance This data set represents the best estimate of snow depth and SWE by land cover/vegetation type for the immediate vicinity of the snow course on the date of the survey. Care should be exercised when extrapolating the measurements in time and/or to a much larger geographic area. 11.4 Other Relevant Information None. 12. Application of the Data Set This data set can be used to develop and validate snow cover algorithms from airborne passive microwave measurements or other remote sensing techniques where SWE can be estimated. 13. Future Modifications and Plans None. 14. Software 14.1 Software Description None. 14.2 Software Access None. 15. Data Access 15.1 Contact Information Ms. Beth Nelson BOREAS Data Manager Bldg. 22, Rm. G87 Code 923 NASA GSFC Greenbelt, MD 20771 (301) 286-4005 (301) 286-0239 (fax) beth@ltpmail.gsfc.nasa.gov 15.2 Data Center Identification See Section 15.1. 15.3 Procedures for Obtaining Data Users may place requests by telephone, electronic mail, or fax. 15.4 Data Center Status/Plans The HYD-04 standard snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory (865)241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products None. 16.2 Film Products None. 16.3 Other Products The data and documentation are available as American Standard Code for Information Interchange (ASCII) files. 17. References 17.1 Platform/Sensor/Instrument/Data Processing Documentation Atmospheric Environment Service. 1973. Snow surveying. 2nd Ed. Environ. Can., Downsview. Ont. Farnes, P.E., N.R. Peterson, B.E. Goodison and R.P. Richards. 1982. Metrication of Manual Snow Sampling Equipment by Western Snow Conference Metrication Committee. 39th Annual Proceedings of the Eastern Snow Conference, Reno, NV, Apr. 19-23, 1982, pp. 120-132. Goodison, B.E., J.E. Glynn, K.D. Harvey, and J.E. Slater, 1987. Snow Surveying in Canada: A Perspective. Can. Wat. Res. Jr., Vol. 12, 2, pp. 27-42. U.S. Soil Conservation Service. 1972. Snow survey and water supply forecasting. Section 22, SCS Nat. Eng. Handb., U.S. Dept. Agric., Washington, DC. World Meteorological Organization. 1974. Guide to hydrological practices. 3rd Ed. Ch. 2, Instruments and Methods of Observation. WMO No.168, World Meteorological Organization, Geneva, pp. 2.1-2.90. 17.2 Journal Articles and Study Reports Chang, A.T.C., J.L. Foster, D.K. Hall, B.E. Goodison, A.E. Walker, J.R. Metcalfe, and A. Harby. 1997. Snow Parameters Derived from Microwave Measurements During the BOREAS Winter Field Campaign. J. Geophys. Res., Vol.102 (D24), 29663-29671. Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0, NASA BOREAS Report (EXPLAN 94). Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1996-2.0, NASA BOREAS Report (EXPLAN 96). Sellers, P. and F. Hall. 1997. BOREAS Overview Paper. JGR Special Issue (in press). Sellers, P., F. Hall, and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers, P., F. Hall, and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPS DOC 96). Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society. 76(9):1549-1577. Walker, A.E., B.E. Goodison, and J.R. Metcalfe. 1995. Investigation of Boreal Forest Snow Cover Variations During the BOREAS 1994 Winter Campaign. 17th Canadian Symposium on Remote Sensing, Saskatoon, Saskatchewan, June 13-15, 1995. Walker, A.E., B.E. Goodison and J.R. Metcalfe. 1995. Investigation of Boreal Forest Snow Cover Variations During the BOREAS 1994 Winter Campaign. 29th Canadian Meteorological and Oceanographic Society Congress, Kelowna, British Columbia, May 30-June 2, 1995. 17.3 Archive/DBMS Usage Documentation None. 18. Glossary of Terms None. 19. List of Acronyms AES - Atmospheric Environment Service ASCII - American Standard Code for Information Interchange BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System CGR - Certified by Group CPI - Certified by Principal Investigator CPI-??? - CPI but questionable DAAC - Distributed Active Archive Center EOS - Earth Observing System EOSDIS - EOS Data and Information System ESC - Eastern Snow Conference FFC-W - Focused Field Campaign - Winter GSFC - Goddard Space Flight Center HYD - Hydrology NAD83 - North American Datum of 1983 NASA - National Aeronautics and Space Administration NSA - Northern Study Area ORNL - Oak Ridge National Laboratory PANP - Prince Albert National Park PI - Principal Investigator PRE - Preliminary SSA - Southern Study Area SWE - Snow Water Equivalent URL - Uniform Resource Locator 20. Document Information 20.1 Document Revision Date Written: 01-May-1995 Last Revised: 04-May-1998 20.2 Document Review Date(s) BORIS Review: 26-Feb-1998 Science Review: 13-Jan-1998 20.3 Document ID 20.4 Citation The snow course data in the SSA were collected by the study area managers Mary Dalman and Paula Pacholek, assisted by Vivian Heap. The NSA snow courses were carried out by AES weather specialist Bill Palmer, assisted by Martha Evaluardjuk. 20.5 Document Curator 20.6 Document URL Keywords --------- SNOW DENSITY SNOW WATER EQUIVALENT HYD04_Std_Snow.doc Page 1 of 1 05/26/98