Scaling Environmental Processes in Heterogeneous Arid Soils: Construction of Large Weighing Lysimeter Facility Karletta Chief Michael H. Young Brad F. Lyles John Healey Jeremy Koonce Eric Knight Elizabeth Johnson Jarai Mon Markus Berli Menoj Menon Gayle Dana December 2009 Publication No. 41249 prepared by Division of Hydrologic Sciences Desert Research Institute, Nevada System of Higher Education
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PREFACE The inability to upscale or downscale arid environmental processes influences research areas of hydrology, biogeosciences, mathematical modeling, and global environmental change. Research facilities that span small (column) to larger (basin) scales are either rare or nonexistent. To address this issue, researchers from Nevada’s universities constructed a weighing lysimeter facility in Boulder City, NV, under the NSF-funded program entitled “Scaling Environmental Processes in Heterogeneous Arid Soils” or SEPHAS. Four stainless steel lysimeters (three installed to date) are weighed on separate scales, each with a live mass of approximately 28,000 kg with a resolution of roughly 72-408 g or 0.018-0.102 mm of water. Each lysimeter is equipped with dataloggers that can be accessed remotely so investigators can monitor individual sensors and weather systems as needed. This meso-scale facility is devoted to investigating the near-surface interactions of soil, water, biota, and atmospheric processes that affect desert environments similar to those found in the southwestern United States such as the Mojave Desert and will bridge existing eco-scale, laboratory, and micro-scale research efforts. Three lysimeters are cylindrical (2.258 m inner diameter x 3 m height), and one is square (2 m length x 2 m width x 3 m height). Each contains 12 m3 of either repacked or intact desert soil and is instrumented with 152 sensors that include 17 different technologies measuring water content, matric potential, temperature, thermal properties, electrical conductivity, soil settlement, and erosion; sampling pore water; and obtaining soil and root imagery. Specifically, a relatively new technology called distributed temperature sensing system was installed to obtain horizontal temperature profiles at six depths and a continuous vertical temperature profile. Also, during packing, four conservative tracers were applied uniformly at four depths from 0.15 to 0.55 m. Solution samplers installed at seven depths from 0.50 to 2.9 m to collect soil solution during irrigation experiments. Native desert shrubs will be installed in two replicate lysimeters in the spring of 2010 and three horizontal (installed at depths from 0.60 to 1.50 m) and one vertical mini-rhizotron tubes will be used to examine rooting behavior and water balance in recently disturbed soil. This report contains 7 chapters that describe the construction and installation of this unique facility. The lysimeters were designed to investigate: 1) landscape dynamics, restoration, and water balance; 2) carbon sequestration; and 3) characteristics of soil properties at different scales. Within these general categories, the following hypotheses were developed: •
disturbance of structured desert soils will alter near-surface soil water balance, rates of biogeochemical weathering, water flow, plant rooting, and thermal and water content profiles;
•
an increase in water infiltration will result in an increase in both soil PCO2 and water content; while an increase in Ca availability will favor carbon sequestration through the precipitation of CaCO3; and
•
effective soil hydraulic properties can be estimated using only moisture content and without complex numerical techniques; characterizing heterogeneity in soil hydraulic properties can be accomplished with fewer physical property measurements; and scale effects create discrepancies in the measurement of hydrologic variables.
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ACKNOWLEDGEMENTS Funding for this research was provided by the National Science Foundation EPSCoR project EPS 0447416. The authors thank projector leader, Michael H. Young (Desert Reseach Institute-DRI) who managed the project with assistance from representatives of University of Nevada, Reno-UNR (Scott Tyler) and University of Nevada, Las Vegas-UNLV (Zhongbo Yu). We also thank 1) the steering committee which included Dale A. Devitt (UNLV), Paul S.J. Verburg (DRI), and the project leaders; 2) the external review panel consisting of Steve Evett (USDA-ARS), Robert Graham (University of California, Riverside), and Peter Wierenga (University of Arizona); and 3) the senior personnel in the soils focal area including Darko Koracin (DRI), John A. Arnone III (DRI), Clay Cooper (DRI), Dave Decker (DRI), Michael J. Nicholl (UNLV), Robert S. Nowak (UNR), Henry Sun (DRI), and Paul S.J. Verburg (DRI). These individuals contributed valuable input for the proposal, facility design, and research development. The installation, and implementation of this research facility was made possible by the collaborative efforts of the Boulder City SEPHAS team, including Karletta Chief, John Healey, Elizabeth Johnson, Jeremy Koonce, Eric Knight, and Michael H. Young, as well as input received from many principal investigators, staff scientists, post-doctoral fellows, and students. Specifically, the authors would like to thank Markus Berli for the design and installation of surface alteration probes; Todd Caldwell for the initial heat dissipation unit (HDU) datalogger program and soil physical analysis; Karletta Chief for calibration of HDUs and time-domain reflectometers, soil characterization, soil hydraulic and physical analysis, and site management and; Dale Devitt for use of the rain simulator and assistance in obtaining a uniformity coefficient; Karen Gray for database development; Mark Hausner, Jeremy Koonce, and Menoj Menon for the design, construction, and calibration of the distributed temperature sensing system; John Healey for the design, construction, and implementation of instruments and tools needed to install the lysimeters; Elizabeth Johnson for instrument mapping, design, cataloging, and installation, re-vegetation of facility field plot, and data quality assurance; Eric Knight for the installation of the lysimeters and site management; Jeremy Koonce for obtaining baseline rhizotron tube scans; Brad Lyles for assistance in extensive datalogger programming, datalogger and instrument design, setup, and wiring, data quality assurance, and database development; Jarai Mon, Hongwei Liu, and Xiang Long for laboratory sorption studies and application of tracers; Menoj Menon and Paul S.J. Verburg for design and application of nitrate tracers; and Michael H. Young for overall project management, instrument design research and field direction, data quality assurance, and database development. We acknowledge seed grant researchers (funded to generate data for proposals and papers) at DRI (Kumud Acharya, John A. Arnone III, Markus Berli, Richard Jasoni, Giles Marion, Daniel Obrist, Mark C. Stone, Paul S.J. Verburg, Michael H. Young, and Jianting Zhu); UNLV (James Cizdziel, Dale A. Devitt, Michael J. Nicholl, Adam Simon, and Zhongbo Yu); UNR (Glenn Miller, Robert S. Nowak, and Scott W. Tyler); College of Southern Nevada (Kaveh Zarrabi); and Lawrence Berkeley National Laboratory (Teamrat Ghezzehei). The SEPHAS funded post-doctoral fellows including Karletta Chief (DRI), Manoj Menon (UNR), and Jarai Mon (UNLV) and one doctoral student, Jeremy Koonce (UNLV), are also acknowledged. Finally, we thank the reviewers and their suggestions which made this report a stronger publication.
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CONTENTS PREFACE ....................................................................................................................................iii ACKNOWLEDGEMENTS.......................................................................................................... v LIST OF FIGURES ..................................................................................................................... ix LIST OF TABLES.....................................................................................................................xiii ACRONYMS AND ABBREVIATIONS .................................................................................. xvi 1. INTRODUCTION .................................................................................................................. 1 1.1. Statement of Problem ..................................................................................................... 1 1.2. Purpose ........................................................................................................................... 1 1.3. Hypotheses ..................................................................................................................... 2 1.4. Location.......................................................................................................................... 3 1.5. Experimental Design ...................................................................................................... 6 1.6. Outline ............................................................................................................................ 7 2. SOIL MATERIAL AND INSTALLATION .......................................................................... 9 2.1. Background..................................................................................................................... 9 2.2. Criteria............................................................................................................................ 9 2.3. Search Area .................................................................................................................. 11 2.4. Layered Excavation and Bulk Density ......................................................................... 15 2.5. Soil Storage .................................................................................................................. 19 2.6. Soil Physical and Chemical Properties......................................................................... 19 2.7. Soil Installation............................................................................................................. 20 2.8. Lysimeter Soil Physical and Chemical Properties........................................................ 21 3. MONITORING METHODS AND INSTRUMENTATION................................................ 29 3.1. Soil, Water, and Meteorological Variables .................................................................. 29 3.2. Water Content............................................................................................................... 32 3.2.1. Weighing Lysimeter............................................................................................ 32 3.2.2. TDR..................................................................................................................... 37 3.2.3. CS616.................................................................................................................. 38 3.2.4. ECH2O ................................................................................................................ 39 3.2.5. DPHP and TPHP................................................................................................. 40 3.2.6. NAT .................................................................................................................... 42 3.3. Matric Potential ............................................................................................................ 43 3.3.1. HDU.................................................................................................................... 43 3.4. Temperature and Thermal Properties ........................................................................... 44 3.4.1. STherm................................................................................................................ 44 3.4.2. TCAV.................................................................................................................. 45 3.4.3. SHF ..................................................................................................................... 45 3.4.4. DTS ..................................................................................................................... 47 3.5. Soil Physical Properties................................................................................................ 48 3.5.1. MRT.................................................................................................................... 48 3.5.2. SET ..................................................................................................................... 49 3.5.3. SSAP ................................................................................................................... 49 3.6. Gas and Water Sampling .............................................................................................. 51 3.6.1. CO2 ...................................................................................................................... 51 3.6.2. SSSS.................................................................................................................... 52 3.6.3. Tracers................................................................................................................. 52
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3.7. Meteorological Variables ............................................................................................. 54 3.7.1. OPEC .................................................................................................................. 54 3.7.2. DAMIT ............................................................................................................... 57 4. INSTRUMENT LAYOUT AND INSTALLATION............................................................ 61 4.1. Instrument Nomenclature ............................................................................................. 64 4.2. TDR, FDR, ECH2O, DPHP, TPHP, HDU, Stherm, TCAV, SHF, and SSSS .............. 65 4.3. DTS, NAT, and vertical MRT...................................................................................... 67 4.4. DTS............................................................................................................................... 67 4.5. MRT ............................................................................................................................. 69 4.6. SET ............................................................................................................................... 70 4.7. SSAP............................................................................................................................. 72 4.8. SSSS ............................................................................................................................. 73 4.9. Tracers .......................................................................................................................... 75 4.10. OPEC............................................................................................................................ 77 4.11. DAMIT ......................................................................................................................... 78 5. INSTRUMENT CALIBRATION......................................................................................... 79 5.1. Weighing Lysimeter ..................................................................................................... 79 5.2. TDR .............................................................................................................................. 82 5.3. CS616 ........................................................................................................................... 85 5.4. ECH2O.......................................................................................................................... 85 5.5. DPHP and TPHP .......................................................................................................... 85 5.6. HDU ............................................................................................................................. 87 5.7. SHF............................................................................................................................... 89 5.8. DTS............................................................................................................................... 89 5.9. MRT ............................................................................................................................. 94 5.10. CO2 ............................................................................................................................... 94 5.11. SSSS ............................................................................................................................. 95 5.12. CSAT3.......................................................................................................................... 95 5.13. LI-7500 ......................................................................................................................... 95 5.14. HMP45C....................................................................................................................... 95 5.15. Net radiometer .............................................................................................................. 95 5.16. Rain Gage ..................................................................................................................... 95 5.17. DAMIT ......................................................................................................................... 95 6. MONITORING PLAN ......................................................................................................... 97 6.1. Infrastructure ................................................................................................................ 97 6.2. Programming Logic...................................................................................................... 98 6.3. Program ...................................................................................................................... 100 6.4. Output ......................................................................................................................... 102 6.5. OPEC (Open Path Eddy Covariance System) ............................................................ 103 6.6. DAMIT (Directional Anemometer and Micro-Instrument Tower)............................ 103 7. SUMMARY........................................................................................................................ 105 8. REFERENCES ................................................................................................................... 107 APPENDIX A. Lysimeter Soil Filling...................................................................................... 109 APPENDIX B. Lysimeter Construction and Installation ......................................................... 119 APPENDIX C. Lysimeter Instrument Maps............................................................................. 121 APPENDIX D. Tracers............................................................................................................. 141
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APPENDIX E. HDU and TDR Calibration Parameters ........................................................... 145 APPENDIX F. BRUGG DTS Cable......................................................................................... 165 APPENDIX G. Matlab Program for DTS................................................................................. 167 APPENDIX H. LoggerNet Program for Lysimeter 1............................................................... 173 APPENDIX I. LoggerNet Program for Lysimeter 2 ................................................................ 211 APPENDIX J. LoggerNet Program for Lysimeter 3 ................................................................ 249 APPENDIX K. LoggerNet Program for OPEC........................................................................ 289 APPENDIX L. LoggerNet Program for Rainfall Simulator..................................................... 315 APPENDIX M. Example Data Outputs.................................................................................... 319 APPENDIX N. Lysimeter Data Map........................................................................................ 343 APPENDIX O. Naming Convention for Sensor Number......................................................... 353
LIST OF FIGURES 1-1. 1-2.
1-3. 1-4.
1-5. 2-1. 2-2. 2-3. 2-4. 2-5. 2-6. 2-7. 2-8.
The SEPHAS Weighing Lysimeter Facility in Boulder City, NV, is located in the desert southwestern United States. ................................................................................... 3 Google earth map of Boulder City, NV with markers indicating the location at KNVBOULD3 meterological station, center of Boulder City, the SEPHAS Lysimeter Facility located at 1500 Buchanan Blvd, the WRCC meterological station, and the Arizo soil which was used to fill the lysimeters...................................... 4 Monthly average, minimum, and maximum temperature and precipitation for WRCC Boulder City, NV meterological station for a 30 year period from 1971 to 2000................................................................................................................................... 4 A) Aerial photograph of the lysimeter facility in Boulder City, NV, showing the location of underground tunnel (shown in black), four lysimeters (shown in orange), re-vegetated field plot (shaded box), and existing lab. B) A southern view of field re-vegetated with creosote bush and white bur sage taken on Dec. 9, 2009................................................................................................................................... 5 Three cylindrical lysimeters (2.258 m diameter and 3 m height) and one square lysimeter (2 m by 2 m by 3 m height)............................................................................... 6 Land jurisdiction in Clark County, Nevada, with inset identifying 25 km radius from Boulder City. ............................................................................................................ 9 SW-NE transect 3 km in length where soil borings were advanced in Eldorado Valley, Nevada................................................................................................................ 11 Depth profile for boreholes advanced along 3 km SW-NE Eldorado transect for A) silt and clay; B) gravel; C) CaCO3; and D) salt......................................................... 12 A) Photo illustrating plant density of identified area in Eldorado Valley for soil excavation and B) photo of desert pavement for soil in Eldorado Valley. ..................... 12 Map of drilled soil borings and excavation of trench in Eldorado Valley, NV. ............. 13 Eldorado boreholes depth profile of A) K, Mg, and Na; B) phosphorous and nitrogen; C) pH and CEC; and D) soluble salts and sulfate. .......................................... 14 Soil pit (3 m deep) excavated in Eldorado Valley. ......................................................... 15 A) Cavity compliant method to measure bulk density; and B) photo of 0-120 cm Arizo soil profile. ............................................................................................................ 16
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2-9. 2-10. 2-11. 2-12.
2-13. 2-14. 2-15. 2-16. 3-1. 3-2. 3-3. 3-4. 3-5. 3-6. 3-7. 3-8. 3-9. 3-10. 3-11. 3-12. 3-13. 3-14. 3-15.
A) Excavation of soil layers to be repacked in lysimeters 1, 2, and 3; B) transportation of soil layers; and C) storage of soil layers in storage containers. ....................................................................................................................... 16 Arizo depth profile of A) Ca, K, Mg, and Na; B) soil texture; C) bulk density and moisture content; D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfur. ............................................................................................................... 17 Wooden hopper and connected PVC tube used to funnel soil into the lysimeter........... 20 A) Gravimetric moisture content was determined for each soil layer installed in lysimeter using a microwave oven. B) Soil layer was leveled and compacted (with soil compactor when necessary) until required thickness for desired bulk density was reached. ....................................................................................................... 21 A) Lysimeter 3 at 160 cm depth has large gravel content; and B) close up of 5-10 cm gravel. ............................................................................................................... 23 Lysimeter 1 depth profile of A) soil textural components; B) moisture content and bulk density; C) K, Mg, Na, and Ca; D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. ..................................................................... 24 Lysimeter 2 depth profile of A) soil textural components, B) moisture content and bulk density; C) K, Mg, Na, and Ca; C, D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. ..................................................................... 26 Lysimeter 3 depth profile of A) soil textural components, B) moisture content and bulk density; C) K, Mg, Na, and Ca; C, D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. ..................................................................... 28 Examples of small scale weighing lysimeter .................................................................. 33 Plan view of the underground lysimeter tunnel (not to scale). ....................................... 34 Vertical cross section of the underground lysimeter tunnel............................................ 34 A) Construction of underground lysimeter tunnel; B) installation of stainless steel lysimeters; and C) placement of lysimeters on weighing scale. ..................................... 35 A photo of a Precision Scale Incorporated manufacturer assembling the lysimeter scale................................................................................................................................. 35 Cross section of lysimeter tank and scale system. .......................................................... 36 Load cell connected to weigh beam and data logger while counterbalanced by counterweights made of steel plates. .............................................................................. 37 TDR (CS 605) moisture probe with 3-30.5 cm probes................................................... 38 CS616 to measure moisture content. .............................................................................. 39 ECH2O or (model ECH2O-TE, Campbell Scientific, Inc., Logan, UT) measures soil water content, temperature and electrical conductivity............................................ 40 Dimensions and components of a Dual-Probe Heat-Pulse (DPHP) and TripleProbe Heat-Pulse (TPHP). .............................................................................................. 41 Components of a neutron probe to measure soil moisture including a probe that emits and detects neutrons, a shield and standard, and a scaler to collect data .............. 43 A heat dissipation unit (HDU) (model 229, Campbell Scientific Inc., Logan, UT) is shown on top. .............................................................................................................. 44 STherm is a soil thermistor (model 108L, Campbell Scientific Inc., Logan UT) that measures the temperature of the soil........................................................................ 45 TCAV (model TCAV-L, Campbell Scientific, Inc., Logan, UT) measures average soil temperature using four parallel probes ....................................................... 45
x
3-16. 3-17. 3-18. 3-19. 3-20. 3-21. 3-22.
3-23. 3-24. 3-25. 3-26. 3-27. 3-28. 3-29. 3-30. 3-31. 3-32. 4-1. 4-2. 4-3. 4-4. 4-5. 4-6. 4-7. 4-8.
Soil heat flux plate (model HFP01SC, Campbell Scientific Inc., Logan, UT)............... 46 Placement of heat flux plates. ......................................................................................... 47 Schematic of DTS system............................................................................................... 47 Cross section showing outer protective jackets and fibers of A) AFL Fiber Optic (1F) cable; and B) BRUGG Fiber Optic (4F) cable........................................................ 48 Settlement plate is a mild steel mesh plate (6 in (l) x 6 in (w) by 1/8 in thick) coated with thick layer of epoxy to prevent corrosion and stainless steel cable attached to center. ........................................................................................................... 49 A) Top view of the SSAP prototype (larger base, rod without stainless steel rings); and B) final SSAP design and installation sketch (SSAP designed by John Healey)............................................................................................................................ 50 SSAP in the soil with nine stainless steel rings as markers and A) measuring distance between top ring and reference level with the metal ruler resting on horizontal leg of the L-shaped aluminum rod and B) counting number of visible rings (taken on the lysimeter 1 on Sept. 16, 2008). ........................................................ 50 Dimensions and components of CO2 sensors (CARBOCAP Carbon Dioxide Transmitter Series GMT220, Vaisala Instruments, Woburn, MA) ................................ 51 Short and long stainless steel solution samplers (SSSS) with 20 and 50 cm porous cylinders shown on top and bottom. ............................................................................... 52 A) FDC Green No. 3 sorption curve for 25-80 cm soil. B) FDC Green No. 3 and NO3- breakthrough curves for 25-80 cm soil. ................................................................. 54 CSAT3 three dimensional sonic anemometer (Campbell Scientific Inc., Logan, UT).................................................................................................................................. 55 Components of the LI-7500 (model LI-7500, LI-COR Biosciences, Lincoln, NE) ....... 55 HMP45C temperature and relative humidity probe (model HMP45C, Campbell Scientific, Inc., Logan, UT) ............................................................................................ 56 NR-LITE net radiometer (model NR-LITE, Campbell Scientific Inc., Logan, UT).................................................................................................................................. 56 TE525WS-L Texas Electronics 8in rain gage ................................................................ 57 A 3-cup anemometer and a wind van mounted on a cross arm (model 03002 wind sentry set, Campbell Scientific Inc., Logan, UT) ........................................................... 58 Dimensions of relative humidity and temperature sensor SHT75 .................................. 59 Aerial photograph of the lysimeter facility in Boulder City, NV, showing location of instruments installed in adjacent natural soil (yellow star), OPEC (blue triangle) and DAMIT (light blue box). ........................................................................... 61 Full instrument suite in lysimeter 1 at 50 cm.................................................................. 66 Instrument placement in lysimeter 1 at 5 cm .................................................................. 66 Instrument placement in lysimeter 1 at 10 cm. ............................................................... 66 DTS Pole, Vertical Mini-Rhizotron Tube (MRT), and Neutron Access Tube (NAT) extend through the entire vertical depth of each lysimeter. DTS pole, vertical MRT, and NAT installed in empty lysimeter 2. ................................................ 67 DTS pole showing A) insulation foam; and B) threat pitch of 4.5 threads per cm glued onto schedule 40 PVC pipe................................................................................... 68 Installation design for optical fiber loops. ...................................................................... 68 DTS loops being installed at 95 cm in a lysimeter 2. ..................................................... 69
xi
4-9. 4-10. 4-11. 4-12. 4-13. 4-14. 4-15. 4-16. 4-17. 4-18. 4-19. 4-20. 5-1. 5-2. 5-3.
5-4. 5-5.
5-6. 5-7. 5-8. 5-9. 5-10.
Repaired vertical MRT with sleeve at 50 cm depth in lysimeter 1 and broken MRT that was removed and replaced with repaired MRT. ............................................ 70 Two settlement plates in the SE and NW quadrants at 190 cm depth in lysimeter 2....................................................................................................................................... 71 Photo of caliper instrument to measure settlement plates............................................... 71 Arrangement of SSAP 7, 8 and 9 in lysimeter 3 with aluminum rod across the lysimeter surface as reference base................................................................................. 72 A) 50 cm long stainless steel solution samplers are installed at 295 cm depth in lysimeter to create a vacuum; and B) using a wooden block to place stainless steel solution samplers at a 10° angle. ............................................................................ 74 Stainless steel solution sampler manifold attached to one side of the lysimeter. ........... 74 Routing of SSSS to solution manifold. ........................................................................... 75 Tracer application in lysimeter 1 of A) FDC Green No. 3 at 55 cm; and B) PFBA at 30 cm........................................................................................................................... 76 Schematic of N-15 application in lysimeters. ................................................................. 77 Different instruments and components of the open path eddy covariance (OPEC) system. ............................................................................................................................ 77 Directional Anemometer and Micro-Instrument Tower (DAMIT). ............................... 78 Location of OPEC (blue triangle) and DAMIT (light blue rectangle) at the SEPHAS lysimeter facility. ............................................................................................ 78 Laboratory calibration of load cell with known weights with load cell connected to a datalogger................................................................................................................. 79 A) Upward and downward calibration and load cell output and B) load cell accuracy of lysimeter 3 scale. ......................................................................................... 80 Data from Jun. 27 to 30, 2008 for 1) scale readings converted to change in water in mm as a result of evaporation from aluminum pans filled with equal water volume and placed on lysimeter 1, 2, and 3; and 2) measurements of soil temperature at 5 cm depth in lysimeter 1 and room 1 air temperature. .......................... 81 Scale, roof temperature, and load cell temperature for lysimeter 1 (Oct. 17 to 31, 2008). .............................................................................................................................. 82 Upward infiltration observed dielectric and moisture contents (two replicates) fitted to Eq. [5-1] for A) 25-80 cm soil horizon; B) 80-120 cm soil horizon; C) 120-160 cm soil horizon; D) 160-200 cm soil horizon; E) 0-200 cm soil horizon; and F) all soil data. ........................................................................................... 84 A) Thermal conductivity; and B) volumetric heat capacity as functions of volumetric water content, measured from lysimeter 3 from Nov. 26 through Dec. 16, 2008........................................................................................................................... 87 Air entry pressure (Ψair) of HDU occurs as saturated soil dries and T* becomes less than 1. For HDU 12260 Ψair is 79.49 mb................................................................. 88 Calibration curve for HDU 12260 based on measured normalized T* measurements (using dry and saturated endpoints) for variably saturated conditions........................................................................................................................ 89 BRUGG DTS schematic for lysimeters 1, 2, and 3. ....................................................... 91 AFL DTS schematic for lysimeters 1, 2, and 3............................................................... 91
xii
5-11. A) Lysimeters 1 through 3 soil temperature collected on Nov. 4, 2008, using AFL optical fiber. B) Lysimeters 1 and 3 soil temperature collected on February 21, 2009, using AFL optical fiber................................................................................... 92 5-12. Lysimeter 1 soil temperature collected Nov. 4 through 6, 2008, using BRUGG optical fiber. .................................................................................................................... 92 5-13. Lysimeter 2 soil temperature collected Nov. 4 through 6, 2008, using BRUGG optical fiber. .................................................................................................................... 93 5-14. Lysimeter 3 soil temperature collected Nov. 4 through 6, 2008, using BRUGG optical fiber. .................................................................................................................... 93 5-15. Lysimeter 2 inner and outer soil temperatures collected Nov. 4 through 6, 2008, using BRUGG optical fiber. ........................................................................................... 94 6-1. A) Plan view of instrument panel. B) Illustration of automated data storage................. 97 6-2. General flowchart of the CR3000 datalogger program................................................. 101 6-3. Flowchart of sensor measurements based on user flags 1, 2, and 3.............................. 102 B-1. SEPHAS lysimeter construction. .................................................................................. 119 B-2. Installation of lysimeter and scale................................................................................. 120 C-1. A) Lysimeter dimensions, porthole numbers, and quadrants. Instrument map for lysimeters at depth B) 0 cm; C) 5 cm; D); 10 cm; E) 25 cm; F) 50 cm; G) 60 cm; H) 75 cm; I) 90 cm; J) 95 cm; K) 100 cm; L) 140 cm; M) 150 cm; N) 190 cm; O) 200 cm; P) 250 cm; and Q) 295 cm. R) Depth profile for the placement of Heat Flux Plates and TCAVS at 2, 6, and 8 cm depth. ......................................................... 125
LIST OF TABLES 1-1. 2-1. 2-2. 2-3. 2-4. 2-5. 2-6. 2-7. 3-1. 3-3. 4-1. 4-2. 4-3. 4-4.
Experimental design of SEPHAS weighing lysimeters. ................................................... 7 Soil series, depth, and textural class along the 3 km SW-NE transect. .......................... 10 GPS Coordinates for soil borings in Eldorado Valley. ................................................... 11 GPS coordinates for coreholes in Eldorado Valley. ....................................................... 13 Eldorado Valley bulk density measurements from 0 to 200 cm using the compliant cavity and short core bulk density methods................................................... 18 Taxonomic identification of Arizo soil series in Eldorado Valley, NV. ........................ 19 Completed schedule for filling lysimeter 1, 2, and 3 with Arizo soil............................. 21 Average concentrations of organic matter (OM), phosphorous (P-Weak Bray), pH, cation exchange capacity (CEC), nitrogen (NO3-N), sulfur (SO4-S) and soluble salts in the native Arizo and repacked lysimeter 1, 2, and 3 soils...................... 25 List of instruments installed in each lysimeter. .............................................................. 30 Meteorological instruments and measurement parameters for the extended open path eddy covariance (OPEC) system and the directional anemometer and microinstrument tower (DAMIT)............................................................................................. 32 Catalogue and number of instruments at each depth for each lysimeter. ....................... 62 Comparison of temperature measured in lysimeter and adjacent natural soil east of lysimeter 3 (data stored as BC_Lys3_TC.dat). Gray cells indicate temperature measured at same depth. ................................................................................................. 63 Comparison of temperature measured in lysimeter and adjacent natural soil east of lysimeter 3. Gray cells indicate water content measured at same depth. ................... 64 Initial caliper measurements of settlement plates taken on June 12, 2008. .................... 72 xiii
4-5. 4-6. 5-1.
Initial measurements of SSAPs from Jul. 18, 2008. ....................................................... 73 Depth and mass of tracers applied in lysimeters 1, 2, and 3........................................... 76 Calibration curves for three lysimeter load cells for decreasing and increasing mass increments. ............................................................................................................. 79 5-2. Upward and downward standard error of lysimeter scales............................................. 80 5-3. Bulk densities for upward infiltration experiment .......................................................... 83 5-4. Results of fitting Eq. [5-1] to observed data. .................................................................. 83 5-5. Distance to lysimeter depth conversion for AFL fiber optic cables. .............................. 90 6-1. Frequency of data collection for various instruments..................................................... 97 6-2. Multiplexer channel assignments and sensor associations. ............................................ 98 6-3. Parameters measured every 15 min by sensors............................................................... 98 6-4. User-defined flag assignments for programming blocks. ............................................... 99 A-1. Filling lysimeter 1 with homogeneous soil with targeted and measured bulk densities of soil layers................................................................................................... 109 A-2. Filling lysimeter 2 with homogeneous soil 200-300 cm depth and heterogeneous soil 0-200 cm depth with targeted and measured bulk densities of soil layers............. 112 A-3. Filling lysimeter 3 with homogeneous soil 200-300 cm depth and heterogeneous soil 0-200 cm depth with targeted and measured bulk densities of soil layers............. 115 C-1. Serial number, placement, and datalogger variable ID of DPHP in lysimeters 1, 2, and 3.......................................................................................................................... 126 C-2. Serial number, placement, and datalogger variable ID of Heat Dissipating Units (HDUs) in lysimeters 1, 2 and 3. .................................................................................. 128 C-3. Serial number, placement, and datalogger variable ID of settlement plates in lysimeters 1, 2, and 3. ................................................................................................... 131 C-4. Serial number, placement, and datalogger variable ID of SSS in lysimeters 1, 2, and 3.............................................................................................................................. 132 C-5. Serial number, placement, and datalogger variable ID of TPHPs in lysimeters 1, 2, and 3.......................................................................................................................... 134 C-6. Serial number, placement, and datalogger variable ID of TDRs in lysimeters 1, 2, and 3.............................................................................................................................. 136 C-7. Serial number, placement, and datalogger variable ID of 108L in lysimeters 1, 2, and 3.............................................................................................................................. 138 C-8. Serial number, placement, and datalogger variable ID of ECH2O-TE in lysimeters 1, 2, and 3. ................................................................................................... 138 C-9. Serial number, placement, and datalogger variable ID of heat flux plates in lysimeters 1, 2, and 3. ................................................................................................... 138 C-10. Serial number, placement, and datalogger variable ID of TCAVs in lysimeters 1, 2, and 3.......................................................................................................................... 138 C-11. Serial number, placement, and datalogger variable ID of FDR (CS616) in lysimeters 1, 2, and 3. ................................................................................................... 139 D-1. Parameters to determine tracer volume and mass need for lysimeter application*...... 141 D-2. Volume of water in lysimeter at specific water content (in liters)................................ 141 D-3. Tracer mass needed (in g) for different water contents.** ........................................... 142 D-4. Amount of CaBr need at different water contents.*** ................................................. 142
xiv
D-5.
Determining volume of CaBr mixture for each mesh square using 2 pipettes for the total application of 15.02 g of CaBr for 12.01 g of Br mixed in 1000 L of water.............................................................................................................................. 142 D-6. Mass of CaBr mixed with 1000 L of water................................................................... 143 E-1. HDU serial numbers and Bilskie fitting parameters. .................................................... 145 E-2. TDR calibration data for individual calibration curves 1 through 6............................. 149 E-3. TDR calibration data for individual calibration curves 7 through 12 and Topp's curve.............................................................................................................................. 162 F-1. Linear length of daisy chained BRUGG DTS loops through three lysimeters............. 165 F-2. Linear length and depth of 150 cm inner and 200 cm outer Brugg DTS loops............ 166 M-1. Example output “BC_Eddy_dly.dat”............................................................................ 319 M-2. Example output table “CO2.dat”. ................................................................................. 319 M-3. Example transposed output table “Daily.dat”............................................................... 320 M-4. Example transposed output table “DPHP.dat”.............................................................. 320 M-5. Example transposed output table "HDU.dat". .............................................................. 325 M-6. Example transposed output table “Scale.dat”. .............................................................. 326 M-7. Example transposed output table "SHT75.dat”. ........................................................... 327 M-8. Example transposed output table “TDR.dat”................................................................ 327 M-9. Example transposed output table “TDR_Wave.dat”. ................................................... 328 M-10. Example transposed output table “TEData.dat”. .......................................................... 334 M-11. Example transposed output table “TPHP.dat”. ............................................................. 335 N-1. Variable definition for lysimeter 1 scale.dat................................................................. 343 N-2. Variable definition for lysimeter 2 scale.dat................................................................. 344 N-3. Variable definition for lysimeter 3 scale.dat................................................................. 345 N-4. Variable definition for lysimeter 1,2, and 3 tdr.dat....................................................... 345 N-5. Variable definition for lysimeter 1,2, and 3 hdu.dat. .................................................... 346 N-6. Definition of open path eddy covariance system (OPEC) sensors. .............................. 346 N-7. Variable definition for BC_Eddy_dly.dat..................................................................... 347 O-1. Definition of sensor number. ........................................................................................ 353 O-2. Sensor ID naming convention for TPHP cluster or "Titanic."...................................... 354 O-3. Sensor ID naming convention for thermocouples (TC) located in natural soil outside of lysimeter 3.................................................................................................... 354
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ACRONYMS AND ABBREVIATIONS CEC CO2 CSI DAMIT DPHP DRI DTS ECH2O EPSCoR CS616 HDU ID MRT NAT NSF OD OM OPEC SEPHAS SET SHF SSAP SSSS STherm TCAV TDR TPHP UNLV UNR VWC
Cation Exchange Capacity CO2 sensor Campbell Scientific, Inc. Directional Anemometer and Micro-Instrument Tower Dual-Probe Heat Pulse Sensor Desert Reseach Institute Distributed Temperature Sensing ECH2O-TE Decagon Soil Moisture Sensor Experimental Program to Stimulate Competitive Research Frequency Domain Reflectometry Probe Heat Dissipation Unit Inside Diameter Mini-Rhizotron Tube Neutron Access Tube National Science Foundation Outside Diameter Organic Matter Open Path Eddy Covariance Scaling Environmental Processes in Heterogeneous Arid Soils Settlement Plate Soil Heat Flux Plate Soil Surface Alteration Probe Stainless Steel Solution Sampler Soil Thermistor Averaging Thermocouple Time Domain Reflectometry Probe Tri-Probe Heat Pulse Sensor University of Nevada, Las Vegas University of Nevada, Reno Volumetric Water Content
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1.
INTRODUCTION
1.1.
Statement of Problem
The vadose and saturated zones represent a critical interface between the earth’s bio-, hydro-, and geospheres. Mass and energy movement across this critical boundary strongly influence a suite of environmentally important processes including local and global element cycling (e.g., CO2, nutrients, and metals), water cycling, and many coupled biogeochemical processes. Many of these processes are typically monitored and characterized at a small spatial scale and the findings are subsequently applied at a larger scale. A better understanding of these fundamental processes will have direct application to many environmental issues, including the impact of global climate change in arid environments, predictions of water recharge, flooding, and fate and transport of contaminants. Furthermore, deserts make up a large portion of the western U.S. where the economy and environment are constrained by water availability. This is particularly true in Nevada, which is one of the driest states in the U.S. and home to one of the fastest growing cities in the country. 1.2.
Purpose
For scientists to obtain a better understanding of the processes that control water, CO2, nutrients, and microbes in desert soils, Nevada researchers developed a statewide program supported by the National Science Foundation (NSF) entitled “Scaling Environmental Processes in Heterogeneous Arid Soils” or SEPHAS. This program focuses on scaling, which is the transfer of knowledge from one spatial or temporal scale to another, of subsurface and landscape-interface environmental processes. Scaling of environmental processes is often hampered by natural heterogeneity, which is not well represented by the scale at which the experiments are conducted. The disparity between the scale of measurement and the scale of interest limits our ability to characterize large-scale environmental processes, and perhaps more importantly, how the processes influence one another. The inability to upscale or downscale these processes influences research areas of hydrology, pedology, agriculture, biogeosciences, mathematical modeling, and global environmental change, in part because facilities that permit multi-scale environmental research are either rare or nonexistent. Thus, limited data are available to test hypotheses or make meaningful predictions. To address these research challenges, the researchers in Nevada, led by Desert Research Institute (DRI), constructed the SEPHAS Weighing Lysimeter Facility (“lysimeter facility” or simply “facility”) in Boulder City, NV. The facility is devoted to investigating the near-surface interactions of soil, water, biotic, and atmospheric processes that affect desert environments like those found in the southwestern U.S., in particular the Mojave Desert in southern Nevada. The lysimeters were constructed at the meso-scale and play an important role in bridging existing eco-scale, laboratory, and micro-scale research efforts. The SEPHAS project includes four lysimeters (three installed to date), containing repacked and intact soil caissons. Three of the lysimeters are cylindrical, measuring 2.258 m inner diameter x 3 m height, and the fourth is square, measuring 2 m length x 2 m width x 3 m height. The lysimeters are instrumented to measure near-surface processes of mass and energy movement through the land-atmosphere interface in the desert environment. This facility will be used to
1
attract researchers and students across the U.S. and abroad who are interested in obtaining high-resolution measurements and answering scaling-related questions in arid settings. 1.3.
Hypotheses
The hypotheses were developed based on several focal themes including 1) landscape dynamics, restoration, and water balance; 2) carbon sequestration; and 3) characteristics of soil properties at different scales. Specificaly for theme 2, the SEPHAS Weighing Lysimeter Facility has close links to ongoing research funded by NSF at the Nevada Desert Research Center (NDRC) (“Biotic and Abiotic Controls on CaCO3 Formation in Mojave Desert ecosystems,” PI Paul Verburg), specifically at the Mojave Global Change Facility (MGCF), which was constructed in part under an earlier NSF EPSCoR award. Faculty on the NDRC project provided hypotheses for the SEPHAS project regarding impacts of subsurface plant activity on CaCO3 formation and dissolution, in response to experimental nitrogen additions and field irrigation. The facility will address more in-depth studies which would not be possible in the field, including the use of isotopic tracers to study carbon and nitrogen allocation in plants and soil. It will also allow closer study of CaCO3 formation and dissolution immediately surrounding root surfaces. Very little information is available about plant microbe interactions in desert systems, an issue that typically cannot be studied in the field because of difficulties associated with accessing the subsurface without impacting the plant-microbe system. One of the initial findings of the research conducted at NDRC is that below-ground activity does not respond to N additions. However, increased precipitation resulted in sustained increases in soil CO2 concentrations despite very little changes in root turnover. These increases in soil CO2 concentrations in combination with increased soil moisture could potentially result in increased CaCO3 dissolution and transport down the soil profile. This raises questions with respect to N uptake in desert systems and the role of deep NO3 reservoirs in the soil. The experimental control provided by the SEPHAS lysimeters allows these issues to be addressed and the results to be applied at larger-scale landscapes. The scientific motivation for the SEPHAS facility is summarized by the hypotheses developed by the NSHE science consortium. For the first theme of landscape dynamics, the hypotheses posed in this project are: A) disturbance of structured desert soils will alter near-surface soil water balance and rates of biogeochemical weathering; B) water flow patterns and plant rooting distributions are dependent on pedological development; and, C) thermal and water-content profiles will differ when soil is disturbed, but profiles will equilibrate quickly. For the second theme of carbon sequestration, the hypotheses are as follows: A) increased precipitation will result in higher soil PCO2 and soil moisture; and, B) increased Ca availability will favor C sequestration in CaCO3. For the third theme of characterizing soil properties at different scales, the hypotheses are as follows: A) effective soil hydraulic properties can be estimated using only moisture content and without computationally demanding numerical techniques;
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B) characterizing the heterogeneity in soil hydraulic properties can be accomplished with fewer measurements of physical properties; and, C) scale effects create discrepancies in the measurement of hydrologic variables. 1.4.
Location
The lysimeter facility is located in Boulder City, NV, approximately 40 km southeast of Las Vegas, NV and approximately 120 km from MGCF and the Nevada Desert FACE Facility. (Figure 1-1). The closest Western Regional Climate Center (WRCC) meterological station is the Boulder City, NV station located at 35.98°, -114.85° (11S 693829 m Easting, 3984237 m Northing) (Figure 1-2). The elevation is 768 m (2520 ft) and the average total precipitation is 16.3 cm (6.42 in). The average minimum and maximum temperature was 13.9°C and 25.7°C or 57.0°F and 78.3°F over a 30 year period (Boulder City, NV meterological data located at http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?nv1071). A local weather station, KNVBOULD3 in Boulder City, NV, is located at 35.97°, -114.84° (11S 694763 m Easting 3982777 m Northing) (Figure 1-2). KNVBOULD3 measured a maximum wind speed of 22 mph from the west-southwest and a maximum wind gust of 38.0 mph from the east (KNVBOULD3 meterological data at http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KNVBOULD3). Meterological data is also collected near and directly above the lysimeters (See Section 3.7).
Figure 1-1.
The SEPHAS Weighing Lysimeter Facility in Boulder City, NV, is located in the desert southwestern United States.
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Figure 1-2.
Google earth map of Boulder City, NV with markers indicating the location at KNVBOULD3 meterological station, center of Boulder City, the SEPHAS Lysimeter Facility located at 1500 Buchanan Blvd, the WRCC meterological station, and the Arizo soil which was used to fill the lysimeters.
Figure 1-3.
Monthly average, minimum, and maximum temperature and precipitation for WRCC Boulder City, NV meterological station for a 30 year period from 1971 to 2000. 4
The 3.5 acre research facility, formerly named Desert Research Institute Solar, is owned by the Nevada System for Higher Education, but is still operated by DRI. The facility is equipped with offices, a high-bay, laboratory space, machine shop, computer servers, and fiber optic communications. The lysimeters are located 150 m west of the main building and are aligned in a NW to SE direction (Figure 1-4). There are four lysimeter rooms that are accessed by a central underground tunnel (Figure 3-4). Briefly, each lysimeter is weighed on a separate scale and has a live mass of about 28,000 kg with a resolution of +72 to 409 g (equivalent to 0.018 to 0.102 mm water on the surface). Each lysimeter is equipped with dataloggers that can be accessed remotely so that investigators can monitor individual sensors and systems as needed. Finally, the bottom boundary of the lysimeter is controlled using stainless steel tubing connected to a vacuum system. This provides the ability to: 1) mimic an infinitely deep soil profile by creating uniform soil water potential; 2) create shallow water table conditions; and 3) allow sampling of soil solution. The overall goal of the design is flexibility to conduct multiple simultaneous experiments without antagonistic effects.
Figure 1-4.
A) Aerial photograph of the lysimeter facility in Boulder City, NV, showing the location of underground tunnel (shown in black), four lysimeters (shown in orange), re-vegetated field plot (shaded box), and existing lab. B) A southern view of field re-vegetated with creosote bush and white bur sage taken on Dec. 9, 2009.
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1.5.
Experimental Design
The experimental design is based on three factors: undisturbed versus disturbed soil, existence or absence of vegetation, and treatment (Table 1-1). Lysimeters 1, 2, and 3 are cylindrical (2.258 m inner diameter x 3 m height), containing disturbed soil that was repacked to bulk densities found in the field. Lysimeter 4 is square (2 m width x 2 m length x 3 m height) and will contain an undisturbed block of soil. The differences in shape will allow differences in boundary effects due to a cylindrical and square shapes to be seen. Lysimeter 1 is filled with homogenized soil and will have no vegetation or other treatment. Lysimeters 2 and 3 are filled with soil, repacked according to the soil horizons found in the field, and will be planted with native desert plants (creosote bush [Larrea tridentada] and white bur sage [Ambrosia dumosa]). Lysimeter 4 will contain an undisturbed square block of desert soil, in its natural depositional order, with native vegetation intact. This experimental design will allow a comparison between lysimeter 1 and lysimeters 2 and 3 to assess the impact of bare soil versus a vegetated upper boundary. Also, lysimeters 2 and 3 will serve as replicates. Lysimeter 2 and 3 will be compared to lysimeter 4 to evaluate the differences between reestablished native plants versus intact plants. Finally, a comparison of lysimeter 1 with lysimeters 2 and 3 will assess the effects of no layering (homogenized soil) versus layered soil horizons. When the experiments begin, the soil borrow site will be instrumented with sensors to measure water content, water pressure, and temperature a short period of time. This data will provide a comparison between natural field conditions to those created in the lysimeters.
Figure 1-5.
Three cylindrical lysimeters (2.258 m diameter and 3 m height) and one square lysimeter (2 m by 2 m by 3 m height).
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Table 1-1.
Experimental design of SEPHAS weighing lysimeters.
Lysimeter Soil Vegetation Treatment Shape
1.6.
1 Disturbed Homogeneous Bare None Cylindrical
2 Disturbed Soil Horizons Desert Plants Irrigation Cylindrical
3 Disturbed Soil Horizons Desert Plants Irrigation Cylindrical
4 Undisturbed Soil Horizons Desert Plants Irrigation Square
Outline
The purpose of this report is to provide detailed information on the design, construction, installation, and operation of the lysimeters for present and future scientists conducting research at the lysimeter facility. The general outline of this report includes information on properties and installation of soil material used to fill the lysimeters; monitoring methods and instrumentation including lysimeter construction; instrument layout and installation; instrument calibration; and monitoring plan including data acquisition and management.
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2.
SOIL MATERIAL AND INSTALLATION
2.1.
Background
A search for soil was undertaken to identify soil suitable for lysimeters. It was also desirable to locate a borrow site proximal to the facility to facilitate linking lysimeter data to natural field conditions, and to minimize transportation costs. Therefore, the search for a suitable desert soil was limited to a 25 km radius from the facility (within Clark County). This quickly narrowed the search to a site on private land owned by Boulder City in Eldorado Valley that had not been developed. Other surrounding areas were federal lands and they were quickly eliminated because of the challenges associated with land excavation on federal and protected lands (Figure 2-1).
Figure 2-1.
2.2.
Land jurisdiction in Clark County, Nevada, with inset identifying 25 km radius from Boulder City.
Criteria
Several criteria were selected for the borrow site in Eldorado Valley, including soil texture, plant type and density, existence of desert pavement, and topography. The preferred soil type was moderately drained, loamy sand, located on a shallow slope of an alluvial fan. The soil should have creosote bush (L. tridentada) at a density with a maximal spacing of 2 m, so that collection of a soil caisson with an intact plant could be possible. The depth to bedrock should be greater than 3.5 m and incipient desert pavement present on surface would be desirable. These criteria were used to facilitate addressing hypotheses listed in Section 1.3 within a reasonable amount of time. Very fine or clayey soil would require significant time periods for deep water percolation, and coarse soil could reduce water holding capacity, thus affecting desert plant growth and vigor. In sum, the use of loamy sand would provide the balance needed to conduct experiments within an acceptable time period.
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As a preliminary step, six soil borings were advanced along a 3 km SW-NE transect in Eldorado Valley. Borings were spaced at 0.6 to 1 km intervals, with the exception of the interval between locations 1 and 2, which was 80 m. Soil at each location was sampled from 0-30 cm and 30-60 cm depth intervals. All soil samples were sent to A&L Western Laboratories for basic soil analysis plus soluble salts, excess lime, nitrate-nitrogen, soil physical and chemical analysis (S2N Package, A&L Western Laboratories, Modesto, CA). The first two soil borings were advanced within the Arizo series, the third was within the Caliza series, and the last three were within the Bluepoint series (Figure 2-2). The Arizo soil series is described as a sandy-skeletal, mixed, thermic Typic Torriorthents with very deep, excessively drained soils that is formed in mixed alluvium (Soil Survey Staff, 2008). Arizo soil is found on recent alluvial fans, inset fans, fan aprons, fan skirts, and floodplains and slope ranges from 0 to 15 percent. The Calizo series is described as a sandy-skeletal, mixed, thermic Typic Haplocalcids with deep, well-drained soils that formed in gravelly alluvium (Soil Survey Staff, 2008). Caliza soils are found on alluvial fans or river deposits of Pleistocene age and have slopes of 1 to 50 percent. The Bluepoint series is described as mixed, thermic Typic Torripsamments with very deep, somewhat excessively drained soils that formed in eolian materials from mixed rock sources (Soil Survey Staff, 2008). This series is found on dunes and sand sheets on slopes ranging from 0 to 50 percent. Table 2-1.
Soil series, depth, and textural class along the 3 km SW-NE transect. Soil Arizo 1
Arizo 2
Caliza 1
Bluepoint 1
Bluepoint 2
Bluepoint 3
Depth [cm] 0.0 30.5 61.0 0.0 30.5 61.0 0.0 30.5 61.0 0.0 30.5 61.0 0.0 30.5 61.0 0.0 30.5 61.0
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Textural Class VGLS VGLS VGLS VGLS VGLS VGS VGLS VGLS VGLS VGS VGS VGLS S S S LS VGLS VGS
Figure 2-2.
SW-NE transect 3 km in length where soil borings were advanced in Eldorado Valley, Nevada.
Table 2-2.
GPS Coordinates for soil borings in Eldorado Valley. Sample Arizo 1 Arizo 2 Caliza 1 Bluepoint 1 Bluepoint 2 Bluepoint 3
Easting 688692 688736 689390 689786 690213 690692
Northing 3978052 3978108 3978929 3979432 3979958 3980555
The Arizo soil samples were very gravelly loamy sands and had higher average silt and clay contents than the Caliza and Bluepoint samples (Figure 2-3A). The Bluepoint demonstrated a range of soil textures from sand to loamy sand with stratifications of gravelly layers. All soils had high gravel content (Figure 2-3B) and similar CaCO3 profiles (Figure 2-3C). In general, accumulations of CaCO3 are evident, especially in Bluepoint 1. Furthermore, all soils had relatively homogenized salt concentrations except for Arizo 1, which had elevated salt concentrations not conducive to plant growth (Figure 2-3D). 2.3.
Search Area
In summary, the soil physical and chemical properties of the Caliza, Arizo, and Bluepoint soil series were considered suitable soil as long as the salt concentration was not high and depth to bedrock was greater than 3.5 m. Furthermore, the area of investigation had a sufficient density of creosote bush (L. tridentada) and sufficient plant density to demonstrate that the soil could sustain desert shrubs (Figure 2-4A). Also, the site did show evidence of soil development through the presence of gravel lag at the surface (Figure 2-4B) and layering in near-surface materials.
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Figure 2-3.
Depth profile for boreholes advanced along 3 km SW-NE Eldorado transect for A) silt and clay; B) gravel; C) CaCO3; and D) salt.
Figure 2-4.
A) Photo illustrating plant density of identified area in Eldorado Valley for soil excavation and B) photo of desert pavement for soil in Eldorado Valley.
The Caliza and Arizo soil series in a 1 km2 area were chosen for further investigation due to the proximity to the access roads for excavating large quantities of soil. DRI obtained excavation permits from the Boulder City to conduct a three-phase excavation project, which included soil reconnaissance and search, preliminary soil investigation, and soil removal. The excavation permit on Boulder City property in Eldorado Valley is approximately 30 km from Las Vegas and 5 km from the lysimeter facility. The first permit allowed DRI to conduct a 12
reconnaissance across 1 km2 to narrow down the potential excavation area (Figure 2-5). During the reconnaissance, six soil borings (5.08 cm ID or 2 in ID) were drilled to 4.3 m depth. Soil samples were collected at 30.5 cm intervals to identify soil stratigraphy. Soil borings 1, 3, and 6 were analyzed for chemical and physical properties (Figure 2-5).
Figure 2-5.
Map of drilled soil borings and excavation of trench in Eldorado Valley, NV.
Table 2-3.
GPS coordinates for coreholes in Eldorado Valley. Boring # 1 2 3 4 5 6 7 8 9 10
Easting 689526 689325 689449 689494 689637 689442 689448 689483 689513 689572
Northing 3978813 3978813 3978847 3978910 3978953 3978952 3978808 3978830 3978856 3978869
Figure 2-6A illustrates the K, Mg, and Na profiles of three borings aligned in a SE to NW direction. The chemical profiles for K and Mg were relatively homogeneous with low K concentrations throughout the profile. The borings indicated a low surface concentration of Na, with concentration increases with depth. On the other hand, P concentrations were high in the surface but decreased considerably at depths below 100 cm (Figure 2-6B). Boring 1 had a relatively homogeneous N profile, with an average N concentration of 7.5 ppm and a maximum N of 16 ppm at 152 cm. Boring 3 also had a relatively homogeneous N profile, with an average N concentration of 6.0 ppm and a sharp increase in concentration of 15 and 10 ppm at 0 and 213 cm, respectively. Boring 6 had an average N 13
concentration of 6.7 ppm, with maximum concentrations of 9 ppm from 0 to 31 cm and 11 ppm at 122 cm. The chemical analyses indicate a layer of elevated N concentrations from 122 to 213 cm in all three borings. The pH and CEC profiles were relatively homogeneous, with averages of 8.4+0.2 and 14.9+1.6 meq (100 g)-1, respectively (Figure 2-6C). The average soluble salt concentrations in borings 1, 3, and 6 were 1.3+0.7, 0.4+0.2, 0.7+0.4 mmhos cm-1, respectively (Figure 2-6D). The average sulfate concentrations in borings 1, 3, and 6 were 87.7+50.5, 16.7+5.1, and 28.0+14.9 ppm, respectively (Figure 2-6D). Boring 1 also contained higher soluble salt and sulfate concentrations below 153 cm.
Figure 2-6.
Eldorado boreholes depth profile of A) K, Mg, and Na; B) phosphorous and nitrogen; C) pH and CEC; and D) soluble salts and sulfate.
A borrow site near boring 1 (classified as Arizo soil) was identified as a desirable site for further soil investigation because of its elevated nitrate concentration at 213 cm ideal for plant growth and higher concentrations of sulfate and soluble salts below 153 cm. DRI thus obtained a second permit to temporarily excavate 2 soil pits, 10 m (l) x 2 m (w) 4 m (h), so that the entire profile would be available for sampling for physical and chemical analyses and for making detailed soil descriptions (Figure 2-7). The site is located on a south-facing alluvial fan composed of reworked fluvially deposited volcanic parent material. The uppermost section of the soil profile is a poorly structured aeolian-deposited sand that grades
14
into a loamy fine sand with gravel clasts and very gravelly sand with gravel lenses. Once the soil pit was completely analyzed and the soil was confirmed as suitable for the lysimeters, DRI obtained a third permit from the City of Boulder City that would allow soil excavation and removal of 80 m3 (100 yd3) of Arizo soil to be installed in the three large weighing lysimeters.
Figure 2-7. 2.4.
Soil pit (3 m deep) excavated in Eldorado Valley.
Layered Excavation and Bulk Density
At the SE corner of the plot shown above in Figure 2-5, two soil pits were sectioned off for excavation (Figure 2-8). Using a backhoe, each layer was carefully excavated and placed in a truck (Figure 2-9A). Another section south of the soil pit was identified to obtain a mix of soil from the entire stratigraphic section (0 to 200 cm), which was designated as the “homogeneous soil.” Although the lysimeters are 300 cm in depth, excavation was stopped at 200 cm because the petrocalcic layer from 200-300 cm made it difficult to excavate the soil any deeper. A homogeneous soil was used to fill the 200-300 cm soil layer in lysimeters 1, 2, and 3. Bulk density was measured at depths 10, 30, 60, 100, 110, 130, 140, 160, 175, and 190 cm ( Figure 2-10C). Within the identified soil layers, the lysimeters were re-packed similarly to these measured bulk densities. The bulk densities for each horizon were measured using the compliant cavity and short core bulk density method. The compliant cavity bulk density measurements were corrected for gravel content by volume. Bulk density measurements were averaged to obtain a target bulk density for each identified soil horizon (Table 2-4). For 25-80 cm soil horizon, 15
the bulk density from the short core bulk density measurement was used. The target bulk density for horizons 1, 2, 3, 4, and 5 were 1.71, 1.74, 1.89, 1.71, and 1.74 g cm-3, respectively.
Figure 2-8.
A) Cavity compliant method to measure bulk density; and B) photo of 0-120 cm Arizo soil profile.
Figure 2-9.
A) Excavation of soil layers to be repacked in lysimeters 1, 2, and 3; B) transportation of soil layers; and C) storage of soil layers in storage containers.
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Figure 2-10.
Arizo depth profile of A) Ca, K, Mg, and Na; B) soil texture; C) bulk density and moisture content; D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfur.
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Table 2-4.
Eldorado Valley bulk density measurements from 0 to 200 cm using the compliant cavity and short core bulk density methods. Soil Layers
Texture
S
Compliant Cavity Method†
Top Bottom Depth --------------[cm]-------------0 0 25 10
Short Core Method
Depth Layer ρb1 ρb2 ρb3 Avg. ρb Avg. ρb -------------------------[g cm-3]------------------------1.74 1.84 1.71 1.77 1.71 1.69 1.63 1.80 1.71
20 30 60 70
1.75 1.74 2.33 1.91
1.72 -2.18 2.41
1.51 -2.19 2.10
1.66 1.74 2.23 2.14
2.12
Depth [cm] 0 10 -40 80 --
Layer ρb4 Avg. ρb -----[g cm-3]---1.71 1.71 1.71 -1.75 1.73 --
25
80
VGS
80
120
100 110
1.83 1.91
2.03 1.87
1.87 1.83
1.91 1.87
1.89
---
---
---
VGLS
120
160
130 140 160
1.66 1.61 1.73
1.52 1.75 1.95
1.77 1.84 1.60
1.65 1.73 1.76
1.71
----
----
----
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S
1.74
175 1.95 1.52 1.88 1.78 ---1.74 190 1.60 --1.60 ----3 †ρT=(1-fv)ρb+(fv)ρd from Russo (1983) where ρd is 2.65 g cm and fv is the depth weighted average course fraction less than 2 mm by volume or 0.214. VGS
160
200
2.5.
Soil Storage
Approximately 80 m3 (100 yd3) of excavated soil was transported from the borrow site (Figure 2-9B) and stored in four weather-tight transportainers at the lysimeter facility (Figure 2-9C). Transportainers were located within 30 m of the lysimeters to facilitate skidsteer loader transference of the soil into the lysimeter. Within each container, partitions were constructed to retain the segregated soil into the identified soil layers. The storage containers served three major purposes: 1) management and storage of the soil until needed; 2) protection from wind and water erosion, particularly removal of fine-grained particles; and 3) denial of access to animal and insect population. Containers 1 and 2 held 12 m3 (16 yd3) and 9 m3 (12 yd3), respectively, of homogenous soil. The back section of container 2 also held 3 m3 (4 yd3) of 0-25 cm soil. Containers 3 and 4 held 3 m3 (4 yd3) each of partitioned soil from the profile depths of 25 to 80 cm, 80 to 120 cm, 120 to 160 cm, and 160 to 200 cm. 2.6.
Soil Physical and Chemical Properties The soil was formed through fluvial reworking and aeolian aggradation and has very little structure and cohesion. The fluvial deposit and aeolian accretion buried an older soil horizon near 200 cm depth. Table 2-5 provides detailed information regarding the borrow site and taxonomic identifications (Soil Survey Staff, 1993). Table 2-5. Soil Series
Arizo
Taxonomic identification of Arizo soil series in Eldorado Valley, NV. Textural Class
VGS VGSL
Family Taxonomic Classification Coarse-loamy, calcareous, mixed, thermic Typic Torriorthents
Horizon
Depth [cm]
Structure
A1
0-2
A
2-160
Bk
160-200
weak coarse granular weak coarse granular massive
C
200-300
massive
The soil physical and chemical properties of Arizo soil were analyzed in 5 cm depth increments up to 200 cm depth. Five distinct layers were identified according to lithology and transitions of major cation concentrations: 0-25 cm, 25-80 cm, 80-120 cm, 120-160 cm, and 160-200 cm (Figure 2-10A). The surface horizon from 0-25 cm was highly bioturbated sand with decreasing Ca and increasing K concentrations with increasing depth. Semi-arid plant roots were pervasive in this low Na stratum. A distinct transition of major cations existed in the underlying soil horizon, from 25-80 cm depth where Ca concentrations stabilized, K decreased, and Na and Mg increased. The soil texture in the 80-120 cm horizon changed to sandy layers interspersed with very gravelly sand layers, as indicated by a maximum bulk density of 1.91 g cm-3 (Figure 2-10C). At this layer, K and Mg stabilized to near constant concentrations while Na increased. The underlying strata transitioned into very gravelly loamy sand layers with elevated Na concentrations reaching a maximum of 16.4% (Figure 2-10B). Poor plant growth was observed when Na exceeded 15% and root development and advancement below 120-160 cm was found to be minimal. Furthermore, Ca reached a minimum of 10.5%.
19
In the 160-200 cm depth horizon, the silt and clay concentrations decreased to the average profile percentages and the horizon consisted of very gravelly sand. This horizon has nearly uniform percentage of high gravel content (Figure 2-10B). The alternating spikes of increased Ca and decreased Mg and Na concentrations support visual evidence of carbonate deposition underneath gravel clasts, which supports the concept of early stage caliche development (Figure 2-10A). Below 200 cm, the profile consisted of more advanced petrocalcic development accompanied with a high gravel fraction. This resistant layer prevented soil excavation to greater depths. In summary, the Arizo soil 0-25 cm horizon had the highest P and N concentrations, averaging 3.6+1.8 ppm (Figure 2-10D). The soil is basic, with pH increasing with depth to the 120-160 cm horizon (Figure 2-10E). The cation exchange capacity (CEC) was also highest where the pH is the lowest at about 175 cm (Figure 2-10E). Also, sulfate is highest at 175 cm depth, because low pH leads to dissolution of gypsum (i.e., Ca concentriaton was highest at 175 cm depth). Possible cause is sulfur mineral oxidation reaction causing drop in pH. Finally, an accumulation of soluble salts and sulfur was observed in the 120-160 cm horizon (Figure 2-10F). 2.7.
Soil Installation
Efficient on-site transportation of soil from the borrow source and installation into the lysimeters with minimal soil loss was important. Thus, a “hopper” (i.e. a large funnel-shaped apparatus) was used to convey soil from ground surface into the lysimeter (Figure 2-11). The hopper was constructed from common lumber, with its overall dimensions tailored to work with the rented skid steer. A PVC pipe was fastened to the hopper channeled soil into the lysimeter, thereby reducing loss of fine-grained sediment (Figure 2-11). Other than moving soil from place to place, the soil was not sieved or treated in any other way, although some stones (greater than 25 cm diameter), were removed from the soil when encountered.
Figure 2-11.
Wooden hopper and connected PVC tube used to funnel soil into the lysimeter.
Lysimeters 1, 2, and 3 were packed as closely as possible to field bulk density values (Table 2-4), with the average thickness of each soil layer added into lysimeters 1, 2, and 3 being 11+3 cm, 9+4 cm, and 10+5 cm, respectively. Before each soil layer was added to the lysimeter, the gravimetric moisture content was measured using a microwave oven (Figure 20
2-12A). A subsample of the soil was dried in a 1200-watt oven at 50% heating capacity for 5 minutes. The total mass of the soil (including the water mass) was determined using the lysimeter weighing scale, and the water mass was removed so that the oven-dry mass would be used for the bulk density. The targeted thickness was then computed by the known target bulk density (thickness = soil mass / cross sectional area / bulk density). Soil compaction was performed manually, but when necessary, a 300 cm2 metal plate was used to compact the soil until the target soil thickness was achieved (Figure 2-12B). Table 2-6 shows the progress and rate of lysimeter filling.
Figure 2-12.
A) Gravimetric moisture content was determined for each soil layer installed in lysimeter using a microwave oven. B) Soil layer was leveled and compacted (with soil compactor when necessary) until required thickness for desired bulk density was reached. Soil thickness was determined using depth markings on lysimeter interior wall.
Table 2-6.
Completed schedule for filling lysimeter 1, 2, and 3 with Arizo soil.
Lysimeter 1 2 3
2.8.
Start to End 03/12-04/07/08 03/26-06/01/08 04/21-05/30/08
Total Days 11 13 14
Avg. Lifts day-1 2.5 2.5 2.1
Max. Lifts day-1 5 4 4
Lysimeter Soil Physical and Chemical Properties
Lysimeter 1 consists of soil collected from 0-200 cm depth. Soil was mixed and homogenized in the field. As expected, the soil texture was nearly uniform for the entire depth in lysimeter 1, except for a peak in gravel content at the bottom (depth equal to 287 cm). The average sand, silt plus clay, and gravel contents were 93.8+0.4%, 6.2+0.4%, and 18.8+5.4%, respectively (Figure 2-14A). The homogenized soil was texturally classified as gravelly fine sand. The average error in bulk density was 1+4%. Therefore, the final bulk density was similar to the target bulk density profile (Figure 2-14B, shown in gray), though minor deviations occurred at 56, 72, and 149 cm depth, which were associated with the presence of the mini-rhizotron tubes (MRT) installed at 60, 100, and 150 cm depths, respectively. The MRTs created 8700 cm3 of void space, and even though this space was accounted for in the calculation of lift thickness, it was difficult to densely pack soil above
21
the tube without the potential for breaking the Plexiglass. The average volumetric moisture content was 4.55+7.2%. With respect to soil chemical profiles in lysimeter 1, the soil chemical profile illustrates a nearly uniform concentration of the major cations (Figure 2-14C). The average concentrations of K, Mg, Na, and Ca were 5.5+0.5%, 8.0+0.4%, 3.7+0.6%, and 82.8+1.0%, respectively. Lysimeter 1 has an average phosphorous concentration of 5.0+3.0 ppm and an average N concentration of 10.4+4.8 ppm (Figure 2-14D), which is higher than the values found in situ Arizo soil. The CEC changes in stepwise manner through each horizon and the pH is nearly constant throughout the profile at 8.3+0.1 (Figure 2-14E). The accumulation of soluble salts and S that was found in the undisturbed Arizo soil at 160-200 cm depth was ultimately homogenized and distributed throughout the lysimeter 1 profile (Figure 2-14F) at a concentration of 0.6+0.1 ppm. The average sulfur concentration of 34.9+8.2 ppm is lower than the average sulfur concentration found in the Arizo soil of 73.9+176.7 ppm (Table 2-7). Lysimeter 2 was repacked with the five designated Arizo soil horizons observed in the field from 0-200 cm depth. The 200-300 cm lysimeter interval was repacked with homogeneous soil consisting of material collected from 0-200 cm in the field. The average sand, silt+clay, and gravel contents were 92.7+2.9%, 7.3+2.9%, and 23.9+11.9%, respectively (Figure 2-15A). Lysimeter 2 was repacked with sand layers, 0-25 cm and 25-80 cm, with higher gravel content than was found in the field. The three layers, 80-120, 120160, and 160-200 cm, had gravel contents that were lower than those found in Eldorado Valley. Furthermore, the gravel lenses were not preserved as indicated by the less steep gravel gradients. The average volumetric moisture content was 4.18+1.13% through the profile. Obtaining the target bulk densities in lysimeters 2 and 3 was more difficult than lysimeter 1, especially in the 120-160 cm depth horizon where the soil was very gravelly loamy sand (Figure 2-15B and Figure 2-16B), though densities from 200-300 cm depth were close to target values. The greatest deviation in bulk density was associated with the MRT installed at 150 cm depth in lysimeter 2 (bulk density errors were 21 to 32%). The shallower tubes installed at 60 and 100 cm depths did not cause large errors in the bulk density. One potential cause of error is the large gravel content from 150-200 cm depth. When the lysimeter was filled with gravelly loamy sand and gravelly sand, the thickness of the lift had a larger standard error of measurement due to the non-uniformity of the layer and it was more difficult to estimate the thickness of the soil layer since coarse gravel (2-7.5 cm) and cobbles (7.5 cm to 25 cm) protruded (Figure 2-13). Also, at 25 cm, there was a bulk density error of 16% and at 0 cm, a bulk density error of -10%. Nevertheless, the average bulk density error was 2+8%.
22
Figure 2-13.
A) Lysimeter 3 at 160 cm depth has large gravel content; and B) close up of 5-10 cm gravel.
The soil chemical profile in lysimeter 2 indicates that the gradual step-wise increase of Na from 0-170 cm depth was retained for each horizon up to 180 cm depth (Figure 2-15A). In addition, the gradual decrease of Ca to 71% of the maximum surface concentration from 0-160 cm depth in the field was retained, as general downward trend to 77% of the maximum Ca concentration at the surface. For Mg, a uniform minimum concentration in 0-25 cm horizon, increasing Mg in 25-80 cm horizon, and relatively uniform Mg from 80-200 cm was retained with a minimum Mg in 0-25 cm horizon and relatively uniform Mg concentration from 25-200 cm. Likewise, instead of a gradual increase and decrease of K in 0-25 cm and 25-80 cm depth horizons, the K concentration decreased stepwise from 0-25, 25-80, and 80-200 cm. Soil chemical concentrations in the bottom homogenized horizon in the lysimeter 2 (from 200-300 cm), were similar to values found in lysimeter 1 (Figure 2-14A and Figure 2-15A). The soil P and N profiles are similar to field Arizo soil with a more pronounced maximum concentration of P and N at the soil surface. Average concentrations of P and N are respectively 5.3+5.6 ppm and 11.8+11.6 ppm (Figure 2-15D; Table 2-7). The pH is uniform throughout the soil profile and averages 8.4+0.3 (Figure 2-15E). The increase in pH and CEC in the Arizo soil found in the field at 120-160 cm depth is absent in lysimeter 2 because the soil was reworked and inadvertently homogenized in the removal, transportation, and repacking process. The CEC profile is similar to the homogenized soil profile installed in lysimeter 1 (Figure 2-14E). Evidence of soluble salts and sulfur accumulation exists in soil found at 160-200 cm depth and is not as prominent as in the undisturbed Arizo soil horizons (Figure 2-15F). The concentration of soluble salts is nearly uniform throughout the soil profile at 0.8+0.7 ppm. The average sulfur concentration of 47.6+50.3 ppm is lower than the average sulfur concentration found in the field Arizo soil of 73.9+176.7 ppm.
23
Figure 2-14.
Lysimeter 1 depth profile of A) soil textural components, B) moisture content and bulk density; C) K, Mg, Na, and Ca; D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. The shaded area represents the target bulk density for each soil horizon. 24
Table 2-7.
Average concentrations of organic matter (OM), phosphorous (P-Weak Bray), pH, cation exchange capacity (CEC), nitrogen (NO3-N), sulfur (SO4-S) and soluble salts in the native Arizo and repacked lysimeter 1, 2, and 3 soils.
Soil Group
OM
P-Weak Bray
-------[%]-------
-----[ppm] -----
pH
CEC
-------[-]-------
Sulfur SO4-S
Soluble Salts
-1
Nitrogen NO3-N
----------------- [ppm]-----------------
[mmhos cm-1]
[meq (100 g) ]
μ
σ
μ
σ
μ
σ
μ
σ
μ
σ
μ
σ
μ
σ
Arizo
0.5
0.1
3.8
3.5
8.7
0.3
15.2
2.4
3.6
1.8
73.9
176.7
1.0
1.4
Lysimeter 1
0.5
0.1
5.0
3.0
8.3
0.1
16.3
1.5
10.4
4.8
34.9
8.2
0.6
0.1
Lysimeter 2
0.7
0.2
5.3
5.6
8.4
0.3
14.9
2.4
11.8
11.6
47.6
50.3
0.8
0.7
Lysimeter 3
0.9
0.1
4.4
5.7
8.4
0.2
14.1
2.8
9.2
8.6
45.1
54.0
0.7
0.8
25
Figure 2-15.
Lysimeter 2 depth profile of A) soil textural components, B) moisture content and bulk density; C) K, Mg, Na, and Ca; C, D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. The shaded area represents the target bulk density for each soil horizon.
26
Lysimeter 3, like lysimeter 2, was repacked with the five designated Arizo soil horizons observed in the field from 0-200 cm depth. The 200-300 cm lysimeter interval was repacked with homogeneous soil consisting of material collected from 0-200 cm in the field. The average sand, silt+clay, and gravel contents were 92.4+3.1%, 7.6+3.1%, and 21.5+14.2%, respectively (Figure 2-16A). Again it was observed that like lysimeter 2, lysimeter 3 was repacked with sand layers, 0-25 cm and 25-80 cm, that were higher in gravel content than field measurements. Furthermore, like lysimeter 2, the three layers, 80-120, 120160, and 160-200 cm, had gravel contents that were lower than those found in the intact Arizo soil in Eldorado Valley. As in lysimeter 2, the gravel lenses were not preserved in lysimeter 3. The average volumetric moisture content was 4.24+1.3% through the profile. Obtaining the target bulk density was challenging, especially in the 120-160 cm depth horizon where there was very gravelly loamy sand (Figure 2-15B and Figure 2-16B), though densities from 200-300 cm depth were close to target values. The greatest deviation in bulk density was associated with the MRT installed at 150 cm depth. The average bulk density error was 1+8%. The soil chemical profile in lysimeter 3 is similar to the profile in lysimeter 2 (Figure 2-15C and Figure 2-16C). However, the stepwise increases of Na and stepwise decrease of Ca and K are more evident. In particular, the major cations in the bottommost soil layer installed at 200-300 cm depth are more uniform and closer to the average concentrations found in lysimeter 1. The average volumetric moisture content was 4.24+1.3% throughout the profile (Figure 2-15C). Phosphorus concentrations in lysimeter 3 soil from 80-160 cm depth differed from the field Arizo soil and soil installed in lysimeters 1 and 2 (Figure 2-16D). The P profile is similar to that found in lysimeter 2, with the maximum P concentration found at ground surface. The pH and CEC profiles are similar to those found in lysimeters 1 and 2 and the CEC continues to change in a stepwise manner with each soil horizon (Figure 2-16E). Lysimeter 3 is similar to the field Arizo and lysimeter 2 soils in the accumulation of soluble salts and sulfur in the 160-200 cm soil horizon.
27
Figure 2-16.
Lysimeter 3 depth profile of A) soil textural components, B) moisture content and bulk density; C) K, Mg, Na, and Ca; C, D) phosphorous and nitrogen; E) pH and CEC; and F) soluble salts and sulfate. The shaded area represents the target bulk density for each soil horizon.
28
3.
MONITORING METHODS AND INSTRUMENTATION A description of the monitoring methods, theory, and selected instruments are detailed in this section. 3.1.
Soil, Water, and Meteorological Variables
To understand the movement and characteristics of unsaturated water in the vadose zone, it is important to obtain gas and water samples, monitor state variables that determine the movement of water, and meteorological conditions that include precipitation, and soil and air temperature. Changes in soil gas concentrations will help us to understand diurnal and seasonal gas diffusion with varying moisture content. Obtaining samples of natural pore water will allow us to understand water chemistry, and collecting samples of tracers will help to track the rate of water movement through the soil. State variables like water content, matric potential, temperature, and thermal properties will help us to understand the soilmoisture regime under different conditions. Water content and matric potential are related in a soil-water retention curve where at saturation, the matric potential is near atmospheric pressure but as the soil dries, the matric potential becomes negative. The rate of water content and matric potential decrease is a characteristic of the soil, and describes the state and flux of soil water. Measuring metrological conditions help us to estimate the total amount of precipitation that enters the soil and the total amount of evaporation. A total of 17 different types of instruments were installed in lysimeters 1, 2, and 3. Table 3-1 lists the abbreviations for each instrument. Water content is measured with the weighing lysimeter, TDR, CS616, ECH2O, DPHP, TPHP, and NAT. Soil water potential is measured by the HDU sensors. Temperature is measured by the STherm, TCAV, SHF, DTS Loops, and DTS Pole. Soil particle movement and root distribution is measured with the MRT. Soil settlement is measured with the settlement plates and scouring probes. Pore water solution is extracted with stainless steel solution samplers (SSSS) and carbon dioxide concentration in the soil is measured with the CO2 sensors. Meteorological data is collected using an extended open path eddy covariance (OPEC) system and a directional anemometer and micro-instrument tower (DAMIT). The instruments and the meteorological measurement parameters are listed in Table 3-3.
29
Table 3-1.
List of instruments installed in each lysimeter.
Abbreviation
Instrument
Measurement Parameter
Electrical Conductivity
Gas and Water Sample
Matric Potential
Physical Properties
Temperature
Thermal Properties
Water Content
Water Content 1
Scale_Kg
2
TDR
3
CS616
Weighing Lysimeter Time Domain Reflectometry Probe Frequency Domain Reflectometry Probe
soil water content soil water content
X
X
X
X
soil water content soil electrical conductivity, temperature, and water content soil temperature, thermal properties, and water content soil temperature, thermal properties, and water content
ECH2O
5
DPHP
Dual-Probe Heat Pulse
6
TPHP
Tri-Probe Heat Pulse
7
NAT
Neutron Access Tube
soil water content
Heat Dissipation Unit
soil matric potential and temperature
30
4
ECH2O-TE Decagon Soil Moisture Sensor
X
X
X
X
X
X
X
X
X X
Matric Potential 1
HDU
Temperature and Thermal Properties 108L Soil 1 STherm Thermistor
soil temperature
X
X
X
X
Table 3-2.
List of instruments installed in each lysimeter (continued).
Abbreviation
Instrument
31
Type E Thermocouple 2 TCAV Averaging Soil Temperature Probe Soil Heat Flux 3 SHF Plates Distributed Temperature Sensing Loops 4 DTS Loops (BRUGG BRUSteel Fiber Optic (4F) Cable) Distributed Temperature Sensing Pole (AFL 5 DTS Pole Telecommunicatio ns Fiber Optic (1F) Cable) Soil Physical Properties Mini-Rhizotron 1 MRT Tube 2 SET Settlement Plate Soil Surface 3 SSAP Alteration Probe Gas and Water Sampling 1
CO2
2
SSSS
3
TCR
CO2 sensor Stainless Steel Solution Sampler Tracers
Measurement Parameter
Electrical Conductivity
Gas and Water Sample
Matric Potential
Physical Properties
soil temperature
Temperature
Water Content
X
ground conduction
X
soil temperature
X
soil temperature
X
soil and root imagery soil settlement soil settlement, and wind erosion
X X X
CO2 concentration soil solution sampling water velocity Total
Thermal Properties
X X X 1
4
1
3
8
3
7
Table 3-3.
Meteorological instruments and measurement parameters for the extended open path eddy covariance (OPEC) system and the directional anemometer and micro-instrument tower (DAMIT).
Abbreviation
Instrument
Measurement Parameter
Extended Open Path Eddie Covariance (OPEC) System: 1
CSAT3
3D Sonic Anemometer
wind speed and direction
2
LI-7500
Open Path Infrared Gas analyzer (CO2 and H2O)
CO2 and H2O concentration
3
HMP45C
Temperature and Relative Humidity Probe
air temperature and relative humidity
4 5
CNR2 TE525/TE525WS
Net Radiometer Rain Gauge
net radiation rain
6
CS616
Frequency Domain Reflectometry Probe
soil water content
7
TCAV
Type E Thermocouple Averaging Soil Temperature Probe
soil temperature
8
HFP01SC
Soil Heat Flux Plates
ground conduction
Directional Anemometer and Micro-Instrument Tower (DAMIT): 1
DAMIT
SHT-75
air temperature and relative humidity
2 3
DAMIT DAMIT
Anemometer Wind vane
wind speed wind direction
3.2.
Water Content
Water content is expressed either as a fraction on a volumetric basis (cm3 water cm-3 of soil) or on a mass basis (g water g-1 soil). There are several ways to directly and indirectly measure water content including the traditional oven drying method, electrical resistance blocks, neutron scattering, gamma-ray absorption, time-domain reflectometry, and remote sensing, amongst others. 3.2.1. Weighing Lysimeter Weighing lysimeters are buried containers of soil resting on scales. They are used to study several phases of the hydrological cycle (e.g. infiltration, evapotranspiration, deep drainage), and soluble constitutents removed in drainage. Lysimeters aim to represent existing soil, vegetation, and climatic conditions to improve the accuracy of measurements of physical processes (Hillel, 1998). As a result, lysimeters are freely floating with the top flush with the soil surface to reduce influences and errors associated with lateral wind shear (Figure 3-1). The change in mass due to water gain and loss through precipitation and 32
evaporation allows determination of evaporation and plant water use. Lysimeters can be as small as 10 cm ID x 10 cm (h) (Fox et al., 2004) and as large as 250 ID x 400 cm (h) (Young et al., 1996). The advantage of large lysimeters is the ability to study water movement and solute transport in deep soil profiles.
Figure 3-1.
Examples of small scale weighing lysimeter (Source: http://www.lysimeter.com).
The SEPHAS lysimeter facility was constructed according to an experimental design detailed in section 1.5. During construction, a single trench (85 m2, 0.01 ha) was excavated to a depth of 4.6 m (15 ft), where four concrete square and circular footings were formed for the lysimeter and wall of the lysimeter room (See Appendix B; Figure B-1 for more information on lysimeter facility construction). The lysimeter housing and underground access tunnel was constructed with corrugated highway culvert (Figure 3-4A). Highway culverts were selected as the primary building material because of its strength and low cost. To construct the lysimeter rooms, corrugated metal pipe (12 GA, 5.49 m diameter) were installed vertically onto each concrete pad, bolted into place, and sealed with concrete to prevent leakage into the lysimeter room (Figure 3-2). For the 3.7 m (12 ft) long tunnels connecting each lysimeter room and for the 21.34 m (70 ft) long main entrance, horizontal culverts, 14 GA 2.44 m (8 ft diameter) corrugated metal pipe were used. The lysimeter rooms are 3.66 m (12 ft) high and the concrete roof is 30.5 cm (1 ft) below the ground surface (Figure 3-3). The main entrance on the south is angled at 6° from horizontal. In addition, vertical culvert 0.91 m (3 ft) in diameter is used on the north side as an alternatve exit and equipped with a vertical ladder. All connections between horizontal and vertical culverts where caulked and bolted together.
33
Emergency exit
NW
Square lysimeter
Lysimeter Rooms
Round lysimeter on scale 70’ Datalogger board
113’6”
5’-6” 12’ 7”
5’ 14 GA 3’ diameter Connecting tunnels Weighing corrugated metal 12 GA 18’ diameter beam pipe w. 5”x1” Floor drain corrugated metal corrugations pipe w. 6”x2” corrugations
SE
8’ Main entrance 5’
7”
14 GA 8’ diameter corrugated metal pipe w. 5”x1” corrugations
Figure 3-2.
Plan view of the underground lysimeter tunnel (not to scale).
Figure 3-3.
Vertical cross section of the underground lysimeter tunnel.
Once in place, the spaces around the culverts and access tunnel were backfilled with soil to prevent settling. The facility is equipped with ventilation ducts in each room and equipped with high-volume fans to improve air flow. Ventilation fans can be controlled individually or through a central switch. In addition, each room can be isolated from the outdoor air, if needed, using heavy plastic sheeting. A key element of the design is 360° access to each lysimeter, allowing researchers to sample the soil environment from all sides of the tank. The distance from the floor of the lysimeter room to final ground surface was designed to accommodate the lysimeter height plus the scale height, so that the tops of each lysimeter coincided with final grade. A ring flashing was installed and bolted to the concrete ceiling, and backfilled with 30.5 cm (1 ft) of soil (Figure 3-4B). This ring flashing keeps soil and rocks from falling into the annular space between the lysimeter tank and flashing, allowing the lysimeter to move freely (Figure 3-4C). A water-resistant ripstop nylon fabric (Joannes Fabrics, Las Vegas, NV) was wrapped over the 13-20 mm (½ -¾ in) free space between the lysimeter and ring flashing to prevent debris from becoming lodged in the free space. The ripstop was held in place by inserting it about 10 mm (4 in) into the lysimeter soil in the lysimeter and 30.5 cm (12 in) into the native soil outside of the ring flashing.
34
Figure 3-4.
A) Construction of underground lysimeter tunnel; B) installation of stainless steel lysimeters; and C) placement of lysimeters on weighing scale.
The lysimeter facility houses four weighing lysimeters. The square lysimeter will be housed in the most northern lysimeter room at a future date and is 2 m (w) x 2 m (l) x 3 m (h). The remaining lysimeter rooms house three cylindrical lysimeters measuring 2.258 m ID x 3 m (h). The lysimeter is constructed of stainless steel (type 304) and was fabricated by Moore’s Blacksmith Shop (Red Bluff, CA). The lysimeters rest on scales (model FS-8, Cardinal Scale Manufacturing Co., Webb City, MO) (Figure 3-4C) and was designed and assembled by Fred Lourence (Precision Lysimeters, Red Bluff, CA) (Figure 3-5). The lysimeter has a live mass of about 28,000 kg. The scale is bolted to the floor (Figure 3-6) and is outfitted with a weigh beam connected to an electronic 45 kg load cell (Model Z-100, Cardinal Scale Manufacturing Co., Webb City, MO).
Figure 3-5.
A photo of a Precision Scale Incorporated manufacturer assembling the lysimeter scale.
35
Figure 3-6.
Cross section of lysimeter tank and scale system (looking west).
The tension load cell is fabricated from aircraft-quality aluminum alloy and potted with sealant that provides water protection for the strain gauges and that has no effect on the precision over a large temperature range. At the heart of a load cell is a strain gauge that changes resistance when it is deformed or stressed. The strain gauge is a resistive transducer and returns a higher resistance when the gauge is under tensile load and lower when under a compressive load. A strain gauge is cemented to the surface of a column within the load cell. As the surface to which it is attached becomes strained, the fine wires of the strain gage wires expand or compress, changing their resistance proportional to the applied load (1:45 kg ratio or 1:99 lb ratio; see section 5.1). The lysimeter load is transferred to the weigh beam which is counterbalanced by the counter weights (Figure 3-7). The load cell is connected to a datalogger (model CR3000, Campbell Scientific, Inc.) and voltage measurements are collected every 0.25 second for 25 s (Figure 3-7), every 15 minutes. Each loadcell was individually calibrated, off the scale, by hanging a series of calibrated weights and measuring the voltage response. Standard error of mass measurements was in the range of ±10 g. Calibration of the scale and loadcell together discussed in section 5.1. Counterweights should be added when the load on the loadcell exceeds 70% of capacity.
36
Figure 3-7.
Load cell connected to weigh beam and data logger while counterbalanced by counterweights made of steel plates. The mass of each plate is known.
Each lysimeter is outfitted with sampling portholes, through which sensors (or sensor wires) can be installed and soil could be sampled (Figure 1-5). A total of 24 portholes were made available at each level and divided into four quadrants. The quadrants were designated as quadrant I, oriented on the NE area of the lysimeter, and rotating clockwise to quadrant IV on the NW area (Appendix A). Each quadrant had a group of six portholes and spaced at 90°. The exception was installation of portholes at 295 cm depth where only six portholes were installed in two groups of three each (spaced 180° apart) in quadrants II and IV. Portholes were spaced 21 cm apart, center to center. Lysimeters 1, 2, and 4 were outfitted with six porthole levels (60, 80, 100, 150, 250, and 295 cm depth) and lysimeter 3 was outfitted with an additional porthole level at 200 cm depth. Lysimeters 1, 2, and 4 have a total of 126 portholes and lysimeter 3 has a total of 150 portholes. 3.2.2. TDR Time-domain reflectometry was established in the 1970’s as a non-destructive method to measure soil water content (Davis and Annan, 1977; Wobschall, 1977; Topp et al., 1980; Wang and Schmugge, 1980). The TDR method is a transmission line technique, and determines the apparent permittivity (εa) of material, using the travel time of an electromagnetic wave that propagates along a transmission line, usually two or more parallel metal rods embedded in soil or sediment. The dielectric constant is strongly related to the soil water content, and is typically expressed using third-order polynomials (Topp et al., 1980; Campbell, 1990; Herkelrath et al., 1991) and semi-empirical four-component dielectric equations (Dobson et al., 1985; Roth et al., 1992). The TDR sensor (model CS 605, Campbell Scientific Inc., Logan, UT) consists of three stainless steel rods (30.5 cm long, 0.48 cm in diameter and 4.5 cm spacing) with large probe head, into which coaxial cable is soldered and potted with epoxy. The probes act as a wave guide. The CS605 probe uses RG58 cable, which is suitable for applications requiring cable lengths of less than 15 m.
37
Figure 3-8.
TDR (CS 605) moisture probe with 3-30.5 cm probes (Illustration from Campbell Scientific Inc.).
3.2.3. CS616 The frequency domain reflectometry sensors (model CS616, Campbell Scientific, Inc., Logan, UT) measures the volumetric water content from 0% to saturation in less than 500 ms. The probe consists of two 30 cm long stainless steel rods 3.2 mm in diameter with a probe head that is 63 mm (l) x 18 mm (w) x 85 mm (h). The probe rods can be inserted from the surface or the probe can be buried at any orientation to the surface. Maximum cable length is 1000 feet (305 m). The sensor contains a bistatic multivibrator that measures the travel time (i.e., the period) for an electromagnetic wave to travel from the sensor head to the probe end, and back. Polarity of the waveguides is changed after each full cycle. The manufacturer supplied quadractic equation provides a +2.5% accuracy for volumetric water contents that range from 0 to saturation for soils with electrical conductivity less than 0.5 dS m-1, and bulk density less than 1.55 g cm-3. The precision is 0.05% VWC and the resolution is 0.1% VWC. The linear equation for period in ms is, VWC = -0.4677 + 0.0283*period.
[3-1]
The equation for the quadratic equation is, VWC = -0.0663 – 0.0063*period + 0.0007*period2. The VWC is in fractional form. The linear calibration is within +1.25% VWC of the quadratic but underestimates at the dry and wet ends and overestimates by 1.2% at 20% VWC. The quadratic calibration equation with a temperature correction was used in this study (see section 5.3).
38
[3-2]
Figure 3-9.
CS616 to measure moisture content.
3.2.4. ECH2O The ECH2O probe is an ECH2O-TE instrument (Decagon Devices, Inc., Pullman, WA) that measures soil water content, electrical conductivity, and temperature using an oscillator at 70 MHz frequency . A thermister is located at the prongs of the probes and provides an average temperature. The gold traces on probe’s surface forms a four-probe electrical array to measure electrical conductivity. The dimensions of the ECH2O are 3.2 cm (w) x 0.7 cm (d) x 10 cm (h), with a prong length of 5.2 cm. The measurement accuracy for volumetric water content and electrical conductivity using calibration curves for mineral soil, rockwool, and potting soil is ±3% VWC and 8 dS m-1; ±3% VWC and 0.5 to 8 dS m-1; and ±3% VWC and 3 to 14 dS m-1, respectively. If the probe is calibrated to a site-specifc soil, the accuracy for volumetric water content is ±1-2% VWC. The temperature range of ECH2O probes are -40 to +50 ºC with a resolution of 0.1 ºC and an accuracy: ±1 ºC. The manufacter standard calibration equation for mineral soil was used in this study and is: VWC = 1.087*10-3*Raw – 0.629
[3-3]
where Raw is the output of the probe sensor. This linear equation provides a comparable fit to higher order polynomials for water contents ranging from 0-35% VWC. The standard calibration curve for dielelectric permittivity is εb = 7.64*10-8*Raw3 – 8.85*10-5*Raw2 = 4.85*10-2*Raw – 10
[3-4]
To determine the pore water electrical conductivity the following equation is used, σp = εp' σb /(εpb' – εσb'=0)
[3-5]
where σp is the pore water electrical conductivity (dS m-1), εp is the real portion of the dielectric permittivity of the soil pore water (unitless); σb is the bulk electrical conductivity (dS m-1). The real portion of the dielectric permittivity, εp, is calculated from the soil temperature as,
39
εp = 80.3 – 0.37 *(Tsoil – 20)
Figure 3-10.
[3-6]
ECH2O or (model ECH2O-TE, Campbell Scientific, Inc., Logan, UT) measures soil water content, temperature and electrical conductivity. The dimensions are 3.2 cm width x 0.7 cm thickness x 10 cm length with a 5.2 cm long probe (Illustration from Campbell Scientific Inc.).
3.2.5. DPHP and TPHP Campbell et al. (1991) introduced the dual-probe heat-pulse (DPHP) method to measure soil volumetric heat capacity and volumetric water content. Triple-probe heat-pulse (TPHP) sensors were developed to achieve the same purpose, but to also measure water flux. The DPHP and TPHP sensors consists of 30 mm long stainless steel needles, 0.9 mm in diameter, and spaced 6 mm apart (East 30 Sensors, Inc., Pullman, WA). The heater probe contains an Evanohm heater and the temperature probe(s) contains a chromel-constantan (type E) thermocouple. For the TPHP, the temperature probe is between two heater probes. After the DPHP or TPHP sensor is inserted into a medium, a current is applied to the heater for 8 s. The temperature rise of the thermocouple is then monitored. The specific heat of the material is inversely proportional to the height of the sensed temperature rise, and the thermal diffusivity of the material is related to the time taken for the pulse peak to pass the temperature sensor. The thermal conductivity can then be computed as the product of the thermal diffusivity and the specific heat.
40
DPHP 30 mm 6 mm
TPHP Temperature Probe Heater Probe
0.9 mm diameter
Figure 3-11.
Dimensions and components of a Dual-Probe Heat-Pulse (DPHP) and TripleProbe Heat-Pulse (TPHP).
Analysis of DPHP and TPHP data was fully described by Young et al. (2008). Briefly, temperature changes at a distance, r, from a heating needle, are estimated using the analytical solution to the heat flow equation for a short-duration heat pulse through an infinite line source (de Vries, 1952; Kluitenberg et al., 1993; Mori et al., 2003):
⎛ − rn2 q' ⎡ ⎛ − rn2 ⎞ ⎟ − Ei⎜⎜ ΔT (r , t ) = ⎢ Ei⎜ 4πCκ ⎣⎢ ⎜⎝ 4κ (t − t o ) ⎟⎠ ⎝ 4κt
⎞⎤ ⎟⎟⎥ + st ⎠⎦⎥
[3-7]
where T is temperature (ºC); t is time from beginning of heating (unit); t0 is time after heating ceases (unit); q´ is the quantity of heat liberated per unit length of heater per unit time (J m−1); C is the volumetric heat capacity (J m-3 ºC); κ is the thermal diffusivity (m2 s-1), which is the quotient of λ/C, where λ is the thermal conductivity (J m-1 s-1 ºC-1); Ei is the exponential integral; and s (oC) is the ambient temperature drift occurring during the DPHP measurements. Thermal conductivity is a function of water content, through (Campbell et al., 1994):
λ=
φ wξ w λ w + φ g ξ g λ g + φ m ξ m λ m φ wξ w + φ g ξ g + φ m ξ m
[3-8]
where φ is the volume fraction; ξ is a weighting factor; and subscripts w, g, and m denote water, gas, and mineral, respectively (i.e., φw is used here instead of the commonly used θv for volumetric water content). The weighting factors ξi and temperature dependencies of parameters in Eq. [3-8] are described in Campbell and Norman (1997). In this approach, a Levenburg-Marquardt (LM) parameter optimization scheme is used with three independent variables, including soil water content (φw in Eq. [3-8]), apparent DPHP needle spacing, rw 41
(equivalent to r in Eq. [3-7]), and ambient temperature drift (s in Eq. [3-7]) in ambient (background) temperature. The approach, which bypasses the need to obtain thermal properties, was shown by Young et al. (2008) to effectively incorporate changes in ambient temperatures, which can be significant in near-surface desert environments, and to yield realistic values of water content and thermal properties. They showed that the new algorithm significantly reduced the water-content fluctuations occurring from background temperature from ±0.05 cm3 cm-3 to ±0.005 cm3 cm-3, and that it could be used to identify the occurrence of precipitation events as low as about 2 mm. Data collected during each measurement includes change in temperature every 2 s for 80 s and the power (q´ in Eq. [3-7]), which is calculated during the 8 s heating period. For these measurements, all values of time (e.g., heating period, change in temperature) are the true time measured at the datalogger and not integer values of time, in seconds; thus, the time needed to switch multiplexers and for the temperature reading is accounted for. No functional difference exists in data analysis for DPHP and TPHP sensors. In the case of TPHP sensors, thermistor needles are located on either side of the heater needle. Thus, two analyses are conducted for each sensor, yielding thermal properties and water contents for discrete depths of 6 mm each. 3.2.6. NAT The neutron scattering method (NAT) was developed in the 1950’s to measure volumetric moisture content. It is easy to deploy, rapid, non-destructive, and repeatable. Furthermore, results are independent of soil temperature and pressure. However, using this method is expensive, can be hazardous to operator health if not properly used, has a low spatial resolution, and is prone to measurement error near ground surface. The probe has a Ra-Be source that emits fast neutrons (mixture of radioactive emitter of alpha particles (helium nuclei) with beryllium. The probe also has a detector that detects slow neutrons. The probe is lowered into an access tube (2 ½ in schedule 40 stainless steel pipe, Ferguson Wholesale Metals, Las Vegas, NV) and the neutrons are emitted radially into the soil where they collide with protons of hydrogen that are nearly equal in mass with the fast neutrons, which are “thermalized” into slow neutrons. The detector measures the density of slow neutrons which is proportional to the concentration of hydrogen or water content in the soil.
42
Figure 3-12.
3.3.
Components of a neutron probe to measure soil moisture including a probe that emits and detects neutrons, a shield and standard, and a scaler to collect data (Image Source: http://www.fao.org/docrep/T0231E/t0231e47.gif).
Matric Potential
Matric potential is defined as the negative gauge pressure, relative to the external gauge pressure on soil water, to which solution identical to soil solution must be subjected to be in equilibrium through a porous membrane wall with the soil water (Hillel, 1998). Other synonymous terms include matric suction, soil-water suction, water potential, and capillary potential. Matric potential is a result of interactive capillary and adsorptive forces between water and soil. Water bound to soil exhibits a lower potential energy than that of bulk water. Water pressure under a free-water surface is greater than atmospheric pressure and is positive in quantity, while water rising in a capillary tube (i.e., soil pores) is at a pressure below atmospheric, and this is negative in quantity. A wide range of methods is available to measure matric potential directly and indirectly, including dew point potentiometer (water activity meter), tensiometers, vapor equilibration, electrical resistance sensors, and heat dissipation, among others (Scanlon et al., 2002). 3.3.1. HDU The heat dissipation unit (HDU) (model 229, Campbell Scientific Inc., Logan, UT) indirectly measures the soil water matric potential from approximately -0.1 to -10 bars (-10 to -1000 kPa or -100 to -10,000 mb) by measuring the thermal conductivity of the HDU porous ceramic. The HDU has a resolution of approximately 1 kPa at matric potentials greater than -100 kPa. The HDU has a heating element and a thermocouple epoxied in a hypodermic needle and encased in a porous ceramic matrix. The HDU is 1.5 cm in diameter and 6 cm in length. A current excitation module applies a 50 mA current to the heating element for 30 s and the thermocouple measures the temperature rise. The amount of temperature rise depends on the water content in the porous ceramic matrix, which changes as the surrounding soil wets and dries. The soil water matric potential is calculated using a power law equation calibrated to a dimensionless temperature rise.
43
to output on CE8 to ground on datalogger to differential on datalogger to ground on CE8 to input channel on datalogger
Figure 3-13.
3.4.
A heat dissipation unit (HDU) (model 229, Campbell Scientific Inc., Logan, UT) is shown on top. The epoxied heating element and thermocouple in hypothermic needle is shown on bottom (Illustration from Campbell Scientific Inc.).
Temperature and Thermal Properties
Soil temperature is important for determining rates and directions of soil physical processes, and energy and mass exchange with the atmosphere. Therefore, monitoring the change of soil temperature in time and space helps to understand these processes. Specifically, temperature drives evaporation, aeration, biological and chemical reactions, seed germination, root growth and development, and microbiological activity. Soil temperature is a function of radiant, thermal, and latent energies. Understanding the propagation of heat into the soil requires knowledge of soil parameters such as volumetric heat capacity, thermal conductivity, and thermal diffusivity. These parameters also depend on soil bulk density and water content. 3.4.1. STherm STherm is a thermistor that measures soil temperature (model 108L, Campbell Scientific Inc., Logan, UT). It can also measure temperature in other media, like air and water, from -5° to +95 °C. The probe consists of a thermistor encapsulated in cylindrical aluminum housing, and is designed for durability and ease of installation and removal.
44
Figure 3-14.
STherm is a soil thermistor (model 108L, Campbell Scientific Inc., Logan UT) that measures the temperature of the soil (Illustration from Campbell Scientific Inc.).
3.4.2. TCAV The TCAV (model TCAV-L, Campbell Scientific Inc., Logan, UT) measures the average soil temperature in the top 6 to 8 cm using four parallel probes and is used in combination with soil heat flux plates. The TCAV consist of four type E thermocouples (chromel-constantan) that come together at a junction in one 24-gauge wire. Each member of a thermocouple pair is buried at different depths. The two pairs are separated at a distance of up to 1 m.
Figure 3-15.
TCAV (model TCAV-L, Campbell Scientific, Inc., Logan, UT) measures average soil temperature using four parallel probes (Illustration from Campbell Scientific Inc.).
3.4.3. SHF The soil heat flux plate (SHF) (model HFP01SC, Campbell Scientific Inc., Logan, UT) measures heat flux, typically as a component within energy balance or Bowen ratio flux systems. The SHF is 8 cm in diameter and 5 mm thick and outputs a voltage signal that is proportional to the heat flux of the surrounding medium. At least two SHFs are required for each site to provide spatial averaging. An on-board heater allows calibration via the "Van den Bos-Hoeksema" method which takes 8 minutes and is performed daily at midnight.
45
Figure 3-16.
Soil heat flux plate (model HFP01SC, Campbell Scientific Inc., Logan, UT) (Illustration from Campbell Scientific Inc.).
To determine the soil heat flux at the surface, two SHF are used to measure heat flux at 8 cm depth, TCAVs measure temporal change in the soil layer above SHF, and a CS616 is used to determine water content (Figure 3-17). Soil heat flux is calculated as the sum of the measured energy flux at a fixed depth and the energy stored in the soil above the plates. The soil specific heat and change in soil temperature, ∆Ts, over a time interval are needed to determine the stored energy. The volumetric heat capacity is determined by adding the volumetric fractions of specific heats of dry soil and soil water, converting specific heats to a volumetric basis through respective bulk densities, and ignoring the heat capacity of air. The heat capacity of moist soil is determined by, Cs = ρb (Cd + θmCw) = ρbCd + θv + ρwCw
[3-9]
where, θm = (ρw/ρb)θv
[3-10]
where Cs is the heat capacity of moist soil, ρb is the soil bulk density, ρw is the water density, Cd is the heat capacity of dry mineral soil (840 J kg-1 K-1), θm is the soil water content on a mass basis, θv is the soil water content on a volumetric basis measured by the CS616, and Cw is the heat capacity of water. Finally, the storage term is determined by Eq. [3-11] and soil heat flux at the surface is given by Eq. [3-12]. S = (∆TsCsd)/t
[3-11]
Gsfc = G8cm + S
[3-12]
46
Figure 3-17.
Placement of heat flux plates (Illustration from Campbell Scientific Inc.).
3.4.4. DTS Digital temperature sensing systems (DTS) were developed in the 1980s for fire monitoring, pipeline monitoring, and other industrial applications (Dakin et. al., 1985; Kurashima et al., 1990; Tyler et al., 2009). In 2006, Raman spectra DTS using fiber-optic cables became a promising and applied tool for hydrologic sciences to track thermal pulses, estimate fluid fluxes, trace surface water-ground water exchange, and estimate groundwater recharge, amongst other uses. DTS systems collects temperature of air, water, and solid media at much greater spatial and temporal resolutions than conventional instrumentation. DTS systems rely on Raman spectra scattering (Smith and Dent, 2006) and the known speed of light within an optical fiber to calculate the average temperature integrated over a specified length of fiber. DTS systems measure temperature in the fiber by pulsing a laser and timing the return signal, thus allowing multiple and frequent temperature measurements rather than a single measurement (Figure 3-18). Commercially available DTS systems can measure temperatures at spatial intervals as short as 1 m and as frequent as 10 s. For more detailed descriptions of the theory and operation of fiber optic DTS instruments, see publications by Selker et al. (2006) and Tyler et al. (2009).
Figure 3-18.
Schematic of DTS system
Many configurations are available for fiber optics including different protective coverings, fillers, and number of fibers. However, two types of fiber optic cables were used in the lysimeters. The first (single fiber [1F], AFL) cable has 1 fiber surrounded by a polymer 47
buffer tube, an Aramid strength member, and an outer protective jacket. The AFL fiber optic (FO) cable is very thin, flexible, and fragile. Great care must be taken to not break the fiber. The second FO cable is a BRUGG FO (4F) cable which has 4 fibers covered with a gel-filled steel loose tube, reinforced with steel wires, and an outer protective jacket. The BRUGG cable is more durable than the AFL cable. The steel wires add memory to the BRUGG cable, requiring careful shaping to allow desired placement and orientation.
Figure 3-19.
3.5.
Cross section showing outer protective jackets and fibers of A) AFL Fiber Optic (1F) cable; and B) BRUGG Fiber Optic (4F) cable.
Soil Physical Properties
Soil is the growth medium for living organisms and interacts with the atmosphere above and the strata below. Soil is the bio-physical-chemical reactor that breaks down waste products into nutrients for microorganisms and plants. Soil is defined as the weathered and fragmented outer layer of the earth and is formed through disintegration and decomposition of rocks by physical and chemical processes (Hillel, 1998). Natural soil is not homogeneous but is continually changing due to its dynamic interactions with the atmosphere and its role as a growth medium. As a result of these processes, soil will begin to develop, age, and form its own characteristics. There are many methods that can be applied to understand soil formation processes through physical, chemical, and biological processes. 3.5.1. MRT Soil and root imagery is obtained using a root scanner (CI-600, CID Inc., Camas, WA), and a an acrylic mini-rhizotron tube (MRT) (6.35 cm OD, 3.1 mm wall thickness). Scanner size is 6.4 cm diameter x 34.3 cm length. The MRT is used to investigate soil particle movement and root growth. The system is a modified scanner, similar to those for flatbed scanners, that allows a head rotation of 345 º. An image is obtained when the scanner head is inserted into the MRT and the scanning program is started on the computer. The scan head will automatically rotate creating an image (21.58 cm (w) × 19.56 cm (l) size with maximum resolution of 1200 dpi or 188 million pixels) of the soil and roots in 5 to 15 s. However, initial scans revealed that the image quality did not improve significantly beyond 200 dpi. The scanner is moved progressively into the MRT to obtain images at different depths.
48
3.5.2. SET Settlement plates are used to determine vertical displacement due to settling by measuring the change in length of a stainless steel cable that is attached to the center of a mild steel mesh plate. As the soil settles, the plate will be pulled deeper into the soil, causing the cable to shorten. The settlement plate is 15.24 cm (l) x 15.24 cm (w) x 0.32 cm thick (6 in (l) x 6 in (w) x 1/8 in thick) and is coated with a thick layer of epoxy to prevent corrosion.
Figure 3-20.
Settlement plate is a mild steel mesh plate (6 in (l) x 6 in (w) by 1/8 in thick) coated with thick layer of epoxy to prevent corrosion and stainless steel cable attached to center.
3.5.3. SSAP Soil surface alteration probes (SSAP) are used to determine settlement and erosion of the soil surface. Each SSAP consists of a round base (80 mm diameter and 6.5 mm thick that is perforated with 6 mm diameter holes that are spaced 25.4 mm apart) connected to a cylindrical rod (10 mm diameter and 200 mm high) (Figure 3-21A). The rod and base are made of Delrin®, and are equipped with nine stainless steel rings spaced 10 mm apart. The top and bottom rings are located 180 and 100 mm, respectively, above the base plate surface (Figure 3-21B). Please note that Figure 3-21A shows a prototype with a larger base diameter (130 instead of 80 mm) than the SSAP installed and has no stainless steel rings.
49
Figure 3-21.
A) Top view of the SSAP prototype (larger base, rod without stainless steel rings); and B) final SSAP design and installation sketch (SSAP designed by John Healey).
Surface settlement is determined by measuring the distance between the top ring on each SSAP and a fixed reference level (L-shaped aluminum bar). Two parameters for each measurement are obtained: 1) distance from the top ring of the probe to the reference level and 2) number of rings visible above the soil surface. For parameter 1, a metal ruler (Figure 3-22A) is used to measure the distance between the top ring of the probe and the surface of the horizontal leg of the L-shaped aluminum rod, representing the reference level. For parameter 2, the number of rings visible above the soil surface is counted (Figure 3-22B). First, a change in distance with time between a marker on the SSAP and the reference level indicates that the SSAP moved vertically up or down relative to the reference level, which indicates vertical movement of the lysimeter soil surface. Second, a change in the number of visible rings indicates soil deposition or erosion on the soil surface. An increase in the number of visible rings indicates erosion, and a decrease indicates deposition.
Figure 3-22.
SSAP in the soil with nine stainless steel rings as markers and A) measuring distance between top ring and reference level with the metal ruler resting on horizontal leg of the L-shaped aluminum rod and B) counting number of visible rings (taken on the lysimeter 1 on Sept. 16, 2008).
50
3.6.
Gas and Water Sampling This section describes the instruments used to measure carbon dioxide in the soil pore and how pore water will be sampled.
3.6.1. CO2 The CO2 sensors are designed to measure carbon dioxide in harsh and humid environments (CARBOCAP Carbon Dioxide Transmitter Series GMT220, Vaisala Instruments, Woburn, MA). The CO2 sensor is easy to install, has interchangeable probes for several measurement ranges, and is easy to maintain. The housing is dust- and waterproof to IP65 standards. The critical parts of the sensor are made of silicon and gives the sensor stability over time and temperature. Model type GMT222 CO2 sensor measures 0 to 3000 ppm CO2 and model type GMT221 measures 0 to 2% CO2 (0 to 20,000 ppm CO2). The output is in volts. A 10:1 voltage divider is used to reduce the output voltage of the transmitter to an acceptable range of the datalogger; and a multiplier is applied to determine the % CO2 [Eq. 3-13] and ppm CO2 [Eq. 3-14]. For more accurate CO2 values, CO2 measurements can be corrected for pressure and temperature.
Figure 3-23.
CO2_pct = CO2_volt * 0.002
[3-13]
CO2_ppm = CO2_pct*1000000
[3-14]
Dimensions and components of CO2 sensors (CARBOCAP Carbon Dioxide Transmitter Series GMT220, Vaisala Instruments, Woburn, MA) (Illustration from Vaisala Instruments).
51
3.6.2. SSSS Stainless steel solution samplers (SSSS) are used to extract pore water from unsaturated soil for water quality analyses. The single-chamber solution sampler (model SW074, Soil Measurement Systems, Tucson, AZ) is made of stainless steel porous tube (2.22 cm OD) with two stainless steel tubes (3 mm OD) inserted into the body of the sampler. There are two lengths of SSSS where the short (top SSSS in Figure 3-24) and long (bottom SSSS in Figure 3-24) samplers have porous cylinders that are 20 and 50 cm in length, respectively. There are are also two lengths of stainless steel tubing that extrude 10.2 and 17.7cm from the porous sampler. The stainless steel tubes are connected to flexible Teflon tubing (0.125 OD and 0.030 ID, model PFA-T2-030-100, Swagelok, Tempe, AZ) using stainless steel connectors (model SS-200-6, Swagelok, Tempe, AZ). The short stainless steel tube is a tube that is used for maintaining vacuum in the sampler and conveying pore water into the porous tube. The long stainless steel tube is a vacuum release tube that is used to release vacuum levels (Figure 3-24). Solutions samplers are placed in the soil at a 10º to the horizontal, with the long tubing at the top, placing the solution withdrawal tubing to be located at the base of the sampler (Figure 4-13B). This orientation facilitates water collection at the closed end of the sampler. Bubbling pressure is approximately 600 cm H2O. The Teflon tubing runs through the portholes and is sealed with compression fittings (model RSP-200-W and RSB200, Remke, Wheeling, IL).
Figure 3-24.
Short and long stainless steel solution samplers (SSSS) with 20 and 50 cm porous cylinders shown on top and bottom. Each SSSS has two stainless steel tubing that extrude 10.2 and 17.7 cm from the porous sampler.
3.6.3. Tracers Tracers play an important role in understanding flow and transport processes in the subsurface. Particularly, tracers are used to measured flow velocity and travel time, flow direction, and hydrodynamic dispersion. Several tracers such as isotopes, anions, fluorobenzoates, polyaromatic sulfonates, and dyes have been recommended and used for hydrological investigations. However, some tracers sorb to sediments and may degrade during the investigation period. An ideal water tracer moves in a manner similar to water (i.e., conservative in behavior), has low background concentration, and is not sensitive to changes in solution chemistry.
52
Isotopic tracers are used by substituting one or more atoms of the molecule of interest with the same chemical element but with a different isotope. It will contain the same number of protons, thus not changing its chemical reaction with other compounds, but the difference in neutrons will allow it to be detected separately from the other atoms of the same element using neutron magnetic resonance spectroscopy (NMR). Once the system is ‘labeled’ with an isotope, tracking its passage through a system is possible. A specific isotopic tracer is nitrogen-15 which is a stable, non-radioactive isotope of nitrogen and is regularly used in agricultural research for plant nutrition and soil fertility. Nitrogen and nitrogen isotopic compositions in soil and water is controlled by biologically-mediated reactions (e.g., assimilation, nitrification and denitrification). Nitrification produces nitrate (NO3-) through the oxidation of ammonium (NH4+) under aerobic conditions. Besides N2 (g), nitrate is the most stable form of nitrogen and is present in most groundwater. Denitrification occurs under anaerobic conditions by bacteria and plant assimilation. Analysis of N-15 with O-18 provides information about nitrates in water and soils. Nitrification:
NH4+ + 2O2 = NO3- + 2H+ + H2O
Denitrification: NO3- + 5/4CH2O = 1/2N2 +5/4HCO3- +1/4H+ +1/2H2O
[3-15] [3-16]
Studies have reported that bromide, pentafluorobenzoic acid (C6F5CO2H or PFBA), and 2,6-difluorobenzoic acid (F2C6H3CO2H2 or 2,6-DFBA) move at a similar rate as water in most soil conditions (Bowman, 1984; Bowman and Gibbens, 1992; Jaynes, 1994). Contrarily, most dye tracers sorb to soils to some extent. The sorption of these tracers (bromide, PFBA, 2,6-DFBA) in Arizo has not been tested. However, many investigators have reported that bromide, PFBA, and 2,6-DFBA showed no sorption under most soil conditions and thus are suitable for tracing water movement in soils (Bowman, 1984; Bowman and Gibbens, 1992; Mayes et al., 2003). Despite non-conservative behavior, dyes are often used as water tracers because of their unique properties—visibility, ease of detection, low background concentration, and simple technology. Among dye tracers tested in sandy soils, Food, Drug, and Cosmetics (FDC) Green No. 3 demonstrated relatively low sorption levels (Mon et al., 2006). For the Arizo soils used in the lysimeters, analyses of sorption isotherms for FDC Green No. 3 shows a maximum sorption capacity of 0.338 mmol kg-1 and a Langmuir coefficient of 10.707 kg-1 (Figure 3-25A). A column experiment, conducted under water-saturated conditions to determine the retardation factor for the dye compared to that of NO3-, showed that the retardation factor for the dye was 1.89, where the retardation factor for NO3- was 0.99 (Figure 3-25B).
53
0.5
1.2
A)
B)
FDC Green No.3
1.0
0.4
NO3
C C0
-1
mmol kg
-1
0.8
0.3
0.6
0.2
0.4
0.1
0.2
FDC Green No. 3 0
0.0
0
0.5
1
1.5
-1
2
2.5
0
mmol L
Figure 3-25.
3.7.
2
4
6
8 10 Pore Volume
12
14
16
A) FDC Green No. 3 sorption curve for 25-80 cm soil. B) FDC Green No. 3 and NO3- breakthrough curves for 25-80 cm soil.
Meteorological Variables
The extended open path eddy covariance (OPEC) system and directional anemometer and micro-insrument tower (DAMIT) are used to measure micrometeorological components needed to measure actual evapotranspiration and to close the energy balance. 3.7.1. OPEC The eddy covariance system consists of a datalogger, a three-dimensional anemometer (model CSAT3, Campbell Scientific, Inc.), a open path infrared gas analyzer (IRGA; model LI-7500, Licor, Inc.), a temperature and humidity probe (model HMP45C, Cambell Scientific, Inc.), net radiometer (model NR-LITE, Kipp and Zonen), tipping bucket rain gage, soil heat flux plates (model HFP01SC, Campbell Scientific, Inc.), soil temperature probes (model TCAV, Campbell Scientific, Inc.), and soil water content sensors (model CS616, Campbell Scientific, Inc.). This system allows measurements of carbon dioxide flux, latent heat flux, sonic and computed sensible heat flux, momentum flux, temperature, humidity, wind vectors (speed and direction), net radiation, soil heat flux, soil temperature, and soil water content. 3.7.1.1. CSAT3 The CSAT3 is a three dimensional ultrasonic anemometer that measures wind speed in three dimensions using three pairs of non-orthogonally oriented transducers to sense the horizontal wind by transmitting and receiving ultrasonic signals (Figure 3-26). The wind speeds are transformed to orthogonal wind components, ux, uy, and uz and are referenced to the anemometer head. The speed of sound (c) or the sonic virtual temperature (Ts) is directly related to the air density, using temperature and humidity, and is determined by combining the out and back time-of-flight measurements. The speed of sound is measured on all three axes and then averaged to find a single value. It is also corrected for crosswinds or wind blowing normal to the sonic measurement path. By measuring the average wind speed and direction, the turbulent fluctuations of horizontal and vertical wind speeds and momentum flux can be determined. The sensible, latent heat, and carbon dioxide flux can also be determined using the covariance between the humidity and wind speed. The anemometer head is 47.3 cm (l) x 42.4 cm (h) and weighs 1.7 kg (3.7 lb). The transducers are 0.64 cm
54
(0.25 in) diameter and 60° from the horizontal. The right-handed orthogonal coordinate system is oriented with the CSAT3 pointing into the negative x direction, so that if the anemometer is pointing into the wind, it will report a positive ux wind. Measurement resolution for ux, uy, uz, and c are 1, 1, 0.5, and 15 mm s-1 and the reporting ranges are 32.8, 65.5, 8.2, and 300 mm s-1, respectively.
Figure 3-26.
CSAT3 three dimensional sonic anemometer (Campbell Scientific Inc., Logan, UT) (Illustration from Campbell Scientific Inc.).
3.7.1.2. LI-7500 The LI-7500 (model LI-7500, LI-COR Biosciences, Lincoln, NE) is a high speed precision, non-dispersive infrared gas analyzer that measures the concentration of carbon dioxide and water vapor. It is used with a sonic anemometer to determine CO2 and H2O fluxes. The flux, Fc of a gas c, is given by Fc = w’pc’ where c’ is the density fluctuations of the gas (mmoles m-3) and w’ is the vertical wind velocity fluctuations (m s-1). The precision is 0.16 ppm CO2 and 0.0067 ppt H2O.
Figure 3-27.
Components of the LI-7500 (model LI-7500, LI-COR Biosciences, Lincoln, NE) (Illustration from LI-COR Biosciences).
55
3.7.1.3 HMP45C The HMP45C (model HMP45C, Campbell Scientific, Inc., Logan, UT) is a temperature and relative humidity probe that measures the air temperature and relative humidity using a Platinum Resistance Temperature detector (PRT) and a Vaisala HUMICAP® 180 capacitive relative humidity sensor (Figure 3-28). It is 25.4 cm (10 in) long with a 2.5 cm (1 in) body diameter, operates from -40° to 60°C and has a relative humidity accuracy of +2-3%. Measurements can be used to determine air and vapor density.
Figure 3-28.
HMP45C temperature and relative humidity probe (model HMP45C, Campbell Scientific, Inc., Logan, UT) (Illustration from Campbell Scientific Inc.).
3.7.1.4 Net Radiometer The net radiometer is a high-output thermopile sensor which measures incoming and outgoing long- and short-wave radiation (model NR-LITE Net Radiometer, Campbell Scientific Inc., Logan, UT). Incoming radiation includes direct (beam) and diffuse solar radiation and long-wave irradiance from the sky. Outgoing radiation includes reflected solar radiation and terrestrial long-wave radiation. The result is a measure of the total net radiation (W m-2) using the difference between incoming radiation measured by the upward facing sensor and the outgoing radiation measured by the downward facing sensor. The sensor is 8.0 cm (3.1 in) in diameter and weighs 635 g (23 oz) with a spectral range of 0.2 to 100 μm and a directional error less than 30 W m-2.
Figure 3-29.
NR-LITE net radiometer (model NR-LITE, Campbell Scientific Inc., Logan, UT) (Illustration from Campbell Scientific Inc.).
56
3.7.1.5 Rain Gage The tipping bucket rain gage (model TE525WS-L, Campbell Scientific, Inc., Logan, UT) measures in 0.254 mm (0.01 in) increments with an accuracy of +1% at rates up to 2.54 cm hr-1. Precipitation funnels into a bucket that tips over when filled and a magnetic switch is activated to count the number of tips using a datalogger to determine the volume. The rain gage has an orifice diameter of 20.3 cm (8 in), a height of 26.7 cm (10.5 in), and is made of anodized aluminum.
Figure 3-30.
TE525WS-L Texas Electronics 8in rain gage (Illustration from Campbell Scientific Inc.).
3.7.1.6 CS616 Please see section 3.2.3. 3.7.1.7 TCAV Please see section 3.4.2. 3.7.1.8 SHF Please see section 3.4.3. 3.7.2. DAMIT The directional anemometer and micro-instrument tower (DAMIT) is a near-ground surface monitoring system that consists of 1) wind sentry set that measures wind speed and direction 10 cm above ground surface; and 2) a temperature and relative humidity microinstrument tower made from several stacked relative humidity and temperature sensors (model SHT-75, Sensirion, Westlake Village, CA) located at 5, 10, 25, and 50 cm above the ground surface (igure 4-19). The system is installed outside of lysimeter 3. The wind sentry set has a 3-cup anemometer and wind vane that is mounted on a crossarm (model 03002 wind sentry set, Campbell Scientific, Inc., Logan, UT). The rotation of the anemometer cups produces an AC sine wave that is proportional to wind speed. The frequency of the AC signal is a pulse counted converted to wind speed using a datalogger. The range of wind speed measured by the anemometer is 0 to 50 m s-1 (0 to 112 mph) with an 57
accuracy of +0.5 m s-1 (+1.1 mph). The wind direction is determined by the potentiometer that is connected to the 360° rotating wind vane. The output signal is an analog voltage that is directly proportional to the azimuth of the wind direction. The wind direction accuracy is +8°.
Figure 3-31.
A 3-cup anemometer and a wind van mounted on a cross arm (model 03002 wind sentry set, Campbell Scientific Inc., Logan, UT) (Illustration from Campbell Scientific Inc.).
The micro-instrument tower has four relative humidity and temperature sensors. The sensors integrate elements with signal processing in a very compact form, while providing a fully calibrated digital output. Relative humidity is measured with a unique capacitive sensor element and has an accuracy of +3.0% with a typical resolution of 0.05%. Temperature is measured with a band-gap sensor and has an accuracy of +0.4°C, a range from -40 to 123.8°C, and has a resolution of 0.01°C.
58
Figure 3-32.
Dimensions of relative humidity and temperature sensor SHT75 (Illustration from Sensirion).
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60
4.
INSTRUMENT LAYOUT AND INSTALLATION
Approximately 152 sensors and instruments were installed in each lysimeter. Table 4-1 lists the number of each sensors in each lysimeter. In addition to the sensors installed in the lysimeter, immediately east of the main room for lysimeter 3, six TDR probes were installed in the adjacent natural soil at 50 cm depth intervals from ground surface, and nine thermocouples were installed at depths of 5, 10, 25, 75, 100, 150, 200, 250, and 300 cm (Table 4-2 and Table 4-3). These sensors were installed so that conditions inside the lysimeter tank could be directly compared to conditions in undisturbed soil. The DAMIT system is installed immediately above lysimeter 3, on the north side, and an OPEC system is installed 112 m W of lysimeter 1.
Figure 4-1.
Aerial photograph of the lysimeter facility in Boulder City, NV, showing location of instruments installed in adjacent natural soil (yellow star), OPEC (blue triangle) and DAMIT (light blue box).
61
Table 4-1.
Catalogue and number of instruments at each depth for each lysimeter.
Depth [cm]
CO2
0
1
CS616†
DPHP
DTS Loops
DTS Pole‡
HDU
1
2
1
NAT‡
1
1
SET
SHF
SSAP
4
50
1
4
1
STherm
TCAV
TDR
TPHP 4&
1
1
2
2
1
4
1
4
4
4
1
4
4
4
1
4
2
2 4
2**
4
4
2
4
4
2
2
2
2
2
2
62
4
250
2
1
2
290
9 8
6
3
1
14
8
1
2
32
3
1
1
6
2
3
26
6 4
† - CS616 was installed only in lysimeter 1. ‡ - One vertical tube extends through entire depth of lysimeter. & - This is a TPHP Cluster between 0-5 cm at approximately 1 cm interval. * - In Lysimeter 1, there is a total of 8 TPHPs instead of 18 and 24 DPHPs instead of 14. This results in 2, 2, 4, and 4 DPHPs at 10, 25, 50, and 75 cm. ** - DTS loop is at 95 cm.
18
17
2 2
18
2
1
190 200
15
17
2 4
15
3
1
140 150
11
2
4
1
100
Subtotal
9 4
1
75
Subtotal
SSSS
3
4 2
90
MRTv‡
4
25 60
MRTh
1
5 10
ECH2O
1
26
18
152
Table 4-2.
Comparison of temperature measured in lysimeter and adjacent natural soil east of lysimeter 3 (data stored as BC_Lys3_TC.dat). Gray cells indicate temperature measured at same depth. Adjacent Natural Soil
Lysimeter Soil Depth [cm] 0 5 10 25 50 60 75 90 100 140 150 190 200 250 290 Subtotal
DPHP
DTS Loops
DTS Pole‡ 1
ECHO
1 1 2 1
HDU
STherm
TCAV
TPHP
TC
4& 2
4 4 4 4
1
1
1 1
4 4 4
1 1 1
1
2
1
2
1
4
4
2
4
1
4
1
4
1
2 2
1 1 1**
2 2
14
9
1
2
32
4
1
18
9
† - CS616 was installed only in lysimeter 1. ‡ - One vertical tube extends through entire depth of lysimeter. & - This is a TPHP Cluster between 0-5 cm at approximately 1 cm interval. * - In Lysimeter 1, there is a total of 8 TPHPs instead of 14 and 24 DPHPs instead of 14. This results in 4, 4, 2, and 2 DPHPs at 75, 50, 25, and 10 cm. ** - This thermocouple is actually at 300 cm depth. Note: This data is collected in BC_Lys3_TC.dat.
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Table 4-3.
Comparison of temperature measured in lysimeter and adjacent natural soil east of lysimeter 3. Gray cells indicate water content measured at same depth. Lysimeter Soil
Depth [cm] 0 5 10 25 50 60 75 90 100 140 150 190 200 250 290 Sub-total
Adjacent Natural Soil
CS616†
TDR
TDR
1
4 4 4
1
4 2
1
4
1
2 2
1
26
3
† - CS616 was installed only in lysimeter 1. ‡ - One vertical tube extends through entire depth of lysimeter. & - This is a TPHP Cluster between 0-5 cm at approximately 1 cm interval. * - In Lysimeter 1, there is a total of 8 TPHPs instead of 14 and 24 DPHPs instead of 14. This results in 4, 4, 2, and 2 DPHPs at 75, 50, 25, and 10 cm.
4.1.
Instrument Nomenclature
An identification system was developed for instrument placement and portholes through which instruments are wired. It was necessary to classify portholes so that wiring of instrument cables could be identified, especially given that no portholes are shallower than 60 cm; thus, instruments were not necessarily strung through a porthole adjacent to the instrument. The location of the sensors is identified by lysimeter room#_depth_quadrant. For example, an instrument placed at 50 cm depth in the NW quadrant of lysimeter 1 (or room 1) is identified as being located at R1_50_NW. Portholes are identified by lysimeter#_quadrant/port#_depth. Portholes are numbered one to six in a clockwise direction for each of four clusters for each depth. For instance, a port numbered R1_NW1_D295 identifies a porthole located in lysimeter 1 in the NW quadrant and is the first porthole in the quadrant at a depth of 295 cm (Appendix C). Placement of instruments was accomplished using plan view maps showing instrument type and serial number for each depth (Appendix C, Figure C-1). A table is attached to each map designating the port through which the instrument cable is wired (Appendix C, Table C-1 to 64
Table C-17). 4.2.
TDR, FDR, ECH2O, DPHP, TPHP, HDU, Stherm, TCAV, SHF, and SSSS
Above 25 cm, instrument were placed at 0, 5, and 10 cm depths. The vertical spacing between instrumented layers from 25 to 100 cm depth is 25 cm and between 100 cm and 300 cm depths, the vertical spacing is approximately 50 cm. The typical suite in an instrumented layer includes 16 sensors placed in the horizontal plane with four sensors in each of four quadrants: an HDU, a TDR, either a DPHP or a TPHP, and an SSSS (Figure 4-2; Figure C-1M). Instruments were positioned radially toward the center of the lysimeter with the center of the instrument at 56.5 cm (half the lysimeter radius) from the wall of the lysimeter. Full instrument suites exist at four depths between 50 and 150 cm (Figure C-1F, H, K, and M). At depths of 200 and 250 cm, instruments were installed using only two quadrants at each depth. At 200 cm, sensors were placed in quadrants II and IV (SE and NW respectively) (Figure C-1O) and at 250 cm, sensors were placed in quadrants I and III (NE and SW, respectively) (Figure C-1P). Near the ground surface, instrument density was higher because a greater spatial and temporal variability of heat and moisture occur in shallower soil zones. The shallowest instruments were placed at approximately 5 cm depth (Figure 4-3). This instrument suite has one HDU in each quadrant; an ECH2O, SHF and corresponding TCAV in quadrants I and III; a soil thermistor (STherm or model 108L) positioned between quadrants I and II on the eastwest centerline; and a TPHP cluster or “Titanic” in quadrant II (Figure 4-3; Figure C-1C). The Titanic is oriented vertically instead of in the plane and has four TPHPs aligned in a downward trend. Certain instruments, such as the soil heat flux plates (SHF) and averaging thermocouples (TCAV) were only placed near the surface (i.e., upper 10 cm of soil) (Figure 4-4). Lysimeter 1 included a CS616 which was not installed in lysimeters 2 and 3. The FDR was placed in SW quadrant (quadrant III) at the same depth as the SHF to measure moisture content (Figure 4-4). Also, at depths of 10 and 25 cm, solution samplers were not used so the instrument suite consisted of an HDU, a TDR, and either a DPHP or a TPHP in each quadrant (Figure C-1D and E). TPHP sensors allow for the measurement of water content and thermal properties at 6 mm intervals, and therefore, were installed at shallower depths where possible. In addition, the TPHP sensors became available during soil installation phase, and thus the decision was made to augment the DPHP instruments with TPHP sensors in the top 75 cm.
65
Figure 4-2.
Full instrument suite in lysimeter 1 at 50 cm.
Figure 4-3.
Instrument placement in lysimeter 1 at 5 cm
Figure 4-4.
Instrument placement in lysimeter 1 at 10 cm.
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4.3.
DTS, NAT, and vertical MRT
Three types of instruments were installed through the entire depth of the lysimeters: neutron probe tube, DTS pole, and the vertical MRT (Figure 4-5). The neutron access probe was installed vertically (with 6.35 cm of NAT protruding from the filled lysimeter surface) and the vertical MRT was installed at a 31º angle to the vertical. This offset from vertical was made to improve viewing of roots as they grow through the soil.
Figure 4-5.
4.4.
DTS Pole, Vertical Mini-Rhizotron Tube (MRT), and Neutron Access Tube (NAT) extend through the entire vertical depth of each lysimeter. DTS pole, vertical MRT, and NAT installed in empty lysimeter 2.
DTS
The AFL FO (1F) and BRUGG FO (4F) cables were installed in the each lysimeter. The AFL FO cable was wrapped around a vertical pole to measure high-resolution vertical temperature gradients, and the BRUGG FO cable was placed in loops at 6 depths to measure temperatures on a horizontal plane at regular depths. The DTS pole is a threaded 5.08 cm (2 in, nominal) schedule 40 PVC pipe wrapped with a 900 micron cable containing a simplex tight-buffered 50/125 multi-mode fiber (AFL Telecommunications, Duncan, SC). The pipe, 6.03 cm (2.375 in) OD, has standard threads with a thread pitch of 4.5 threads per cm (11.5 threads per in). The inside of the pipe is filled with insulating foam to prevent internal heat convection from interfering with temperature measurements (Figure 4-6A). The fiber is seated in the threads, with a diameter of rotation similar to the outside diameter of the pipe. This configuration, shown in Figure 4-6B, provides a vertical resolution of 1.17 vertical centimeters per meter of optical fiber.
67
Figure 4-6.
DTS pole showing A) insulation foam; and B) threat pitch of 4.5 threads per cm glued onto schedule 40 PVC pipe.
DTS loops were installed to study the wall effects on the thermal regime of the lysimeters. DTS loops are concentric loops at different depths of BRUSteel (BRUGG) Flexible Mini Fiber Optic Cable 4F (an armored cable with duplexed loose-tube 50/125 multi-mode fibers, Brugg Kabel, Brugg, Switzerland). The diameter of the inner loop is 1 m and outer loop is 1.5 m. The depths of installation of the inner loops in the lysimeter are 5, 25, 50, 75, 95, and 200 cm. Outer loops are present at 25 and 95 cm depths. The different diameter loops were used to estimate the potential for lateral heat movement between the soil and the lysimeter wall. Each loop was centered as closely as possible in the lysimeter, with the larger loop placed 13 cm from the lysimeter wall, and the smaller loop placed 38 cm from the wall (Figure C-1J). Each fiber optic DTS deployment includes a free length of cable (approximately 30–50 m) on either end of the measurement area (at the bottom 150 cm porthole and at the top 60 cm porthole). This length of cable is required to calibrate the installation prior to making any measurements. A schematic diagram of the DTS loops are shown in (Figure 4-7) and a photo of the DTS loops during installation is shown in Figure 4-8.
Cable end 1
5 cm 25 cm
Outer loop (d=2 m)
50 cm 75 cm
Inner loop (d=1.5 m)
95 cm 200 cm
Figure 4-7.
Cable end 2
Installation design for optical fiber loops.
68
Figure 4-8.
4.5.
DTS loops being installed at 95 cm in a lysimeter 2.
MRT
Three horizontal (MRTh) and one vertical (MRTv) were installed in each lysimeter. Lysimeter ports were modified to accommodate the larger diameter clear acrylic pipe through the lysimeter. The MRTh were installed at depths of 60, 100, and 150 cm, oriented NW to SE through porthole 3. The MRTv was placed at a 31° angle from SW porthole 4. While soil was being installed, the extruding portion of the MRTv was protected with rubber tubing to prevent accidental scratching. However, the MRTv in lysimeter 1 broke at 50 cm depth and was repaired with a clear outside sleeve (ca. 6.35 cm ID x 50 cm long). After installation, the complete length of inside each MRT was cleaned with a terry cloth and mild soap to remove excess soil and the ends were covered with a plastic cap to prevent dust from entering the tube. MRTh were cut such that 10 cm length was extruding from the lysimeter wall. The MRTv was cut approximately 10 cm above ground and then covered with a painted-white PVC tubing to protect the MRT from the sun and other disturbances.
69
Figure 4-9.
4.6.
Repaired vertical MRT with sleeve at 50 cm depth in lysimeter 1 and broken MRT that was removed and replaced with repaired MRT.
SET
Two settlement plates each were placed at depths of 90, 140, and 190 cm in quadrants 1 and 3, 2 and 4, and 1 and 3, respectively, and their cables were fed through portholes at 60, 100 and 150 cm, respectively (Figure 4-10). The distances of the SET from the inner wall of the lysimeters were 22.5, 30, and 30 cm, respectively, meaning that the angles of the cables were 36.9º in each case. The cable was placed inside a rigid outer tubing or “cable housing” so that the cable could move freely while being held in place with the compression fitting in the porthole. Approximately 100 mm of cable protrudes perpendicular out of the lysimeter and is periodically measured using a digital caliper. The digital caliper is placed parallel to the cable, so that the caliper’s inner diameter measurement stops are against the inside portion of the cable housing and cable tip (Figure 4-11). Light tension is placed on the caliper adjustment wheel to make sure the cable is straight. (Note: The cable at 60 cm depth is not perpendicular because the frame structure of the lysimeter room obstructs it and because measurements are made at an angle, it is more prone to error.)
70
Figure 4-10.
Two settlement plates in the SE and NW quadrants at 190 cm depth in lysimeter 2.
Figure 4-11.
Photo of caliper instrument to measure settlement plates.
71
Table 4-4.
Initial caliper measurements of settlement plates taken on June 12, 2008.
Settlement Plate NE_60 cm SW_60 cm NW_100 cm SE_100 cm NE_150 cm SW_150 cm
4.7.
Lysimeter 1 105.22 101.97 102.48 104.43 102.10 101.75
Lysimeter 2 Lysimeter 3 -------------------[mm] ------------------102.40 102.48 100.47 101.60 102.68 100.71 101.47 102.93 103.57 103.86 100.43 101.70
SSAP
All nine SSAPs were installed on Jul. 18, 2008 with the upper surface of the base plate installed 100 mm beneath the soil surface. The L-shaped aluminum bar used as fixed reference level was attached to the ring flashing of the lysimeter when measurements were taken (Figure 3-22A; Figure 4-12). Table 4-5 provides the values of the initial measurement.
Figure 4-12.
Arrangement of SSAP 7, 8 and 9 in lysimeter 3 with aluminum rod across the lysimeter surface as reference base (on Sept. 16, 2008).
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Table 4-5.
Initial measurements of SSAPs from Jul. 18, 2008. Lysimeter 1
2
3
4.8.
SSAP #
Distance [mm]
# Rings
1
55
8
2
57
9
3
58
9
4
55
8
5
55
8
6
58
8
7
40
9
8
48
8
9
48
8
SSSS
Four 20 cm length stainless steel solution samplers (SSSS) were installed at 50, 75, 100 and 150 cm and two 20 cm long SSSS were installed at 200 and 250 cm depth. Six SSSS of 60 cm length were installed at 290 cm depth. The sole purpose of the shorter SSSS is to extract pore water while the long SSSS near the lysimeter bottoms are primarily intended to create a specified constant matric potential using an applied vacuum in dry soils (Figure 4-13A and Figure C-1). The samplers were oriented with the closed end towards the center of the lysimeter and the open end (of stainless steel tubes) towards the sample porthole. Flexible 1 /8” Teflon tubing is attached to the short and long stainless steel tubings with one pair wired through an adjacent porthole. The mid-points of SSSS were placed 57.5 cm from the lysimeter wall (50% of the radius) (Figure 4-13B). The solution-vacuum manifold consists of a panel of 40 mL glass vials for pore water collection and one schedule 80 ½-in PVC pipe connected to a vacuum pump to establish a vacuum (Figure 4-14). The vacuum manifold was constructed so that solution samplers could be placed under vacuum individually, thereby allowing targeted pore water sampling by depth or quadrant. Each lysimeter has two solution-vacuum manifolds that are attached to the lysimeter at 180º from each other. Each PVC pipe was drilled and tapped to ¼-in National Pipe Thread (NPT) to accommodate ¼-in vacuum valve (model HVN2-N2U, PISCO, Bensenville, IL). Each manifold is connected to a central GAST vacuum pump thereby, placing both manifolds under identical vacuum levels. Compression fittings were secured on to the vacuum control valves situated along the length of the PVC pipe at the same depths as the portholes (i.e. one for each solution sampler).
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Figure 4-13.
A) 50 cm long stainless steel solution samplers are installed at 295 cm depth in lysimeter to create a vacuum; and B) using a wooden block to place stainless steel solution samplers at a 10° angle.
Figure 4-14.
Stainless steel solution sampler manifold attached to one side of the lysimeter. There are two SSSS manifolds per lysimeter.
From the SSSS, the vacuum/extraction line is routed through a two-hole rubber stopper to a glass vial mounted along-side a vacuum manifold (Figure 4-15). The vacuum release tube is connected to flexible Teflon tubing and sealed off with a pinch valve. A short length of Teflon tubing is routed through the other hole in the rubber stopper and is terminated into a compression fitting.
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The two lines from the solution samplers are routed as follows: •
The short steel tube or the “extraction tube,” is connected to tubing (flagged red) and is routed to the vial;
•
A Teflon tube connects the vial to the vacuum manifold; and
•
The long steel tube or the “vacuum release tube” is connected to Teflon tubing (flagged white) and sealed with a pinch valve.
Figure 4-15.
Routing of SSSS to solution manifold.
At the time of this writing, no pore water was available for sampling. However, the plan to operate the system is known, with operations to differ depending on purpose. The bottommost samplers are used to control unsaturated conditions. Thus, the vacuum level will be set at the approximate soil water potential as measured in the soil, in this case at 250 cm depth where the deepest HDUs are located (Table 4-1). In this way, the soil water potential would be approximately equal toward the base of the lysimeter, thus creating unit gradient conditions in the soil. Samplers installed at shallower depths, to be used specifically for sampling and analyzing soil pore water, will be placed at a vacuum level that facilitates a hydraulic gradient toward the sampler, such that water will collect in the porous tubing. 4.9.
Tracers
Tracers will be used to determine the hydrodynamics of the lysimeter soil and to track the upward and downward movement and the mixing behavior of water in soil profiles. Five different tracers are applied on the lysimeter at 6 different depths including N-15 at 10 ppm at 5 cm in lysimeter 1 when the experiment starts; 2,6 DFBA and PFBA at 15 and 30 cm in all lysimeters; Br at 40 cm in all lysimeters; FDC Green No. 3 at 55 cm in all lysimeters; and N-15 at 210.5 ppm in lysimeter 2. Table 4-6 lists each tracer, depth of application, chemical form, amount of water used for solution, and target concentration. The amount of mass
75
needed for each tracer is determined using a volumetric water content of 0.20 cm3 cm-3 and an assumed minimum detectable tracer concentration. The tracer is dissolved in deionized water. A wire mesh with 178 squares is placed in the lysimeter over the soil and the solution volume is divided equally among all squares using precision pipettes (Eppendorf pipette, Eppendorf North America, Hauppauge, NY). See Appendix D for specific calculations.
Figure 4-16.
Tracer application in lysimeter 1 of A) FDC Green No. 3 at 55 cm; and B) PFBA at 30 cm.
Table 4-6.
Depth and mass of tracers applied in lysimeters 1, 2, and 3.
Lysimeter
Depth [cm]
Tracer
Mass [g]
Chemical Form
1
1†
5
N-15
0.7
KNO3
Water [mL] 1000
2
1,2, and 3
15
2,6-DFBA
2.4
2,6-DFBA
1000
1 mg L-1 2,6 DFBA
3
1,2, and 3
30
PFBA
2.4
PFBA
1000
1 mg L-1 PFBA
15.01
Br
1000
5 mg L-1 Br
12.01
Br
1000
5 mg L-1 Br
12.01
FDC Green No. 3 (dye)
1050
5 mg L-1 Br
5
dry CaNO3
0
49.2
N-15 (KNO3)
890
4
1 2 and 3
40
Br‡
5
1,2, and 3
55
FDC Green No. 3 (dye)
6
2
150-200
N-15
Target Conc. 10 ppm N-15
210.5 ppm N-15
† Applied only lysimeter 1 when experiment begins. ‡ Calculated 80% of Br- in KBr-
To study the interaction between vegetation and the nitrogen reservoir in deserts of the southwestern U.S. (Walvoord et al., 2003), a 50 cm layer of N (200 mg kg-1 N) was created in lysimeter 2 using a total mass of 5.0 kg CaNO3 and 49.2 g N-15 (KNO3). The
76
compounds were divided into 5 equal portions. After each 10 cm thick layer of soil, CaNO3 prills (3 mm diameter) were broadcast on the surface. The dissolved N-15 was injected on the surface, dried, and mixed gently using a garden claw. The procedure was repeated five times to make a 50 cm thick layer of spiked N. Zones of application are given in Figure 4-17. In lysimeter 1, 0.3 g (1% of the total N in the soil) of N-15 as KNO3 will be applied at the soil surface when the experiment begins. This facilitates the study of the fate of N-15 in desert soils under natural conditions. 150 cm
15 cm of N-15
50 cm of N-15
285 cm
100 cm
Lysimeter 1 Figure 4-17. 4.10.
Lysimeter 2 Schematic of N-15 application in lysimeters.
OPEC
The OPEC is located 28.5 m (93.33 ft) directly west of the southern midpoint of lysimeter 1. The rain gage and radiometer is located 3.2 m (10 ft) south and 3.2 m (10 ft) east of the eddy covariance tower.
Figure 4-18.
Different instruments and components of the open path eddy covariance (OPEC) system.
77
4.11.
DAMIT
DAMIT was installed in fall 2008 with the micro-instrument tower directly above north side of lysimeter 3 (Figure 4-19). The directional anemometer is located 2.7 m (8.9 ft) north of the micro-instrument tower (Figure 4-20). These sensors provide a better measure of the environmental conditions that are affecting the lysimeters.
Figure 4-19.
Directional Anemometer and Micro-Instrument Tower (DAMIT).
Figure 4-20.
Location of OPEC (blue triangle) and DAMIT (light blue rectangle) at the SEPHAS lysimeter facility.
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5.
INSTRUMENT CALIBRATION
5.1.
Weighing Lysimeter
Load cells were calibrated individually in the laboratory by incrementally adding known weight and regressing total weight to voltage output. The voltage output for the load cell with no weight was used as the offset. Then, the load cell was hung on a steel bar with its tension and compression hook, and steel plates were hung onto the bottom of the load cell to create the vertical load (Figure 5-1). Each steel plate was weighed on a digital balance with a precision of +0.1 g and the average mass of a plate was 9 kg. A total of 5 plates were added in incrementally until the capacity of the load cell was reached. Plates were then removed incrementally until all plates were removed. A calibration curve for voltage output and weight was obtained for loading and unloading of weights. Linear regression analysis was then used to examine linearity, hysteresis, and offsets. Table 5-1 shows the results.
Figure 5-1.
Laboratory calibration of load cell with known weights with load cell connected to a datalogger.
Table 5-1.
Calibration curves for three lysimeter load cells for decreasing and increasing mass increments.
Serial Number Z2065568 Z20655A4 Z2065577
Slope
Offset
Standard Error
-------------------------------------[mV]------------------------------------Incr. Decr. Incr. Decr. Incr. Decr. 23.20158 23.21437 0.78902 0.77488 25.076 7.467 23.33832 23.34993 -0.06694 -0.07564 26.923 6.402 23.22169 23.22977 -0.08144 -0.08966 24.358 6.436
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R2
Incr. 0.999999 0.999999 0.999999
Decr. 1.0000 1.0000 1.0000
Scale calibration was completed several times before and after the filling of the lysimeters. Prior to the start of soil installation, lysimeters 1, 2, and 3 were filled with 100 cm of water or approximately 3586 kg of mass. This step was necessary because the scale required several thousand kg of load before accurate operation. By using water, we could demonstrate the performance of the scale when connected to the load cell, and we could examine accuracy and hysteresis of the system before adding soil to the lysimeter. The scale legs were adjusted until the system was level: the lysimeter was at the proper height relative to the ring flashing (also known as the flange), and the gap spacing was uniform with no impingements around the circumference between the inside of the ring flashing and the outside of the lysimeter tank. The weight on the weigh beam was then adjusted approximately 50% of the capacity of the 45 kg load cell. The area under the legs was then grouted. Each scale and its load cell was calibrated by adding weights of known mass to the scale surface in at least 5 increments, collecting 20 measurements and then removing the weights in the same order to estimate hysteresis of the scale. Figure 5-2 shows a typical scale output and regression line. The standard error of each scale is shown in Table 5-2 and ranged from 72 to 409 g, which correlates to 0.018 to 0.102 mm of water in the lysimeter (assuming water density is 1000 kg m-3 and m of water depth = mass in kg / 1000 kg m-3 / 4.0044 m2 lysimeter). 1.17
1.17
B) Load Cell Output [mV]
Load Cell Output [mV]
A) 1.16 1.15 1.14 1.13 1.12
1.16 1.15 1.14
y = 0.0005x + 1.1275 R2 = 0.9998
1.13 1.12
0
200
400 600 800 Measurements
1000
1200
0
20
40 Applied Mass [kg]
60
80
Figure 5-2.
A) Upward and downward calibration and load cell output and B) load cell accuracy of lysimeter 3 scale.
Table 5-2.
Upward and downward standard error of lysimeter scales.
Scale 1 2 3
Upward Standard Error [g] 153 301 409
Downward Standard Error [g] 72 280 229
Shortly after soil filling, data collected in June 2008 revealed that all three lysimeters revealed similar mass measurements (Figure 5-3). Data from this figure was taken by filling aluminum pans with equal volumes of water and allowing them to evaporate over a period of several days. The change in water (shown on y-axis) is a positive number indicating water loss from the pans.
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In October 2008, lysimeter 1 revealed large mass fluctuations under ambient conditions. Temperature was monitored at the load cell and different heights to determine temperature effects. It was revealed that the settling of cold air during the night and its rising during the day impacted the weight measurements. Subsequent experiments indicated that the cause of the fluctuations was primarily the scale itself (i.e., the loadcell did not exhibit significant temperature affects). When a tarp was used to cover the tunnel in lysimeter room 1, the mass readings stabilized (Figure 5-4). Therefore, to minimize the movement of this air, a permanent sliding door (typically used for garage doors) was installed. In addition, a load cell cage was placed over the load cell to minimize disturbance from any local movements (e.g. someone brushing up against the load cell). Finally, the load cell should be calibrated every year to ensure accuracy.
3.5 3
ΔWater [mm]
60
Lysimeter 1 Lysimeter 2 Lysimeter 3 5 cm Soil Temp Room Temp
55 50
2.5
45
2
40
1.5
35
1
30
0.5
25
0 6/27
6/28
6/28
6/29
6/29
6/30
Temperature [deg C]
4
20 6/30
Time
Figure 5-3.
Data from Jun. 27 to 30, 2008 for 1) scale readings converted to change in water in mm as a result of evaporation from aluminum pans filled with equal water volume and placed on lysimeter 1, 2, and 3; and 2) measurements of soil temperature at 5 cm depth in lysimeter 1 and room 1 air temperature.
81
3.0
27
Tarp OFF
ΔWater [mm]
room traffic
load cell enclosure
2.5
25
2.0
23
1.5
21
1.0
19
0.5
0.0 10/15
17
Scale Roof Temp Load Cell Temp 10/17
10/19
Temperature [deg C]
Tarp ON
10/21
10/23
10/25
10/27
10/29
10/31
15 11/02
Date
Figure 5-4.
5.2.
Scale, roof temperature, and load cell temperature for lysimeter 1 (Oct. 17 to 31, 2008).
TDR
Prior to use, the TDR probe offset was calculated to account for the probe head material, which was not exposed to the soil medium. Probe offset was calculated using information from PCTDR (Campbell Scientific, Inc.), where the standard probe offset for CS605 is 0.090 m for a 3 m long cable. The probe rods were immersed into water of known temperature, and dielectric permittivity of water was then calculated from the water temperature. The container was large enough so the rods were at least 5 cm from the container walls (see manual on using PCTDR to calculate probe offset). An average probe offset for five replicates was found to be 0.150+0.004 m. The upward infiltration method (Young et al., 1997) was used to calibrate the TDR probes to the soil. Air-dry soil was sieved to pass through a 2 mm mesh and packed into a polycarbonate column (14.7 cm ID x 33 cm length). Duplicate soil columns were packed within 10% of the bulk densities found in the field for five soil types (Table 5-3). The TDR probe was carefully inserted into the column to minimize soil compaction around the rods. Water containing 0.01 M CaSO4•2H2O (with no bacterial inhibitor) was pumped at a constant rate with a piston pump at an average rate of 543 mL h-1. The wetting solution, stored in a beaker, was placed on a digital balance (Sartorius Corp., Bohemia, NY). Paired values of dielectric constant, obtained from the TDR traces, and beaker weights were acquired every 3 min. Each experiment ran approximately 3 hours, collecting between 78 and 346 paired values, until water was seen leaking from the upper entry ports for the TDR probe.
82
Table 5-3. Soil
Bulk densities for upward infiltration experiment Replicate
Cm 25-80 25-80 80-120 80-120 120-160 120-160 160-200 160-200 0-200 0-200
Target Bulk Density
Actual Bulk Density
-----------------[g cm-3]----------------1.49 1.60 1.49 1.63 1.68 1.58 1.68 1.62 1.46 1.53 1.46 1.54 1.47 1.61 1.47 1.62 1.51 1.62 1.51 1.61
1 2 1 2 1 2 1 2 1 2
Calibration data collected for soil specific to each soil horizon, homogenized soil, and for a combined data set containing all observed data were fitted to Eq. [5-1] θv = A + Bεa + Cεa2 + Dεa3
[5-1]
which is the form of the equation from Topp et al. (1980), where θv is the volumetric water content (cm3 cm-3), and εa is the apparent dielectric constant. The coefficients A, B, C, and D, shown in Table 5-4, were determined by fitting a third-order polynomial to the observed data using Table Curve (Version 1.12, Jandel Scientific, San Rafael, CA) ( Figure 5-5A–F). The fitted calibration curves were checked for similarity with each other and with Topp’s curve (Topp et al., 1980) using Student’s t-tests (SigmaStat, Version 3.5, San Jose, CA). The tests were conducted at a significance level of p1, b=length(num2str(f));k=0; while k0 errorflag(1)=1; fprintf(fid,'***Settings mismatch between file %i (%s) and file 1.***\n',f,files(f).name); fprintf(fid,'\t\t\t File %i Settings \t File 1 Settings\n',f); for i=1:13, fprintf(fid,'%s \t %f \t\t %f\n',settingslabels(i,:),settings(i),settings1(i));end if settings(13)>settings1(13), fprintf(fid,'**Not all points in file %i read.**\n',f);end fprintf(fid,'****************************************************************\n\ n'); end % save date/time previoustime=elapsedtime; [t,elapsedtime,datetime] = sephasfooter(files,f,t1,dt); %if f==1, elapsedtime=dt; end %first trace is after time dt datetimes(f,1)=cellstr(datetime); times(f,1)=elapsedtime; % verify time interval between files; if gap exists, write warning to output file
170
if (elapsedtime-previoustime)>dt errorflag(2)=1; fprintf(fid,'****************Temporal gap between file %i and file %i.****************\n',f-1,f); fprintf(fid,'File %i (%s) at time %f from start.\n',f-1,files(f-1).name,previoustime); fprintf(fid,'File %i (%s) at time %f from start.\n',f,files(f).name,elapsedtime); fprintf(fid,'Time difference, %f > time interval %f.\n',elapsedtime-previoustime,dt); fprintf(fid,'****************************************************************** ****\n\n'); end % read data from file data = dlmread(files(f).name,';',62,1); %data begin on row 65, changed to 62 for SEPHAS if f==1, cabledistance(:,f) = data(1:nM,1); end %writes once distance measured from unit, 0m = unit temperatures(:,f) = data(1:nM,2); %stop data storage at nM rows to omit footer losses(:,f) = data(1:nM,4); %stop data storage at nM rows to omit footer %data extracted from current file; loop to next file until all are read end fprintf('\n\n') %verify and display array sizes, write warning to output file nD=size(distances,1); if nD~=nM errorflag(3)=1; fprintf(fid,'****************Mismatch in number of points (distances).****************\n'); fprintf(fid,'%i Distances ~= %i Measurements\n',nD,nM); fprintf(fid,'****************************************************************** ******\n\n'); end %display flagged errors if errorflag(1)>0, fprintf('*** Settings mismatch error - see output file. ***\n'); end if errorflag(2)>0, fprintf('*** Temporal gaps in data - see output file. ***\n'); end if errorflag(3)>0, fprintf('*** Mismatch in number of points (distances) - see output file. ***\n'); end fprintf('\nBatch import of %i files completed. \n',N) z=toc; fprintf('\nProcessing time: %f (sec).\n',z) fprintf('See output file for parameter values and errors.\n') fprintf(fid,'End batch import of %i files. \nProcessing time: %f (sec).\n',N,z);
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save agilent.mat %close output file fclose(fid); fprintf('================================ProcessAgD end================================\n\n')
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APPENDIX H. LOGGERNET PROGRAM FOR LYSIMETER 1
'CR3000 Series Datalogger ' SEPHAS Lysimeter 1 ' program by Brad F Lyles ' version 2.0 ' May 9, 2008 '... ver 1_11 5-12-08 added DPHP Ref and heater sesistance ' 5-13-08 changed For/Loop delay to heat for 8 seconds and measure every 2 seconds ' '... ver 2_0 6-2-08 modified with new output format for TPHP, DPHP and TDR ' ' ver 2_1 6-11-08 Brad Lyles ' modified code for CO2 sensors - changed from ppm to percent to match sensors, set to measure every 15 minutes ' changed output format for table "scale" to include sensorID and calibration coeficients for SHF ' changed TPHP and DPHP format to use initial temperature before heated started rather than the first measurement ' after heated stopped. ' changed TPHP and DPHP output format to list differential temperature as a block that can be easilly plotted. ' ' ver 2_2 6-16-08 Brad Lyles ' changed the time into from 55 to 0 to that the table time stamps would be on the hour ' added if then statement to save old shf calib if newly computed value was zero ' ' ver 2_3 6-17-08 Brad Lyles ' moved TDR to be measured at moderate frquency ' added code to measure upper 10 TDRs hourly ' fixed programing error in the DPHP_out stream ' added 1 sec delay after turning on SDM via SW12_2 ' ' ver 2_4 07-09-08 Michael Young ' -- MY noted that duplicate measurements of sensors are being taken at midnight, likely becuase the flags for ' high and low resolution measurements are TRUE at the same time. To avoid this duplication, IF/THEN statements ' were added to shut down high resolution measurements at the time that low resolution measurements are being ' taken. All changes to the code are marked by "MOD by MY" in commented line preceding change. ' ' ver 2_5 07-15-08 Michael Young ' -- MY increased cable lengths for the TDR probes to increase upper end of water content measurement capability.
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' New cable lengths are as shown. Probe offset also change from 0.085 to 0.115, based on laboratory measurements ' made by Karletta Chief. ' --B Lyles changed table allocation values to allow 10 days storage on logger ' ' Ver 2_6 7-24-08 Brad Lyles ' remove SW12 (2,1) commands from all but the ECHO probe in the if FLAG 16 command sequence ' moved ECHO to a slow sequence ' changed CS616 temperature correction upper limit from 40 to 60 ' changed soil heat flux time into interval from "Min" to "3" ' ' Ver 2_7 7-24-08 Michael Young ' alter method for obtaining data from scale - 100 measurements in single burst ' B. Lyles modified code to write data to cards if present (assumed 4mb in logger and 2Gb on card to determine size). ' removed HF_scale table ' fixed Scale_Kg_Max sign error ' ' Ver 2_8 7-28-08 Brad Lyles ' alter soil heat flux code to match the code on the BC Eddy station ' changed DPHP Vref measurement from singe ended to differential ' ' Ver 2_9 8-13-08 Brad Lyles ' changed TPHP Vref measurement from single ended to differential ' ' Ver 2_10 8-19-08 Brad Lyles ' changed SHF to measure at 16 after the hour until 19 after rather than 1 and 3 minutes after ' (SHF calib was not initiating properly due to a table over run at the top of the hour) ' changed ECHO code - move code from slow sequence back to the main routine, and changed ' maeasurement delay from 100 to 200 dSec ' ' Ver 2_11 8-25-08 Brad Lyles ' changed code to include TDR calibration factors ' changed code to compute HDU T* and matrix potential values and included calibration factors ' ' Ver 2_12 9-19-08 Brad Lyles ' fixed TPHP Rref values back to 1 ohm resistor values used in v2_9 ' ' Ver 2_13 10-23-08 Brad Lyles ' Added code in slow sequence to measure scale temperature via AM25T#2 chan 25 ' ' Ver 2_14 12-3-08 Brad Lyles ' added code to compute intermediate statistics for scale in scratch table
174
' removed code to manually compute scale statistics ' added table for intermediate statics table ' added code to test HDU_Tstar, if < 0 then = 1e-6 ' added code to convert CO2 fractional percent to ppm ' ' Ver 2_15 12-16-08 Brad Lyles ' changed ECHO VWC from p (potting soil) to m (mineral soil) in ECHO table. ' changed the HDU hourly sensors from 8 to 16; turned on CE8#2 and measured more sensors ' ' Ver 2_17 2-5-09 Brad Lyles (note: skipped ver2_15 and Ver2_16 to match tank3) ' changed sensor IDs for the top four TPHPs ' ' Ver 2_18 4-3-09 Brad Lyles ' changed sensor ID code for CS616 from 14 to 19 ' ' Note sensors so far ' HDU, CS616, TCAV, SHF, DPHP, TPHP, ECHO, 108temp, TDR100, CO2 ' 'Wiring 'H1 = scale (red) 'L1 = scale (white) 'G = scale (black) 'H2 = S_Therm(1) (red) 'L2 = S_Therm(2) (red) 'G = purple, clear (1&2) 'H3 = S_Therm(3) (red) 'L3 = S_Therm(4) (red) 'G = purple, clear (3&4) 'H4 = TCAV(1) 'L4 = TCAV(1) 'G = 'H5 = SHF V_Rf(1) yellow 'L5 = SHF V_Rf(2) 'G = purple, clear 'H6 = SHF(1) white 'L6 = SHF(1) green 'G = clear 'H7 = SHF(2) white 'L7 = SHF(2) green 'G = clear 'H8 = HDU AM25T#1 Hi 'L8 = HDU AM25T#1 Lo 'G = 'H9 = HDU AM25T#2 Hi 'L9 = HDU AM25T#2 Lo
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'G = 'H10 = DPHP & TPHP AM16/32#1 even Lo 'L10 = DPHP & TPHP AM16/32#1 odd Hi 'G = 'H11 = DPHP & TPHP AM16/32#1 odd Lo 'L11 = CS616 (green) 'G = 'H12 = Vref AM16/32#2 even Lo 'L12 = 'G = 'H13 = VDIV10.1 to AM16/32 #3 even Hi CO2 sensors 'L13 = VDIV10.1 to AM16/32 #3 even Lo CO2 sensors 'G = 'H14 = 'L14 = 'G = 'VX1 = scale (green) 'VX2 = AM25T 'G = scale (yellow) 'VX3 = DPHP AM16/32#1 even Hi 'VX4 = 108 probes 1-4 (black) 'G = 'SW12_1 = SHF auto calibration (red) 'SW12_2 = TDR100 and ECHO (1&2) 'G = SHF (black) 'C1 = Tx 'C2 = Rx ECHO #1 'C3 = enable AM16/32 #2 'C4 = clock all mux 'C5 = enable AM25T #1 'C6 = enable AM25T #2 'C7 = Tx 'C8 = Rx ECHO #2 'G = 'SDM_C1 = SDMCD16D & TDR100 & tdr mux 'SDM_C2 = SDMCD16D & TDR100 & tdr mux 'SDM_C3 = SDMCD16D & TDR100 & tdr mux 'G = '5V = CS-616 (orange) ' ' ECHO TE 'SW12V-2 ALL WHITE (EXCITATION) WIRES 'C2 TE #1 OUTPUT (RED) WIRE 'C8 TE #2 OUTPUT (RED) WIRE 'GND ALL BARE (GND) WIRES
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'Flags 'Flag(1) = High Freqency Sample Mode 'Flag(2) = Moderate Sample Freq. Sensor Set 'Flag(3) = Intermediate Sample Freq. Sensor Set 'Flag(4) = Low Sample Freq. Sensor Set 'Flag(10) = all CO2 sensors 'Flag(11) = all HDU sensors 'Flag(12) = all TPHP sensors 'Flag(13) = all DPHP sensors 'Flag(14) = all TDR sensors 'Flag(15) = all TDR waveforms 'Flag(16) = ECHO TE sensors 'SDM CD16D channels defs 'chan(1) = CE8(1) 'chan(2) = CE8(2) 'chan(3) = CE8(3) 'chan(4) = CE8(4) 'chan(5) = TPHP heater card (1) 'chan(6) = TPHP heater card (2) 'chan(7) = DPHP heater card (1) 'chan(8) = DPHP heater card (2) (card has been removed) 'chan(9) = DPHP heater card (3) 'chan(10) = DPHP heater card (4) 'chan(11) = DPHP heater card (5) 'chan(12) = DPHP heater card (6) 'chan(13) = enable AM16/32 #1 (output from TPHP and DPHP sensors) 'chan(14) = enable AM16/32 #3 (output from CO2 sensors) 'chan(15) = 'chan(16) = SequentialMode 'Output period Const OUTPUT_INTERVAL = 15
'data output interval in minutes.
Const CAL_INTERVAL = 1440 'HFP01SC insitu calibration interval (minutes). Const END_CAL = OUTPUT_INTERVAL-1 'End HFP01SC insitu calibration one minute before the next Output. Const HFP01SC_CAL_1 = 1000/61.6 #1 (1000/sensitivity). Const HFP01SC_CAL_2 = 1000/62.6 #2 (1000/sensitivity).
'Unique multiplier for HFP01SC 'Unique multiplier for HFP01SC
'Declare Public Variables
177
Public Scale_mV, Scale_Kg, ScaleMult, ScaleTar, Scale_array(5) Public Scale_Kg_SD, Scale_Kg_Min, Scale_Kg_Max, Scale_Kg_Mean, Scale_mV_Mean Public batt_volt, Scale_temp_C Public Ptemp Public Flag(16) As Boolean Public Src(16) Dim HDU_output_flag As Boolean Public HF_scale As Boolean Public old_ScaleKg, del_Scale, HF_event Dim MassID Dim ST_ID Dim TCAV_ID Dim SHF1_ID Dim SHF2_ID Dim ST1_ID, ST2_ID, ST3_ID, ST4_ID Dim Ptemp_ID Dim CS616_ID Public shf(2) Public tcav_1 Public cs616_uS Public cs616_uS_tc Public soil_water_VMC correction. Public S_Therm(4) Public shf_cal(2) Units shf = W/m^2 Units cs616_uS = uSeconds Units soil_water_VMC = frac_v_wtr Units shf_cal = W/(m^2 mV) Units S_therm() = C Dim sw12_1_state
'Water content reflectometer period. 'Volumetric soil water content with temperature
'State of the switched 12Vdc port 1.
'Soil heat flux calibration variables. Public shf_mV(2) Public shf_mV_run(2) Public shf_mV_0(2) Public shf_mV_180(2) Public shf_mV_end(2) Public V_Rf(2) Public V_Rf_run(2) Public V_Rf_180(2) Public shf_cal_on As Boolean Public shf_calib
178
Public TRef Public ST1(16), ST2(16), Public del_T1(16), del_T2(16) Public T1_1sec(16), T2_1sec(16) Public T1_30sec(16), T2_30sec(16) Dim HDU_sen(32) Dim HDU_wet(32) Dim HDU_dry(32) Dim HDU_alpha(32) Dim HDU_beta(32) Dim HDU_Tstar Dim HDU_Psi Public HDU_out(12) Units Scale_mV = mV Units Scale_Kg = Kg Units ST1 = deg C Units ST2 = deg C Units del_T1 = deg C Units del_T2 = deg C Units T1_1sec = deg C Units T2_1sec = deg C Units T1_30sec = deg C Units T2_30sec = deg C Public Vref(24,8),Vrefacc(24), Power(24), LNR Public DPHP_mv(24,41), DPHP_C(24,41), DPHP_out(200) Public DPHP_timer(24,41), DPHP_timer_final Public DPHP_ref(24), DPHP_Rht(24), DPHP_sen(24), dt(24,41) Public TPHP_mV1(8,41), TPHP_C1(8,41), TPHP_sen(8) Public TPHP_mV2(8,41), TPHP_C2(8,41),dT1(8,41), dT2(8,41), TPHP_ref(8), TPHP_Rht(8) Public TPHP_out(302) Public TPHP_timer(8,49), TPHP_timer_final Public LaL(26), LaL2(26) Public TDR_EC(26), ToppVWC(26) Public WavePT(260), MuxChan, TDR_sen(26) Public TDR_out(8), TDRraw(8) Const a0 = -0.0789 Const a1 = 0.03481 Const a2 = -0.00122 Const a3 = 0.00002323 Const high = true Const low = false
179
'Declare Other Variables Dim i,j, k, kk Dim ProbeNum, DPHP_num(24), TPHP_num(8) 'Declare ECHO Public Variables Const TE_Num = 2 reading
'change this constant for the number of ECHO TE probes you are '4 is the maximum
number of TE probes readable without a multiplexer Const eb0 = 6 'empirical constant loosely representing the dielectric of dry soil Public TEout(4,1) As String * 32 Public Pos_RawVWC(TE_Num) as LONG Public Pos_RawEC(TE_Num) as LONG Public Pos_RawT(TE_Num) as LONG Public RawVWC(TE_Num) as LONG,RawEC(TE_Num) as FLOAT,RawT(TE_Num) as LONG Public VWCm(TE_Num) as FLOAT,VWCp(TE_Num) as FLOAT 'VWCm for mineral soil, VWCp for potting soil Public Temp(TE_Num) As Float Public eb(TE_Num) as float, ep(TE_Num) as float 'eb is bulk dielectric and ep is the 'dielectric of the pore water Public ECb(TE_Num) as float ' this is bulk dielectric measured by the TE Public ECp(TE_Num) as float ' this is the pore water dielectric estimated by Public x As Float Public TE_sen(TE_Num) 'Declare Viasala CO2 Sensor Public Variables Public CO2_volt Public CO2_pct, CO2_ppm Public CO2_sen(4), sensor_num 'Define Data Tables DataTable (TEData,True,96) CardOut (0 ,48000) Sample (TE_Num,TE_sen(),IEEE4) Sample (TE_Num,VWCm(),FP2) Sample (TE_Num,ECp(),FP2) Sample (TE_Num,Temp(),FP2) EndTable DataTable (Daily,1,40)
180
CardOut (0 ,20000) DataInterval (0,1440,Min,10) Average (1,Scale_Kg,FP2,False) StdDev (1,Scale_Kg,FP2,False) Minimum (1,Scale_Kg,FP2,False,False) Maximum (1,Scale_Kg,FP2,False,False) Minimum (1,batt_volt,FP2,0,False) Sample (1,Ptemp,FP2) EndTable DataTable (TDR_Wave,True,104) CardOut (0 ,52000) Sample(1,MuxChan,IEEE4) Sample(260,WavePT(),FP2) FieldNames ("sensorID:,WavePT_1:,WavePT_2:,WavePT_3:,etc") EndTable DataTable (TDR,True,1376) CardOut (0 ,688000) Sample(8,TDR_out(),IEEE4) FieldNames ("sensorID:,LaL:,ToppVWC:,TDR_EC:,a0:,a1:,a2:,a3") EndTable DataTable (Scale,True,-1) CardOut (0 ,-1) DataInterval (0,OUTPUT_INTERVAL,Min,0) Sample (1,MassID,IEEE4) Average (1,Scale_mV,IEEE4,False) Sample (1,Scale_Kg_Mean,IEEE4) Sample (1,Scale_Kg_SD,IEEE4) Sample (1,Scale_Kg_Min,IEEE4) Sample (1,Scale_Kg_Max,IEEE4) Sample (1,TCAV_ID,IEEE4) Average (1,tcav_1,FP2,False) Sample (1,SHF1_ID,IEEE4) Average (1,shf(1),IEEE4,shf_cal_on) Sample (1,shf_cal(1),IEEE4) Sample (1,SHF2_ID,IEEE4) Average (1,shf(2),IEEE4,shf_cal_on) Sample (1,shf_cal(2),IEEE4) Sample (1,ST1_ID,IEEE4) Average (1,S_Therm(1),FP2,False) Sample (1,ST2_ID,IEEE4) Average (1,S_Therm(2),FP2,False) Sample (1,ST3_ID,IEEE4) Average (1,S_Therm(3),FP2,False)
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Sample (1,ST4_ID,IEEE4) Average (1,S_Therm(4),FP2,False) Sample (1,ptemp_ID,IEEE4) Average (1,Ptemp,FP2,False) Sample (1,CS616_ID,IEEE4) Average (1,cs616_uS,FP2,False) Average (1,soil_water_VMC),FP2,False) Average (1,Scale_temp_C,FP2,False) EndTable DataTable (HDU,true,896) CardOut (0 ,448000) Sample(12,HDU_out(),IEEE4) FieldNames ("sensorID:,SoilTemp:,deltaTemp:,T_1sec:,T_30sec:,RefTemp:,Tstar:,Psi:,wet:,dry:,alpha:,b eta") EndTable DataTable (DPHP,Flag(13),2304) CardOut (0 ,1152000) Sample (179,DPHP_out(),IEEE4) FieldNames ("sensorID:,timer_1:,temp_C_1:,temp_mV_1:,Vref1:,Vref2:,Vref3:,Vref4:,Vref5:,Vref6:,Vre f7:,Vref8:,Power:,Vref:avg,Rht:,Rref:,heat_time:total") EndTable DataTable (TPHP,Flag(12),768) CardOut (0 ,384000) Sample (302,TPHP_out(),IEEE4) FieldNames ("sensorID:,timer_1:,temp1_C_1:,temp1_mV_1:,temp2_C_1:,temp2_mv_1:,Vref1:,Vref2:,V ref3:,Vref4:,Vref5:,Vref6:,Vref7:,Vref8:,Power:,Vref:avg,Rht:,Rref:,heat_time:total") EndTable DataTable (CO2,Flag(10),1536) CardOut (0 ,768000) Sample (1,sensor_num,IEEE4) Sample (1,CO2_ppm,IEEE4) Sample (1,CO2_volt,IEEE4) EndTable DataTable (scale_int,true,20) DataInterval (57,60,Sec,10) Average (1,Scale_mV,IEEE4,False) Average (1,Scale_Kg,IEEE4,False) StdDev (1,Scale_Kg,IEEE4,False)
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Minimum (1,Scale_Kg,IEEE4,False,False) Maximum (1,Scale_Kg,IEEE4,False,False) EndTable 'Subroutines Sub Initialize MassID = 100015 TCAV_ID = 120208 SHF1_ID = 110207 SHF2_ID = 130207 ST1_ID = 160206 ST2_ID = 160406 ST3_ID = 160506 ST4_ID = 160706 Ptemp_ID = 100016 CS616_ID = 130219 'reset SDM-CD16D For i=1 To 16 Src(i) = 0.0 Next i ' HDU HDU_sen(1) = 110204 HDU_sen(2) = 120204 HDU_sen(3) = 130204 HDU_sen(4) = 140204 HDU_sen(5) = 110304 HDU_sen(6) = 120304 HDU_sen(7) = 130304 HDU_sen(8) = 140304 HDU_sen(9) = 110404 HDU_sen(10) = 120404 HDU_sen(11) = 130404 HDU_sen(12) = 140404 HDU_sen(13) = 110504 HDU_sen(14) = 120504 HDU_sen(15) = 130504 HDU_sen(16) = 140504 HDU_sen(17) = 110704 HDU_sen(18) = 120704 HDU_sen(19) = 130704 HDU_sen(20) = 140704 HDU_sen(21) = 110904 HDU_sen(22) = 120904 HDU_sen(23) = 130904 HDU_sen(24) = 140904
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HDU_sen(25) = 111104 HDU_sen(26) = 121104 HDU_sen(27) = 131104 HDU_sen(28) = 141104 HDU_sen(29) = 121304 HDU_sen(30) = 141304 HDU_sen(31) = 111404 HDU_sen(32) = 131404 HDU_dry(1) = 2.59881148 HDU_dry(2) = 2.70462444 HDU_dry(3) = 2.82089616 HDU_dry(4) = 2.80437224 HDU_dry(5) = 2.81965852 HDU_dry(6) = 2.888096 HDU_dry(7) = 3.00122832 HDU_dry(8) = 2.86658692 HDU_dry(9) = 2.75317956 HDU_dry(10) = 2.86660744 HDU_dry(11) = 3.12010208 HDU_dry(12) = 2.72838524 HDU_dry(13) = 2.85276352 HDU_dry(14) = 2.80000616 HDU_dry(15) = 2.75912244 HDU_dry(16) = 2.80957012 HDU_dry(17) = 2.73580292 HDU_dry(18) = 2.79983616 HDU_dry(19) = 2.7455 HDU_dry(20) = 2.65062036 HDU_dry(21) = 2.81804052 HDU_dry(22) = 2.79185552 HDU_dry(23) = 2.90636272 HDU_dry(24) = 2.8038744 HDU_dry(25) = 2.77459496 HDU_dry(26) = 2.88021276 HDU_dry(27) = 2.85478572 HDU_dry(28) = 2.82709532 HDU_dry(29) = 2.81268692 HDU_dry(30) = 2.86043096 HDU_dry(31) = 2.9190336 HDU_dry(32) = 2.86946756 HDU_wet(1) = 0.703747176 HDU_wet(2) = 0.703838656 HDU_wet(3) = 0.810288008 HDU_wet(4) = 0.771614448 HDU_wet(5) = 0.708999024 HDU_wet(6) = 0.703212964
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HDU_wet(7) = 0.685451508 HDU_wet(8) = 0.696603628 HDU_wet(9) = 0.709443964 HDU_wet(10) = 0.696791152 HDU_wet(11) = 0.750799332 HDU_wet(12) = 0.703956376 HDU_wet(13) = 0.696686024 HDU_wet(14) = 0.70873314 HDU_wet(15) = 0.702029424 HDU_wet(16) = 0.718705524 HDU_wet(17) = 0.693649144 HDU_wet(18) = 0.683669208 HDU_wet(19) = 0.707328732 HDU_wet(20) = 0.699073712 HDU_wet(21) = 0.724320896 HDU_wet(22) = 0.750586608 HDU_wet(23) = 0.713331444 HDU_wet(24) = 0.698470996 HDU_wet(25) = 0.720076524 HDU_wet(26) = 0.707718816 HDU_wet(27) = 0.72762528 HDU_wet(28) = 0.730897676 HDU_wet(29) = 0.73069938 HDU_wet(30) = 0.724401312 HDU_wet(31) = 0.728108524 HDU_wet(32) = 0.706802448 HDU_alpha(1) = 129.922999 HDU_alpha(2) = 129.922999 HDU_alpha(3) = 114.243419 HDU_alpha(4) = 114.243419 HDU_alpha(5) = 129.922999 HDU_alpha(6) = 129.922999 HDU_alpha(7) = 129.922999 HDU_alpha(8) = 129.922999 HDU_alpha(9) = 129.922999 HDU_alpha(10) = 129.922999 HDU_alpha(11) = 129.922999 HDU_alpha(12) = 129.922999 HDU_alpha(13) = 129.922999 HDU_alpha(14) = 129.922999 HDU_alpha(15) = 129.922999 HDU_alpha(16) = 129.922999 HDU_alpha(17) = 129.922999 HDU_alpha(18) = 129.922999 HDU_alpha(19) = 129.922999 HDU_alpha(20) = 129.922999
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HDU_alpha(21) = 129.922999 HDU_alpha(22) = 129.922999 HDU_alpha(23) = 129.922999 HDU_alpha(24) = 129.922999 HDU_alpha(25) = 129.922999 HDU_alpha(26) = 129.922999 HDU_alpha(27) = 129.922999 HDU_alpha(28) = 129.922999 HDU_alpha(29) = 129.922999 HDU_alpha(30) = 129.922999 HDU_alpha(31) = 129.922999 HDU_alpha(32) = 129.922999 HDU_beta(1) = 0.279008781 HDU_beta(2) = 0.313076862 HDU_beta(3) = 0.318432533 HDU_beta(4) = 0.300570406 HDU_beta(5) = 0.269957178 HDU_beta(6) = 0.274867941 HDU_beta(7) = 0.309858714 HDU_beta(8) = 0.287730289 HDU_beta(9) = 0.30998111 HDU_beta(10) = 0.296082004 HDU_beta(11) = 0.303684967 HDU_beta(12) = 0.251981704 HDU_beta(13) = 0.297084951 HDU_beta(14) = 0.29292206 HDU_beta(15) = 0.293541319 HDU_beta(16) = 0.292264595 HDU_beta(17) = 0.269075145 HDU_beta(18) = 0.267939204 HDU_beta(19) = 0.283146012 HDU_beta(20) = 0.259178832 HDU_beta(21) = 0.26778083 HDU_beta(22) = 0.261317113 HDU_beta(23) = 0.25900204 HDU_beta(24) = 0.262502192 HDU_beta(25) = 0.262720418 HDU_beta(26) = 0.288194103 HDU_beta(27) = 0.254327558 HDU_beta(28) = 0.256842527 HDU_beta(29) = 0.303756937 HDU_beta(30) = 0.27935846 HDU_beta(31) = 0.327189805 HDU_beta(32) = 0.287492086
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' TPHP TPHP_ref(1) = 1.0014 TPHP_ref(2) = 1.0011 TPHP_ref(3) = 1.0035 TPHP_ref(4) = 1.0021 TPHP_ref(5) = 1.0057 TPHP_ref(6) = 1.0025 TPHP_ref(7) = 1.0067 TPHP_ref(8) = 1.0045 TPHP_Rht(1) = 40.0 TPHP_Rht(2) = 40.2 TPHP_Rht(3) = 40.1 TPHP_Rht(4) = 40.1 TPHP_Rht(5) = 40.5 TPHP_Rht(6) = 40.4 TPHP_Rht(7) = 40.6 TPHP_Rht(8) = 40.2 TPHP_sen(1) = 125003 TPHP_sen(2) = 125103 TPHP_sen(3) = 125203 TPHP_sen(4) = 125303 TPHP_sen(5) = 110303 TPHP_sen(6) = 130303 TPHP_sen(7) = 110403 TPHP_sen(8) = 130403 'DPHP DPHP_ref(1) = 1.013 DPHP_ref(2) = 1.026 DPHP_ref(3) = 1.021 DPHP_ref(4) = 1.014 DPHP_ref(5) = 1.015 DPHP_ref(6) = 1.017 DPHP_ref(7) = 1.037 DPHP_ref(8) = 1.019 DPHP_ref(9) = 1.017 DPHP_ref(10) = 1.014 DPHP_ref(11) = 1.020 DPHP_ref(12) = 1.016 DPHP_ref(13) = 1.012 DPHP_ref(14) = 1.019 DPHP_ref(15) = 1.017 DPHP_ref(16) = 1.016 DPHP_ref(17) = 1.022 DPHP_ref(18) = 1.015
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DPHP_ref(19) = 1.015 DPHP_ref(20) = 1.017 DPHP_ref(21) = 1.017 DPHP_ref(22) = 1.029 DPHP_ref(23) = 1.023 DPHP_ref(24) = 1.016 DPHP_Rht(1) = 39.6 DPHP_Rht(2) = 39.5 DPHP_Rht(3) = 40.1 DPHP_Rht(4) = 39.8 DPHP_Rht(5) = 40.0 DPHP_Rht(6) = 39.7 DPHP_Rht(7) = 39.5 DPHP_Rht(8) = 40.0 DPHP_Rht(9) = 40.1 DPHP_Rht(10) = 39.6 DPHP_Rht(11) = 40.1 DPHP_Rht(12) = 39.6 DPHP_Rht(13) = 39.9 DPHP_Rht(14) = 39.8 DPHP_Rht(15) = 39.7 DPHP_Rht(16) = 39.5 DPHP_Rht(17) = 39.4 DPHP_Rht(18) = 39.8 DPHP_Rht(19) = 39.6 DPHP_Rht(20) = 39.9 DPHP_Rht(21) = 39.7 DPHP_Rht(22) = 39.4 DPHP_Rht(23) = 39.4 DPHP_Rht(24) = 39.5 DPHP_sen(1) = 120302 DPHP_sen(2) = 140302 DPHP_sen(3) = 120402 DPHP_sen(4) = 140402 DPHP_sen(5) = 110502 DPHP_sen(6) = 120502 DPHP_sen(7) = 130502 DPHP_sen(8) = 140502 DPHP_sen(9) = 110702 DPHP_sen(10) = 120702 DPHP_sen(11) = 130702 DPHP_sen(12) = 140702 DPHP_sen(13) = 110902 DPHP_sen(14) = 120902 DPHP_sen(15) = 130902 DPHP_sen(16) = 140902
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DPHP_sen(17) = 111102 DPHP_sen(18) = 121102 DPHP_sen(19) = 131102 DPHP_sen(20) = 141102 DPHP_sen(21) = 121302 DPHP_sen(22) = 141302 DPHP_sen(23) = 111402 DPHP_sen(24) = 131402 'TDR TDR_sen(1) = 110301 TDR_sen(2) = 120301 TDR_sen(3) = 130301 TDR_sen(4) = 140301 TDR_sen(5) = 110401 TDR_sen(6) = 120401 TDR_sen(7) = 130401 TDR_sen(8) = 140401 TDR_sen(9) = 110501 TDR_sen(10) = 120501 TDR_sen(11) = 130501 TDR_sen(12) = 140501 TDR_sen(13) = 110701 TDR_sen(14) = 120701 TDR_sen(15) = 130701 TDR_sen(16) = 140701 TDR_sen(13) = 110701 TDR_sen(14) = 120701 TDR_sen(15) = 130701 TDR_sen(16) = 140701 TDR_sen(17) = 110901 TDR_sen(18) = 130901 TDR_sen(19) = 111101 TDR_sen(20) = 121101 TDR_sen(21) = 131101 TDR_sen(22) = 141101 TDR_sen(23) = 121301 TDR_sen(24) = 141301 TDR_sen(25) = 111401 TDR_sen(26) = 131401 'CO2 CO2_sen(1) = 120114 CO2_sen(2) = 120914 CO2_sen(3) = 121114 CO2_sen(4) = 121414 'ECHO TE_sen(1) = 110209
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TE_sen(2) = 130209 EndSub 'Hukseflux HFP01SC insitu calibration routine. Sub hfp01sc_cal 'Begin HFP01SC calibration one minute into very CAL_INTERVAL minutes. If ( IfTime (16,CAL_INTERVAL,Min) ) Then shf_cal_on = TRUE Move (shf_mV_0(1),2,shf_mV_run(1),2) sw12_1_state = TRUE 'turn on heaters EndIf If ( IfTime (19,CAL_INTERVAL,Min) ) Then Move (shf_mV_180(1),2,shf_mV_run(1),2) Move (V_Rf_180(1),2,V_Rf_run(1),2) sw12_1_state = FALSE 'turn off heater after 4 minutes EndIf 'End HFP01SC calibration sequence. If ( IfTime (29,CAL_INTERVAL,Min) ) Then Move (shf_mV_end(1),2,shf_mV_run(1),2) 'Compute new HFP01SC calibration factors. For j = 1 To 2 shf_calib = V_Rf_180(j)*V_Rf_180(j)*128.7/ABS (shf_mV_0(j)shf_mV_180(j)) If (shf_calib 0.) Then shf_cal(j) = shf_calib EndIf Next j shf_cal_on = FALSE EndIf EndSub 'Main Program BeginProg Call Initialize 'Load the HFP01SC factory calibration. shf_cal(1) = HFP01SC_CAL_1 shf_cal(2) = HFP01SC_CAL_2 ScaleMult = 2145.92 ScaleTar = -2133.0 SerialOpen (Com1,1200,19,0,10000)
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SerialOpen (Com4,1200,19,0,10000) Scan (250,mSec,3,0) PanelTemp (Ptemp,250) Battery (Batt_volt) '================================================================== == ' New section of code calculates the scale mass in 100 measurement burst, takes the average and SD If IfTime (14,15,Min) Then 'Measure scale ExciteV (Vx1,5000,200) For i = 1 To 100 BrFull (Scale_mV,1,AutoRange,1,Vx1,1,5000,True,True,0,_60Hz,1.0,0.0) Scale_Kg = Scale_mV * ScaleMult + ScaleTar CallTable scale_int Next i ExciteV (Vx1,0,0) EndIf CallTable scale_int GetRecord (Scale_array,scale_int,1) Scale_mV_Mean = Scale_array(1) Scale_Kg_Mean = Scale_array(2) Scale_Kg_SD = Scale_array(3) Scale_Kg_Min = Scale_array(4) Scale_Kg_Max = Scale_array(5) 'Measure the HFP01SC soil heat flux plates. VoltDiff (shf_mV(1),2,mV50C,6,TRUE,200,250,1,0) 'Apply HFP01SC soil heat flux plate calibration. For j = 1 To 2 shf(j) = shf_mV(j)*shf_cal(j) Next j 'Power the HFP01SC heaters. PortSet (9,sw12_1_state) 'Measure voltage across the heater (Rf_V). VoltSe (V_Rf(1),2,mV5000,9,TRUE,200,250,0.001,0) 'Maintain filtered values for calibration. AvgRun (shf_mV_run(1),2,shf_mV(1),100) AvgRun (V_Rf_run(1),2,V_Rf(1),100)
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'Measure the TCAV soil thermocouples. TCDiff (tcav_1,1,mV20C,4,TypeE,Ptemp,TRUE,200,250,1,0) 'Measure soil 108 thermistors Therm108 (S_Therm(),4,3,Vx4,0,250,1.0,0) 'Measure the CS616 soil water content probes. CS616 (cs616_uS,1,22,4,2,1,0) 'Apply temperature correction to CS616 period and find volumetric water content. If ( (10 measure HDU measure TPHP monitor TPHP temperature every 2 seconds for 80 seconds 'TPHP_timer = Timer (1,mSec,2) 'reset timer For j=2 To 41 '40 measurements every 2 seconds For i = 1 To 8 'measure 8 probes PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) BrHalf (TPHP_mv1(i,j),1,AutoRange,19,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv1(i,j)*10.)/TPHP_mv1(i,j))/10.) TPHP_C1(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 BrHalf (TPHP_mv2(i,j),1,AutoRange,20,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv2(i,j)*10.)/TPHP_mv2(i,j))/10.) TPHP_C2(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dT1(i,j) = TPHP_C1(i,j) - TPHP_C1(i,1) dT2(i,j) = TPHP_C2(i,j) - TPHP_C2(i,1) TPHP_timer(i,j) = Timer (1,mSec,4)/1000. - TPHP_timer_final Next i Src(13) = 0 SDMCD16AC (Src(),1,3) Delay(0,1,Sec) 'turn off mux and wait 1 sec to turn on to reset to channel 1 Src(13) = 1 SDMCD16AC (Src(),1,3) Delay(0,510,mSec) 'value was set based on the amount time it took to progress through the program Next j Src(13) = 0 SDMCD16AC (Src(),1,3) '******************--> compute TPHP power and build output table For i=1 To 8 Power(i) = (Vrefacc(i)/8.0)^2 * ((TPHP_Rht(i)*TPHP_timer_final)/(TPHP_ref(i)^2*0.03))
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TPHP_out(1) = TPHP_sen(i) TPHP_out(2) = TPHP_timer(i,1) TPHP_out(3) = TPHP_C1(i,1) TPHP_out(4) = TPHP_mv1(i,1) TPHP_out(5) = TPHP_C2(i,1) TPHP_out(6) = TPHP_mv2(i,1) k=7 For j=1 To 8 TPHP_out(k) = Vref(i,j) k = k+1 Next j TPHP_out(k) = power(i) k = k+1 TPHP_out(k) = Vrefacc(i)/8 k = k+1 TPHP_out(k) = TPHP_Rht(i) k = k+1 TPHP_out(k) = TPHP_ref(i) k = k+1 TPHP_out(k) = TPHP_timer_final k = k+1 For j=2 To 41 TPHP_out(k) = dT1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = dT2(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_timer(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_mv1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C2(i,j) k = k+1 Next j For j=2 To 41
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TPHP_out(k) = TPHP_mv2(i,j) k = k+1 Next j CallTable TPHP Next i 'SW12 (2,0) Flag(12) = FALSE EndIf '**********************************--> measure DPHP monitor DPHP temperature every 2 seconds for 80 seconds For j=2 To 41 '40 measurements every 2 seconds k=1 For i=1 To 8 'skip first 8 channels PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) Next i For i = 1 To 8 'measure 24 probes 3 at a time, ie. 8 loops PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) BrHalf (DPHP_mv(k,j),1,mV1000,19,Vx3,1,1000,True,0,_60Hz,1.0,0) LNR = LOG(((10.-DPHP_mv(k,j)*10.)/DPHP_mv(k,j))/10.) DPHP_C(k,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dt(k,j) = DPHP_C(k,j) - DPHP_C(k,1) DPHP_timer(k,j) = Timer (1,mSec,4)/1000. - DPHP_timer_final k = k+1 BrHalf (DPHP_mv(k,j),1,mV1000,20,Vx3,1,1000,True,0,_60Hz,1.0,0) LNR = LOG(((10.-DPHP_mv(k,j)*10.)/DPHP_mv(k,j))/10.) DPHP_C(k,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dt(k,j) = DPHP_C(k,j) - DPHP_C(k,1) DPHP_timer(k,j) = Timer (1,mSec,4)/1000. - DPHP_timer_final k = k+1 BrHalf (DPHP_mv(k,j),1,mV1000,21,Vx3,1,1000,True,0,_60Hz,1.0,0) LNR = LOG(((10.-DPHP_mv(k,j)*10.)/DPHP_mv(k,j))/10.) DPHP_C(k,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dt(k,j) = DPHP_C(k,j) - DPHP_C(k,1) DPHP_timer(k,j) = Timer (1,mSec,4)/1000. - DPHP_timer_final k = k+1 Next i Src(13) = 0 SDMCD16AC (Src(),1,3) Delay(0,100,mSec) 'turn off mux and wait 1 sec to turn on to reset to channel 1 Src(13) = 1 SDMCD16AC (Src(),1,3) Delay(0,130,mSec) 'value was set based on the amount time it took to progress through the program Next j Src(13) = 0 SDMCD16AC (Src(),1,3)
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'******************--> compute DPHP power and build output table For i=1 To 24 Power(i) = (Vrefacc(i)/8.0)^2 * ((DPHP_Rht(i)*DPHP_timer_final)/(DPHP_ref(i)^2*0.03)) DPHP_out(1) = DPHP_sen(i) DPHP_out(2) = DPHP_timer(i,1) DPHP_out(3) = DPHP_C(i,1) DPHP_out(4) = DPHP_mv(i,1) k=5 For j=1 To 8 DPHP_out(k) = Vref(i,j) k = k+1 Next j DPHP_out(k) = power(i) k = k+1 DPHP_out(k) = Vrefacc(i)/8 k = k+1 DPHP_out(k) = DPHP_Rht(i) k = k+1 DPHP_out(k) = DPHP_ref(i) k = k+1 DPHP_out(k) = DPHP_timer_final k = k+1 For j=2 To 41 DPHP_out(k) = dt(i,j) k = k+1 Next j For j=2 To 41 DPHP_out(k) = DPHP_timer(i,j) k = k+1 Next j For j=2 To 41 DPHP_out(k) = DPHP_C(i,j) k = k+1 Next j For j=2 To 41 DPHP_out(k) = DPHP_mv(i,j) k = k+1 Next j CallTable DPHP Next i 'SW12 (2,0) Flag(13) = FALSE EndIf
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'**********************--> measure all TDR theta and EC measure ECHO compute TPHP power and build output table For i=1 To 18 Power(i) = (Vrefacc(i)/8.0)^2 * ((TPHP_Rht(i)*TPHP_timer_final)/(TPHP_ref(i)^2*0.03)) TPHP_out(1) = TPHP_sen(i) TPHP_out(2) = TPHP_timer(i,1) TPHP_out(3) = TPHP_C1(i,1) TPHP_out(4) = TPHP_mv1(i,1) TPHP_out(5) = TPHP_C2(i,1) TPHP_out(6) = TPHP_mv2(i,1) k=7 For j=1 To 8 TPHP_out(k) = Vref(i,j) k = k+1 Next j TPHP_out(k) = power(i) k = k+1 TPHP_out(k) = Vrefacc(i)/8 k = k+1 TPHP_out(k) = TPHP_Rht(i) k = k+1
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TPHP_out(k) = TPHP_ref(i) k = k+1 TPHP_out(k) = TPHP_timer_final k = k+1 For j=2 To 41 TPHP_out(k) = dT1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = dT2(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_timer(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_mv1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C2(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_mv2(i,j) k = k+1 Next j CallTable TPHP Next i Flag(12) = FALSE EndIf '**********************************--> measure DPHP measure TDR measure ECHO measure CO2 sensors measure HDU measure TPHP monitor Vref from the TPHPs during heating Timer (1,sec,2) Src(5) = 1 'set values for SDM-CD16D PortSet(3,1) Delay(0,150,msec)'warmup AM16/32#2 SDMCD16AC (Src(),1,3) 'set channel 5 high on CD16D - turn on 4 HP cards TPHP_timer(1,1) = Timer (1,mSec,2) 'reset and start timer For j=1 To 8 'step through 8 seconds For i=1 To 18 'measure the reference supply voltage for 18 TPHPs PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) ' VoltSe (Vref(i,j),1,mV5000,23,1,0,250,0.001,0) VoltDiff (Vref(i,j),1,mV5000,12,True ,0,250,0.001,0.0) Vrefacc(i) = Vrefacc(i)+Vref(i,j) Next i
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PortSet(3,0) 'turn off AM16/31#2 to reset to channel 1 Delay(0,110,mSec) PortSet(3,1) 'enable AM16/32#2 Delay(0,365,mSec) 'wait the remainder of 1 sec before making the next measurement Next j Src(13) = 1 Src(15) = 0 'turn off heater board contactor Src(5) = 0 SDMCD16AC (Src(),1,3) 'turn off 4 HP control cards and enable AM16/32#1 TPHP_timer_final = Timer (1,mSec,4)/1000. PortSet(3,0) '******************--> monitor TPHP temperature every 2 seconds for 80 seconds 'TPHP_timer = Timer (1,mSec,2) 'reset timer For j=2 To 41 '40 measurements every 2 seconds For i = 1 To 16 'measure 16 probes PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) BrHalf (TPHP_mv1(i,j),1,AutoRange,19,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv1(i,j)*10.)/TPHP_mv1(i,j))/10.) TPHP_C1(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 BrHalf (TPHP_mv2(i,j),1,AutoRange,20,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv2(i,j)*10.)/TPHP_mv2(i,j))/10.) TPHP_C2(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dT1(i,j) = TPHP_C1(i,j) - TPHP_C1(i,1) dT2(i,j) = TPHP_C2(i,j) - TPHP_C2(i,1) TPHP_timer(i,j) = Timer (1,mSec,4)/1000. - TPHP_timer_final Next i Src(13) = 0 Src(14) = 1 SDMCD16AC (Src(),1,3) Delay(0,150,msec)'warmup AM16/32#3 For i = 17 To 18 'measure 2 probes PortSet(4,1) Delay (0,20,mSec)'clock mux PortSet(4,0) BrHalf (TPHP_mv1(i,j),1,AutoRange,19,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv1(i,j)*10.)/TPHP_mv1(i,j))/10.) TPHP_C1(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 BrHalf (TPHP_mv2(i,j),1,AutoRange,20,Vx3,1,1000,True ,0,250,1.0,0) LNR = LOG(((10.-TPHP_mv2(i,j)*10.)/TPHP_mv2(i,j))/10.)
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TPHP_C2(i,j)=24.996+22.8148*LNR+1.5437*LNR^2+0.0974*LNR^3+0*LNR^4+0*LNR^5 dT1(i,j) = TPHP_C1(i,j) - TPHP_C1(i,1) dT2(i,j) = TPHP_C2(i,j) - TPHP_C2(i,1) TPHP_timer(i,j) = Timer (1,mSec,4)/1000. - TPHP_timer_final Next i Src(14) = 0 SDMCD16AC (Src(),1,3) Delay(0,500,mSec) 'turn off mux and wait 1 sec to turn on to reset to channel 1 Src(13) = 1 SDMCD16AC (Src(),1,3) Delay(0,510,mSec) 'value was set based on the amount time it took to progress through the program Next j Src(13) = 0 Src(14) = 0 SDMCD16AC (Src(),1,3) '******************--> compute TPHP power and build output table For i=1 To 18 Power(i) = (Vrefacc(i)/8.0)^2 * ((TPHP_Rht(i)*TPHP_timer_final)/(TPHP_ref(i)^2*0.03)) TPHP_out(1) = TPHP_sen(i) TPHP_out(2) = TPHP_timer(i,1) TPHP_out(3) = TPHP_C1(i,1) TPHP_out(4) = TPHP_mv1(i,1) TPHP_out(5) = TPHP_C2(i,1) TPHP_out(6) = TPHP_mv2(i,1) k=7 For j=1 To 8 TPHP_out(k) = Vref(i,j) k = k+1 Next j TPHP_out(k) = power(i) k = k+1 TPHP_out(k) = Vrefacc(i)/8 k = k+1 TPHP_out(k) = TPHP_Rht(i) k = k+1 TPHP_out(k) = TPHP_ref(i) k = k+1 TPHP_out(k) = TPHP_timer_final k = k+1 For j=2 To 41 TPHP_out(k) = dT1(i,j) k = k+1 Next j
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For j=2 To 41 TPHP_out(k) = dT2(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_timer(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_mv1(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_C2(i,j) k = k+1 Next j For j=2 To 41 TPHP_out(k) = TPHP_mv2(i,j) k = k+1 Next j CallTable TPHP Next i Flag(12) = FALSE EndIf '**********************************--> measure DPHP 0.10 Then ECp(i) = (ep(i)*ECb(i))/(eb(i)-eb0) Else ECp(i) = ECb(i) EndIf Else TEout(i) = "No Probe" EndIf
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Next i SW12 (2,0) CallTable (TEData) Flag(16) = False EndIf '**************************--> measure InSitu TDR outside the wall Right handed coordinate system ' ' ' (N) ' {-y} ' | ' | ' | ' | ' | ' (W) {+x} comment out a line fw_raw if fine wire thermocouple is not used. bfl 728-08 fw = t_hmp 'fw = fw_raw 'Measure battery voltage. Battery (batt_volt) 'Find the HMP45C vapor pressure (kPa). VaporPressure (e_hmp,t_hmp,rh_hmp) 'CNR2 Net Radiation Measurements VoltDiff (Rn_shortwave,1,mV50,3,TRUE,200,250,CNR2_SW_CAL,0) VoltDiff (Rn_longwave,1,mV50,4,TRUE,0,250,CNR2_LW_CAL,0) Rn = Rn_shortwave+Rn_longwave 'Measure the HFP01SC soil heat flux plates. VoltDiff (shf_mV(1),2,mV50C,7,TRUE,200,250,1,0) 'Apply HFP01SC soil heat flux plate calibration. For j = 1 to 4 shf(j) = shf_mV(j)*shf_cal(j) Next j 'Power the HFP01SC heaters. PortSet (9,sw12_1_state) 'Measure voltage across the heater (Rf_V). VoltSe (V_Rf(1),4,mV5000,25,TRUE,200,250,0.001,0) 'Maintain filtered values for calibration. AvgRun (shf_mV_run(1),4,shf_mV(1),100) AvgRun (V_Rf_run(1),4,V_Rf(1),100) 'Measure the TCAV soil thermocouples.
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TCDiff (tcav_1,2,mV20C,11,TypeE,panel_temp_raw,TRUE,200,250,1,0) 'TE525/TE525WS Rain Gauge measurement Rain_mm: PulseCount(Rain_mm,1,1,2,0,0.108,0) 'calibrated 4-30-08 by Brad Lyles 50.5 ml/10 tips => 0.108 mm/tip 'Measure the CS616 soil water content probes. CS616 (cs616_wcr(1),2,3,1,2,1,0) 'Apply temperature correction to CS616 period and find volumetric water content. For j = 1 to 2 If ( (10 = &h00ff) ) 'Save only the four most significant bits of the LI-7500 diagnostic word. diag_irga = INT (diag_irga_work/&h0010) 'Filter data in the covariance instruction if the CSAT3 or LI-7500 reports bad data. cov_disable_flag = disable_flag_on(1) OR disable_flag_on(2)
'Start saving the time series data on an even minute boundary.
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If ( (NOT (save_ts_flag_on)) AND (IfTime (0,1,Min)) ) Then ( save_ts_flag_on = TRUE ) 'Save adjusted time series data. CallTable ts_data 'Load the arrays that hold the input data for the covariance instructions. cov_array(1,1) = Ts Move (cov_array(1,2),3,Ux,3) cov_array(2,1) = co2 Move (cov_array(2,2),3,Ux,3) cov_array(3,1) = h2o Move (cov_array(3,2),3,Ux,3) cov_array(4,1) = fw Move (cov_array(4,2),3,Ux,3) 'Compute the online covariances. CallTable comp_cov If ( comp_cov.Output(1,1) ) Then GetRecord (cov_out(1),comp_cov,1) Tsoil_avg(1) = comp_cov.tcav_1_Avg(1,1) Tsoil_avg(2) = comp_cov.tcav_2_Avg(1,1) 'Compass wind direction will be between 0 and 360 degrees. wnd_dir_compass = (wnd_dir_compass+CSAT3_AZIMUTH) MOD 360 'CSAT3 wind direction will be between 0 to 180 degrees and 0 to -180 degrees. If ( wnd_dir_csat3 ) > 180 Then ( wnd_dir_csat3 = wnd_dir_csat3-360 ) h2o_hmp_mean = e_hmp_mean/((t_hmp_mean+273.15)*RV) rho_d_mean = (press_meane_hmp_mean)/((t_hmp_mean+273.15)*RD) rho_a_mean = (rho_d_mean+h2o_hmp_mean)/1000 'Compute online fluxes. Fc_irga = cov_co2_Uz LE_irga = LV*cov_h2o_Uz Hs = rho_a_mean*CP*cov_Ts_Uz
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H = rho_a_mean*CP*cov_fw_Uz tau = SQR ((cov_Ux_Uz*cov_Ux_Uz)+(cov_Uy_Uz*cov_Uy_Uz)) u_star = SQR (tau) tau = rho_a_mean*tau 'Compute the standard deviation from the variance. stdev_Ts = SQR (stdev_Ts) stdev_Ux = SQR (stdev_Ux) stdev_Uy = SQR (stdev_Uy) stdev_Uz = SQR (stdev_Uz) stdev_co2 = SQR (stdev_co2) stdev_h2o = SQR (stdev_h2o) stdev_fw = SQR (stdev_fw) sigma_wpl = h2o_hmp_mean/rho_d_mean 'LI-7500 Webb et al. term for water vapor Eq. (25). h2o_wpl_LE = MU_WPL*sigma_wpl*LE_irga h2o_wpl_H = (1+(MU_WPL*sigma_wpl))*h2o_hmp_mean/(t_hmp_mean+273.15)*LV*cov_Ts_Uz LE_wpl = LE_irga+h2o_wpl_LE+h2o_wpl_H 'Compute a sensible heat flux from Hs and LE_wpl. Hc = (Hs(rho_a_mean*CP*0.51*RD*(t_hmp_mean+273.15)*(t_hmp_mean+273.15)*LE_wpl)/(press _mean*LV))*((t_hmp_mean+273.15)/(Ts_mean+273.15)) 'LI-7500 Webb et al. term for carbon dioxide Eq. (24). co2_wpl_LE = MU_WPL*co2_mean/rho_d_mean*cov_h2o_Uz co2_wpl_H = (1+(MU_WPL*sigma_wpl))*co2_mean/(t_hmp_mean+273.15)*Hc/(rho_a_mean*CP) Fc_wpl = Fc_irga+co2_wpl_LE+co2_wpl_H 'Compute the change in soil temperature. del_Tsoil(1) = Tsoil_avg(1)-prev_Tsoil(1) del_Tsoil(2) = Tsoil_avg(2)-prev_Tsoil(2) prev_Tsoil(1) = Tsoil_avg(1) prev_Tsoil(2) = Tsoil_avg(2) EndIf
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CallTable flux Else scan_count = scan_count+1 EndIf Call hfp01sc_cal NextScan EndProg
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APPENDIX L. LOGGERNET PROGRAM FOR RAINFALL SIMULATOR
'CR1000 'Boulder City Sprinkler Irregation Program 'Version 1 'by Brad Lyles 10-10-08 ' ' This program will control three irregation zones and a master valve. ' Flowrate will be monitored seperately for each zone via Omega roatary sensors. ' If the wind speed exceeds a threshold, the master valve will close and a ' warning will be documented. ' 'Declare Variables and Units Public Batt_Volt Public WS_ms, WS_threshold, AvgWS_ms Public WindDir Public Flag(8) As Boolean Public FlowRaw(3), FlowRate(3) Public Total(3), RunTime, Volume(3), T_Vol, sum_Vol Public WaitTime Dim i 'declare Email parameter strings (as constants), Message String & Result Variable 'Const ServerAddr="198.200.3.45" Const ServerAddr="mail-n.dri.edu" Const ToAddr="
[email protected]" Const FromAddr="
[email protected]" Const Subject="BC Sprinkler Warning Message" Const Attach="" Const UserName="brad" Const Password="" Const CRLF = CHR(13)+CHR(10) Public Result As String * 50 Public AlarmTrigger As Boolean Public Message As String * 250 Public EmailSuccess As Boolean Units Batt_Volt=Volts Units WS_ms=meters/second Units WS_threshold=meters/second Units WindDir=Degrees Units Total=Liter Units Volume=Liter Units RunTime=Seconds 'Define Data Tables 315
DataTable(Wind,True,-1) DataInterval(0,10,Min,10) WindVector (1,WS_ms,WindDir,FP2,False,0,0,2) FieldNames("WS_ms_S_WVT,WS_ms_U_WVT,WindDir_DU_WVT,WindDir_SD U_WVT") EndTable DataTable(daily,True,-1) Average (1,WS_ms,FP2,False) Maximum (1,WS_ms,FP2,False,False) Sample (1,Total,FP2) Sample (1,RunTime,FP2) Sample (1,Volume,FP2) Sample (1,WS_threshold,FP2) Minimum(1,Batt_Volt,FP2,False,False) EndTable DataTable (WSwarn,True,100) Sample (1,WS_ms,FP2) Sample (1,AvgWS_ms,FP2) EndTable 'Main Program BeginProg Volume(1) = 100 Volume(2) = 100 Volume(3) = 100 T_Vol = Volume(1) + Volume(2) + Volume(3) WS_threshold=10 Scan(1,Sec,1,0) 'Default Datalogger Battery Voltage measurement Batt_Volt: Battery(Batt_Volt) '03001 Wind Speed & Direction Sensor measurements WS_ms and WindDir: PulseCount(WS_ms,1,1,1,1,0.75,0.2) If WS_ms=360 Then WindDir=0 'START Time If IfTime (600,1440,Min) Then 'start time Flag(1) = 1 'turn on sprinkler system For i=1 To 3 Total(i) = 0 'reset volume totalizers Next i EndIf 'High wind shut down If (WS_ms > WS_threshold) Then
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Flag(2) = 1 'high wind flag PortSet (1,0) 'turn off master valve CallTable (WSwarn) WaitTime = Timer(1,Sec,2) EndIf WaitTime = Timer(1,Sec,4) '60 second running average for WS AvgRun (AvgWS_ms,1,WS_ms,60) If (WaitTime > 60 ) Then 'wait 60 seconds before restarting sprinklers If (AvgWS_ms < WS_threshold) Then Flag(2) = 0 CallTable (WSwarn) EndIf EndIf 'turn on sprinklers and monitor flowrate PulseCount (FlowRate(),3,16,0,0,40.,0) If (Flag(1)=1 AND Flag(2)=0) Then PortSet (1,1) 'turn on master valve PortSet (2,1) 'turn on zone 1 valve PortSet (3,1) 'turn on zone 2 valve PortSet (4,1) 'turn on zone 3 valve For i=1 To 3 Total(i) = Total(i) + FlowRate(i) Next i RunTime = RunTime + 1 EndIf 'check if total volume is greater then the user specified Volumes If (Total(1) > Volume(1)) Then PortSet (2,0) If (Total(2) > Volume(2)) Then PortSet (3,0) If (Total(3) > Volume(3)) Then PortSet (4,0) sum_Vol = Total(1) + Total(2) + Total(3) If (sum_Vol > T_Vol) Then Flag(1) = 0 'STOP time If IfTime (660,1440,Min) Then CallTable (daily) Flag(1) = 0 EndIf 'turn off all valves If (Flag(1) = 0) Then PortSet (1,0) 'turn off master valve PortSet (2,0) 'turn off zone 1 valve PortSet (3,0) 'turn off zone 2 valve PortSet (4,0) 'turn off zone 3 valve EndIf
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CallTable (Wind) If (Flag(1) = 0 AND Flowrate(1)+Flowrate(2)+Flowrate(3) 0) Then AlarmTrigger = True EndIf NextScan SlowSequence Scan(1,Hr,1,0) If AlarmTrigger = False Then If AlarmTrigger Then Message = "Warning!" + CRLF + CRLF Message = Message + "This is a automatic email message from the datalogger station " + Status.StationName + ". " Message = Message + "An alarm condition has been identified. " Message = Message + "Flowrate(1) = " + Flowrate(1) + " L/sec" + CRLF Message = Message + "Flowrate(2) = " + Flowrate(2) + " L/sec" + CRLF Message = Message + "Flowrate(3) = " + Flowrate(3) + " L/sec" + CRLF + CRLF + CRLF EmailSuccess=EMailSend (ServerAddr,ToAddr,FromAddr,Subject,Message,Attach,UserName,Password,Result) EndIf EndIf If Flowrate(1)+Flowrate(2)+Flowrate(3) = 0 Then AlarmTrigger=False NextScan EndProg
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APPENDIX M. EXAMPLE DATA OUTPUTS
Table M-1. Example output table “BC_Eddy_dly.dat”. Variable Name Statistic Example Type * Record 1 RECORD RN 5:59:28 PM panel_temp_raw C Smp 92537333 Ux_raw m/s Smp 19.5365 Uy_raw m/s Smp -2.22175 Uz_raw m/s Smp -0.3865 Ts_raw C Smp -0.22425 diag_csat_raw unitless Smp 20.35373 co2_raw umol/m^3 Smp 39 h2o_raw mmol/m^3 Smp 12.59977 press_raw kPa Smp 400.9868 diag_irga_raw unitless Smp 93.97631 Statistic types (Smp = sample)
Table M-2. Example output table “CO2.dat”. TIMESTAMP RECORD sensor_num TS RN smp 4/3/2009 15:45 0 120114 4/3/2009 15:45 1 120914 4/3/2009 15:45 2 121114 4/3/2009 15:45 3 121414 4/3/2009 16:00 4 120114 4/3/2009 16:00 5 120914 4/3/2009 16:00 6 121114 4/3/2009 16:00 7 121414 Statistic types (Smp = sample)
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CO2_ppm smp 213.787 18.22989 266.8194 223.7305 99.4362 9.94362 208.816 323.1676
Example Record 2 5:59:29 PM 92537334 19.5365 -2.11675 -0.3225 -0.24175 20.35202 40 12.59836 401.7864 93.97631
CO2_volt smp 0.106894 0.009115 0.13341 0.111865 0.049718 0.004972 0.104408 0.161584
Table M-3.
Example transposed output table “Daily.dat”. Statistic Variable Name Type * Example Record 1 Example Record 2 TIMESTAMP TS 4/5/2009 0:00 4/6/2009 0:00 RECORD RN 1 2 Scale_Kg_Avg Kg Avg 205.7 204.4 Scale_Kg_Std Kg Std 1.087 0.648 Scale_Kg_Min Kg Min 204.2 203.2 Scale_Kg_Max Kg Max 207.8 206.8 batt_volt_Min Min 12.96 12.97 Ptemp C Smp 19.86 20.01 * Statistic types (Smp = sample, Avg = average, Std = Standard Deviation, Min = minimum, Max = Maximum) Table M-4. Example transposed output table “DPHP.dat”. Variable Variable Statistic Example Name Definition Type* Record 1 TIMESTAMP 4/3/2009 18:00 RECORD 0 sensorID Smp: 120302 timer_1 Smp: 0 temp_C_1 Smp: 17.80201 temp_mV_1 Smp: 0.4200662 Vref1 Smp: 0.2133741 Vref2 Smp: 0.2130426 Vref3 Smp: 0.212794 Vref4 Smp: 0.2127112 Vref5 Smp: 0.2125454 Vref6 Smp: 0.2124626 Vref7 Smp: 0.2123797 Vref8 Smp: 0.2122969 Power Smp: 428.3227 Vref Smp:avg 0.2127008 Rht Smp: 39.6 Rref Smp: 1.013 heat_time Smp:total 7.36 DPHP_out(18) diff temp 1 Smp 0.3282528 DPHP_out(19) diff temp 2 Smp 0.5692577 DPHP_out(20) diff temp 3 Smp 0.8516731 DPHP_out(21) diff temp 4 Smp 1.097139 DPHP_out(22) diff temp 5 Smp 1.262777 DPHP_out(23) diff temp 6 Smp 1.363354 DPHP_out(24) diff temp 7 Smp 1.407724
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Example Record 2 4/3/2009 18:00 1 140302 0 16.58591 0.4064762 0.2813223 0.280908 0.2806594 0.2804937 0.280328 0.2801622 0.2800794 0.2799965 724.2778 0.2804937 39.5 1.026 7.36 1.565058 1.754314 2.014547 2.227478 2.332472 2.401976 2.40641
Table M-5. Example transposed output table “DPHP.dat” (continued). Variable Variable Statistic Example Name Definition Type* Record 1 DPHP_out(25) diff temp 8 Smp 1.410685 DPHP_out(26) diff temp 9 Smp 1.403286 DPHP_out(27) diff temp 10 Smp 1.382582 DPHP_out(28) diff temp 11 Smp 1.352999 DPHP_out(29) diff temp 12 Smp 1.316021 DPHP_out(30) diff temp 13 Smp 1.279047 DPHP_out(31) diff temp 14 Smp 1.239115 DPHP_out(32) diff temp 15 Smp 1.197701 DPHP_out(33) diff temp 16 Smp 1.159254 DPHP_out(34) diff temp 17 Smp 1.054255 DPHP_out(35) diff temp 18 Smp 1.045383 DPHP_out(36) diff temp 19 Smp 1.01285 DPHP_out(37) diff temp 20 Smp 0.9817982 DPHP_out(38) diff temp 21 Smp 0.9640522 DPHP_out(39) diff temp 22 Smp 0.9256077 DPHP_out(40) diff temp 23 Smp 0.8945541 DPHP_out(41) diff temp 24 Smp 0.8664589 DPHP_out(42) diff temp 25 Smp 0.8383636 DPHP_out(43) diff temp 26 Smp 0.8147049 DPHP_out(44) diff temp 27 Smp 0.791048 DPHP_out(45) diff temp 28 Smp 0.7659111 DPHP_out(46) diff temp 29 Smp 0.7437325 DPHP_out(47) diff temp 30 Smp 0.723032 DPHP_out(48) diff temp 31 Smp 0.7023315 DPHP_out(49) diff temp 32 Smp 0.6860695 DPHP_out(50) diff temp 33 Smp 0.6653671 DPHP_out(51) diff temp 34 Smp 0.6476231 DPHP_out(52) diff temp 35 Smp 0.6313572 DPHP_out(53) diff temp 36 Smp 0.6150932 DPHP_out(54) diff temp 37 Smp 0.5988312 DPHP_out(55) diff temp 38 Smp 0.5855236 DPHP_out(56) diff temp 39 Smp 0.5692577 DPHP_out(57) diff temp 40 Smp 0.5574303 DPHP_out(58) timer sec 1 Smp 0.2799997 DPHP_out(59) timer sec 2 Smp 2.27 DPHP_out(60) timer sec 3 Smp 4.26 DPHP_out(61) timer sec 4 Smp 6.25 DPHP_out(62) timer sec 5 Smp 8.24 DPHP_out(63) timer sec 6 Smp 10.23 DPHP_out(64) timer sec 7 Smp 12.21 DPHP_out(65) timer sec 8 Smp 14.19
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Example Record 2 2.2955 2.276274 2.231913 2.203815 2.159452 2.10918 2.064819 2.013067 1.96575 1.355097 1.390585 1.368404 1.38763 1.412766 1.366926 1.337355 1.313698 1.295952 1.263424 1.229412 1.20723 1.193922 1.176178 1.156956 1.139212 1.114069 1.103718 1.088932 1.072662 1.053438 1.038651 1.026821 1.010555 0.3399997 2.329999 4.32 6.31 8.299999 10.29 12.27 14.25
Table M-6. Example transposed output table “DPHP.dat” (continued). Variable Variable Statistic Example Name Definition Type* Record 1 DPHP_out(66) timer sec 9 Smp 16.17 DPHP_out(67) timer sec 10 Smp 18.16 DPHP_out(68) timer sec 11 Smp 20.15 DPHP_out(69) timer sec 12 Smp 22.14 DPHP_out(70) timer sec 13 Smp 24.13 DPHP_out(71) timer sec 14 Smp 26.12 DPHP_out(72) timer sec 15 Smp 28.09 DPHP_out(73) timer sec 16 Smp 30.07 DPHP_out(74) timer sec 17 Smp 32.06 DPHP_out(75) timer sec 18 Smp 34.05 DPHP_out(76) timer sec 19 Smp 36.04 DPHP_out(77) timer sec 20 Smp 38.03 DPHP_out(78) timer sec 21 Smp 40.02 DPHP_out(79) timer sec 22 Smp 42.01 DPHP_out(80) timer sec 23 Smp 43.99 DPHP_out(81) timer sec 24 Smp 45.97 DPHP_out(82) timer sec 25 Smp 47.95 DPHP_out(83) timer sec 26 Smp 49.94 DPHP_out(84) timer sec 27 Smp 51.93 DPHP_out(85) timer sec 28 Smp 53.92 DPHP_out(86) timer sec 29 Smp 55.91 DPHP_out(87) timer sec 30 Smp 57.89 DPHP_out(88) timer sec 31 Smp 59.87 DPHP_out(89) timer sec 32 Smp 61.86 DPHP_out(90) timer sec 33 Smp 63.84 DPHP_out(91) timer sec 34 Smp 65.83 DPHP_out(92) timer sec 35 Smp 67.82 DPHP_out(93) timer sec 36 Smp 69.8 DPHP_out(94) timer sec 37 Smp 71.78 DPHP_out(95) timer sec 38 Smp 73.76 DPHP_out(96) timer sec 39 Smp 75.75 DPHP_out(97) timer sec 40 Smp 77.74 DPHP_out(98) temp C 1 Smp 18.13026 DPHP_out(99) temp C 2 Smp 18.37127 DPHP_out(100) temp C 3 Smp 18.65368 DPHP_out(101) temp C 4 Smp 18.89915 DPHP_out(102) temp C 5 Smp 19.06479 DPHP_out(103) temp C 6 Smp 19.16537 DPHP_out(104) temp C 7 Smp 19.20974 DPHP_out(105) temp C 8 Smp 19.2127 DPHP_out(106) temp C 9 Smp 19.2053
322
Example Record 2 16.23 18.22 20.21 22.2 24.19 26.18 28.15 30.13 32.12 34.11 36.1 38.09 40.08 42.07 44.05 46.03 48.01 50 51.99 53.98 55.97 57.95 59.93 61.92 63.9 65.89 67.88 69.86 71.84 73.82 75.81 77.8 18.15096 18.34022 18.60045 18.81339 18.91838 18.98788 18.99232 18.88141 18.86218
Table M-7. Example transposed output table “DPHP.dat” (continued). Variable Variable Statistic Example Name Definition Type* Record 1 DPHP_out(107) temp C 10 Smp 19.18459 DPHP_out(108) temp C 11 Smp 19.15501 DPHP_out(109) temp C 12 Smp 19.11803 DPHP_out(110) temp C 13 Smp 19.08106 DPHP_out(111) temp C 14 Smp 19.04113 DPHP_out(112) temp C 15 Smp 18.99971 DPHP_out(113) temp C 16 Smp 18.96127 DPHP_out(114) temp C 17 Smp 18.85627 DPHP_out(115) temp C 18 Smp 18.84739 DPHP_out(116) temp C 19 Smp 18.81486 DPHP_out(117) temp C 20 Smp 18.78381 DPHP_out(118) temp C 21 Smp 18.76606 DPHP_out(119) temp C 22 Smp 18.72762 DPHP_out(120) temp C 23 Smp 18.69657 DPHP_out(121) temp C 24 Smp 18.66847 DPHP_out(122) temp C 25 Smp 18.64038 DPHP_out(123) temp C 26 Smp 18.61672 DPHP_out(124) temp C 27 Smp 18.59306 DPHP_out(125) temp C 28 Smp 18.56792 DPHP_out(126) temp C 29 Smp 18.54574 DPHP_out(127) temp C 30 Smp 18.52504 DPHP_out(128) temp C 31 Smp 18.50434 DPHP_out(129) temp C 32 Smp 18.48808 DPHP_out(130) temp C 33 Smp 18.46738 DPHP_out(131) temp C 34 Smp 18.44963 DPHP_out(132) temp C 35 Smp 18.43337 DPHP_out(133) temp C 36 Smp 18.4171 DPHP_out(134) temp C 37 Smp 18.40084 DPHP_out(135) temp C 38 Smp 18.38754 DPHP_out(136) temp C 39 Smp 18.37127 DPHP_out(137) temp C 40 Smp 18.35944 DPHP_out(138) temp mV 1 Smp 0.4237365 DPHP_out(139) temp mV 2 Smp 0.4264313 DPHP_out(140) temp mV 3 Smp 0.4295891 DPHP_out(141) temp mV 4 Smp 0.4323335 DPHP_out(142) temp mV 5 Smp 0.4341852 DPHP_out(143) temp mV 6 Smp 0.4353094 DPHP_out(144) temp mV 7 Smp 0.4358054 DPHP_out(145) temp mV 8 Smp 0.4358385 DPHP_out(146) temp mV 9 Smp 0.4357558 DPHP_out(147) temp mV 10 Smp 0.4355244
323
Example Record 2 18.81782 18.78972 18.74536 18.69509 18.65073 18.59897 18.55166 17.941 17.97649 17.95431 17.97354 17.99867 17.95283 17.92326 17.8996 17.88186 17.84933 17.81532 17.79314 17.77983 17.76208 17.74286 17.72512 17.69998 17.68962 17.67484 17.65857 17.63935 17.62456 17.61273 17.59646 0.4239679 0.4260841 0.4289939 0.4313746 0.4325485 0.4333255 0.4333751 0.4321351 0.4319202 0.4314242
Table M-8. Example transposed output table “DPHP.dat” (continued). Variable Variable Statistic Example Name Definition Type* Record 1 DPHP_out(148) temp mV 11 Smp 0.4351937 DPHP_out(149) temp mV 12 Smp 0.4347804 DPHP_out(150) temp mV 13 Smp 0.4343671 DPHP_out(151) temp mV 14 Smp 0.4339207 DPHP_out(152) temp mV 15 Smp 0.4334578 DPHP_out(153) temp mV 16 Smp 0.4330279 DPHP_out(154) temp mV 17 Smp 0.4318541 DPHP_out(155) temp mV 18 Smp 0.4317549 DPHP_out(156) temp mV 19 Smp 0.4313911 DPHP_out(157) temp mV 20 Smp 0.431044 DPHP_out(158) temp mV 21 Smp 0.4308456 DPHP_out(159) temp mV 22 Smp 0.4304157 DPHP_out(160) temp mV 23 Smp 0.4300685 DPHP_out(161) temp mV 24 Smp 0.4297544 DPHP_out(162) temp mV 25 Smp 0.4294403 DPHP_out(163) temp mV 26 Smp 0.4291758 DPHP_out(164) temp mV 27 Smp 0.4289112 DPHP_out(165) temp mV 28 Smp 0.4286302 DPHP_out(166) temp mV 29 Smp 0.4283822 DPHP_out(167) temp mV 30 Smp 0.4281507 DPHP_out(168) temp mV 31 Smp 0.4279193 DPHP_out(169) temp mV 32 Smp 0.4277374 DPHP_out(170) temp mV 33 Smp 0.4275059 DPHP_out(171) temp mV 34 Smp 0.4273075 DPHP_out(172) temp mV 35 Smp 0.4271257 DPHP_out(173) temp mV 36 Smp 0.4269438 DPHP_out(174) temp mV 37 Smp 0.426762 DPHP_out(175) temp mV 38 Smp 0.4266132 DPHP_out(176) temp mV 39 Smp 0.4264313 DPHP_out(177) temp mV 40 Smp 0.426299 DPHP_out(178) Smp 0 DPHP_out(179) Smp 0 * Statistic types (Smp = sample, Smp:Avg = Average, Smp:Total = Total)
324
Example Record 2 0.4311101 0.4306141 0.430052 0.429556 0.4289774 0.4284483 0.4216202 0.422017 0.4217691 0.421984 0.4222651 0.4217525 0.4214219 0.4211573 0.4209589 0.4205952 0.420215 0.419967 0.4198182 0.4196198 0.4194049 0.4192065 0.4189254 0.4188097 0.4186443 0.4184625 0.4182476 0.4180822 0.41795 0.4177681 0 0
Table M-9.
Example transposed output table "HDU.dat". Example Variable Name Abbreviation Statistic Type* Record 1 TIMESTAMP TS 6/3/09 10:00 RECORD RN Smp 24304 sensorID Smp 110204 SoilTemp Smp 30.91109 deltaTemp Smp 2.629807 T_1sec Smp 32.14703 T_30sec Smp 34.77684 RefTemp Smp 26.94606 Tstar Smp 0.000001 Psi Smp 2.4604E+19 wet Smp 0.7037472 dry Smp 2.598811 alpha Smp 129.923 beta Smp 0.2790088 * Statistic types (Smp = sample)
325
Example Record 2 6/3/09 10:00 24305 120204 31.68461 2.746502 32.7258 35.47231 26.94606 0.000001 1.12444E+17 0.7038386 2.704624 129.923 0.3130769
Table M-10. Example transposed output table “Scale.dat”. Statistic Example Variable Name Units Type * Record 1 TIMESTAMP Date Time 4/3/2009 16:00 RECORD 1 MassID Smp 100015 Scale_mV_Avg mV Avg 1.091006 Scale_Kg_Mean Smp 208.4792 Scale_Kg_SD Smp 0.5804706 Scale_Kg_Min Smp 206.8081 Scale_Kg_Max Smp 209.7876 TCAV_ID Smp 120208 tcav_1_Avg Avg 17.4 SHF1_ID Smp 110207 shf_Avg(1) W/m^2 Avg -14.86542 shf_cal(1) W/(m^2 mV) Smp 16.23377 SHF2_ID Smp 130207 shf_Avg(2) W/m^2 Avg -11.59741 shf_cal(2) W/(m^2 mV) Smp 15.97444 ST1_ID Smp 160206 S_Therm_Avg(1) C Avg 17.91 ST2_ID Smp 160406 S_Therm_Avg(2) C Avg 19.02 ST3_ID Smp 160506 S_Therm_Avg(3) C Avg 18.82 ST4_ID Smp 160706 S_Therm_Avg(4) C Avg 18.23 Ptemp_ID Smp 100016 Ptemp_Avg Avg 20.35 CS616_ID Smp 130219 cs616_uS_Avg uSeconds Avg 19.05 soil_water_VMC_Avg frac_v_wtr Avg 0.069 Scale_temp_C_Avg Avg 18.02 * Statistic types (Smp = sample, Avg = average)
326
Example Record 2 4/3/2009 16:15 2 100015 1.091252 209.091 0.5267347 207.9563 211.5034 120208 17.15 110207 -17.08253 16.23377 130207 -13.40317 15.97444 160206 17.68 160406 19.01 160506 18.82 160706 18.23 100016 20.35 130219 19.05 0.069 18.01
Table M-11. Example transposed output table "SHT75.dat”. Variable Statistic Example Name Abbreviation Type* Record 1 TIMESTAMP TS 5:15:00 PM RECORD RN 0 T75(1) Degrees_C Smp 2.632 T75(2) Degrees_C Smp 2.743 T75(3) Degrees_C Smp 2.975 T75(4) Degrees_C Smp 3.077 RH75(1) RH_Percent Smp 90.5 RH75(2) RH_Percent Smp 89 RH75(3) RH_Percent Smp 86.1 RH75(4) RH_Percent Smp 85 WS_ms_S_WVT meters/second WVc 0 WindDir_D1_WVT Deg WVc 0 * Statistic types (Smp = sample, Avg = average)
Example Record 2 5:30:00 PM 1 2.692 2.763 2.945 3.248 89.2 87.6 85.9 83.5 0.071 224.9
Table M-12. Example transposed output table “TDR.dat”. Variable Statistic Name Abbreviation Type* Example Record 1 Example Record 2 TIMESTAMP TS 4/3/2009 16:00 4/3/2009 16:00 RECORD RN 0 1 sensorID Smp: 110301 120301 LaL Smp: 2.336395 2.272633 ToppVWC Smp: 0.078544 0.0715449 TDR_EC Smp: 0.003843971 0.003279238 a0 Smp: -0.0789 -0.0789 a1 Smp: 0.03481 0.03481 a2 Smp: -0.00122 -0.00122 a3 Smp 2.32E-05 2.32E-05 * Statistic types (Smp = sample)
327
Table M-13. Example transposed output table “TDR_Wave.dat”. Variable Name TIMESTAMP RECORD MuxChan sensorID WavePT_1 WavePT_2 WavePT_3 Etc WavePT(6) WavePT(7) WavePT(8) WavePT(9) WavePT(10) WavePT(11) WavePT(12) WavePT(13) WavePT(14) WavePT(15) WavePT(16) WavePT(17) WavePT(18) WavePT(19) WavePT(20) WavePT(21) WavePT(22) WavePT(23) WavePT(24) WavePT(25) WavePT(26) WavePT(27) WavePT(28) WavePT(29) WavePT(30) WavePT(31) WavePT(32) WavePT(33) WavePT(34) WavePT(35) WavePT(36) WavePT(37) WavePT(38) WavePT(39) WavePT(40) WavePT(41) WavePT(42) WavePT(43)
Abbreviation TS RN
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
328
Example Record 1 4/4/2009 0:00 0 110301 4 1 251 15 3 0.3 0.155 1000 0 22.65 23.74 23.74 22.65 24.82 22.65 23.74 23.74 22.65 24.82 23.74 23.74 25.9 25.9 25.9 24.82 23.74 25.9 25.9 25.9 25.9 25.9 26.99 26.99 26.99 26.99 26.99 26.99 29.15 31.32 35.65 43.24 54.07 65.99
Example Record 2 4/4/2009 0:00 1 120301 4 1 251 15 3 0.3 0.155 1000 0 23.11 23.11 23.11 23.11 24.2 23.11 23.11 23.11 23.11 23.11 23.11 22.03 22.03 23.11 23.11 22.03 22.03 22.03 23.11 24.2 23.11 23.11 23.11 24.2 23.11 24.2 24.2 24.2 23.11 23.11 23.11 23.11 23.11 19.86
Table M-14. Example transposed output table “TDR_Wave.dat” (continued). Variable Name WavePT(44) WavePT(45) WavePT(46) WavePT(47) WavePT(48) WavePT(49) WavePT(50) WavePT(51) WavePT(52) WavePT(53) WavePT(54) WavePT(55) WavePT(56) WavePT(57) WavePT(58) WavePT(59) WavePT(60) WavePT(61) WavePT(62) WavePT(63) WavePT(64) WavePT(65) WavePT(66) WavePT(67) WavePT(68) WavePT(69) WavePT(70) WavePT(71) WavePT(72) WavePT(73) WavePT(74) WavePT(75) WavePT(76) WavePT(77) WavePT(78) WavePT(79) WavePT(80) WavePT(81) WavePT(82) WavePT(83) WavePT(84) WavePT(85) WavePT(86) WavePT(87) WavePT(88) WavePT(89)
Abbreviation
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
329
Example Record 1 83.3 101.7 120.2 136.4 150.5 161.3 170 178.7 185.2 189.5 192.7 194.9 191.7 189.5 186.2 183 178.7 178.7 174.3 171.1 170 165.7 161.3 161.3 157 157 152.7 152.7 148.3 148.3 148.3 147.2 144 144 139.7 139.7 135.3 135.3 131 131 129.9 126.7 126.7 122.3 122.3 119.1
Example Record 2 20.94 20.94 23.11 22.03 22.03 23.11 23.11 23.11 23.11 24.2 24.2 24.2 24.2 24.2 24.2 24.2 24.2 25.28 26.37 28.54 32.88 38.3 46.98 58.92 74.11 90.4 108.8 126.2 141.4 155.5 165.2 176.1 182.6 188 192.4 194.5 193.5 193.5 190.2 188 184.8 180.4 179.3 176.1 171.8 171.8
Table M-15. Example transposed output table “TDR_Wave.dat” (continued). Variable Name WavePT(90) WavePT(91) WavePT(92) WavePT(93) WavePT(94) WavePT(95) WavePT(96) WavePT(97) WavePT(98) WavePT(99) WavePT(100) WavePT(101) WavePT(102) WavePT(103) WavePT(104) WavePT(105) WavePT(106) WavePT(107) WavePT(108) WavePT(109) WavePT(110) WavePT(111) WavePT(112) WavePT(113) WavePT(114) WavePT(115) WavePT(116) WavePT(117) WavePT(118) WavePT(119) WavePT(120) WavePT(121) WavePT(122) WavePT(123) WavePT(124) WavePT(125) WavePT(126) WavePT(127) WavePT(128) WavePT(129) WavePT(130) WavePT(131) WavePT(132) WavePT(133) WavePT(134) WavePT(135)
Abbreviation
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
330
Example Record 1 118 118 114.7 113.7 113.7 109.3 109.3 109.3 108.2 105 105 105 103.9 100.7 100.7 100.7 100.7 100.7 100.7 100.7 103.9 109.3 118 128.8 144 161.3 178.7 197.1 218.7 236.1 253.4 270.7 289.2 302.2 317.3 331.4 344.4 354.2 366.1 375.8 387.8 396.4 405.1 414.8 422.4 430
Example Record 2 167.4 164.2 163.1 160.9 158.7 158.7 154.4 154.4 154.4 150.1 150.1 150.1 149 145.7 143.5 141.4 141.4 137 137 132.7 132.7 132.7 128.4 128.4 127.3 124 124 124 122.9 124 119.7 119.7 119.7 116.4 115.3 115.3 115.3 112.1 111 111 111 111 114.2 117.5 122.9 130.5
Table M-16. Example transposed output table “TDR_Wave.dat” (continued). Variable Name WavePT(136) WavePT(137) WavePT(138) WavePT(139) WavePT(140) WavePT(141) WavePT(142) WavePT(143) WavePT(144) WavePT(145) WavePT(146) WavePT(147) WavePT(148) WavePT(149) WavePT(150) WavePT(151) WavePT(152) WavePT(153) WavePT(154) WavePT(155) WavePT(156) WavePT(157) WavePT(158) WavePT(159) WavePT(160) WavePT(161) WavePT(162) WavePT(163) WavePT(164) WavePT(165) WavePT(166) WavePT(167) WavePT(168) WavePT(169) WavePT(170) WavePT(171) WavePT(172) WavePT(173) WavePT(174) WavePT(175) WavePT(176) WavePT(177) WavePT(178) WavePT(179) WavePT(180) WavePT(181)
Abbreviation
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
331
Example Record 1 437.6 443 451.7 458.2 463.6 469 475.5 480.9 486.3 492.8 498.3 502.6 506.9 511.3 515.6 519.9 523.2 526.4 528.6 532.9 536.2 538.3 541.6 542.7 545.9 550.3 551.3 554.6 555.7 558.9 562.2 563.3 566.5 569.8 571.9 575.2 578.4 580.6 584.9 588.2 591.4 593.6 596.8 600.1 602.3 604.4
Example Record 2 143.5 158.7 177.2 196.7 216.2 236.8 257.5 272.7 291.1 307.4 321.5 335.6 351.9 363.8 373.6 386.6 397.4 407.2 415.9 424.5 433.2 441.9 449.5 457.1 464.7 470.1 475.5 482 488.6 495.1 500.5 505.9 510.3 516.8 521.1 526.5 530.9 535.2 538.5 541.7 543.9 548.2 552.6 555.8 558 561.3
Table M-17. Example transposed output table “TDR_Wave.dat” (continued). Variable Name WavePT(182) WavePT(183) WavePT(184) WavePT(185) WavePT(186) WavePT(187) WavePT(188) WavePT(189) WavePT(190) WavePT(191) WavePT(192) WavePT(193) WavePT(194) WavePT(195) WavePT(196) WavePT(197) WavePT(198) WavePT(199) WavePT(200) WavePT(201) WavePT(202) WavePT(203) WavePT(204) WavePT(205) WavePT(206) WavePT(207) WavePT(208) WavePT(209) WavePT(210) WavePT(211) WavePT(212) WavePT(213) WavePT(214) WavePT(215) WavePT(216) WavePT(217) WavePT(218) WavePT(219) WavePT(220) WavePT(221) WavePT(222) WavePT(223) WavePT(224) WavePT(225) WavePT(226) WavePT(227)
Abbreviation
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
332
Example Record 1 605.5 606.6 607.7 607.7 607.7 607.7 607.7 607.7 607.7 607.7 606.6 604.4 603.3 603.3 603.3 603.3 603.3 603.3 603.3 602.3 602.3 602.3 602.3 600.1 599 599 599 599 599 599 599 599 599 599 599 599 599 599 600.1 601.2 602.3 602.3 602.3 602.3 603.3 603.3
Example Record 2 564.5 565.6 569.9 571 574.3 577.5 578.6 581.9 584 586.2 589.5 591.6 596 599.2 602.5 604.6 609 612.2 615.5 617.7 618.8 622 623.1 623.1 623.1 623.1 623.1 623.1 623.1 623.1 622 618.8 619.8 618.8 618.8 618.8 618.8 618.8 618.8 617.7 616.6 614.4 614.4 614.4 614.4 614.4
Table M-18. Example transposed output table “TDR_Wave.dat” (continued). Variable Name Abbreviation WavePT(228) WavePT(229) WavePT(230) WavePT(231) WavePT(232) WavePT(233) WavePT(234) WavePT(235) WavePT(236) WavePT(237) WavePT(238) WavePT(239) WavePT(240) WavePT(241) WavePT(242) WavePT(243) WavePT(244) WavePT(245) WavePT(246) WavePT(247) WavePT(248) WavePT(249) WavePT(250) WavePT(251) WavePT(252) WavePT(253) WavePT(254) WavePT(255) WavePT(256) WavePT(257) WavePT(258) WavePT(259) WavePT(260) * Statistic types (Smp = sample)
Statistic Type* Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp Smp
333
Example Record 1 603.3 603.3 603.3 603.3 603.3 605.5 604.4 605.5 606.6 606.6 606.6 607.7 607.7 607.7 608.8 610.9 610.9 610.9 610.9 610.9 612 612 612 612 612 612 612 612 612 612 614.2 615.3 616.4
Example Record 2 614.4 614.4 614.4 614.4 614.4 614.4 614.4 613.3 613.3 613.3 613.3 613.3 613.3 613.3 614.4 614.4 614.4 614.4 614.4 614.4 614.4 614.4 616.6 617.7 617.7 617.7 618.8 618.8 618.8 618.8 618.8 619.8 622
Table M-19. Example transposed output table “TEData.dat”. Variable Statistic Name Abbreviation Type* Value Value TIMESTAMP 4/3/2009 16:00 4/3/2009 17:00 RECORD RN 0 1 TE_sen(1) Smp 110209 110209 TE_sen(2) Smp 130209 130209 VWCm(1) Smp 0.069 0.068 VWCm(2) Smp 0.074 0.074 ECp(1) Smp 0.03 0.03 ECp(2) Smp 0.02 0.03 Temp(1) Smp 17.8 17.5 Temp(2) Smp 17.6 17.2 * Statistic types (Smp = sample)
334
Table M-20. Example transposed output table “TPHP.dat”. Variable Variable Statistic Example Record Name Definition Type* 1 TIMESTAMP TS 4/3/2009 16:00 RECORD RN 0 sensorID Smp: 125003 timer_1 Smp: 0 temp1_C_1 Smp: 13.62661 temp1_mV_1 Smp: 0.3735516 temp2_C_1 Smp: 13.01524 temp2_mv_1 Smp: 0.3667936 Vref1 Smp: 0.2940826 Vref2 Smp: 0.2927568 Vref3 Smp: 0.2924253 Vref4 Smp: 0.2923424 Vref5 Smp: 0.2920938 Vref6 Smp: 0.2920938 Vref7 Smp: 0.2919281 Vref8 Smp: 0.2919281 Power Smp: 900.6807 Vref Smp:avg 0.2924564 Rht Smp: 40 Rref Smp: 1.0014 heat_time Smp:total 7.92 TPHP_out(20) diff temp_1 1 Smp 0.03599358 TPHP_out(21) diff temp_1 2 Smp 0.1019793 TPHP_out(22) diff temp_1 3 Smp 0.2203999 TPHP_out(23) diff temp_1 4 Smp 0.3941803 TPHP_out(24) diff temp_1 5 Smp 0.6067457 TPHP_out(25) diff temp_1 6 Smp 0.8340931 TPHP_out(26) diff temp_1 7 Smp 1.059759 TPHP_out(27) diff temp_1 8 Smp 1.267344 TPHP_out(28) diff temp_1 9 Smp 1.44644 TPHP_out(29) diff temp_1 10 Smp 1.598597 TPHP_out(30) diff temp_1 11 Smp 1.71938 TPHP_out(31) diff temp_1 12 Smp 1.813294 TPHP_out(32) diff temp_1 13 Smp 1.881855 TPHP_out(33) diff temp_1 14 Smp 1.929538 TPHP_out(34) diff temp_1 15 Smp 1.95934 TPHP_out(35) diff temp_1 16 Smp 1.972748 TPHP_out(36) diff temp_1 17 Smp 1.972748 TPHP_out(37) diff temp_1 18 Smp 1.96381 TPHP_out(38) diff temp_1 19 Smp 1.945928 TPHP_out(39) diff temp_1 20 Smp 1.920599
335
Example Record 2 4/3/2009 16:00 1 125103 0 15.44885 0.3937925 14.12111 0.3790311 0.2933368 0.2923424 0.2919281 0.2917624 0.2916795 0.2915967 0.2915967 0.2915967 902.778 0.2919799 40.2 1.0011 7.92 0.02086544 0.06557655 0.1579599 0.2950068 0.4692297 0.6582708 0.8487234 1.021268 1.175918 1.305259 1.410794 1.492543 1.553475 1.599542 1.62926 1.648578 1.654525 1.654525 1.647092 1.633718
Table M-21. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(40) diff temp_1 21 Smp 1.893776 1.618857 TPHP_out(41) diff temp_1 22 Smp 1.860988 1.596566 TPHP_out(42) diff temp_1 23 Smp 1.82373 1.574277 TPHP_out(43) diff temp_1 24 Smp 1.786465 1.550504 TPHP_out(44) diff temp_1 25 Smp 1.749198 1.523751 TPHP_out(45) diff temp_1 26 Smp 1.710434 1.497001 TPHP_out(46) diff temp_1 27 Smp 1.668683 1.470248 TPHP_out(47) diff temp_1 28 Smp 1.631408 1.442008 TPHP_out(48) diff temp_1 29 Smp 1.59263 1.413768 TPHP_out(49) diff temp_1 30 Smp 1.550869 1.387015 TPHP_out(50) diff temp_1 31 Smp 1.513577 1.358773 TPHP_out(51) diff temp_1 32 Smp 1.477773 1.330529 TPHP_out(52) diff temp_1 33 Smp 1.440473 1.303775 TPHP_out(53) diff temp_1 34 Smp 1.404659 1.277015 TPHP_out(54) diff temp_1 35 Smp 1.370335 1.251742 TPHP_out(55) diff temp_1 36 Smp 1.336009 1.22498 TPHP_out(56) diff temp_1 37 Smp 1.304663 1.199706 TPHP_out(57) diff temp_1 38 Smp 1.273313 1.175918 TPHP_out(58) diff temp_1 39 Smp 1.24047 1.150638 TPHP_out(59) diff temp_1 40 Smp 1.210607 1.128337 TPHP_out(60) diff temp_2 1 Smp 0.07670307 0.01347065 TPHP_out(61) diff temp_2 2 Smp 0.178937 0.03891754 TPHP_out(62) diff temp_2 3 Smp 0.3381948 0.09129715 TPHP_out(63) diff temp_2 4 Smp 0.5453596 0.1735897 TPHP_out(64) diff temp_2 5 Smp 0.7703228 0.282774 TPHP_out(65) diff temp_2 6 Smp 0.9875803 0.4113474 TPHP_out(66) diff temp_2 7 Smp 1.183703 0.5443459 TPHP_out(67) diff temp_2 8 Smp 1.348268 0.6772833 TPHP_out(68) diff temp_2 9 Smp 1.481346 0.8012085 TPHP_out(69) diff temp_2 10 Smp 1.584475 0.9116526 TPHP_out(70) diff temp_2 11 Smp 1.659181 1.01162 TPHP_out(71) diff temp_2 12 Smp 1.709971 1.092167 TPHP_out(72) diff temp_2 13 Smp 1.741338 1.160767 TPHP_out(73) diff temp_2 14 Smp 1.754781 1.215935 TPHP_out(74) diff temp_2 15 Smp 1.757767 1.25917 TPHP_out(75) diff temp_2 16 Smp 1.748806 1.293457 TPHP_out(76) diff temp_2 17 Smp 1.730882 1.317306 TPHP_out(77) diff temp_2 18 Smp 1.709971 1.333699 TPHP_out(78) diff temp_2 19 Smp 1.683084 1.342645 TPHP_out(79) diff temp_2 20 Smp 1.653205 1.348605 TPHP_out(80) diff temp_2 21 Smp 1.621831 1.345624
336
Table M-22. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(81) diff temp_2 22 Smp 1.587462 1.341155 TPHP_out(82) diff temp_2 23 Smp 1.551597 1.333699 TPHP_out(83) diff temp_2 24 Smp 1.515725 1.323268 TPHP_out(84) diff temp_2 25 Smp 1.479849 1.309851 TPHP_out(85) diff temp_2 26 Smp 1.445466 1.294947 TPHP_out(86) diff temp_2 27 Smp 1.40958 1.278549 TPHP_out(87) diff temp_2 28 Smp 1.37519 1.260659 TPHP_out(88) diff temp_2 29 Smp 1.342287 1.242774 TPHP_out(89) diff temp_2 30 Smp 1.30938 1.221899 TPHP_out(90) diff temp_2 31 Smp 1.277966 1.202517 TPHP_out(91) diff temp_2 32 Smp 1.248045 1.181643 TPHP_out(92) diff temp_2 33 Smp 1.218119 1.163748 TPHP_out(93) diff temp_2 34 Smp 1.18819 1.14287 TPHP_out(94) diff temp_2 35 Smp 1.161252 1.121995 TPHP_out(95) diff temp_2 36 Smp 1.135809 1.101115 TPHP_out(96) diff temp_2 37 Smp 1.108866 1.081726 TPHP_out(97) diff temp_2 38 Smp 1.084915 1.062335 TPHP_out(98) diff temp_2 39 Smp 1.062455 1.042947 TPHP_out(99) diff temp_2 40 Smp 1.037001 1.025044 TPHP_out(100) timer 1 Smp 0.04999971 0.1099997 TPHP_out(101) timer 2 Smp 2.03 2.09 TPHP_out(102) timer 3 Smp 4.01 4.07 TPHP_out(103) timer 4 Smp 5.99 6.05 TPHP_out(104) timer 5 Smp 7.97 8.03 TPHP_out(105) timer 6 Smp 9.950001 10.01 TPHP_out(106) timer 7 Smp 11.93 11.99 TPHP_out(107) timer 8 Smp 13.91 13.97 TPHP_out(108) timer 9 Smp 15.89 15.95 TPHP_out(109) timer 10 Smp 17.87 17.93 TPHP_out(110) timer 11 Smp 19.85 19.91 TPHP_out(111) timer 12 Smp 21.83 21.89 TPHP_out(112) timer 13 Smp 23.81 23.87 TPHP_out(113) timer 14 Smp 25.79 25.85 TPHP_out(114) timer 15 Smp 27.77 27.83 TPHP_out(115) timer 16 Smp 29.75 29.81 TPHP_out(116) timer 17 Smp 31.73 31.79 TPHP_out(117) timer 18 Smp 33.71 33.77 TPHP_out(118) timer 19 Smp 35.68 35.74 TPHP_out(119) timer 20 Smp 37.66 37.72 TPHP_out(120) timer 21 Smp 39.64 39.7 TPHP_out(121) timer 22 Smp 41.61 41.67
337
Table M-23. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(122) timer 23 Smp 43.59 43.65 TPHP_out(123) timer 24 Smp 45.57 45.63 TPHP_out(124) timer 25 Smp 47.55 47.61 TPHP_out(125) timer 26 Smp 49.53 49.59 TPHP_out(126) timer 27 Smp 51.5 51.56 TPHP_out(127) timer 28 Smp 53.48 53.54 TPHP_out(128) timer 29 Smp 55.46 55.52 TPHP_out(129) timer 30 Smp 57.44 57.5 TPHP_out(130) timer 31 Smp 59.41 59.47 TPHP_out(131) timer 32 Smp 61.39 61.45 TPHP_out(132) timer 33 Smp 63.37 63.43 TPHP_out(133) timer 34 Smp 65.35 65.41 TPHP_out(134) timer 35 Smp 67.33 67.39 TPHP_out(135) timer 36 Smp 69.31001 69.37 TPHP_out(136) timer 37 Smp 71.29 71.35 TPHP_out(137) timer 38 Smp 73.27 73.33 TPHP_out(138) timer 39 Smp 75.25 75.31001 TPHP_out(139) timer 40 Smp 77.23 77.29 TPHP_out(140) temp C_1 1 Smp 13.6626 15.46971 TPHP_out(141) temp C_1 2 Smp 13.72859 15.51442 TPHP_out(142) temp C_1 3 Smp 13.84701 15.60681 TPHP_out(143) temp C_1 4 Smp 14.02079 15.74385 TPHP_out(144) temp C_1 5 Smp 14.23335 15.91808 TPHP_out(145) temp C_1 6 Smp 14.4607 16.10712 TPHP_out(146) temp C_1 7 Smp 14.68637 16.29757 TPHP_out(147) temp C_1 8 Smp 14.89395 16.47012 TPHP_out(148) temp C_1 9 Smp 15.07305 16.62477 TPHP_out(149) temp C_1 10 Smp 15.22521 16.75411 TPHP_out(150) temp C_1 11 Smp 15.34599 16.85964 TPHP_out(151) temp C_1 12 Smp 15.4399 16.94139 TPHP_out(152) temp C_1 13 Smp 15.50846 17.00232 TPHP_out(153) temp C_1 14 Smp 15.55615 17.04839 TPHP_out(154) temp C_1 15 Smp 15.58595 17.07811 TPHP_out(155) temp C_1 16 Smp 15.59936 17.09743 TPHP_out(156) temp C_1 17 Smp 15.59936 17.10337 TPHP_out(157) temp C_1 18 Smp 15.59042 17.10337 TPHP_out(158) temp C_1 19 Smp 15.57254 17.09594 TPHP_out(159) temp C_1 20 Smp 15.54721 17.08257 TPHP_out(160) temp C_1 21 Smp 15.52038 17.06771 TPHP_out(161) temp C_1 22 Smp 15.4876 17.04541 TPHP_out(162) temp C_1 23 Smp 15.45034 17.02312
338
Table M-24. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(163) temp C_1 24 Smp 15.41307 16.99935 TPHP_out(164) temp C_1 25 Smp 15.37581 16.9726 TPHP_out(165) temp C_1 26 Smp 15.33704 16.94585 TPHP_out(166) temp C_1 27 Smp 15.29529 16.9191 TPHP_out(167) temp C_1 28 Smp 15.25802 16.89086 TPHP_out(168) temp C_1 29 Smp 15.21924 16.86262 TPHP_out(169) temp C_1 30 Smp 15.17748 16.83586 TPHP_out(170) temp C_1 31 Smp 15.14019 16.80762 TPHP_out(171) temp C_1 32 Smp 15.10438 16.77938 TPHP_out(172) temp C_1 33 Smp 15.06708 16.75262 TPHP_out(173) temp C_1 34 Smp 15.03127 16.72586 TPHP_out(174) temp C_1 35 Smp 14.99694 16.70059 TPHP_out(175) temp C_1 36 Smp 14.96262 16.67383 TPHP_out(176) temp C_1 37 Smp 14.93127 16.64855 TPHP_out(177) temp C_1 38 Smp 14.89992 16.62477 TPHP_out(178) temp C_1 39 Smp 14.86708 16.59949 TPHP_out(179) temp C_1 40 Smp 14.83722 16.57718 TPHP_out(180) temp mV_1 1 Smp 0.3739501 0.394025 TPHP_out(181) temp mV_1 2 Smp 0.3746807 0.3945231 TPHP_out(182) temp mV_1 3 Smp 0.3759925 0.3955526 TPHP_out(183) temp mV_1 4 Smp 0.3779186 0.3970802 TPHP_out(184) temp mV_1 5 Smp 0.3802764 0.3990229 TPHP_out(185) temp mV_1 6 Smp 0.3828003 0.4011317 TPHP_out(186) temp mV_1 7 Smp 0.3853076 0.4032571 TPHP_out(187) temp mV_1 8 Smp 0.3876157 0.4051832 TPHP_out(188) temp mV_1 9 Smp 0.3896082 0.4069101 TPHP_out(189) temp mV_1 10 Smp 0.3913018 0.4083547 TPHP_out(190) temp mV_1 11 Smp 0.3926468 0.4095336 TPHP_out(191) temp mV_1 12 Smp 0.3936929 0.4104469 TPHP_out(192) temp mV_1 13 Smp 0.3944567 0.4111276 TPHP_out(193) temp mV_1 14 Smp 0.394988 0.4116424 TPHP_out(194) temp mV_1 15 Smp 0.3953201 0.4119745 TPHP_out(195) temp mV_1 16 Smp 0.3954696 0.4121903 TPHP_out(196) temp mV_1 17 Smp 0.3954696 0.4122567 TPHP_out(197) temp mV_1 18 Smp 0.3953699 0.4122567 TPHP_out(198) temp mV_1 19 Smp 0.3951707 0.4121737 TPHP_out(199) temp mV_1 20 Smp 0.3948884 0.4120243 TPHP_out(200) temp mV_1 21 Smp 0.3945895 0.4118582 TPHP_out(201) temp mV_1 22 Smp 0.3942242 0.4116091 TPHP_out(202) temp mV_1 23 Smp 0.3938091 0.4113601 TPHP_out(203) temp mV_1 24 Smp 0.393394 0.4110944
339
Table M-25. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(204) temp mV_1 25 Smp 0.3929789 0.4107955 TPHP_out(205) temp mV_1 26 Smp 0.3925472 0.4104967 TPHP_out(206) temp mV_1 27 Smp 0.3920822 0.4101978 TPHP_out(207) temp mV_1 28 Smp 0.3916672 0.4098823 TPHP_out(208) temp mV_1 29 Smp 0.3912354 0.4095668 TPHP_out(209) temp mV_1 30 Smp 0.3907705 0.4092679 TPHP_out(210) temp mV_1 31 Smp 0.3903554 0.4089524 TPHP_out(211) temp mV_1 32 Smp 0.3899569 0.408637 TPHP_out(212) temp mV_1 33 Smp 0.3895418 0.4083381 TPHP_out(213) temp mV_1 34 Smp 0.3891433 0.4080392 TPHP_out(214) temp mV_1 35 Smp 0.3887613 0.4077569 TPHP_out(215) temp mV_1 36 Smp 0.3883795 0.407458 TPHP_out(216) temp mV_1 37 Smp 0.3880308 0.4071757 TPHP_out(217) temp mV_1 38 Smp 0.3876821 0.4069101 TPHP_out(218) temp mV_1 39 Smp 0.3873168 0.4066278 TPHP_out(219) temp mV_1 40 Smp 0.3869847 0.4063787 TPHP_out(220) temp C_2 1 Smp 13.09194 14.13458 TPHP_out(221) temp C_2 2 Smp 13.19417 14.16003 TPHP_out(222) temp C_2 3 Smp 13.35343 14.21241 TPHP_out(223) temp C_2 4 Smp 13.5606 14.2947 TPHP_out(224) temp C_2 5 Smp 13.78556 14.40388 TPHP_out(225) temp C_2 6 Smp 14.00282 14.53246 TPHP_out(226) temp C_2 7 Smp 14.19894 14.66545 TPHP_out(227) temp C_2 8 Smp 14.3635 14.79839 TPHP_out(228) temp C_2 9 Smp 14.49658 14.92232 TPHP_out(229) temp C_2 10 Smp 14.59971 15.03276 TPHP_out(230) temp C_2 11 Smp 14.67442 15.13273 TPHP_out(231) temp C_2 12 Smp 14.72521 15.21327 TPHP_out(232) temp C_2 13 Smp 14.75657 15.28187 TPHP_out(233) temp C_2 14 Smp 14.77002 15.33704 TPHP_out(234) temp C_2 15 Smp 14.773 15.38028 TPHP_out(235) temp C_2 16 Smp 14.76404 15.41457 TPHP_out(236) temp C_2 17 Smp 14.74612 15.43841 TPHP_out(237) temp C_2 18 Smp 14.72521 15.45481 TPHP_out(238) temp C_2 19 Smp 14.69832 15.46375 TPHP_out(239) temp C_2 20 Smp 14.66844 15.46971 TPHP_out(240) temp C_2 21 Smp 14.63707 15.46673 TPHP_out(241) temp C_2 22 Smp 14.6027 15.46226 TPHP_out(242) temp C_2 23 Smp 14.56683 15.45481 TPHP_out(243) temp C_2 24 Smp 14.53096 15.44438 TPHP_out(244) temp C_2 25 Smp 14.49508 15.43096
340
Table M-26. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(245) temp C_2 26 Smp 14.4607 15.41605 TPHP_out(246) temp C_2 27 Smp 14.42482 15.39966 TPHP_out(247) temp C_2 28 Smp 14.39043 15.38177 TPHP_out(248) temp C_2 29 Smp 14.35752 15.36388 TPHP_out(249) temp C_2 30 Smp 14.32462 15.34301 TPHP_out(250) temp C_2 31 Smp 14.2932 15.32362 TPHP_out(251) temp C_2 32 Smp 14.26328 15.30275 TPHP_out(252) temp C_2 33 Smp 14.23335 15.28486 TPHP_out(253) temp C_2 34 Smp 14.20343 15.26398 TPHP_out(254) temp C_2 35 Smp 14.17649 15.2431 TPHP_out(255) temp C_2 36 Smp 14.15104 15.22222 TPHP_out(256) temp C_2 37 Smp 14.1241 15.20283 TPHP_out(257) temp C_2 38 Smp 14.10015 15.18344 TPHP_out(258) temp C_2 39 Smp 14.07769 15.16405 TPHP_out(259) temp C_2 40 Smp 14.05224 15.14615 TPHP_out(260) temp mV_2 1 Smp 0.3676404 0.3791806 TPHP_out(261) temp mV_2 2 Smp 0.3687695 0.3794628 TPHP_out(262) temp mV_2 3 Smp 0.3705296 0.380044 TPHP_out(263) temp mV_2 4 Smp 0.372821 0.3809572 TPHP_out(264) temp mV_2 5 Smp 0.3753117 0.3821694 TPHP_out(265) temp mV_2 6 Smp 0.3777193 0.3835973 TPHP_out(266) temp mV_2 7 Smp 0.3798946 0.3850752 TPHP_out(267) temp mV_2 8 Smp 0.381721 0.386553 TPHP_out(268) temp mV_2 9 Smp 0.3831989 0.3879311 TPHP_out(269) temp mV_2 10 Smp 0.3843445 0.3891599 TPHP_out(270) temp mV_2 11 Smp 0.3851748 0.3902724 TPHP_out(271) temp mV_2 12 Smp 0.3857393 0.391169 TPHP_out(272) temp mV_2 13 Smp 0.386088 0.3919328 TPHP_out(273) temp mV_2 14 Smp 0.3862375 0.3925472 TPHP_out(274) temp mV_2 15 Smp 0.3862707 0.3930287 TPHP_out(275) temp mV_2 16 Smp 0.386171 0.3934106 TPHP_out(276) temp mV_2 17 Smp 0.3859718 0.3936763 TPHP_out(277) temp mV_2 18 Smp 0.3857393 0.3938589 TPHP_out(278) temp mV_2 19 Smp 0.3854404 0.3939586 TPHP_out(279) temp mV_2 20 Smp 0.3851084 0.394025 TPHP_out(280) temp mV_2 21 Smp 0.3847597 0.3939918 TPHP_out(281) temp mV_2 22 Smp 0.3843778 0.393942 TPHP_out(282) temp mV_2 23 Smp 0.3839793 0.3938589 TPHP_out(283) temp mV_2 24 Smp 0.3835807 0.3937427 TPHP_out(284) temp mV_2 25 Smp 0.3831822 0.3935933 TPHP_out(285) temp mV_2 26 Smp 0.3828003 0.3934272
341
Table M-27. Example transposed output table “TPHP.dat” (continued). Variable Variable Statistic Example Record Example Record Name Definition Type* 1 2 TPHP_out(286) temp mV_2 27 Smp 0.3824018 0.3932446 TPHP_out(287) temp mV_2 28 Smp 0.3820199 0.3930453 TPHP_out(288) temp mV_2 29 Smp 0.3816546 0.3928461 TPHP_out(289) temp mV_2 30 Smp 0.3812893 0.3926136 TPHP_out(290) temp mV_2 31 Smp 0.3809406 0.3923977 TPHP_out(291) temp mV_2 32 Smp 0.3806085 0.3921653 TPHP_out(292) temp mV_2 33 Smp 0.3802764 0.391966 TPHP_out(293) temp mV_2 34 Smp 0.3799444 0.3917336 TPHP_out(294) temp mV_2 35 Smp 0.3796455 0.3915011 TPHP_out(295) temp mV_2 36 Smp 0.3793632 0.3912686 TPHP_out(296) temp mV_2 37 Smp 0.3790643 0.3910528 TPHP_out(297) temp mV_2 38 Smp 0.3787987 0.3908369 TPHP_out(298) temp mV_2 39 Smp 0.3785496 0.3906211 TPHP_out(299) temp mV_2 40 Smp 0.3782673 0.3904218 TPHP_out(300) Smp 0 0 TPHP_out(301) Smp 0 0 TPHP_out(302) Smp 0 0 * Statistic types (Smp = sample, Smp:Avg = Average, Smp:Total = Total)
342
APPENDIX N. LYSIMETER DATA MAP
Table N-1.
Variable definition for lysimeter 1 scale.dat.
Variable Name TIMESTAMP RECORD MassID Scale_mV_Avg Scale_Kg_Mean Scale_Kg_SD Scale_Kg_Min Scale_Kg_Max TCAV_ID tcav_1_Avg SHF1_ID shf_Avg(1) shf_cal(1) SHF2_ID shf_Avg(2) shf_cal(2) ST1_ID S_Therm_Avg(1) ST2_ID S_Therm_Avg(2) ST3_ID S_Therm_Avg(3) ST4_ID S_Therm_Avg(4) Ptemp_ID Ptemp_Avg CS616_ID cs616_uS_Avg soil_water_VMC_Avg
Explanation Time of measurement Data record number ID number for scale Loadcell output Average scale mass Std deviation of scale mass Minimum mass for time period Maximum mass for time period ID number for averaging thermocouple Average temperature ID number for heat flux plate #1 at 10 cm depth Average soil heat flux for #1 Calibration data - SHF #1 ID number for heat flux plate #2 at 10 cm depth Average soil heat flux for #2 Calibration data - SHF #2 ID number for thermocouple at 5 cm depth Average temperature at 5 cm depth ID number for thermocouple at 25 cm depth Average temperature at 25 cm depth ID number for thermocouple at 50 cm depth Average temperature at 50 cm depth ID number for thermocouple at 75 cm depth Average temperature at 75 cm depth ID number for datalogger panel temperature Datalogger panel temperature ID number for FDR probe at 5 cm Return period for FDR measurement] Vol water content
343
Units ---mv kg kg kg kg -C -W/m^2 W/(m^2 mV) -W/m^2 W/(m^2 mV) -C -C -C -C -C -uSeconds frac_v_wtr
Table N-2.
Variable definition for lysimeter 2 scale.dat.
Variable Name TIMESTAMP RECORD MassID Scale_mV_Avg Scale_Kg_Mean Scale_Kg_SD Scale_Kg_Min Scale_Kg_Max TCAV_ID tcav_1_Avg SHF1_ID shf_Avg(1) shf_cal(1) SHF2_ID shf_Avg(2) shf_cal(2) ST1_ID S_Therm_Avg(1) ST2_ID S_Therm_Avg(2) ST3_ID S_Therm_Avg(3) ST4_ID S_Therm_Avg(4) Ptemp_ID Ptemp_Avg
Explanation Time of measurement Data record number ID number for scale Loadcell output Average scale mass Std deviation of scale mass Minimum mass for time period Maximum mass for time period ID number for averaging thermocouple Average temperature ID number for heat flux plate #1 at 10 cm depth Average soil heat flux for #1 Calibration data - SHF #1 ID number for heat flux plate #2 at 10 cm depth Average soil heat flux for #2 Calibration data - SHF #2 ID number for thermocouple at 5 cm depth Average temperature at 5 cm depth ID number for thermocouple at 25 cm depth Average temperature at 25 cm depth ID number for thermocouple at 50 cm depth Average temperature at 50 cm depth ID number for thermocouple at 75 cm depth Average temperature at 75 cm depth ID number for datalogger panel temperature Datalogger panel temperature
344
Units ---mv kg kg kg kg -C -W/m^2 W/(m^2 mV) -W/m^2 W/(m^2 mV) -C -C -C -C -C
Table N-3.
Variable definition for lysimeter 3 scale.dat.
Variable Name TMSTAMP RECNBR MassID Scale_mV_Avg Scale_Kg_Mean Scale_Kg_SD Scale_Kg_Min Scale_Kg_Max TCAV_ID tcav_1_Avg SHF1_ID shf_Avg(1)
Explanation Time of measurement Data record number ID number for scale Loadcell output Average scale mass Std deviation of scale mass Minimum mass for time period Maximum mass for time period ID number for averaging thermocouple Average temperature ID number for heat flux plate #1 at 10 cm depth Average soil heat flux for #1
shf_cal(1) SHF2_ID shf_Avg(2)
Calibration data - SHF #1 ID number for heat flux plate #2 at 10 cm depth Average soil heat flux for #2
shf_cal(2) ST1_ID S_Therm_Avg(1) ST2_ID S_Therm_Avg(2) ST3_ID S_Therm_Avg(3) ST4_ID S_Therm_Avg(4) Ptemp_ID Ptemp_Avg
Calibration data - SHF #2 ID number for thermocouple at 5 cm depth Average temperature at 5 cm depth ID number for thermocouple at 25 cm depth Average temperature at 25 cm depth ID number for thermocouple at 50 cm depth Average temperature at 50 cm depth ID number for thermocouple at 75 cm depth Average temperature at 75 cm depth ID number for datalogger panel temperature Datalogger panel temperature
Table N-4.
Units ---mv kg kg kg kg -C -W/m^2 W/(m^2 mV) -W/m^2 W/(m^2 mV) -C -C -C -C -C
Variable definition for lysimeter 1,2, and 3 tdr.dat.
Variable Name TMSTAMP RECNBR sensorID
Explanation Time of measurement Data record number TDR probe ID Ratio of apparent length to physical length (SQRT dielectric constant) LaL ToppVWC Vol Water content using third order polynomial TDR_EC Bulk electrical conductivity a0 offset a1 coefficient A a2 coefficient B a3 coefficient C Note: theta = a0 + a1*(LaL)+a2*(LaL)^2+a3*(LaL)^3
345
Units ----m3/m3 S/m -----
Table N-5.
Variable definition for lysimeter 1,2, and 3 hdu.dat.
Variable Name Explanation TIMESTAMP Time of measurement RECORD Data record number sensorID HDU probe ID SoilTemp Initital soil temperature deltaTemp Change in temperature from 0 - 30 seconds T_1sec Temperature at 1 s T_30sec Temperature at 30 s RefTemp Panel T Tstar Normalized change in temperature Psi Soil water potential wet deltaTemp for fully wetted probe dry deltaTemp for very dry probe alpha Calibration coefficient beta Calibration coefficient Note:HDU_Tstar = (HDU_dry(i)-del_T1(i))/(HDU_dry(i)-HDU_wet(i)) HDU_Psi = HDU_Tstar^(-1/HDU_beta(i))/HDU_alpha(i) If HDU_Tstar< 0, then HDU_Tstar= 1e-6
Table N-6. Sensors CSAT3 LI-7500 HMP45C FW05 CNR2 HFP01SC TCAV CS616
Units ---C C C C C -Mpa C C ---
Definition of open path eddy covariance system (OPEC) sensors. Definition three dimensional sonic anemometer open path infrared gas analyzer (CO2 and H2O) temperature and relative humidity probe type E fine wire (0.0005 inch diameter) thermocouple net radiometer soil heat flux plates (four sensors) type E thermocouple averaging soil temperature probes (two sensors) water content reflectometer (volumetric soil moisture)(two sensors)
346
Table N-7. Variable Name TIMESTAMP RECORD Hs H Fc_wpl LE_wpl Hc tau u_star Ts_mean 347
stdev_Ts cov_Ts_Ux cov_Ts_Uy cov_Ts_Uz co2_mean stdev_co2 cov_co2_Ux
Variable definition for BC_Eddy_dly.dat. Explanation Units Time of measurement -Data record number -Sensible heat flux using sonic temperature W/m^2 Sensible heat flux using hmp temperature Carbon dioxide flux (LI-7500), with Webb et al. term mg/(m^2 s) Latent heat flux (LI-7500), with Webb et al. term W/m^2 Sensible heat flux computed from Hs and LE_wpl W/m^2 Momentum flux kg/(m s^2) Friction velocity m/s Air temperature C standard deviation of Air temperature C covariance of btwn air temperature and wind speed in x direction m C/s covariance of btwn air temperature and wind speed in y direction m C/s covariance of btwn air temperature and wind speed in z direction m C/s mean CO2 concentration mg/m^3 standard deviation of CO2 concentration mg/m^3 covariance btwn CO2 concentration and wind speed mg/(m^2 s) in the x direction
Equation ---
Table N-8. Variable Name cov_co2_Uy
cov_co2_Uz
h2o_Avg
stdev_h2o
cov_h2o_Ux 348 cov_h2o_Uy
cov_h2o_Uz fw_Avg stdev_fw
cov_fw_Ux
cov_fw_Uy
Variable definition for BC_Eddy_dly.dat (continued). Explanation covariance btwn CO2 concentration and wind speed in the y direction covariance btwn CO2 concentration and wind speed in the z direction 10-min average water vapor density measured by LI7500 10-min standard deviation of water vapor density measured by LI-7500 covariance btwn water vapor density and wind speed in the x direction covariance btwn water vapor density and wind speed in the y direction covariance btwn water vapor density and wind speed in the z direction average temperature with fine-wire thermocouple standard deviation of temp with fine-wire thermocouple covariance btwn air temperature and wind speed in x direction covariance btwn air temperature and wind speed in y direction
Units
Equation
mg/(m^2 s)
mg/(m^2 s)
g/m^3
g/m^3
g/(m^2 s)
g/(m^2 s)
g/(m^2 s) C C
m C/s
m C/s
fw = t_hmp
Table N-9.
Variable definition for BC_Eddy_dly.dat (continued).
Variable Name
cov_fw_Uz Ux_Avg stdev_Ux
cov_Ux_Uy
cov_Ux_Uz Uy_Avg 349
stdev_Uy
cov_Uy_Uz Uz_Avg stdev_Uz press_mean t_hmp_mean h2o_hmp_mean rho_a_mean
Explanation covariance btwn air temperature and wind speed in z direction Average wind speed in the x direction Standard deviation of wind speed in the x direction Covariance btwn wind speed in the x and y directions Covariance btwn wind speed in the x and z directions Average wind speed in the y direction Standard deviation of wind speed in the y direction Covariance btwn wind speed in the y and z directions Average wind speed in the z direction Standard deviation of wind speed in the x direction Air pressure measured by LI-7500 Air temperature measured by HMP45C Mean HMP45C vapor density Mean air density
Units
Equation
m C/s m/s m/s
(m/s)^2
(m/s)^2 m/s m/s
(m/s)^2 m/s m/s kPa C g/m^3 kg/m^3
h2o_hmp_mean = e_hmp_mean/((t_hmp_mean+273.15)*RV)
Table N-10.
Variable definition for BC_Eddy_dly.dat (continued).
Variable Name wnd_dir_compass
wnd_dir_csat3 wnd_spd rslt_wnd_spd std_wnd_dir
Fc_irga LE_irga 350
co2_wpl_LE co2_wpl_H h2o_wpl_LE h2o_wpl_H n_Tot csat_warnings irga_warnings del_T_f_Tot sig_lck_f_Tot
Explanation Wind direction CSAT3 wind direction will be between 0 to 180 degrees and 0 to -180 degrees. wind speed measured by CSAT3 wind vector standard wind vector Carbon dioxide flux (LI7500), without Webb et al. term Latent heat flux (LI-7500), without Webb et al. term LI-7500 Webb et al. term for carbon dioxide Eq. (24) LI-7500 Webb et al. term for carbon dioxide Eq. (24) LI-7500 Webb et al. term for water vapor Eq. (25) LI-7500 Webb et al. term for water vapor Eq. (25) Warnings collected during time period Warnings collected during time period Warnings collected during time period Warnings collected during time period Warnings collected during time period
Units degrees
Equation wnd_dir_compass = (wnd_dir_compass+CSAT3_AZIMUTH) MOD 360
degrees
If ( wnd_dir_csat3 ) > 180 Then ( wnd_dir_csat3 = wnd_dir_csat3-360 )
m/s m/s degrees
WindVector (1,wnd_spd,wnd_dir_csat3,FP2,False,0,0,0)
mg/(m^2 s) W/m^2 mg/(m^2 s)
Fc_irga = cov_co2_Uz LE_irga = LV*cov_h2o_Uz
W/m^2
co2_wpl_LE = MU_WPL*co2_mean/rho_d_mean*cov_h2o_Uz co2_wpl_H = (1+(MU_WPL*sigma_wpl))*co2_mean/(t_hmp_mean+273.15)*Hc/(rho_a_mean*CP) h2o_wpl_H = (1+(MU_WPL*sigma_wpl))*h2o_hmp_mean/(t_hmp_mean+273.15)*LV*cov_Ts_Uz
W/m^2
h2o_wpl_LE = MU_WPL*sigma_wpl*LE_irga
mg/(m^2 s)
samples samples samples samples samples
Table N-11.
Variable definition for BC_Eddy_dly.dat (continued).
Variable Name amp_h_f_Tot amp_l_f_Tot chopper_f_Tot detector_f_Tot pll_f_Tot
351
sync_f_Tot agc_Avg panel_temp_Avg batt_volt_Avg Rn_shortwave_Avg Rn_Avg hfp01sc_1_Avg hfp01sc_2_Avg hfp01sc_3_Avg hfp01sc_4_Avg del_Tsoil(1) del_Tsoil(2)
soil_water_T_Avg(1)
Explanation Units Equation Warnings collected during time period samples Warnings collected during time period samples Warnings collected during time period samples Warnings collected during time period samples Warnings collected during time period samples Warnings collected during time period samples Automatic gain control unitless agc = INT ((diag_irga_work AND &h000f)*6.25+0.5) Datalogger panel temperature C Measure battery voltage V Shortwave CNR2 Net Radiation Measurements Average CNR2 Net Radiation Measurements W/m^2 Rn = Rn_shortwave+Rn_longwave Average soil heat flux from HFP01SC soil heat flux plate 1 W/m^2 Average soil heat flux from HFP01SC soil heat flux plate 2 W/m^2 Average soil heat flux from HFP01SC soil heat flux plate 3 W/m^2 Average soil heat flux from HFP01SC soil heat flux plate 4 W/m^2 Change in soil temperature 1 C del_Tsoil(1) = Tsoil_avg(1)-prev_Tsoil(1) Change in soil temperature 2 C del_Tsoil(2) = Tsoil_avg(2)-prev_Tsoil(2) CS616 Volumetric soil water content with temperature frac_v_wtr soil_water_T(j) = -0.0663+cs616_T(j)*(-0.0063+cs616_T(j)*0.0007) correction 1
Table N-12.
Variable definition for BC_Eddy_dly.dat (continued).
Variable Name
soil_water_T_Avg(2) Tsoil_avg(1) Tsoil_avg(2) cs616_wcr_Avg(1) cs616_wcr_Avg(2) par_totflx_Tot
352
par_flxdens_Avg wnd_spd_WVc(1) wnd_spd_WVc(2) wnd_spd_WVc(3) wnd_spd_Max wnd_spd_TMx
Explanation CS616 Volumetric soil water content with temperature correction 2 Average Soil Temperature from TCAV 1 Average Soil Temperature from TCAV 2 CS616 soil water content probe 1 CS616 soil water content probe 2 Total Flux measured by LI190SB PAR Sensor Flux Density measured by LI190SB PAR Sensor
Maximum Wind Speed
Units
frac_v_wtr
Equation
soil_water_T(j) = -0.0663+cs616_T(j)*(-0.0063+cs616_T(j)*0.0007)
C
--
C
--
uSeconds
--
uSeconds
--
mmol/m^2
par_totflx = par_mV*par_mult_totflx
umol/s/m^2 m/s m/s m/s m/s m/s
par_flxdens = par_mV*par_mult_flxdens
Total Rain measured by TE525/TE525WS Rain mm calibrated 4-30-08 by Brad Lyles 50.5 ml/10 tips => 0.108 mm/tip Rain_mm_Tot Gauge Rn_longwave_Avg Longwave CNR2 Net Radiation Measurements HFP01SC factory calibration W/(m^2 shf_cal(1) 1 mV) HFP01SC factory calibration W/(m^2 shf_cal(2) 2 mV) HFP01SC factory calibration W/(m^2 shf_cal(3) 3 mV) HFP01SC factory calibration W/(m^2 4 mV) shf_cal(4) Note: The sign convention for the fluxes, except net radiation, is positive away from the surface and negative towards the surface.
APPENDIX O. NAMING CONVENTION FOR SENSOR NUMBER
Data for each instrument is filed according to the sensor number: ‘WXY1Y2Z1Z2’. where, W: X: Y:
lysimeter number, 1 through 4, oriented south to north quadrant (NE, SE, SW, NW), 1 through 4, oriented clockwise beginning from upper right quadrant or NE quadrant depth of instrument
Examples: 130505: Lysi 1, SW quad, 50 cm depth, SSS 211401: Lysi 2, NE quad, 250 cm depth, TDR 340303: Lysi 3, NW quad, 10 cm depth, TPHP Table O-1.
Definition of sensor number.
W
Lysimeter
X
Quadrant
Y1Y2
1 2 3 4
1 2 3 4 natural soil
1 2 3 4
NE SE SW NW
01 02 03 04 05
Depth [cm] 0 5 10 25 50
06 07 08 09 10 11 12 13 14 15 16
5
353
Z1Z2
Instrument
01 02 03 04 05
TDR DPHP TPHP HDU SSSS
60 75 90 100
06 07 08 09
Stherm SHF TCAV ECH2O
140 150 190 200 250 290 300
10 11 12 13 18
SET DTS Loops DTS Pole CS616 TC
An exception to this naming convention is the TPHP cluster or “Titanic” at 5 cm. To make the Sensor ID unique, the depth was ‘50’ for the highest TPHP and ‘53’ for the lowest. An example is given for lysimeter 1 in the table below. Table O-2. Sensor_ID 125003 125103 125203 125303 110303 130303 110403 130403
Table O-3. Sensor_ID 520318 520418 520518 520718 520918 521118 521318 521418 521618
Sensor ID naming convention for TPHP cluster or "Titanic." Lysimeter 1 1 1 1 1 1 1 1
Quadrant SE SE SE SE SW SW NW NW
Depth Titanic 50 (highest) Titanic 51 Titanic 52 Titanic 53 (lowest) 10 10 25 25
Instrument TPHP TPHP TPHP TPHP TPHP TPHP TPHP TPHP
Sensor ID naming convention for thermocouples (TC) located in natural soil outside of lysimeter 3. Lysimeter natural soil natural soil natural soil natural soil natural soil natural soil natural soil natural soil natural soil
Quadrant near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3 near SE corner of lysimeter 3
Depth 10 25 50 75 100 150 200 250
near SE corner of lysimeter 3
300
354
Instrument TC TC TC TC TC TC TC TC TC
