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The Necessity of Sensor Calibration for the Precise Measurement of Water Fluxes in Forest Ecosystems

  • Shin’ichi IidaEmail author
  • Takanori Shimizu
  • Yoshinori Shinohara
  • Shin’ichi Takeuchi
  • Tomo’omi Kumagai
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Part of the Ecological Studies book series (ECOLSTUD, volume 240)

Abstract

In forested watersheds, interception loss (EI) and transpiration (ET) constitute the majority of evapotranspiration. Accordingly, their precise evaluations are necessary to understand and quantify fluxes within the hydrologic cycle. EI is commonly measured by tipping-bucket rain gauges and flow meters, while ET is often estimated by sap flow techniques. To obtain reliable estimations of EI and ET, we describe detailed procedures to calibrate tipping-bucket rain gauges and flow meters as well as sap flow techniques. For tipping-bucket rain gauges and flow meters, we measure the one tip static volume, and then changes in the one tip amount with different inflow rates for dynamic calibration. Without proper calibration, the significant evaluation error in EI can range from 40% underestimation to 20% overestimation. We calibrate three sap flow techniques—thermal dissipation (TD), heat field deformation (HFD), and heat ratio (HR) methods—for Japanese cedar (Cryptomeria japonica) from two sites. The clear radial and azimuthal trends in sap flux density (FD) are confirmed for the artificial sap flow generated by a vacuum pump. Among segments sampled at a site, TD and HFD methods do not have any tendencies to overestimate and underestimate FD. While at the other site, TD and HFD methods underestimate FD, and therefore ET, by at least 30%, the HR method shows a 30% overestimation. Thus, we highly recommend the calibration of tipping-bucket rain gauges, flow meters, and sap flow techniques to obtain valid estimates of EI and ET.

Notes

Acknowledgments

We wish to thank Akita Forestry Research and Training Center for supporting our studies in the Nagasaka Experimental Watershed. This study was partially supported by the project “Research on adaptation to climate change for forestry and fisheries” founded by the Agriculture, Forestry and Fisheries Research Council, Japan, the Global Environmental Research Coordination System from Ministry of the Environment of Japan, and JSPS KAKENHI Grant Numbers JP21710021, JP26450495, JP18K05714 and JP19K06135. We also wish to recognize the anonymous reviewer as well as editors of this volume, especially DE Carlyle-Moses and DF Levia whose comments greatly improved this chapter.

References

  1. Bosch JM, Hewlett JD (1982) A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J Hydrol 55:3–23.  https://doi.org/10.1016/0022-1694(82)90117-2 CrossRefGoogle Scholar
  2. Bosch DD, Marshall LK, Teskey R (2014) Forest transpiration from sap flux density measurements in a southeastern coastal plain riparian buffer system. Agric For Meteorol 187:72–82.  https://doi.org/10.1016/j.agrformet.2013.12.002 CrossRefGoogle Scholar
  3. Burgess SSO, Adams MA, Turner NC, Beverly CR, Ong CK, Khan AA et al (2001) An improved heat pulse method to measure low and reverse rates of sap flow in woody plants. Tree Physiol 21:589–598.  https://doi.org/10.1093/treephys/21.9.589 CrossRefGoogle Scholar
  4. Bush SE, Hultine KR, Sperry JS, Ehleringer JR (2010) Calibration of thermal dissipation sap flow probes for ring-and diffuse-porous trees. Tree Physiol 30:1545–1554.  https://doi.org/10.1093/treephys/tpq096 CrossRefGoogle Scholar
  5. Carlyle-Moses DE, Gash JHC (2011) Rainfall interception loss by forest canopies. In: Levia DF, Carlyle-Moses DE, Tanaka T (eds) Forest ecology and biogeochemistry: synthesis of past research and future directions, ecol studies 216. Springer, Dordrecht, pp 407–423.  https://doi.org/10.1007/978-94-007-1363-5_20 CrossRefGoogle Scholar
  6. Carlyle-Moses DE, Lishman CE (2015) Temporal persistence of throughfall heterogeneity below and between the canopies of juvenile lodgepole pine (Pinus contorta). Hydrol Process 29:4051–4067.  https://doi.org/10.1002/hyp.10494 CrossRefGoogle Scholar
  7. Carlyle-Moses DE, Laureano JF, Price AG (2004) Throughfall and throughfall spatial variability in Madrean oak forest communities of northeastern Mexico. J Hydrol 297:124–135.  https://doi.org/10.1016/j.jhydrol.2004.04.007 CrossRefGoogle Scholar
  8. Carlyle-Moses DE, Lishman CE, McKee AJ (2014) A preliminary evaluation of throughfall sampling techniques in a mature coniferous forest. J For Res 25:407–413.  https://doi.org/10.1007/s11676-014-0468-8 CrossRefGoogle Scholar
  9. Carlyle-Moses DE, Iida S, Germer S, Llorens P, Michalzik B, Nanko K et al (2018) Expressing stemflow commensurate with its ecohydrological importance. Adv Water Resour 121:472–479.  https://doi.org/10.1016/j.advwatres.2018.08.015 CrossRefGoogle Scholar
  10. Clearwater MJ, Meinzer FC, Andrade JL, Goldstein G, Holbrook NM (1999) Potential errors in measurement of nonuniform sap flow using heat dissipation probes. Tree Physiol 19:681–687.  https://doi.org/10.1093/treephys/19.10.681 CrossRefGoogle Scholar
  11. Dunlap F (1912) The specific heat of wood. USDA Forest Service Bulletin No. 110, p 28Google Scholar
  12. Edwards WRN, Warwick NWM (1984) Transpiration from a kiwifruit vine as estimated by the heat pulse technique and the penman-Monteith equation. New Zealand J Agric Res 27:537–543.  https://doi.org/10.1080/00288233.1984.10418016 CrossRefGoogle Scholar
  13. Edwards IJ, Jackson WD, Fleming PM (1974) Tipping bucket gauges for measuring runoff from experimental plots. Agric Meteorol 13:189–201.  https://doi.org/10.1016/0002-1571(74)90046-6 CrossRefGoogle Scholar
  14. Fan J, Guyot A, Ostergaard KT, Lockington DA (2018) Effects of earlywood and latewood on sap flux density-based transpiration estimates in conifers. Agric Meteorol 249:264–274.  https://doi.org/10.1016/j.agrformet.2017.11.006 CrossRefGoogle Scholar
  15. Flo V, Martinez-Vilalta J, Steppe K, Schuldt B, Poyatos R (2019) A synthesis of bias and uncertainty in sap flow methods. Agric For Meteorol 271:362–374.  https://doi.org/10.1016/j.agrformet.2019.03.012 CrossRefGoogle Scholar
  16. Friesen J, Van Beek C, Selker J, Savenije HHG, Van de Giesen N (2008) Tree rainfall interception measured by stem compression. Water Resour Res 44:W00D15.  https://doi.org/10.1029/2008WR007074 CrossRefGoogle Scholar
  17. Fuchs S, Leuschner C, Link R, Coners H, Schuldt B (2017) Calibration and comparison of thermal dissipation, heat ratio and heat field deformation sap flow probes for diffuse-porous trees. Agric Meteorol 244:151–161.  https://doi.org/10.1016/j.agrformet.2017.04.003 CrossRefGoogle Scholar
  18. Fujiwara K, Yamashita K, Hirakawa Y (2004) Mean basic density and density variation within individual trees in major plantation species. Bull FFPRI 3:341–348. (in Japanese with English abstract)Google Scholar
  19. Granier A (1985) Une nouvelle méthod pour la mesure du flux de sève brute dans le tronc des arbres. Ann Sci For 42:193–200.  https://doi.org/10.1051/forest:19850204. [Granier A (1985) A new method of sap flow measurement in tree trunks. English translation by Gash JHC, Granier A (2007) In Evaporation, Benchmark Papers in Hydrology 2. Gash JHC, Shuttleworth, WJ (eds). IAHS Press: Oxfordshire; 61–63.]CrossRefGoogle Scholar
  20. Helvey JD, Patric JH (1965) Canopy and litter interception of rainfall by hardwoods of eastern United States. Water Resour Res 1:193–206.  https://doi.org/10.1029/WR001i002p00193 CrossRefGoogle Scholar
  21. Herbst M, Roberts JM, Rosier PT, Gowing DJ (2007) Seasonal and interannual variability of canopy transpiration of a hedgerow in southern England. Tree Physiol 27:321–333.  https://doi.org/10.1093/treephys/27.3.321 CrossRefGoogle Scholar
  22. Herwitz SR (1985) Interception storage capacities of tropical rainforest canopy trees. J Hydrol 77:237–252.  https://doi.org/10.1016/0022-1694(85)90209-4 CrossRefGoogle Scholar
  23. Hubbard RM, Stape J, Ryan MG, Almeida AC, Rojas J (2010) Effects of irrigation on water use and water use efficiency in two fast growing Eucalyptus plantations. For Ecol Manag 259:1714–1721.  https://doi.org/10.1016/j.foreco.2009.10.028 CrossRefGoogle Scholar
  24. Iida S (2009) Rainfall redistribution by vegetation. In: Sugita M, Tanaka T (eds) Hydrologic laboratory of the university of Tsukuba, Japan hydrologic science. Kyoritsushuppan, Tokyo, pp 103–117. (in Japanese)Google Scholar
  25. Iida S, Tanaka T (2010) Effect of the span length of Granier-type thermal dissipation probes on sap flux density measurements. Ann For Sci 67:408.  https://doi.org/10.1051/forest/2009128 CrossRefGoogle Scholar
  26. Iida S, Shimizu T, Kabeya N, Nobuhiro T, Tamai K, Shimizu A et al (2012) Calibration of tipping-bucket flow meters and rain gauges to measure gross rainfall, throughfall, and stemflow applied to data from a Japanese temperate coniferous forest and a Cambodian tropical deciduous forest. Hydrol Process 26:2445–2454.  https://doi.org/10.1002/hyp.9462 CrossRefGoogle Scholar
  27. Iida S, Levia DF, Shimizu A, Shimizu T, Tamai K, Nobuhiro T et al (2017) Intrastorm scale rainfall interception dynamics in a mature coniferous forest stand. J Hydrol 548:770–783.  https://doi.org/10.1016/j.jhydrol.2017.03.009 CrossRefGoogle Scholar
  28. Iida S, Levia DF, Nanko K, Sun X, Shimizu T, Tamai K et al (2018) Correction of canopy interception loss measurements in temperate forests: a comparison of necessary adjustments among three different rain gauges based on a dynamic calibration procedure. J Hydrometeorol 19:547–553.  https://doi.org/10.1175/JHM-D-17-0124.1 CrossRefGoogle Scholar
  29. Kimmins JP (1973) Some statistical aspects of sampling throughfall precipitation in nutrient cycling studies in British Columbian coastal forests. Ecology 54:1008–1019.  https://doi.org/10.2307/1935567 CrossRefGoogle Scholar
  30. Kumagai T, Aoki S, Nagasawa H, Mabuchi T, Kubota K, Inoue S et al (2005a) Effects of tree-to-tree and radial variations on sap flow estimates of transpiration in Japanese cedar. Agric Forest Meteorol 135:110–116.  https://doi.org/10.1016/j.agrformet.2005.11.007 CrossRefGoogle Scholar
  31. Kumagai T, Nagasawa H, Mabuchi T, Ohsaki S, Kubota K, Kogi K et al (2005b) Sources of error in estimating stand transpiration using allometric relationships between stem diameter and sapwood area for Cryptomeria japonica and Chamaecyparis obtusa. For Ecol Manag 206:191–195.  https://doi.org/10.1016/j.foreco.2004.10.066 CrossRefGoogle Scholar
  32. Kumagai T, Tateishi M, Shimizu T, Otsuki K (2008) Transpiration and canopy conductance at two slope positions in a Japanese cedar forest watershed. Agric Forest Meteorol 148:1444–1455.  https://doi.org/10.1016/j.agrformet.2008.04.010 CrossRefGoogle Scholar
  33. Kumagai T, Tateishi M, Miyazawa Y, Kobayashi M, Yoshifuji N, Komatsu H et al (2014) Estimation of annual forest evapotranspiration from a coniferous plantation watershed in Japan (1): water use components in Japanese cedar stands. J Hydrol 508:66–76.  https://doi.org/10.1016/j.jhydrol.2013.10.047 CrossRefGoogle Scholar
  34. Levia DF, Germer S (2015) A review of stemflow generation dynamics and stemflow-environment interactions in forests and shrublands. Rev Geophys 53:673–714.  https://doi.org/10.1002/2015RG000479 CrossRefGoogle Scholar
  35. Lloyd CR, Marques ADO (1988) Spatial variability of throughfall and stemflow measurements in Amazonian rainforest. Agric Forest Meteorol 42:63–73.  https://doi.org/10.1016/0168-1923(88)90067-6 CrossRefGoogle Scholar
  36. Lopez JG, Licata J, Pypker T, Asbjornsen H (In press) Effects of heater wattage on sap flux density estimates using an improved tree-cut experiment. Tree Physiol.  https://doi.org/10.1093/treephys/tpy137
  37. Lu P, Chacko E (1998) Evaluation of Granier’s sap flux sensor in young mango trees. Agronomie 18:461–471.  https://doi.org/10.1051/agro:19980703 CrossRefGoogle Scholar
  38. Marshall DC (1958) Measurement of sap flow in conifers by heat transport. Plant Physiol 33:385–396.  https://doi.org/10.1104/pp.33.6.385 CrossRefGoogle Scholar
  39. McCulloh KA, Winter K, Meinzer FC, Garcia M, Aranda J, Lachenbruch B (2007) A comparison of daily water use estimates derived from constant-heat sap-flow probe values and gravimetric measurements in pot-grown saplings. Tree Physiol 27:1355–1360.  https://doi.org/10.1093/treephys/27.9.1355 CrossRefGoogle Scholar
  40. Nadezhdina N, Vandegehuchte MW, Steppe K (2012) Sap flux density measurements based on the heat field deformation method. Trees 26:1439–1448.  https://doi.org/10.1007/s00468-012-0718-3 CrossRefGoogle Scholar
  41. Oishi AC, Oren R, Stoy PC (2008) Estimating components of forest evapotranspiration: a footprint approach for scaling sap flux measurements. Agric Forest Meteorol 148:1719–1732.  https://doi.org/10.1016/j.agrformet.2008.06.013 CrossRefGoogle Scholar
  42. Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science 313:1068–1072.  https://doi.org/10.1126/science.1128845 CrossRefGoogle Scholar
  43. Ouyang S, Xiao K, Zhao Z, Xiang W, Xu C, Lei P et al (2018) Stand transpiration estimates from recalibrated parameters for the granier equation in a Chinese fir (Cunninghamia lanceolata) plantation in southern China. Forests 9:162.  https://doi.org/10.3390/f9040162 CrossRefGoogle Scholar
  44. Peters RL, Fonti P, Frank DC, Poyatos R, Pappas C, Kahmen A et al (2018) Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method. New Phytol 219:1283–1299.  https://doi.org/10.1111/nph.15241 CrossRefGoogle Scholar
  45. Puckett LJ (1991) Spatial variability and collector requirements for sampling throughfall volume and chemistry under a mixed-hardwood canopy. Can J For Res 21:1581–1588.  https://doi.org/10.1139/x91-220 CrossRefGoogle Scholar
  46. Reid LM, Lewis J (2009) Rates, timing, and mechanisms of rainfall interception loss in a coastal redwood forest. J Hydrol 375:459–470.  https://doi.org/10.1016/j.jhydrol.2009.06.048 CrossRefGoogle Scholar
  47. Roth BE, Slatton KC, Cohen MJ (2007) On the potential for high-resolution lidar to improve rainfall interception estimates in forest ecosystems. Front Ecol Environ 5:421–428.  https://doi.org/10.1890/060119.1 CrossRefGoogle Scholar
  48. Schmidt-Walter P, Richter F, Herbst M, Schuldt B, Lamersdorf NP (2014) Transpiration and water use strategies of a young and a full-grown short rotation coppice differing in canopy cover and leaf area. Agric For Meteorol 195:165–178.  https://doi.org/10.1016/j.agrformet.2014.05.006 CrossRefGoogle Scholar
  49. Seibert J, Morén AS (1999) Reducing systematic errors in rainfall measurements using a new type of gauge. Agric For Meteorol 98:341–348.  https://doi.org/10.1016/S0168-1923(99)00107-0 CrossRefGoogle Scholar
  50. Shedekar VS, King KW, Fausey NR, Soboyejo AB, Harmel RD, Brown LC (2016) Assessment of measurement errors and dynamic calibration methods for three different tipping bucket rain gauges. Atmos Res 178:445–458.  https://doi.org/10.1016/j.atmosres.2016.04.016 CrossRefGoogle Scholar
  51. Shimizu T, Kumagai T, Kobayashi M, Tamai K, Iida S, Kabeya N et al (2015) Estimation of annual forest evapotranspiration from a coniferous plantation watershed in Japan (2): comparison of eddy covariance, water budget and sap-flow plus interception loss. J Hydrol 522:250–264.  https://doi.org/10.1016/j.jhydrol.2014.12.021 CrossRefGoogle Scholar
  52. Shimizu T, Kobayashi M, Iida S, Levia DF (2018) A generalized correction equation for large tipping-bucket flow meters for use in hydrological applications. J Hydrol 563:1051–1056.  https://doi.org/10.1016/j.jhydrol.2018.06.036 CrossRefGoogle Scholar
  53. Shinohara Y, Oda T, Kume T, Iida S, Chiu CW, Katayama A et al (2016) Calibration of granier method for Japanese larch and oak. In: 63rd annual meeting of ecological society of Japan, P2–069 (in Japanese)Google Scholar
  54. Shiraki K, Yamato T (2004) Compensation of tipping-bucket flow meters. J Japan Soc Hydrol Water Resour 17:159–162.  https://doi.org/10.3178/jjshwr.17.159. (in Japanese with English abstract)CrossRefGoogle Scholar
  55. Shiraki K, Sun J, Kagami S, Nagai K, Yokoyama Y, Koyama Y et al (2018) Accracy of low-cost flow meters for stemflow observation including handmade tipping buckets flow meter. J Japan Soc Hydrol Water Resour 31:380–392.  https://doi.org/10.3178/jjshwr.31.380. (in Japanese with English abstract)CrossRefGoogle Scholar
  56. Steppe K, DJW DP, Doody TM, Teskey RO (2010) A comparison of sap flux density using thermal dissipation, heat pulse velocity and heat field deformation methods. Agric Forest Meteorol 150:1046–1056.  https://doi.org/10.1016/j.agrformet.2010.04.004 CrossRefGoogle Scholar
  57. Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J et al (eds) (2014) Climate change 2013: the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York, p 1535Google Scholar
  58. Su L, Zhao C, Xu W, Xie Z (2016) Modelling interception loss using the revised gash model: a case study in a mixed evergreen and deciduous broadleaved forest in China. Ecohydrology 9:1580–1589.  https://doi.org/10.1002/eco.1749 CrossRefGoogle Scholar
  59. Sun H, Aubrey DP, Teskey RO (2012) A simple calibration improved the accuracy of the thermal dissipation technique for sap flow measurements in juvenile trees of six species. Trees 26:631–640.  https://doi.org/10.1007/s00468-011-0631-1 CrossRefGoogle Scholar
  60. Swanson RH, Whitfield DWA (1981) A numerical analysis of heat pulse velocity theory and practice. J Exp Bot 32:221–239.  https://doi.org/10.1093/jxb/32.1.221 CrossRefGoogle Scholar
  61. Takahashi M, Giambelluca TW, Mudd RG, DeLay JK, Nullet MA, Asner GP (2011) Rainfall partitioning and cloud water interception in native forest and invaded forest in Hawai’i volcanoes National Park. Hydrol Process 25:448–464.  https://doi.org/10.1002/hyp.7797 CrossRefGoogle Scholar
  62. Takeuchi S, Sugio Y, Shinozaki K, Matsushima D, Iida S (2017) Calibration of heat ratio method by direct measurements of transpiration with weighing root-ball method: a study with Acer palmatum Thunb. J Japan Soc Reveg Tech 43:109–114.  https://doi.org/10.7211/jjsrt.43.109. (in Japanese with English abstract)CrossRefGoogle Scholar
  63. Taneda H, Sperry JS (2008) A case-study of water transport in co-occurring ring-versus diffuse-porous trees: contrasts in water-status, conducting capacity, cavitation and vessel refilling. Tree Physiol 28:1641–1651.  https://doi.org/10.1093/treephys/28.11.1641 CrossRefGoogle Scholar
  64. Tokyo Climate Center, Japan Meteorological Agency (2018) Primary factors behind the heavy rain event of July 2018 and the subsequent heatwave in Japan from Mid-July Onward. https://ds.data.jma.go.jp/tcc/tcc/news/press_20180822.pdf
  65. Turner B, Hill DJ, Carlyle-Moses DE, Rahman M (2019) Low-cost, high-resolution stemflow sensing. J Hydrol 570:62–68.  https://doi.org/10.1016/j.jhydrol.2018.12.072 CrossRefGoogle Scholar
  66. van Dijk AI, Gash JH, van Gorsel E, Blanken PD, Cescatti A, Emmel C et al (2015) Rainfall interception and the coupled surface water and energy balance. Agric Forest Meteorol 214:402–415.  https://doi.org/10.1016/j.agrformet.2015.09.006 CrossRefGoogle Scholar
  67. van Emmerik T, Steele-Dunne S, Hut R, Gentine P, Guerin M, Oliveira RS et al (2017) Measuring tree properties and responses using low-cost accelerometers. Sensors 17:1098.  https://doi.org/10.3390/s17051098 CrossRefGoogle Scholar
  68. Wilson KB, Hanson PJ, Mulholland PJ, Baldocchi DD, Wullschleger SD (2001) A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance. Agric Forest Meteorol 106:153–168.  https://doi.org/10.1016/S0168-1923(00)00199-4 CrossRefGoogle Scholar
  69. Ziegler AD, Giambelluca TW, Nullet MA, Sutherland RA, Tantasarin C, Vogler JB et al (2009) Throughfall in an evergreen-dominated forest stand in northern Thailand: comparison of mobile and stationary methods. Agric Forest Meteorol 149:373–384.  https://doi.org/10.1016/j.agrformet.2008.09.002 CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shin’ichi Iida
    • 1
    Email author
  • Takanori Shimizu
    • 1
  • Yoshinori Shinohara
    • 2
  • Shin’ichi Takeuchi
    • 3
  • Tomo’omi Kumagai
    • 4
  1. 1.Forestry and Forest Products Research InstituteTsukubaJapan
  2. 2.University of Miyazaki, MiyazakiMiyazakiJapan
  3. 3.Tokai University, ShizuokaShizuokaJapan
  4. 4.University of TokyoBunkyoku, TokyoJapan

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