Boundary-Layer Meteorology

, Volume 165, Issue 2, pp 295–310 | Cite as

Edge-to-Stem Variability in Wet-Canopy Evaporation From an Urban Tree Row

  • John T. Van StanIIEmail author
  • Zachary Norman
  • Adrian Meghoo
  • Jan Friesen
  • Anke Hildebrandt
  • Jean-François Côté
  • S. Jeffrey Underwood
  • Gustavo Maldonado
Research Article


Evaporation from wet-canopy (\(E_\mathrm{C}\)) and stem (\(E_\mathrm{S}\)) surfaces during rainfall represents a significant portion of municipal-to-global scale hydrologic cycles. For urban ecosystems, \(E_\mathrm{C}\) and \(E_\mathrm{S}\) dynamics play valuable roles in stormwater management. Despite this, canopy-interception loss studies typically ignore crown-scale variability in \(E_\mathrm{C}\) and assume (with few indirect data) that \(E_\mathrm{S}\) is generally \({<}2\%\) of total wet-canopy evaporation. We test these common assumptions for the first time with a spatially-distributed network of in-canopy meteorological monitoring and 45 surface temperature sensors in an urban Pinus elliottii tree row to estimate \(E_\mathrm{C}\) and \(E_\mathrm{S}\) under the assumption that crown surfaces behave as “wet bulbs”. From December 2015 through July 2016, 33 saturated crown periods (195 h of 5-min observations) were isolated from storms for determination of 5-min evaporation rates ranging from negligible to 0.67 \(\hbox {mm h}^{-1}\). Mean \(E_\mathrm{S}\) (0.10 \(\hbox {mm h}^{-1}\)) was significantly lower (\(p < 0.01\)) than mean \(E_\mathrm{C}\) (0.16 \(\hbox {mm h}^{-1}\)). But, \(E_\mathrm{S}\) values often equalled \(E_\mathrm{C}\) and, when scaled to trunk area using terrestrial lidar, accounted for 8–13% (inter-quartile range) of total wet-crown evaporation (\(E_\mathrm{S}+E_\mathrm{C}\) scaled to surface area). \(E_\mathrm{S}\) contributions to total wet-crown evaporation maximized at 33%, showing a general underestimate (by 2–17 times) of this quantity in the literature. Moreover, results suggest wet-crown evaporation from urban tree rows can be adequately estimated by simply assuming saturated tree surfaces behave as wet bulbs, avoiding problematic assumptions associated with other physically-based methods.


Pinus elliottii Rainfall interception Tree surface temperature Urban forest Wet-canopy evaporation Wet-bulb temperature 



This work was supported by the US-NSF (EAR-1518726). Student support for Zachary Norman was provided by the Environmental Protection Division of the Georgia Department of Natural Resources (EPD-WQ-5419). The authors thank the Georgia Southern University physical plant for assistance installing, maintaining and securing sensors within the canopy, and students Daniel Cirincione, Ravon Elam, Dylan Mesta, and Sandra Yankine for assistance with terrestrial lidar data collection and processing.


  1. Baynes J, Dunn GM (1997) Estimating foliage surface area index in 8-year-old stands of Pinus elliottii var. elliottii x Pinus caribaea var. hondurensis of variable quality. Can J For Res 27:1367–1375Google Scholar
  2. Carlyle-Moses D, Gash JHC (2011) Rainfall interception loss by forest canopies. Chapter 20 in: Forest hydrology and biogeochemistry: synthesis of past research and future directions. Springer, Heidelberg, pp 407–423Google Scholar
  3. Crockford RH, Richardson DP (2000) Partitioning of rainfall into throughfall, stemflow and interception: effect of forest type, ground cover and climate. Hydrol Process 14:2903–2920CrossRefGoogle Scholar
  4. Dassot M, Constant T, Fournier M (2011) The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Ann For Sci 68:959–974CrossRefGoogle Scholar
  5. Dirmeyer PA, Gao X, Zhao M, Guo Z, Oki T, Hanasaki N (2006) GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull Am Meteorol Soc 87:1381–1397CrossRefGoogle Scholar
  6. Dolman AJ (1987) Summer and winter rainfall interception in an oak forest. Predictions with an analytical and a numerical simulation model. J Hydrol 90:1–9CrossRefGoogle Scholar
  7. Dunin F, O’Loughlin E, Reyenga W (1988) Interception loss from eucalypt forest: Lysimeter determination of hourly rates for long term evaluation. Hydrol Process 2:315–329CrossRefGoogle Scholar
  8. Fassnacht KS, Gower ST, Norman JM, McMurtrie RE (1994) A comparison of optical and direct methods for estimating foliage surface area index in forests. Agr For Meteorol 71:183–207CrossRefGoogle Scholar
  9. 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:W00D15CrossRefGoogle Scholar
  10. Friesen J, Lundquist J, Van Stan JT (2015) Evolution of forest precipitation water storage measurement methods. Hydrol Process 29:2504–2520CrossRefGoogle Scholar
  11. Gash JHC (1979) An analytical model of rainfall interception by forests. Q J R Meteorol Soc 105:43–55CrossRefGoogle Scholar
  12. Guevara-Escobar A, Gonzalez-Sosa E, Veliz-Chavez C, Ventura-Ramos E, Ramos-Salinas M (2007) Rainfall interception and distribution patterns of gross precipitation around an isolated Ficus benjamina tree in an urban area. J Hydrol 333:532–541CrossRefGoogle Scholar
  13. Hackenberg J, Spiecker H, Calders K, Disney M, Raumonen P (2015) SimpleTree—An efficient open source tool to build tree models from TLS Clouds. Forests 6:4245–4294CrossRefGoogle Scholar
  14. Herwitz SR (1985) Interception storage capacities of tropical rainforest canopy trees. J Hydrol 77:237–252CrossRefGoogle Scholar
  15. Holder CD, Gibbes C (2017) Influence of leaf and canopy characteristics on rainfall interception and urban hydrology. Hydrol Sci J 62:182–190CrossRefGoogle Scholar
  16. Holwerda F, Bruijnzeel LA, Scatena FN, Vugts HF, Meesters AGCA (2012) Wet canopy evaporation from a Puerto Rican lower montane forest: the importance of realistically estimated aerodynamic conductance. J Hydrol 414–415:1–15CrossRefGoogle Scholar
  17. Kimbauer MC, Baetz BW, Kenny WA (2013) Estimating the stormwater attenuation benefits derived from planting four monoculture species of deciduous trees on vacant and underutilized urban land parcels. Urban For Urban Green 12:401–407CrossRefGoogle Scholar
  18. Linhoss AC, Siegert CM (2016) A comparison of five forest interception models using global sensitivity and uncertainty analysis. J Hydrol 538:109–116CrossRefGoogle Scholar
  19. Livesley SJ, Baudinette B, Glover D (2014) Rainfall interception and stem flow by eucalypt street trees—the impacts of canopy density and bark type. Urban For Urban Green 13:192–197CrossRefGoogle Scholar
  20. Levia DF, Herwitz SR (2005) Interspecific variation of bark water storage capacity of three deciduous tree species in relation to stemflow yield and solute flux to forest soils. Catena 64:117–137CrossRefGoogle Scholar
  21. Levia DF, Keim RF, Carlyle-Moses DE, Frost EE (2011) Throughfall and stemflow in wooded ecosystems. Chapter 21 In: Forest hydrology and biogeochemistry. Ecological studies series 216. Springer, Heidelberg, pp 425-443Google Scholar
  22. McPherson GE, van Doorn N, de Goede J (2016) Structure, function and value of street trees in California, USA. Urban For Urban Green 17:104–115CrossRefGoogle Scholar
  23. Miralles DG, Gash JHC, Holmes TRH, de Jeu RAM, Dolman AJ (2010) Global canopy interception from satellite observations. J Geophys Res-Atmos 115:D16122CrossRefGoogle Scholar
  24. Monteith JL, Unsworth MH (2008) Principles of environmental physics. Academic Press, LondonGoogle Scholar
  25. Oliver HR (1971) Wind profiles in and above a forest canopy. Q J R Meteorol Soc 97:548–553CrossRefGoogle Scholar
  26. Pereira FL, Gash JHC, David JS, Valente F (2009) Evaporation of intercepted rainfall from isolated evergreen oak trees: Do crowns behave as wet bulbs? Agr For Meteorol 149:667–679CrossRefGoogle Scholar
  27. Pereira FL, Valente F, David JS, Jackson N, Minunno F, Gash JHC (2016) Rainfall interception modelling: Is the wet bulb approach adequate to estimate mean evaporation rate from wet/saturated canopies in all forest types? J Hydrol 534:606–615CrossRefGoogle Scholar
  28. Raumonen P, Kaasalainen M, Åkerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, Holopainen M, Disney M, Lewis P (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520CrossRefGoogle Scholar
  29. Rutter AJ, Morton AJ (1977) A predictive model of rainfall interception in forests. III. Sensitivity of the model to stand parameters and meteorological variables. J Appl Ecol 14:567–588CrossRefGoogle Scholar
  30. Sadeghi SMM, Attarod P, Van Stan JT, Pypker TG (2016) The importance of considering rainfall partitioning in afforestation initiatives in semiarid climates: a comparison of common planted tree species in Tehran. Iran Sci Total Environ 568:845–855CrossRefGoogle Scholar
  31. Smolander H, Stenberg P (1996) Response of LAI-2000 estimates to changes in plant surface area index in a Scots pine stand. Tree Physiol 16:345–349CrossRefGoogle Scholar
  32. Stull R (2011) Wet-bulb temperature from relative humidity and air temperature. J Appl Meteorol Climatol 50:2267–2269CrossRefGoogle Scholar
  33. Sypka P, Starzak R (2013) Simplified, empirical model of wind speed profile under canopy of Istebna spruce stand in mountain valley. Agric For Meteorol 171–172:220–233CrossRefGoogle Scholar
  34. University of Georgia Weather Network (2016). Accessed 8 Aug 2016
  35. Valente F, David JS, Gash JHC (1997) Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models. J Hydrol 190:141–162CrossRefGoogle Scholar
  36. van der Ent RJ, Wang-Erlandsson L, Keys PW, Savenije HHG (2014) Contrasting roles of interception and transpiration in the hydrological cycle—Part 2: moisture recycling. Earth Syst Dyn 5:471–489CrossRefGoogle Scholar
  37. van Dijk AIJM, Bruijnzeel LA (2001) Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description. J Hydrol 247:230–238CrossRefGoogle Scholar
  38. van Dijk AIJM, Gash JHC, van Gorsel W, Blanken PD, Cescatti A, Emmel C, Gielen B, Harman LN, Kiely G, Merbold L, Montagnani L, Moors E, Sttocornola M, Varlagin A, Williams CA, Wohlfahrt G (2015) Rainfall interception and the coupled surface water and energy balance. Agric For Meteorol 214–215:402–415CrossRefGoogle Scholar
  39. Van Stan JT, Van Stan JH, Levia DF (2014) Meteorological influences on stemflow generation across diameter size classes of two morphologically distinct deciduous species. Int J Biometeorol 58:2059–2069CrossRefGoogle Scholar
  40. Van Stan JT, Levia DF, Jenkins RB (2015) Forest canopy interception loss across temporal scales: implications for urban greening initiatives. Prof Geogra 67:41–51CrossRefGoogle Scholar
  41. Van Stan JT, Lewis ES, Hildebrandt A, Rebmann C, Friesen J (2016) Impact of interacting bark structure and rainfall conditions on stemflow variability in a temperate beech-oak forest, Central Germany. Hydrol Sci J 61:2071–2083CrossRefGoogle Scholar
  42. Wang J, Endreny TA, Nowak DJ (2008) Mechanistic simulation of tree effects in an urban water balance model. J Am Water Resour Assoc 44:75–85CrossRefGoogle Scholar
  43. Wang-Erlandsson L, van der Ent RJ, Gordon LJ, Savenije HHG (2014) Contrasting roles of interception and transpiration in the hydrological cycle—part 1: temporal characteristics over land. Earth Syst Dyn 5:441–469CrossRefGoogle Scholar
  44. Xiao Q, McPherson EG, Ustin SL, Grismer ME, Simpson JR (2000) Winter rainfall interception by two mature open-grown trees in Davis, California. Hydrol Process 14:763–784CrossRefGoogle Scholar
  45. Xu X, Yi C, Kutter E (2015) Stably stratified canopy flow in complex terrain. Atmos Chem Phys 15:7457–7470CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Geology and GeographyGeorgia Southern UniversityStatesboroUSA
  2. 2.Department of Catchment HydrologyHelmholtz Centre for Environmental Research – UFZLeipzigGermany
  3. 3.Institute of GeoscienceFriedrich Schiller University JenaJenaGermany
  4. 4.Canadian Wood Fibre CentreNatural Resources CanadaQuébecCanada
  5. 5.Department of Civil Engineering and Construction ManagementGeorgia Southern UniversityStatesboroUSA
  6. 6.Office of the Vice President for ResearchCalifornia State UniversityLos AngelesUSA

Personalised recommendations