Temporal variations of transpiration and latent heat fluxes from isolated linden crowns and lawns in a park at Strasbourg, France
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1 Temporal variations of transpiration and latent heat fluxes from isolated linden crowns and lawns in a park at Strasbourg, France Ngao J., Colin J. 2, Améglio T., Saudreau M., Kastendeuch P. 2, Granier A. 3, Najjar G. 2 UMR PIAF (INRA-UBP), Clermont-Ferrand (France) 2 UMR icube (CNRS-UStra-INSA), Strasbourg (France) 3 UMR EEF (INRA-UL), Nancy (France) jerome.ngao@clermont.inra.fr
2 Urban Heat Island and Vegetation Climate Global Climate Change Urban Structure Urban Heat Balance Urban Heat Island Effect Source: Human Health Rizwan et al., 28
3 Urban Heat Island and Vegetation Climate Global Climate Change Urban Structure Urban Heat Balance Vegetation Incident radiation interception Urban Heat Island Effect Source: Human Health Matthieu Carton MNHN
4 Urban Heat Island and Vegetation Climate Global Climate Change Urban Structure Urban Heat Balance Vegetation Latent heat evaporation Incident radiation interception Urban Heat Island Effect Source: Human Health Ong et al., 23; Bowler et al., 2; Santamouris, 23; Matthieu Carton MNHN
5 Urban Heat Island and Vegetation Climate Global Climate Change Urban Structure Urban Heat Balance Vegetation Latent heat evaporation Incident radiation interception Urban Heat Island Effect XX W m -2? XX W m -2? Source: Human Health Matthieu Carton MNHN
6 Trees and Lawns Transpiration? 2 OBJECTIVES. To quantify the latent heat flux emitted by a lawn surface (E L ) and isolated linden trees (E T ) in the city centre of Strasbourg (France) 2. To characterize the seasonal evolution of these fluxes with respect to atmospheric variables
7 Trees and Lawns Transpiration? 2 OBJECTIVES. To quantify the latent heat flux emitted by a lawn surface (E L ) and isolated linden trees (E T ) in the city centre of Strasbourg (France) 2. To characterize the seasonal evolution of these fluxes with respect to atmospheric variables Q: Order of magnitude of E L and E T? Q2: Environmental variables influencing E T? Q3: Drought Stress in Urban Environment?
8 Experimental Site 3 OBJECTIVES Strasbourg (France). To quantify > 25 the latent inhab. heat flux (City) emitted by a lawn surface (E L ) and isolated linden trees > 75 (E T ) in the inhab. city (Urb. centre Area) of Strasbourg (France) N 2. To characterize the seasonal evolution of these fluxes with respect to atmospheric variables
9 Experimental Site 3 OBJECTIVES Strasbourg (France). To quantify > 25 the latent inhab. heat flux (City) emitted by a lawn surface (E L ) and isolated linden trees > 75 (E T ) in the inhab. city (Urb. centre Area) of Strasbourg (France) N 2. To characterize the seasonal evolution of these fluxes with respect to atmospheric variables
10 Field Measurements 4 N E L = Lawn transpiration (Closed Transpiration Chamber) 2-min measurements every 2 min
11 Field Measurements N Granier, E T = Tree transpiration (Thermal Dissipation Probes) Half-hourly timestep for all trees
12 Field Measurements N Granier, E T : Corrected for the sapwood width Gebauer et al., 28 Ground surface area - based values Meteorological data: PET computation (Penman formula) Canopy conductance (Penman-Monteith formula) E T = D.(R n G) + r.cp.vpd.g a l.[d + g.( + g a /g c )]
13 Q: Order of Magnitude? 6 Latent Heat Flux (W m -2 ) EL ET RG Global Radiation (W m -2 ) 5/7/24 6/7/24 7/7/24 8/7/24 E L and E T : Similar order of magnitude Maximum le: ~35% (E L ) and 2-4% (E T ) of maximum R G Qiu et al., 23
14 Q2: Influencing Environmental Variables : E L 6 Latent Heat Flux (W m -2 ) 4 9 EL 35 ET RG /7/24 6/7/24 7/7/24 8/7/24 E L and E T : Similar order of magnitude Global Radiation (W m -2 ) Latent Heat Flux (W m -2 ) E L 5 Location Location 2 5/7/24 6/7/24 7/7/24 8/7/24 Daily Spatio-Temporal Variability of E L le: ~35% (E L ) and 2-4% (E T ) of R G
15 Mean E L (W m -2 ) Q2: Influencing Environmental Variables : E L 6 Latent Heat Flux (W m -2 ) 4 9 EL 35 ET RG /7/24 6/7/24 7/7/24 8/7/24 E L and E T : Similar order of magnitude Global Radiation (W m -2 ) Latent Heat Flux (W m -2 ) E L 5 Location Location 2 5/7/24 6/7/24 7/7/24 8/7/24 Daily Spatio-Temporal Variability of E L le: ~35% (E L ) and 2-4% (E T ) of R G Best Model E L = -. VPD VPD. R G R 2 =.83; AIC = 7.78 R G (W m -2 ) VPD (kpa)
16 Q2: Influencing Environmental Variables : E T 7 August 23 E T
17 Q2: Influencing Environmental Variables : E T 7 August 23 E T Latent Heat Flux (W m -2 ) August, 6 24 Tree#4 Tree#6 : 3: 6: 9: 2: 5: 8: 2: : Hour (UTC) Inter-Individual Variability of E T Latent Heat Flux (W m -2 ) August, 6 24 R 2 =.72 R 2 =.73 Tree#4 Tree# VPD (kpa) Strong influence of VPD
18 Q2: Influencing Environmental Variables : E T Tree #4 Tree #6 Strong influence of tree's phenology Maximum ET (W m -2 ) /4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 4/ 3/2 5/3 4/4 4/5 3/6 3/7 2/8 /9 / 3/ /
19 Q2: Influencing Environmental Variables : E T 8 Maximum ET (W m -2 ) Budburst Leaf expansion Leaf fall Tree #4 Tree #6 Strong influence of tree's phenology 5 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 4/ 3/2 5/3 4/4 4/5 3/6 3/7 2/8 /9 / 3/ /
20 Q2: Influencing Environmental Variables : E T 8 Maximum ET (W m -2 ) Budburst Leaf expansion Leaf fall Tree #4 Tree #6 Strong influence of tree's phenology Penman Equation: D.R n + r.cp.vpd.g a PET = l.[d + g] 5 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 4/ 3/2 5/3 4/4 4/5 3/6 3/7 2/8 /9 / 3/ /.8 Tree#4 Tree#6 ET/ETP /4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 23
21 Q2: Influencing Environmental Variables : E T 8 Maximum ET (W m -2 ) Budburst Leaf expansion Leaf fall Tree #4 Tree #6 Strong influence of tree's phenology Penman Equation: D.R n + r.cp.vpd.g a PET = l.[d + g] E T /PET ratio: no major stress 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 4/ 3/2 5/3 4/4 4/5 3/6 3/7 2/8 /9 / 3/ /.8 Tree#4 Tree#6 ET/ETP /4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 23
22 Q3: Drought Stress? 9.8 Tree#4 Tree#6.6 ET/ETP.4.2 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 23
23 Q3: Drought Stress? 9.8 Tree#4 Tree#6 Canopy condutance (Penman- Monteith Equation): ET/ETP.6.4 E T = D.R n + r.cp.vpd.g a l.[d + g.( + g a /g c )].2 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 23
24 Q3: Drought Stress? 9.8 Tree#4 Tree#6 Canopy condutance (Penman- Monteith Equation): ET/ETP.6.4 E T = D.R n + r.cp.vpd.g a l.[d + g.( + g a /g c )] Canopy conductance (cm s - ).2 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/ Tree#4 Tree#6 Canopy conductance (cm s - ) Tree#4 Tree#6 22/8 23/8 24/8 25/8 26/8 27/8 28/8 29/8 25/8 3/6 /7 2/7 3/7 4/7 5/7 6/7 7/7 8/7 9/7
25 Q3: Drought Stress? 9.8 Tree#4 Tree#6 Canopy condutance (Penman- Monteith Equation): ET/ETP.6.4 E T = D.R n + r.cp.vpd.g a l.[d + g.( + g a /g c )] Canopy conductance (cm s - ).2 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/ Tree#4 Tree#6 Canopy conductance (cm s - ) Tree#4 Tree#6 22/8 23/8 24/8 25/8 26/8 27/8 28/8 29/8 25/8 Stomatal limitation of E T at the crown-level Signs of drought stress in urban conditions? 3/6 /7 2/7 3/7 4/7 5/7 6/7 7/7 8/7 9/7
26 Q3: Drought Stress? Drought stress: looking at the tree's hydric status Stem/Branch Diameter Variations
27 Q3: Drought Stress? Drought stress: looking at the tree's hydric status Stem/Branch Diameter Variations 2 5 Ex. Drought experiment Well watered tree Stem increment (µm) 5 /5 8/5 25/5 /6 8/6 5/6 22/6 29/6 Stem skrinkage due to server water shortage and high PET conditions -5
28 Q3: Drought Stress? Drought stress: looking at the tree's hydric status Stem/Branch Diameter Variations 2 5 Ex. Drought experiment Well watered tree Stem increment (µm) 5 /5 8/5 25/5 /6 8/6 5/6 22/6 29/6 Stem skrinkage due to server water shortage and high PET conditions -5 Diameter increment (µm) Tree#4 Tree#6 No significant stem shinkrage Avoidance of drought stress during the growing period 25/8-5 9/4 9/5 8/6 8/7 7/8 6/9 6/ 5/ 5/2 23
29 CONCLUDING REMARKS Q: Order of magnitude of E L and E T? E L and E T : Similar order of magnitude Maximum le: ~35% (E L ) and 2-4% (E T ) of maximum R G
30 CONCLUDING REMARKS Q: Order of magnitude of E L and E T? E L and E T : Similar order of magnitude le: ~35% (E L ) and 2-4% (E T ) of R G Q2: Environmental variables influencing E T? R G and VPD: "Classical" variables driving the ecophysiological functionning of trees and lawns (cf. Penman-Monteith modelling) Necessity to have precise measurements and/or model outputs of variables of biological interest (e.g. wind speed) Phenology: strong influence on fluxes, itself under climate forcing
31 CONCLUDING REMARKS Q: Order of magnitude of E L and E T? E L and E T : Similar order of magnitude le: ~35% (E L ) and 2-4% (E T ) of R G Q2: Environmental variables influencing E T? R G and VPD: "Classical" variables driving the ecophysiological functionning of trees and lawns (cf. Penman-Monteith modelling) Necessity to have precise measurements and/or model outputs of variables of biological interest (e.g. wind speed) Phenology: strong influence on fluxes, itself under climate forcing Q3: Drought Stress in Urban Environment? No drought stress (strong stomatal control, water table?) Other sites with more constraining water conditions?
32 CONCLUDING REMARKS Toward a more complete scheme of energy balance Atmospheric radiative and energy balance: Pyranometers, pyrgeometer, pyrheliometer, sonic anemometer Water balance: soil and plant parameters 3D Geometry of both urban infrastucture and vegetation (tree) for accurate radiative and energy balance simulation (LASER/F model Kastendeuch et Najjar 29 and RATP model Sinoquet et al., 2)
33 CONCLUDING REMARKS Toward a more complete scheme of energy balance Atmospheric radiative and energy balance: Pyranometers, pyrgeometer, pyrheliometer, sonic anemometer Water balance: soil and plant parameters 3D Geometry of both urban infrastucture and vegetation (tree) for accurate radiative and energy balance simulation (LASER/F model Kastendeuch et Najjar 29 and RATP model Sinoquet et al., 2) Poster 22: NOMTM Najjar et al., A three years long fieldwork experiment to monitor the role of vegetation on the urban climate of the city of Strasbourg, France Landes et al., 3D tree architecture modeling from laser scanning for urban microclimate study
34 Thank you for your attention!
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