flows, fluxes and flightpaths: adventures in quantifying
TRANSCRIPT
Sap Flow & Modelling Surface Renewal ET Remotely Sensed ET
Flows, fluxes and flightpaths: Adventures in quantifying vine and vineyard water use
Andrew J. McElrone; [email protected]
Precipitation (P) & Irrigation (I)
Vineyard Water Balance
Drainage (D)
Evaporation & Transpiration (ET)
Change in soil water = P + I – ET – D – R Gains Losses
Runoff (R)
ETc = Kc * ETo
Grapevine evapotranspiration Crop coefficient Reference ET(well-wateredmodel grass)
Kc = ETc / ETo
Obtained from vines in weighing lysimeter
Kearney Agricultural CenterUniv. of California- Parlier CA
California Irrigation Management Information
System (CIMIS)
“…assumes a disease-free plant
grown under optimum soil water
and nutrient conditions…”Doorenbos and Pruitt, 1977
Williams and Ayars (2005)
Scale or load cell
Irrigation supply tank
Concrete pad
Tank filled with soil
Datalogger
Schematic drawing of lysimeter
Thompson Seedless
grapevines
HRM-Downstream temp
CHPM & HRM-
Upstream temp
Heater probe
Bark Sapwood
Sap
flow X
X
Schematic of Dual Heat Pulse Sap Flow Sensors
HRM-
Ideal for low &
reverse flows
CHPM-
Ideal for high
flows
CHPM-Downstream temp
Thermocouples
Xu
Xd
6mm
18, 24 or
36mm
-6mm-6mm
Heartwood
Vh = (Xd + Xu)/2t0 * 3600
Compensation Heat Pulse Method (CHPM)
Vh = (k * ln(V1 / V2))/X * 3600
Heat Ratio Method (HRM)
T up = T down
Vh = heat pulse velocity; X = spacing distances; t0 = time to travel ½ way Vh = heat pulse velocity; k = thermal diffusivity; V = temp increases; X = spacing distance
Bleby et al. 2004
08 Sep 10 Sep 12 Sep 14 Sep
Sap
Flo
w (
L h
r-1)
0
2
4
6
8
10
ETc
HRM
CHPM 18 Field Weighing Lysimeter vs. Dual Sap Flow Method
2008 Field Season
y = 0.9898x
R2 = 0.896
Lysimeter
20 Jul 23 Jul 26 Jul 29 Jul 01 Aug 04 Aug 07 Aug
Sa
p F
low
Ve
loc
ity
(c
m h
r-1)
0
20
40
60
80
100
HRM
CHPM 18
CHPM 24
CHPM 36 Irrigation
events
Pearsall et al. 2014
Trunk
Green 1 year old shoot
2 year old shoot
Converting sap
velocity to volume
Grapevines are hydraulically sectored
Trunk
03-Oct 05-Oct 07-Oct 09-Oct 11-Oct 13-Oct
Sap
Flo
w V
elo
cit
y (
cm
hr-
1)
0
50
100
150
200
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Sensor 5
Sensor 6
ET
o (m
m)
0.0
0.2
0.4
0.6
0.8
1.0
CIMIS ETo
Variability around the trunk
Pearsall et al. 2014 FPB
Vary from 50-200 cm hr-1
Sensor Installation
“Dye colouring of the xylem vessels revealed that even 21 year old
grapevines did not show any development of heartwood and that xylem
vessels of that age still have the capacity to transport water”
Braun and Schmid (1999)
Bouda et al. (in review)
MRI-flow FusionX-ray microCT
Actual vs. modelled flows on same stem
Flow = DY/ Hydraulic Resistance
Model predicted flow rates with single pressure value across whole segment
Bouda et al. (in review)
Bouda et al. 2019
Model predicted flow rates with separate pressure value for sets of vessels
Flow = DY/ Hydraulic Resistance
Well-watered
Severe stress
Different water content in same stem
5 cm hr-1 Flow, Fibers Water-Filled
Heat Propagation in Air-Filled vs. Water-Filled
Fiber Matrix
temp (K)
70% overestimation of sap flow velocity due to
air-filled fiber tissue
Surface Renewal
Paw U & collaborators 1989, 1991, 1995
Goal: inexpensive, site-specific measurement of actual crop water use
=Net
Radiation + +Ground
Heat Flux
Sensible Heat Flux
Latent Heat Flux
Surface Energy Balance: Partitioning of energy at the surface
Bird’s eye view
theoretical
actual
Surface Renewal- Theory vs. Reality
Successfully removed the need to calibrate against expensive research grade system (Shapland et al. 2012a,b, 2014)
Research Grade System~$10,000 New Commercial System from
joint patent between USDA & UC Davis
New Surface Renewal System: A reliable & automated ET measurement system
New Surface Renewal Method vs. Weighing Lysimeter
Proof of concept:
Kearney Agricultural CenterUniv. of California- Parlier CA
New Surface Renewal Method vs. Soil Water Budget
Proof of concept:
Kearney Agricultural CenterUniv. of California- Parlier CA
Refine and apply a multi-scale remote sensing ET toolkit for mapping crop water use and stress for improved irrigation management in CA
Grape Remote sensing Atmospheric Profile
& Evapotranspiration eXperiment
Anderson et al. 2016
Approach: During the 2013 to the 2016 growing seasons, micrometeorogical, biophysical and
remote sensing data from ground, airborne (including UAVs) and satellite platforms have
been collected in adjacent vineyards at different levels of maturity near Lodi. Expanded to
additional sites (near Cloverdale and Ripperdan) in 2017-2019.
Kustas et al. 2018
Parry et al. 2019
Kustas et al. 2018
Kustas et al. 2018
Utah State Aggie Air
Kustas et al. 2018
Knipper et al. 2019
Acknowledgements
• Mimar Alsina- E&J Gallo• Martha Anderson- USDA-ARS• Jim Ayars- USDA-ARS• Felipe Barrios Masias- UC Davis• Mark Battany- UC ANR• Daniel Bosch- Constellation Brands• Arturo Calderon- UC Davis• Sean Castorani- ARS Davis• Nick Dokoozlian- E&J Gallo• Ashley Eustis- USDA-ARS• Kevin Fort- UC Davis• Bill Kustas- USDA-ARS
• Jerry Lohr- Grower Cooperator• Kyaw Tha Paw U- UC Davis• Anji Perry- J Lohr V&W• Rod Scheaffer- Constellation Brands• Tom Shapland- UC Davis• Ruby Stahel- USDA-ARS• Rick Snyder- UC Davis• Gwen Tindula- UC ANR• Yannis Toutountzis- Constellation Vlade
Tudor- Tudor Farms• Andy Walker- UC Davis• Larry Williams- UC Davis• Andrew Zaninovich- Sunview Vineyards
• Funding Sources:– J. Lohr Vineyards and Wines
– American Vineyard Foundation
– National Grape and Wine Initiative
– NIFA-Specialty Crops Research Initiative
– USDA-ARS Sustainable Vit CRIS
– GRAPEX Team and E&J Gallo