Agricultural Water Management 179 (2017) 92–102
2524
Contents lists available at ScienceDirect
Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat
Assessing FAO-56 dual crop coefficients using eddy covariance flux
partitioning
Ray G. Anderson a,∗ , Joseph G. Alfieri b , Rebecca Tirado-Corbalá c,1 , Jim Gartung c ,
Lynn G. McKee b , John H. Prueger d , Dong Wang c , James E. Ayars c , William P. Kustas b
a
US Salinity Laboratory, USDA-Agricultural Research Service, George E. Brown Jr. Salinity Laboratory, 450 W. Big Springs Rd., Riverside, CA, 92507-4617,
USA2
b
Hydrology and Remote Sensing Laboratory, USDA-Agricultural Research Service, 10300 Baltimore Ave., Bldg. 007 BARC-WEST, Beltsville, MD, 20705, USA
c
Water Management Research, USDA-Agricultural Research Service, San Joaquin Valley Agricultural Sciences Center, 9611 S. Riverbend Ave., Parlier, CA,
93648-9757, USA
d
National Laboratory for Agriculture and the Environment, USDA-Agricultural Research Service, 2110 University Blvd., Ames, IA 50011, USA
a r t i c l e
i n f o
Article history:
Received 4 February 2016
Received in revised form 27 July 2016
Accepted 31 July 2016
Available online 8 August 2016
Keywords:
FAO-56
Eddy covariance
Flux partitioning
Crop coefficients
Evapotranspiration
a b s t r a c t
Current approaches to scheduling crop irrigation using reference evapotranspiration (ET0 ) recommend
using a dual-coefficient approach using basal (Kcb ) and soil (Ke ) coefficients along with a stress coefficient (Ks ) to model crop evapotranspiration (ETc ), [e.g. ETc = (Ks *Kcb + Ke )*ET0 ]. However, determining
Ks , Kcb , and Ke from the combined evapotranspiration (ET) is challenging, particularly for Ke , and a new
method is needed to more rapidly determine crop coefficients for novel cultivars and cultivation practices. In this study, we partition eddy covariance ET observations into evaporation (E) and transpiration
(T) components using correlation structure analysis of high frequency (10–20 Hz) observations of carbon
dioxide and water vapor (Scanlon and Sahu, 2008) at three irrigated agricultural sites. These include
a C4 photosynthetic-pathway species (sugarcane—Sacharum officinarum L.) and a C3 pathway species
(peach—Prunus persica) under sub-surface drip and furrow irrigation, respectively. Both sites showed
high overall Kc consistent with their height (>4 m). The results showed differences in Ke , with the subsurface drip-irrigated sugarcane having a low Ke (0.1). There was no significant relationship (r2 < 0.05)
between root zone soil volumetric water content (VWC) in sugarcane and observed Kcb *Ks , indicating that
there was no stress (Ks = 1), while the peach orchard showed mid-season declines in Kcb *Ks when VWC
declined below 0.2. Partitioning of Kc into Kcb and Ke resulted in a better regression (r2 = 0.43) between
the Normalized Differential Vegetation Index (NDVI) and Kcb in sugarcane than between NDVI and Kc
(r2 = 0.11). The results indicate the potential for correlation structure flux partitioning to improve crop
ET coefficient determination by improved use of eddy covariance observations compared to traditional
approaches of lysimeters and microlysimeters and sap flow observations to determine Kc , Ke , Ks , and Kcb .
Published by Elsevier B.V.
∗ Corresponding author.
E-mail addresses: ray.anderson@ars.usda.gov, r.g.anderson@gmail.com (R.G. Anderson), joe.alfieri@ars.usda.gov (J.G. Alfieri), rebecca.tirado@upr.edu (R. Tirado-Corbalá),
jim.gartung@ars.usda.gov (J. Gartung), lynn.mckee@ars.usda.gov (L.G. McKee), john.prueger@ars.usda.gov (J.H. Prueger), dong.wang@ars.usda.gov (D. Wang),
james.ayars@ars.usda.gov (J.E. Ayars), bill.kustas@ars.usda.gov (W.P. Kustas).
1
Present address: Crops and Agro-Environmental Science Department, University of Puerto Rico, PO Box 9000, Mayagüez, Puerto Rico, 00681-9000 USA.
2
Note: The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and
where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an
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for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint
of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202)
720-6382 (TDD). USDA is an equal opportunity provider and employer.
http://dx.doi.org/10.1016/j.agwat.2016.07.027
0378-3774/Published by Elsevier B.V.
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
1. Introduction
Agricultural irrigation is the largest anthropogenic use of fresh
water (Döll and Siebert, 2002). Accurately parameterizing crop
water demand is critical to optimizing irrigation amounts and
timing, maximizing crop growth and water productivity, and minimizing losses due to over-irrigation or crop stress from under
irrigation (Doorenbos et al., 1979; Steduto et al., 2012). One major
approach for calculating crop evapotranspiration (ET) is described
in the Food and Agricultural Organization Irrigation and Drainage
paper 56 (FAO-56 – Allen et al., 1998). FAO-56 combines a reference
evapotranspiration (ET0 ), characterizing meteorological evaporative demand, along with a crop coefficient representing agricultural
field characteristics, including crop type, plant density, phenology,
soil water content status, and others. The FAO-56 method can be
run with either a single crop coefficient (Kc ) or a dual crop coefficient approach that separately represents soil evaporation (Ke )
and plant transpiration and stress (Kcb and Ks ). Currently, numerous hydrologic models combine a multiple coefficient approach
with FAO-56 ET0 (Raes et al., 2009; Rosa et al., 2012). Using a dual
coefficient approach may be more appropriate for many cropping
systems with technologies such as drip irrigation (Paço et al., 2012),
plastic or organic mulching (Ding et al., 2013; Odhiambo and Irmak,
2012), partial root zone drying (O’Connell and Goodwin, 2007), and
deficit irrigation (Ferreira et al., 2012). These techniques minimize
soil evaporation and maximize water productivity, and, as such,
it is important for irrigation studies to be able to independently
determine and assess Kcb , Ks , and Ke .
Despite the increasing relevance of using the dual crop coefficient approach for modeling crop ET, determination of Kcb , Ks , and
Ke is challenging. The most commonly used methods for observing
crop ET, including soil water balance (Bodner et al., 2007), lysimetry
(Liu and Luo, 2010), and micrometeorological approaches including
eddy covariance (EC) (Paço et al., 2006; Zhang et al., 2013), surface
renewal (Testi et al., 2004) and Bowen ratio energy balance (InmanBamber and McGlinchey, 2003; Todd et al., 2000), cannot partition
ET into evaporation (E) and transpiration (T). The FAO-56 approach
and other studies rely on analyzing individual irrigation events and
determining Kcb after an irrigation event when the soil surface is
dry and E is very low or negligible (Allen et al., 1998; Benli et al.,
2006). Other crop water use studies incorporate modeling to determine Kcb and Ke from the single coefficient (Kato and Kamichika,
2006). Separate E and T measurements are ideally needed to determine Kcb , Ks , and Ke .
Direct observations of E and T, including microlysimetery (Liu
et al., 2002) and sap flow techniques (Rana et al., 2005) observe
small areas relative to the whole field and have similar issues of
spatial representativeness (Kool et al., 2014; Williams et al., 2004).
Another option for partitioning of fluxes is micrometeorological
observations of isotopic fluxes (mainly 18 O and 13 C), which can be
used to partition fluxes due to isotopic discrimination during photosynthesis and evaporation (Griffis et al., 2004; Zhang et al., 2011).
However, this method has substantial methodological challenges
due to uncertainty over the relationship between isotopic discrimination and land-atmospheric exchange processes at differing sites
(Griffis et al., 2011; Sutanto et al., 2014); it also is considerably more
expensive than EC. Due to expense and challenges of measuring E
or T, numerous studies measure either E or T and determine the
other component by residual (Ferreira et al., 2012; Liu et al., 2002).
Recently a method of partitioning EC fluxes was developed
based on flux-variance similarity theory, which uses parameterized
leaf-level water use efficiency and analysis of the correlation structure of high frequency CO2 and H2 O time series observations from
each flux averaging interval (Scanlon and Sahu, 2008; Scanlon and
Kustas 2010, 2012). This method (hereafter called “EC FP”) enables
separation of EC fluxes into E and T without additional observa-
93
tions. In this study, we conceptually demonstrate how EC FP can
be applied to efficiently determine Kcb *Ks and Ke from EC observations in irrigated agricultural fields. Using observations during
stressed and non-stressed periods, we illustrate how Ks can be separated from Kcb *Ks . We apply EC FP to three sites in two, contrasting
irrigated agricultural systems. The first two sites are sub-surface
drip-irrigated sugarcane (Sacharum officinarum L.) fields located in
Hawaii, USA, while the third site is a furrow-irrigated peach orchard
(Prunus persica) in California, USA. Finally, we illustrate how EC FP
can also be used to enable soil moisture and satellite observations
for quantifying Kcb , Ks , and Ke in operational settings for irrigation
management.
2. Materials and methods
2.1. Flux partitioning and crop coefficient calculation
EC FP relies on applying flux variance similarity (Scanlon and
Sahu, 2008) to high frequency wind velocity and CO2 and H2 O concentration observations. We followed Scanlon and Kustas (2010,
2012) for data processing to determine half hourly partitioned
fluxes. Briefly, Reynolds averaging and density fluctuation corrections were made to the data, and then wavelet filtering was
applied to remove the impact of large-scale, low-frequency atmospheric processes (Reynolds, 1895; Scanlon and Sahu, 2008; Webb
et al., 1980). Wavelet size decreased until an analytic solution was
found to satisfy similarity theory with the given leaf-level water
use efficiency (WUE), which is the ratio of leaf-level CO2 assimilation to transpiration, and resultant stomatal and non-stomatal
components (Scanlon and Kustas, 2010). Along with the high frequency atmospheric time series data, WUE is the only other input
used in the EC FP model. WUE was estimated following Campbell
and Norman (1998) (see also Eq. (1) in Scanlon and Kustas, 2012).
Intercellular H2 O assumed to be the saturation vapor pressure of
the parameterized leaf temperature, while intercellular CO2 (ci ) is
parameterized differently for the C3 and C4 photosynthetic pathways. For the C4 sugarcane sites, ci is estimated as a constant
fraction (0.44) of atmospheric CO2 (ca ) (Kim et al., 2006). For the C3
peach orchard, the ci /ca ratio is a function of vapor pressure deficit
(VPD) following Katul et al. (2009) as shown below:
ci
=1−
ca
˛ 0.5
ca
D0.5
(1)
Where ␣ is the diffusivity of CO2 to H2 O (1.6), D is VPD, and is
a parameter describing the relationship between ci and ca . was
set at 22 following the leaf-level analyses presented in Katul et al.
(2009).
We scaled the half hourly partitioned fluxes to daily fractions of
transpiration (FT ) and evaporation (FE ) following Eqs. (2) and (3):
FT =
1
j
1
FE =
j
T+
j
1
j
1
T+
T
j
1
(2)
E
E
j
1
(3)
E
Where T and E are the evaporation and transpiration outputs from
the EC FP program and summation subscripts 1 and j denote the
first and last periods of a day where the EC FP program finds a
solution. Periods where the program does not come to a solution
are excluded from the summation. If j < 8 for a given day, Eqs. (2)
and (3) are not run, FT and FE are assumed to be missing for those
days, and the dual crop coefficients are not calculated. We chose 8
periods (4 h) to reduce potential time of day biases in calculating FT
and FE while still maximizing the number of days where the crop
coefficients are calculated. FT and FE can be used to partition the
94
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
Fig. 1. Map of peach study site. (a) Map of United States with southern San Joaquin Valley, California, highlighted. (b) False color image of southern San Joaquin Valley
(Landsat 8 platform—June 13, 2016) with peach orchard indicated. For maps of the Hawaiian sugarcane study sites, please refer to Anderson et al. (2015a,b) or Zhang et al.
(2015).
single crop coefficient (Kc ) into Kcb *Ks and Ke following Eqs. (4)
and (5):
Kcb ∗ Ks = FT ∗ Kc
(4)
Ke = FE ∗ Kc
(5)
Two-week, running means of Kcb *Ks and Ke are calculated from
the daily data. Ks can be separated from Kcb during periods of the
growing season following onset of stress when canopy cover is relatively constant and Kcb can be assumed to be a constant as shown
in Eq. (6):
KS (t2 ) =
Kcb ∗ Ks (t2 )
Kcb ∗ Ks (t1 )
(6)
where t1 and t2 can either be two times in space or two differing
values of soil moisture or salinity stress; Kcb *Ks can be determined
for each time from Eq. (5); and t1 is assumed to be before onset
of stress. Appropriate times for t1 and t2 need to be assessed by
a combination of crop cover data and ancillary observations (soil
water content/matric potential and salinity content), which may
indicate onset of stress.
2.2. Study sites
Two agricultural production systems and three fields were used
to evaluate EC FP and crop coefficients. One system (two fields) was
two-year, sub-surface drip irrigated sugarcane (Sacharum officinarum L. cv. H65-7052) on a commercial plantation in Maui, Hawaii,
United States. Data from these fields (hereafter denoted Windy and
Lee), and the plantation’s cultivation practices have been reported
elsewhere (Anderson and Wang, 2014; Anderson et al., 2015a,b;
Tirado-Corbalá et al., 2015; Zhang et al., 2015) and are briefly
discussed here. Windy and Lee were planted in May and March
2011, respectively, and harvested in June 2013 and November 2012,
respectively, with towers removed 2–6 weeks prior to harvest. For
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
analyses of Kc , data from Windy was truncated to 600 days after
planting (DAP) for comparison with the shorter record from Lee.
In both fields, we observed existing farm agronomic practices. The
farm scheduled irrigations based on reference ET from its own inhouse weather station network and customized crop coefficients.
The Windy and Lee fields and data have several characteristics that
make them a particularly useful testbed for assessing crop coefficients with EC FP, including (1) minimal (<150 mm) growing season
precipitation due to the fields leeward location and Maui’s drought
during the data collection period, (2) presence of a layer of sugarcane residue (also known as “trash”) during the middle and later
parts of the growing season that minimize E, and (3) high photosynthetic and evaporative fluxes. For these two fields we used a
custom Penman-Monteith based ET0 equation due to divergence
between the FAO-56 ET0 and measured ET under high wind speeds
as reported by Anderson et al. (2015b).
The second production system is a mature cling peach orchard
(Prunus persica—cv. Ross) located on a commercial farm (Fig. 1)
in the San Joaquin Valley of California, United States (36.4582◦ N,
119.5793◦ W, and 86 m elevation for eddy covariance tower site).
The orchard soil is a coarse-loamy, alluvial soil, Wasco Series (Soil
Survey Staff, 2016). The climate is typical of the region with warm
to hot, dry summers (maximum air temperature of ∼40 ◦ C) and
cool, rainy winters; average annual (1981–2010) precipitation is
256 mm (PRISM Climate Group, 2016). The 8.3 ha (20 acre) orchard
was planted in 1998 with a spacing of 6.1 m (20 feet) between rows
and 3.0 m (10 feet) between trees within rows.
The trees were planted on a small raised berm (width of
∼150 cm) in between two furrows (bottom width ∼125 cm, maximum depth 25 cm). The trees were trimmed to a height of 4.1 m
(13.5 ft.) at the end of each season, and regrew to a maximum height
of 6 m during the growing season. There was no lateral trimming
(reduction of canopy width) during the growing season. During
the growing season, the orchard was fully vegetated (LAI exceeding 3 m2 m−2 ; fractional canopy cover exceeding 80%). The farmer
irrigated based on his experience and assessment of crop and soil
conditions. The farm used surface water or groundwater depending upon water availability. During normal periods, the orchard
was irrigated to meet full evaporative demand with furrows filled
approximately once every 4–12 days (irrigation application depth
of 52–58 mm per event) depending upon meteorological demand
and proximity to harvest. However, the farm’s well ran dry during
part of the 2014 drought-affected growing season, and the farmer
was unable to fully irrigate during this season. Leaf growth usually
started in March with harvest in early August and leaf senescence
in late November. Like the sugarcane fields, the peach orchard
had little (or no) precipitation during the growing season and also
had high evaporative and photosynthetic fluxes. For this orchard,
ET0 was provided by the Spatial California Irrigation Management
Information (Spatial CIMIS) product (Hart et al., 2009).
2.3. Eddy covariance (EC) and soil volumetric water content
(VWC) observations
Details about the sugarcane EC towers, associated meteorological and soil instrumentation, and data processing are reported
elsewhere (Anderson and Wang, 2014; Anderson et al., 2015a,b).
Both sugarcane sites were instrumented with open-path infrared
gas analyzers (IRGAs). Both fields had adequate fetch distance and
conditions. In each field, VWC was observed at 20 cm depth near
the tower site with water content time-domain reflectometers
underneath the cane row and in between each set of drip lines.
Sensors were custom calibrated for each field’s soil texture, and a
water retention curve was measured for each soil from Tempe cell
samples (Anderson et al., 2015b). As discussed in Anderson et al.
(2015b), VWC observations were not possible in the Lee field due
95
to rockiness. Precipitation data from both sites was measured at an
on farm weather station network with a station within 1500 m of
each EC tower.
Like the sugarcane fields, the instrumentation in the peach
orchard also consisted of an IRGA (Licor 7500, Licor Inc., Lincoln,
Nebraska, USA3 ) and 3D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, Utah, USA). These instruments were mounted on
a custom, non-guyed, telescoping pole which was adjusted to keep
the cross bar with the EC instruments ∼2–2.5 m above the top of
the canopy depending upon the time of year and expected change
of canopy height. A four component net radiometer (CNR1, Hukseflux, Delft, Netherlands) was mounted at the top of the telescoping
pole, 70 cm above the EC instruments. Along with the IRGA and
sonic anemometer, there was an integrated air temperature and
relative humidity sensor (HMP45C, Vaisala, Vantaa, Finland). Due
to the relatively small dimensions of the field, 400 m east-west and
200 m north-south, the tower was not installed in the center of
the field, but instead in the southeast quadrant of the field (30 m
north of the south edge of the field and 118 m west of the eastern
edge of the field). This placement resulted in better fetch distance
to the north and west, which takes advantage of the predominant
northwesterly winds at this site. An on-site rain gauge (TE525WS,
Texas Electronics, Dallas, Texas, USA) was installed at the beginning of 2013; rain data prior to 2013 came from the CIMIS station
at Parlier, California (station #44).
There was also an extensive soil heat flux and moisture monitoring array that included 8 locations immediately around the tower;
three were located on the berm 0.5 m away from the trunk of a
peach tree oriented perpendicular to the berm and 45◦ between
perpendicular and parallel to the berm. The other 5 locations were
located perpendicular to the mound in line with the same peach
tree and were located at 1.5 m (furrow bottom), 2.1 m (furrow
edge), and 2.75 m, 3.75 m, and 4.75 m away from the tree, with the
last three locations being on the inter-row between the irrigation
furrows. At each location type-T, copper-constantan, soil thermocouples were placed at 2 and 6 cm depths to monitor near surface
soil heat storage. A heat flux plate (HFT3, REBS, Seattle, Washington,
USA) was installed at 8 cm to measure soil heat flux, and soil water
content was measured using an impedance dielectric sensor (Hydra
Probe II, Stevens Water Monitoring Systems, Inc., Portland, Oregon, USA), also placed at 8 cm depth. We used the manufacturer’s
default calibration to convert dielectric readings to soil volumetric water content; the default calibration has a standard deviation
of 0.01 m3 m−3 for loamy soils similar to the peach orchard soil.
Soil volumetric water content at field capacity (−0.020 MPa) was
measured with a pressure plate while permanent wilting point
(−15 MPa) was measured with a dew point hygrometer (WP4C,
Decagon Devices, Pullman, Washington, USA) to avoid overestimation at lower soil water potential (Bittelli and Flury, 2009).
Data from the sonic anemometer and IRGA were recorded at
20 Hz during the first two growing seasons of observation (2012
and 2013) and 10 Hz during the 2014 season. After the first two
seasons, the flux collection rate was changed to 10 Hz to make
data processing, handling, and storage more logistically feasible.
Evaluation of flux spectra and energy budget closure did not indicate missing energy and mass transport in the 10–20 Hz range
which would bias ET observations. Flux data were processed to
15 min averaging intervals using open-source software (EddyPro®
(v5.0—Advanced mode), released by LI-COR Biosciences, Lincoln,
Nebraska USA—http://www.licor.com/eddypro) with custom setting and corrections identical to those used by Anderson et al.
3
Mention of trade names or commercial products in this publication is solely for
the purpose of providing specific information and does not imply recommendation
or endorsement by the U.S. Department of Agriculture.
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
2.4. Satellite remote sensing observations
For the sugarcane fields, we calculated Normalized Differential Vegetation Index (NDVI) using reflectance data from the
MODerate resolution Imaging Spectroradiometer (MODIS), Terra
250m, 16 day vegetation product (product ID: MOD13Q1 v.5 –
Huete et al., 2002). Following Anderson et al. (2015a), reflectance
data were extracted for the pixel overlapping the tower footprint using the Oak Ridge National Laboratory subsetting and
visualization tool (http://daac.ornl.gov/cgi-bin/MODIS/GLBVIZ 1
Glb/modis subset order global col5.pl). NDVI observations for the
peach orchard EC tower were obtained from the Landsat 7 satellite
using the TOPS Satellite Irrigation Management System (SIMS) platform (http://ecocast.arc.nasa.gov/dgw/sims/ – Melton et al., 2012).
Although TOPS SIMS produces fractional canopy cover (Fc ) estimates, we chose to use their NDVI values for comparison with the
sugarcane fields and due to erratic mid-season patterns in SIMS
provided Fc that diverged from non-gap filled Landsat NDVI.
3. Results
Table 1
Precipitation days (greater than 1.5 mm) for Maui sugarcane sites. The number of
additional smaller precipitation events (0.25–1.5 mm) was less than 10 for each
tower site. These are excluded for brevity and because of their negligible contribution to the hydrologic fluxes for each site.
DAP – Windy
Precipitation (mm)
– Windy
DAP – Lee
Precipitation – Lee
129
203
214
216
271
272
299
300
301
302
405
498
598
1.5
2
3.3
9.7
1.8
18
4.1
22.4
6.4
10.9
3.3
7.1
2.3
219
257
259
313
314
315
341
342
343
345
540
3.8
3.8
3.8
3.6
2.8
17.5
2.3
2.8
19.8
2.5
2.8
10
8
−1
(2015a). When winds came between 130◦ and 230◦ , fetch conditions were assumed to be inadequate, and data during these periods
were gap-filled. Gap filling and energy budget closure (Reichstein
et al., 2005; Leuning et al., 2012) also followed the procedure used
by Anderson and Wang (2014) and Anderson et al. (2015a), with
the addition that fluxes coming from the south with unsuitably
short fetches were considered as missing and correspondingly gap
filled. All other meteorological and soil data were averaged over the
15 min intervals as the EC data.
ET0 (mm day )
96
6
4
2012 running
2013 running
2014 running
2012 daily
2013 daily
2014 daily
2
3.1. Micro-meteorological observations, ET0 , and EC ET
Details of the micrometeorological, ET0 , and EC ET observations
for the Maui sugarcane sites are reported elsewhere (Anderson
and Wang, 2014; Anderson et al., 2015a,b) with key aspects briefly
discussed here. The overall quality of the EC ET observations was
excellent, with energy balance approaching full closure (97%) at
Windy, few daytime gaps at Lee that would affect ET sums, and 90%
flux footprints that were well within field boundaries at both fields
(Anderson et al., 2015b). Both fields exhibited moderate daily ET
and ET0 , with mean ET values of 3.2 and 3.1 mm day−1 in Windy and
Lee, respectively, and mean ET0 of 3.8 and 4.1 mm day−1 . The limited seasonality and moderate rates of EC ET and ET0 are consistent
with Hawaiian climatology with limited temperature ranges (seasonal maximum daily temperature range of ∼4 ◦ C) and relatively
mild maximum daily temperatures of less than 30 ◦ C. Cumulative
precipitation for both sites was less than 100 mm during the period
of analysis (Table 1).
With respect to the peach orchard, the EC tower had a reasonable energy closure on a daily basis with measured turbulent fluxes
comprising 83% of radiometric fluxes. 10% of the turbulent flux periods (friction velocity greater than 0.1 m s−1 ) needed to be gap filled
due to the wind coming from a southerly direction with unsuitably
short fetch length. The mean 70% flux footprint length during turbulent periods was 109 m while the mean 90% footprint length was
163 m, which both lie within the field boundaries for predominant
wind directions. The climatology for the peach site is characteristic of the San Joaquin Valley and is briefly discussed here with
respect to the 2012–2014 tower record. The site has high solar
irradiance with a mean of 21.4 MJ m−2 day−1 and a maximum of
33.0 MJ m−2 day−1 . Mean air temperature during the entire study
period was 17.3◦ C with daily average temperatures ranging from
−0.5 to 29.3◦ C for the coldest and warmest days. Wind speeds were
0
50
100
150
200
250
300
DOY
Fig. 2. Reference ET (ET0 ) for the Peach orchard. Data are plotted against Day of Year
(DOY) for each of the three years presented. Points show daily values while the lines
show a two week running mean of daily values.
relatively low with a mean value of 1.2 m s−1 and daily averages
ranging from 0.5 to 2.9 m s−1 .
ET0 and ET at the peach orchard had strong seasonality with high
peak daily fluxes (Figs. 2 and 3). During the non-dormant periods
of the year (Day of Year (DOY) between 50 and 300), ET0 ranged
between 1.4 and 9.2 mm day−1 , with a mean of 5.7 mm day−1 .
Cumulative ET0 was similar for the two years with complete growing seasons (2013 and 2014), with 2013 having a cumulative ET0
of 1383 mm and 2014 an ET0 of 1411 mm. Actual ET tracked ET0
closely, with ET ranging between 0.6 and 9.0 mm day−1 with a mean
of 5.2 mm day−1 . Tree trimming, which occurred during the dormant season, had no apparent impact on ET or Kc . With respect
to cumulative seasonal totals, 2013 and 2014 showed significant
(p < 0.001) difference with ET, with 2013 having cumulative ET of
1360 mm and 2014 having an ET of 1059 mm. The ET patterns for
2012 and 2013 closely followed ET0 while 2014 showed some substantial late season decreases in ET (differences of up to 4 mm day−1
compared to 2012 and 2013) due to irrigation deficits resulting
from the farmer’s well failure.
3.2. Single and dual crop coefficients
Single crop coefficients (Kc ) are presented for the sugarcane sites
(Fig. 4) and the peach orchard (Fig. 5). For the sugarcane, both fields
97
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
10
0.6
K Lee running
e
K Windy running
0.5
e
8
Ke Lee
K Windy
−1
ET (mm day )
0.4
Ke
6
2012 daily
2013 daily
2014 daily
2012 running
2013 running
2014 running
2
0
50
100
150
200
0.1
0
0
250
300
Fig. 3. Eddy covariance measured evapotranspiration (ET) for the Peach orchard.
Data are plotted against Day of Year (DOY) for each of the three years presented. As
with Fig. 2, points show daily values while the lines show a two week running mean
of daily values.
1.4
1.2
1
Kc
0.8
0.6
K Lee running
c
0.4
K Windy running
c
K Lee
0.2
c
K Windy
c
100
200
300
DAP
400
500
600
Fig. 4. Single crop coefficients (Kc ) from the Windy and Lee field. Kc is calculated by
dividing measured eddy covariance ET by the custom Penman-Monteith reference
ET for these sites. Data are plotted against Days After Planting (DAP). As in Fig. 2,
points show daily values while the lines show a two week running mean of daily
values for both sites.
1.4
1.2
1
Kc
0.8
0.6
2012 running
2013 running
2014 running
2012 daily
2013 daily
2014 daily
0.4
0.2
100
150
200
250
100
200
300
DAP
400
500
600
Fig. 6. Soil evaporation coefficients (Ke ) from the Windy and Lee field determined
by the EC-FP method and Eq. (5). Lines, points, and x-axis represent same temporal
periods as Fig. 4.
DOY
0
50
0.3
0.2
4
0
0
e
300
DOY
Fig. 5. Single crop coefficients (Kc ) from the Peach orchard. Kc is calculated by dividing eddy covariance ET by Spatial CIMIS reference ET for the location. Data are plotted
against Day of Year (DOY) for each of the three years presented. As in Fig. 2, points
show daily values while the lines show a two week running mean of daily values.
showed a similar maximum and season end Kc and curvilinear Kc
form with respect to days after planting (DAP), with maximum Kc
exceeding 1.2 between 200 and 250 DAP with a gradual decline
to ∼0.4 at 600 DAP. Overall Kc appears to be slightly out of phase
(∼50 days) between the two sites with Lee having a later phase than
Windy. This could be due to slightly slower development of Lee due
to its planting earlier in the spring, which corresponds to the sugarcane farm’s own internal Kc curve with slower increases in Kc for
late winter/early spring planting compared to late spring/summer
planting. The field with earlier relative EC tower establishment
(Windy) showed a rapid increase in Kc from 0.5 at 75 DAP to the
maximum values. For both fields, peak Kc corresponded with a
period of relatively rapid growth in both sugarcane fields as well as
the Hawaiian winter with slightly lower temperatures and reduced
ET0 . For the peach orchard (Fig. 5), Kc was relatively low and variable (0.4–0.6), and then rapidly increased around DOY 100 with
leaf emergence. From DOY 100 (April 8–9) to DOY 250 (September
5–6), Kc ranged between 0.9 and 1.1 for the two years with adequate irrigations (2012 and 2013 – Fig. 5). From DOY 250 until leaf
senescence at DOY 300, Kc increases slightly, but this occurs at a
time of rapidly decreasing ET0 , thus suggesting that this increase is
a result of actual ET decreasing slightly slower than ET0 . In 2014, Kc
initially shows the same pattern as 2012 and 2013, but the impacts
of the farm’s well failure become apparent with significant minima
in Kc at about DOY 160, 235, and 275.
Soil evaporation coefficients (Ke ) at both sugarcane sites
decreased with increasing crop age, cover, and residue deposition
on the ground, but showed significant variation and correspondence with precipitation (Fig. 6; Table 1). Maximum Ke decreased
from ∼0.3 to less than 0.1 with increasing DAP in both fields. Over
the study period, mean Ke was 0.13 in Lee and 0.15 in Windy. Again,
there were some phase differences between the sites, but also some
localized discrepancies as well with a spike in the Lee field Ke at
∼225 DAP, and a spike in Windy field Ke at ∼400 DAP. In the peach
orchard, Ke was substantially higher than at the sugarcane sites, as
would be expected with furrow irrigation with exposed soil compared to shallow sub-surface drip with substantial surface residue.
Between seasons, the Ke for the peach orchard in 2013 was substantially higher than 2012 or 2014. This higher Ke in 2013 may be
due to the higher ecosystem respiration observed by an independent partitioning approach (Reichstein et al., 2005). As observed
using Reichstein et al.’s (2005) approach, ecosystem respiration
was 1061 kg C ha−1 from DOY 110 to 300 in 2013 while it was only
832 kg C ha−1 in 2012 for the same period. Gross photosynthesis
during this time period for the two seasons was much closer (1067
98
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
1.4
1
1.2
1
Kcb*Ks
1.2
1.4
2012 running
2013 running
2014 running
2012 daily
2013 daily
2014 daily
Ke
0.8
0.8
0.6
0.6
K *K Lee running
0.4
cb
cb
0.4
0.2
s
K *K Lee
cb
s
K *K Windy
0.2
0
50
s
K *K Windy running
cb
0
0
100
150
200
250
100
200
s
300
DAP
400
500
600
300
DOY
Fig. 7. Soil evaporation coefficients (Ke ) from the Peach orchard. Ke is calculated by
equation 5. Lines, points, and x-axis represent same temporal periods as Fig. 5.
Fig. 8. Basal crop coefficient and stress coefficient (Kcb *Ks ) from the Windy and
Lee field. Kcb *Ks is calculated by Eq. (4). Lines, points, and x-axis represent same
temporal periods as Fig. 4.
1.4
Table 2
Precipitation days (greater than 1.5 mm) for peach orchard site during the period
between Day of Year (DOY) 50 and DOY 300.
DOY
Precipitation (mm)
4/11/2012
4/13/2012
4/25/2012
2/19/2013
3/7/2013
3/8/2013
3/31/2013
5/7/2013
2/26/2014
2/27/2014
2/28/2014
3/29/2014
3/30/2014
3/31/2014
4/25/2014
102
104
116
50
66
67
151
127
57
58
59
88
89
90
115
18.2
26.9
6.8
6.6
2.3
5.6
5.8
15.1
5.3
5.3
20.3
4.6
3.0
5.6
33.27
and 1007 kg C ha−1 for 2012 and 2013, respectively). The reason for
increased respiration during 2013 is unclear, but it should be noted
that the winter of 2012–2013 was exceptionally dry, and decomposition of woody debris and other residue from the 2012 growing
season may have been delayed until the 2013 growing season when
irrigation water would have increased microbial activity. As carbon and water fluxes are tightly coupled in the EC FP algorithm,
increased respiration would result in a greater proportion of total
ET being partitioned to evaporation, thus increasing Ke and confounding the FP algorithm Early summer in 2012 and 2014 had
similar Ke values near 0.2; divergence between 2012 and 2014 in
later summer followed the divergence in overall water status and
Kc between the two years quite well; Ke values were under 0.05
for the most water-stressed parts of the 2014 season (Fig. 7 and
Table 2).
Basal crop/stress coefficients (Kcb *Ks ) for the sugarcane sites followed the same pattern as Kc , but with lower magnitudes (Fig. 8).
Kcb *Ks values ranged from 0.35 to 1 in both fields with the maximum values again coming about 200–250 DAP. After 300 DAP, there
was less variability in Kcb *Ks with a linear decrease from 0.8 to
less than 0.4. Mean Kcb *Ks was 0.74 in Lee and 0.68 in Windy over
the entire study period. Similar to the Kc curve, Kcb *Ks appeared
to have a phase discrepancy between the two fields that could be
related to differences in planting time. With the peach orchard,
Kcb *Ks increased rapidly with leaf out around DOY 100 (Fig. 9). 2012
had the highest overall Kcb *Ks , with values ranging from ∼0.7 to
1.2
1
Kcb*Ks
Date
2012 running
2013 running
2014 running
2012 daily
2013 daily
2014 daily
0.8
0.6
0.4
0.2
0
50
100
150
200
250
300
DOY
Fig. 9. Basal crop coefficient and stress coefficient (Kcb *Ks ) from the Peach orchard.
Kcb *Ks is calculated by Eq. (4). Lines, points, and x-axis represent same temporal
periods as Fig. 5.
∼1.0. 2013 and 2014 had similar, lower values of Kcb *Ks . The seasonality of Kcb *Ks in 2014 was more closely tied to overall Kc and
water stress, while 2013’s pattern correlates with the issues with
Ke discussed above. Peach volumetric water content (VWC) during the three seasons showed patterns consistent with observed
crop coefficient values (Fig. 10). Maximum mean daily VWC was
over 0.3 m3 m−3 except for 2014, which also showed the longest
periods between irrigation events consistent with the farm’s well
failure.
3.3. Relationship between crop coefficients, remotely sensed
vegetation, and soil moisture
At the sugarcane sites, Kcb *Ks had a significantly stronger
relationship with MODIS NDVI than Kc (Fig. 11), with the r2 of
Kcb *Ks -NDVI of 0.43 and the r2 of Kc -NDVI of 0.11. Both types of
coefficients had substantial scatter, and the relationship did not
change considerably when the relationships in each field were analyzed separately. We did not attempt to separate out water-stressed
periods at the sugarcane sites because there were no stressed periods at Windy and there were no good soil moisture observations at
Lee (Anderson et al., 2015b). The correlation for NDVI-Kc is much
weaker than reported for other crops. One possible explanation
99
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
1.4
0.5
2012
2013
Peach ET coefficient
2014
0.3
3
−3
VWC (m m )
0.4
m =1.60±0.36
Kc
1.2 mKcb=0.82±0.29
b =−0.18±0.24
Kc
1 b =0.13±0.20
Kcb
0.8
0.6
r2 =0.79
Kc
r2 =0.61
Kcb
NDVI−K regression
0.4
c
NDVI−K
0.2
regression
cb
0.2
K
c
K
cb
0
0.2
0.1
0
50
100
150
200
250
300
DOY
Fig. 10. Daily mean volumetric water content (VWC) shown for the furrow bottom
sensors (8 cm depth) shown for the three seasons. Field capacity (green line) and
permanent wilting point (red line) are shown as horizontal lines. (For interpretation
of the references to colour in this figure legend, the reader is referred to the web
version of this article.)
0.3
0.4
0.5
0.6
Landsat 7 NDVI
0.7
0.8
0.9
Fig. 12. Relationship between Landsat 7 (L7) NDVI and single and basal/stress coefficients for the peach orchard. Plotted values are average Kc and Kcb *Ks values for each
16-day L7 overpass period. Data are shown from 2012 and the early portions of the
2014 season that were not confounded by uncertainty in the FP algorithm (2013—see
paragraph 2 of Section 3.2) or water stress (later 2014). Regression statistics including slope (mKc and mKcb ), intercept (bKc and bKcb ), and coefficient of determination
(r2 Kc and r2 Kcb ) are shown.
1
m =3.17±0.70
Kcb
0.9 b =0.11±0.12
Kcb
m =0.62±0.46
Kc
1.2 mKcb=1.34±0.40
b =0.42±0.31
Kc
1 b =−0.19±0.27
0.8 r2 =0.50
s
Kcb
Peach K *K
Kcb
0.8
0.6
r2 =0.11
Kc
r2 =0.43
0.6
0.5
Kcb
0.4
VWC−K
0.3
2012
2014
NDVI−K regression
0.4
c
NDVI−K
cb
0.2
K
K
0
0.3
0.7
cb
Windy and Lee ET coefficients
1.4
0.4
regression
0.2
0.05
c
cb
0.5
0.6
0.7
MODIS Terra NDVI
0.8
0.1
0.15
0.2 0.25 0.3 0.35
VWC (m3 water m−3 soil)
cb
regression
0.4
0.45
0.5
0.9
Fig. 11. Combined relationship between the MODIS Normalized Differential Vegetation Index (NDVI) and single and basal/stress coefficients for the Maui sugarcane
sites. Plotted values are average Kc and Kcb *Ks values for each 16-day MODIS overpass period. Regression statistics including slope (mKc and mKcb ), intercept (bKc and
bKcb ), and coefficient of determination (r2 Kc and r2 Kcb ) are shown.
is that there were relatively few MODIS overpass dates at earlier
growth stages with lower NDVI values (NDVI < 0.5), which results
in a lower dynamic range for NDVI and which may give the NDVIKc relationships more scatter than they would have over an entire
crop cycle. Another explanation for the poor r2 is the role of understory leaves in contributing to sugarcane transpiration. As Hawaiian
sugarcane ages and is dried down, lower leaves on the stalk lose
greenness while the top leaves (most visible to satellites) are still
green. With respect to the peach orchard, there were significantly
stronger relationships between Kcb -NDVI and Kc -NDVI (Fig. 12)
during well-watered periods (2012 and early 2014). For both Kc
and Kcb , the relationship with NDVI had a coefficient of determination greater than 0.6. The peach site also had a greater range
of NDVI values (0.3–0.8) to compare against the crop coefficients.
The peach site had stronger r2 between NDVI and Kc than Kcb . This
might be due to dark, wet soil which would have a higher NDVI,
thus resulting in a positive NDVI-Ke relationship (Bausch, 1993).
Fig. 13. Relationship between peach basal coefficients and shallow soil volumetric
water content (VWC—m3 H2 O m−3 soil volume). VWC is measured at 8 cm beneath
the furrow and plotted for contrasting wet and water stressed growing seasons of
2012 and 2014. For both years, plotted daily coefficients are from the months of
May through August when Kcb is relatively constant. Regression equation is shown
for VWC less than 0.2 m3 m−3 for both years as this is the VWC at which substantial
declines in Kcb *Ks are observed Regression statistics including slope (mKcb ), intercept
(bKcb ), and coefficient of determination (r2 Kcb ) are shown.
With respect to the relationship between soil moisture and
crop coefficients, there was no significant correlation (r2 = 0.05)
between root zone VWC in Windy and mid-period Kcb *Ks (data not
shown), thus indicating that these data cannot be used to assess
Ks relationships. This was not surprising given that root-zone VWC
remained near field capacity for the entire study period. Even near
the minimum VWC of 0.23, matric stress was minimal at −33 kPa
(Anderson et al., 2015b). This was also in line with Anderson et al.’s
(2015b) statistical testing of the days before and after irrigation,
which showed no increase in ET in days immediately after irrigation, thus further discounting the likelihood of water stress. With
respect to the peach orchard, the 2012 and 2014 growing seasons provide an interesting contrast between a well-watered and
water-stressed season. For our assessment of VWC, we used the
two sensors closest to the bottom of the irrigation furrow as our
100
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
proxy for root zone soil water content. The relationship between
the VWC average of these two sensors and Kcb *Ks during the middle of the growing season is shown in Fig. 13. VWC at field capacity
and permanent wilting point were 33% and 3%, respectively. We
assume that Kcb is relatively constant during this period, and can
see that Kcb *Ks declines significantly below VWC of about 0.2–0.25,
which indicates that the fraction of depletion of plant available
water (field capacity-permanent wilting point) prior to onset of
stress is between 0.27–0.43. We developed a regression equation
for VWC-Kcb *Ks for VWC below 0.2. Based on this regression, for
example, we would expect Ks to decrease from 1 to 0.57 as VWC
decreased from 0.2 to 0.1. The VWC at which Ks starts to decrease
below 1 can be imprecise, but a figure such as Fig. 13 can help
independently determine this relationship.
4. Discussion and conclusions
4.1. Comparison of EC FP coefficients to literature values
Maximum observed single coefficients (Kc ) for the peach and
sugarcane towers compared favorably with FAO-56 published values and adjustments for canopy cover and height (Allen et al., 1998;
Allen and Pereira, 2009). Maximum fraction canopy cover (Fc ) from
TOPS-SIMS for the peach orchard was 0.7–0.8, which suggests a
Kcmid of 1.15–1.2 based on Allen and Pereira (2009). This value was
slightly above our observed mean mid-season Kc of ∼1.1 during
2012 and 2013. Similarly, the literature value of Kcmid for sugarcane
(1.25 – Allen et al., 1998) is also above our observed maximum Kc
of 1.1–1.15. We note the inherent daily variation in Kc observations
with micrometeorology that other researchers have found in sugarcane and peach (Inman-Bamber and McGlinchey, 2003; Paço et al.,
2012). Where differences exist are around the relative shape of the
Kc curve, with our peach orchard showing a more rapid increase
to Kc than previous studies in California (Ayars et al., 2003). With
our sugarcane sites, we note that Olivier and Singels (2012) also
found a relatively short Kcmid period and reduction in Kc which they
attributed to sugarcane residue (trash) suppressing E and slightly
reducing overall ET. While our sugarcane sites were technically
sub-surface drip irrigated, the drip lines were buried very close
to the surface (0–5 cm) with some locations having drip line on the
surface. This shallowness combined with the higher initial Ke (∼0.3
maximum), leads us to surmise that burying the drip line had little
impact on Ke .
Determination of dual crop coefficients (Ke , Kcb , and Ks ) is more
challenging due to challenges in independently observing transpiration and evaporation and relatively fewer studies publishing dual
coefficients. FAO-56 and its extension models for Ke (Allen et al.,
1998, 2005) rely on parameterizing maximum Ke as the difference between maximum observed Kc and Kcb , with reductions in
Ke modeled as the soil dries. da Silva et al. (2012) evaluated the
dual crop coefficient approach over Brazilian sugarcane and found
mid-season Ke values on the order of ∼0.1, which is similar to our
results. In peach, Abrisqueta et al. (2013) parameterized Ke from
soil moisture profile observations in the inter row to calculate Kcb .
Like us, they found significant seasonal scatter and year to year variation in Kcb with a maximum Kcb of approximately 1. With respect
to stress coefficients, both peach and sugarcane are known to have
a high sensitivity to deficit irrigation (Mata et al., 1999; Robertson
et al., 1999). Girona et al. (2002) found a 50% relative reduction in
ET with a 0.1 m3 m−3 reduction in total plant available water once
the deficit threshold had been reached. This corresponds well with
our observed decrease in Kcb *Ks from 0.75 to 0.4 when soil VWC
dropped from 0.2 to 0.1 m3 m−3 .
4.2. Integration of EC FP to modeling and decision support
infrastructure
EC has been frequently used in recent studies in a wide variety of agricultural systems to assess water use (see, for example,
Amayreh and Al-Abed (2005), Cammalleri et al. (2013), Facchi et al.
(2013), Li et al. (2008), and Rajan and Maas, (2014) in addition to
studies cited in the introduction for just a few examples). There are
increasingly large databases of EC data (e.g. Fluxnet—http://fluxnet.
fluxdata.org) with improved site coverage as new data are added.
Most of these network-level syntheses focus on analysis and dissemination of processed fluxes as opposed to providing the raw,
high-frequency, atmospheric time series. EC FP could be applied
retroactively to previously gathered EC data to determine individual processes and fluxes from modeling components. Using EC FP
can also assess T and E separately without the requirement of additional instrumentation such as sap flow sensors or microlysimeters
that have spatial footprints that are much smaller than an EC tower.
As farmers change drip irrigation spacing and use different amounts
and types of mulches, canopy structures and planting densities; EC
FP can help rapidly assess controls on Ke , Kcb , and Ks in new production systems and canopy environments. Increased accuracy of
Ke , Kcb , and Ks will be useful for hydrologic and soil models that rely
on crop coefficients to parameterize boundary model conditions.
The results of EC FP can also be integrated into decision support
frameworks for irrigation management. As an example, consider
the relationship between Kcb *Ks and furrow bottom soil water content during the mid-summer at the peach orchard (Fig. 13). While
this shallow measurement location is not in the middle of the root
zone, it does have two advantages for management purposes. One,
it sits near the interface between the peach root zone and the furrow, so it can capture water dynamics and plant stress. Second,
and perhaps more critically, it is at a depth and location that can be
easily accessed by a farmer for periodic sampling of water content.
Using a relationship such as the one presented in Fig. 13, a farmer
could easily and rapidly check his field for moisture stress by assessing soil at this position at multiple sites in the field. This would
enable more efficient irrigation scheduling without a large investment in fixed, in-situ, water content sensors. Depending upon the
data and available modeling frameworks, other simple field checks
or site based instrumentation could be developed to predict Ks and
thus schedule irrigation at the onset of stress to maximize irrigation
efficiency.
4.3. Conclusions
In this study, we analyzed three eddy covariance (EC) sites in
two contrasting agricultural systems to demonstrate how a fluxvariance based partitioning algorithm can be used to partition
evapotranspiration into basal, soil evaporation, and stress coefficients for determination of agricultural water consumption. Our
maximum basal and combined coefficients were similar to previous studies for similar crop cover conditions, but we did find
a differing shape of the Kcb curve compared with FAO-56. Partitioning EC fluxes also improved the relationship between Kcb and
satellite vegetation cover in sugarcane. Analysis of partitioned coefficients against soil moisture indicated a lack of water stress at one
of the sugarcane sites while illustrating how the Ks relationship
could be determined at a peach orchard. Future work is needed to
validate EC FP against independent transpiration and evaporation
measurements across a variety of crops, crop cover (bare soil to
full canopy cover), and irrigations practices. The results illustrate
the value applying flux partitioning to determine dual crop coefficients from EC measured ET data across a variety of agricultural
production systems. This type of analysis will become increasingly
feasible as the prevalence of EC sites increases due to decreases in
R.G. Anderson et al. / Agricultural Water Management 179 (2017) 92–102
monetary and logistical costs; this analysis may also be feasible for
existing tower data if the high frequency EC tower data are shared
on commonly used data repositories (e.g. Ameriflux and Fluxnet).
Acknowledgements
We thank the editors and two anonymous reviewers for their
constructive critiques on a previous version of this manuscript. We
thank Hawaiian, Commercial and Sugar Company for access to the
Maui sites and Ryan Palm, for access to the peach orchard. We thank
our cooperators on the Maui project and Stella Zambrzuski and Alex
Jimenez from ARS Parlier and Dennise Jenkins from ARS Riverside
for field assistance in the orchard. Dennise also measured the water
retention curve from the orchard site. 30 min flux data for the Maui
sites are available on the Ameriflux website. This study was supported by the United States Department of Agriculture-Agricultural
Research Service, National Program 211: Water Availability and
Watershed Management (project numbers 2036-61000-015-00,
2034-13000-011-00, and 8042-13611-028-00) and National Program 212: Climate Change, Soils, and Emissions (project number
5030-11610-002-00) and by the Office of Naval Research.
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