Post on 22-Apr-2023
JAWRA Page 1 2/4/2004
Priestley-Taylor Alpha Coefficient: Variability and Relationship to NDVI in Arctic
Tundra Landscapes
Submitted To:
Journal of the American Water Resources Association (JAWRA)
Ryan N. Engstrom1, Allen S. Hope1, Douglas A. Stow1, George L. Vourlitis2, and Walter
C. Oechel3
1Department of Geography, San Diego State University, San Diego, CA 92182 USA
2Biology Program, California State University, San Marcos, CA 92096 USA
3Global Change Research Group, Department of Biology, San Diego State University,
San Diego, CA 92182 USA
JAWRA Page 2 2/4/2004
Abstract
Average daily values of the Priestley-Taylor coefficient, α, were calculated for
two eddy covariance (flux) tower sites with contrasting vegetation, soil moisture, and
temperature characteristics on the North Slope of Alaska over the 1994 and 1995 growing
seasons. Because variations in α have been shown to be associated with changes in
vegetation, soil moisture, and meteorological conditions in Arctic ecosystems, we
hypothesized that α values would be significantly different between sites. Since
variations in the normalized difference vegetation index (NDVI) follow patterns of
vegetation community composition and state that are largely controlled by moisture and
temperature gradients on the North Slope of Alaska, we hypothesized that temporal
variations in α respond to these same conditions and thus, co-vary with NDVI.
Significant differences in α values were found between the two sites in 1994 under
average precipitation conditions. However, in 1995, when precipitation conditions were
above average, no significant difference was found. Overall, the variations in α over the
two growing seasons showed little relationship to the seasonal progression of the regional
NDVI. The only significant relationship was found at the drier, upland study site.
Key Terms: Evapotranspiration, Remote Sensing, Priestley-Taylor, Tundra ecosystems,
Climate Change, Statistical Analysis
JAWRA Page 3 2/4/2004
Introduction
Increased air temperatures and the associated rise in surface temperatures due to
global warming are predicted to vary over the globe, with changes in Arctic ecosystems
expected to occur sooner and to be more dramatic than in other ecosystems (Gates et al.,
1992; Manabe and Stouffer 1993; Meehl et al. 1993). These changes may significantly
impact permafrost dynamics, thus altering hydrologic processes and the surface energy
balance in Arctic ecosystems (Hinzman and Kane, 1992). The National Science
Foundation (NSF) Arctic System Sciences (ARCSS) Land Atmosphere Ice Interactions
(LAII) research program was initiated with the goal of improving scientific
understanding of the drivers and processes influencing change in Arctic tundra
landscapes. The primary research objectives of the current LAII project, Arctic
Transitions in the Land-Atmosphere System (ATLAS), are 1) to determine the
geographical patterns of, and controls over, the atmosphere-land surface exchanges of
mass and energy in order to 2) quantify current patterns and processes and to develop
reasonable scenarios of future change in the Arctic system. Additionally, this research
project seeks to expand our understanding of local-scale mass and energy exchange and
to extrapolate this knowledge to the regional and circumpolar scales.
Following snowmelt, evapotranspiration (ET) is the hydrologic process that
dominates the Arctic summer water balance, usually exceeding summer precipitation
(Rovanesk et al., 1996). Therefore, latent heat flux is a primary mechanism of summer
surface energy dissipation (Kane et al., 1990). Higher Arctic air and surface
temperatures, and evaporation rates, and the resulting increase in atmospheric water
vapor may exert a positive feedback on regional or global warming (Maxwell, 1992).
JAWRA Page 4 2/4/2004
Arctic tundra contains a range of vegetation, physiographic, and climatic
conditions that may give rise to significant spatial and temporal variations in ET. Few
studies have investigated this spatial and temporal variability due to the limited number
of meteorological sites, logistic difficulty, and the significant costs associated with
reaching many areas of the Arctic (Kane et al., 1990). Due to this data poor environment,
a number of hydrologic studies have utilized simple ET models including, the Priestley-
Taylor (1972) model (PT) (e.g. Kane et al., 1990). The PT model has been shown to
provide acceptable accuracy for predicting daily evaporation in Arctic ecosystems if the
value of the α coefficient is known (Rouse et al., 1977). However, α values in Arctic
ecosystems have been shown to vary over time and space with variations related to
changes in vegetation type and state, soil moisture, and meteorological conditions (Kane
et al., 1990; Rovanesk et al., 1996; Mendez et al., 1998; Stewart and Rouse, 1976).
Changes in vegetation type and state are controlled by differences in soil moisture
regime and by soil and air temperatures on the North Slope of Alaska (Ostendorf and
Reynolds, 1998). Hope et al. (1993) and Stow et al. (1993) demonstrated that variations
in the remotely sensed normalized difference vegetation index (NDVI) are related to
changes in vegetation type and state in Arctic tundra ecosystems. Therefore, we
hypothesized that changes in the NDVI would be indicative of variability in vegetation
and soil moisture conditions, variables shown to co-vary with α in Arctic landscapes. In
this study, α values were calculated at two eddy flux tower sites located in regions with
contrasting vegetation, surface moisture, and meteorological characteristics to determine
if 1) α values were significantly different between sites, and 2) seasonal variations in α
were correlated with the regional patterns of vegetation phenology as characterized by the
JAWRA Page 5 2/4/2004
NDVI. This research represents a preliminary step towards determining if satellite
remotely sensed data (i.e. NDVI) can be used as a tool to predict seasonal changes in α.
Modeling Evapotranspiration in the Arctic: the Priestley-Taylor Model
The PT model is based on the concept of equilibrium evaporation postulated by
Slatyer and McIlroy (1961). Equilibrium evaporation occurs when air moving large
distances in the lower atmospheric boundary over a homogeneous, well-watered surface
becomes saturated. Since the air is saturated and additions and losses of water vapor to
the lower atmospheric boundary layer are in equilibrium, the atmospheric/mass transfer
term from the Penman-Monteith equation (Monteith, 1965) can be eliminated. Thus, the
evaporation rate becomes simply a function of the available energy (Rn-G) and air
temperature.
Equilibrium evaporation conditions rarely exist in natural environments, with
deviations arising from large-scale or regional advection effects involving horizontal
variations of surface and/or atmospheric conditions (e.g. Eichinger, 1996). In order to
compensate for these deviations, Priestley and Taylor (1972) adjusted the equilibrium
evaporation model via an empirical coefficient, α. The PT model for estimating latent
heat flux (LE) is:
LE = α(Rn - G)[S/(S + U)] (1)
where α (alpha) is an empirical coefficient relating actual evaporation to equilibrium
evaporation, S is the slope of the saturation vapor pressure and air temperature curve
(kPa°C-1), U is the psychometric constant (kPa°C-1), Rn is net radiation (Wm-2), and G is
ground heat flux (Wm-2). Based upon a number of experiments in different climates over
both land and water surfaces, Priestley and Taylor (1972) established a 'best estimate' of
JAWRA Page 6 2/4/2004
α= 1.26 (for mid-latitude environments). The value of 1.26 for α has been supported by
a number of other studies (e.g. McNaughten and Black, 1973; Mukammal and Neumann,
1977; Parlange and Katul, 1992).
Values of the α coefficient have been shown to be generally constant (e.g., 1.26)
in landscapes where vegetation cover is almost complete and of short stature if moisture
availability for evaporation is unrestricted. However, authors such as McNaugton and
Black (1973), Barton (1979) and Shuttleworth and Calder (1979) have reported large
deviations in α under drier or varied vegetation conditions. Stannard (1993) suggests that
as soil moisture decreases and the canopy becomes water stressed, α values decrease due
to the surface restrictions on ET rates. A number of field studies over different types of
vegetation have related decreases in soil moisture to decreases in α values (Davies and
Allen, 1973; Barton 1979; Flint and Childs 1991). Vegetation height and type has also
been shown to cause α to deviate from 1.26 with values of 1.03 in a rainforest
(Viswanadham et al., 1991), 1.05 in a Douglas fir forest (McNaughton and Black, 1973)
and 1.12 over grass (De Bruin and Holtslag, 1982). These results indicate that variations
in soil moisture and vegetation characteristics need to be addressed when attempting to
model values for α over time and space.
Variability of the Alpha Coefficient in the Arctic
The PT α coefficient has been shown to vary over time and space in Arctic
ecosystems (Kane et al., 1990; Rovanesk et al., 1996; Mendez et al., 1998). Results from
previous studies indicate that the value for α in dry upland regions characterized by sedge
tussocks, mosses, lichens and shrubs, is at or near 1.00 (α = 0.95 Rouse and Stewart,
1972 and Rouse et al. 1977; α = 1.00 Stewart and Rouse 1976). Stewart and Rouse
JAWRA Page 7 2/4/2004
(1976) noted that the non-transpiring lichens restricted evaporation rates so α values
tended to be lower than the 1.26 reported by Priestley and Taylor (1972). Furthermore, in
a similar dry, upland study site, Kane et al. (1990) found that the PT model with α set at
0.95 produced daily evapotranspiration rates that were similar to those calculated by
other models, including the water balance and surface energy balance approaches.
In wet, lowland areas characterized by wet sedge tundra and small ponds, a
number of studies have indicated that appropriate α values should be at or above 1.26 (α
= 1.26 Stewart and Rouse 1976; α = 1.3 Roulet and Woo 1986; α = 1.35 Bello and Smith
1990; α = 1.3 Rovanesk et al. 1996). In these areas, water from the early season
snowmelt is present at the surface for much of the growing season therefore surface
restrictions on ET rates are not significant.
Seasonal changes in α have been reported due to changes in surface wetness and
the corresponding transpiration properties of the vegetation. Rovanesk et al. (1996) and
Mendez et al. (1998) found that different α values (decreasing throughout the growing
season) were required to accurately calculate ET rates through an entire growing season
in a coastal wetland near Prudhoe Bay, Alaska. Their calculations of cumulative daily
evapotranspiration for five growing seasons (1992-1996) compared well with the
Penman-Monteith, energy balance and other model estimates. Rovanesk et al. (1996)
adjusted α every 15 days to model ET over a two year period (1992 and 1993) with
values ranging from 1.6 to 1.3 prior to Julian Date (JD) 212 and reduced to 1.1 after JD
213. Mendez et al. (1998) used two values for α (1.15 before JD 200 and 1.1 after JD
200) for the three years of their study (1994-1996). α values are highest early in the
growing season due to open surface water after snowmelt coinciding with the most rapid
JAWRA Page 8 2/4/2004
plant growth during growing season (Hobbie, 1980). As time progresses through the
season, the freestanding water above the surface (due to poor drainage) slowly evaporates
and the active layer depth increases causing the water table to drop exposing the
background surface. Additionally, the vegetation moves toward maturity and finally,
senescence. Together these factors reduce ET rates and α values due to increasing surface
resistance as the growing season progresses (Rovanesk et al., 1996).
Research Hypothesis: Alpha Variability and Relationship with NDVI in Arctic
Tundra Landscapes
Soil moisture differences are a major determinant of productivity patterns in
Arctic tundra vegetation (e.g. Peterson and Billings, 1980; Jasieniuk and Johnson, 1982;
Matthes-Sears et al., 1988; Gold and Bliss, 1995; Ostendorf and Reynolds, 1998).
Variations in the NDVI have been related to changes in the abundance and type of tundra
vegetation (Hope et al., 1993; Stow et al., 1993). It follows that variations in NDVI are
likely to reflect the average, long-term patterns in soil moisture conditions, climate and
soil characteristics that control vegetation distribution and productivity over time and
space (Ostendorf and Reynolds, 1998). These same environmental drivers have also been
shown to affect α (Kane et al., 1990; Rovanesk et al., 1996; Mendez et al., 1998), leading
to the following general hypotheses tested in this study 1) regions with contrasting
surface moisture, vegetation, and meteorological characteristics will have significantly
different α values and 2) seasonal variations in α are related to changes in vegetation
phenology as characterized by the NDVI. We test these hypotheses by a) determining if
there is a significant difference between daily α values at two tower sites located in
regions with contrasting surface moisture regimes, vegetation characteristics, and
JAWRA Page 9 2/4/2004
meteorological conditions and b) determining if seasonal variations in α are correlated
with the regional patterns of vegetation phenology as characterized by the NDVI. The α-
NDVI relationship was examined using (1) the pooled data (both towers and both years)
(2) the data pooled by year (each year with the data pooled for both tower locations) and
(3) the data pooled by tower location (each tower location with the data pooled for both
years).
Study Site
The study was conducted within a rectangular region encompassing 9200 km2
Kuparuk river watershed on the North Slope of Alaska (figure 1). The study area can be
divided into two distinct physiographic regions, the Arctic coastal plain and the foothills
of the Brooks Mountain Range. The coastal plain is dominated by wet sedge and moist
non-acidic tundra vegetation and is characterized by low relief, poor sub-surface
drainage, and a large number of shallow thaw lakes. The foothills consist mostly of moist
acidic tussock and dwarf shrub tundra vegetation and are typified by low rolling hills,
water track drainages and shallow active layer depths. Mean monthly temperatures range
between –12.8 and 10.3° C at the coast and from –11.1 to 6.7° C in the foothills (Oechel,
1989). Total summer (June-August) precipitation in the foothills region is between 90-
170 mm and is approximately 80 mm in the coastal plain (30 year average 1961-1990;
Alaska Cooperative Snow Survey Data 1994).
Eddy correlation tower measurements were collected at two tower sites, U-PAD
in the coastal plain and Happy Valley in the foothills (figure 1) (Vourlitis and Oechel,
1999). The U-PAD tower site (70°16.88' N, 148°53.23' W) is located near Prudhoe Bay,
approximately 10 km south of the Arctic Ocean (figure 1). Vegetation at this site is wet
JAWRA Page 10 2/4/2004
sedge tundra, dominated by Carex aquatilis and Eriophorum angustifolium interspersed
with a number of shallow thaw lakes. The Happy Valley tower site (69°08.54' N,
148°35.47' W) is located 135 km south of the Arctic Ocean and 1.5 km west of the Trans-
Alaska Pipeline Haul Road on the North Slope of Alaska (figure 1). Vegetation at this
site is moist acidic tussock tundra, dominated by Eriophorum vaginatum. These two sites
are representative of the extremes within the Kuparuk River study area in terms of
temperature, latitudinal variation in NDVI values, topography, vegetation type, and
surface moisture values (Eugster et al., 1997).
INSERT FIGURE 1 ABOUT HERE
Tower Measurements
Data from the two flux towers were collected throughout the 1994 and 1995
summer growing seasons (early June to mid-September) and detailed descriptions of the
instrumentation and data collection procedures are given in Vourlitis and Oechel (1997)
and Vourlitis and Oechel (1999). The net fluxes of H2O (LE) and sensible heat (H) were
measured using the eddy covariance (flux) technique. Sensors were mounted at 2.5 m
above the ground and fluxes were computed using a 200-second running mean and stored
on data loggers as 30-minute averages (CR 21X, Campbell Scientific Inc., Ogden Utah).
Net radiation (Rn) was measured at a height of 1 m using a net radiometer (Q-6, REBS,
Seattle, Washington). Ground heat flux (G) was measured using two soil heat flux plates
(HFT-1, REBS, Seattle) buried 2 cm below the moss surface (Vourlitis and Oechel, 1997;
Vourlitis and Oechel, 1999). The measured G values were adjusted prior to our analysis
in order to correct for energy lost to the moss layer because of its high heat capacity
(Miller et al., 1984; Waelbroeck, 1993). Measurements of wet and dry bulb temperatures
JAWRA Page 11 2/4/2004
were made at 25, 100, and 200 cm above ground level using ventilated psychrometers
affixed with cross-calibrated type-t thermocouples. All meteorological and flux
measurements were made every 60 seconds and stored as 30-minute averages (Vourlitis
and Oechel, 1997; Vourlitis and Oechel, 1999).
Derivation of Alpha
Rearranging the Priestley-Taylor evaporation equation (equation 1) to solve for α
yields:
α = [LE (S + U)] / [(Rn – G) S] (2)
Observed 30-minute average LE, Rn and G (G was taken as the average value of the two
measurements made at each site) values were used in this equation along with calculated
values of U and S based on the measured air temperature. α values were calculated for
each 30-minute interval and averaged to daily values using only the 30-minute intervals
in which LE and available energy (Rn-G) were positive. This screening limited α values
to periods when evaporation was or could be occurring (the period of interest in this
study) and so eliminated negative α values. The psychometric constant U, was obtained
from:
U = 100.3/(0.677*T) (3)
where U is in Kpa°C-1 and T is air temperature in °C measured at 2 m above ground level.
S, the slope of the saturation vapor pressure and temperature curve, was calculated as a
function of the 2 m air temperature using the following equation:
S = {25083/(T + 237.3)2 exp(17.3T/(T + 237.3))}*0.1 (4)
where S is in Kpa°C-1 (Dingman, 1994).
JAWRA Page 12 2/4/2004
NDVI
The ultimate goal of this research is to use remote sensing as a tool to predict α
values over the circumpolar region. In order to estimate α at these spatial scales while
accounting for seasonal changes described in previous studies (i.e. 15-days Rovanesk et
al. 1996) the only suitable satellite data available is from the National Oceanic and
Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer
(AVHRR) suite of satellites. Therefore, NDVI values were extracted from the NOAA
AVHRR maximum value composite (MVC) images that have been used as a data input in
a number of other ET studies (e.g., Szilagyi and Parlange, 1999). The MVC NDVI
images are twice monthly or bi-weekly (depending on the year) composites of the highest
NDVI values, derived on a pixel-by-pixel basis and extracted from a geo-referenced,
multi-temporal data set (Holben 1986). MVC is a widely used method to reduce
atmospheric affects and cloud contamination that frequently inhibits optical remote
sensing in Arctic environments (Hope et al., 1999a). NDVI is calculated using
reflectance values for channel 1, red (0.58-0.68 µm) and channel 2, NIR (0.72-1.1 µm)
based on the following equation:
(NIR – Red) / (NIR + Red) (5)
We characterized regional phenology (seasonal growth) by using a 16 km2 (4
pixel by 4 pixel) subset area from each MVC NDVI image for the operational periods of
the two towers during the 1994 (tables 1a and 1b) and 1995 (tables 1c and 1d) growing
seasons. This subset size was used to provide an estimate of the regional change in the
vegetation and the associated energy and moisture regimes, which co-vary with
vegetation type and state and to account for any mis-registration between the images and
JAWRA Page 13 2/4/2004
the actual tower location on the ground. The 4 by 4 pixel NDVI values were averaged,
providing a single NDVI value of ‘relative greenness’ for each composite period around
the tower.
INSERT TABLES 1a, b, c AND d ABOUT HERE
Analytical Approach/Statistical Procedures
The significance of the two components of this study, comparing α values
between tower sites and the α-NDVI relationship, was determined using a matched pairs
t-test and ordinary least squares (OLS) regression respectively. Both statistical tests are
parametric and assume mutually independent random samples and the time series data
used in this study may exhibit trends (temporal autocorrelation) that violate this
assumption. Temporal autocorrelation can influence the variance, thus affecting the
confidence intervals used to determine the significance of the relationship. Therefore, the
possible influences of temporal autocorrelation were addressed to ensure correct
interpretation of the statistical results.
Comparison Between Towers-Spatial Differences
A matched pairs t-test was used to determine if daily average α values were
significantly different between the tower sites. The daily average α values may not be
mutually independent random samples due to longer-term (i.e. longer than one day)
variations in surface and weather conditions which can affect the daily average α values
(Bello and Smith, 1990; Viswandham, 1991). Therefore, the autocorrelation function
(ACF) was used to determine the length of the skip/lag that would be used to subset the
data prior to analysis.
JAWRA Page 14 2/4/2004
The ACF is an important metric of the serial dependence within a stationary
random function (Diggle, 1990). The ACF describes the amount of correlation and the
corresponding significance between pairs of data in a time series. This function can be
used to determine when pairs of data (values that follow each other in the time series) are
not dependent on a previous value or values and are, therefore, not temporally auto-
correlated.
Alpha-NDVI Relationship
Ordinary least squares (OLS) regression was used to examine the relationship
between α and NDVI. Because α and NDVI values are sequential in nature, the Durbin-
Watson test was used to determine if the regression results were influenced by temporal
autocorrelation.
Results and Discussions
Daily Average Alpha Values
Figures 2 and 3 display the daily average α values over time for both towers and
both years. α was highly variable with coefficients of variation (CV) between 40 and
50% (Table 2). From the results given in table 2, it is evident that day-to-day variability
in α is greater than the differences in the mean α value between the two sites. This
indicates that meteorological variability at the sites causes greater variability in α then the
differences in surface conditions at the two sites. Overall, average daily α values at the
upland, Happy Valley site were smaller, less variable and closer to the value of 1.26
compared to the U-PAD site. Both the average and maximum values of α for both towers
were larger in 1995 compared 1994. The increase in average and maximum values may
be due to the greater summer precipitation totals in 1995. In 1994 Happy Valley and U-
JAWRA Page 15 2/4/2004
PAD received 137.5 mm and 77.5 mm of precipitation, respectively, compared to 449.6
and 195.8 mm in 1995. This increase in precipitation at both sites during 1995 led to a
shallower depth to water table and increased surface moisture at both sites (Vourlitis and
Oechel 1997; Vourlitis and Oechel 1999). Moisture availability at the surface affects the
partitioning of available energy into sensible (H) and latent heat (LE) fluxes. The
relationship of α to the partitioning of energy at the Earth’s surface into these
components can be examined using the Bowen Ration (β) (H/LE). α is related to β as
follows:
α = (1 +U/S) / (1 + β) (6)
Therefore, as surface moisture increases and a greater proportion of the available energy
at the surface is partitioned into LE, the β decreases causing an increase in α values.
INSERT FIGURES 2 AND 3 AND TABLE 2 ABOUT HERE
Comparison Between Towers
ACF results from the 1994 growing season at both the Happy Valley and U-PAD
tower sites indicate that correlation between pairs of data fell within the 95% confidence
limit after a one-day lag (figure 4a,b). Therefore, the matched pairs t-test utilized daily
average α values sub-sampled every other day from both tower sites during the 1994
growing season. ACF results from U-PAD tower in 1995 indicated that the correlation
between pairs of values fell within the 95% confidence interval after a one-day lag (figure
4d). However, at the Happy Valley tower in 1995 the correlation between pairs of α
values fell within the 95% confidence interval after a two-day lag (figure 4c). Thus, the
matched pairs t-test utilized every 3rd daily average α value in 1995.
INSERT FIGURE 4 ABOUT HERE
JAWRA Page 16 2/4/2004
Differences between the mean daily α coefficients from the U-PAD and Happy
Valley tower sites were significant in 1994 but not significant in 1995 at the 0.05
confidence level (Table 3). Moisture availability was greater in 1995 than in 1994 due to
greater precipitation, decreased depth to water table, and greater soil moisture (Vourlitis
and Oechel 1997; Vourlitis and Oechel 1999). If the surface moisture availability was
largely unrestricted in 1995, differences in surface resistance to LE associated with
transpiring vegetation and/or a drying soil surface would be reduced and inter-site
differences would be due solely to meteorological forcing which may explain the non
statistically significant results in 1995 compared to 1994.
INSERT TABLE 3 ABOUT HERE
Alpha-NDVI Relationship
Average values for α were calculated for the MVC composite periods
(approximately 15 days) in order to correspond with the intervals for the NDVI images.
This time scale is commensurate with time periods used in previous studies (e.g.
Rovanesk et al., 1996). Furthermore, since the α calculations were made using eddy flux
tower measurements, the α values are representations of an area approximately 1km2 in
extent. This assumes a 500m fetch and variations in wind speed and direction over the 14
to 16 day averaging period (Hope et al. 1995). Thus, the α values averaged over the 15 to
16 day period using the eddy flux tower measurements are time and space averaged
values.
The α-NDVI relationship was examined using three OLS regression models,
namely, (1) the pooled data (both towers and both years), (2) the data pooled by year
(each year with the data pooled for both tower locations), and (3) the data pooled by
JAWRA Page 17 2/4/2004
tower location (each tower location with the data pooled for both years). Only the pooled
data from both towers and years had an adequate sample size, (N=25) to test for temporal
auto-correlation with the Durbin-Watson test. The Durbin-Watson test from the pooled
regression produced a value of 1.81 that was above the upper threshold (Du) value, 1.20,
at the 0.05 significance level. Therefore, the residuals were not temporally auto-
correlated. The other regression models (i.e. individual towers and years) were comprised
of small sample sizes (i.e. <15 samples), therefore the residuals were visually examined
for patterns and/or trends that may have been due to temporal auto-correlation. Visual
examination of the scatterplots indicated no temporal autocorrelation.
The OLS regression model results from the pooled data and each year at both
tower locations were not significant. The R2 values were 0.121 for the pooled data, 0.157
for the 1994 growing season, and 0.092 for the 1995 growing season. The results from
the individual towers indicated no significant relationship at the U-PAD site with an R2
value of 0.272. However, at the Happy Valley tower site the relationship was significant
at the 0.05 level and the R2 value was 0.551 (figure 5 and table 4).
INSERT FIGURE 5 AND TABLE 4 ABOUT HERE
Overall, there was no agreement between α and NDVI at the tower sites. The
lack of a relationship was especially evident at the U-PAD tower site where none of the
regressions were significant. This may be a result of the large amount of standing water
freely available at the surface for most and/or all of the growing season. Under these
conditions vegetation is not the dominant surface control over LE because water can
freely move into the atmosphere regardless of the amount of and type of vegetation
present. Since the NDVI characterizes the abundance and type of vegetation and these
JAWRA Page 18 2/4/2004
factors did not restrict water availability to the atmosphere, α values were not related to
changes in NDVI at this site.
Conversely, the Happy Valley tower site is located in a drier upland landscape
where the amount of water freely available at the surface was significantly less than at the
U-PAD tower site. Therefore, the amount and abundance of vegetation characterized by
the NDVI is a stronger influence on the surface resistance to LE through the transpiration
properties of the vegetation at the Happy Valley tower site (Vourlitis and Oechel, 1999).
This leads to the significant α-NDVI relationship over time at the Happy Valley tower
site.
Summary and Conclusions
The objective of this research was to determine 1) the variability of the Priestley-
Taylor α coefficient between two regions with contrasting vegetation, surface moisture,
and meteorological characteristics and 2) if seasonal variations in α were correlated to the
satellite-derived regional NDVI in Arctic tundra ecosystems. This research was
performed by comparing site-to-site differences between α values calculated at two eddy
flux towers located in contrasting regions and by determining the significance of the
correlation between α and the regional NDVI over time.
Differences in daily average α values between the two tower sites were
significant in only one of the two years, 1994. In 1995 the lack of a significant difference
between the tower sites appears to be due to the significantly higher precipitation totals
leading to greater moisture availability, thus diminishing differences in the surface
restrictions on α values between sites. Under these conditions, atmospheric restrictions
JAWRA Page 19 2/4/2004
such as vapor pressure deficit (VPD) may have become the dominant factor influencing
ET rates and α values.
Overall, the results indicate that the regional NDVI is a poor predictor of changes
in α at the time/space scales of this study in Arctic ecosystems. Originally it was
hypothesized that changes in vegetation amount and state, as reflected by the regional
NDVI, would be indicative of average soil moisture conditions and the highly related
meteorological conditions in Arctic ecosystems, and that these conditions were the major
factors controlling the temporal variability in α. From this research it is apparent that
NDVI is not significantly related to α at the time/space scales investigated. Other factors
may have affected the α values (e.g. significant inter-annual differences in precipitation),
and/or the regional scale NDVI values did not provide an adequate representation of
vegetation amount and state, average soil moisture conditions, and the related
meteorological conditions at the tower sites during the study period that were affecting α
values.
This study used regional estimates of NDVI derived from the NOAA AVHRR
suite of satellites because temporal fidelity of the data set. The regional scale NDVI
provides an acceptable estimate of changes in vegetation greenness over time in Arctic
ecosystems however, fine-scale variations in surface characteristics due to topography
and/or soil characteristics, such as open water conditions after snowmelt, are not well
captured by the regional NDVI and may have affected the results in this study.
Furthermore, one of the major limiting characteristics of NDVI is its inability to
distinguish between different background components such as soil, non-
photosynthetically active vegetation (NPV), and/or water (Peddle et al., 1999). Because
JAWRA Page 20 2/4/2004
fine-scale variations in surface conditions such as open water and NPV may influence α
values, and because NDVI cannot distinguish between them, other remote sensing
techniques may improve predictions of α. One remote sensing technique that might be
used is Spectral Mixture Analysis (SMA), which provides estimates of the fractions of
green vegetation, NPV, soil and water within individual pixels. Changes in these
fractions over time and space may be better predictors of α than a single NDVI value,
particularly in areas where vegetation is not the dominant control over LE (e.g. the U-
PAD site). Hope et al. (1999b) have shown that water cover, in the form of lakes, in the
Arctic can be identified using SMA with AVHRR data. In addition to different analytical
methods, new sensors such as the Moderate Resolution Imaging Spectrometer (MODIS)
aboard the NASA Earth Observing System (EOS) Terra satellite may allow for improved
models of α over time and space. With its similar temporal resolution and increased
spectral and spatial resolutions compared to the NOAA AVHRR sensor, the MODIS
sensor may provide improved model inputs for the prediction of α through SMA, as well
as for the estimation of surface temperature, and other variables that may be strongly
related to the α coefficient over time and space in Arctic ecosystems.
Acknowledgements
This research was funded by the National Science Foundation’s Office of Polar Programs
as part of the Arctic Systems Science (ARCSS) Land Atmosphere Ice Interactions (LAII)
FLUX study, Grant No OPP-9318527 and OPP-9216109. The authors would like to
thank Dr. Arthur Getis for his help with the statistical analysis and Steve Hastings,
Rommel Zulueta, and Joe Verfaille Jr. for their field and technical assistance.
JAWRA Page 21 2/4/2004
Literature Cited
Barton, I. J., 1979. A Parameterization of the Evaporation from Nonsaturated Surfaces. Journal of Applied Meteorology 18:43-47. Bello, R. and J. D. Smith, 1990. The effect of Weather Variability on the Energy Balance of a Lake in the Hudson Bay Lowlands, Canada. Arctic and Alpine Research 22(1):98-107. Davies, J. A. and C. D. Allen, 1973. Equilibrium, Potential, and Actual Evaporation from Cropped Surfaces in Southern Ontario. Journal of Applied Meteorology 12:649-657. De Bruin, H. A. R. and A. A. M. Holtstag, 1982. A Simple Parameterization of the Surface Fluxes of Sensible and Latent Heat during Daytime Compared with Penman-Monteith Concept. Journal of Applied Meteorology 21:1610-1621. Diggle, P. J., 1990. Time Series: A Biostatistical Introduction. Oxford University Press, New York, New York. Dingman, S. L., 1994. Physical Hydrology. Prentice-Hall, Inc., Upper Saddle River, New Jersey. Eichinger, W. E., M. B. Parlange, and H. Stricker, 1996. On the Concept of Equilibrium Evaporation and the Value of the Priestley-Taylor Coefficient. Water Resources Research 32:161-164. Eugster, W., J. P. McFadden, and F. S. Chapin III, 1997. A Comparative Approach to Regional Variation in Surface Fluxes using Mobile Eddy Correlation Towers. Boundary-Layer Meteorology 85:293-307. Flint, A. L. and S. W. Childs, 1991. Use of the Priestley-Taylor Evaporation Equation for Soil Water Limited Conditions in a Small Forest Clearcut. Agricultural and Forest Meteorology 56:247-260. Gates, W. L., J. F. B. Mitchell, G. J. Boer, U. Cubasch, and V. P. Meleshko, 1992. Climate Modeling, Climate Prediction and Model Validation. In: Climate Change 1992: The Supplemental Report to the IPCC Scientific Assessment, J. T. Houghton, B. A. Callander and S. K. Varney (Editors). Cambridge University Press, Cambridge, UK, pp. 97-135. Gold, W. G. and L. C. Bliss, 1995. Water Limitations and Plant Community Development in a Polar Desert. Ecology 76(5):1558-1568. Hinzman, L. D. and D. L. Kane, 1992. Potential Response of an Arctic Watershed during a Period of Global Warming. Journal of Geophysical Research 97(D3):2811-2820.
JAWRA Page 22 2/4/2004
Hobbie, J. E. 1980. Introduction and Site Description. In: Liminology of Tundra Ponds, J. E. Hobbie (Editor). Dowden, Hutchinson, and Ross, Stroudsburg, Pennsylvania pp. 19-50. Holben, B. N., 1986. Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data. International Journal of Remote Sensing 7(11):1417-1434. Hope, A. S., J. S. Kimball, and D. A. Stow, 1993. The Relationship Between Tussock Tundra Spectral Reflectance Properties and Biomass and Vegetation Composition. International Journal of Remote Sensing 14(10):1861-1874. Hope, A. S., J. B. Fleming, G. Vourlitis, D. A. Stow, W. C. Oechel and T. Hack, 1995. Relating CO2 fluxes to spectral vegetation indices in tundra landscapes: importance of footprint definition. Polar Record 31:245-250. Hope, A. S., L. L. Coulter, and D. A. Stow, 1999b. Estimating Lake Area in the Arctic Landscape Using Linear Mixture Modeling with AVHRR Data. International Journal of Remote Sensing 20(4):829-835. Hope, A. S., K. R. Pence, D. A. Stow, 1999a. Response of the Normalized Difference Vegetation Index to Varying Cloud Conditions in Arctic Tundra Environments. International Journal of Remote Sensing 20(1):207-212. Jasieniuk, M. A. and E. A. Johnson, 1982. Peatland Vegetation Organization and Dynamics in the Western Subarctic, Northwest Territories, Canada. Canadian Journal of Botany 60:2581-2593. Kane, D. L., R. E. Gieck, and L. D. Hinzman, 1990. Evapotranspiration from a Small Alaskan Arctic Watershed. Nordic Hydrology 21:253-272. Manabe, S. and R. J. Stouffer, 1993. Century-Scale Effects of Increased Atmospheric CO2 on the Ocean-Atmosphere System. Nature 364:215-218. Matthes-Sears, U., W. C. Matthes-Sears, S. J. Hastings and W. C. Oechel, 1988. The Effects of Topography and Nutrient Status on the Biomass, Vegetative Characteristics, and Gas Exchange of Two Deciduous Shrubs on an Arctic Tundra Slope. Arctic and Alpine Research 20:342-351. Maxwell, B., 1992. Arctic Climate: Potential for Change under Global Warming. In: Arctic Ecosystems in a Changing Climate: An Ecophysiological Perspective, F. S. Chapin III, J. L. Jeffries, J. F. Reynolds, G. R. Shaver and J. Svoboda (Editors). Academic Press, Inc., San Diego, California, pp. 11-34. McNaughton, K. G. and T. A. Black, 1973. A Study of Evapotranspiration from a Douglas Fir Forest Using the Energy Balance Approach. Water Resources Research 9:1579-1590.
JAWRA Page 23 2/4/2004
Meehl, G. A., W. M. Washington, and T. R. Karl, 1993. Low-frequency Variability and CO2 Transient Climate Change: Part 1. Time Averaged Differences. Climate Dynamics 8:117-133. Mendez, J., L. D. Hinzman, and D. L. Kane, 1998. Evapotranspiration from a Wetland Complex on the Arctic Coastal Plain of Alaska. Nordic Hydrology 29(4/5):303-330. Miller, P. C., P. M. Miller, et al., 1984. Plant-Soil Processes in Eriophorum Vaginatum Tussock Tundra in Alaska: A Systems Modeling Approach. Ecological Monographs 54(4):361-405. Monteith, J. L., 1965. Evaporation and environment. In: Proceedings of the 19th Symposium of the Society for Experimental Biology. Cambridge University Press, New York, New York, pp. 205-233. Mukammal, E. I. and H. H. Neumann, 1977. Application of the Priestley-Taylor Evaporation Model to Assess the Influence of Soil Moisture on the Evaporation from A Large Weighing Lysimeter and Class A Pan. Boundary-Layer Meteorology 14:243-256. Oechel, W. C., 1989. Nutrient and Water Flux in a Small Arctic Watershed: An Overview. Holarctic Ecology 12:187-201. Ostendorf, B. and J. F. Reynolds, 1998. A Model of Arctic Tundra Vegetation Derived from Topographic Gradients. Landscape Ecology 13:187-201. Parlange, M. B. and G. G. Katul, 1992. Estimation of the Diurnal Variation of Potential Evaporation from a Wet Bare-Soil Surface. Journal of Hydrology 132:71-89. Peddle, D. R., F. G. Hall, and E. F. LeDrew, 1999. Spectral Mixture Analysis and Geometrical Optical Reflectance Modeling of Boreal Forest Biophysical Structure. Remote Sensing of Environment 67:288-297. Peterson, K. M. and W. D. Billings, 1980. Tundra Vegetational Patterns and Succession in Relation to Microtopography near Atkasook, Alaska. Arctic and Alpine Research 12:473-482. Priestley, C. H. B. and R. J. Taylor, 1972. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review 100(2):81-92. Roulet, N. T. and M. Woo, 1986. Hydrology of a Wetland in the Continuous Permafrost Region. Journal of Hydrology 89:73-91. Rouse, W. R., P. F. Mills, and R. B. Stewart, 1977. Evaporation in High Latitudes. Water Resources Research 13:909-914.
JAWRA Page 24 2/4/2004
Rouse, W. R. and R. B. Stewart, 1972. A Simple Model for Determining the Evaporation from High Latitude Upland Sites. Journal of Applied Meteorology 11:1063-1070. Rovansek, R. J., L. D. Hinzman, and D. L. Kane, 1996. Hydrology of a Tundra Wetland Complex on the Alaskan Arctic Coastal Plain, U.S.A. Arctic and Alpine Research 28(3):311-317. Shuttleworth, W. J. and I. R. Calder, 1979. Has the Priestley-Taylor Equation Any Relevance to Forest Evaporation? Journal of Applied Meteorology 18:639-646. Slatyer, R. O. and I. C. McIlroy, 1967. Practical Microclimatology. CISRO, Melbourne, Australia. Stannard, D. I., 1993. Comparison of Penman-Monteith, Shuttleworth-Wallace, and Modified Priestley-Taylor Evapotranspiration Models for Wildland Vegetation in Semiarid Rangeland. Water Resources Research 29(5):1379-1392. Stewart, R. B. and W. R. Rouse, 1976. Simple Models for Calculating Evaporation from Dry and Wet Tundra Surfaces. Arctic and Alpine Research 8(3):263-274. Stow, D. A., B. H. Burns, A. S. Hope, 1993. Spectral, Spatial and Temporal Characteristics of Arctic Tundra Reflectance. International Journal of Remote Sensing 14(13):2445-2465. Szilagyi, J. and M. G. Parlange, 1999. Defining watershed-scale evaporation using a normalized difference vegetation index. Journal of the American Water Resources Association 35:1245-1255. Viswanadham, Y., V. P. Silva Filho, and R. G. B. Andre, 1991. The Priestley-Taylor Parameter Alpha for the Amazon Forest. Forest Ecology and Management 38:211-225. Vourlitis, G. L. and W. C. Oechel, 1997. Landscape-scale CO2, H2O Vapor, and Energy Flux of Moist-Wet Coastal Tundra Ecosystems Over Two Growing-Seasons. Journal of Ecology 85:575-590. Vourlitis, G. L. and W. C. Oechel, 1999. Eddy Covariance Measurements of Net CO2 Flux and Energy Balance of an Alaskan Moist-Tussock Tundra Ecosystem. Ecology 80(2):686-701. Waelbrock, C., 1993. Climate-Soil Processes in the Presence of Permafrost: A Systems Modeling Approach. Ecological Modeling 69:185-225.
JAWRA Page 25 2/4/2004
Table 1a. AVHRR NDVI values for the U-PAD tower during the 1994 growing season. Composite
Period Julian Days Min Max Mean N Std.
Deviation
June 1-15 152-166 -0.10 0.03 0.017 16 0.015
June 16-30 167-181 0.07 0.15 0.112 16 0.019
July 1-15 182-196 0.09 0.26 0.176 16 0.040
July 16-31 197-212 0.22 0.33 0.276 16 0.036
Aug 1-15 213-227 0.17 0.27 0.216 16 0.034
Aug 16-31 228-243 0.09 0.17 0.124 16 0.025
Sep 1-15 244-258 0.11 0.23 0.164 16 0.036 Table 1b. AVHRR NDVI values for the Happy Valley tower during the 1994 growing season.
Composite Period
Julian Days Min Max Mean N Std.
Deviation
June 1-15 152-166 0.19 0.35 0.302 16 0.048
June 16-30 167-181 0.25 0.42 0.349 16 0.058
July 1-15 182-196 0.33 0.51 0.436 16 0.057
July 16-31 197-212 0.38 0.52 0.469 16 0.044
Aug 1-15 213-227 0.25 0.48 0.379 16 0.064
Aug 16-31 228-243 0.23 0.39 0.323 16 0.058
Sep 1-15 244-258 0.17 0.35 0.283 16 0.058
Table 1c. AVHRR NDVI values for the U-PAD tower during the 1995 growing season. Composite
Period Julian Days min max mean N Std.
DeviationMay 27-June 9 147-160 0.00 0.02 0.004 16 0.006
June 10-23 161-174 0.02 0.14 0.093 16 0.037
June 24-July 7 175-188 0.13 0.28 0.201 16 0.038
July 8-21 189-202 0.20 0.29 0.245 16 0.028
July 22-Aug 4 203-216 0.21 0.33 0.283 16 0.036
Aug 5-18 217-230 0.21 0.32 0.270 16 0.032
Aug 19-Sep 1 231-244 0.18 0.30 0.246 16 0.032
Sep 2-15 245-258 0.03 0.03 0.042 16 0.008
JAWRA Page 26 2/4/2004
Table 1c. AVHRR NDVI values for the Happy Valley tower during the 1995 growing season.
Composite Period
Julian Days Min Max Mean N Std.
Deviation
May 27-June 9 147-160 0.12 0.31 0.203 16 0.069
June 10-23 161-174 0.25 0.39 0.327 16 0.050
June 24-July 7 175-188 0.33 0.50 0.431 16 0.055
July 8-21 189-202 0.35 0.51 0.447 16 0.053
July 22-Aug 4 203-216 0.35 0.52 0.461 16 0.047
Aug 5-18 217-230 0.37 0.51 0.446 16 0.046
Aug 19-Sep 1 231-244 0.32 0.44 0.389 16 0.044
Sep 2-15 245-258 0.20 0.32 0.246 16 0.039
JAWRA Page 27 2/4/2004
Table 2. Descriptive statistics for daily average α values
Tower/Year N Min. Max. Mean Std. Deviation
Coefficient of Variation.
Happy Valley 1994 56 0.47 3.07 1.24 0.50 40.32 U-PAD 1994 62 0.12 3.37 1.45 0.81 55.86 Happy Valley 1995 86 0.28 4.78 1.35 0.69 51.11 U-PAD 1995 85 0.13 4.45 1.48 0.78 52.70
JAWRA Page 28 2/4/2004
Table 3. Paired samples t-test results
Year N t-value t-critical* Null Hypothesis Rejected
1994 19 -2.310 0.033 Yes 1995 26 0.534 0.598 No
* 0.05 Confidence Level
JAWRA Page 29 2/4/2004
Table 4. Regression statistics for the 1994 and 1995 data from the Happy Valley tower
site.
N R R2 Adj. R2 F-Value Sig. F* Durbin-Watson
12 0.742 0.551 0.506 12.275 0.006 2.341
Variable Coefficient Std. Error t-Value Sig. t
Constant -0.002 0.384 -0.042 0.967 NDVI 3.400 0.970 3.504 0.006
*0.05 Confidence level
JAWRA Page 30 2/4/2004
List of figures:
Figure 1: Kuparuk river watershed within the North Slope of Alaska and the location of
the study tower sites.
Figure 2: Daily average α values from the 1994 growing season at both tower sites.
Figure 3: Daily average α values from the 1995 growing season at both tower sites.
Figure 4. Auto-correlation functions for a) Happy Valley 1994 b) U-PAD 1994 c) Happy
Valley 1995 and d) U-PAD 1995 with 95% confidence intervals.
Figure 5. Scatterplot of α versus NDVI during the 1994 and 1995 growing
seasons at the Happy Valley tower site.
JAWRA Page 31 2/4/2004
1470
710
680
1520
25 0 50
Kuparuk WatershedBoundary
Brooks Range kilometers
Arctic Ocean
U-Pad
Happy Valley
Tower Site
Alaska
JAWRA Page 32 2/4/2004
0
1
2
3
4
5
151
157
163
169
175
181
187
193
199
205
211
217
223
229
235
241
247
Julian Day
Alp
ha
Happy Valley
UPAD
JAWRA Page 33 2/4/2004
0
1
2
3
4
5
151
157
163
169
175
181
187
193
199
205
211
217
223
229
235
241
247
Julian Day
Alp
haHappy ValleyUPAD
JAWRA Page 34 2/4/2004
Lag Number
1615
1413
1211
109
87
65
43
21
ACF
1.0
.5
0.0
-.5
-1.0
Confidence Limits
Coefficient
a
Lag Number
1615
1413
1211
109
87
65
43
21
AC
F
1.0
.5
0.0
-.5
-1.0
Confidence Limits
Coefficient
b
Lag Number
1615
1413
1211
109
87
65
43
21
AC
F
1.0
.5
0.0
-.5
-1.0
Confidence Limits
Coefficient
d
Lag Number
1615
1413
1211
109
87
65
43
21
AC
F
1.0
.5
0.0
-.5
-1.0
Confidence Limits
Coefficient
c