Download 036 Sass et al 2008 - Characterizing

HYDROLOGICAL PROCESSES
Hydrol. Process. 22, 1687– 1699 (2008)
Published online 1 May 2007 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/hyp.6736
Characterizing hydrodynamics on boreal landscapes using
archived synthetic aperture radar imagery
G. Z. Sass1 and I. F. Creed1,2 *
1
Department of Geography, The University of Western Ontario, London, Ontario, Canada
Department of Biology, The University of Western Ontario, London, Ontario, Canada
2
Abstract:
Characterizing the spatial and temporal variation in surface hydrological dynamics of large boreal landscapes is vital, since these
patterns define the occurrence of key areas of land-to-lake and land-to-atmosphere hydrological and biogeochemical linkages
that are critical in the movement of matter and energy at local to global scales. However, monitoring surface hydrological
dynamics over large geographic extents and over long periods of time is a challenge for hydrologists, as traditional point
measurements are not practical. In this study we used European Remote Sensing satellite radar imagery to monitor the
variation in surface hydrological patterns over a 12-year period and to assess the change in the organization of saturated and
inundated areas of the landscape. Using the regional Utikuma River drainage basin (2900 km2 ) as the test area, the analyses of
patterns of wetlands indicated that, during dry climatic conditions, wetland sizes were small and disconnected from each other
and receiving bodies of water. As climatic conditions changed from dry to mesic, wetland numbers increased but were still
disconnected. Very wet climatic conditions were required before the disconnected wetlands coalesced and connected to lakes.
During these wet conditions, the response of the lake level at Utikuma Lake was observed to be much higher than under drier
conditions. Analyses of individual wetland maps and integrated wetland probability maps have the potential to inform future
biogeochemical and ecological investigations and forest management on the Boreal Plain. Copyright  2007 John Wiley &
Sons, Ltd.
KEY WORDS
boreal; forest; hydrology; wetland; soil moisture; radar; SAR; ERS
Received 27 June 2006; Accepted 26 February 2007
INTRODUCTION
Recent acceleration of human activities such as oil, gas
and timber extraction on the Boreal Plain of northern
Alberta has raised concerns over possible disruptions
and/or alterations of existing surface hydrological dynamics and associated impacts on terrestrial and aquatic
ecosystems (Prepas et al., 2001; Schindler, 2001; Smith
et al., 2003). Surface hydrological features (i.e. saturated
and inundated areas) demand monitoring and potentially
protection, since they play a significant role in mediating the transfer of water (e.g. Bowling et al., 2003;
Lindsay et al., 2005), nutrients (e.g. Creed and Band,
1998; Devito et al., 2000; Evans et al., 2000; Macrae
et al., 2005), sediments (e.g. Rustomji and Prosser, 2001;
Chaplot et al., 2005), and biota (e.g. Cottenie and De
Meester, 2003; Pringle, 2003; Ray et al., 2004) from
terrestrial to aquatic systems and in the exchange of
gases between terrestrial and atmospheric systems (e.g.
Savage et al., 1997). In order to monitor surface hydrological dynamics, techniques are needed that provide
hydrological data over large areas (e.g. regional drainage
basins) because management decisions are being made at
these scales. Ground-based techniques provide point measurements of soil moisture (Munoz-Carpena, 2002) and,
* Correspondence to: I. F. Creed, Department of Biology, The University
of Western Ontario, London, Ontario N6A 5B7, Canada.
E-mail: [email protected]
Copyright  2007 John Wiley & Sons, Ltd.
therefore, they are not feasible for large-scale mapping.
Advances in remote sensing allow hydrologists to move
beyond point measurements and to monitor soil moisture
and patterns of inundation at broad spatial scales (Toyra
et al., 2001; Moran et al., 2004; Western et al., 2004).
This paper explores the use of satellite radar imagery
in mapping wetland areas, including both saturated and
inundated lands.
The dominant processes controlling hydrological dynamics of wetlands are functions of interacting climatic
and landscape controls (Devito et al., 2005a). The western Boreal Plain, characterized by sub-humid climate,
heterogeneous glacial deposits and flat terrain, has complex surface water–groundwater interactions, resulting
in surface hydrological dynamics that do not necessarily reflect local topography (Smerdon et al., 2005). This
landscape complexity makes it difficult to extend processbased understanding from one or two catchments to other
nearby catchments or to scale hydrological findings from
low-order catchments to regional catchments (e.g. surface
hydrological dynamics in some catchments may be driven
by topography, but in an adjacent catchment the surface
hydrological dynamics maybe driven more by subsurface storage features). A complete picture of hydrological
dynamics will only emerge when considering a large area
that captures the full spatial heterogeneity in landscape
properties and a long time to capture the multiple climatic
oscillations that influence the hydrology of this region.
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G. Z. SASS AND I. F. CREED
Satellite data provide an opportunity for delineating
surface hydrological dynamics over large geographic
extents and over many years (Smith, 1997; Frazier
et al., 2003). In particular, synthetic aperture radar (SAR)
images have been shown to be very useful in detecting changes in soil moisture (Oldak et al., 2003; Moran,
et al., 2004) and inundated areas (Townsend, 2002;
Bourgeau-Chavez et al., 2005). Advantages of SAR sensors (in relation to optical sensors such as Landsat TM)
include all-weather, all-day and canopy penetration capabilities. The European Remote Sensing (ERS)-1 and 2 satellites have been collectively the longest running
commercial systems (1992 to present) collecting fine resolution (¾25 m) SAR imagery. ERS is a C-band sensor (5Ð6 cm wavelength) with vertical send and receive
polarization and a 35-day repeat cycle (Attema, 1991).
ERS has been shown to be useful in detecting soil wetness and inundation in temperate and boreal landscapes
(Morrissey et al., 1994; Kasischke et al., 1995; Gineste
et al., 1998; Quesney et al., 2000). ERS data are available
from archives managed by various agencies (e.g. Alaska
Satellite Facility (ASF)).
In this study, we monitored variations in soil saturation
and inundation in a remote and relatively undisturbed part
of the boreal forest. The aim was to evaluate the use of
archived SAR imagery in establishing surface hydrological dynamics over a representative range of hydrological
conditions. Specifically, the objectives were to: (1) map
year-to-year changes in surface hydrological dynamics
using ERS imagery; (2) quantify the relation between climate and hydrological dynamics; (3) quantify the change
in magnitude and organization of surface hydrological
patterns under a range of climatic conditions; (4) quantify
the probability of surface saturation and inundation in
space and time.
STUDY AREA
The Utikuma River drainage basin was selected
as the study area (outlet located at 56° 040 28Ð400 N,
115° 080 51Ð900 W; Figure 1). This basin lies in the western
portion of the Boreal Plain ecozone on an upland plateau,
approximately 600 m a.s.l. in the larger Peace River
drainage basin (Figure 1, inset). It contains Utikuma
Lake, a large regional lake with a surface area of 288 km2
(Mitchell and Prepas, 1990).
The climate is continental with cold, long winters
and short, cool summers. Based on the 1971–2000 normals for the closest year-round meteorological recording
station (Slave Lake, 55° 180 , 114° 460 W), located within
80 km of the centre of our study area, average annual
temperature is 1Ð7 ° C, with average monthly temperatures ranging from 14Ð5 ° C in January to 15Ð6 ° C in
July (Environment Canada, 2006). Average annual precipitation P is 503 mm, 70% of which falls as rain from
May to September (Environment Canada, 2006). The
highest average daily precipitation occurs between early
June and mid July (¾3Ð5 mm day1 ), followed by a relatively drier period from mid July to the end of August
Figure 1. Map of the Utikuma River drainage basin with locations of soil moisture sampling sites marked. Inset: location of study region on the
Boreal Plain ecozone
Copyright  2007 John Wiley & Sons, Ltd.
Hydrol. Process. 22, 1687– 1699 (2008)
DOI: 10.1002/hyp
CHARACTERIZING HYDRODYNAMICS ON BOREAL LANDSCAPES
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(<4 m) with open canopies. Vegetation on moderately
well-drained uplands is dominated by trembling aspen
(Populus tremuloides Michx.) with occasional stands of
white spruce (Picea glauca (Moench) Voss) (Mitchell
and Prepas, 1990). Vegetation on well-drained uplands
is dominated by pine (Pinus banksiana Lamb.). Aspen
stands have closed but simple (one-layer) canopies ranging from 8 to 15 m in height. Pine stands have more open
canopies ranging from 7 to 13 m in height.
METHODS
Figure 2. Average daily precipitation based on the 1970– 2004, 35-year
normal for Lesser Slave Lake, the closest year-round meteorological
recording station, located within 80 km of the centre of the study area
(¾2Ð1 mm day1 ) (Figure 2). Average annual potential
evapotranspiration (PET) is approximately 517 mm. During the past 35 years P was less than PET (based on
1 May to 31 August totals that were available for the
Utikuma River drainage basin), except once every 5 to
10 years when P was greater than PET (Figure 3). This
has resulted in an overall drying trend since the mid 1970s
(Figure 3).
The Utikuma River drains an area that is characterized
by flat to gently undulating glaciated terrain with depositional landforms (including moraines, glacio-fluvial outwash, and glacio-lacustrine clay plains) (Paulen et al.,
2004). Most flat areas are covered by peatlands, and
many rivers and lakes are surrounded by swamps or
marshes. Soils in poorly drained locations are Organic
soils, whereas soils on better drained till and outwash
deposits are dominated by Orthic and Podzolic Gray
Luvisols (Wynnyk et al., 1963).
Wetland vegetation is dominated by black spruce
(Picea mariana (Mill.) B.S.P.) and tamarack (Larix
laricina (Du Roi) K. Koch) in peat-forming fens and bogs
and by willow (Salix spp.), birch (Betula spp.), alder
(Alnus spp.), sedges (Carex spp.) and grasses in nonpeat-forming swamps and marshes (Mitchell and Prepas,
1990). Tree stands on the lowlands are relatively short
Preprocessing archived ERS imagery
ERS-1 and ERS-2 images were acquired through the
ASF. All images were acquired in the descending orbit at
noon local time. The images were orthorectified using a
25 m pixel resolution digital elevation model and an additional 20 to 30 ground control points collected from georeferenced geographical information system (GIS) layers
of hydrography (i.e. lakes and streams), infrastructure
(i.e. roads) and a recent Landsat TM image (i.e. road
intersections). The root-mean-square error was <25 m in
both Northing and Easting directions for all images. During orthorectification, radar images were resampled from
12Ð5 m to 25 m pixel resolution using a bilinear resampling algorithm and then filtered twice using a 3 ð 3
gamma filter to reduce speckle (Lopes et al., 1993). We
assumed that the resolution of the images (25 m ð 25 m)
would be sufficient to resolve the saturated and inundated
features of the landscape that are often many times the
size of one pixel given the relatively flat landscape.
Once orthorectified, digital numbers were converted
to backscatter coefficient (with units of decibels) using
standard radiometric calibration techniques (ASF, 2004).
ERS-1 images were applied an offset of 0Ð5 dB to make
them comparable to the ERS-2 images (W. Albright
(ASF), personal communication). ERS-2 images were
corrected for a constant rate of loss in transmitter pulse
power by applying a linear correction factor (Meadows
et al., 2004). The rate of loss was approximately 0Ð66 dB
year1 from launch (1995) to the end of 2000 and then
Figure 3. Seasonal and cumulative long-term climate trends of P PET (bars represent 1 May–31 August total)
Copyright  2007 John Wiley & Sons, Ltd.
Hydrol. Process. 22, 1687– 1699 (2008)
DOI: 10.1002/hyp
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G. Z. SASS AND I. F. CREED
approximately 0Ð82 dB year1 from 2001 to February
2003 (Meadows et al., 2004). In February 2003, a gain
increase of 3 dB was applied. Since then the rate of
loss has been 0Ð66 dB year1 . Finally, for both ERS1 and ERS-2 images, a systematic offset was observed
between images taken from the two adjacent descending
orbits, which is most likely due to differences in relative
proportions of lake pixels introducing different levels of
auto-normalization (Meadows et al., 1998). An offset of
0Ð8 dB was applied to images acquired from the orbit
centred on 56° N, 116 ° W.
Interpreting synthetic aperture radar imagery for
hydrological applications
SAR images capture the amount of incident microwave
radiation backscattered by a surface. On landscapes
where interferences are minimal (e.g. bare nonagricultural soils), the intensity of this backscattered
radiation is highly dependent on the amount of water
present within the top soil layer.
There are two dominant scattering mechanisms at work
as the hydrological state changes from dry through to
saturated and then to inundated soils (Figure 4). The first
mechanism, over non-inundated areas, is controlled by
the dielectric properties of the surface. Dielectric properties are related to the way a medium reacts when
an electric field is applied to it (Ulaby et al., 1996).
Since the dielectric constant of wet soils (¾80) is much
higher than the dielectric constant of dry soils (¾1),
the microwave radiation is very sensitive to changes in
wetness (Ulaby et al., 1996). The relation between the
backscatter coefficient and wetness is non-linear because
the backscatter coefficient reaches signal saturation at
high wetness levels (Kasischke et al., 1995). Surface
roughness and vegetation may change this basic backscatter response in complex ways, sometimes by increasing
the backscatter coefficient (Griffiths and Wooding, 1996)
or sometimes by decreasing the backscatter coefficient
(Kasischke et al., 2003). The mixed-wood boreal forest
of the Utikuma River drainage basin has a relatively simple and open canopy, giving good penetration capability;
Figure 4. Schematic of radar backscatter response to change in hydrological state from unsaturated to inundated over surfaces without vegetation
Copyright  2007 John Wiley & Sons, Ltd.
therefore, we considered interferences to the backscattering signal minimal. For example, for the northern portion
of the Utikuma River drainage basin, vegetation density
data from the Alberta Vegetation Inventory indicated that,
of the total land area, 47% has a canopy closure of 50%
or less, 89% has a canopy closure of 70% or less, and
only 12% has a canopy closure of greater than 100%.
These data suggest that vegetation will have a relatively
minor effect on the backscatter response for the majority
of the watershed.
The second scattering mechanism, over inundated
areas, is controlled by specular reflection from smooth
water surfaces. Specular reflection implies that most of
the incident energy is reflected away from the sensor and,
therefore, that the backscatter coefficient will be very
low from these areas. This basic backscatter response
is modified where there is emergent vegetation over
inundated areas. In such cases, double-bounce scattering
from the water surface and plant stems substantially
increases the backscatter coefficient (Townsend, 2002).
The backscatter coefficient may also be enhanced over
inundated areas if there is significant wind-induced wave
action (Horritt et al., 2003). This can sometimes increase
the backscatter signal of a lake to match that of noninundated areas. Potential wind effects on open water
areas of lakes in the Utikuma River drainage basin were
reduced by excluding all open water areas of lakes as
defined by a provincial hydrography layer.
Developing a hydrological classification for ERS images
We wanted to assess the nature of the relation between
satellite-based backscatter coefficient and ground-based
volumetric soil moisture (VSM) over non-inundated
areas. Ideally, we would have measured VSM directly
using a gravimetric method (e.g. by oven-drying soil
samples) and related it to the backscatter coefficient.
However, direct sampling of VSM was not possible, as
we needed to cover a large geographic region in a short
period of time. Therefore, we estimated VSM using a soil
impedance probe and a calibration function that related
gravimetrically determined VSM and soil impedance.
The indirect measure of VSM may have introduced error
in the estimation of VSM, but collecting soil moisture
measurements with a probe had the advantage of covering
the range of sites we needed.
To calibrate soil impedance (millivolts) to VSM (per
cent) we collected soil samples and corresponding soil
impedance measurements from representative upland and
lowland sites. Twenty sites were sampled during summer
of 2004, with four sites sampled twice. Soil impedance
measurements were taken with a ThetaProbe (Delta-T
Devices, 1998). The ThetaProbe measures the impedance
of an electromagnetic wave travelling along a central rod
surrounded by three outer rods, inserted 6 cm into the
ground surface (Munoz-Carpena, 2002). The impedance
probe was inserted into the top 6 cm of the soil, which
consisted mostly of a leaf, fibric, and humic (LFH)
layer in uplands and peat or moss in lowlands. For the
Hydrol. Process. 22, 1687– 1699 (2008)
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CHARACTERIZING HYDRODYNAMICS ON BOREAL LANDSCAPES
Figure 5. Regression model relating soil impedance measured by ThetaProbe and VSM estimated gravimetrically from soil samples. The data
points represent 20 soil moisture sampling sites, 11 of which overlap
with the 18 sites shown in Figure 1. The data used in the figure were
collected during the July and September collection campaigns with four
of the sites sampled twice, giving a total sample size of 24
same soil volume (30 cm3 ), a soil sample was taken
and its moisture content determined gravimetrically. A
regression model was generated to relate soil impedance
and gravimetrically determined VSM (Figure 5). Since
both upland (LFH) and lowland (organic) samples fell
on the same regression line that related VSM to soil
impedance, only one regression model was used (r 2 D
0Ð74; SE D 0Ð42; p < 0Ð0001, n D 24).
To calibrate backscatter coefficient (decibels) to VSM,
we extracted backscatter coefficients and coincident soil
impedance measurements across 18 sites (Figure 1) during four sampling periods in the summer of 2004 (26
June, 12 July, 16 August, and 20 September). Data collection started the evening prior to and continued through
to the afternoon after the satellite flyover. The sites were
selected to represent the dominant cover types of the
region (i.e. black spruce and tamarack forests in lowlands, aspen and pine forests in uplands). Each site was
located within a larger homogeneous region (minimum
area of 1 ha) with respect to vegetation cover to ensure
minimal mixed pixels and to reduce noise introduced
by speckle when co-registering with satellite imagery
(Griffiths and Wooding, 1996). At each of the 18 sites,
soil impedance readings were collected on a rectangular grid (24 nodes laid out over a 50 m ð 50 m area)
(Figure 1, inset). At each node of the 50 m ð 50 m rectangular grid, impedance readings were taken in each of
the cardinal directions, with a set of four readings in
upland sites and two sets of four readings (one in a hollow
and one on a hummock) in lowland sites. Soil impedance
measurements were averaged for each of the 18 sites
and subsequently converted to VSM using the calibration function shown in Figure 5. Corresponding satellitebased backscatter data were extracted from four ERS
images by digitizing polygons on the computer screen
with the aid of geographic coordinates of sampling sites
and a land cover map derived from a recent Landsat TM
image. An average backscatter coefficient was calculated
Copyright  2007 John Wiley & Sons, Ltd.
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Figure 6. Regression model relating ground and satellite-based estimates
of soil moisture based on four sampling periods conducted during the
summer of 2004. Each data point represents one of the 18 soil moisture
sampling sites shown on Figure 1. VSM was derived from soil impedance
measurements using the regression shown in Figure 5
for each polygon. Regression models were developed that
related ground-based VSM to satellite-based backscatter
coefficient. Prior to developing the regression models, the
VSM data (which were left skewed) were transformed by
taking the natural logarithm to meet the assumption of
normally distributed regression residuals. Following convention, the backscatter coefficient was regressed against
VSM. All four models were statistically significant and
explained between 31 and 67% of the variance. When the
four samples (each representing the 18 sites sampled during four different sampling periods) were combined into
one global dataset, the regression model captured 45%
of the variation in backscatter coefficient (p < 0Ð0001;
SE D 0Ð90, n D 72) (Figure 6).
Although statistically significant, the regression model
explained less than 50% of the variation in radar
backscatter, which we considered inadequate for characterizing VSM content. However, our targets were the
mapping of inundated and saturated parts of the landscape. To map these areas, a regression model that provided a continuous measure of soil moisture was not
needed; rather, a simple classification of unsaturated, saturated, and inundated classes was developed. We used
the regression model (shown in Figure 6) to transform
all pixels from decibels to VSM. Next, we applied classification rules to separate the derived VSM layer into
three hydrologically relevant classes. For non-inundated
parts of the landscape, we defined two classes: unsaturated (0–60% VSM) and saturated (>60% VSM). Our
threshold in VSM for identifying saturated areas was
based on the gravimetric samples collected for both mineral and organic soils. To complete the classification we
defined inundated areas as anything less than 12Ð5 dB.
This was estimated from frequency distributions of inundated and non-inundated pixels derived from polygons
visually delineated on the images.
In order to allow for uncertainty in the transition
zone between the three hydrological classes, fuzzy
classification rules were implemented using a sigmoidal
Hydrol. Process. 22, 1687– 1699 (2008)
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G. Z. SASS AND I. F. CREED
Figure 7. Fuzzy classification rules applied to spatial layers of VSM originally derived from spatial layers of backscatter coefficient using the
regression model in Figure 6. bx values represent the point at which the probability of an object belonging to that class is 50%. dx values define the
width of the fuzzy zone between the 0Ð5 and the 1Ð0 probability point of a particular class. The b and d values were based on the class threshold
determined from field data and reflected the uncertainty in separating the classes. Although not shown on the graph, the fuzzy zones do intersect
Figure 8. Frequency distribution of backscatter coefficients extracted under masks defined by field-based unsaturated and saturated soil moisture data
and lake inundation data. Kruskall–Wallis H test indicated statistically significant differences between the three classes (2 D 14 737; p < 0Ð0001)
membership function (Burrough and McDonnell, 1998),
implemented in TAS 2Ð07 (Lindsay, 2005). The membership function is governed by the two parameters b
and d, where b represents the point at which the probability of an object belonging to that class is 0Ð5 and
d represents the width of the fuzzy zone between the
0Ð5 and the 1Ð0 probability point of a particular class.
The b and d values were based on the class threshold
determined from field data and reflected the uncertainty
in separating the classes. The fuzzy boundary rules used
are shown in Figure 7. Each ERS image was assigned a
fuzzy membership for each hydrological class, creating
three different GIS layers, which were then combined by
assigning each pixel to a class with the highest probability
(Clark, 2004).
We assessed the accuracy of our hydrological classification using two methods. First, we assessed the statistical separability of the classes. We grouped each soil
moisture sampling site into unsaturated or saturated based
on the 60% VSM threshold and we used the provincial
lake layer to identify inundated areas. For each of the
three classes, we extracted backscatter coefficients and
tested the separatibility by running a Kruskal–Wallis H
Copyright  2007 John Wiley & Sons, Ltd.
test, which compared the mean ranking of the groups.
The Kruskall–Wallis H test indicated statistically significant differences among the three classes ( 2 D 14 737;
p < 0Ð0001; Figure 8). Open-water areas (without windinduced backscatter artefacts) were used in assessing the
separability of the three classes, but because of the random and, therefore, unpredictable effect of wind-induced
backscatter increases in open-water areas, these areas
were masked out for the rest of the hydrological pattern
analyses. In contrast, shoreline and open-water wetland
areas, where wind effects are reduced by nearby vegetation, were not masked out.
Second, we tested the accuracy of the classification
of unsaturated versus saturated classes for 16 validation
sites that were not part of the data used to develop
the classification. Each of the four field collection dates
was represented by these validation data. We extracted
the dominant hydrological class (i.e. mode) for each
site using the final classification maps and compared
the classified data with the field data. The accuracy
assessment indicated high overall agreement between the
field-based and the satellite-based classifications (of 16
validation sites, 14 (88%) were correctly classified).
Hydrol. Process. 22, 1687– 1699 (2008)
DOI: 10.1002/hyp
CHARACTERIZING HYDRODYNAMICS ON BOREAL LANDSCAPES
Monitoring the dynamics of surface hydrological
features using archived ERS imagery
In order to capture the full range of hydrological
conditions, all available descending images were acquired
from the ASF archives between 1992 and 2004. One
image was selected per year for the relatively dry latesummer period from year-days 200 to 240 (20 July to
30 August). This period reflects the cumulative effect of
hydrological forcing on surface hydrological dynamics
that result from the precipitation peak in June and July
(Figure 2).
Four ERS-1 (1992–1995) and five ERS-2 (1996–2004)
images were selected. We tested how representative
the summer hydrological conditions during these nine
‘image’ years were of the longer 1970–2004 period of
climate record. Summer hydrological conditions were
approximated by summing the daily difference between
precipitation P and potential evapotranspiration PET
between 1 May and 31 August. PET was computed from
daily average temperature T record according to Hamon
(1964). P and T were measured at five climate stations within or near the Utikuma River basin (maximum
40 km from drainage divide). The five climate records
were averaged to give a basin-wide representative climate record. P PET was calculated from this combined
record. A Kolmogorov–Smirnov test showed no statistically significant difference (z D 0Ð43; p D 0Ð99) between
the distribution of the P PET sample of the nine years
with satellite coverage and the P PET sample of the
35-year (1970–2004) period. Closer inspection of the climate record revealed that both the second wettest (1996:
137 mm) and fourth driest (1995: 164 mm) years over
the past 35 years occurred between 1992 and 2004. These
results suggest that the seasonal climate totals of the nine
available ERS-image years were representative of the previous 35-year (1970–2004) record.
For the analyses of surface hydrological dynamics, we
combined saturated and inundated classes into a wetland
class, resulting in a binary (non-wet versus wet) image.
For each of the nine images, the percentage cover of
wetlands (%WET) within the Utikuma River drainage
basin was calculated (approximately 10% of the Utikuma
River drainage basin did not have satellite coverage and
was masked out in all subsequent analyses). We related
%WET to other basin-wide measures of hydro-climatic
indicators, including P PET and fluctuations in water
levels within Utikuma Lake. The change in lake level
was computed as the difference between the lake levels
measured on 1 May and 31 August.
Using the wetland maps, simple landscape metrics
were calculated to capture the organization of these
hydrologically important areas under different climatic
conditions. We computed the total number of wetland
patches for the Utikuma River drainage basin and the
percentage cover of wetlands that were connected to
lakes (%WETCONN ). Wetlands had to be located not more
than 25 m from the shoreline of lakes to be considered
connected. This buffer allowance was implemented to
factor in any locational inaccuracies in the images.
Copyright  2007 John Wiley & Sons, Ltd.
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Characterizing the probability of wetland occurrence
We computed the probability of wetland occurrence
using an aspatial and spatial approach. With the aspatial
approach, we wanted to answer the question about what
the probability of observing a certain percentage of the
landscape covered by wetlands was during the month of
August. For example, what was the probability of finding
5% of the landscape covered by wetlands? In order to
calculate this probability, we ranked %WET from lowest
to highest. The lowest value of %WET was assigned
a probability of occurrence of 100% (i.e. nine out of
nine images had at least 4Ð4% of the landscape covered
by wetlands). The second lowest value of %WET was
assigned a probability of 89% (i.e. 8/9), and so on. The
highest value of %WET was assigned a probability of
11Ð1%.
With the spatial approach, we wanted to answer the
question about what the probability of finding any point
in the landscape belonging to the wetland class was
during the month of August. The probability of wetland
occurrence was computed by summing the nine binary
(non-wet versus wet) image layers and dividing by the
sample size. This technique was used to compute the
probability of inundation in boreal forests (Clark, 2004)
and the probability of belonging to a depression from
digital elevation models (Lindsay and Creed, 2006).
RESULTS AND DISCUSSION
Boreal landscapes occupy large and remote areas that
remain relatively untouched by human disturbance. As
the human footprint changes the boreal landscape, there
is a need for hydrological data to establish the range of
natural variability (i.e. a reference condition) and to estimate changes to this range of natural variability caused
by human activities (i.e. altered condition). A challenge is
to develop databases and analyses at the scales at which
these human activities are being manifested. We retrieved
SAR imagery from the archives of the ASF to characterize surface hydrological dynamics over a regional
catchment (that included hundreds of smaller low-order
catchments) and over a longer time period (1992–2004)
that captured the representative range of hydrological
conditions.
Wetland dynamics
A time-series and scatter plot of %WET and corresponding changes in lake levels are presented in
Figure 9a and b. Both of these graphs suggest significant temporal coherence of surface hydrological patterns within the terrestrial and the aquatic systems of the
Utikuma River drainage basin.
An examination of climatic controls on %WET and
lake level was conducted (Figure 10). On the graph, a
1 : 1 line is drawn that reflects a lake level response
(millimetres) that is identical in magnitude to inputs
of P PET (millimetres). We observed that, when P <
PET, an increase in P PET resulted in an equal increase
Hydrol. Process. 22, 1687– 1699 (2008)
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Figure 9. (a) Time-series of lake level change and percentage wetland cover. (b) Scatter-plot of change in lake level versus percentage wetland cover
Figure 10. Scatter plot of indicator of climate forcing (P PET) versus indicators of surface hydrological dynamics (lake level change and percentage
wetland cover)
in lake level and the data points fell parallel to the 1 : 1
line. However, when P > PET, an increase in P PET
resulted in a greater increase in lake level such that the
data points fell above the 1 : 1 line. There was a two to
three times increase in lake level for each unit increase
of P PET; for example, for a P PET increase of
137 mm, the lake level increased by 630 mm in 1996.
Copyright  2007 John Wiley & Sons, Ltd.
This suggested that as P PET > 0 mm, the storage
capacity within the terrestrial system started to fill, and
excess water was transferred via surface pathways (i.e.
streams and wetlands) and possibly subsurface pathways
to the lake.
However, not all years fit the general pattern observed
in Figure 10. In some years (1971, 1979, 1994) the lake
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CHARACTERIZING HYDRODYNAMICS ON BOREAL LANDSCAPES
levels were more responsive than anticipated based on
the P PET data. This suggests that the storage capacity
was already near full due to precipitation in previous
years and/or that the timing of precipitation during the
course of the summer was more important than the
total seasonal precipitation. The daily precipitation record
during these years indicated that there was a 45 mm or
greater precipitation event in all of these years during
the month of June. In fact, from 23 to 26 June 1971,
115 mm of rain fell, making it the largest 4-day total over
the 35-year climate record. In contrast, lake levels during
2000 were less responsive than anticipated, suggesting
that the storage capacity was not reached within the
terrestrial system. The small hydrological response may
be explained by two exceptionally dry years (1998, 1999)
that preceded 2000, which had the effect of substantially
increasing the storage capacity. Scatter around the pattern
observed in Figure 10 may also be attributed to the fact
that PET does not represent actual evapotranspiration
(AET) well in all years. Devito et al. (2005b) showed
that, for a first-order catchment on the Boreal Plain, AET
matched PET fairly well in wet years but was less than
PET during dry years. In a regional basin where there
are relatively more open water areas, AET is more likely
closer to PET even in drier years.
The surface hydrological dynamics in the Utikuma
River drainage basin showed the following characteristics:
1. there was a general trend showing immediate responsiveness of wetland size and water depth in lakes to
climatic forcing;
2. there was evidence for a threshold in P PET ¾0 mm, below which vertical hydrological
fluxes were important and above which both vertical
and horizontal fluxes were important;
3. there was evidence for the importance of within-year
timing of precipitation (e.g. extreme rain events during
June or July) and/or among-year timing of drought
and flood years (e.g. multiple years of P < PET) that
resulted in changes to the amount of water stored on the
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landscape leading to time lags between climate forcing
and associated hydrological response.
Wetland connectivity
A time-series of maps of unsaturated, saturated or inundated areas of the Utikuma River drainage basin was generated from archived SAR imagery. These maps showed
a complex pattern, with saturated and inundated areas
distributed along topographic divides rather than around
streams and shorelines (e.g. Figure 11). This pattern suggested that surface hydrological dynamics may be regulated more by subsurface (e.g. stratigraphy) than surface
(e.g. topography) controls. The importance of substrate
control on the movement of water in this relatively flat
region was emphasized by Devito et al. (2005a), who
provided evidence for surface and subsurface water flow
that did not always follow topographic drainage patterns.
There was substantial variation in wetland connectivity as hydrological conditions changed from dry to wet
(Figures 11 and 12). The total number of wetland patches
increased initially, peaked when %WET was approximately 15%, and then decreased gradually as %WET
reached the limit of 35% (Figure 12). The connectivity
of these wetland patches (%WETCONN ) increased slowly,
and then more rapidly, suggesting that a threshold was
reached in the water storage capacity of the contributing land areas (Figure 12). From these landscape patterns
in wetlands, the following general observations can be
made. When climatic inputs were small (P − PET), patterns of wetlands were disorganized and disconnected
from lakes. In this dry state, there were few patches
(0Ð08 ha1 ) with small areas (¾1 ha). When climatic
inputs were balanced by outputs (P D PET), patterns of
wetlands were still disorganized but were distributed in
newly formed patches. Patch density peaked in this mesic
transition state (0Ð13 ha1 ). When climatic inputs were
large (P × PET), patterns of wetlands became organized
and connected to lakes. In this wet state, wet patches
were relatively few (0Ð10 ha1 ) but were large (¾4 ha).
The trends observed in Figure 12 suggested that
surface hydrological dynamics in the Utikuma River
Figure 11. Maps showing hydrological classifications of images acquired during dry, mesic and wet hydrological conditions (see Figure 1 for location
within Utikuma River drainage basin)
Copyright  2007 John Wiley & Sons, Ltd.
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G. Z. SASS AND I. F. CREED
Figure 12. Scatter plot showing relations between percentage wetland cover, number of wetland patches and percentage wetland connected to lakes
drainage basin have two dominant states: a dry, disconnected state, and a wet, connected state. Such dual systems have been observed in other environments (Grayson
et al., 1997; Cook, 2001; Western et al., 2001). Grayson
et al. (1997) reported that soil moisture patterns switched
between two preferred states in temperate regions of Australia. During the dry state, vertical fluxes of water dominated and the pattern of surface saturation was seemingly
random, whereas during the wet state, horizontal movement of water became more dominant and the pattern of
saturation became more organized (Grayson et al., 1997).
Similarly, Cook (2001) reported that vertical fluxes of
water dominate during dry periods, but temporary surface
connections between adjacent wetlands were observed
during wet periods in temperate regions of central North
America.
The surface hydrological dynamics on the Utikuma
River drainage basin have important implications for
the movement of nutrients. Lakes may receive relatively
little surface hydrological and biogeochemical inputs
from their catchments during the frequent dry conditions,
supporting findings from field investigations conducted in
small, low-order catchments on the Boreal Plain (Ferone
and Devito, 2004). That study suggested that most of
the surface hydrological action takes place in riparian
zones surrounding shallow lakes and wetlands. However,
lakes become more connected to wetlands within their
catchments and may receive larger hydrological and
biogeochemical inputs during the less frequent periods of
wet conditions (Devito et al., 2000; Evans et al., 2000).
We have assessed the surface hydrological dynamics in
a regional drainage basin; however, there may be large
sub-basin differences between climatic forcing and magnitude and organization of surface hydrological features
due to a variety of landscape factors, as hypothesized
by Devito et al. (2005a). For example, in groundwater
discharge zones, connectivity of wetlands may be more
stable over time than in areas with limited groundwater
upwelling. Current research is focusing on understanding
Copyright  2007 John Wiley & Sons, Ltd.
groundwater and surface water interactions in the dominant hydro-geological units of this region (e.g. Smerdon
et al., 2005).
Probability of wetland occurrence
Surface hydrological dynamics captured by archived
ERS imagery in the Utikuma River drainage basin were
synthesized to compute the probability of wetland occurrence. The computed probabilities of wetland occurrence
were based on the probability of observing wetlands
after the peak in the rainy season (late July and August)
between the years 1992 and 2004. However, as shown
in the ‘Monitoring the dynamics of surface hydrological
features using archived ERS imagery’ section, the years
of this study period captured the 35-year extremes in climatic forcing; therefore, the argument can be made that
the probability of wetland occurrence based on the nine
study years would be valid over a longer period.
A plot of %WET against the probability of occurrence indicated the probability of observing a certain
percentage of the landscape being covered with wetlands
(Figure 13). For example, at any given time the probability of observing 5% of the landscape being covered with
wetlands was 100%. The probability decreased as %WET
increased. There was a more drastic decrease in probability until %WET reached about 12%. This inflection
point reflected the transition zone in wetland reorganization when going from the dry state to the wet state
(Figure 12).
A map of probability of wetland occurrence exhibited a
pattern where most of the Utikuma River drainage basin
had a low probability of being a wetland (Figure 14a).
However, the eastern portion of the basin (dominated
by clayey soils and larger peatland complexes) showed
more areas with a higher probability of being a wetland
(Figure 14a). Figure 14b is an example of three small
lakes surrounded by an extensive peatland located on
the eastern side of the Utikuma River drainage basin.
The probability map showed that most of the areas
surrounding the lakes had a high probability of being
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CHARACTERIZING HYDRODYNAMICS ON BOREAL LANDSCAPES
Figure 13. Probability of wetland coverage during the relatively dry
late-summer period from late July to August between 1992 and 2004
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a wetland; however, the pattern was not uniform. The
areas of highest probability straddled topographic divides,
which illustrates that topography may not be the primary
control on patterns of soil wetness (Devito et al., 2005a).
More research is needed to identify the landscape controls
on this observed pattern.
The synthesis of surface hydrological dynamics into
probability maps provides a useful tool in identifying
parts of the landscape that are most susceptible to human
disturbance, such as saturated and inundated areas. The
identification of such hydrologically sensitive areas for
improved land management has received recent attention
(Walter et al., 2000; Agnew et al., 2006). Wolniewicz
et al. (2002) mapped the return period of surface saturation using radar imagery and used it to provide a
Figure 14. Maps of probability of wetland occurrence during the relatively dry late-summer period from late July to August between 1992 and 2004:
(a) entire drainage basin; (b) enlarged area identified in Figure 1
Copyright  2007 John Wiley & Sons, Ltd.
Hydrol. Process. 22, 1687– 1699 (2008)
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G. Z. SASS AND I. F. CREED
buffer design that was adaptive to the probability of formation of hydrologically sensitive areas. Accurate delineation of hydrologically sensitive areas may also facilitate
improved road placement and lead generally to ‘surprisefree’ forest operations (Tague and Band, 2001). Future
work needs to focus on how best to incorporate maps of
probability of wetland occurrence into forest management
plans on the Boreal Plain.
CONCLUSIONS
The Utikuma River regional drainage basin, located on
the Boreal Plain of northern Alberta, exhibited surface
hydrological dynamics that switched between two states:
a dry state characterized by a low aerial coverage of wetlands that were not well connected to lakes and a wet state
characterized by much higher aerial coverage of wetlands
that were well connected to lakes and other wetlands.
We mapped wetlands (areas of saturation and inundation)
over a 12-year period using ERS images acquired after
the peak in the rainy season. The mapping was based
on a simple classification where the boundaries between
the classes were calibrated by ground-based soil moisture
measurements. Analyses of the patterns of wetlands indicated that, during dry climatic conditions, wetland sizes
were small and disconnected from each other and receiving bodies of water. As climatic conditions changed from
dry to mesic, wetland numbers increased but were still
disconnected from the rest of the landscape. It required
very wet climatic conditions before the disconnected wetlands coalesced and connected to the lakes. During these
wet conditions, the response of the lake level at Utikuma
Lake was observed to be much higher than under drier
conditions. Analyses of individual wetland maps and
integrated probability maps may be useful in identifying not only important sites for land-to-lake horizontal
hydrological linkages, but also land-to-atmosphere vertical linkages. These maps have the potential to inform
future scientific studies, as well as management practices,
on the Boreal Plain.
ACKNOWLEDGEMENTS
Funding for this study was provided by an NSERC-CRD
grant to IFC for the Hydrology, Ecology, and Disturbance
(HEAD) research project in collaboration with Ducks
Unlimited Canada, Alberta-Pacific Forest Industries Inc.,
Weyerhaeuser Canada, Syncrude Canada Ltd, and Suncor
Energy Inc. Funding was also provided to GZS from
the Natural Sciences and Engineering Research Council
(NSERC) Industrial Postgraduate Scholarship and the
Ontario Graduate Scholarship—Science and Technology.
We gratefully acknowledge a grant from NASA for SAR
imagery through the ASF, without which this study would
not have been possible. We thank D. Aldred, S. Kelly,
and T. Cui, who provided invaluable assistance in the
field.
Copyright  2007 John Wiley & Sons, Ltd.
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