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\begin{document}

\title{The role of snow processes and hillslopes on runoff generation in
present and future climates in a recently constructed watershed in the
Athabasca oil sands region}



\author[1]{Kelly Biagi}%
\author[1]{Sean Carey}%
\affil[1]{McMaster University}%


\vspace{-1em}



  
  \date{April 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Mine reclamation in the Athabasca oil sands region Canada, is required
by law where companies must reconstruct disturbed landscapes into
functioning ecosystems such as forests, wetlands and lakes that existed
in the Boreal landscape prior to mining. Winter is a major hydrological
factor in this region as snow covers the landscape for 5 to 6 months and
is \textasciitilde{}25\% of the annual precipitation, yet few studies
have explored the influence of winter processes on the hydrology of
constructed watersheds. One year (2017-2018) of intensive snow hydrology
measurements are supplemented with six years (2013-2018) of
meteorological measurements from the constructed Sandhill Fen Watershed
to: 1) understand snow accumulation and redistribution, snowmelt timing,
rate and partitioning, 2) apply a physically-based model for simulating
winter processes on hillslopes and 3) evaluate the impact of soil
prescriptions and climate change projections on winter processes in
reclaimed systems. The 2017-2018 snow season was between November and
April and SWE ranged between 40-140 mm. Snow distribution was primarily
influenced by topography with little influence of snow trapping from
developing vegetation. Snow accumulation was most variable on hillslopes
and redistribution was driven by slope position, with SWE greatest at
the base of slopes and decreased towards crests. Snowmelt on hillslopes
was controlled by slope aspect, as snow declined rapidly on west and
south-facing slopes, compared to east and north-facing slopes. Unlike
results previously reported on constructed uplands, snowmelt runoff from
uplands was much less (\textasciitilde{}30\%), highlighting the
influence of different construction materials. Model simulations
indicate that antecedent soil moisture and soil temperature have a large
influence on partitioning snowmelt over a range of observed conditions.
Under a warmer and wetter climate, average annual peak SWE and snow
season duration could decline up to 52 \% and up to 61 days,
respectively while snowmelt runoff ceases completely under the warmest
scenarios. Results suggest considerable future variability in snowmelt
runoff from hillslopes, yet soil properties can be used to enhance
vertical or lateral flows.%
\end{abstract}%



\sloppy


\textbf{1. INTRODUCTION}

Surface mining in the Athabasca oil sands region (AOSR) of northern
Alberta, Canada, has disturbed \textgreater{}900 km\textsuperscript{2}
of forests and wetlands in the Western Boreal Plains (WBP), as entire
landscapes are removed and excavated up to 75 m to access the
bitumen-rich McMurray formation (Canada's Oil \& Natural Gas Producers,
2017). These mining activities have permanently altered pre-disturbance
vegetation communities, carbon storage capacity (Rooney, Bayley, \&
Schindler, 2012), soil layers (Nwaishi et al., 2015) and hydrologic
functioning of these natural systems (Price, McLaren, \& Rudolph, 2010;
Rooney et al., 2012; Trites \& Bayley, 2009). In addition to the
physical disturbance, excessive salts are ubiquitous in the post-mining
landscape from the tailings material that is used to backfill open pits
(Biagi, Oswald, Nicholls, \& Carey, 2019; Kessel, Ketcheson, \& Price,
2018; Kessler, Barbour, van Rees, \& Dobchuk, 2010; Simhayov et al.,
2017). As part of their legal requirements, oil companies must reclaim
disturbed landscapes into a mosaic of functioning ecosystems including
wetlands, peatlands, forests and pit lakes (OSWWG, 2008) however,
reclamation involves the complete reconstruction of these ecosystems,
which is complicated given the regional sub-humid climate, limited
research and knowledge, ubiquitous salinity and the unprecedented scale
of the disturbance (Daly et al., 2012; Elshorbagy, Jutla, Barbour, \&
Kells, 2005; Wytrykush, Vitt, Mckenna, \& Vassov, 2012). Currently, only
two watersheds that include peatlands have been constructed and
instrumented which vary considerably in design (Daly et al., 2012;
Ketcheson et al., 2016; Wytrykush et al., 2012).

~

The AOSR has a sub-humid climate which controls water availability
primarily through precipitation and evapotranspiration (Devito, Mendoza,
\& Qualizza, 2012). Most of the annual precipitation in the WBP occurs
during the growing season when evapotranspiration is highest, and excess
of soil water is minimal (Ferone \& Devito, 2004). Only
\textasciitilde{}25\% of annual precipitation falls as snow (Environment
Canada, 2019), yet snowmelt produces a large, but variable, surplus of
water in a relatively short amount of time before the growing season
when evapotranspiration is low (Devito, Creed, \& Fraser, 2005; Devito
et al., 2012). Autumn rainfall events and snowmelt are often the only
source to replenish groundwater stores depleted by evapotranspiration
during the previous growing season (Redding \& Devito, 2011; Smerdon,
Mendoza, \& Devito, 2008). Additionally, snowmelt water is a critical
source of freshwater to constructed watershed ecosystems that can have
highly saline waters with elevated sodium concentrations which originate
from the construction materials used in reclamation (Biagi et al.,
2019). Winter processes therefore play an important role in reclamation
hydrology as they influence snow storage, redistribution, melt and water
transmission (Price, 1987; Price \& Fitzgibbon, 1987; Rouse, 2000; Woo
\& Winter, 1993). Variations in blowing snow, sublimation, interception,
frozen ground and vegetation communities among landscapes impact snow
accumulation and melt regimes (Pomeroy et al., 2006, 1998; Price \&
Fitzgibbon, 1987; Spence \& Woo, 2003; Whittington, Ketcheson, Price,
Richardson, \& Di Febo, 2012; Woo \& Marsh, 2005; Woo \& Winter, 1993).
Soil ice formation is another critical component of winter hydrology as
frozen soil water is unavailable in the early growing season for
evapotranspiration (Devito et al., 2012; Van Huizen, Petrone, Price,
Quinton, \& Pomeroy, 2020). In addition, frozen ground can enhance
snowmelt runoff to lowlands that may rely on this seasonal input of
water prior to the growing season (Devito et al., 2012; Van Huizen et
al., 2020).

~

Surface overland flow typically makes up a small component of the annual
water balance in the undisturbed WBP due to the large storage capacity
of soils even when frozen (Devito et al., 2005, 2012; Ferone \& Devito,
2004; Redding \& Devito, 2011). At the regional scale, a recent study
reported that peatland-swamp ecosystems across the WBP are the primary
producers of runoff (3 -- 27 \% of annual precipitation) and
downgradient water transfer, while open-water wetlands and forestlands
act as water sinks (Devito et al., 2017). Drivers of runoff generation
are variable among WBP ecosystems and include snowpack depth (Devito et
al., 2005; Ferone \& Devito, 2004), concrete frost development (Redding
\& Devito, 2011), depth to confining layer and soil storage (Devito et
al., 2005), snowmelt rate, autumn antecedent moisture conditions (Ireson
et al., 2015; Redding \& Devito, 2011) and to a lesser extent,
vegetation canopy and soil type (Redding \& Devito, 2011). Surface
runoff also varies across the reclaimed landscape, where hillslopes with
low antecedent soil water content and the presence of macropores only
produces runoff once the high soil storage is exceeded (Kelln, Barbour,
\& Qualizza, 2009; Shurniak \& Barbour, 2002), while hillslopes designed
with a high antecedent soil moisture and low infiltration capacity (with
finer textures) produces a high volume of surface runoff during snowmelt
and heavy rain events (Ketcheson \& Price, 2016b, 2016a). Soil materials
used in reconstruction are harvested from the pre-mining landscape and
can include coarse textured, fine textured and veneer-type soils, all of
which represent different hydrologic response areas due to their unique
soil properties (Devito et al., 2012). These soils are prescribed to
reconstructed ecosystems based on desired hydrologic behaviour and
storage capacity of the system and therefore construction practice can~
play a large role in dictating surface runoff from hillslopes in
reclaimed watersheds in the AOSR (Kelln et al., 2009; Ketcheson \&
Price, 2016b; Shurniak \& Barbour, 2002).

~

Modelling hydrological processes in constructed landscapes, particularly
those that include wetlands, is challenging because natural peatlands
are complex heterogeneous systems that provide many key ecosystem
functions and are resilient to change through feedback mechanisms
(Waddington et al., 2014). Many studies have been successful at using
models to highlight and explain hydrological processes observed in WBP,
as well as potential ecosystem responses to disturbance or stress
(Hilbert, Roulet, \& Moore, 2000; Smerdon, Mendoza, \& Devito, 2007;
Thompson, Mendoza, Devito, \& Petrone, 2015). Modelling constructed
systems in the AOSR presents an additional challenge because of the
dynamic nature of these systems within the first several years of
development. There is no consistency of models used to represent
constructed systems in the AOSR, as models have been chosen and
developed to examine a specific process or operational questions. Of the
models used, many have focussed on subsurface water fluxes
(Carrera-Hern\selectlanguage{ngerman}ández, Smerdon, \& Mendoza, 2012; Dobchuk, Shurniak,
Barbour, Kane, \& Song, 2013; Huang, Barbour, \& Carey, 2015; Kelln,
Barbour, \& Qualizza, 2007; Kelln et al., 2009; Lukenbach et al., 2019;
Shurniak \& Barbour, 2002; Sutton \& Price, 2019) and/or inorganic
solute transport (Huang, Hilderman, \& Barbour, 2015; Kelln, Barbour, \&
Qualizza, 2008; Kelln et al., 2009) with little focus on
surface-atmosphere interactions, even though these are the dominant
water fluxes in the WBP (Devito et al., 2012; Strilesky, Humphreys, \&
Carey, 2017). There are limited studies that focus on overall system
performance that incorporate multiple components of the hydrological
cycle in addition to subsurface processes (Elshorbagy et al., 2005;
Elshorbagy, Jutla, \& Kells, 2007; Keshta, Elshorbagy, \& Carey, 2009),
but few have been conducted on constructed peatlands or focus
specifically on winter processes. ~An additional challenge in building
landscapes in the AOSR is the uncertainty associated with climate
change, as the WBP is expected to experience increases in temperature
and precipitation in the coming century (Ireson et al., 2015; Meehl et
al., 2007). We have a limited understanding of the role of climate
change on constructed systems in the AOSR that already face challenges
associated with water supply and quality. Such influences are not fully
understood but are important for reclamation and closure plans as they
could significantly impact the outcome of constructed systems in the
long-term.~

\par\null

Constructed ecosystems provide a unique opportunity to study hydrologic
functioning and development as these systems mature and develop in a
continuously changing landscape and will likely differ considerably from
their natural analogues (Elshorbagy et al., 2005). Despite the
importance of winter processes, few studies (Kelln et al., 2009;
Ketcheson \& Price, 2016b; Meier \& Barbour, 2002) have examined their
influence on system hydrology of constructed watersheds in the AOSR. In
addition, a range of soil prescriptions are used in reclamation and
snowmelt partitioning will vary greatly depending on the soil properties
which will influence water availability and down-gradient water transfer
prior to the growing season. As up to 4800 km\textsuperscript{2} of the
AOSR is suitable for surface mining, understanding how winter processes
influence system hydrology and water availability is critical for the
long-term success of current and future constructed ecosystems. To
address this, the objectives of this research are to use data from a
constructed upland-wetland watershed to: 1) quantify snow accumulation
and redistribution, melt timing, rate and partitioning, 2) apply a
widely used physically-based model for simulating winter processes and
runoff generation on upland hillslopes, and 3) evaluate the impact of
different reclamation soils and climate projections on winter processes.
This information will help guide future landscape construction practice
and provide information on the potential long-term importance of winter
processes in a changing climate.~

\textbf{2. SANDHILL FEN WATERSHED}

The Sandhill Fen Watershed (SFW) is a constructed peatland-upland system
in the northwest corner of East-In-Pit, a previously mined area
(1977-1999), and is part of Syncrude Canada Ltd.'s Base Mine (57°02'N,
111°35'W) which is approximately 42 km north of Fort McMurray, Alberta.~
This region is situated in the Western Boreal Plains ecoregion of
western Canada which is characterized by a cold and sub-humid climate
where mean annual potential evapotranspiration (607 mm) is greater than
mean annual precipitation (456 mm) (Environment Canada, 2019). Climate
normals (1971 -- 2010) for the Fort McMurray Airport indicates that most
of the annual precipitation falls as rain (342 mm) and the remainder
falls as snow (113 mm of water equivalent) with a regional average peak
snow depth of 31 cm (February). Winter temperatures are typically
coldest in January averaging -17.4 ± 5 \selectlanguage{english}@C and averages -12.1 \selectlanguage{ngerman}°C from
November to March. The annual mean temperature is 1 ± 1.3 \selectlanguage{english}@C
(Environment Canada, 2019).~

~

Over the course of four years (2009 -- 2012), East-In-Pit was filled
with 35 m of inter-bedded composite tailings and tailings sand layers
followed by a 10 m tailings sand structural cap as part of the
reclamation strategy. The SFW was then constructed on top of these
materials and is 52 ha total with a 17 ha fen wetland, 35 ha upland
including 20 ha of upland hillslopes (referred to as hummocks)
(Wytrykush et al., 2012). Due to the relatively low grade of the wetland
and upland areas, (0.1-0.5 \%), the hummocks were constructed to create
distinct groundwater recharge areas as well as add topographic variation
to the watershed.~ The hummocks were constructed of tailings sand that
was mechanically placed and capped with Pleistocene fluvial sand (see
Table 1 for specific soil properties). A peat-mineral mix was placed in
the lower upland areas while 0.5 m of clay followed by 0.5 m of donor
peat materials were placed in the wetland area. Details of watershed
design, construction materials and soil stratigraphy are outlined in
Biagi et al. (2019). The SFW was vegetated in 2011 with species native
to this region (Nicholls, Carey, Humphreys, Clark, \& Drewitt, 2016;
Wytrykush et al., 2012), but species composition has changed
considerably throughout the watershed with time (Vitt, House, \&
Hartsock, 2016). Unique to the SFW, inflow and outflow can be managed
via a pump system that was installed during construction. Fresh water
can be supplied to the Water Storage Pond (Figure 1) from a near-by
natural lake (Mildred Lake) which flows westward through the wetland
area towards the outlet where water can be pumped out of the SFW and
back into East-In-Pit. It should be noted that aside from deeper
groundwater flow paths, the outflow of surface and near-surface water
can only occur when the outflow pump is activated. The intention of
these pumps was to mitigate the elevated salinity levels from
construction materials by providing fresh water and flushing out saline
water in the first few years while the system established within the
disturbed landscape. These pumps have remained largely off since 2013
(Biagi et al., 2019; Nicholls et al., 2016).

\textbf{3. METHODS}

\emph{3.1 Meteorological Measurements}

Air temperature, windspeed and direction, relative humidity, short, and
long-wave radiation were measured at each of the three meteorological
towers (Figure 1) on SFW. Instrument details can be found in Nicholls et
al., (2016). Precipitation was measured with a tipping bucket rain gauge
(Model CS700, Campbell Scientific Inc. (CSI), Logan UT, USA) for
rainfall, a CSI \emph{CS725} to measure snow water equivalent (SWE) as
well as a CSI SR50A sonic ranger to monitor snow depth. All measurements
were recorded on an hourly basis with CSI CR1000 data loggers since
2013. In addition, one eddy covariance tower that was instrumented in
2013 measured turbulent fluxes year-round (Clark, Humphreys, \& Carey,
2019; Nicholls et al., 2016).

~

\emph{3.2 Snow Accumulation and Melt Measurements}

Intensive field measurements were made during the 2018 winter season
(November 2017 -- April 2018) and included snow survey transects
starting on 15 February 2018 throughout the different landscape units
within SFW including the wetland, uplands and hummocks (Figure 1). Snow
survey measurements included snowpack depth every 10 m using an
avalanche probe, snow water equivalent (SWE) every 30 m using a Mount
Rose corer and ground ice presence every 30 m using a metal rod. To
supplement SWE measurements, snow pits were completed in the upland and
wetland to quantify snowpack density using standard approaches. SWE
measurements on the hummocks were divided into slope position and aspect
to assess differences in snow accumulation patterns. Slope position was
assigned using visual observations and contour lines where the bottom
slope was within the first meter of the hillslope, the crest was the
entire flat portion at the top and mid-slope was the remainder of the
hillslope area in between. Slope aspect was assigned using the Aspect
tool in the Spatial Analyst toolbox in ArcGIS. All statistical analysis
of snow accumulation trends was completed using the R language for
statistical computing (R Core Team, 2018).

~

To quantify and partition snowmelt from hummock hillslopes, surface
runoff collectors were constructed on the north- and south-facing slopes
that drained into v-notch bucket weirs with a pressure transducer.
Runoff collectors, modified from Ketcheson and Price (2016b), were
constructed prior to melt by digging shallow trenches
(\textasciitilde{}15 cm) that extended out from a bucket weir in a ``V''
shape where each arm of the ``V'' was approximately 3-4 m. Flexible,
plastic garden edging was sealed into the bottom of the shallow trenches
using hydraulic cement to ensure meltwater could not flow across the
trenches. At the base of the ``V'', a plastic eavestrough was used to
direct surface runoff into the bucket weirs and was also sealed with
hydraulic cement to limit leakage (Figure 2). Large patches of ground
that were cleared for runoff collector construction were filled with
snow after construction was completed to limit any change to ground
surface albedo. A pressure transducer was installed in each bucket weir
to continuously measure water level and discharge during melt. Discharge
measurements for each bucket weir were made to create independent rating
curves for each runoff collector. A one-meter resolution LiDAR digital
elevation model (DEM) of the study area was used to determine the
drainage direction and~delineate the~contributing area for each runoff
collector using the Hydrology toolset in ArcGIS. Calculated contributing
areas of runoff collector one (RC1) and two (RC2) were 331
m\textsuperscript{2} and 160 m\textsuperscript{2}, respectively (Figure
1). The horizontal and vertical accuracy of the LiDAR data was 30 cm and
15 cm, respectively at 95\% confidence through comparison to
independently surveyed ground points.

~

Snow surveys were also completed on older reclaimed sites on the
Syncrude Canada Ltd. property that are in more advanced stages of
regrowth to compare snow accumulation and melt patterns using the same
methods as above. Sites were within 5 km from the SFW and included two
mature reclaimed forests, South Bison Hill (\textasciitilde{}20-year-old
mature aspen/white spruce stand) and Jack Pine
(\textasciitilde{}30-year-old jack pine stand) have delayed melt
compared with the newly reclaimed Coke Beach site
(\textasciitilde{}10-year young aspen) and the SFW (\textasciitilde{}6
years).

~

\emph{~3.3 Soil parameters}

Soil properties of hummocks have been measured as part of the active
research at the SFW (2012 -- 2018) and are summarized in Table 1. Both
in-situ field and laboratory methods were used to estimate soil
properties. Soil pits were constructed on hummocks to collect samples at
various depths to capture all soil materials used in hummock
construction. Saturated hydraulic conductivity (Ksat) values are
averaged values from several data collection methods including in-situ
using the Guelph Permeameter and single ring infiltrometer as well as
laboratory methods using the KSAT and Hyprop instruments from METER
Group Inc. Ksat estimation from the Guelph Permeameter followed standard
procedures (Soilmoisture Equipment Corp., 2012). Single ring
infiltrometers were used to estimate field Ksat and infiltration
capacity. Single ring infiltrometers were installed into the ground
surface to depths of at least 1 cm and constant head tests were
conducted until a steady state infiltration rate was reached. Ksat was
assumed to be equal to the steady state infiltration capacity reached
during each test. Porosity is automatically calculated using the Hyprop
software and specific yield can be estimated from the Hyprop data by the
difference between the saturated volumetric water content (VWC) and the
VWC at 330 mb which is equivalent to the field capacity of the sample.
Samples were analyzed for bulk density using standard methods (Freeze \&
Cherry, 1979) with the exception that the samples were oven-dried at 80
\selectlanguage{ngerman}°C to limit any loss of organic matter. Organic matter content was
determined via loss on ignition (LOI) where samples were placed in a
muffle furnace at 550 °C for four hours. LOI was calculated as the
difference between the pre- and post-muffle furnace weight divided by
the pre-muffle furnace weight. The remainder of soil properties in Table
1 were extracted from NorthWind Land Resources Inc. (2012).

\emph{~}

\emph{3.4 Model setup and parameterization} ~

The cold regions hydrological model (CRHM) (outlined in Pomeroy et al.,
2007) provides a platform to assess potential hydrological responses of
constructed peatlands in the AOSR as it continues to develop and in
response to future climate change. CRHM is a physically based
hydrological model that can simulate hydrological processes in a modular
fashion and is particularly strong in representing winter hydrological
processes (Pomeroy et al., 2007). Modules within CRHM are selected by
the user based on what simulated processes are needed which cover a wide
range of hydrological processes. CRHM can assign linked algorithms that
simulate hydrological processes to different hydrological response units
(HRUs) and can route water between HRUs through pathways such as blowing
snow, overland flow, groundwater flow when specific thresholds for that
unit are exceeded. This unique feature allows HRUs to be a series of
cascades across the landscape (Pomeroy et al., 2007; Rasouli, Pomeroy,
Janowicz, Carey, \& Williams, 2014). Because CRHM is physically based
and is a purpose-built model based on process understanding, typical
calibration methods used in many other models are omitted. Divergence
between model and observations are diagnosed based on process
understanding and model structures changed until suitable dynamics are
achieved (Cordeiro, Wilson, Vanrobaeys, Pomeroy, \& Fang, 2017; Rasouli,
Pomeroy, \& Whitfield, 2019). CRHM has been successfully applied to a
variety of catchments including the Canadian Prairies (Fang \& Pomeroy,
2007, 2010; Fang et al., 2010; Shook, Pomeroy, Spence, \& Boychuk,
2013), agricultural catchments (Cordeiro et al., 2017), arctic regions
(Krogh \& Pomeroy, 2018; Quinton \& Baltzer, 2013; Quinton, Bemrose,
Zhang, \& Carey, 2009; Quinton, Carey, \& Goeller, 2004), peatlands
(Knox, Carey, \& Humphreys, 2012; Quinton \& Baltzer, 2013), and
mountainous regions (Ellis, Pomeroy, Brown, \& MacDonald, 2010; Fang et
al., 2013; Pomeroy, Fang, \& Ellis, 2012; Weber et al., 2016).~

~

Several physically based modules were used (detailed in Pomeroy et al.
(2012) and Fang et al. (2013)) to examine the hydrological controls on
winter processes in the SFW and include the following:

1. Observation module: imports and reads observed meteorological data
which include continuous, hourly time-steps of air temperature,
precipitation, relative humidity, wind speed, incoming short-wave
radiation and incoming longwave radiation which are used as forcing data
to drive other CRHM modules.~

2. Radiation module: global radiation, direct and diffuse shortwave
radiation based on site latitude, elevation, slope and azimuth (Garnier
\& Ohmura, 1970). Radiation from this module is used in the sunshine
hour module, energy budget snowmelt module and net all-wave radiation
module.

3. Sunshine hour module: used shortwave radiation and maximum sunshine
hours to estimate total sunshine hour. Estimates from this module are
used in the energy-budget snowmelt module and et all-wave radiation
module.~

4. Slope correction for the shortwave radiation module: the incoming
shortwave radiation at a level surface to estimate the incident
shortwave radiation on a slope. The module uses the measured incoming
shortwave radiation from the observation module as well as the
calculated direct and diffuse solar radiation from the radiation module
to calculate the adjustment ratio for the shortwave radiation on a
slope.~

5. Longwave radiation module: uses the measured shortwave radiation to
estimate incoming longwave radiation (Sicart, Pomeroy, Essery, \&
Bewley, 2006), which is used in the energy-balance snowmelt module.

6. Albedo module: snow albedo is estimated for the duration of winter as
well as the melt period. This module also indicated the beginning of
melt which is used in the energy-balance snowmelt module.

7. SnobalCRHM: designed for deep alpine snowpacks (Marks, Domingo,
Susong, Link, \& Garen, 1999), SnowbalCRHM simulates the mass and energy
balance of the snowpack to estimate snowmelt by calculating the energy
balance of radiation, sensible and latent heat, ground heat flux,
advection from rainfall and change in internal energy for two layers of
the snowpack (an active top layer and a lower layer).

8. frozenAyers: uses Ayers (1959) infiltration to estimate unfrozen soil
infiltration and Zhao \& Gray (1999) to estimate frozen soil
infiltration and subsequent surface runoff. The soil moisture balance
module is linked to both infiltration algorithms. Surface runoff occurs
when snowmelt or rainfall exceeds the infiltration rate.~

~

CRHM was initially set up for the 2018 winter season using detailed
observations outlined above as one hummock HRU to evaluate snow
accumulation and movement of meltwater from hillslopes to the lowlands.
The lowlands were not simulated. Parameters were selected from direct
observation where possible, and a suitable model performance was
obtained (Table 2). CRHM was then run for the five previous years of
observation to provide simulations for five winters under current
climate conditions (T0-P1). Following this, CRHM was used to evaluate
the potential impacts of climate change on these five years. Nine future
climate scenarios for the WBP were selected from the Intergovernmental
Panel on Climate Change report (Meehl et al., 2007) and applied to CRHM
in a delta-change approach though systematically changing temperature,
precipitation, and both, based on ensemble averages. In CRHM,
temperature is an additive change while precipitation is a
multiplicative change that only affects days with existing precipitation
(a value of 1 represents current precipitation conditions). Model
results are grouped based on increases in temperature (T) and
precipitation (P) from their baseline temperature (0 °C), and
precipitation (a value of 1 indicates no change in precipitation).
First, air temperature was increased for 5 years of winter simulation by
2, 4 and 6 °C (T2-P1, T4-P1 and T6-P1, respectively). Second, under
current temperature, days with precipitation had volume increases by
factors of 1.15, 1.2 and 1.3 (T0-P1.15, T0-P1.2 and T0-P1.3,
respectively), based on the IPCC predicted range of increased future
precipitation (Meehl et al., 2007). Finally, to assess the impacts of
both a warmer and wetter future, precipitation was increased by a factor
of 1.2 in addition to the three temperature change scenarios (T2-P1.2,
T4-P1.2 and T6-P1.2). CRHM was also used to test the influence of
different soil parameters on the partitioning of snowmelt (under current
climate conditions only). Soil parameters within the frozenAyers module
that were evaluated were soil temperature at the onset of snowmelt in
the top 40 cm, the initial soil saturation (VWC/porosity), and soil
texture. Values were taken from the observed variability in the six
years of data and directly measured soil physical properties.

\par\null

\textbf{4. RESULTS}

\emph{4.1 Snow accumulation and distribution}

The average peak watershed SWE was 89 ± 17 mm (n=278) during the
2017-2018 winter season with small but significant differences among
landscape units determined by a simple Wilcoxon rank sum test. The
wetland, uplands and hummocks had average peak SWE of 94 ± 13 mm, 90 ±
16 mm and 85 ± 20 mm, respectively (Figure 3a), where the hummocks
exhibited the most variability as a result of slope position and aspect
(Figure 3b). Only the hummocks and uplands were statistically similar to
one another (p=0.07). The smallest snowpack was observed on the crest of
the hummocks (71 ± 16 mm), followed by the mid-slope (88 ± 17 mm) and
lower-slope (94 ± 26 mm) positions. The crest was significantly
different than the mid and lower-slope positions (p\textless{}0.05)
while the mid and lower slopes were similar (p=0.3). SWE accumulation
and distribution were statistically similar among aspects
(p\textgreater{}0.05) except between north and east-facing slopes
(p=0.04). Average SWE on south-, east-, north- and west-facing slopes
averaged 91 ± 22 mm,99 ± 17 mm, 84 ± 17 mm and 90 ± 17 mm, respectively.

~

\emph{4.2 Snowmelt and Melt Partitioning}

The SWE of all landscape units remained relatively stable until 12 March
when SWE began declining during the first melt event which continued to
decline to mid-April when the second (and final) melt event depleted the
remainder of the snowpack (Figure 4). SWE decline was similar among all
sites until the first melt event on 12-15 March. Once this melt started
in mid-March, the hummock SWE was the most variable (Figure 4c) as melt
among aspects was considerably different, where the south and
west-facing slopes had faster melt rates than the north and east-facing
slopes (Figure 4d). West-facing slopes had the largest SWE decline of
\textasciitilde{}25 mm whereas all other sites had a SWE decline \selectlanguage{english}[?] 10
mm. While no snow surveys were conducted between 15-March and 9-April,
SWE decline was the largest during this time period for all sites which
can be attributed to the warmer temperatures in mid-April as
temperatures remained well below zero for the remainder of March. The
second melt event began mid-April when air temperatures were above zero
during most days. ~The snowpacks on the south and west-facing slopes
lost the majority of SWE by the second melt event in April whereas the
remainder of the sites had approximately half of their snowpack left
during the second melt event (12-24 April) (Figure 4).

~

To evaluate meltwater partitioning from hillslopes to the wetland area,
two runoff collectors were constructed on the north- and south- and
facing slopes (Figure 1).~ Snowmelt on the north-facing slope was not
measured in March as it was not fully constructed however, the snowpack
stayed deep and experienced minimal melt during the mid-March warm
period. Snowmelt began on the south-facing slope on 15 March, with
instantaneous discharge peaking the next day at \textasciitilde{}190
cm\textsuperscript{3}/s and total cumulative runoff reaching 10 mm in
two days (Figure 5a). Following this, runoff occurred from the
south-facing slope for the next five days between
\textasciitilde{}10:30-17:30 each day and in total 15 mm was collected
before this hillslope was snow-free on 23 March. In contrast, the
north-facing slope did not begin to generate runoff until daily air
temperatures were consistently near zero in mid-April. Snowmelt began on
12 April with peak instantaneous discharge of 79 cm\textsuperscript{3}/s
on 14 April (Figure 5b). The first several days of melt were the most
productive, yielding between 2 and 5 mm over the first four days with a
shorter duration of daily melt (typically four hours in duration through
mid-day). Runoff then declined as the slope became gradually snow-free
by 24 April and yielded a total of 23 mm for the entire period. While
there were no runoff collectors for upland sites, on-site observations
confirm that surface runoff was only generated from hummocks as opposed
to low-gradient areas surrounding the lowland.

~ \emph{~}~

The average SWE on the south- and north-facing slope prior to melt was
50 mm and 90 mm, respectively and total surface runoff totaled 15 mm and
23 mm, respectively and their corresponding runoff ratios were 0.31 and
0.26, respectively. Sublimation during the two melt periods was
calculated from the eddy-covariance derived latent heat data and
amounted to 0.44 mm from 15-21 March (south-facing melt) and 2 mm from
12-24 April (north- facing melt). Infiltration into the frozen ground
was calculated as the residual after accounting for runoff and
sublimation which was 34 mm and 65 mm for the south- and north-facing
slopes, respectively. When all hillslopes feeding the wetland are
considered and measurements are scaled, snowmelt surface runoff
contributed \textasciitilde{}11 mm to the wetland area of SFW.

~

\emph{4.3 Model Simulation}

\emph{4.3.1 Influence of soil parameters on snowmelt partitioning.}

To evaluate the role of soil properties and antecedent conditions on
runoff generation from the hummocks, parameters within the frozenAyers
module, which is derived from the parametric equations of heat and mass
transfer in Zhao and Gray (1999), were adjusted based on field and
laboratory observations. While the sensitivity of melt partitioning
largely reflects parameter assignments in the model equations, it
provides guidance on the expected influence of soil conditions on runoff
generation from hillslopes based on the ranges of observed values.

~

Three key parameters that influence frozen soil infiltration are soil
texture, the degree of initial soil saturation and ground temperature.
Soil texture is important in a reclamation context as different textures
are used in various landforms which will eventually be integrated into
the closure landscape. The influence of texture on the partitioning of
snowmelt is complex as liquid and frozen water influence capillary
pressure and permeability. There also exists a relation between texture
and soil temperature due to unfrozen water content and latent heat
effects. Soil texture was changed within CRHM to silt and clay from the
base 2018 winter simulation (sand, 34 mm of runoff).~ As expected, finer
soil texture resulted in greater runoff, with silt loam (37 mm) and clay
(41 mm) being 8 and 17 \% greater than sand, respectively. It is
important to note that degree of soil saturation was not adjusted
(Si=0.2), and changes in runoff are from textural differences alone.
However, it is expected that finer textured soils would have greater
antecedent wetness and therefore greater runoff.

~

The degree of initial soil saturation (Si), the upper soil volumetric
water content at freezeback divided by porosity, influences runoff as
soils with greater pore space filled with frozen water restrict
infiltration and promotes runoff. In fall 2017, soils froze in late
October at a relatively low moisture content in the near surface profile
(VWC \textless{} 5 \% and Si = 0.20). This moisture content was the
lowest of any years observed, yet pre-freezeback values as high as 0.5
occurred in some years. As with soil temperature, Si was varied within
the range of the observed six-year record for the 2018 winter case.
Runoff increased approximately linearly with increasing Si, and results
indicate that within the range of observation, pre-freezeback soil
moisture can more than triple the expected runoff for a given year
(Table 3). For every 5 \% increase in Si from 0.2, runoff increased
\textasciitilde{}12 mm with slightly greater increases when dry. It is
important to note that 2018 was a high snow year with low initial Si.

~

Zhao and Gray (1999) use temperature within the top 40 cm at the onset
of melt along with a nighttime refreezing parameter to establish the
influence of soil thermal status on infiltration. The initial soil
temperature (top 40 cm) during 2018 was -2 \textsuperscript{o}C, which
was used to run all climate change scenarios in Section 4.3.1. However,
between 2013 and 2018 soil temperatures at the onset of melt were as low
as -8 \textsuperscript{o}C within the top 40 cm, which reflected cold
years with limited snow. The 2018 winter simulation was run with
temperatures from -0.5 \textsuperscript{o}C to -8 \textsuperscript{o}C,
and results indicate a strong influence of temperature on runoff
generation (Table 3). Runoff generation was zero at temperatures warmer
than -1.5 \textsuperscript{o}C and increased in a negative exponential
manner with declining temperatures and at -8 \textsuperscript{o}C, 94 mm
of runoff was simulated compared with 34 mm as the base case.

~

\emph{4.3.2 Climate Change Scenarios}

To test the accuracy of CRHM against observed data, simulations were set
up under current climate conditions (T0-P1) for snowpack SWE from
2013-2018 (Figure 6a) and surface runoff during the 2017-2018 winter
(Figure 6b). Surface runoff data only exists for 2017-2018 as the runoff
collectors were installed in March 2018. In general, CRHM's simulations
overestimated peak snowpack SWE, total surface runoff and underestimated
mid-winter melt events (Figure 6). Annual peak simulated SWE was higher
by an average of \textasciitilde{}30 mm over simulated years, but the
timing of snow accumulation and the start/end of the snow season is
consistent with the observed data (Figure 6a) and over all simulated
years matched the observed data within 10 days. Simulated surface runoff
did not match observed runoff in terms of timing as simulated runoff
occurs at the very end of the snow season while observed runoff had two
distinct melt periods (Figure 6b). While total observed runoff (15 and
23 mm) was less than simulated runoff (34 mm), snowmelt partitioning
between infiltration and runoff were similar based on the calculated
runoff ratios of 0.3 and 0.2, respectively. Observed and simulated total
runoff may differ between because by the time the runoff collectors were
installed, some of the snowpack had already melted, yielding a smaller
total runoff. ~Additionally, CRHM underestimates the magnitude and
duration of mid-winter melt events in all simulated years. For example,
CRHM simulated an 8 mm melt event over five days in 2018 compared to
observed where the mid-winter melt event caused snowpack SWE to decrease
by 83, 45 and 51 mm, respectively for stations 1, 2 and 3 over
\textasciitilde{}12 days. It is unclear as to why this underestimation
occurred, although it may be due to different locations between the SWE
sensors and the meteorological data used to drive the model. Note that
CRHM was not calibrated but driven by observation data and measured soil
parameters (Table 1 and 2).

~

Modelled increases in temperature without an alteration in precipitation
resulted in an expected reduction in simulated SWE, a shorter snow
season and longer and larger mid-winter melt events (Figure 7a). Over
the simulated years in scenarios T2-P1, T4-P1 and T6-P1, peak snowpack
SWE decreased by an average of 21, 35 and 42 \%, respectively (Table 4),
where deviation from current conditions (T0-P1) was amplified in winters
with a higher peak SWE (Figure 7a). Duration of the snow season
decreased by averages of 10, 37 and 61 days, respectively, some of which
were the result of mid-winter melt that eliminated the entire snowpack
by January or February in low snow years (i.e. 2013-2014, Figure 7a).
The presence of mid-winter melt events is an emergent process with
warming as its timing moves successively earlier into the winter season.
Under these warming scenarios, average mid-winter melt duration
increased from 5 days under T0-P1 to 7, 14 and 11 days, respectively for
scenarios T2-P1, T4-P1 and T6-P1, respectively. Average snowmelt volume
during the mid-winter melt also increased from 12 mm under T0-P1 to 26,
39 and 43 mm, respectively, all of which infiltrated into the frozen
ground. Spring snowmelt runoff was small for all warming-only scenarios
and decreased from a 5-year average of 11 mm under current conditions to
8, 1 and 0 mm, respectively for each of the two-degree temperature
increases (Figure 8a) which decrease the average runoff ratio from 0.38
to 0.1, 0.01 and 0, respectively (Table 4).

~

Simulated increases in daily precipitation without changes in
temperature increased peak SWE all years by averages of 12, 28 and 36 \%
in scenarios T0-P1.15, T0-P1.2 and T0-P1.3, respectively compared to
initial conditions (Figure 7b). Snow season duration is similar to
initial conditions and only increased by an average of 7, 8 and 8 days,
respectively among precipitation scenarios. Unlike the temperature
scenarios, there are no distinct mid-winter melt events among years and
snowpack SWE continues to increase until the spring snowmelt event.
Surface runoff of spring snowmelt increased from the 11 mm average for
current conditions greater daily precipitation, and averaged 20, 25 and
29 mm, respectively (Figure 8b) which kept runoff ratios similar to
current conditions (0.2) and averaged 0.17, 0.18 and 0.21, respectively
(Table 4).

~

An increase of both temperature and precipitation slightly lessened the
effect of climate change on snowpack SWE while surface runoff exhibited
the most variability among all climate scenarios. Peak SWE and snow
duration under T2-P1.2 are similar to initial conditions but decrease
considerably with continued warming with T4-P1.2 and T6-P1.2 (Figure
7c). Average peak snowpack SWE and snow season duration both decreased
by an average of 0.2, 24 and 32 \%, respectively and by an average of 1,
17 and 61 days, respectively in scenarios T2-P1.2, T4-P1.2 and T6-P1.2
(Table 4). Mid-winter melt events become increasingly prominent with
warming and mid-melt duration increased to 6, 12 and 14 days from 5 days
under T0-P1. Mid-winter melt volumes also increased under these
conditions to 19, 40 and 44 mm, respectively from 5 mm under T0-P1.
Surface runoff from end of season snowmelt increased initially in
scenario T2-P1.2 to 20 mm from 11 mm in T0-P1, but then decreased to 10
and 3 mm in scenarios T4-P1.2 and T6-P1.2, respectively (Figure 8c).
Runoff ratios were maintained at T2-P1.2 and averaged 0.21 but decreased
with continued warming to 0.16 and 0.03, respectively indicating that
more meltwater infiltrates under warmer scenarios. A summary of the CRHM
simulations are in Table 4 but detailed results from the climate change
simulations can be found in Table A1.

\par\null

\textbf{5. DISCUSSION}

At present, two watersheds in the AOSR inform much of our understanding
of hydrology (Ketcheson et al., 2017; Nicholls et al., 2016; Spennato,
Ketcheson, Mendoza, \& Carey, 2018), carbon dynamics (Clark et al.,
2019; Clark, Humphreys, \& Carey, 2020) and water quality (Biagi et al.,
2019; Kessel et al., 2018; Simhayov et al., 2017) for integrated
constructed ecosystems. With only two examples over a relatively short
time period, it is uncertain as to how representative these systems are
and whether early findings can be used to evaluate performance and help
guide future design. While ecosystems are built for long-term
sustainability, they are dynamic and rapidly changing and it is expected
that their hydrological behaviour will evolve in response to changes in
vegetation, soil properties, water quality and climate. Examination of
winter processes in constructed ecosystems is scarce (Kelln et al.,
2009; Ketcheson \& Price, 2016b; Meier \& Barbour, 2002), despite five
months of temperatures below freezing. In this work we used detailed
field observations combined with a purpose-built hydrological model to
better understand how ecosystem properties and a changing climate
influence hydrological fluxes.

\par\null

\emph{5.1 What controls patterns of snow accumulation and melt on SFW?}

The greatest SWE variability arose from differences in slope position on
the hummocks (Figure 3), where snow accumulated at the bottoms of the
hillslopes and decreased towards the crest. The base of hillslope
accumulates blowing snow from the relatively flat wetlands and uplands,
whereas the crest of the hummocks are exposed to wind erosion as well as
enhanced sublimation (Pomeroy \& Essery, 1999). SWE distribution among
aspects showed no significant difference (p\textgreater{}0.05), as wind
direction was variable throughout the winter (data not shown). SWE
decline was similar between the wetland and upland areas (Figure 4a and
b) as both have a relatively flat terrain and receive similar amounts of
solar radiation throughout the day. Melt was most variable on the
hummocks as slope aspects are exposed to different irradiances and
south-facing slopes melted earlier and faster than north-facing slopes
(Figure 4).

~

Vegetation often exerts a strong control on snow accumulation and melt
patterns as blowing snow from surrounding landscapes are trapped by
shrubs and trees plus snow interception on their canopies (Boon, 2012;
Pomeroy et al., 2006, 1998). However, due to the relatively young age of
the SFW, topography was the dominant control on snow accumulation and
melt as the vegetation is in the early stages of development and has
similar height and density across landscape units, despite species
differences. This is common among more recently reclaimed ecosystems
(Ketcheson \& Price, 2016b) which are considerably younger than their
natural analogues and require decades for their vegetation to develop
fully. As vegetation emerges, increased roughness will lower the
influence of wind on scouring and enhance snow trapping via decreased
turbulence. However, direct interception may lower snow accumulation due
to enhanced sublimation. From a melt perspective, vegetation reduces
shortwave radiation to the surface yet enhances long-wave fluxes to the
snowpack, thus altering the snowmelt radiative regime (Pomeroy et al.,
2009). While aspect plays an important role on how vegetation influences
melt (Ellis \& Pomeroy, 2007; Ellis, Pomeroy, Essery, \& Link, 2011), as
the canopy closes melt will be delayed further into spring
(Dickerson-Lange et al., 2017). This influence can be observed by
comparing SFW with other nearby reclamation landscapes in more advanced
stages of regrowth (Figure 9). Two mature reclaimed forests, South Bison
Hill (\textasciitilde{}20-year-old mature aspen/white spruce stand) and
Jack Pine (\textasciitilde{}30-year-old jack pine stand) have delayed
melt compared with the newly reclaimed Coke Beach site
(\textasciitilde{}10-year young aspen) and the SFW (\textasciitilde{}6
years).~ Both sites with tall vegetation had deeper snowpacks that
persisted later into April and were less susceptible to early melt.
However, the actual snow-free dates were remarkably similar.

~

\emph{5.2 The importance of hillslopes on snowmelt runoff generation}

The snowmelt period is a critical input of freshwater available to WBP
ecosystems, including constructed systems, which rely heavily on this
input to replace storage deficits for the upcoming growing season as
well as persist in a climate with a long-term water deficit (Devito et
al., 2012). Snowmelt also offers an important input of freshwater to
dilute the elevated salinity and sodium concentrations that are
ubiquitous within the reclaimed landscape (Biagi et al., 2019; Kessel et
al., 2018). Reconstructed landscapes need to balance between optimizing
soil storage for vegetation productivity while providing sufficient
water to surrounding landscapes. The hillslope hummocks in the SFW were
initially designed to provide groundwater to the adjacent lowlands via
recharge and were not expected to produce much surface runoff. The
hummocks have no confining soil layer at depth and most of its meltwater
infiltrates and percolates downwards. Groundwater flow is the primary
delivery of water to the lowlands (Lukenbach et al., 2019), whereas
surface runoff is limited to snowmelt periods and potentially extreme
rain events.

~

While total surface snowmelt runoff was similar on both south and
north-facing slopes (15 mm and 23 mm, respectively), their timing and
rates differed where south-facing slopes melted much earlier and faster
(Figure 5). The increased air temperature and radiation in mid-March
induced melt-generated runoff on the south-facing slope, yet
north-facing slope melt was delayed and no runoff occurred until
mid-April when conditions remained warm and a rapid decline in snowpacks
were observed (Figure 5). The SFW hummocks are constructed from Pf sand
and tailings sand materials which have average hydraulic conductivities
of 1.1 x 10\textsuperscript{-4} m/s and 6.7 x 10\textsuperscript{-4} m/s
and an average unfrozen infiltration capacity of 1.8 x
10\textsuperscript{-4} m/s (Table 1). These hummocks remain unsaturated
all year as water infiltrates and percolates downwards to recharge
groundwater at depth (Lukenbach et al., 2019), which can result in low
antecedent moisture contents (VWC \textless{}5 \% on Nov 1, data not
shown) prior to freeze-up throughout the upper soil column. However,
this varied over the five years of observation. In 2018, these soils
maintain a relatively high infiltration capacity when frozen and exceed
the maximum melt rate on both the north and south-facing slopes (5.8 x
10\textsuperscript{-7} and 3.7 x 10\textsuperscript{-7} m/s,
respectively) therefore partitioning most of the snowmelt water as
infiltration. Surface runoff ratios at SFW (\textasciitilde{}0.3) were
much less than those reported by Ketcheson and Price (2016) (0.7-0.9).
The hillslopes on Nikanotee fen by comparison, have finer soil textures,
considerably higher moisture content overall, and low surface
infiltration capacities (Ketcheson \& Price, 2016a), resulting in much
higher surface runoff during spring snowmelt (Ketcheson \& Price,
2016b).

~

Using CRHM, a well-established platform for simulating winter
hydrological processes, the influence of soil properties on the
partitioning of meltwater was evaluated. Soil texture alone had only a
modest influence, increasing runoff as texture became finer. However,
this is complicated by differences in antecedent moisture that would
typically accompany finer textures. As antecedent soil wetness
increased, melt generated runoff increased as snowmelt infiltration
became more restricted (Table 3). Progressively finer textures will
increase runoff, an important implication for landscape design and an
observation which reconciles results presented here and those of
Ketcheson and Price (2016a). Surprisingly, soil temperature at the onset
of melt had a large influence on limiting infiltration. In 2018, soils
were relatively warm (-2 \textsuperscript{o}C) under a deep snowpack,
and simulations suggest that if soils were colder, considerably more
runoff would have been generated (Table 3). However, a negative feedback
exists as deeper snowpacks that can generate more runoff insulate soils
(enhancing infiltration), whereas thin snowpacks with less potential for
runoff generation would have colder soils, enhancing runoff. While
texture can be used to directly enhance or reduce melt runoff, a subtle
relationship exists that provides for a large range of runoff ratios
based on the combination of SWE and soil temperature.~ From an
operational perspective, upland hillslopes can be designed using
textural classes to either enhance or limit direct runoff from
hillslopes to provision lowland and wetland systems with water. However,
model results suggest a large range of variability driven by moisture
content and temperature, even within coarse textured soils that were
expected to produce little snowmelt runoff. As vegetation on uplands
grows, snow accumulation may slightly increase, and melt will be
delayed. Furthermore, vegetation will increase soil macro-porosity and
infiltration capacity, reducing runoff potential.

\par\null

\emph{5.3 What is the influence of climate change on winter processes?}

While the WBP is expected to be warmer and wetter with the influence of
climate change, an increase in evapotranspiration could lead to an
overall drier environment (Ireson et al., 2015; Thompson, Mendoza, \&
Devito, 2017). CRHM simulations for winter indicate a decrease in snow
season length, runoff, peak SWE and an increase in the presence and
magnitude of mid-winter melt events. While it suggests that the increase
in precipitation by a factor of 1.2 offsets the increase in temperature
by 2 \textsuperscript{o}C, further warming clearly decreases the
snowpack SWE and shortens the snow season and therefore more of the
annual precipitation is delivered as rain instead of snow. Mid-winter
melt events are increasingly prevalent under warming scenarios and are
expected to become more common in reconstructed systems and may result
in the complete loss of the snowpack in drier years when the snowpack is
small. Surface runoff is expected to decrease considerably with
increased warming and may cease under the warmest scenarios. However,
the role of potentially colder runoff-enhancing soils with decreasing
snowpacks was not captured in the model. With a smaller snowpack, less
meltwater is partitioned as runoff, which may increase soil and
groundwater recharge. The influence of climate change may be
underestimated by CRHM, as results indicate that simulations
overestimate SWE and to a lesser extent runoff compared to observed
data. As a result, actual snowpack SWE, runoff and mid-winter melt
events with the onset of climate change could be enhanced compared to
simulated results in this study. While an increase in precipitation
(delivered as rain) may offset the effects of a lengthened growing
season and increase in evapotranspiration, it is expected that the water
balance of these systems will be impacted over time. The influence of
climate change on SFW combined with expected changes in vegetation and
soil development provide considerable uncertainty as to how the rate,
timing and magnitude of hydrological fluxes within the landscape will
change. With the potential for these systems to become increasingly
drier with the onset of climate change, it is critical that future
design of these constructed systems maximize water retention and storage
such as increasing hummock recharge (Ketcheson et al., 2017; Lukenbach
et al., 2019) and maintaining high infiltration capacity-soils to
enhance groundwater recharge.

~

\textbf{CONCLUSIONS}

The future expansion of bitumen extraction via surface mining projected
for the coming decades will result in thousands of hectares of land that
will need to be reclaimed. A better understanding of the evolution of
runoff pathways and surface-groundwater interactions are required to
assess their trajectory and long-term sustainability, which will
influence water management and future design of these systems. Results
presented here highlight that topography currently controls snow
distribution and melt, as vegetation has little influence on winter
processes during these early stages. Surface runoff from hillslopes in a
large snow year was higher than expected (up to 30 \%) and offers a
potentially important transport mechanism of water towards the wetland
that can help replenish water deficits as well as offer a supply of
fresh water to a relatively saline wetland. The remainder of meltwater
infiltrated into the unsaturated frozen soils and was partitioned
between the rooting zone and deeper percolation.~ This study provides an
important contrast to the other constructed wetland-upland system in the
AOSR where the majority of meltwater was transported to the lowland via
surface runoff which highlights the influence of construction design and
practice on the system's hydrology. The Cold Regions Hydrological Model
simulations of snowmelt partitioning with varying soil conditions
indicate that antecedent soil saturation (VWC/porosity) and soil
temperature had the greatest influence on partitioning snowmelt as
surface runoff, while soil texture alone had a moderate affect. Observed
and modelled surface runoff results within the AOSR provide evidence
that partitioning between surface runoff and infiltration can somewhat
be controlled by hillslope construction materials when designed these
reclaimed systems. Under various scenarios of a warmer and wetter
climate, the Cold Regions Hydrological Model predicts that average
annual peak SWE and duration of the snow season could decline by up to
52 \% and up to 61 days, respectively while snowmelt runoff ceases
completely under the warmest scenarios. This may lead to drier
conditions with the onset of climate change as water deficits could
increase each year as an increasingly smaller snowpack cannot replenish
water stores.

\textbf{ACKNOWLEDGEMENTS}

We thank two anonymous reviewers for their contributions in improving
this paper. We would also like to thank Dr. M. Graham Clark for detailed
comments, help with figures, and suggestions to the manuscript. Funding
for this research was provided by Syncrude Canada Ltd. and Global Water
Futures. We would like to thank all field work assistance including
Keegan Smith, Ian Martin, M. Graham Clark, Dr. Michael Treberg and Dr.
Gordon Drewitt. We also acknowledge Angus MacDonald (McMaster
University) for help with CRHM setup, Sean Leipe (McMaster University)
for help with the LiDAR data and ArcGIS and O'Kane Consultants for
assistance with meteorological data.

\textbf{Data Availability Statement}

The data that support the findings of this study are available from the
corresponding authors upon reasonable request.

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\textbf{TABLES}

Table 1. Measured hummock soil properties.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\textbf{Soil Type} & \textbf{Prescribed Depth} & \textbf{Specific Yield}
& \textbf{Porosity} & \textbf{LOI} & \textbf{Bulk Density} &
\textbf{K}\textsubscript{\textbf{sat}} &
\textbf{Sand}\textsuperscript{+}\textbf{{}} &
\textbf{Silt}\textsuperscript{+}\textbf{{}} &
\textbf{Clay}\textsuperscript{+}\textbf{{}}\tabularnewline
\textbf{~} & cm & - & - & \% & g/cm\textsuperscript{3} & m/s & \% & \% &
\%\tabularnewline
LFH & 0 - 15 & 0.23 & 0.31 & 9.3 & 1.14 & 1.75E-04 & - & - &
-\tabularnewline
Pf Sand & 15 - 55 & 0.28 & 0.23 & 3.5 & 1.58 & 1.09E-04 & 94 & 6 &
0\tabularnewline
Tailings Sand & \textgreater{} 55 & 0.29 & 0.26 & 1.1 & 1.54 & 6.74E-05
& 92 & 3 & 5\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{+}(NorthWind Land Resources Inc., 2012)

Table 2. Parameters used to set up CRHM simulations under current and
potential climate

conditions.

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
~\textbf{Parameter} ~ & ~\textbf{Units} ~ & ~\textbf{Assigned} ~ &
~\textbf{Module} ~\tabularnewline
HRU area ~ & km\textsuperscript{2} ~ & 3.31E+4 \& 1.6E+4 ~ & Shared
~\tabularnewline
Aspect ~ & N/E/S/W ~ & S \& N ~ &\tabularnewline
Elevation ~ & m ~ & 318 ~ &\tabularnewline
Slope ~ & \selectlanguage{ngerman}° ~ & 8 ~ &\tabularnewline
Latitude ~ & \selectlanguage{ngerman}° ~ & 57 ~ &\tabularnewline
Vegetation height ~ & m ~ & 1 ~ &\tabularnewline
Si (initial soil saturation) ~ &
mm\textsuperscript{3~}mm\textsuperscript{-3} ~ & 0.2 ~ &\tabularnewline
Max available water holding capacity ~ & mm ~ & 375 ~ &\tabularnewline
Max values for soil recharge zone ~ & mm ~ & 60 ~ &\tabularnewline
Albedo\_bare ~ & - ~ & 0.17 ~ & Albedo ~\tabularnewline
Albedo\_snow ~ & - ~ & 0.85 ~ &\tabularnewline
Groundcover ~ & - ~ & 1 (bare ground) ~ & frozenAyers ~\tabularnewline
Soil Texture ~ & - ~ & 1 (coarse/medium coarse) ~ &\tabularnewline
Soil Temperature ~ & K ~ & 271.1 ~ &\tabularnewline
Coefficient ~ & - ~ & 2.82 ~ &\tabularnewline
Rain\_soil\_snow ~ & - ~ & 1 ~ & SnobalCRHM ~\tabularnewline
T\_g / G\_flux ~ & ~ ~ & 0 ~ &\tabularnewline
Climate Chg Temp ~ & \selectlanguage{ngerman}°C ~ & 0, 2, 4, 6 ~ & obs ~\tabularnewline
Climate Change Ppt factor ~ & - ~ & 1, 1.15, 1.2, 1.3 ~ &\tabularnewline
& & &\tabularnewline
\bottomrule
\end{longtable}

Table 3. CRHM simulation results from the frozenAyers module. Variables
tested were initial soil saturation (Si), soil temperature (Tsoil) and
soil texture.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Si}~ & \textbf{Runoff}~ & ~ & \textbf{Tsoil}~ & \textbf{Runoff}~
& ~ & \textbf{Soil Texture}~ & \textbf{Runoff}~\tabularnewline
-~ & mm~ & ~ & \selectlanguage{ngerman}°C~ & mm~ & ~ & -~ & mm~\tabularnewline
0.2{} & 34{} & ~0{} ~ & 0{} & Sand{} & 34 & &\tabularnewline
0.25{} & 47{} & -1{} & 0{} & Silt loam{} & 37 & &\tabularnewline
0.3{} & 60{} & -2{} & 34{} & Clay{} & 41 & &\tabularnewline
0.35{} & 72{} & -4{} & 69{} & & & &\tabularnewline
0.4{} & 84{} & -6{} & 85{} & & & &\tabularnewline
0.45{} & 95{} & -8{} & 94{} & & & &\tabularnewline
0.5{} & 105{} & & & & & &\tabularnewline
\bottomrule
\end{longtable}

Table 4. Summary of CRHM simulation results for projected climate change
scenarios. Positive and negative signs indicate if values increased or
decreased, respectively from current conditions. Note that values were
averaged over the five simulated years (2013-2018).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Climate Scenario~~ & Change in peak snowpack~SWE~ & Change in
snow~season {} & Mid-winter melt~duration~ & Mid-winter melt~ & Runoff~
& Runoff Ratio~\tabularnewline
2013-2018~ & \%~ & days~ & days~ & mm~ & mm~ & -~\tabularnewline
T0-P1~ & 0~ & 0~ & 5~ & 12~ & 11~ & 0.38~\tabularnewline
T2-P1~ & -21~ & -10~ & 7~ & 26~ & 8~ & 0.1~\tabularnewline
T4-P1~ & -35~ & -37~ & 14~ & 39~ & 1~ & 0.01~\tabularnewline
T6-P1~ & -42~ & -61~ & 11~ & 43~ & 0~ & - ~\tabularnewline
T0-P1.15~ & +12~ & +7~ & 0~ & 0~ & 20~ & 0.17~\tabularnewline
T0-P1.2~ & +28~ & +8~ & 0~ & 0~ & 25~ & 0.18~\tabularnewline
T0-P1.3~ & +36~ & +8~ & 0~ & 0~ & 29~ & 0.21~\tabularnewline
T2-P1.2~ & -0.2~ & -1~ & 6~ & 19~ & 20~ & 0.21~\tabularnewline
T4-P1.2~ & -24~ & -17~ & 12~ & 40~ & 10~ & 0.16~\tabularnewline
T6-P1.2~ & -32~ & -16~ & 14~ & 44~ & 3~ & 0.03~\tabularnewline
\bottomrule
\end{longtable}

\textbf{FIGURE LEGENDS}

Graphical Abstract. Runoff was measured directly on constructed
hillslopes and is an important

freshwater source for wetlands in a sub-humid and saline environment.

~

Figure 1. Instrumentation map of Sandhill Fen Watershed. Dark grey areas
represent the lowland

(wetland) area and lighter grey represents the margins and upland areas.
Circles represent snow

survey points which are coloured based on landscape type. The ``V''
shape at the base of the

runoff collectors indicate the physical constructed boundary and thinner
lines represent the

calculated upslope contributing areas.

~

Figure 2. Runoff collector construction phases. a) Two 10-15 cm trenches
were dug into the frozen

ground, b) hydraulic cement was used to seal the garden edging into the
trenches, c) trenches

tapered at the bottom of the slope to form a ``V'' where an eavestrough
was used to funnel water

towards the bucket with a v-notch, d) a pressure transducer was used to
continuously measure

water height and discharge, e) completed runoff collector where each arm
of the ``V'' was \textasciitilde{}4 m.

~

Figure 3. Boxplots of snowpack SWE prior to melt in a) landscape units
and b) hummock slope

position and aspect. Circular points are data points from the snow
surveys. Outer border of the

boxplots represent the 25\textsuperscript{th} and 75\textsuperscript{th}
percentiles and mid box lines represent the group median.

The diamond points represent the group mean and numbers above the x-axis
indicate sample size.

Means sharing a letter are not significantly different (Wilcoxon rank
sum test).

~

Figure 4. Snowpack SWE of each landscape unit from before and during
melt for the a) wetland,

b) upland, c) hummocks and d) hummock aspects. The data points represent
the mean SWE and

the top and bottom of the shaded areas represent the
75\textsuperscript{th} and 25\textsuperscript{th} percentiles,
respectively.

~

Figure 5. Snowmelt runoff collector discharge (cm\textsuperscript{3}/s)
and cumulative runoff (mm) at 15-minute

intervals for two distinct melt periods of a) South-facing runoff
collector from 15-22 March 2018

and b) North-facing collector during 12-24 April 2018. Note: 1) figure
scales are different and 2) the entire snowpack had melted by the end of
each melt period shown.

~

Figure 6. Observed and CRHM simulated a) hourly snowpack SWE from
2013-2018 and b) daily

snowmelt surface runoff for the 2017-2018 winter season.

~

Figure 7. Simulated SWE under climate scenarios of a) increased
temperature (T2-P1, T4-P1, T6-

P1), b) increased precipitation (T0-P1.5, T0-P1.2, T0-P1.3) and c)
increased temperature and

precipitation (T2-P1.2, T4-P1.2, T6-P1.2). The black dotted line is the
simulated SWE under

current climate conditions (T0-P1).

~

Figure 8. Simulated snowmelt runoff under climate scenarios of a)
increased temperature (T2-P1,

T4-P1, T6-P1), b) increased precipitation (T0-P1.5, T0-P1.2, T0-P1.3)
and c) increased

temperature and precipitation (T2-P1.2, T4-P1.2, T6-P1.2). The dark grey
bar represents the

simulated runoff under current climate conditions (T0-P1).

~

Figure 9. Average SWE of older constructed systems on the Syncrude
Canada Ltd. property.

Data points represent daily average SWE data from snow surveys conducted
during the 2017

-2018 winter. Inset: photographs to indicate maturity differences among
sites for South Bison

(SB), Coke Beach (CB), Jake Pine (JP) and Sandhill Fen Watershed (SFW).

\textbf{APPENDIX}

Table A1. All simulation results from the Cold Regions Hydrological
Model under current and various potential future climate scenarios.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Climate Scenario & Simulation Results\tabularnewline
\textbf{Scenario} & \textbf{Temperature Increase}\tabularnewline
\selectlanguage{ngerman}°C & mm/day\tabularnewline
T0-P1 & 0\tabularnewline
2014-2015 & 22-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T2-P1 & 2\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T4-P1 & 4\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 23-Jan-16\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T6-P1 & 6\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 23-Jan-16\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T0-P1.15 & 0\tabularnewline
2014-2015 & 22-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T0-P1.2 & 0\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T0-P1.3 & 0\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 26-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T2-P-1.2 & 2\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 15-Nov-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T4-P1.2 & 4\tabularnewline
2014-2015 & 21-Nov-14\tabularnewline
2015-2016 & 8-Dec-15\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
T6-P1.2 & 6\tabularnewline
2014-2015 & 22-Nov-14\tabularnewline
2015-2016 & 23-Jan-16\tabularnewline
2016-2017 & 27-Nov-16\tabularnewline
2017-2018 & 19-Nov-17\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{+}Sum of two mid-winter melt events\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}
\caption{{Figure 1. Instrumentation map of Sandhill Fen Watershed. Dark gray areas
represent the lowland (wetland) area and lighter grey represents the
margins and upland areas. Circles represent snow survey points which are
coloured based on landscape type. The ``V'' shape at the base of the
runoff collectors indicate the physical constructed boundary and thinner
lines represent the calculated upslope contributing areas.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure21/Figure21}
\caption{{Figure 2. Runoff collector construction phases. a) Two 10-15 cm trenches
were dug into the frozen ground, b) hydraulic cement was used to seal
the garden edging into the trenches, c) trenches tapered at the bottom
of the slope to form a ``V'' where an~ eavestrough was used to funnel
water towards the bucket with a v-notch, d) a pressure transducer was
used to continuously measure water height and discharge, e) completed
runoff collector where each arm of the ``V'' was \textasciitilde{}4m.
{\label{533441}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure3}
\caption{{Figure 3.~ Boxplots of snowpack SWE prior to melt in a) landscape units
and b) hummock slope position and aspect. Circular points are data
points from the snow surveys. Outer border of the boxplots represent the
25th and 75th percentiles and mid box lines represent the group median.
The diamond points represent the group mean and numbers above the x-axis
indicate sample size. Means sharing a letter are not significantly
different (Wilcoxon rank sum test).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure4}
\caption{{Figure 4. Snowpack SWE of each landscape uit from before and during melt
for the a) wetland, b) upland, c) hummock and d) hummock aspects. The
data points represent the mean SWE and the top and bottom of the shaded
areas represent the 75th and 25th percentiles, respectively.~%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4/Figure4}
\caption{{Figure 5. Snowmelt runoff collector discharge (cm\textsuperscript{3}/s)
and cumulative runoff (mm) at 15-minute intervals for two distinct melt
periods of a) South-facing runoff collector from 15-22 March 2018 and b)
North-facing collector during 12-24 April 2018. Note: 1) figure scales
are different and 2) the entire snowpack had melted by the end of each
melt period shown.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5-2/Figure6}
\caption{{Figure 6. Observed and CRHM simulated a) hourly snowpack SWE from
2013-2018 and b) daily snowmelt surface runoff for the 2017-2018 winter
season.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure6/Figure7}
\caption{{Figure 7. Simulated SWE under climate scenarios of a) increased
temperature (T2-P1, T4-P1, T6-P1), b) increased precipitation (T0-P1.5,
T0-P1.2, T0-P1.3) and c) increased temperature and precipitation
(T2-P1.2, T4-P1.2, T6-P1.2). The black dotted line is the simulated SWE
under current climate conditions (T0-P1).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure7/Figure8}
\caption{{Figure 8. Simulated snowmelt runoff under climate scenarios of a)
increased~(T2-P1, T4-P1, T6-P1), b) increased precipitation (T0-P1.5,
T0-P1.2, T0-P1.3) and c) increased temperature and precipitation
(T2-P1.2, T4-P1.2, T6-P1.2). The dark grey bar represents the simulated
runoff under current climate conditions (T0-P1).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure-9/Figure-9}
\caption{{Figure 9. Average SWE of older constructed systems on the Syncrude
Canada Ltd. property. Data points represent daily average SWE data from
snow surveys conducted during the 2017-2018 winter. Inset: photographs
to indicate maturity differences among sites for South Bison (SB), Coke
Beach (CB), Jack Pine (JP), and Sandhill Fen Watershed (SFW).
{\label{376443}}%
}}
\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

