MODELS, DATASETS, AND METHODS
MESH modelling
framework
The
model utilized here is the Modélisation Environmentale Surface et
Hydrologie model (MESH: Pietroniro et al., 2007). MESH is a
physically-based, semi-distributed modelling system with three main
components: (1) the vertical processes of moisture and heat flux
land-atmosphere transfers, represented either by the Canadian Land
Surface Scheme (CLASS: Verseghy, 1991, 2000) or the Soil, Vegetation and
Snow scheme (SVS: Husain et al. , 2016), (2) the lateral movement
of surface (overland) and subsurface (interflow) flows to the drainage
system, represented by the WATROF (Soulis et al. , 2000) or PDMROF
(Mekonnen et al. , 2014) algorithms, (3) the hydrological routing
between river-network grids, represented by the WATROUTE component of
the WATFLOOD hydrologic model (Kouwen et al. , 1993b).
To
represent the landscape, MESH is based on a model grid that is
subdivided into grouped response units (GRU: Kouwen et al. ,
1993a) based on land cover, soil type, slope/aspect. Water and energy
fluxes are computed at the tile-level (GRUs mapped onto grids) and then
aggregated to the grid-scale using weighted averaging based on the areal
fractions of GRUs in each grid. MESH runs at a sub-daily time-step
forced by seven meteorological variables, namely air temperature,
barometric pressure, incoming longwave and shortwave radiation,
precipitation, specific humidity, and wind speed. MESH has been widely
utilized to simulate land surface-hydrology processes in cold regions
(e.g. Razavi et al. , 2010; Haghnegahdar et al. ,
2014; Davison et al. , 2016; Yassin et al. , 2017; Elshamyet al. , 2020).
In this study, CLASS is used as the underlying land surface model. CLASS
simulates the coupled water and energy balances for a user-defined soil
layering that is generalized across the modelled watershed. Above
ground, CLASS encompasses four plant functional types, needle-leaf
forest, broadleaf forest, grassland and cropland. Below ground, soil
parameters (defined by Sand, Clay, and Organic matter percentages)
implicitly link soil thermal (i.e . heat capacity and thermal
conductivity), and soil hydraulic properties (e.g . porosity and
saturated hydraulic conductivity); the latter being defined using Cosbyet al. (1984). During runtime, each soil layer’s temperature and
moisture content can evolve and update the associated thermal
properties. This occurs down to the depth of bedrock, or the soil
permeable depth (SDEP), below which no moisture migration is allowed;
only heat can transfer vertically between the soil layers in this
region.
CLASS incorporates the Neumann-type boundary condition at the bottom of
the soil column (constant geothermal flow) by which the user can
replicate the presence of an upward geothermal flux. Flux exchanges with
the atmosphere determine the upper boundary condition through the
solution of the surface energy balance. Initial conditions include
prognostic variables for each soil layer, such as temperature and
volumetric moisture content, in addition to other surface state
variables.
Fully organic soils can be handled by CLASS using three predefined
organic peat types (i.e . fibric, hemic and sapric) based on the
work of Letts et al. (2000). Compared to mineral soil, peats have
higher porosities (0.93, 0.88,0.83 for the three sub-types respectively
compared to 0.49 for clay), higher retention capacity (0.275, 0.62,
0.705), higher residual water content (0.04, 0.15, 0.22), higher heat
capacity (2.5 x 106Jm-3K-1), and lower thermal
conductivity (0.25 Wm-1K-1) than
mineral soils. Thermal properties are taken to be the same for all
organic sub-types. Further details are provided in Verseghy (2012).
MESH/CLASS is usually run at a 30-min time-step, and different
permafrost characteristics can be output from the simulated temperature
profiles. For the present study, the following related aspects of
permafrost are considered: temperature envelopes (Tmax and Tmin), mean
annual ground temperature profile at the top of the permafrost (MAGTp),
active layer thickness (ALT), depth of the zero-annual amplitude point
(DZAA), depth to the base of permafrost (BP), thermal offset, surface
offset, and date of maximum thaw (ALT-DOY) (see Fig. 1 andTable 1 ).
Possible Position of Fig. 1 .
Possible Position of Table 1 .
Study area and
data
The
experimental sites selected for this study are near the outlet of the
Liard River Basin, Northwest Territories, Canada (Fig. 2 ). The
area is located along the divide between sporadic and discontinuous
permafrost regions based on the permafrost Map of Canada (Hegginbottomet al ., 1995). More than half of the basin is underlain by
sporadic permafrost, mainly in the south, discontinuous permafrost
underlays the northern third of the basin, and the rest of the basin is
underlain by patchy permafrost (Fig. 2 ).
The climate is characterized as
subarctic according to the Köppen-Geiger classification (Peel et
al. , 2007). The basin is underlain by warm-permafrost (near 0°C), where
a rapid reduction in the extent of permafrost in the Canadian sub-arctic
has been observed due to climate change (DeBeer et al. , 2016;
Connon et al. , 2018). The Liard River basin plays a central role
in the sub-continental Makenzie River Basin’s hydrology, as it has the
highest runoff coefficient and contributes the largest mean annual flow
to the Mackenzie River at the outlet (Woo, 2012).
The availability of soil
temperature data is limited to a few experimental sites (e.g.Scotty-Creek (Quinton and Marsh, 1999)), and measurements made
during/after the construction of infrastructure for maintenance and
monitoring purposes (e.g. Norman Wells-Zama pipeline (Smithet al. , 2004); Yukon Alaska Highway (Oldenborger et al. ,
2015)). In this study, two representative permafrost sites were selected
due to the availability of soil temperature data at multiple depths and
corresponding borehole logs (Fig. 3 ). These were Jean Marie
Creek (borehole 85-12B), underlain by sporadic permafrost, and Wrigley
Highway (borehole 99TC03), underlain by discontinuous permafrost
(Table 2 ), initially installed to monitor the Norman Wells-Zama
pipeline’s impact on permafrost. Several Geological Survey of Canada
(GSC) reports have been used to extract the thermal and geological data
for the current study (Smithet al. , 2004, 2009, 2010, 2016; Ednie et al. , 2012;
Chartrand et al. , 2014).
The Jean Marie Creek (JMC) site is dominated by
boreal forest (mainly needleleaf)
and scattered ericaceous shrubs on peat plateaux where permafrost is
warm (Mean Annual Ground Temperature (MAGT) of -0.1°C) and of limited
thickness (~ 4m) and the active layer is shallow
(~ 1.5m). The data span 1986 to 2000, with no records
available in the 21st century. The
Wrigley Highway (WH) site is
dominated by shrubs with a small
black spruce thicket and moss. At this site, permafrost is also warm
(MAGT of -0.2 °C) but has a larger thickness (~ 10m)
than JMC while the active layer is slightly deeper (~
2m). Since the two sites are relatively close (~60 km
apart), they have similar climatic conditions with an average annual
daily air temperature over the 1979-2017 period of -2.5°C and -1.9°C,
and average annual precipitation of 430 mm yr-1 and
420 mm yr-1 for the JMC and WH sites respectively.
MESH requires seven climatic variables at a sub-daily resolution to
drive CLASS, as mentioned in Section 2.1 . Selection of a
forcing dataset is constrained by the quality of atmospheric data and
the availability of permafrost data, which spans 1986 - 2000 for JMC and
2007-2015 for WH (Table 2 ). A few forcing datasets start prior
to the 1980s such as WFD (WATer and global CHange (WATCH) Forcing Data)
(Weedon et al. , 2011), Princeton (Sheffield et al. , 2006),
and WFDEI (WFD with the ERA-Interim analysis) (Weedon et al. ,
2014). However, the WFD and Princeton datasets end in 2001 and 2012
respectively. The combined Global Environmental Model (GEM; Côtéet al. , 1998) atmospheric forecasts and the Canadian
Precipitation Analysis (CaPA; Mahfouf et al. , 2007) have been
found to compare well with ground observations (Wong et al. ,
2017), but GEM-CaPA is not available prior to 2002. Since the WFDEI
dataset provides reasonable estimates of climate fields, as shown by
Wong et al. (2017) for precipitation, and is available from 1979,
covering the duration of the permafrost records, WFDEI is used for
driving CLASS for the period 1979-2016, at 3-hour resolution.