SWAT-CS: Revision and testing of SWAT for Canadian Shield watersheds
Congsheng Fua, April L. Jamesa, Huaxia Yaob,a
aDept.
of Geography, Nipissing University, Ontario, Canada (email: congshengf@nipissingu.ca); bDorset Environmental Science Centre, The Ministry of Environment, Dorset, Ontario, Canada.
Results – Snow Water Equivalent
Results - Streamflow and Interflow
Abstract
Canadian Shield watersheds are under increasing pressure from development (e.g. mining,
nearshore development) and climate change. Dynamic watershed models including
representation of terrestrial, stream, and lake components are needed to simulate and explain
nutrient and chemical processes in this region. Within the Canadian Shield, forested
headwater watersheds are generally characterized by shallow forested soils with high
infiltration rates and low bedrock infiltration, generating little overland flow, and macropore
and subsurface flow are important streamflow generation processes. Large numbers of
wetlands and lakes are also key physiographic features, and snow-processes are critical to
watershed modeling in this climate. We have developed and tested SWAT-CS, a revised version
of the publicly available SWAT, to represent hydrological processes dominating headwater
Canadian Shield watersheds. SWAT-CS revisions include introduction of macropore flow, new
limitations on bedrock percolation, and a small change to snow pack temperature.
Table 2. SWAT-CS improved Nash–Sutcliffe efficiency (NSE) for daily streamflow compared to original SWAT for
both calibration (1978-1992) and verification (193-2007) periods. Bolded numbers are maximum NSEs.
Sub-catchment
Original SWAT
(1978-1992)
Original SWAT
(1993-2007)
SWAT-CS
(1978-1992)
SWAT-CS
(1993-2007)
HP3
HP3a
HP4
HP5
HP6a
HP6
HP0
(Lake
outflow)
-0.55~0.54 -1.76~0.34 -0.37~0.54 -0.21~0.51 -0.53~0.51 -1.36~0.49 -1.12~0.58
-0.80~0.50 -2.23~0.26 -0.50~0.58 -0.33~0.49 -0.69~0.47 -2.04~0.46 -1.74~0.56
-0.68~0.63 -2.01~0.59 -0.37~0.62 -0.20~0.60 -0.66~0.57 -1.46~0.67 -0.80~0.72
-0.75~0.60 -2.28~0.54 -0.44~0.66 -0.32~0.61 -0.72~0.59 -1.95~0.65 -1.27~0.68
Fig 6. SWAT-CS simulated daily
snow water equivalent (SWE) well,
with a NSE ~0.86 for snow water
equivalent (SWE) in HP4 from
1987 to 1992. Parameters in subfigure (a) are shown in Table 3. For
sub-figure (b), Timp is 0.1, other
parameters are as same as those
in Table 3.
Fig 1. SWAT-CS was tested on the
Harp Lake catchment (5.42 km2),
located in the Muskoka District of
central
Ontario.
Very
few
applications of SWAT exist on
Canadian Shield watersheds.
Fig 3. SWAT-CS simulated daily streamflow well, with Nash–Sutcliffe efficiency (NSE) for the daily
streamflow of HP4 in calibration (1978-1992) and verification (1993-2007) being 0.53 and 0.57,
respectively. Parameters shown in Table 3.
Harp Lake SWAT Model
 Harp Lake is part of the Dorset Environmental Science Centre, an Ontario Ministry of
Environment research station with ~ 30 yrs of streamflow (6 stations), lake outflow, and 5
years of snow water equivalent (SWE) data.
 Original SWAT was tested against key model revisions (Table 1) that reflect forested
Canadian Shield hydrology, including limiting of bedrock percolation and introduction of
macropore flow.
Fig 4. SWAT-CS allows
significant amounts of rain
and snowmelt to enter soil
macropores (c) instead of
soil matrix (b), shown here
for a HRU in sub-basin 23 in
HP4 for 2007 (location
shown in the top right of Fig.
2). Sub-figure (d) is daily
streamflow for HP4.
Fig 7. HP4 sub-catchment snow water equivalent (SWE) Nash-Sutcliff efficiency (NSE) showed negative relationships with
daily streamflow NSE by SWAT ( top, a–c), whereas corresponding relationships using SWAT-CS were positive (bottom, d–f)
(SWE NSE: 1987 – 1992; streamflow NSE: 1978 – 1992). Results are from 8000 Monte Carlo simulations.
Table 3. Parameters for illustrations of model results (SWAT-CS).
Additional examination (not
shown) shows significant
daily lateral flow (interflow)
occurring at the soil-bedrock
interface, especially during
the snowmelt period. Key
parameters shown in Table
3.
Fig 2. Harp Lake is fairly uniform in
landuse (forested, top left). Detailed
soil type information is available for
only part of the watershed (subcatchment HP4). Terrain is hilly (right)
with shallow soils and significant
wetlands. Numbers in top right subfigure are indexes for sub-basins in
SWAT and SWAT-CS.
Overland flow
Original SWAT
Linear relationships also existed between
HP4 sub-catchment streamflow NSE and
other sub-catchment streamflow NSE
(bottom, d–f), illustrating parameters were
transferable between sub-catchments.
Daily results shown here for SWAT-CS
calibration period for 8000 Monte Carlo
simulations.
SWAT-CS
CN =35 (fixed)
CN =35 (fixed)
CN = 98 (frozen soil; bedrock outcrop) CN = 98 (frozen soil; bedrock outcrop)
Infiltration
Kinematic storage
Kinematic storage
Macropore flow
Snowmelt
Revision of snow pack temperature
Revision of snow pack temperature
Bedrock
percolation
Virtual soil layer at soil-bedrock
interface
i) Virtual soil layer at soil-bedrock interface
ii) Added limitations (Par2)
Parameters scaled with area; subWetland outflow
basin 15 obs.
Lake outflow Target release method
Parameter
Canmx (mm)
Sftmp (°C)
Smtmp (°C)
Snowmelt
Smfmx (mm H2O /°C-day)
Smfmn (mm H2O /°C-day)
Timp
Esco
Evapotranspiration Gw_Revap
Revapmn (mm H2O)
Overland flow
Surlag (day)
Infiltration
Par
Fig 5. Linear relationships exist between
modeled Lake outflow (HP0) NSE and subcatchment streamflow NSE (top, a–c).
Table 1. SWAT-CS revisions tested with the Harp Lake SWAT model.
SWAT Module
Module
Interception
Parameters scaled with area; sub-basin 15
obs.
Target release method
Select References
Eckhardt, K., Haverkamp, S., Fohrer, N., Frede, H.G., 2002. SWAT-G, a version of SWAT99.2 modified for application to low mountain
range catchments. Phys Chem Earth. 27, 641–644.
Watson, B.M., McKeown, R.A., Putz, G., MacDonald, J.D., 2008. Modification of SWAT for modelling streamflow from forested
watersheds on the Canadian Boreal Plain. J. Environ. Eng. Sci. 7, 145–159.
Wu, K., Johnstone, C.A., 2007. Hydrologic response to climatic variability in a Great Lakes Watershed: A case study with the SWAT
model. J. Hydrol. 337(1–2), 187–199.
Yao, H., McConnell, C., Somers, K.M., Yan, N.D., Watmough, S., Scheider, W., 2011. Nearshore human interventions reverse patterns of
decline in lake calcium budgets in central Ontario as demonstrated by mass-balance analyses. Water Resour. Res. 47, W06521,
doi:10.1029/2010WR010159.
Value
2.548
0.332
0.931
1.398
1.486
0.793
0.073
0.001
74.878
0.781
6.983
Module
Interflow
Parameter
Lat_ttime (day)
Par2
Bedrock percolation
Rchrg_dp
Alpha_bf
Groundwater flow Gw_delay (day)
Gwqmn (mm H2O)
River routing
Ch_n2
Vpr (104 m3)
Reservoir
Vem (104 m3)
Ndtarg (day)
Value
2.527
0.486
0.347
0.003
12.620
0.978
0.011
925.430
943.553
3.196
Conclusions
 SWAT-CS significantly improves daily streamflow model performance compared to original SWAT.
 Land cover and soil properties are transferable between sub-catchments, providing a basis for extrapolation to
larger scale applications.
 SWAT-CS corrects a negative correlation between model efficiency for streamflow and snow water equivalent by
altering dominant streamflow generation processes, e.g. from surface runoff to subsurface interflow generation.
Using SWAT-CS, efficient modeling of SWE and streamflow are positively correlated.
 More than 90 % of interflow can be generated at the soil-bedrock interface by SWAT-CS, and the contribution of
groundwater flow to total runoff can approach zero, consistent with hydrologic understanding of these
headwater systems.
 Using 8000 Monte Carlo simulations, the common challenge of equifinality is documented for both streamflow
and snow water equivalent (SWE) but select parameters were identifiable.
 The reservoir-based target releasing method is shown to be a reasonable and efficient method in modeling
natural lake storage (results not shown here).
Acknowledgements
This study was funded in part by the Ontario Ministry of Research and Innovation (MRI) Post Doctoral Fellowship Round 4, Nipissing
University, Canada Foundation of Innovation Leaders Opportunity Fund and the Canada Research Chair program. Authors thank
Murray Richardson for providing the LiDAR DEM data. Thanks also to many Ontario Ministry of Environment staff and their partners
for provision of and assistance in field data collection.