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Uncertainty assessment of gridded climate datasets and their application to hydrological modelling over the Lower Nelson River Basin, Manitoba, Canada
Rajtantra Lilharea, Stephen J. Dérya,b,*, Scott Pokornyc, Tricia A. Stadnykc, and Kristina Koenigd
aNatural Resources and Environmental Studies (NRES), University of Northern British Columbia, Prince George, British Columbia, Canada, V2N 4Z9bEnvironmental Science and Engineering Program, University of Northern British Columbia, Prince George, British Columbia, Canada, V2N 4Z9cDepartment of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada, R3T 5V6dManitoba Hydro, Winnipeg, Manitoba, Canada, R3C 0G8
*Correspondence to: Stephen J. Déry ([email protected])
Abstract
Several different gridded datasets are now available to provide consistent sets of input climate
forcings for various hydro-climatogical and hydrological modelling studies. Recent
modifications in land-surface schemes, access to more powerful computational resources, and
advances in distributed hydrological models have required even higher-resolution gridded
dataset. However, it remains a challenge to identify the most suitable dataset for hydrological
modelling, especially for data sparse, remote and physically complex regions due to paucity of
observational records. This study evaluates spatiotemporal differences in the input forcing
datasets as well as the associated predictive uncertainties in hydrologic simulations over the
Lower Nelson River Basin (LNRB), Manitoba, Canada, using the Variable Infiltration Capacity
(VIC) model. These datasets include the Inverse Distance Weighted (IDW) interpolated
observations from 14 Environment and Climate Change Canada (ECCC) meteorological stations,
the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN), North
American Regional Reanalysis (NARR), ERA-Interim (ERA-I), and Watch forcing data ERA-
Interim (WFDEI) gridded products. Inter-comparison of these datasets performed over the
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LNRB and VIC hydrologic responses of ten unregulated sub-watersheds examined at seasonal
and annual timescales for 1979–2009. Results suggest that the gridded datasets have systematic
differences, which vary with different seasons and regional characteristics with the most
significant differences arising in precipitation (~0.5–5.0 mm) and air temperature (±1.5°C)
during summer and autumn across the LNRB. The hydrologic simulations driven by these five
forcing datasets and their ensemble show substantial differences in modelled flows, (~0.5 to -3.0
mm day-1), and seasonal water balances (~90 mm month-1) for ten LNRB sub-basins. The
NARR-VIC and ENSEMBLE-VIC simulations match more closely the observations and better
represent the LNRB’s hydrology amongst other datasets. The ANUSPLIN-VIC manifests
considerable underestimation (~2.5 mm day-1) in simulated flows due to a dry bias in
precipitation whereas ERA-I and WFDEI yield high flows (~0.5–3.0 mm day-1) and an
overestimation in water balance terms for most of the sub-basins. Overall, analyses of the
different climate datasets and their derived VIC simulations reveal that the choice of input
forcing plays a crucial role in the accurate estimation of hydrologic responses for the LNRB, but
all datasets remain valuable in estimating the range of uncertainty in the VIC model simulations.
Keywords: VIC model; gridded climate data; inter-comparison; water balance uncertainty;
Lower Nelson River basin
1. Introduction
Numerical modelling of a river basin iremains essential in both climate research and ecological
studies as it provides vital information on its hydrological cycle and water availability for human
society and ecosystems. Although recent developments and advances have been achieved in
hydrological modelling along with increases in computational power, how to efficiently address
associated uncertainties in hydrological simulations remains critical and challenging (Liu and
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Gupta, 2007). To achieve a hydrological model’s optimal contribution to decision making, there
is a growing need for proper uncertainty assessments mainly associated mainly with the
observations required to drive these models and validate their outputs. Input climate forcings for
numerical modelling, particularly precipitation and air temperature, remain vitally important for
accurate streamflow simulations and water balance calculations (Eum et al., 2014; Fekete et al.,
2004; Reed et al., 2004; Tobin et al., 2011). For cold regions, these input forcings alter the phase
and magnitude of modelled precipitation and influence the hydrological model’s response. Input
forcings uncertainty (measurement errors, etc.) cascade through all hydrological processes during
numerical simulations, impacting the reliability of model output (Anderson et al., 2008; Tapiador
et al., 2012; Wagener and Gupta, 2005).
In recent decades, multiple global forcing datasets have been produced using different
input sources such as remote sensing products, climate model simulations, and in situ
observations. These datasets systematically agree over the major temporal trends and spatial
distribution of climate variables (i.e., precipitation and air temperature), but they frequently show
notable differences at regional scales (Adler et al., 2001; Costa and Foley, 1998). Essou et al.
(2016b) compared hydrological simulations over the continental United States (US) from
different observed input forcings and found significant differences among the datasets; however,
all forcings were essentially interpolated from the same climate databases. Moreover, they
investigated the hydrological response of three reanalysis products and uncovered biases in all,
especially in winter and summer over the southeastern US (Essou et al., 2016a). Overall, these
observation errors in climate variables induce uncertainties in a hydrological model’s outcome;
hence, numerical simulations driven by different forcing datasets effectively provide an
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uncertainty estimate of essential hydrological variables for water resource management and
planning.
Solid precipitation underestimationcatch due to wind undercatch (Adam and Lettenmaier,
2003) and underestimation in precipitation due to lack offrom a paucity of observations in
topographically complex river basins (Adam et al., 2006) are well-known sources of errors in
climate datasets. Tian et al. (2007) performed simulations using both undercatch corrected and
uncorrected data toand concluded that bias-corrected precipitation resultsed in an increase of
5%–25% increases in simulated streamflow over the circumpolar north (poleward of 45°N).
Several studies raise tThe question of which forcing dataset is the most suitable and accurate to
drive hydrological models but has not yet been responded with consensusremains elusive and
inconclusive. Steps toward answering that question were undertaken by Pavelsky and Smith
(2006) who concluded that observations covered the trends significantly better than two
reanalysis products when they assessed the quality of four global precipitation datasets against
the discharge observations from 198 pan-Arctic rivers. Fekete et al., (2004) described impacts
ofthe input data uncertainty effects on runoff estimates at a grid scale by driving a global water
balance model with six different global forcing datasets. They demonstrated that the uncertainty
in precipitation yields similar or higher levels of uncertainty in the simulated runoff and other
water balance terms. The bias and uncertainty in global hydrological modelling due to input
datasets and associated over- or underestimations in modelled streamflows over several basins
have also been identified in previous studies (e.g., Döll et al., 2003; Gerten et al., 2004; Nijssen
et al., 2001). However, its individual contribution to overall water balance estimation has not yet
been identified at watershed and sub-watershed scales. Moreover, the interannual and seasonal
patterns of discharge are essential for water resource assessments since both water demand and
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supply vary throughout the year. Thus, the input forcing uncertainty assessments should also be
performed on seasonal and annual timescales. While there may be uncertainties in other input
datasets (e.g., soil, land use, etc.), this paper focuses primarily on the uncertainty in the input
climate forcing datasets, which is perhaps the most significant source of uncertainty for any
hydrological modelling related study.
ObservedSeveral gridded datasets for precipitation and air temperature – based on
available observations, post-processing techniques and sometimes climate modelling – are
available for the Canadian landmass to force hydrological simulations (Hopkinson et al., 2011;
Mesinger et al., 2006). Long-term records of tThese gridded datasets are available at hourly
and/or daily temporal resolution and play a significant role in hydrological modelling,
particularly over large areas with low density of in-situ observations. Nevertheless, these datasets
are assimilated, spatially interpolated and constructed to grid cells. Since observational data are
incorporated to derive the gridded datasets, they may also contain measurement errors and
missing records. These missing values translate into the data interpolation and aidd to the overall
uncertainty in resulted gridded products. Such uncertainties associated with forcing datasets are
assessed in many studies (Eum et al., 2014; Horton et al., 2006; Kay et al., 2009). Choi et al.
(2009) obtained satisfactory results for hydrological simulations of three northern Manitoba
watersheds over 1980-2004 useding North American Regional Reanalysis temperature and
precipitation as driving datasets to perform hydrological simulations (1980–2004) and obtained
satisfactory results for three selected watersheds in northern Manitoba. However, iIn Canada,
however, numerous studies have also used multiple forcing datasets to assess the performance of
hydrological simulations. For example, Sabarly et al. (2016) used four reanalysis products to
evaluate the terrestrial branch of the water cycle over Québec, Canada with acceptable results for
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the period 1979–2008. In this study, we perform the inter-comparison of available forcing
datasets and uncertainty associated with their surface water balance estimations over the LNRB.
To achieve this goal, six forcing datasets, i.e. Inverse Distance Weighted interpolated
observations from 14 Environment and Climate Change Canada (ECCC) meteorological stations
(IDW hereafter; Gemmer et al., 2004; Shepard, 1968), the Canadian Precipitation Analysis and
the thin-plate smoothing splines (ANUSPLIN hereafter; Hopkinson et al., 2011), North
American Regional Reanalysis (NARR hereafter; Mesinger et al., 2006), ERA-Interim (ERA-I
hereafter; Dee et al., 2011), Watch forcing data (WFD) ERA-I (WFDEI hereafter; Weedon et al.,
2014), and their ensemble (ENSEMBLE hereafter; Morice et al., 2012) are ingested into the
VICa hydrological model over the LNRB. These datasets are examined separately against the
IDW gridded data over the study domain and with the ECCC station observations across the
LNRB. NARR is the only dataset that was used by Choi et al. (2009) for the hydrological
modelling of three LNRB sub-watersheds whereas the four other forcing datasets, namely IDW,
ANUSPLIN, ERA-I and WFDEI, have not yet been evaluated with the Variable Infiltration
Capacityhydrological models over the LNRB. However, these datasets are used in various other
studies over different Canadian regions (Boucher and Best, 2010; Islam and Déry, 2017;
Sauchyn et al., 2011; Seager et al., 2014; Woo and Thorne, 2006). To our knowledge, for the
LNRB, this is the first comprehensive study that collectively examines available gridded datasets
against observations, establishes the most suitable datasets for the LNRB’s VIC hydrological
modelling, and performs uncertainty assessment for their hydrological responses.
Overall, the main objectives of this study are to: (i) compare and identify the most
reliable available gridded forcing datasets for hydrological simulations over the LNRB; (ii)
evaluate a hydrological modelling’s responses from different driving datasets over the LNRB;
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and (iii) evaluate uncertainties induced with the water balance estimations from different forcing
datasets. To achieve these objectives, a semi-distributed macroscale hydrological model, i.e., the
Variable Infiltration Capacity (VIC) model (Liang et al., 1994, 1996), is used for simulations
over the LNRB. The VIC model conserves surface water and energy balances for large-scale
watersheds (Cherkauer et al., 2003) and it has been successfully implemented, calibrated, and
validated over major Canadian river basins (Islam et al., 2017; Kang et al., 2014; Shi et al.,
2013).
2. Study area
2.1 The Lower Nelson River Basin (LNRB)
The Nelson River Basin (NRB) is one of the major river systems in Canada (third largest by area
and volumetric discharge to the coastal ocean) that drains water mainly from the interior of
Canada, cutting through the Canadian Shield of northern Manitoba before emptying into Hudson
Bay (Figure 1a) (Newbury and Malaher, 1973). The Churchill River system covers the
northwestern part of the NRB and is considered here since it was joined to the Nelson River by a
diversion in 1976. The entire Nelson-Churchill River Basin extends geographically between
~45.5°N to 59.5°N, and ~90°W to 117.5°W. This system ranges in elevation from 3,200 m at the
western headwaters in the Rocky Mountain Ranges (Nelson River headwaters) to 0 m (sea level)
at the river outlets of Hudson Bay.
In this study, the downstream segment of the Nelson River system fed by Lake Winnipeg
constitutes the LNRB (Figure 1b). The LNRB spans an area of ~90,500 km2 and collects all
water from the drainage area upstream of the Nelson River (~970,000 km2) before discharging
into Hudson Bay. In the LNRB, the main stem river (Nelson) and its largest tributary – the
Burntwood, which also carries diverted flows from the Churchill River – have less seasonal flow
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variability due to streamflow regulation and a large drainage area. Most of the areas in the LNRB
hasve very low gentle slopes, with common channelized lakes moderating flow variability.
Wetlands abound within the LNRB and store significant volumes of water, cover large areas and
moderate streamflow responses to rainfall and snowmelt events. Shallow soils and permafrost
limit infiltration, groundwater storage and groundwater flows. To increase its hydroelectric
capacity, Manitoba Hydro regulatmanages flows in the LNRB. River diversions, current and
proposed hydroelectric developments within the LNRB are shown in Figure 1b. The LNRB has
with two major sources of streamflow regulation: the Churchill River Diversion (CRD) and the
Lake Winnipeg Regulation (LWR) (Figure 1b). On the LNRB’s northwestern boundary,
Manitoba Hydro operates the CRD. In 1977, a portion of the Churchill River Basin (licensed
maximum of 850 m3 s-1) was diverted into the LNRB and regulated at Notigi Lake by the Notigi
Control Structure on the Rat River. In 1972, Manitoba Hydro started the LWR project, which is
key to hydropower development on the Nelson River system. Presently, Manitoba Hydro
operates six hydroelectric generating stations and one station is under construction (Keeyask)
(Figure 1b) within the LNRB.
Our study region, tThe LNRB, ha experiences a sub-arctic continental climate characterized
by moderate precipitation and humidity, cool summers, and cold winters. The snow-free season
remains brief, generally beginning in May and ending in October, with a daily average summer
temperature of 11.5°C over the 1981–2010 climate normal period (Environment and Climate
Change Canada, 2016). Most of the precipitation that occurs during the summer months falls as
rain, accounting for ~65% nearly two-thirds of the total annual precipitation. The precipitation
peaks in July, the warmest month of the year with an average daily temperature of 16.2°C. Given
that the average annual precipitation over the LNRB totals ~500 mm, evapotranspiration in the
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region is high, with a loss of ~300–350 mm annually, and the surface water evaporation being
even higher at ~450 mm annually (Environment and Climate Change Canada, 2016; Smith et al.,
2015).
The most expansive land cover class in the LNRB is temperate or sub-polar needleleaf forest
covering ~33% of its total area with secondary classes being mixed forests (19%) and temperate
or sub-polar shrublands (9%) (North American Land Change Monitoring System, 2010). Various
types of wWetlands then prevail (bogs and fens, 21%) and open surface water cover ~21% and
(13%) then prevail in of the region, respectively.
The 30-m Shuttle Radar Topography Mission (SRTM) digital elevation datamodel (DEM)
(United States Geological Survey, 2013), provides the required topography used at that is
aggregated to 0.10° resolution for the VIC model setup, is shown in (Figure 1c). The entire
region exhibits low relief with a maximum elevation and average basin slope of 390 m.a.s.l. and
0.037%, respectively. Shallow depths characterize LNRB soils, leaving the underlying
precambrian igneous and metamorphic rocks of the Canadian Shield near the surface (Centre for
Land and Biological Resources Research, 1996). Permafrost abounds in the LNRB with sporadic
discontinuous permafrost (between 10% to 50%) spanning ~68% of theits total area. The
downstream northeastern portion comes under extensive discontinuous (between 50% to 90%)
and continuous (between 90% to 100%) permafrost region and covers approximately 9% and
0.8% of the areaLNRB, respectively (Natural Resources Canada, 2010). The southern part of the
LNRB covers around 16% of the total area with isolated patches of permafrost.
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3. Data and methods
3.1 Datasets
Required soil parameters for the VIC model are sourced from the multi-institution North
American Land Data Assimilation System (NLDAS) project at a resolution of 0.50° resolution
(Cosby et al., 1984). Since unavailable from the NLDAS project; frost-related parameters (e.g.,
bubbling pressure) are extracted from the conterminous United States soil (CONUS-SOIL)
database (Miller and White, 1998) or set to default values (Mao and Cherkauer, 2009). These soil
parameters are then aggregated to the VIC model resolution following Mao and Cherkauer
(2009). Land cover data are obtained from the Natural Resources Canada’s (NRCan) GeoBase -
Land Cover, circa 2000-Vector (LCC2000-V) product and vegetation parameters estimated for
the VIC model following Sheffield and Wood (2007). Each of the landcover classes are mapped
into standard VIC model vegetation classes. The Leaf Area Index (LAI) for each vegetation class
in each grid cell is estimated from Myneni et al. (1997). Rooting depths are obtained from
Maurer et al. (2002), while other vegetation parameters are taken from Nijssen et al. (2001).
Fractions of the open water and wetland class are estimated from the NLDAS map and
aggregated for each of the VIC model grid cells within the study domain. The VIC model lake
and wetland algorithm is used to represent all potential open water areas (wetlands, natural lakes,
and ponds). North-central Canada is dominated by smaller (1-10 km2) inland lakes, and only a
few large lakes (>10 km2) are present inspan the study domain (Halsey et al., 1997). These lakes
have areas smaller than a model grid cell and share multiple grid cells, therefore VIC does not
consider any horizontal redistribution within the lake. The depth-area relationship of the lake and
wetland tile is established empirically, which allows the prediction of a variable inundated area
with surface volume storage (Cherkauer and Lettenmaier, 1999).
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Observed daily hydrometricstreamflow records for 10 hydrometric stations (Table 2) are
obtained from the Water Survey of Canada’s Hydrometric Database (HYDAT; Water Survey of
Canada, 2016). Additional hydrometric data are provided by Manitoba Hydro, which are
recorded and maintained by them. These flow records are used to calibrate and evaluate the VIC
model estimates of streamflow.
Various observation-based gridded forcing datasets such as ANUSPLIN, NARR, ERA-I,
and WFDEI are available to drive the hydrological model (Table 1). These forcing datasets are
derived using advanced interpolation and data assimilation (for NARR, ERA-I, and WFDEI)
techniques. To compare these products, we constructed a gridded forcing dataset from 14 ECCC
meteorological stations, within the LNRB, using squared IDW interpolation technique. Further,
these forcings have been used to investigate the VIC model’s hydrological response over the
LNRB.
High resolution observation-based interpolated daily gridded datasets, i.e., the
ANUSPLIN developed by Natural Resources Canada (NRCan) and improved by Hopkinson et
al. (2011) and McKenney et al. (2011) for the Canadian landmass south of 60° N at 10 km
resolution (Natural Resources Canada, 2014). This dataset uses a trivariate thin-plate smoothing
spline technique and includes daily data of total precipitation (mm), maximum and minimum air
temperatures (Tmax and Tmin) (°C) at a 10 km spatial-resolution based on 7514 meteorological
stations (1950–2011) over the entire Canadian landmass (Eum et al., 2014).
The first reanalysis product used in this study is an improved version of the National
Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research
(NCAR) global reanalysis data (Kalnay et al., 1996; Kistler et al., 2001). The North American
Regional Reanalysis product was developed at 32 km spatial and 3-hourly temporal resolution by
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utilizing a version of the Eta Model and its 3D variational data assimilation system (EDAS)
(Mesinger et al., 2006) for the North American continent, available from 1979 to presentthe
current year. The accuracy of NARR air temperature and winds are improved, and interannual
variability of the seasonal precipitation is enhanced, relative to earlier versions of the
NCEP/NCAR reanalysis datasets (Mesinger et al., 2006; Nigam and Ruiz-Barradas, 2006)
allowing the generation of accurate water balance estimates (Luo et al., 2007; Sheffield et al.,
2012). Choi et al. (2009) used the NARR air temperature and precipitation data for hydrological
modelling of selected watersheds in northern Manitoba and found that these datasets are well
correlated with observations rather than the NCEP–NCAR Global Reanalysis-1 dataset. NARR
outputs are also used in regional water balance studies (Luo et al., 2007; Sheffield et al., 2012).
In contrast, many studies have reported that NARR forcing is comparatively suitable for the
hydrological modelling of Canadian river basins (Choi et al., 2009; Keshta and Elshorbagy,
2011). For example, Woo and Thorne (2006) used various reanalysis products includingobtained
improvement hydrological simulations when using NARR as an input for a hydrological model
over the Liard River Basin in western sub-arctic Canada and found significant improvements in
hydrological simulations.
ERA-I (Dee et al., 2011; Simmons, 2006) is a global reanalysis product from the
European Centre for Medium-Range Weather Forecasts (ECMWF) at ~80 km spatial resolution
for January 1979 through near real-time. The ERA-I product has a 4D variational assimilation
system and a lag of around one month from real-time. The ERA-I atmospheric reanalysis has a
consistent assimilatesion of a comprehensive set of observations,data from satellite remote
sensing, in situ, radio sounding, profilers, etc., distributed worldwide. The product combines
observations with a prior estimate of the atmospheric state generated by a global forecast model
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in a statistically optimal way. The ERA datasets have been evaluated and widely used in a
variety of studies related to pan-Arctic hydroclimatology (Betts et al., 2003; Finnis et al., 2009;
Slater et al., 2007; Su et al., 2006; Troy et al., 2011).
The WFDEI dataset is based on a method by the EU WATCH project (http://www.eu-
watch.org) and incorporates in situ observations in reanalysis datasets (Weedon et al., 2011). The
WATCH forcing dataset (WFD) is based on the ERA40 (the 40-year ECMWF Re-Analysis 40
year) reanalysis correction (Uppala et al., 2005) and an elevation correction was performed for
numerous variables. Extensive corrections were applied for rainfall and snowfall measurements
to remove biases in the reanalysis data. Furthermore, to retain the monthly statistics similar to in
situ observations of the Global Precipitation Climatology Centre (Schneider et al., 2008), an
undercatch correction was adopted whereas the daily variability of the reanalysis product is
conserved (Weedon et al., 2011). The WFDEI dataset used in this study was produced
employing the WFD method to the ERA-I reanalysis data (Dee et al., 2011; Weedon et al.,
2011). The 1979–2009 WFDEI daily precipitation, Tmax, Tmin, and wind speed datasets are
downloaded from the DATAGURU website (http://dataguru.nateko.lu.se/) at 0.50°.
The IDW (Shepard, 1968) dataset of daily precipitation, Tmax and Tmin are derived
primarily from 14 ECCC meteorological stations. These observation stations were spatially
interpolated by applying the IDW interpolation method, and gridded datasets have been procured
at 0.10° horizontal resolution for the LNRB. The grid cell values are calculated by weighted
averaging of the station data, and it assumes that each measured point has a local influence that
diminishes with distance (Huisman and De By, 2009). The IDW method requires a choice of
power parameter and a search radius, which control the significance of station observations on
the interpolated values. High power value in the IDW interpolation ensures a high degree of local
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influence, gives more emphasis to the nearest point, and produces output surfaces with more
detail. In this interpolation, the power parameter was set to two and the search radius specified as
241.17 km based on the selected ECCC stations (see Bill, 1999 for more details).
The NARR, ERA-I, and WFDEI datasets are acquired at 32, ~13, and ~55 km spatial
resolutions, respectively, and at a daily timescale. To simplify the forcing datasets inter-
comparison and to provide consistent VIC input, the NARR, ERA-I, and WFDEI were then
regridded to 10 km (~0.10°) spatial resolution using bilinear interpolation that matches the VIC
implementation scale. The NARR (32 km) dataset’s curvilinear grids whereasand the ERA-I and
WFDEI datasets’ Gaussian grids were interpolated from coarser resolution to slightly higher
resolution (10 km). No elevation correction during the interpolation frorm coarser to highfiner
spatial resolutions was performed as elevations changes within the study area vary no more than
±10 % in the study area; hence regridding of the NARR, ERA-I and WFDEI datasets from 32,
~13 and ~55 km, respectively, to 10 km spatial resolution results in negligible elevation-
dependent uncertainty. Indeed, LNRB grid cells exhibit almost no difference in orography;
therefore, atmospheric variables (i.e., air temperature) and basin elevation remain nearly
identical at both spatial resolutions.
Daily wind speeds, which is an essential input variable for the VIC model, areis not
available for the ANUSPLIN and IDW forcing datasets. Thus, the NARR wind speeds have been
used to run the VIC model using the ANUSPLIN and IDW datasets. The observed wind speeds,
both upper air and near-surface are assimilated in the NARR reanalysis product; thus, it shows
satisfactory correspondence with the ECCC observations (Hundecha et al., 2008).
Spatially regridded datasets (IDW, ANUSPLIN, NARR, ERA-I and WFDEI) at daily
temporal and 10 km spatial resolutions are then used to produce an ensemble mean forcing
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dataset from 1979 to 2009. For this multi-product ensemble forcing dataset, daily precipitation,
Tmax and Tmin are derived from the average of all five gridded products, while daily wind speed
ensemble is calculated from the mean of three reanalysis products (NARR, ERA-I, and WFDEI)
as the other two datasets (IDW and ANUSPLIN) have no such records. Further, performance of
this multi-product ensemble precipitation and air temperature dataset was evaluated against the
observed ECCC station data for the period of 1979–2009 (Figure 2). We found that for the study
period, the spatially regridded multi-product ensemble data can satisfactorily reproduce
precipitation and air temperature with some intermodel variation (Figure 3). The multi-product
ensemble approach was used previously over globally, over a large and regional domains, in
previous studies to evaluate changes in water balance components under historical and projected
future climate conditions (Fowler et al., 2007; Fowler and Kilsby, 2007; Mishra and Lilhare,
2016; Wang et al., 2009).
3.2 The Variable Infiltration Capacity (VIC) model
Development of the VIC model with an addition of the variable infiltration capacity curve was
an alternative to the earlier bucket model type representation (Liang et al., 1994, 1996; Wood et
al., 1992). Several modifications and updates have been made to render the VIC model more
physically-based, mainly for cold season processes, incorporating snow, canopy interception of
snow, and soil frost (Cherkauer et al., 2003; Cherkauer and Lettenmaier, 1999). The VIC model
is a semi-distributed macroscale hydrological model that has parameters for each grid cell;
however, it excludes horizontal interaction between model grid cells (Mitchell et al., 2004).
Therefore, it must be applied at various scales where the subsurface flow between grid cells is
minimal. In the VIC model, vegetation is represented using a mosaic schemeapproach represents
tiles with multiple vegetation types co-existing in a single grid cell. These vegetation types are
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specified using the root-fraction, canopy resistance, LAI, and other related parameters.
Advantages of the VIC model over other hydrological models are: it considers sub-grid
variability in land surface vegetation classes and soil moisture storage capacity; it assumes non-
linear recession of baseflow from lower soil layers; and it considers topographic variation, which
allows orographic precipitation and temperature lapse rates, yielding more realistic estimates in
mountainous regions but not applicable (although not a factor in the current application toover
the LNRB). VIC uses a stand-alone routing model to route the combined runoff and baseflow
from each grid cell to the basin outlet (Lohmann et al., 1998). In the routing model, water is not
allowed tocannot flow back to theupstream grid cell once it has reachesd the channel. This model
relies on the linear transfer function by considering flow direction and unit hydrograph for
simulating streamflow (Lohmann et al., 1996, 1998).
3.2.1 The VIC model implementation
In this study, the VIC (version 4.2.d) model (Liang et al. 1994, 1996) with more recent
modifications (Bowling et al., 2003; Bowling and Lettenmaier, 2010; Cherkauer et al., 2003) is
used to simulate streamflow at a daily time-step in full water and energy balance mode that
includes soil ice formation (Table 1). The VIC model has also been widely applied using various
forcing datasets from weather and climate prediction models as climate change has been an
important issue since the 1990s (Shukla et al., 2013, 2014; Wang et al., 2009).
The VIC model grid cells over the LNRB comprise 41 rows and 90 columns with a 5°
range of latitudes (53°-58° N) and a 12° range of longitudes (103°-91° W) Thise VIC model
setup over the LNRBapplication uses three soil layers, five soil thermal nodes (the default value)
that are solved using the method of Cherkauer and Lettenmaier (1999), and a constant bottom
boundary temperature at a damping depth of 10 m for our study region (Williams and Gold,
16
1976). The LNRB’s tiles are characterized by soil and vegetation fractions, which were
proportionally partitioned proportionally within a grid cell. For cold region hydrology, VIC
follows the U.S. Army Corps of Engineers’ empirical snow albedo decay curve (USACE, 1956),
the total precipitation is distributed based on the 0.10° grid cell, and the air temperature is
adjusted based on the lapse rate to resolve the precipitation type. The default single elevation
band is used whereby VIC assumes that each grid cell is flat and takes the mean grid elevation
into account for simulations over the LNRB. Natural lakes and wetlands are considered in this
VIC model implementation to the LNRB; however, anthropogenic structures (i.e., dams,
reservoirs) and flow regulation are not incorporated in the VIC model. Future work will integrate
these components that may influence streamflow simulations of the Nelson River, which is
highly regulated for the hydropower generation (Lee et al., 2011). Ten unregulated tributaries of
the lower Nelson River for which observed streamflow records are available for the study period
(1979–2009) are selected for the model calibration and subsequent analyses (Table 2). Even
though the effects of CRD and LWR are not completely removed, the streamflow and water
balance estimation in the LNRB’s unregulated sub-watersheds satisfy the aim of the VIC model
input forcings and water budget uncertainty assessment. Across the basin, ten gauged sub-
watersheds outlets are selected to evaluate the routed streamflow from the VIC simulations. The
routing network and other essential inputs for the routing model (e.g., flow direction, fraction,
and mask) are created at 10 km resolution for the entire LNRB using the 30 m Shuttle Radar
Topography Mission (SRTM) digital elevation model (DEM;) (United States Geological Survey,
2013) (Figure 1c). Since the permafrost, which covers more than 68% of the basin’s total
areaLNRB and, plays a vital role in cold region hydrology, the “frozen soil” option is switched
on in the VIC model simulations.
17
3.2.2 Calibration and evaluation
The VIC model simulations from 1979 to 2009 are used for model calibration and evaluation at
ten hydrometric stations (Table 2). The model’s optimization process is carried out by
minimiziesng the difference between observed and simulated monthly streamflow at unregulated
hydrometric gauge locations within the LNRB. The model performance is examined by using
tThe Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), Kling–Gupta efficiency (KGE)
(Gupta et al., 2009), and Pearson’s correlation (r) coefficients in addition to percent bias
(PBIAS) provide metrics of the model’s performances.
Separate calibration is applied to all ten sub-watersheds within the LNRB to determine the
most optimized parameters against the observed streamflow. Further, we selected a minimum 10-
year period for model calibration and the remainder of the years (≥5) with available observations
for evaluation. The University of Arizona multiobjective complex evolution (MOCOM-UA)
optimizer yields the VIC model calibration at monthly time scale (Shi et al., 2008; Yapo et al.,
1998). The MOCOM-UA optimizer searches a group of VIC input parameters (Table S3) using
the population method; it trieattempts to maximize the NSE coefficient between observed and
simulated streamflow at each iteration. At each trial, the multiobjective vector consisting of VIC
parameters is determined, and the population is rankordered by the Pareto rank of Goldberg
(1989). In the MOCOM-UA optimization process, the user defines the training parameter set is
defined by the user. Here, six VIC soil parameters are used as the training parameter set for the
optimization process (Table S3): b_infilt (a parameter of the variable infiltration curve), Dsmax
(the maximum velocity of base flow for each grid cell), Ds (the fraction of the Dsmax parameter
at which nonlinear base flow occurs), D2 and D3 (depth of the second and third soil layers
depth), and Ws (the fraction of maximum soil moisture where nonlinear base flow occurs). These
18
six parameters are optimized separately for all input forcings, by minimizing the difference
between modeled and observed monthly runoff for all ten sub-watersheds. Tables 1 and S3
provide details of input forcings, VIC configuration, soil parameters, definitions, ranges, and
final values for all selected sub-watersheds.
3.3 Experimental set-up and analysis approach
A series of different VIC model setups was derived to (i) compare the VIC model’s response
when forced by different gridded datasets, and (ii) evaluate the uncertainties associated with the
water budget estimation using different forcings. For objective (i), we used all five datasets and
their ensemble to run VIC simulations and facilitate detailed comparison of different input
forcing datasets and their hydrological response. In objective (ii), rather than doing the inter-
comparison of datasets, our goal is to examine the uncertainty that mainly occurs by input
forcings and influence overall water balance results in the LNRB. We thus calibrated and
validated the VIC model with each input forcing dataset, estimated water balance components
separately, and selected IDW as the reference dataset to compare different outputs as it was
derived from the ECCC meteorological stations. The experiments are categorized as follows:
Inter-comparison simulations: the VIC model was driven by each forcing dataset for 31
years (1979 to 2009) including calibration and validation periods, for each sub-watershed (Table
2). The VIC simulations driven by IDW, ANUSPLIN, NARR, ERA-I, WFDEI, and
ENSEMBLE forcings from 1979–1983 are used to generate the VIC model initial state
parameter file, to allow model spin-up time for five years, for each forcing dataset. The VIC
model validation runs were also initialized with these six different state files to produce
hydrological simulations for the entire period (1979–2009). The VIC simulations driven by IDW,
ANUSPLIN, NARR, ERA-I, WFDEI, and ENSEMBLE are referred to as IDW-VIC,
19
ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC and ENSEMBLE-VIC, respectively.
These validated simulations were run for the LNRB’s ten selected sub-watersheds: BRL, FRF,
GRS, GRJ, KRG, LRB, ORT, SRN, TRT, and WRM (Table 2).
VIC model calibration and evaluation: here we used an individual calibration strategy
using each forcing dataset for ten selected sub-watersheds within the LNRB. There was a
necessity of separately calibratinged and validatinged the VIC model using different forcing
datasets to investigate uncertainties in the water balance estimation over the LNRB. In thise
model calibration and evaluation process, we selected a minimum ten and five years within
1979–2009, respectively (Table 2). Based on the observed hydrometric records for some of the
sub-watersheds, these calibration and evaluation time periods vary within 1979–2009. The initial
state for each input forcing dataset was prepared individually and used in the respective model
runs for the water balance estimation. This diminishes simulation uncertainty during the
calibration and validation process, and in the modelled water balance for the entire study period.
We performed the calibration of six soil parameters, i.e., b_infilt, Dsmax, Ws, D2, D3, and Ds, in
six optimization setups using different forcing datasets (IDW, ANUSPLIN, NARR, ERA-I,
WFDEI, and ENSEMBLE). The VIC calibration for each forcing dataset was run using different
ranges of the calibration parameters in the MOCOM-UA optimizer as these ranges of parameter
limits are sensitive to model calibration process (Islam and Déry, 2017). The final optimized
values for all sub-watersheds from different model calibrations are then extended to the
remaining study period that further facilitates validation and spatial water balance analysis over
the LNRB.
Water balance estimations: here we used the calibrated IDW-VIC simulation as a
reference to investigate the uncertainties in the water balance estimation from 1979–2009 using
20
different forcing datasets. Five different calibrated setups, namely ANUSPLIN-VIC, NARR-
VIC, ERA-I-VIC, WFDEI-VIC and ENSEMBLE-VIC, were performed for the water year
(October to September) in the entire study period of 1979–2009. To examine seasonal
differences at the grid scale, between IDW-VIC and each other VIC model simulations, in water
balance components such as evapotranspiration (ET), total runoff (TR), and average soil
moisture (SM), we selected four seasons: winter (DJF), spring (MAM), summer (JJA), and
autumn (SON). For each experiment the routed streamflows for selectedthe 10 sub-watersheds
were also examined based on the availability of observed hydrometric records availability and
the NSE, KGE, Pearson’s r and PBIAS were calculated for both the calibration and validation
periods at every sub-watershed outlets (Tables 4, 5, S1, and S2).
4. Results and discussion
We first examine the ANUSPLIN, NARR, ERA-I, and WFDEI gridded datasets to investigate
differences in precipitation and air temperature against the ECCC meteorological stations, and at
several temporal and spatial scales across the LNRB. The VIC simulations using these forcing
datasets, including IDW and ENSEMBLE, are then discussed to examine uncertainties in water
balance components (ET, TR, and SM).
4.1 Inter-comparison of gridded climate data with station observations
Although each selected gridded dataset incorporates station-based climate observations, the
reliability of these datasets varies with the density and quality of in situ stations and topography
of the region. There is thus a necessity of an additional comparison of each gridded dataset
corresponding to the different regions and timescales (seasonal and annual) within the LNRB
(Eum et al., 2014; Lindau and Simmer, 2013; Petrik et al., 2011). Observational data for this
inter-comparison were obtained from four ECCC meteorological stations (Climate ID) within the
21
LNRB: Norway House A (506B047), Flin Flon A (5050960), Gillam A (5061001), and
Thompson A (5062922). These have non-homogenized continuous daily records of precipitation
and mean air temperature for the study period (Environment and Climate Change Canada, 2016).
These stations are well maintained, monitored, and processed by the ECCC and cover different
sub -regions of the study domain. Instead of analyzing nearby grids with the station data, since
each gridded dataset has a different spatial resolution, we performed area-averaged comparisons
from the four ECCC stations with each gridded dataset (Figures 4 and S1–S2). Here, we
hypothesize that the mean of precipitation and air temperature using four different stations
represent the basin average observational condition that integrates only continuous records for
the inter-comparison analysis. Even though the station observations have been used in
developing the climate products, comparison with mean observations is still meaningful since
archived (raw) station datasets are used in producing most of the gridded datasets, and there is a
difference between archived and adjusted values. We did not incorporate the ENSEMBLE and
IDW datasets in this analysis as they were used separately for the detailed comparison and
discussed in following sections.
To examine the consistency and pattern of gridded datasets against the ECCC
observations, each dataset was spatially averaged over the LNRB from 1979 to 2009. Figure 4a
presents the long-term mean annual precipitation and air temperature for the period 1979–2009.
Overall, yearly precipitation from the ERA-I and WFDEI is higher thansurpasses that from the
ANUSPLIN, ECCC, and NARR datasets, which is notable for across the entire study period.
ANUSPLIN underestimates consistently mean annual precipitation whereas NARR shows better
agreement with the observations for most years. The differences in annual precipitation from
four different datasets increase in recent years, mainly from 2004 to 2009. These emerging
22
differences (post 2003) are likely because of the Canadian precipitation observations not being
assimilated into most of the gridded products as of 2004 (Boucher and Best, 2010; Mesinger et
al., 2006; Uppala et al., 2005). Table 3 shows mean annual and seasonal statistics (1979–2009),
i.e., root mean square error (RMSE) and PBIAS against the ECCC data for precipitation and air
temperature. Long-term annual precipitation for the NARR dataset shows less positive PBIAS
and RMSE values among all other datasets while ERA-I and WFDEI show high RMSEs and
PBIAS due to systematic overestimation in precipitation (Table 3). The ANUSPLIN data show
dry bias (-5.8%) in annual precipitation but low RMSE (37.3%) amongst other datasets. We also
performed long-term seasonal analyses (Table 3 and Figure S1) that reveal ANUSPLIN
underestimates precipitation during all seasons apart from winter whereas NARR data better
represent seasonality with lower RMSE (12.7-37.2 mm) for most of the years when compared
withto ECCC stations (Figure S1). The ERA-I and WFDEI show substantial overestimation in
summer precipitation (44.47% and 21.27%) that declined by ~10% in spring and autumn (14.6-
36.1%).
Similar to that for annual and seasonal precipitation, an inter-comparison for mean annual
and seasonal air temperature is also performed for all four gridded datasets with the ECCC
observations. Figure 4b shows area-averaged long-term mean annual air temperature from 1979
to 2009. Apart from precipitation differences, Tthe NARR dataset exhibits ~1°C
differenceviation in annual air temperature, and high RMSE (0.90°C) over the LNRB, when
compared withrelative to the ECCC datasets whereas the ERA-I shows better agreement with the
loweast RMSE (0.25°C) among all other datasets (Table 3). The ANUSPLIN and WFDEI are
~0.5°C colder than the observations, a negative bias that persists throughout the study period at -
0.16% and -0.18%, respectively. The seasonal analysis reveals colder mean air temperature from
23
the ANUSPLIN, ERA-I, and WFDEI, which ranges from -0.02% to -0.29% for all datasets, with
similar inter-annual variability and trends during all four seasons (Figure S2). The NARR dataset
shows warm air temperatures during all seasons and the highest (lowest) positive biases, 0.38%
(0.17%), in summer (spring). In general, the ERA-I and ANUSPLIN have lower biases and
RMSEs than the NARR for mean seasonal air temperature while WFDEI nestles in between
ERA-I and ANUSPLIN for these statistics. Moreover, the NARR dataset shows larger RMSEs
than the others and has a strong positive bias in mean seasonal and annual air temperature over
the LNRB. These findings are consistent with the trend analysis of Aziz and Burn (2006) and an
inter-comparison performed by Eum et al. (2014), where they found high inter-annual and
seasonal uncertainty between the observed and gridded precipitation and air temperature
estimates over theirs different part of study areas.
4.2 Basin average inter-comparison of forcing datasets
The domain averaged daily mean precipitation magnitudes vary significantly among datasets
(Figure 2). Summer precipitation that begins in March and persists until August shows greater
inter-dataset differences over the LNRB. The ANUSPLIN precipitation is underestimated
consistently throughout the study period as compared relative to the IDW and NARR datasets,
with ~0.5 to 1 mm day-1 differences, especially in summer. This underestimation is more distinct
in the IDW-ANUSPLIN spatial difference, showingwith up to 60 mm month-1 in total summer
precipitation over most part of the LNRB (Figure S3). The spatial precipitation difference in the
NARR dataset varies within ±20 mm month-1 for all seasons, a minimum total seasonal
difference amongst all other datasets. For peak spring and summer precipitation, the range of
inter-dataset spread varies from 2.0 to 5.0 mm day-1 as overestimated by the ERA-I and WFDEI
datasets, respectively, during the study period. These overestimations are evident in the spatial
24
differences of IDW-ERA-I and IDW-WFDEI, which show more than 20 mm month-1 wet bias in
ERA-I precipitation for spring, summer, and autumn whereas ~15 mm month-1 in WFDEI for all
seasons.
The daily mean air temperature of the IDW, ANUSPLIN, NARR, ERA-I, WFDEI, and
ENSEMBLE datasets remainfalls below 0°C from November to March and rises above 0°C in
early spring over the LNRB domain (Figure 2). While the inter-datasets seasonal variability of
air temperature is quite similar, winter in the IDW and NARR is ~2°C warmer compared to the
remaining datasets. The grid-scale seasonal differences (IDW minus ANUSPLIN, NARR, ERA-
I, and WFDEI) of mean air temperature spatially quantify the inter-dataset disagreements (Figure
S4). The IDW-NARR difference is within ±1°C whereas the IDW-ANUSPLIN difference
exceeds ~2.5°C over most of the LNRB in all seasons, revealing ANUSPLIN air temperatures to
be quite colder than in the IDW dataset. While the IDW-ERA-I shows >2°C difference over
most of the LNRB in spring, summer and autumn, the IDW-WFDEI difference remains within
±1°C, which shows WFDEI air temperatures are slightly warmer than the ERA-I.
The dry bias in the ANUSPLIN precipitation arises possibly from the thin plate smoothing
spline surface fitting technique used in its preparation, a feature reported in previous studies
(Islam and Déry, 2017; Milewska et al., 2005; O’Neil et al., 2017; Wong et al., 2017). In the
reanalysis products, NARR shows the best agreement with ECCC stations interpolated gridded
dataset, IDW, while other products such as (ERA-I and WFDEI) reveal considerable differences
in air temperature and precipitation, which may have been induced by the climate model used to
assimilate and generate these products. However, WFDEI shows an improvement over the ERA-
I dataset when compared to the IDW data, in agreement with other studies (Boucher and Best,
2010; Weedon et al., 2011, 2014; Wong et al., 2017).
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4.3 Hydrological simulations
The IDW-VIC, ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC, and ENSEMBLE-
VIC simulation performance was evaluated using the NSE, KGE, Pearson’s correlation (r), and
PBIAS coefficients by calibrating and validating against observed daily streamflow for the ten
selected unregulated rivers within the LNRB (Figure 5, Tables 4–5 and S1–S2). The mean of
NSE and KGE scores for all ten sub-watersheds are much higher for the NARR-VIC and
ENSEMBLE-VIC simulations compared to the IDW-VIC, ANUSPLIN-VIC, ERA-I-VIC, and
WFDEI-VIC. The lower NSE and KGE scores in the IDW-VIC and ANUSPLIN-VIC
simulations reflect the precipitation undercatch and a dry precipitation bias in their respective
datasets. As the model resolution, configuration, and soil data were identical for all VIC
simulations, different NSE and KGE values show uncertainty associated only with each
observational gridded dataset. Despite the low NSE and KGE scores of the IDW-VIC,
ANUSPLIN-VIC, ERA-I-VIC, and WFDEI-VIC simulations, the correlation coefficients remain
significantly high for all sub-basins. The negative biases in simulated streamflows are
contributeing to the lower NSE and KGE coefficients, whereas the timing of seasonal flows is
quite similar to the observed flows in the IDW-VIC and ANUSPLIN-VIC simulations. The
ERA-I-VIC and WFDEI-VIC simulations reveal strong positive biases for most of the sub-
watersheds due to their wet biases in the precipitation forcing datasets. However, these
simulations show acceptable NSE and KGE coefficients for most of the sub-watersheds.
The VIC simulated total runoff (surface runoff and baseflow) is routed to produce
hydrographs for the LNRB’s ten unregulated sub-basins (Figure 9). Comparison of simulated
runoff with observations shows the NARR-VIC, and ENSEMBLE-VIC simulations show highly
consistent model performance, while the IDW-VIC and ANUSPLIN-VIC values are
26
considerably lower for all sub-watersheds. ANUSPLIN-VIC and IDW-VIC runoffs show
substantial disagreement with the observed hydrograph, especially in the KRG, LRB, ORT, SRN
and WRM sub-basins, owing to the dry bias in the precipitation forcing and undercatch at the
ECCC stations, respectively. The ERA-I-VIC and WFDEI-VIC simulations overestimate
summer and autumn runoffs and reasonably capture reasonably well winter and spring flows for
all sub-watersheds. Consistent with the spatial differences of precipitation, mean air temperature
and runoff (Figures S3–S4, and Figure 6), the wet (warmer) ERA-I and WFDEI precipitation
(mean air temperature) over the LNRB in spring, summer and autumn induce more surface
runoff and snowmelt that overestimate simulated flows. Simulated flows for the BRL, FRF and
TRT sub-watersheds from all VIC model setups are comparable in magnitude with observations,
but the timing is considerably shifted (~20 days), yielding more spring runoff and earlier decline
of summer recession flows. Shifts in the hydrographs may be associated with the warmer air
temperatures over these sub-basins that cause earlier snowmelt runoff. Difference in the air
temperature during spring and summer for these sub-watersheds are evident in the spatial
seasonal comparisons (Figure S4). In contrast, the NARR air temperature comparatively shows
minimum differences amongst other datasets in winter, spring and autumn when compared with
the IDW dataset. This may satisfy the snowmelt-driven runoff in the NARR-VIC simulation,
causing a better representation of simulated flows for these seasons over each LNRB sub-
watershed. The ENSEMBLE-VIC and NARR-VIC hydrographs are better in most of the sub-
watersheds with high NSE and KGE scores (Figure 5, Tables 4 and S1).
The basin average and station based inter-comparison analysis shows that forcing
datasets uncertainties influence the VIC model performance significantly. This is consistent with
other studies whereby model structure contributes less uncertainty in the water balance
27
simulations (Troin et al., 2015, 2016), whereas input forcing datasets are often the major source
of uncertainty in hydrological modelling (Chen et al., 2011; Fekete et al., 2004; Islam and Déry,
2017; Kay et al., 2009). Moreover, the NARR dataset was used in other studies to examine
systematic biases in simulations and the substantial effects of lateral boundary conditions on the
regional model’s performance (de Elía et al., 2008; Eum et al., 2014; Luca et al., 2012). In this
study, we obtained optimal results from the NARR-VIC simulation amongst all other input
datasets; therefore, Table S3 provides thea list of final values for the VIC soil parameters is
provided in Table S3.
Although the precipitation differences are acceptable for regional hydrological modelling,
the air temperature uncertainties play a vital role in cold region hydrological simulations. In the
LNRB, air temperature controls spring freshets and summer water availability, which makes
regional water balances associated with snowmelt runoff more susceptible to air temperature,
rather than precipitation. Some of the selected sub-watersheds are comparatively small than other
basins that show less sensitivity to air temperature and precipitation. However, uncertainties in
daily air temperature and precipitation are critical for the runoff timing in VIC simulations over
the majority of the LNRB’s sub-watersheds.
4.4 Uncertainty in the water budget estimation
Table 6 presents a summarizesy of the observational average annual precipitation and VIC
simulated water budgets of the LNRB’s sub-watersheds, from all five input forcings, and their
estimated standard errors. For 1979–2009, the GRJ sub-watershed shows high average annual
inter-dataset variability (53.0 mm year-1) in precipitation that results ~60, ~50 and 70 mm year-1
standard errors in the total runoff, evapotranspiration, and average soil moisture, respectively.
The decrease in precipitation uncertainty yields less deviation in simulated water budgets; for
28
example, the GRS sub-watershed exhibits a 29.6 mm year-1 deviation in precipitation estimates,
which shows a minimum error in simulated water balances among all other sub-watersheds. The
smaller SRN, FRF and TRT (area < 900 km2) sub-basins manifest similar inter-dataset errors
(~36 mm year-1) for annual precipitation whereas relatively larger sub-watersheds (GRJ and
ORT) show significant differences in the standard error, which reveal higher spatial variability
from different forcing datasets. Consequently, these precipitation uncertainties among all
selected sub-watersheds translate to 20-60 mm year-1 errors in the water balance estimates. These
results correspond well with those concluded by Fekete et al., (2004) who found that the
uncertainty in precipitation translates to at least the same, and typically much more significant,
level of uncertainty in runoff and relative water balance terms.
4.4.1 Total Runoff (TR)
Figure 10 (b1–b4) shows spatially averaged seasonal TR for ten selected sub-watersheds during
the study period (1979–2009). Domain-averaged seasonal TR shows higher uncertainty for
relatively larger sub-watersheds, for example, (e.g. GRJ, KRG, LRB, ORT, and WRM),
especially in spring and summer. The simulated TR uncertainty is higher in spring and summer
than fall and winter, which is mainly due to the more substantial seasonal variation in inter-
datasets precipitation and air temperature. The ENSEMBLE-VIC simulations of mean spring TR
match significantly with multidata-VIC simulations. For instance, eight out of ten sub-basins
mean TR is well captured by the ENSEMBLE-VIC whereas two of them showed
underestimation, and this underestimation extends into summer in six sub-watersheds (Figure 10
b2, b3). Inter-seasonal air temperature analysis shows that due to extreme minimum air
temperature in winter, simulated multi data and ENSEMBLE-VIC TRs over each sub-watershed
are low and result less inter simulations uncertainty. The simulated error increases in early spring
29
and persists until late autumn, consistent with seasonal precipitation for all sub-watersheds.
However, there remains much uncertainty in air temperature records over the LNRB from the
different forcing datasets, which can be translated into inter-seasonal water balance estimation in
the region. For annual TR estimates, the GRJ, KRG, LRB, and WRM sub-watersheds reveal high
inter-simulation error whereas relatively smaller sub-basins show less deviation in their results
and better TR estimation from ENSEMBLE-VIC (Figure S5). Moreover, an interplay between
changes in precipitation type (solid and liquid) and increases in air temperature may play a
crucial role in our understanding of the modelled water balance (Barnett et al., 2005; Fowler and
Archer, 2006; Immerzeel et al., 2010).
4.4.2 Evapotranspiration (ET)
Figure 10 (c1–c4) shows area-averaged mean seasonal ET for all ten sub-watersheds within the
LNRB. Due to cold temperatures in winter, ET shows smaller value (<3 mm) for all sub-
watersheds (Figure 10c1-c4). It increases through spring and peaks in summer with 35 mm
multidata-VIC simulation error, which can be attributed to a substantial rise in air temperature
and precipitation. The multidata-VIC uncertainty decreases in autumn that essentially reveals
less regional variability in ET estimates (~60 mm) over the LNRB’s sub-basins. Conditions such
as different air temperature records and precipitation undercatch in summer may lead to
detrimental impacts on soil moisture. Depleted soil moisture conditions induce basin water
limitations that yield uncertainty in ET estimates; for example, the largest sub-watersheds (GRS
and GRJ) within the basinLNRB show higher uncertainty in ET estimates (Figure 10). The
ENSEMBLE-VIC simulation shows a better representatsion of the winter, spring, and autumn
ET with overestimates in summer for all sub-watersheds. For annual ET, the GRJ and SRN sub-
30
basins show high variability within VIC simulations, but other sub-watersheds have a less inter-
simulation error and better ET estimates from ENSEMBLE-VIC (Figure S5).
4.4.3 Soil Moisture (SM)
Figure 10 (d1–d4) shows area-averaged mean seasonal SM for all ten selected sub-watersheds
within the LNRB. Among all other seasons, the highest SM reported in the spring season
followed by summer and autumn due to seasonal transitions and snowmelt runoff, which is more
evident in relatively large sub-watersheds (BRL, GRJ, GRS, LRB, and WRM) (Figure 10d1-d4).
This increased SM values for spring, summer and autumn with concomitant effects on runoff
conditions in respective seasons. Furthermore, the FRF sub-watershed is smaller relative to
others; however, it shows considerable inter-dataset variation (~90 mm) in SM for all seasons.
Moreover, eight out of ten sub-basins demonstrate substantial multi datasets uncertainty in SM
for all seasons but mean seasonal SM is well captured by the ENSEMBLE-VIC for these sub-
watersheds. The highest annual SM arises in the GRS, FRF, and GRJ sub-basins with significant
inter-datasets variation whereas other sub-watersheds showed less error in SM simulations with
nearly identical annual values (Figure S5).
This analysis shows that the hydrological model performances and water budget estimations
change considerably with different driving datasets, an essential part of the hydrological
simulation. The proper practical implementation for present and future climatic conditions
requires a well calibrated and validated hydrological model using reliable input forcing dataset.
Thus, it can produce trustworthy hydrological information important for end users and water
resource managers.
31
5. Conclusions
This study used the IDW, ANUSPLIN, NARR, ERA-I, and WFDEI observation-based gridded
datasets to examine systematic inter-dataset uncertainties and their implications on VIC
hydrological simulations over the LNRB. The uncertainties in modelled water balance estimation
at different temporal resolutions were also investigated.
The air temperature in the ERA-I and WFDEI were comparable, while precipitation from
both datasets remains quite high across the basin compared to the IDW and NARR datasets. The
ANUSPLIN precipitation had a significant dry bias over the LNRB compared to all other forcing
datasets. The ECCC has already reported that meteorological stations used to prepare IDW
gridded datasets experienced some precipitation undercatch resulting in dry biases during the
study period. The NARR seasonal air temperature was ~1°C warmer than the other datasets over
most of the LNRB. The NARR-VIC and ENSEMBLE-VIC simulations had higher NSE, KGE,
and Pearson's r values and more reasonable hydrographs compared with observed flows for more
than six sub-basins of the LNRB. The ERA-I-VIC and WFDEI-VIC simulations revealed higher
total runoff compared to other datasets, likely due to their precipitation overestimates. The IDW-
VIC and ANUSPLIN-VIC simulations had noticeably lower runoff, NSE, and KGE values along
with less evapotranspiration and soil moisture amounts owing to their reduced precipitation
estimates. The NARR dataset showed warm area-averaged winter, summer, and autumn area-
averaged air temperatures, which influenced its streamflow simulations for some of the sub-
basins by shifting runoff peaks and increased ET, and hence lower total runoff. The IDW-VIC
simulations underestimated flows for most of the sub-watersheds showrevealing precipitation
undercatch and air temperature biases in the observed records. Moreover, the ANUSPLIN-VIC
and IDW-VIC water balance estimates were considerably lower for all sub-basins. Nevertheless,
32
the ENSEMBLE-VIC was not affected much by precipitation biases and undercatch, and
ENSEMBLE-VIC and NARR-VIC simulations were identical in most of the cases over the
LNRB’s sub-basins.
This study’s inter-comparison exhibited spatiotemporal differences between the IDW,
ANUSPLIN, NARR, ERA-I, and WFDEI datasets over the LNRB that is essential to capture the
uncertainties in hydrologic modelling responses. Overall, the NARR and ENSEMBLE datasets
provided reliable results for the LNRB’s hydrology, whereas the IDW, ANUSPLIN, ERA-I, and
WFDEI datasets had issues with either air temperature or with precipitation. The LNRB’s natural
lakes and wetlands dominated hydrology along with its existing and proposed regulation
structures require highly accurate gridded data products to increase the reliability of the
hydrological simulations. This could be possible through improving meteorological station
density or by obtaining the best suitable and accurate gridded dataset. However, the air
temperature plays a vital role in hydrological simulations, enhancing the quality of precipitation
records that can lead to more precise hydrological modelling of the LNRB. Significant
precipitation bias can considerably degrade the model performance. There is a necessity of
distinct methods to deal with the increasing uncertainty associated with models themselves, and
with the observed records required for driving and validating hydrological models.
In this study, our primaryncipal focus was on the input forcing datasets induced
uncertainty in hydrological simulations using the VIC model. However, other sources of
uncertainties are not discussed that may be responsible for a series of impacts on hydrological
outcomes. First, the VIC model structure uncertainty caused by model parameters may result in
different estimates of hydrological terms. Thus, it may be useful to use various hydrological
models and quantify inter-model structure uncertainty. The model used in this study drives at a
33
daily temporal resolution that can be improved to hourly to obtain optimal model performance.
Along with these, the in-situ soil moisture observations and satellite-derived evapotranspiration
estimates can be used for the VIC model evaluation. Moreover, VIC simulations usually evaluate
based on observed and simulated flows comparison that partially depends on a standalone
routing model, which may comprise structural uncertainties. Finally, natural lakes, wetlands, and
frozen ground in the present VIC model setup may be sensitive to streamflows and other water
balance variables. Further, a sensitivity analysis of the drainage basin physical characteristics
(natural lakes, wetlands, and frozen ground) can provide useful insights in hydrological
modelling. Apart from this, we selected ten unregulated sub-watersheds for model calibration
and evaluation, and there is a need to extend calibrated parameters for the entire LNRB. Our
future work will therefore investigate inter-hydrological model uncertainties, a possible
sensitivity of natural lakes, wetlands and frozen ground, and an extension of calibrated
parameters to the entire study domain using efficient interpolation techniques.
Acknowledgements
Financial support for this research was provided by Manitoba Hydro and the Natural Sciences
and Engineering Research Council of Canada (NSERC) through the BaySys project. We thank
Siraj Ul Islam (UNBC) for assistance in setting up the VIC model over the LNRB. Mark
Gervais, Phil Slota, Mike Vieira, and Shane Wruth (Manitoba Hydro) provided helpful advice
and logistical support throughout this work and beneficial reviews on an earlier version of the
manuscript.
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Table 1. VIC inter-comparison experiments performed using different observational forcings.
VIC model input forcing datasets Description VIC configuration
IDW
Inverse Distance Weighted interpolated observations from 14 ECCC meteorological stations (Gemmer et al., 2004; Shepard, 1968)
Domain = 53°−58° N 91°−103° WResolution = 0.10° × 0.10°Time step: dailySoil Layers: 3Vertical elevation band: OffNatural lakes and frozen ground: OnTime span: 1980–1989 (calibration*), 1990–1999 (evaluation*)
ANUSPLINThe Canadian Precipitation Analysis and the thin-plate smoothing splines (Hopkinson et al., 2011)
NARR North American Regional Reanalysis
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(Mesinger et al., 2006)
ERA-I ERA-Interim (Dee et al., 2011)
WFDEI Watch forcing data (WFD) ERA-Interim (Weedon et al., 2014)
*Calibration and evaluation periods vary between 1979–2009 based on the availability of continuous observed hydrometric records.
Table 2. List of ten selected unregulated hydrometric stations, maintained by the Water Survey of Canada and Manitoba Hydro, for the VIC model calibration and evaluation with sub-watershed characteristics and mean annual discharge (Water Survey of Canada, 2016).
Station name (abbreviation) [Gauge ID]
Mean sub-watershed
elevation (m)
Drainage area (km2)
Mean annual discharge (m3 s-1)
Calibration period
Validation period
Burntwood River above Leaf Rapids (BRL) [05TE002]
302.44 5,810 22.9 1980-1989 1990-1999
Footprint River above Footprint Lake (FRF)
273.75 643 3.2 1980-1989 1990-1999
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[05TF002]
Grass River above Standing Stone Falls (GRS) [05TD001]
265.02 15,400 64.6 1991-2000 1979-1983
Gunisao River at Jam Rapids (GRJ) [05UA003] 260.88 4,610 18.0 1990-1999 2000-2004
Kettle River near Gillam (KRG) [05UF004] 164.67 1,090 13.2 1981-1990 1991-1995
Limestone River near Bird (LRB) [05UG001] 173.59 3,270 21.5 1980-1989 1990-1999
Odei River near Thompson (ORT) [05TG003] 253.46 6,110 34.3 1980-1989 2000-2009
Sapochi River near Nelson House (SRN) [05TG006] 259.13 391 2.2 1980-1989 1990-1999
Taylor River near Thompson (TRT) [05TG002]
236.15 886 5.1 1980-1989 1992-1996
Weir River above the Mouth (WRM) [05UH002] 125.84 2,190 15.6 1980-1989 1991-1995
Table 3. Seasonal and annual total precipitation and mean air temperature statistics for the domain-averaged ANUSPLIN, NARR, ERA-I, and WFDEI datasets against four ECCC stations average values across the LNRB, water years 1979–2009. Water year begins on 1 October and ends on 30 September of the following calendar year.
Precipitation (1979–2009)
Scores Datasets Winter Spring Summer Autumn Annual
RM
SE
(mm
)
ANUSPLIN 6.83 12.34 25.20 12.16 37.26
NARR 12.72 24.34 37.23 16.55 44.71
ERA-I 10.07 17.29 42.57 16.64 59.16
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WFDEI 21.42 22.73 22.25 25.22 80.31
PBIA
S (%
) ANUSPLIN 3.77 -13.86 -28.41 -7.50 -5.76
NARR 7.42 27.49 -16.32 -0.88 2.22
ERA-I -5.91 22.17 44.47 14.58 9.43
WFDEI 32.63 33.05 21.27 36.09 15.41
Mean air temperature (1979–2009)
Scores Datasets Winter Spring Summer Autumn Annual
RM
SE (o C
) ANUSPLIN 0.70 0.77 0.32 0.29 0.49
NARR 1.23 0.62 1.08 1.09 0.90
ERA-I 0.43 0.41 0.19 0.19 0.25
WFDEI 0.79 0.60 0.37 0.41 0.52
PBIA
S (%
) ANUSPLIN -0.21 -0.29 -0.11 -0.09 -0.16
NARR 0.35 0.17 0.41 0.38 0.31
ERA-I -0.08 -0.13 -0.02 -0.02 -0.06
WFDEI -0.26 -0.22 -0.13 -0.14 -0.18
Table 4. Monthly [daily] performance metrics for the VIC inter-comparison simulations. Calibration, based on the availability of continuous observed records, for the ten selected unregulated tributaries of the LNRB, is evaluated using the Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE) coefficients.
Sub-watershedsNSE Calibration (1980–1989): Monthly [daily]
IDW ANUSPLIN NARR ERA-I WFDEI ENSEMBLE
BRL 0.37 [0.04] 0.36 [0.08] 0.58 [0.03] 0.60 [-0.36] 0.50 [-0.71] 0.53 [-0.08]
FRF 0.32 [-0.26] 0.25 [-0.53] 0.37 [-0.22] -0.19 [-1.55] 0.14 [-0.86] 0.36 [-0.83]
GRS (1991-2000) 0.13 [-0.10] 0.02 [-0.45] 0.16 [-0.08] -0.14 [-1.76] -0.10 [-1.03] 0.21 [-0.10]
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GRJ (1990-1999) 0.32 [0.03] 0.20 [0.02] 0.42 [0.03] 0.28 [-2.88] 0.44 [0.27] 0.41 [-0.12]
KRG (1981-1990) 0.52 [0.33] 0.60 [0.37] 0.70 [0.37] 0.69 [-0.16] 0.56 [-0.35] 0.77 [0.45]
LRB 0.52 [0.45] 0.68 [0.46] 0.69 [0.49] 0.67 [-0.09] 0.64 [-0.07] 0.73 [0.39]
ORT 0.54 [0.28] 0.61 [0.39] 0.66 [0.29] 0.55 [-0.31] 0.48 [-0.27] 0.65 [0.24]
SRN 0.46 [0.25] 0.62 [0.36] 0.60 [0.27] 0.48 [-0.72] 0.52 [-0.32] 0.60 [0.32]
TRT 0.58 [0.26] 0.55 [0.23] 0.63 [0.21] 0.52 [-0.57] 0.55 [-0.30] 0.66 [0.22]
WRM 0.50 [0.41] 0.61 [0.42] 0.65 [0.43] 0.66 [-0.04] 0.63 [-0.02] 0.70 [0.38]
Mean 0.43 [0.18] 0.45 [0.14] 0.55 [0.20] 0.45 [-0.74] 0.44 [-0.20] 0.56 [0.10]
Sub-watershedsKGE Calibration (1980–1989): Monthly [daily]
IDW ANUSPLIN NARR ERA-I WFDEI ENSEMBLE
BRL 0.47 [0.44] 0.43 [0.44] 0.69 [0.53] 0.80 [0.36] 0.74 [0.26] 0.62 [0.48]
FRF 0.53 [0.35] 0.53 [0.33] 0.62 [0.44] 0.39 [0.06] 0.53 [0.27] 0.62 [0.28]
GRS (1991-2000) 0.54 [0.48] 0.47 [0.38] 0.59 [0.52] 0.49 [0.00] 0.44 [0.22] 0.59 [0.51]
GRJ (1990-1999) 0.37 [0.37] 0.28 [0.29] 0.50 [0.45] 0.62 [-0.48] 0.60 [0.49] 0.46 [0.44]
KRG (1981-1990) 0.39 [0.42] 0.43 [0.46] 0.68 [0.63] 0.51 [0.27] 0.55 [0.21] 0.73 [0.66]
LRB 0.37 [0.37] 0.54 [0.53] 0.71 [0.71] 0.54 [0.36] 0.50 [0.35] 0.60 [0.66]
ORT 0.46 [0.42] 0.54 [0.54] 0.63 [0.55] 0.60 [0.27] 0.66 [0.33] 0.69 [0.56]
SRN 0.32 [0.34] 0.47 [0.49] 0.52 [0.49] 0.66 [0.21] 0.76 [0.39] 0.54 [0.52]
TRT 0.52 [0.48] 0.48 [0.46] 0.57 [0.50] 0.59 [0.23] 0.78 [0.43] 0.57 [0.59]
WRM 0.35 [0.36] 0.49 [0.48] 0.71 [0.66] 0.56 [0.38] 0.57 [0.39] 0.69 [0.70]
Mean 0.43 [0.40] 0.47 [0.44] 0.62 [0.55] 0.58 [0.17] 0.61 [0.33] 0.61 [0.54]
Table 5. Same as Table 34 but for the Pearson’s correlation coefficient (r, p-value < 0.05 for all) and percent bias (PBIAS).
Sub-watershedsPearson’s r Calibration (1980–1989): Monthly [daily]
IDW ANUSPLIN NARR ERA-I WFDEI ENSEMBLE
BRL 0.69 [0.53] 0.72 [0.59] 0.80 [0.64] 0.81 [0.65] 0.78 [0.59] 0.77 [0.61]
FRF 0.61 [0.37] 0.61 [0.41] 0.70 [0.52] 0.48 [0.34] 0.53 [0.36] 0.67 [0.46]
GRS (1991-2000) 0.64 [0.57] 0.59 [0.48] 0.67 [0.62] 0.64 [0.50] 0.46 [0.32] 0.69 [0.60]
GRJ (1990-1999) 0.71 [0.53] 0.71 [0.55] 0.75 [0.58] 0.75 [0.60] 0.76 [0.63] 0.74 [0.56]
KRG (1981-1990) 0.81 [0.62] 0.85 [0.66] 0.85 [0.71] 0.92 [0.72] 0.88 [0.75] 0.90 [0.74]
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LRB 0.84 [0.71] 0.88 [0.70] 0.85 [0.76] 0.91 [0.66] 0.93 [0.70] 0.90 [0.70]
ORT 0.84 [0.64] 0.86 [0.73] 0.86 [0.71] 0.88 [0.74] 0.83 [0.72] 0.84 [0.73]
SRN 0.81 [0.60] 0.86 [0.68] 0.83 [0.66] 0.79 [0.61] 0.78 [0.64] 0.82 [0.68]
TRT 0.81 [0.60] 0.80 [0.60] 0.83 [0.61] 0.83 [0.63] 0.76 [0.59] 0.84 [0.65]
WRM 0.82 [0.68] 0.84 [0.68] 0.81 [0.71] 0.89 [0.67] 0.90 [0.70] 0.86 [0.72]
Mean 0.76 [0.59] 0.77 [0.61] 0.80 [0.65] 0.79 [0.61] 0.76 [0.60] 0.80 [0.65]
Sub-watershedsPBIAS Calibration (1980–1989): Monthly [daily]
IDW ANUSPLIN NARR ERA-I WFDEI ENSEMBLE
BRL -30.91 [-30.96] -38.22 [-38.24] -21.26 [-21.22] 2.52 [2.43] -6.30 [-6.43] -23.25 [-23.28]
FRF -17.82 [-17.81] -22.63 [-22.55] -22.15 [-21.98] 34.40 [34.38] 0.30 [0.34] -4.62 [-4.52]
GRS (1991-2000) -28.96 [-28.84] -29.67 [-29.55] -21.25 [-21.02] 41.44 [41.58] -18.45 [-18.31] -22.77 [-22.63]
GRJ (1990-1999) -39.18 [-39.13] -48.76 [-48.68] -36.31 [-36.24] 64.69 [65.03] -29.21 [-29.19] -33.03 [-32.87]
KRG (1981-1990) -36.14 [-36.10] -37.62 [-37.63] -21.07 [-21.00] 45.60 [45.73] 31.36 [31.57] -21.86 [-21.79]
LRB -41.62 [-41.67] -28.57 [-28.66] -15.58 [-15.64] 42.18 [42.06] 40.08 [40.00] -17.38 [-17.44]
ORT -40.25 [-40.33] -37.71 [-37.79] -32.80 [-32.82] 17.00 [16.86] 3.75 [3.71] -26.53 [-26.67]
SRN -44.56 [-44.67] -37.84 [-37.91] -37.86 [-37.87] 23.56 [23.32] 1.76 [1.70] -35.88 [-35.98]
TRT -29.65 [-29.76] -33.12 [-33.22] -31.40 [-31.42] 35.22 [35.05] 3.77 [3.73] -20.16 [-20.25]
WRM -40.60 [-40.59] -33.36 [-33.35] -17.15 [-17.11] 41.76 [41.70] 35.82 [35.79] -3.93 [-3.88]
Mean -34.97 [-34.99] -34.75 [-34.76] -25.68 [-25.63] 34.84 [34.81] 6.29 [6.28] -20.94 [-20.93]
Table 6. Components of the water budget in the LNRB’s sub-watersheds, average annual values for 1979–2009. The average annual precipitation (PCP) based on the mean of five forcing datasets, and other terms are the total runoff (TR), evapotranspiration (ET), and average soil moisture (SM), all based on the mean of VIC simulations from five different input forcing datasets. Standard deviation (SD) shows inter VIC simulations variation in the water balance estimations.
Sub-watershedsPCP (mm) TR (mm) ET (mm) SM (mm)
Mean SD Mean SD Mean SD Mean SD
BRL 498.30 31.27 98.04 26.55 404.20 19.54 80.05 17.90
FRF 522.00 37.67 110.60 28.70 409.60 31.69 169.80 88.26
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GRS 506.10 29.64 87.21 24.42 416.00 27.44 192.80 61.63
GRJ 539.10 52.95 100.30 59.92 435.20 49.10 135.30 70.33
KRG 523.50 51.25 158.20 56.00 369.50 23.61 86.72 16.24
LRB 515.40 48.77 139.70 55.34 378.60 24.78 95.79 26.77
ORT 525.30 38.03 147.20 48.67 381.80 32.70 91.24 18.47
SRN 523.70 36.65 111.80 31.09 415.10 40.85 98.53 22.21
TRT 521.30 31.27 137.80 36.23 385.70 33.37 94.54 19.31
WRM 510.00 50.39 141.10 58.32 373.10 26.39 90.45 24.01
Mean 518.47 40.79 123.20 42.52 396.88 30.95 113.52 36.51
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Figure 1. Maps of the LNRB. (a) The Nelson River Basin (NRB), Churchill River Basin (CRB), and Lower Nelson River Basin (LNRB). (b) major rivers within the LNRB are labelled, red diamonds denote current generating stations, and the yellow circle shows a proposed generating station by Manitoba Hydro, the Notigi Control Structure is represented by a green box, and the Churchill River diversion is indicated with a red star. (c) VIC model domain for the LNRB with 0.10° resolution and selected unregulated sub-watersheds (black line): BRL, FRF, GRS, GRJ, KRG, LRB, ORT, SRN, TRT, and WRM (Table 2) used in the study.
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Figure 2. Area-averaged time series of (left y-axis) mean daily precipitation (solid lines) and (right y-axis) daily air temperature (dotted lines) over the LNRB for the IDW, ANUSPLIN, NARR, ERA-I, WFDEI, and ENSEMBLE forcing datasets, water years 1979–2009. Water year starts on 1 October and ends on 30 September of the following calendar year.
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Figure 3. Area-averaged ensemble mean of monthly average (a) precipitation and (b) air temperature over the LNRB. Error bars show inter-data variation in the five forcing datasets (i.e., IDW, ANUSPLIN, NARR, ERA-I, WFDEI), water years 1979–2009.
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Figure 4. Area-averaged (a) mean annual precipitation and (b) mean annual air temperature over the LNRB for the ANUSPLIN, NARR, ERA-I and WFDEI datasets against four ECCC stations average values across the basin, years 1979–2009.
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Figure 5. Boxplots for monthly calibration (1st column) (1980-1989) and validation (2nd column) (1990-1999) performance metrics, NSE (a1-a2), KGE (b1-b2), r (p-value < 0.05 for all) (c1-c2) and PBIAS (d1-d2), for ten selected sub-watersheds within the LNRB based on IDW-VIC, ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC and ENSEMBLE-VIC simulations. The black dots within each box show the mean, the red lines show the median, the vertical black dotted lines show a range of minimum and maximum values excluding outliers, and the red + signs show the outliers defined as the values greater than 1.5 times the interquartile range of each metrics.
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Figure 6. Spatial differences of seasonal total runoff (TR) (mm) for the LNRB’s ten unregulated sub-basins based on IDW-VIC minus (1st row) ANUSPLIN-VIC, (2nd row) NARR-VIC, (3rd row) ERA-I-VIC, (4th row) WFDEI-VIC and (5th row) ENSEMBLE simulations, water years 1979–2009, for the winter (DJF), spring (MAM), summer (JJA) and autumn (SON) seasons represented by different columns.
55
Figure 7. Same as Figure 6 but for seasonal evapotranspiration (ET).
56
Figure 8. Same as Figure 6 but for seasonal soil moisture (SM).
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Figure 9. The simulated and observed daily runoff (mm day-1) for the LNRB’s ten unregulated sub-basins: (a) Burntwood River above Leaf Rapids (BRL), (b) Footprint River above Footprint Lake (FRF), (c) Grass River above Standing Stone Falls (GRS), (d) Gunisao River at Jam Rapids (GRJ), (e) Kettle River near Gillam (KRG), (f) Limestone River near Bird (LRB), (g) Odei River near Thompson (ORT), (h) Sapochi River near Nelson House (SRN), (i) Taylor River near Thompson (TRT) and (j) Weir River Above the Mouth (WRM) averaged over water years 1979–2009. An external routing model is used to calculate runoff for the IDW-VIC, ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC and ENSEMBLE-VIC simulations.
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Figure 10. Area averaged multidata-VIC simulated seasonal water balance mean (mm) of precipitation (PCP, a1-a4), total runoff (TR, b1-b4), evapotranspiration (ET, c1-c4) and soil moisture (SM, d1-d4), represented by different columns, for the LNRB’s ten unregulated sub-basins based on IDW-VIC, ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC and WFDEI-VIC simulations, water years 1979–2009, for the winter (DJF, 1st row), spring (MAM, 2nd row), summer (JJA, 3rd row) and autumn (SON, 4th row) seasons. Red bars show multi VIC simulations mean, black error bars show inter VIC simulations variation using standard deviation, while black dots represent the area averaged water balance estimations from the ENSEMBLE-VIC simulations.
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