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 (stephen.dery@unbc.ca)

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

datasets. 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 month-1) and air temperature

(±1.5°C month-1) 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 (~0.5–3.0 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 remains 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 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 and regional forcing datasets have been produced using

different input sources such as remote sensing products, climate model simulations, and in situ

observations (Dee et al., 2011; Mesinger et al., 2006). 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

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datasets effectively provide an uncertainty estimate of essential hydrological variables for water

resource management and planning.

Solid precipitation underestimation due to wind undercatch (Adam and Lettenmaier,

2003) and from 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 to conclude that bias-corrected

precipitation results in 5%–25% increases in simulated streamflow over the circumpolar north

(poleward of 45°N). The question of which forcing dataset is the most suitable and accurate to

drive hydrological models remains 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 of input data uncertainty 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 supply vary throughout the year. Thus, the

input forcing uncertainty assessments should also be performed on seasonal and annual

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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.

Several 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). These 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 add to the overall uncertainty in resulted gridded products (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 using North

American Regional Reanalysis temperature and precipitation as driving data. In 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

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 Lower

Nelson River Basin (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

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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 a 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 is 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 hydrological

models over the LNRB. However, these datasets are used in various other studies over different

Canadian regions (Boucher and Best, 2010; Islam and Déry, 2016; Sauchyn et al., 2011; Seager

et al., 2014; Woo and Thorne, 2006). To our knowledge, this is the first comprehensive study

that collectively examines available gridded datasets against observations, establishes the most

suitable datasets for the LNRB’s hydrological modelling, and performs uncertainty assessment

for their hydrological responses.

Overall, the main objectives of this study are to: (i) evaluate input datasets uncertainty

and identify the most reliable available gridded forcing datasets for hydrological simulations

over the LNRB; (ii) evaluate a hydrological model’s response from different driving datasets

over the LNRB; 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

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implemented, calibrated, and validated over major Canadian river basins (Islam et al., 2017;

Kang et al., 2014; Shi et al., 2013).

2. Study area: the Lower Nelson River Basin (LNRB)

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, whoseich downstream segment carries diverted flows from the Churchill River –

have less seasonal flow variability due to streamflow regulation and a large drainage area. Most

of the LNRB has 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

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capacity, Manitoba Hydro manages flows in the LNRB 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.

The LNRB 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

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 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).

Wetlands (bogs and fens, 21%) and open surface water (13%) thenalso prevail in the region. The

entire region exhibits low relief with a maximum elevation and average basin slope of 390

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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

withhere the downstream, northeastern portion comes underlain by continuous (between 90% to

100%) and extensive discontinuous (between 50% to 90%) permafrost and covers

(approximately 0.8% and 9% of the LNRB, respectively) (Natural Resources Canada, 2010). The

while sporadic discontinuous permafrost (between 10% to 50%) and isolated permafrost spans

~68%, and the southern part covers 16% of the LNRB’s total area, respectively (Natural

Resources Canada, 2010) with isolated patches of permafrost.

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 0.50° resolution (Cosby et al.,

1984). These soil parameters are then aggregated to the VIC model resolution (0.10°) following

Mao and Cherkauer (2009). 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). 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 every 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).

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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) span the study domain (Halsey et al., 1997). The depth-area

relationship of the lake and wetland tile is established empirically in the VIC model, which

allows the prediction of a variable inundated area with surface volume storage (Cherkauer and

Lettenmaier, 1999).

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 dataset, i.e., the

ANUSPLIN developed by Natural Resources Canada (NRCan) is available for the Canadian

landmass south of 60° N at 10 km resolution (Hopkinson et al., 2011; McKenney et al., 2011;

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; Sharma and Déry,

2016).

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The first reanalysis product used in this study iconsists of 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 is developed at 32 km spatial and 3-hourly temporal

resolution by utilizing a version of the Eta Model and its 3D variational data assimilation system

(Mesinger et al., 2006) for the North American continent, available from 1979 to present. 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). Woo and Thorne

(2006) obtained improvement in hydrological simulations when using NARR as an input for a

hydrological model over the Liard River Basin in western sub-arctic Canada.

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 and 6-

hourly temporal 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 assimilates a comprehensive set of 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 in a

statistically optimal way. The ERA-I 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).

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The WFDEI dataset relies 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) reanalysis correction (Uppala et al., 2005) and an elevation correction is performed

for numerous variables. Extensive corrections are 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 is adopted whereas the daily variability of the reanalysis product is

conserved (Weedon et al., 2011). The WFDEI dataset obtained for this study is produced by

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 from 14

ECCC meteorological stations. These observation stations are spatially interpolated by applying

the IDW interpolation method, and gridded datasets are procured at 0.10° horizontal resolution

for the LNRB. The grid cell values are calculated by weighted averaging of the station data, and

IDW 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 influence, gives more

emphasis to the nearest point, and produces output surfaces with more detail. In this

interpolation, the power parameter is set to two and the search radius specified as 241.2 km. The

search radius describes an extent for the interpolated IDW grids, which depends on the

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meteorological station's density. Here, stations beyond 241.2 km are considered too far to

represent the precipitation events based on selected ECCC stations (see Bill, 1999 for more

details).

The NARR and ERA-I daily precipitation and wind speed are obtained from the sum and

average of hourly values for one day, for respectively variables. To obtain daily Tmax (Tmin) for

these products, we extract the maximum (minimum) value for one day from the 3-hourly NARR

and 6-hourly ERA-I air temperatures. Further, 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 are 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 grid and the ERA-I and WFDEI datasets’ Gaussian grids are interpolated from

coarser resolution to slightly higher resolution (10 km). No elevation correction during the

interpolation from coarser to finer spatial resolutions is performed as elevations 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 speed, which is an essential input variable for the VIC model, is not available

for the ANUSPLIN and IDW forcing datasets. The observed wind speeds, both upper air and

near-surface values, are assimilated in the NARR reanalysis product and it they shows

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satisfactory correspondence with the ECCC observations (Hundecha et al., 2008). Thus, the

NARR wind speeds are used to run the VIC model using the ANUSPLIN and IDW datasets.

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

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 is evaluated against the

observed ECCC station data for the period of 1979–2009 (Figure 2). We found that fFor 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 has been used previously over global and regional domains 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 is 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).

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Therefore, it must be applied at various scales where the subsurface flow between grid cells is

minimal. In the VIC model, a mosaic approach represents tiles with multiple vegetation types co-

existing in a single grid cell. These vegetation types are 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 (although not a

factor in the current application to the LNRB). VIC uses a stand-alone routing model, which

transports grid cell simulated surface runoff and baseflow generated by VIC to the outlet of that

grid cell and then into the river system (Lohmann et al., 1998). In the routing model, water

cannot flow upstream once it reaches 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;

Cherkauer and Lettenmaier, 1999) is used to setup and simulate streamflow at a daily time-step

in full water and energy balance mode that includes soil ice formation (Table 1). 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). This VIC model application 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

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m for our study region (Williams and Gold, 1976). The LNRB’s tiles are characterized by soil

and vegetation fractions, which are 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 cells, and the air

temperature is adjusted based on the lapse rate to resolve the precipitation type. The VIC model

uses a linear method ( with a 0°C threshold) to determine the discriminate rainfall/-snowfall

partitioning. 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. A frozen

soil algorithm (Cherkauer et al., 2003; Cherkauer and Lettenmaier, 1999), which represents

permafrost, is implemented into the VIC model to improve its modelling abilities. Since

permafrost covers 68% of the LNRB and plays a vital role in cold region hydrology, the “frozen

soil” option is switched on in the VIC model simulations. Natural lakes and wetlands are

considered in this VIC model implementation (Bowling and Lettenmaier, 2010) 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 of the lower Nelson River’s unregulated tributaries (including

the unregulated, upstream portion of the Burntwood River), on the basis of observed records

availability for the study period (1979–2009), are selected for the model calibration and

subsequent analyses based on the availability of observational streamflow records through 1979-

2009 (Table 2). Even though the effects of the 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,

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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).

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 minimizes the difference

between observed and simulated monthly streamflow at unregulated hydrometric gauge locations

within the LNRB. The 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 select 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 attempts 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 ordered by the Pareto rank of Goldberg (1989).

In the MOCOM-UA optimization process, the user defines the training parameter set. Here, six

VIC soil parameters are used as the training parameter set for the optimization process (Table

17

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), and Ws (the fraction of

maximum soil moisture where nonlinear base flow occurs). These six parameters are optimized

separately for all input forcings, by minimizing the difference between modelled 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

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 are obtained from four ECCC meteorological stations (Climate ID) within the

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 perform area-averaged comparisons

from the four ECCC stations with each gridded dataset. Here, we hypothesize that the mean of

precipitation and air temperature using four different stations represent the basin average

18

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 iremains 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. The ENSEMBLE and IDW datasets do not participate in this

analysis as they are used separately for the detailed comparison and discussed in following

sections.

A series of different VIC model setups is 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 use all five datasets and

their ensemble to run VIC simulations and to 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 calibrate and validate

the VIC model with each input forcing dataset, estimate water balance components separately,

and select IDW as the reference dataset to compare different outputs as it is derived from the

ECCC meteorological stations. The experiments are categorized as follows:

Inter-comparison simulations: the VIC model is driven by each forcing dataset for 31

years (1979 to 2009) including the 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 are also initialized with these six different state files to produce

19

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,

ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC and ENSEMBLE-VIC, respectively.

These simulations are 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 use an individual calibration strategy

using each forcing dataset for ten selected sub-watersheds within the LNRB. There is a necessity

of separately calibrating and validating the VIC model using different forcing datasets to

investigate uncertainties in the water balance estimation over the LNRB. In this process, we

select 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 is 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 perform 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 is run using different ranges of the calibration parameters in

the MOCOM-UA optimizer as these ranges of parameter limits are sensitive to the model

calibration process (Islam and Déry, 2016). The final optimized values for all sub-watersheds

from different model calibrations are then extended to the remaining study period.

Water balance estimations: here we use the calibrated IDW-VIC simulation as a reference

to investigate the uncertainties in the water balance estimation from 1979–2009 using different

20

forcing datasets. Five different calibrated setups, namely ANUSPLIN-VIC, NARR-VIC, ERA-I-

VIC, WFDEI-VIC and ENSEMBLE-VIC, are performed for the water year (October to

September) inacross 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 select four seasons: winter (DJF), spring (MAM), summer (JJA), and autumn (SON). For

each experiment the routed streamflows for the 10 sub-watersheds are also examined based on

the availability of observed hydrometric records and the NSE, KGE, Pearson’s r and PBIAS are

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

To examine the consistency and pattern of gridded datasets against the ECCC observations, each

dataset is spatially averaged over the LNRB from 1979 to 2009 (Figures 4 and S1–S2). Overall,

yearly precipitation from the ERA-I and WFDEI surpasses that from the ANUSPLIN, ECCC,

and NARR datasets 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

21

years, mainly from 2004 to 2009. These emerging 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). 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 showexhibit a dry bias (-5.8%)

in annual precipitation but low RMSE (37.3%) amongst other datasets. We also perform 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 compared to 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%).

Apart from precipitation differences, the NARR dataset exhibits ~1°C deviation in annual

air temperature and high RMSE (0.90°C) over the LNRB relative to the ECCC dataset whereas

the ERA-I shows better agreement with the lowest 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 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.

22

Moreover, the NARR dataset shows larger RMSEs than the othersits counterparts and has a

strong positive bias in mean seasonal and annual air temperature over the LNRB. These findings

are consistent with the results from Aziz and Burn (2006) and Eum et al. (2014), where they

found high inter-annual and seasonal uncertainty between the observed and gridded precipitation

and air temperature estimates over their study areas.

4.2 Basin average inter-comparison of forcing datasets

The domain averaged daily mean precipitation magnitudes vary substantially among datasets

(Figure 2). Summer precipitation that begins in March and persists until August showswith

greater inter-dataset differences over the LNRB from March to August (Figure 2). The

ANUSPLIN consistently underestimates precipitation is underestimated consistently throughout

the study period 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, with 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 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 falls 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

23

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, 2016; 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 (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).

4.3 Hydrological simulations

The IDW-VIC, ANUSPLIN-VIC, NARR-VIC, ERA-I-VIC, WFDEI-VIC, and ENSEMBLE-

VIC simulation performance is 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

24

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 are 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 contribute to

the lower NSE and KGE coefficients, whereas the timing of seasonal flows iresembles 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 daily runoff is routed to produce hydrographs for the LNRB’s

ten unregulated sub-basins (Figure 9). Comparison of simulated daily 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 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 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

25

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. Differences 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 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

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, 2016; Kay et al., 2009). In this study, we obtain optimal results from the NARR-VIC

simulation amongst all other input datasets; therefore, Table S3 provides the list of final values

for the VIC soil parameters.

26

4.4 Uncertainty in the water budget estimation

The observational average annual observed precipitation and VIC simulated water budgets of the

LNRB’s sub-watersheds, from all five input forcings, and their estimated standard errors are

estimated to find the uncertainty in annual water budgets (Table 6). 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 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)

Domain-averaged seasonal TR shows higher uncertainty for relatively larger sub-watersheds

(e.g. GRJ, KRG, LRB, ORT, and WRM), especially in spring and summer (Figure 10b1–b4).

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

27

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 show 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 multidata and

ENSEMBLE-VIC TRs over each sub-watershed are low and result in less uncertainty

betweeninter simulations uncertainty. The simulated error increases in early spring 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)

Due to cold air temperatures in winter, ENSEMBLE-VIC ETs show smaller value (<3 mm) and

correspond well with multidata-VIC simulations for all sub-watersheds (Figure 10c1–c4). It

increases through spring (~100 mm) and peaks in summer (~250 mm) with 35 mm multidata-

VIC simulation error, which can be attributed to a substantial rise in air temperature and

precipitation. The multidata-VIC standard error shows identical values in autumn that essentially

reveals less regional variability in ET estimates (~60 mm) from all forcing datasets over the

28

LNRB’s sub-basins. 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

LNRB show higher uncertainty in ET estimates (Figure 10). The ENSEMBLE-VIC simulation

better represents the winter, spring, and autumn ET with overestimates in summer for all sub-

watersheds. For annual ET, the GRJ and SRN sub-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)

Among all other seasons, the highest SM ioccurs 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.

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 multidatasets 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 show less error in SM simulations with nearly identical annual values (Figure S5).

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.

29

The air temperature in the ERA-I and WFDEI were comparable, while precipitation from

both datasets remained quite high across the basin comparedrelative to the IDW and NARR

datasets. The ANUSPLIN precipitation had a significant dry bias over the LNRB compared to all

other forcing datasets. 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 the seven LNRB’s sub-basins. 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 winter, summer, and autumn area-

averaged air temperatures, which influenced its streamflow simulations for some of the sub-

basins by shifting runoff peaks and increasinged ET, and hence lower total runoff. The IDW-VIC

simulations underestimated flows for most of the sub-watersheds revealing 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,

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 was essential to capture

and interpret the uncertainties in hydrologic modelling responses. Overall, the NARR and

ENSEMBLE datasets provided reliable results for the LNRB’s hydrology, whereas the IDW,

30

ANUSPLIN, ERA-I, and WFDEI datasets had issues with either air temperature or with

precipitation. To increase the reliability of the LNRB’s hydrological simulations, model required

highly accurate gridded data products. This was possible through improving meteorological

station density or by obtaining the best suitable and accurate gridded dataset.

In this study, our primary 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 andto quantify

inter-model structure uncertainty. The model used in this study driveruns at a 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. Finally, natural lakes, wetlands, and frozen ground in

the present VIC model setup may be sensitive to 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.

31

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° W

Resolution = 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 (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.

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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]

Latitude (°N)

Longitude (°W)

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]

55.49 -99.22 302.44 5,810 22.9 1980-1989 1990-1999

Footprint River above Footprint Lake (FRF) [05TF002]

55.93 -98.88 273.75 643 3.2 1980-1989 1990-1999

Grass River above Standing Stone Falls (GRS) [05TD001]

55.74 -97.01 265.02 15,400 64.6 1991-2000 1979-1983

Gunisao River at Jam Rapids (GRJ) [05UA003] 53.82 -97.77 260.88 4,610 18.0 1990-1999 2000-2004

Kettle River near Gillam (KRG) [05UF004] 56.34 -94.69 164.67 1,090 13.2 1981-1990 1991-1995

Limestone River near Bird (LRB) [05UG001] 56.51 -94.21 173.59 3,270 21.5 1980-1989 1990-1999

Odei River near Thompson (ORT) [05TG003] 55.99 -97.35 253.46 6,110 34.3 1980-1989 2000-2009

Sapochi River near Nelson House (SRN) [05TG006] 55.90 -98.49 259.13 391 2.2 1980-1989 1990-1999

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Taylor River near Thompson (TRT) [05TG002]

55.48 -98.19 236.15 886 5.1 1980-1989 1992-1996

Weir River above the Mouth (WRM) [05UH002] 57.02 -93.45 125.84 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 (m

m) 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

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

PBI

AS ANUSPLIN -0.21 -0.29 -0.11 -0.09 -0.16

42

(%)

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]

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]

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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 4 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]

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]

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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

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 generating station (under construction) 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.

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,

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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.

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 winter (DJF), spring (MAM), summer (JJA) and autumn (SON).

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Figure 7. Same as Figure 6 but for seasonal evapotranspiration (ET).

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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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