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

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,

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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.

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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

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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

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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

43

(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.

51

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.

52

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.

53

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).

57

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.

58

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.

59