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  • View in gallery

    (a) Digital elevation map of the FRB with identification of major subbasins: Fraser–Shelley (UF), Stuart (SU), Nautley (NA), Quesnel (QU), Chilko (CH), Thompson–Nicola (TN), and Fraser at Hope (LF). (b) FRB mean elevation (m) per VIC grid cell (see Table 1 for further details).

  • View in gallery

    Block diagram of experimental setup and analysis.

  • View in gallery

    Simulated (VIC) and observed (OBS) runoff for the Fraser River and its major subbasins for the calibration period (1979–90).

  • View in gallery

    Area-averaged time series of CMIP5 MME mean daily air temperature and precipitation over the (a) Interior Plateau, (b) Rocky Mountains, and (c) Coast Mountains for the 1990s and 2050s (RCP 4.5 and RCP 8.5). Shading corresponds to intermodel uncertainties.

  • View in gallery

    Future change (2050s − 1990s; %) in the spatial distribution of mean annual (a) rainfall, (b) snowfall, and (c) total precipitation for RCP 8.5 only.

  • View in gallery

    Future change (2050s − 1990s; %) in the spatial distribution of mean SWE over the FRB simulated by MME VIC for RCP 8.5 as (a) DJF and (b) MAM seasonal means. (c) Areally averaged annual cycle of daily SWEmelt over the FRB for the 1990s and 2050s for both RCP 4.5 and RCP 8.5. Shading corresponds to intermodel variability.

  • View in gallery

    Spatial distribution of mean future change (2050s − 1990s; %) of (a) runoff, (b) SWEmelt, and (c) . White areas at the outlet of the Fraser do not experience snow and are therefore masked in the percentage differences.

  • View in gallery

    MME-based VIC-simulated mean daily runoff at Fraser River at Hope. Black, blue, and red curves represent the daily climatology for the base period (1990s), 2050s RCP 4.5, and 2050s RCP 8.5, respectively.

  • View in gallery

    VIC–CMIP5 MME mean daily runoff for the (a) UF, (b) SU, (c) NA, (d) QU, (e) CH, and (f) TN subbasins. Black, blue, and red curves represent the daily climatology for the base period (1990s), 2050s RCP 4.5, and 2050s RCP 8.5, respectively.

  • View in gallery

    MME future change (2050s − 1990s; %) variation of mean (a) SWE and (b) snow cover for a range of elevations across the Interior Plateau (green), Rocky Mountains (blue), and Coast Mountains (red) regions. Curves are for RCP 8.5 only.

  • View in gallery

    MME future change (2050s − 1990s) of daily mean (left) SWE (mm) and (right) snow cover (as a fraction) for range of elevations across the (a),(b) Interior Plateau; (c),(d) Rocky Mountains; and (e),(f) Coast Mountains for RCP 8.5 only. Annual cycles are filtered using a 5-day running mean.

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Future Climate Change Impacts on Snow and Water Resources of the Fraser River Basin, British Columbia

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  • 1 Environmental Science and Engineering Program, University of Northern British Columbia, Prince George, British Columbia, Canada
  • | 2 Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada
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Abstract

Changes in air temperature and precipitation can modify snowmelt-driven runoff in snowmelt-dominated regimes. This study focuses on climate change impacts on the snow hydrology of the Fraser River basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity model (VIC). Statistically downscaled forcing datasets based on 12 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used to drive VIC for two 30-yr time periods, a historical baseline (1980–2009) and future projections (2040–69: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The ensemble-based VIC simulations reveal widespread and regionally coherent spatial changes in snowfall, snow water equivalent (SWE), and snow cover over the FRB by the 2050s. While the mean precipitation is projected to increase slightly, the fraction of precipitation falling as snow is projected to decrease by nearly 50% in the 2050s compared to the baseline. Snow accumulation and snow-covered area are projected to decline substantially across the FRB, particularly in the Rocky Mountains. Onset of springtime snowmelt in the 2050s is projected to be nearly 25 days earlier than historically, yielding more runoff in the winter and spring for the Fraser River at Hope, BC, and earlier recession to low-flow volumes in summer. The ratio of snowmelt contribution to runoff decreases by nearly 20% in the Stuart and Nautley subbasins of the FRB in the 2050s. The decrease in SWE and loss of snow cover is greater from low to midelevations than in high elevations, where temperatures remain sufficiently cold for precipitation to fall as snow.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Stephen J. Déry, sdery@unbc.ca

Abstract

Changes in air temperature and precipitation can modify snowmelt-driven runoff in snowmelt-dominated regimes. This study focuses on climate change impacts on the snow hydrology of the Fraser River basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity model (VIC). Statistically downscaled forcing datasets based on 12 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used to drive VIC for two 30-yr time periods, a historical baseline (1980–2009) and future projections (2040–69: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The ensemble-based VIC simulations reveal widespread and regionally coherent spatial changes in snowfall, snow water equivalent (SWE), and snow cover over the FRB by the 2050s. While the mean precipitation is projected to increase slightly, the fraction of precipitation falling as snow is projected to decrease by nearly 50% in the 2050s compared to the baseline. Snow accumulation and snow-covered area are projected to decline substantially across the FRB, particularly in the Rocky Mountains. Onset of springtime snowmelt in the 2050s is projected to be nearly 25 days earlier than historically, yielding more runoff in the winter and spring for the Fraser River at Hope, BC, and earlier recession to low-flow volumes in summer. The ratio of snowmelt contribution to runoff decreases by nearly 20% in the Stuart and Nautley subbasins of the FRB in the 2050s. The decrease in SWE and loss of snow cover is greater from low to midelevations than in high elevations, where temperatures remain sufficiently cold for precipitation to fall as snow.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Stephen J. Déry, sdery@unbc.ca

1. Introduction

Projected changes in air temperature and precipitation, associated with increasing greenhouse gases, will have wide-ranging impacts on the snowmelt-dominated hydrological regimes of western North America. Any change in these driving factors has the potential to modify the distribution of mountainous snowpack that serves as a natural reservoir for cold-season precipitation (Barnett et al. 2005). Western Canada’s snowmelt-dominated regimes are no exception; they too are susceptible to these climate change–driven modifications (Morrison et al. 2002; Ferrari et al. 2007). The Fraser River basin (FRB) of British Columbia (BC), where complex topography dominates the landscape, is a prime example of a susceptible basin. It spans one-fourth of BC, covering roughly 230 000 km2, and thus forms a large basin with significant environmental, economic, and cultural importance. The presence of a seasonal snowpack in the FRB is an essential component of its water resources and serves as a sensitive indicator of climate change. Snowmelt in the FRB plays an important role in regulating water temperature, which supports the reproductive fitness of salmon (Rand et al. 2006). Change in FRB snowpack levels, extent, and duration will alter the associated snowmelt runoff and water temperatures and may reduce the survival of aquatic species such as salmon, which in turn will affect local communities and the economy of western Canada.

There have been several studies investigating the impacts of climate change on snow and its contribution to the hydrology of western North America. Mote et al. (2005) found a declining trend in 1 April snow water equivalent (SWE) across western North America from 1925 to 2000 that was driven mainly by rising air temperature. Using observational datasets, Park et al. (2012) reported declining trends in snow depth across North America between 1948 and 2006. Increased freezing levels (Abatzoglou 2011) and amplified warming at high elevations (Rangwala and Miller 2012) were related to climate change. Choi et al. (2010) reported a general decline in snow cover duration in BC using remote sensing data for 1972–2008. Déry et al. (2012) concluded that there is increasing interannual variability in runoff across many streams and rivers in the FRB, which create flow conditions unfavorable to salmon that are related to changing snow cover conditions. Danard and Murty (1994) reported a decline in SWE across the FRB between 1966 and 1989 concurrent with streamflow decreases in October. Using hydrological model simulations spanning 1948–2006, Kang et al. (2014) revealed a 19% decline in the contribution of snow to runoff generation for the Fraser River at Hope, BC, induced mainly by climate warming. Kang et al. (2016) showed that the rapidly declining mountain snowpack and earlier melt onsets result in a 10-day advance of the Fraser River’s spring freshet, with subsequent reductions in summer flows over the historical period.

Additional studies examined the teleconnections of El Niño–Southern Oscillation (ENSO) and Pacific decadal oscillation (PDO) to western Canada’s hydrology and snow (Moore 1991; Hsieh and Tang 2001; Whitfield et al. 2010). Hsieh and Tang (2001) investigated the influence of teleconnections on 1 April snowpack accumulations in the upper Columbia River basin, with La Niña and a low Pacific–North American pattern yielding large positive SWE anomalies. Rodenhuis et al. (2009) reported that SWE was lower than average over most of southern BC during warm PDO phases. Shrestha et al. (2016) noted lower flows in the FRB during warm PDO phases and higher flows in the FRB during cool PDO phases.

Climate change is projected to continue into the twenty-first century and its impacts along with it. Over the western United States, Rauscher et al. (2008) found that by the end of the twenty-first century, projected future warming could cause snow-driven runoff to occur as much as 2 months earlier than present because of an amplified snow-albedo feedback. Elsner et al. (2010) reported that 1 April SWE is projected to decrease by approximately 38%–46% by the 2040s over Washington State and the greater Columbia River basin. Vano et al. (2010) found declines, and eventual disappearance, of the springtime snowmelt peak in the Puget Sound Basin by the 2040s and 2080s, respectively. Kerkhoven and Gan (2011) projected earlier onset of spring snowmelt and decreased monthly annual peak flow by the end of the twenty-first century for the Fraser River at Hope using the Interactions between Soil, Biosphere, and Atmosphere (ISBA) hydrological model driven by seven global climate models (GCMs). Ferrari et al. (2007) and Shrestha et al. (2012) found that parts of the FRB may already be transitioning to a hybrid, and perhaps even a rain-dominated, system due to significant declines of snow accumulations over recent decades, especially in its lowlands and the Interior Plateau.

The seasonal variability and amount of water released from the FRB’s snowpack sets the pace of regional irrigation, water management, and hydropower generation. Assessing how the FRB’s freshwater resources may change in the future in a way that allows land and water managers, planners, and government to make informed decisions requires rigorous scientific attention. This includes the use of improved future climate projections to drive hydrological models selected for their representation of key processes in the FRB. The use of large GCM datasets in comprehensive hydrologic-model-based projections is becoming more common because of the advancement in GCM dynamics as well as the availability of their output datasets. It is therefore important to use a combination of such tools over the FRB to project climate change impacts on its snowpack and hydrology. Particular attention to the FRB’s mountainous terrain, which exhibits a wide variety of elevation distributions, is also advisable. Given the strong, often linear, relationship between snowpack and elevation (Abatzoglou 2011; Sospedra-Alfonso et al. 2016), snowpack loss will vary from catchment to catchment within different elevations. In estimating future impacts, it is imperative that one simulates and characterizes the rate of projected warming-driven changes at different elevations within the FRB.

While the historical hydrology of the FRB has been addressed in previous studies, the current literature lacks quantification of future changes in FRB snowpack levels and their variation with different elevations using phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012) GCM projection forcings. A quantitative evaluation of the CMIP5-projected climate change and its impact on the FRB’s snowpack requires a systematic analysis of GCM-driven hydrological model simulations that has not been accomplished thus far. The present study therefore investigates the impact of increasing air temperature, coupled with changing precipitation phases, on the FRB’s mountainous snowpack accumulation and melting as well as its contribution to runoff generation. Moreover, the sensitivity of mountainous elevations to such climate change is examined in terms of snowpack levels. Thus, the overarching goal of this study is to project changes in FRB snow hydrology with an emphasis on seasonal variations.

The simulation tool in this study is the hydrological macroscale Variable Infiltration Capacity model (VIC), which is a surface water and energy balance model designed for large-scale hydrological regions such as the FRB (Cherkauer et al. 2003). To evaluate spatially distributed future responses at ¼° grid cells, VIC is forced by statistically downscaled outputs of CMIP5 GCMs (Taylor et al. 2012) used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5; IPCC 2014). CMIP5 GCMs run under representative concentration pathways (RCPs) 4.5 and 8.5, two different pathways with stabilized (RCP 4.5) and rising (RCP 8.5) greenhouse gas emissions, over the future time period are considered. Analyses are performed on the daily and annual data for the entire FRB, six of its major subbasins, and three of its hydroclimatic subregions, contrasting projected conditions in the future (2040–69) time periods to those from the historical baseline (1980–2009). To estimate future changes related to the snowmelt-dominated regions of the FRB, SWE, snow cover, and runoff variables are analyzed along with the ratio of snowmelt to runoff to estimate the direct contribution of snow to runoff generation for the FRB. These snow hydrology variables are crucial for estimating climate change impacts on the FRB’s snowmelt-driven runoff.

2. Fraser River basin

The FRB forms one of the largest and most important basins of western North America, with elevations ranging from sea level to its tallest peak Mount Robson at 3954 m (Benke and Cushing 2005). It spans 240 000 km2 of diverse landscapes from the dry Interior Plateau to the cool, snowy Rocky Mountains to the northeast and the Coast Mountains to the west (Fig. 1a). Because of its varying terrain, the FRB covers 11 biogeoclimatic zones, including various vegetation and land-cover types such as alpine terrain, subalpine, montane, and coastal forests, with drier inland forests and grasslands in the Interior Plateau (Bocking 1997; Benke and Cushing 2005). Some of the major tributaries of the Fraser River are the Nechako, Quesnel, Chilcotin, and Thompson Rivers within the Interior Plateau (Kang et al. 2014).

Fig. 1.
Fig. 1.

(a) Digital elevation map of the FRB with identification of major subbasins: Fraser–Shelley (UF), Stuart (SU), Nautley (NA), Quesnel (QU), Chilko (CH), Thompson–Nicola (TN), and Fraser at Hope (LF). (b) FRB mean elevation (m) per VIC grid cell (see Table 1 for further details).

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

Owing to its complex topography, the FRB exhibits substantial spatial variation of mean annual air temperature and precipitation. Mean annual air temperature ranges from 0.5°C in the mountains in the northwest to 7.5°C in the lower-elevation area in the south, close to BC’s Okanagan region. The interior portions of the FRB have warmer air temperature on average than the high-elevation Coast and Rocky Mountains. The range in mean daily air temperature in summer (from 11.0° to 16.5°C) is less than the range in winter (from −11.0° to −1.0°C). Precipitation varies greatly across the FRB. The mountainous coastal areas receive up to 3000 mm yr−1 of precipitation while the Interior Plateau and regions to the lee side of the Coast Mountains receive only 400–800 mm yr−1 (Kang et al. 2014). The Interior Plateau is the driest section in the FRB. More precipitation falls in the Coast and Rocky Mountains, predominantly as snow. Snow normally accumulates throughout the winter in the FRB, except perhaps at lower elevations and coastal areas. Peak flow in the main stem of the Fraser River and its many tributaries usually occurs in late spring and early summer and is driven by snowmelt primarily from high elevations (Moore and Wondzell 2005). The snowmelt-driven discharge declines rapidly following the depletion of the snow storage in late summer and plays an important role in the regional water supply. Hydrologic response varies considerably across the FRB, including snowmelt, hybrid (rainfall and snowmelt), and rainfall-dominated regimes (Wade et al. 2001).

Changes to the climate of the FRB over the last few decades have been documented. Based on the University of Washington gridded observational climate dataset (Shi et al. 2013; Adam and Lettenmaier 2008), the 1948–2006 annual precipitation shows no clear trend over the 1948–2006 record, whereas a warming of 1.5°C in annual mean temperature occurred over the interior sections of the FRB. The northernmost subbasins experienced the greatest warming rate, at 0.48°C decade−1 as compared to 0.28°C decade−1 for the remainder of the FRB (Kang et al. 2014). Such changes have modified the ratio of snowfall to rainfall and induced considerable changes in the hydrological regime of the FRB.

3. Hydrological model and data

a. VIC

VIC is a semidistributed macroscale model (Liang et al. 1994, 1996) that resolves energy and water balance by grid cells that are subdivided into elevation bands with equal vegetation fractions per band to represent subgrid variability of the land surface. Base flow and runoff from a grid cell are determined at the end of each time step. To consider subgrid variability of the land surface, VIC uses a statistical approach to cope with different land and soil types that control soil moisture storage capacity. It captures subgrid variability of snow depth using a statistical function that can be parameterized. VIC requires a large number of parameters, including soil, vegetation, elevation, and daily meteorological forcings, at each grid cell (Liang et al. 1994; 1996). To convert runoff and base flow from grid cells into discharge from a basin, an external routing model (Lohmann et al. 1996; 1998a,b) needs to be employed once the VIC simulation is completed. The routing model simulates a channel network with a number of nodes, each of which represents information from a grid cell. Detailed descriptions of VIC are available in Liang et al. (1994, 1996) and Gao et al. (2009).

VIC (version 4.1.2) with more recent modifications, such as a parameterization of blowing-snow sublimation and inclusion of lakes and wetlands (Bowling et al. 2004; Bowling and Lettenmaier 2010), has been widely applied to assess water resources, land–atmosphere interactions, and hydrological responses over various river basins around the world (Nijssen et al. 2001a,b; Maurer et al. 2002; Su et al. 2005; Haddeland et al. 2007; Adam et al. 2009; Gao et al. 2010; Ashfaq et al. 2010; Oubeidillah et al. 2014; Zhou et al. 2016). As climate change has been a critical issue since the 1990s, VIC has also been extensively used with various forcing datasets provided by outputs from climate and weather prediction models (Wang et al. 2008; Shukla et al. 2012; 2013). VIC is used for climate change research because it is a process-based model that should respond to changing air temperature and precipitation in a more physically realistic way than empirically based or lumped models (Dwarakish and Ganasri 2015). It is also commonly used to simulate hydrologic response to climate change in snowmelt-dominated basins (Christensen and Lettenmaier 2007; Hidalgo et al. 2009; Cherkauer and Sinha 2010; Schnorbus et al. 2011).

b. Elevation bands

In mountainous regions with a significant range in elevation, VIC can lead to inaccurate snowpack estimates if the effects of elevation on snowpack accumulation and ablation are not modeled properly. To simulate the hydrology of complex terrain, VIC can be set up by dividing each grid cell into a number of snow elevation bands (Nijssen et al. 2001b). The subgrid variability in topography and precipitation is then modeled with a mosaic-type representation by partitioning elevation bands into a number of topography tiles. These tiles are based on high-resolution spatial elevations and fractional area. Each band’s mean elevation is used to lapse the gridcell-average air temperature, pressure, and precipitation to a more accurate local estimate. The snow model is then applied to each elevation tile separately. The simulated output variables (SWE, snow cover, etc.) for each grid cell are calculated as the area averages of the tiles (Gao et al. 2009).

In this study, 10 elevation bands were used in each grid cell (¼°) to better characterize future climate change impacts on different elevations. Bands were divided into different elevation bins based on the minimum and maximum elevations at finer-spatial-resolution tiles (30-arc-s DEM) over the FRB. The total area and average elevation of the tiles that fall into each bin are equal to the band area and elevation, respectively. This allows in-depth analysis of the variation of SWE and snow cover with elevation that is of particular importance for the Rocky and Coast Mountains of the FRB, which cover a wide range of elevations.

c. Climate projections data

Statistically downscaled outputs of 12 CMIP5 GCM projections were used as forcing datasets for the VIC simulations. The CMIP5 projections (Taylor et al. 2012) were generated using a set of improved GCMs that collectively reflect varying degrees of advancement in climate science and modeling since CMIP3. The CMIP5 GCMs were run at higher spatial resolution, with some models being more comprehensive in terms of their physical dynamics (Knutti and Sedláček 2012). A new set of climate forcing scenarios defined as RCPs were used to develop CMIP5 climate projections (van Vuuren et al. 2011). These RCPs reflect recent progress in integrated assessment modeling to characterize future greenhouse gas emissions since the scenarios released under the Special Report on Emissions Scenarios (SRES; Nakicenovic and Swart 2000). Each of the 12 GCMs, as run under the pathways of stabilized (RCP 4.5) and rising (RCP 8.5) greenhouse gas emissions, were statistically downscaled and used to drive VIC, for a total of 24 GCM–RCP simulations.

In the downscaling process, GCMs were selected based on the subset with the widest spread in future projected climate for western North America (Giorgi and Francisco 2000), following Cannon (2015). Downscaled GCMs datasets for the FRB were accessed through the Pacific Climate Impacts Consortium (PCIC) data portal (https://pacificclimate.org/data/statistically-downscaled-climate-scenarios). Those downscaled using the bias-correction spatial disaggregation (BCSD; Wood et al. 2004) were selected to align this work with others in the region (Schnorbus et al. 2014; Shrestha et al. 2012; Elsner et al. 2010; Werner et al. 2013). This implementation of BCSD followed Maurer and Hidalgo (2008) and was modified to incorporate monthly minimum and maximum air temperature instead of monthly mean air temperature, as suggested by Bürger et al. (2012). The bias correction was applied using detrended quantile mapping with delta method extrapolation following Bürger et al. (2013). Although BCSD has been applied to daily GCM data and there are other downscaling techniques that have proven success with downscaling daily data, the monthly application was maintained, again to align with other studies and also because the daily evolution of precipitation and temperature events was not thought to be necessary to investigate the ratio of annual snowmelt to runoff or total contribution of snow to runoff generation in the FRB, the primary focus of this paper. The target historical dataset (1950–2005) used in the bias correction and spatial disaggregation was the 10-km resolution observation-based daily gridded climate data interpolated using the method implemented by the Australian National University spline (ANUSPLIN hereafter) (McKenney et al. 2011; Hopkinson et al. 2011).

4. VIC implementation and methodology

The model was run at ¼° spatial resolution using a daily time step over the entire domain of the FRB in water balance mode along with the built-in snow model. The exception to this is that the snow algorithm still solves the surface energy balance to determine the fluxes needed to drive accumulation and ablation processes. Integrations of VIC over the entire FRB were performed using 34 rows and 42 columns of model grid cells spanning 48°–55°N and 119°–131°W. Different tiles characterizing soils and vegetation were proportionally partitioned within a grid cell. In this model application, three soil layers were used with depth ranges of 0–0.3, 0.4–0.9, and 1.0–2.0 m, in the top, middle, and bottom layers, respectively. The soil and vegetation parameters, leaf area index (LAI), and albedo data were implemented as they were in Kang et al. (2014). Other soil parameters were calibrated based on the agreement between simulated and observed hydrographs (Mitchell et al. 2004).

a. VIC calibration and validation

VIC’s ability to simulate FRB hydrology has been reported in recent work by Kang et al. (2014), which provides details of the model implementation, including the approach to calibration and validation. For the current study, the VIC configuration (resolution, soil layers and datasets, snowbands, etc.) was maintained as in Kang et al. (2014), except the forcing data were changed to the ANUSPLIN. (Hutchinson et al. 2009; McKenney et al. 2011; Hopkinson et al. 2011). This is because the CMIP5 GCMs were downscaled using the ANUSPLIN observation dataset, and forcing VIC with these downscaled outputs required the model to be calibrated using the same observations. The ANUSPLIN data were developed by Natural Resources Canada (NRCan) and contain gridded station data of daily maximum air temperature (°C), minimum air temperature (°C), and total precipitation (mm) for the Canadian landmass south of 60°N at ~10-km resolution (NRCan 2014).

Previous studies that applied the ANUSPLIN dataset to modeling watersheds with complex topography in BC and Alberta revealed underestimates in runoff, owing to a dry bias in ANUSPLIN precipitation at high altitudes (Eum et al. 2014). As the low precipitation values would make it impossible to calibrate VIC, a precipitation correction was applied to the ANUSPLIN data prior to calibration instead of including a precipitation adjustment in the calibration parameters. This correction was applied to adjust for the dry bias found most notably in the FRB’s mountainous areas. Gridded observations (1/16°) developed by PCIC following the technique of Maurer et al. (2002) and Hamlet and Lettenmaier (2005) were regridded using bilinear interpolation to match the scale of the current VIC implementation (¼°). The precipitation adjustments were applied at each grid cell, based on the daily climatology bias for the 1979–2006 period of overlap between the two datasets. Although elevation was not a direct determinate in the bias correction, the difference between PCIC and ANUSPLIN did have a relationship with elevation due to the increased number of stations and adjustment of precipitation in the PCIC gridded climate dataset with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data [see Schnorbus et al. (2011) for more details].

The same adjustment was used to correct the BCSD-downscaled GCM projections prior to the simulations. The calibration of VIC was performed for the years 1979–90, while the years 1991–2006 were used to validate the model. These years were chosen to remain consistent with the CMIP5 baseline period as well as to avoid a shift of the PDO phase in year 1976/77 (Marcus et al. 2011) during the calibration period. The calibration used the optimization process conducted by minimizing the difference between observed and simulated monthly streamflow at Hope using the Nash–Sutcliffe efficiency (NSE) coefficient (Nash and Sutcliffe 1970). Along with the Fraser River at Hope the calibration was applied to six subbasins to determine the optimal parameters by comparing simulated to observed streamflow in each basin. The University of Arizona multiobjective complex evolution (MOCOM-UA) optimizer was selected for the calibration process (Yapo et al. 1998; Shi et al. 2008). MOCOM-UA searches a set of VIC input parameters using the population method to maximize the NSE coefficient between observed and simulated runoff. Six training soil dataset parameters were used in the optimization process, that is, a parameter of the variable infiltration curve (b_infilt), the maximum velocity of base flow for each grid cell (Dsmax), the fraction of maximum soil moisture where nonlinear base flow occurs (Ws), the depths of the second and third soil layers (D2 and D3), and the fraction of the Dsmax parameter at which nonlinear base flow occurs (Ds). By minimizing the difference (increasing the NSE coefficient) between the simulated and corresponding observed monthly flow, final values of these six calibrated parameters were estimated and were used for all the simulations for their respective subbasin and for the Fraser River at Hope.

To calibrate and validate the simulated flow by VIC, daily streamflow data from Environment and Climate Change Canada’s Hydrometric Dataset (HYDAT; Water Survey of Canada 2014) were used. Déry et al. (2012) extracted and compiled a comprehensive streamflow dataset for the FRB spanning 1911–2010.

Once VIC was calibrated and validated, the series of VIC simulations were performed for base and future time periods for the years 1980–2009 (1990s hereafter) and 2040–69 (2050s hereafter), respectively. Each simulation was initiated 5 years prior to the 1990s and 2050s time periods to allow model spinup. VIC was driven by the statistically downscaled GCMs output data of daily precipitation and maximum and minimum air temperature. Daily wind speed data were created by regridding estimates of 10-m wind speed from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses (Kalnay et al. 1996; Schnorbus et al. 2011) because of their unavailability with the downscaled CMIP5 datasets on the PCIC data portal and overall challenges with downscaling wind. The VIC output variables were found insensitive to different wind forcing datasets during the VIC sensitivity experiments. Downscaled GCM projections (~10 km), 12 models for both RCP 4.5 and RCP 8.5, were regridded to a common resolution of ¼° to match the resolution of VIC.

b. VIC ensembles

A realistic model always contains random components and uncertainties such as those in boundary forcing or in initial conditions. To alleviate the impact of these random fluctuations the Multi-Model Ensemble (MME) strategy was used throughout the analyses. While the simulated climate change varies, to a smaller or larger extent, between different CMIP5 models, the MME can provide better estimates of first-order future change as compared to single models (Duan and Phillips 2010; Miao et al. 2013). Each independent simulation of VIC, driven by individual GCM output, was used to produce the MME mean. The intermodel spread among GCMs was also estimated along with MME and was made up of 12 GCMs run under two RCPs (4.5 and 8.5) for a 24-member ensemble.

c. Analysis strategy

Analyses were performed for the entire FRB and its six major subbasins, namely, the upper Fraser at Shelley (UF), Stuart (SU), Nautley (NA), Quesnel (QU), Chilko (CH), and Thompson–Nicola (TN) basins (Fig. 1a, Table 1). The six subbasins examined in the present study contribute 75% of the annual observed discharge for the Fraser River at Hope (LF), with the largest contributions from the TN, UF, and QU subbasins (Déry et al. 2012). However, the focus of this study is on the main stem of the Fraser River at Hope since it is farthest downstream, covering 94% of the basin’s drainage area, and has a continuous streamflow record over the 1980–2009 historical baseline period. To simulate streamflow for these subbasins, a routing network was adapted from Wu et al. (2011).

Table 1.

Details of the Fraser River main stem at Hope (i.e., LF) and major subbasins within the FRB. The list includes mean elevation, gauged area, percentage gauged area relative to LF, and latitude and longitude of the gauge at the outlet of each subbasin (Déry et al. 2012).

Table 1.

The analyses were further expanded for three FRB hydroclimatic regimes: the Interior Plateau (central part of the basin), the Rocky Mountains (Rocky and Columbia Mountains in the eastern part of the basin), and the Coast Mountains (southwestern part of the basin; Moore 1991). Considering that the future responses for each region could vary considerably, these hydroclimatic regions represent the FRB’s distinct land surface features and hydroclimatic conditions. For example, this division helps to evaluate the snow hydrology of the Coast Mountains, especially in the lower Fraser Valley, where there is a unique runoff pattern, which is higher in December–March than the other regions reflecting a rainfall-driven response (Shrestha et al. 2012). Figure 1b shows the gridcell division of these three regions and their elevations.

To capture total SWE melt at all locations, including those at high elevations where snow does not disappear entirely every year, we use the difference between SWEmax and SWEmin. We then quantified the contribution of snow to runoff generation across the basin for each water year as (Déry et al. 2005; Kang et al. 2014):
eq1
where R is the runoff (mm day−1) and N is 365 or 366 for a leap year, with t = 1 indicating 1 October of a given water year. The is defined by
eq2
where is and is .

For each VIC simulation, streamflow was converted to areal runoff by dividing it by the corresponding subbasin area. Daily runoff at the outlet cell was integrated over time to obtain total annual runoff for a selected basin.

Future changes were calculated between the 2050s versus 1990s for both the RCP 4.5 and RCP 8.5 driving datasets of the VIC outputs for 12 GCMs. Seasonal variations were assessed by averaging December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) months for winter, spring, summer, and autumn, respectively. Figure 2 shows an overall framework of this study highlighting the GCMs datasets, VIC configuration, and analysis approach.

Fig. 2.
Fig. 2.

Block diagram of experimental setup and analysis.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

5. Results and discussion

a. VIC performance

Table 2 summarizes the performance of the VIC calibration and validation process for LF and all six subbasins in the FRB. The NSE and correlation values reveal a good agreement between modeled streamflow and observations, exhibiting NSE scores greater than 0.70 for most subbasins. The calibrated results, including low-flow volumes and the timing of runoff peaks, match those in observations for all subbasins in the FRB closely, except Fraser–Shelley (Fig. 3). Model performance is best at the outlet, Fraser at Hope, with high NSE and correlation values and relatively low bias in both the calibration and validation periods. The larger Thompson–Nicola subbasin is also well calibrated considering the high NSE values, whereas the smaller headwater basins, especially the Nautley and Fraser–Shelley, are the only basins with NSE scores less than 0.70. These discrepancies are probably due to the problems in the forcing data, which tend to be more acute at higher elevations where there are fewer meteorological stations. Additionally, they are more common on the lee side of mountains, where interpolation procedures commonly do not weigh windward and leeward stations accordingly (Daly 2006).

Table 2.

The NSE and correlation coefficients r (all significant at the p level of 0.05) and the relative biases for the VIC calibration (1979–90) and validation (1991–2006) periods for six major subbasins of the FRB and Fraser River main stem at Hope (i.e., LF).

Table 2.
Fig. 3.
Fig. 3.

Simulated (VIC) and observed (OBS) runoff for the Fraser River and its major subbasins for the calibration period (1979–90).

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

Some of the discrepancies between observations and simulations may be due to glacier dynamics not being represented in the model physics of the version of VIC applied in this study. However, there are some grid cells in the FRB domain where a perennial snowpack exists that does not melt out, year after year. This is a recognized challenge with VIC and is dealt with differently in each implementation, ranging from eliminating these cells from analysis to introducing a simple, conceptual representation of glacier mass balance into VIC to model perennial snow with VIC’s built-in snow routines. In this approach, a portion of the model grid cells is identified as glacierized and used to form a glacier mask (Schnorbus et al. 2011). In this study, those cells with perennial snowpack were compared to baseline thematic mapping to confirm their location (as per observed glaciers) and were masked in the SWE analysis. Nonetheless, the effects of glaciers may not change the results significantly as VIC is implemented on ~25-km gridcell resolution. Furthermore, glaciers cover only 1.5% of the FRB (Shrestha et al. 2012) and provide only a modest contribution to streamflow at the Fraser River at Hope, primarily in late summer (August/early September).

The reasonably high validation results of the VIC simulations over the FRB demonstrate that it is a useful simulation tool to assess future climate change impacts. The calibration and validation periods were chosen to be the periods before and after the transition from the cool to warm PDO phases, respectively. VIC was also tested for sensitivity to variable climate with a series of sensitivity experiments (results not shown).

b. CMIP5 precipitation and air temperature climatology

Being bias corrected and spatially disaggregated, the downscaled CMIP5 GCMs simulate the baseline mean climatology of precipitation and air temperature well by design. MME precipitation shows a projected increase in winter (~6%), spring (~12%), and autumn (~12%) over the northern and southern FRB for the 2050s while summer precipitation is projected to decrease by 12% over the same period, particularly in the southern FRB under RCP 8.5 (not shown). Precipitation is not projected to change by a high percentage during the winter in the southern parts of the FRB, but some of the largest annual precipitation in the FRB occurs in this region. The spatial pattern of precipitation change for RCP 4.5 is more or less similar to that for RCP 8.5; however, the magnitude of change is not as great (not shown).

MME mean air temperature is projected to increase in all seasons, with higher seasonal increases projected under RCP 8.5 than RCP 4.5 in the 2050s. Mean annual air temperature increases range from 2.0° to 2.4°C for RCP 4.5 and 2.4° to 3.0°C for RCP 8.5 across grid cells. Absolute increases are greater in winter than in summer, over the northern FRB, for both pathways. The projected air temperature change over the Interior Plateau is especially notable in summer for RCP 8.5 considering the relatively high temperature already on record in this region (not shown).

Figure 4 shows the CMIP5 MME mean daily climatology and intermodel spread of daily precipitation and air temperature areally averaged over the FRB’s three regions for the base and future periods. The seasonal precipitation begins in November and persists until April, approaching a maximum of 4.0, 6.0, and 10.0 mm day−1 in winter for the Interior Plateau, Rocky Mountains, and Coast Mountains, respectively, for the 1990s. The maximum intraseasonal variability for CMIP5 MME precipitation occurs in the Coast Mountains, where daily rates range from nearly 0.0 mm day−1 in summer to 10.0 mm day−1 in winter. Daily precipitation rates are projected to change less in late spring and summer than in winter and autumn, when rates are projected to increase, particularly in the mountainous regions. The range of intermodel spread for peak precipitation varies between 8.0 and 12.0 mm day−1 during winter for the Coast Mountains. Precipitation changes in the Coast Mountains are more variable across GCMs than in other regions of the FRB because of its proximity to the Pacific Ocean, where interactions between steep elevation gain and storm-track position are more complex and are represented differently in each GCM. The high range of uncertainties in projections for the Rocky and Coast Mountains is likely due to the inability of climate models to resolve complex topography.

Fig. 4.
Fig. 4.

Area-averaged time series of CMIP5 MME mean daily air temperature and precipitation over the (a) Interior Plateau, (b) Rocky Mountains, and (c) Coast Mountains for the 1990s and 2050s (RCP 4.5 and RCP 8.5). Shading corresponds to intermodel uncertainties.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

Over the 1990s, air temperature remains below 0.0°C between November and March on average, rises above 0.0°C in early spring, inducing snowmelt across all three FRB regions. In the 2050s, while the annual variation of air temperature remains similar to the baseline, a consistent increase of from 2.0° to 3.0°C in air temperature is projected for all three regions. The range of intermodel spread is small in winter and wide in summer, with a range of ±1.5°C around the warmest MME mean temperature for RCP 8.5. In the case of RCP 4.5, the spread is almost the same as that for RCP 8.5 while the mean temperature is lower.

c. Hydrological response to future climate change

In this section, CMIP5-driven VIC simulations are analyzed to estimate future changes in SWE, snow cover, and runoff for the RCP 8.5 pathway. Total runoff is calculated using the sum of base flow and runoff. The spatial patterns for the RCP 4.5 pathway are similar to those for RCP 8.5 for the 2050s, but with slightly lower magnitudes (not shown). In most of the temporal plots, for example, in Fig. 6c (described in greater detail below), the response under RCP 8.5 is somewhat greater than and distinguishable from the RCP 4.5. It is expected that differences between RCPs will be greater at the end of the twenty-first century than they are in the 2050s.

While the total precipitation slightly increases in the 2050s, the simulated changes show future increases of up to 40% in mean annual rainfall and nearly 50% decreases in mean annual snowfall (Fig. 5). The increased rainfall covers the Rocky and Coast Mountains while the decreased snowfall dominates the Interior Plateau and Rocky Mountains. These changes in total precipitation and fractional snowfall have competing effects on the SWE distribution.

Fig. 5.
Fig. 5.

Future change (2050s − 1990s; %) in the spatial distribution of mean annual (a) rainfall, (b) snowfall, and (c) total precipitation for RCP 8.5 only.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

The spatial pattern of projected SWE changes (as a percentage) shows decreases over the Interior Plateau and Rocky and Coast Mountains for both winter and spring, with larger magnitudes in spring (Figs. 6a,b). Most of the Interior Plateau has positive SWEmelt change in winter, while the Rocky and Coast Mountains show nearly zero SWEmelt in the future owing to their high elevations with subfreezing conditions (not shown). The areally averaged seasonal change of SWEmelt (Fig. 6c) shows an earlier onset of about 25 (±10) days by the 2050s. This earlier melting of snow increases spring runoff. SWEmelt is projected to decrease in spring and summer, yielding lower total contributions of SWE to runoff generation. The intermodel range shows a large spread in future SWEmelt, particularly in late spring and early summer. The annual cycle also shows a decrease in peak SWEmelt magnitude in the 2050s as compared to the 1990s being more obvious in RCP 8.5 than RCP 4.5. The earlier mountain snowmelt and associated runoff by the 2050s will result in a longer dry season and diminished summer and autumn streamflow in the Interior Plateau. Snowpacks are projected to decrease because of rising air temperatures accelerating snowmelt and causing more precipitation to fall as rain versus snow and increasing the evapotranspiration (ET). The projected decrease of seasonal snow cover over the entire FRB arises from warming air temperature (not shown). The snowmelt-dominated areas diminish more in winter as compared to other seasons, which reinforces that less precipitation is falling as snow and melting is occurring more often. Snow cover in the Rocky and Coast Mountains decreases substantially in spring and summer. These results are consistent with those reported in Shrestha et al. (2012) using the VIC-driven simulations with CMIP3 model output.

Fig. 6.
Fig. 6.

Future change (2050s − 1990s; %) in the spatial distribution of mean SWE over the FRB simulated by MME VIC for RCP 8.5 as (a) DJF and (b) MAM seasonal means. (c) Areally averaged annual cycle of daily SWEmelt over the FRB for the 1990s and 2050s for both RCP 4.5 and RCP 8.5. Shading corresponds to intermodel variability.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

MME mean seasonal runoff increases in winter and spring and decreases in summer over most of the FRB in the 2050s (not shown). The spring increase and summer decrease in runoff is more obvious in the Rocky and Coast Mountains, which is consistent with declines in the snowmelt-dominated areas (Figs. 6a,b) and the projected precipitation changes (Fig. 5) in these regions. Over the Rocky and Coast Mountains, the projected increase in spring runoff is induced by earlier snowmelt driven by rising air temperature and changing precipitation phase. The more pronounced changes are over the Rocky and Coast Mountains, with spatially varying increases of 40%–100% in spring runoff and nearly 50% decreases in summer runoff, especially over the Coast Mountains.

The spatial distribution of mean change (Fig. 7c) shows spatially varying decreases of 15%–30% in the northern portions of the FRB, with maximum decreases in the Interior Plateau and the Rocky Mountains. Decreases in mean SWEmelt of up to 20% in the Rocky Mountains dominate future declines in (Fig. 7b) compared to the relative change in future runoff (Fig. 7a). The future decrease (16% for RCP 8.5) in arises mainly from declines in snow accumulation rather than changes in runoff amounts (Table 3). The for six subbasins also exhibits considerable decreases in the 2050s, except for the high-elevation CH. As the UF, SU, and NA subbasins experience more future warming in winter, snowmelt accelerates, contributing more to runoff in that season. For RCP 8.5, decreases by nearly 13% (with a range of ±9%) in the UF, 19% (±5%) in the SU, and 22% (±1%) in the NA subbasins in the 2050s (Table 3). The uncertainties associated with changes show a wide range of spread depending on the model, probably due to the accumulated intermodel differences in SWEmelt and runoff. For example, some of the CMIP5 GCMs are warmer and may cause more melting than others.

Fig. 7.
Fig. 7.

Spatial distribution of mean future change (2050s − 1990s; %) of (a) runoff, (b) SWEmelt, and (c) . White areas at the outlet of the Fraser do not experience snow and are therefore masked in the percentage differences.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

Table 3.

Future change (2050s − 1990s) in for FRB’s water basins. Differences are calculated using 30-yr MME mean for both RCP 4.5 and RCP 8.5 pathways. The range of uncertainties is also tabulated along with MME.

Table 3.

The estimates mentioned above neglect the snowmelt contribution to other components of the water balance, such as ET and deep percolation. The ET is projected to increase in spring and summer over the Rocky and Coast Mountains (not shown). Therefore, spring snowmelt could be lost to ET, which may cause the snowmelt contribution to the water balance to be underestimated. However, this could be offset by intermittent snowmelt, particularly at lower elevations. Furthermore, in most of the FRB’s snowmelt-dominated areas, SWE remains consistent in terms of its accumulation and ablation phases (Déry et al. 2014). Therefore, the underestimation of snowmelt-induced runoff is likely minimal for the FRB. Thus, the approach used to calculate provides reasonable estimates of snowmelt contributions to runoff. The projected snowmelt changes for most of the FRB are in agreement with projected changes in the Pacific Northwest of North America (Chang and Jung 2010; Elsner et al. 2010).

Historically, the low-flow period takes place in winter in the FRB. Streamflow starts to increase in spring, reaches its maximum in early summer, and eventually declines in late summer. While this general seasonal pattern continues in the future, the timing of half the annual flow shows seasonal shifts approaching 25 days earlier for RCP 8.5 (Fig. 8) with the intermodel spread of nearly 10 days. The flows increase in early spring and are followed by an earlier and steeper recession in summer. The seasonal shifts are mainly induced by the changing phase of projected precipitation in winter and spring, along with a transition of snowmelt-dominated and hybrid regimes to hybrid and rainfall-dominated regimes in some regions due to increases in air temperature. The early decline of future SWE storage induces such seasonal shifting. The shift in winter and spring runoff leads to lower summer and autumn runoff due to the lower precipitation and snowpack depletion in the previous spring months. Such projected changes in the FRB runoff are the continuation of observed climate change that has already been started in the FRB as seen by Stewart (2009) and Kang et al. (2016). Daily mean runoff at the NA, QU, CH, TN, UF, and SU subbasins exhibits almost the same features as the LF. A noticeable difference, for all subbasins, is the change in timing of the runoff peak that varies between 20 and 30 days (median values) for RCP 8.5 within different subbasins (Fig. 9) with the range of nearly 10 days.

Fig. 8.
Fig. 8.

MME-based VIC-simulated mean daily runoff at Fraser River at Hope. Black, blue, and red curves represent the daily climatology for the base period (1990s), 2050s RCP 4.5, and 2050s RCP 8.5, respectively.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

Fig. 9.
Fig. 9.

VIC–CMIP5 MME mean daily runoff for the (a) UF, (b) SU, (c) NA, (d) QU, (e) CH, and (f) TN subbasins. Black, blue, and red curves represent the daily climatology for the base period (1990s), 2050s RCP 4.5, and 2050s RCP 8.5, respectively.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

The projected changes in FRB flows have significant implications for hydropower generation, such as from the Nechako Reservoir and Bridge River. The water availability appears to diminish during the period of highest demand (summer) in the 2050s, and the water resources managers may experience greater year to year variability and uncertainty in flows because of reduced snowpacks. The Fraser is one of the world’s most productive salmon rivers, with many First Nations relying on annual upriver migrations for food and as a livelihood (Xie and Hsieh 1989; Beamish et al. 1997; Benke and Cushing 2005; Fraser Basin Council 2006, 2009). The considerable changes in future flows along with increased water temperature may significantly degrade the success rates of upriver salmon migrations into the FRB inlands and their reproduction rates (Morrison et al. 2002; Eliason et al. 2011; Déry et al. 2012).

d. Elevation dependence

Elevation is one of the key factors governing cold-season snow processes because of its relationship with air temperature. This controls the partitioning of precipitation into snowfall and rainfall and regulates the melting of the snowpack during the ablation season. To investigate how SWE and snow cover may change at different elevations, analyses are conducted for different elevation ranges using the information from elevation bands within the Rocky Mountains, Interior Plateau, and Coast Mountains regions. To facilitate elevation-dependent projected change, the elevation distribution within 10 bands is clustered into different elevation ranges. While VIC mean SWE output matches observations well, its output for different elevation bands cannot be compared with observational data because of the paucity of SWE and snow cover data at different elevations across the FRB.

Future change in mean SWE for several elevation ranges shows SWE increases (decreases) with higher (lower) elevations, which is consistent in all three regions (Fig. 10a). Future declines in SWE are accentuated at low elevations reaching −50% in the 2050s. The impacts of future warming will be more noticeable at low- and midelevation regions, with implications for the mass balance of snow and associated runoff.

Fig. 10.
Fig. 10.

MME future change (2050s − 1990s; %) variation of mean (a) SWE and (b) snow cover for a range of elevations across the Interior Plateau (green), Rocky Mountains (blue), and Coast Mountains (red) regions. Curves are for RCP 8.5 only.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

The fractional snow cover decreases nonlinearly from high to low elevations. The future variation in relative snow cover with different elevation ranges shows an overall decrease of snow-covered areas within the FRB (Fig. 10b). Snow cover decreases as much as 40% for the lower elevations from sea level to 1200 m, whereas for elevations greater than 1200 m a 20% decrease persists.

Seasonal analyses show that the daily SWE climatology peaks in late winter followed by a sharp decrease in the snowmelt season, with SWE reaching its lowest value in late summer (not shown). With increasing elevation, the magnitude of SWE increases considerably for higher elevations. Although all the three regions follow the same SWE variability pattern, the magnitude varies strongly within each region. In the 1990s, the peak SWE value exceeds 1000 mm at higher elevations of the Rocky Mountains, whereas its maximum value surpasses 800 mm in the wetter Coast Mountains. The daily climatology of snow cover shows a peak value of more than 0.90 (fraction) for higher elevations that remains persistent in spring and decreases to its minimum (0.20) in late summer.

SWE changes are evident in the snow accumulation and snowmelt seasons, with a higher rate of SWE decrease at lower elevations (Figs. 11a,c,e). Compared to the Interior Plateau, the elevation ranges across the Rocky Mountains show larger SWE decreases, with decreases of up to 400 mm in late summer at higher elevations. While the higher elevations show substantial diminishing of SWE magnitude, their relative change compared to the 1990s is less pronounced than those at lower elevations (not shown). In the high elevations of the Rocky Mountains, maximum SWE occurs nearly 60 days later than it occurs at low elevations. A mixed response is seen in the Coast Mountains over different elevations because of the complex topography of the region interacting with influences from the Pacific Ocean. These results are consistent with those of Schnorbus et al. (2014) for the Peace and Campbell River basins, where SWE was found to decline substantially at low elevations while at high elevations, where winter temperatures are projected to remain below freezing, SWE may either remain unchanged or increase with increased winter precipitation.

Fig. 11.
Fig. 11.

MME future change (2050s − 1990s) of daily mean (left) SWE (mm) and (right) snow cover (as a fraction) for range of elevations across the (a),(b) Interior Plateau; (c),(d) Rocky Mountains; and (e),(f) Coast Mountains for RCP 8.5 only. Annual cycles are filtered using a 5-day running mean.

Citation: Journal of Hydrometeorology 18, 2; 10.1175/JHM-D-16-0012.1

The daily mean snow cover shows quite complex differences at low, mid-, and high elevations, particularly for the Coast Mountains (Figs. 11b,d,f). Considerable fractional differences are found in both snow accumulation and melt seasons for all regions. While the fractional decrease of snow cover intensifies with increasing elevations, some high elevations, particularly in the Rocky and Coast Mountains, show less snow cover change compared to mid- and low elevations. This may be due to the large snow masses covered by these elevations that remain below freezing throughout the year. In addition, the increase of future precipitation at some elevations results in deeper snowpacks and may partially mask the effects of warming. In the Rocky Mountains, snow cover decreases during the snow accumulation season, with a maximum fractional decrease of more than 0.30 for most elevations. At high elevations in the Coast Mountains, snow cover decreases in both the snow accumulation and melt seasons. Additionally, there is a considerable shift in the timing (advancement) of the snowmelt season in this region.

About one-third of the FRB’s snow cover will be lost by the 2050s according to the MME (Figs. 10b, 11). Increased winter and spring surface temperature results in retreating snow cover in mountainous terrain, which reduces the surface albedo. This accentuates the snow-albedo feedback and consequently increases the surface temperature, accelerating the melting (Déry and Brown 2007; Hernández-Henríquez et al. 2015). With continued warming in the 2050s, declines in snow accumulation and increased melt are projected to no longer be offset by winter precipitation increases at some elevations in the FRB.

The projected snowpack and hydrological changes estimated in this study are influenced by the different uncertainties associated with future emission scenarios, GCM dynamics, natural climate variability, downscaling methodology, and hydrological modeling (Wilby and Harris 2006). The uncertainties in climate projections (Tebaldi et al. 2005; Bae et al. 2011) and downscaling methods (Fowler et al. 2007; Chen et al. 2013) are the predominant sources of uncertainty in projecting hydrologic changes (Kay et al. 2009; Chen et al. 2011). Our future work will systematically explore these uncertainties and those present in the model setup and calibration parameters. Generally, air temperature is the leading controller of summer water availability in snowmelt-dominated basins, which makes regional snowpacks more vulnerable to temperature-induced effects, rather than precipitation. The IPCC AR5 GCMs have improved since the AR4, and there continues to be a very high confidence that these models reproduce the observed large-scale time–mean temperature pattern (Stocker et al. 2013). The AR5 temperature projections are considered more certain than precipitation and therefore the effect of temperature-based uncertainties in snowmelt-driven runoff may be minimal for the FRB. The future estimates presented in this study can therefore prove to be useful information for impact assessments of climate change.

Rising air temperature has already resulted in substantial hydrologic changes in catchments where hydrology is dominated by snow processes (Kang et al. 2014, 2016; Stewart et al. 2005; Mote et al. 2005). The results reported in this study reveal considerable changes in snowpack in the 2050s with an earlier occurrence and decreased magnitude of maximum SWE, which are consistent with observation- and simulation-based studies (Boyer et al. 2010; Kerkhoven and Gan 2011; Shrestha et al. 2012; Kang et al. 2014, 2016; Troin et al. 2016) conducted in different regions across Canada. These results also echo those found in studies conducted in other snowmelt-dominated regions in North America (e.g., Adam et al. 2009; Elsner et al. 2010; Sproles et al. 2013).

6. Conclusions

This study investigated the possible impacts of twenty-first century climate change on the Fraser River basin (FRB) snow hydrology. VIC driven by 24 statistically downscaled CMIP5 GCM scenarios applied over the FRB provided estimates of spatial and temporal changes in air temperature, precipitation, SWE, snow cover, and runoff for the 2040–69 (2050s) period relative to the 1980–2009 (1990s) baseline. The changes in the contribution of snow to runoff generation and variation in change of SWE and snow cover with different elevation ranges were also analyzed.

The results show that future climate change will considerably affect the FRB hydrology and its snowpack accumulation and ablation with earlier onset of snowmelt and enhanced winter and spring runoff. The overall findings of this study can be summarized with the following key points:

  1. An overall decrease in SWE in winter and spring is projected over the FRB owing to rising air temperature that accelerates snowmelt. The SWEmelt increases in the Interior Plateau in winter but declines in spring and summer in the Rocky and Coast Mountains, yielding lower contributions of SWEmelt to runoff.
  2. SWE and snow cover decrease at a higher rate at lower elevations. SWE and snow cover at low elevations are more sensitive to increased air temperature because the climate is warmer. About one-third of the FRB snow cover will be lost by the 2050s. In the FRB, even the most modest warming will convert snowfall to rainfall because large areas hover around 0°C in the present-day climate. Thus, warming will accelerate melt at low elevations, contributing more SWEmelt to early runoff.
  3. Simulations yield declining for the Fraser River at Hope and all six of its major subbasins driven by projected increases in air temperature. Diminished SWE along with substantial declines in winter snow accumulation and snow-covered areas are projected for the FRB. Analyses suggest that the snowmelt contribution to streamflow in the FRB will decline considerably by the 2050s due to warming.
  4. The timing and amount of runoff is altered substantially by declining mountain snowpacks. Higher temperatures in the 2050s will result in less winter precipitation falling as snow and accelerate (by 25 days) melting of winter and spring snowpacks. Both of these effects lead to a shift in the Fraser River peak runoff toward winter and early spring, when water demand is potentially at its lowest and storage facilities are already at capacity. Hence, in the future, a larger proportion of annual runoff could be lost to the Pacific Ocean. Although, given temperature and resulting evaporative changes, water demand for irrigation will probably also shift to earlier in the year and could utilize some of this water.
The key points mentioned above provide critical information on the range of possible future flows and their timing and hence establish possible impacts on the Fraser River. Such information can be used in the assessment of future water supplies for the region.

While this study facilitates the estimation of future changes in snowpack and water availability of the FRB, some caution in interpreting the results is needed. First, the hydrological model used in this study is integrated on a relatively coarse resolution (¼°), which may not represent small-scale dynamics related to the FRB’s complex topography. Our future work will therefore explore the robustness of the results of this study with an increase of resolution up to 1/16°. Similarly, to improve physical integration, the daily time step used in VIC integrations may be increased to hourly in future efforts. Finally, the accuracy of the forcing data at the model grid resolution and modification of air temperature and precipitation with preset, linear lapse rates across elevation ranges may also introduce uncertainties in VIC simulations. Such alternative approaches will be investigated in our future studies.

Nevertheless, this work provides useful information for climate change impact assessments for the FRB, especially for water management. Previous studies focusing on the FRB (e.g., Kerkhoven and Gan 2011; Ferrari et al. 2007; Shrestha et al. 2012) have estimated the future hydrological changes using CMIP3 or older versions of GCMs. The results from this study are based on the new and improved CMIP5 GCMs and may leverage existing literature by providing future estimates of FRB snowpack and hydrological changes. For similar locations and climate models, CMIP5 projections are warmer than CMIP3 projections because of slightly higher emissions for the closest comparable forcing scenarios, such that CMIP5/RCP 4.5 is warmer than CMIP3/B1, for example. The results reported here therefore offer valuable insight to the FRB’s future state and fate that could have important ramifications for the development of strategies to address challenges created by climate change and to design water management systems and plans for snowmelt-dominated regions of western Canada.

Acknowledgments

This work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC)-funded Canadian Sea Ice and Snow Evolution (CanSISE) Network. The authors are grateful to the colleagues from Pacific Climate Impacts Consortium (PCIC) for providing the statistically downscaled CMIP5 boundary forcing data and their assistance. We thank the World Climate Research Programme’s Working Group on Coupled Modelling and the climate modeling groups who participated in CMIP5 for producing and making their model output available. We also thank Do Hyuk Kang (NASA GSFC), Huilin Gao (Texas A&M), and Xiaogang Shi (Xi’an Jiaotong-Liverpool University) for assisting with this effort. Thanks to Jeannine St. Jacques (Concordia University) for her input in revising this paper. The authors are thankful to Michael Allchin (UNBC) for plotting Fig. 1a. Thanks to the anonymous reviewers and the handling editor for constructive comments that greatly improved the paper.

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