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Gridded Hydrologic Model Output

The Gridded Hydrologic Model Output page provides access to gridded, 1/16-degree (roughly 30 km2 at the latitudes covered) projections of hydrologic states and fluxes for three watersheds that originate in British Columbia, Canada; the Peace, Fraser and Columbia. Data were simulated using an upgraded version of the Variable Infiltration Capacity (VIC-GL) model, reconfigured to couple with an external dynamic glacier model (Schnorbus, in prep). Model set-up and deployment is described in Schnorbus (in prep).


Future simulations were prepared by forcing the VIC-GL model using 12 statistically downscaled global climate model (GCM) projections from the Coupled Model Intercomparison Project Phase 5 (Taylor et al., 2011). Six models were selected to span a wide range in future climate extremes with a cluster initialization algorithm (Cannon, 2015) and each was run under two Representative Concentration Pathways (RCPs), RCP 4.5 and 8.5. The RCP 8.5 scenario is a high emissions scenario, with concentrations in 2100 that are three times those that we see today. The RCP 4.5 scenario represents an intermediate emissions trajectory in which policies are implemented to reduce anthropogenic greenhouse gas emissions and the radiative forcing stabilizes before 2100. (See van Vuuren et al., 2011.) The end-of-century radiative forcing in RCP 4.5 also roughly corresponds to the end-of-century forcing that we would see if the international pledges for the Paris Agreement were to be met. Statistical downscaling was carried out using Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) using PNWNAmet as a target dataset, which combines the bias-corrected constructed analogue (BCCA) (Maurer et al., 2010) and bias-corrected climate imprint (BCCI) (Hunter and Meentemeyer, 2005) techniques. For more information on BCCAQ see (Cannon et al., 2015; Hiebert et al., 2018; Sobie and Murdock, 2017; Werner and Cannon, 2016). The BCCAQ technique is also described on the Statistically Downscaled Climate Scenarios page; however, data provided there used NRCANmet as its target dataset instead of PNWNAmet and covers Canada versus northwest North America, respectively.

Simulated data includes Baseflow, Evapotranspiration, Glacier Area, Glacier Mass Balance, Glacier Outflow, Potential Evapotranspiration, Precipitation, Rainfall, Snow Melt, Snow Water Equivalent, Surface Runoff, Total Column Soil Moisture and Transpiration.

The user interface features an interactive map of the province that allows users to zoom, pan and select their region of interest using a rectangular selection tool. See below for a brief description, notes on citation, references and the terms of use.


Each variable is stored in three-dimensional latitude x longitude x time array (i.e. a time series of 2-dimensional spatial fields) native in NetCDF (Network Common Data Form). Although the data can be downloaded in several formats (NetCDF, ASCII and Arc/Info ASCII Grid), ASCII and Arc/Info are inefficient and we encourage downloading the data in NetCDF, a format which is ideally suited for array-oriented scientific data.

The user interface features an interactive map of northwestern North America that allows users to zoom, pan and select their region of interest using a rectangular selection tool. The displayed legend represents the range of values for all time steps and latitude-longitudes grids. To evaluate change in a given hydrometric state or flux compare the future to the past within the same GCM/RCP scenario, such as rcp45->ACCESS1-0r1->Snow Melt.

To allow hosting of these large datasets on our server, the data are stored as “packed” values (i.e. floating point values are converted to decimal values using a scale factor and offset unique to each data set) to reduce file size. When downloading data as NetCDF, the data are automatically “unpacked” (i.e. converted pack to floating point). If downloading in any other format the data must be unpacked manually. Please see the Metadata for the appropriate scale_factor and add_offset parameters. Note that the packing algorithm is lossy and that some precision is lost during unpacking. This should not be a concern for most practical applications other than to note that values less than or equal to ≈ 0.0009 should be treated as zero.

See the User Docs for more details. See below for notes on Data Citation, Terms of Use, No Warranty and References.

Data Citation

When referring to the Gridded Hydrologic Model Output data retrieved from the website or found otherwise, the source must be clearly stated:
Pacific Climate Impacts Consortium, University of Victoria, (January 2020). VIC-GL BCCAQ CMIP5: Gridded Hydrologic Model Output. Downloaded from <Permalink> on <Date>.

Terms of Use

The data is subject to PCIC's terms of use.


This data product is provided by the Pacific Climate Impacts Consortium with an open license on an “AS IS” basis without any warranty or representation, express or implied, as to its accuracy or completeness. Any reliance you place upon the information contained here is your sole responsibility and strictly at your own risk. In no event will the Pacific Climate Impacts Consortium be liable for any loss or damage whatsoever, including without limitation, indirect or consequential loss or damage, arising from reliance upon the data or derived information.


Cannon, A.J., 2015. Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices. J. Clim., 28, 1260–1267. https://doi.org/10.1175/JCLI-D-14-00636.1

Hunter, R.D., Meentemeyer, R.K., 2005. Climatologically Aided Mapping of Daily Precipitation and Temperature. J. Appl. Meteorol., 44, 1501–1510. https://doi.org/10.1175/JAM2295.1

Liang, X., Lettenmaier, D.P., Wood, E.F., Burges, S.J., 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmospheres, 99, 14415–14428. https://doi.org/10.1029/94JD00483

Liang, X., Wood, E.F., Lettenmaier, D.P., 1996. Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Glob. Planet Change, 13, 195–206.

Maurer, E.P., Hidalgo, H.G., Das, T., Dettinger, M.D., Cayan, D.R., 2010. The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci., 14, 1125–1138. https://doi.org/10.5194/hess-14-1125-2010

Schnorbus, M.A., in prep. Modelling glacier surface mass and energy balance with the VIC model. Dev.

Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2011. An Overview of CMIP5 and the Experiment Design. Bull. Am. Meteorol. Soc., 93, 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1

van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., Rose, S.K., 2011. The representative concentration pathways: an overview. Clim. Change, 109, 5. https://doi.org/10.1007/s10584-011-0148-z

Werner, A.T., Schnorbus, M.A., Shrestha, R.R., Cannon, A.J., Zwiers, F.W., Dayon, G., Anslow, F., 2019. A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Sci. Data, 6, 180299. https://doi.org/10.1038/sdata.2018.299