. The interaction of mountain terrain with meteorological processes causes substantial temporal and
spatial variability in snow accumulation and ablation. Processes impacted by complex terrain
include large-scale orographic enhancement of snowfall, small-scale processes such as
gravitational and wind-induced transport of snow, and variability in the radiative balance such as
through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate
the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines
atmospheric data from a numerical weather prediction system at the kilometre scale with process-based
downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions
allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by
using the efficient terrain representation by unstructured triangular meshes. The model simulates
processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation
and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and
snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate
Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic
Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In
particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving
wind model at 50  m resolution to perturb the mesoscale HRDPS wind and to account for the
influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate
snow conditions down to 50  m resolution during winter 2017/2018 in a domain around the
Kananaskis Valley ( ∼1000 km2 ) in the Canadian Rockies. Simulations were
evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and
snow persistence indexes derived from remotely sensed imagery. Results included model
falsifications and showed that both wind-induced and gravitational snow redistribution need to be
simulated to capture the snowpack variability and the evolution of snow depth and persistence with
elevation across the region. Accumulation of windblown snow on leeward slopes and associated
snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not
capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric
helped to identify these lee-side areas and improved the wind field and the associated snow
redistribution. An overestimation of snow redistribution from windward to leeward slopes and
subsequent avalanching was still found. The results of this study highlight the need for further
improvements of snowdrift-permitting models for large-scale applications, in particular the
representation of subgrid topographic effects on snow transport.

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Multi-scale snowdrift-permitting modelling of mountain snowpack

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows:
 The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.


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The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

 Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS) Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses All systems used are close to operational model versions and configurations In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations

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A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model

Canada, like other high latitude cold regions on Earth, is experiencing some of the most accelerated and intense warming resulting from global climate change. In the northern regions, Arctic amplification has resulted in warming two to three times greater than global mean temperature trends. Unprecedented warming is matched by intensification of wet and dry regions and hydroclimatic cycles, which is altering the spatial and seasonal distribution of surface waters in Canada. Diagnosing and tracking hydrologic change across Canada requires the implementation of continental-scale prediction models owing the size of Canada’s drainage basins, their distribution across multiple eco- and climatic zones, and the scarcity and paucity of observational networks. This review examines the current state of continental-scale climate change across Canada and the anticipated impacts to freshwater availability, including the role of anthropogenic regulation. The review focuses on continental and regional-scale prediction that underpins operational design and long-term resource planning and management in Canada. While there are significant process-based changes being experienced within Canadian catchments that are equally—if not more so—critical for community water availability, the focus of this review is on the cumulative effects of climate change and anthropogenic regulation for the Canadian freshwater supply.

Canadian Continental-Scale Hydrology under a Changing Climate: A Review

Influence of Hurricane Wind Field Variability on Real-Time Forecast Simulations of the Coastal Environment

The Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis...

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Djamel Bouhemhem

Papers

The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally