ALBERT

Description: From 19 to 22 June 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins.

Description: A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

Description: A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction.

Description: Land surface schemes (LSSs) are of potential interest both to hydrologists looking for innovative ways to simulate river flow and the land surface water balance and to atmospheric scientists looking to improve weather and climate predictions. This paper discusses three ideas, which are grounded in hydrological science, to improve LSS predictions of streamflow and latent heat fluxes. These three possibilities are 1) improved representation of lateral flow processes, 2) the appropriate representation of surface heterogeneity, and 3) calibration to streamflow as a way to account for parameter uncertainty. The current understanding of lateral hydrological processes is described along with their representation of a selected group of LSSs. Issues around spatial heterogeneity are discussed, and calibration in hydrologic models and LSSs is examined. A case study of an evapotranspiration-dominated basin with over 10 years of extensive observations in central Canada is presented. The results indicate that in this particular basin, calibration of streamflow presents atmospheric modelers with a unique opportunity to improve upon the current practice of using lookup tables to define parameter values. More studies are needed to determine if model calibration to streamflow is an appropriate method for generally improving LSS-modeled heat fluxes around the globe.

Description: Monitoring, understanding, and forecasting the hydrologic cycle of large freshwater basins often requires a broad suite of data and models. Many of these datasets and models, however, are susceptible to variations in monitoring infrastructure and data dissemination protocols when watershed, political, and jurisdictional boundaries do not align. Reconciling hydrometeorological monitoring gaps and inconsistencies across the international Laurentian Great Lakes–St. Lawrence River basin is particularly challenging because of its size and because the basin’s dominant hydrologic feature is the vast surface waters of the Great Lakes. For tens of millions of Canadian and U.S. residents that live within the Great Lakes basin, seamless binational datasets are needed to better understand and predict coastal water-level fluctuations and other conditions that could potentially threaten human and environmental health. Binational products addressing this need have historically been developed and maintained by the Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data (Coordinating Committee). The Coordinating Committee recently held its one-hundredth semiannual meeting and reflected on a range of historical accomplishments while setting goals for future work. This article provides a synthesis of those achievements and goals. Particularly significant legacy and recently developed datasets of the Coordinating Committee include historical Great Lakes surface water elevations, basin-scale tributary inflow to the Great Lakes, and basin-scale estimates of both over-lake and over-land precipitation. Moving forward, members of the Coordinating Committee will work toward customizing state-of-the-art hydrologic and meteorological forecasting systems across the entire Great Lakes basin and toward promoting their products and protocols as templates for successful binational coordination across other large binational freshwater basins.

Description: Sensible and latent heat fluxes over Lake Superior are computed using a variational approach with a Bowen ratio constraint and inputs of 7 years of half-hourly temporal resolution observations of hydrometeorological variables over the lake. In an advancement from previous work focusing on the sensible heat flux, in this work computations of the latent heat flux are required so that a new physical constraint of the Bowen ratio is introduced. Verifications are made possible for fluxes predicted by a Canadian operational coupled atmosphere–ocean model due to recent availabilities of observed and model-predicted fluxes over Lake Superior. The observed flux data with longer time periods and higher temporal resolution than those used in previous studies allows for the examination of detailed performances in computing these fluxes. Evaluations utilizing eddy-covariance measurements over Lake Superior show that the variational method yields higher correlations between computed and measured sensible and latent heat fluxes than a flux-gradient method. The variational method is more accurate than the flux-gradient method in computing these fluxes at annual, monthly, daily, and hourly time scales. Under both unstable and stable conditions, the variational method considerably reduces mean absolute errors produced by the flux-gradient approach in computing the fluxes. It is demonstrated with 2 months of data that the variational method obtains higher correlation coefficients between the observed and the computed sensible and latent heat fluxes than the coupled model predicted, and yields lower mean absolute errors than the coupled model. Furthermore, comparisons are made between the coupled-model-predicted fluxes and the fluxes computed based on three buoy observations over Lake Superior.

Description: A one-dimensional mixed layer dynamic lake model is enhanced with snow and ice physics for an examination of processes governing ice cover and phenology in a small boreal lake. The complete snowpack physics module of the Canadian Land Surface Scheme along with a new snow-ice parameterization have been added to the Canadian Small Lake Model, and detailed meteorological and temperature profile data have been acquired for the forcing and evaluation of two wintertime simulations. During the first winter, simulated ice-on and ice-off biases were −3 and −5 days, respectively. In the second winter simulation, ice-on bias was larger, likely due to the absence of a frazil ice scheme in the model, and simulated ice-off was 6 days late, evidently due to insufficient convective mixing beneath the ice in the weeks leading up to ice-off. Ice cover was simulated about 25% too thin between January and March for this year, though late January simulated snow and snow-ice amounts were close to observed. The impact of snow-ice production on simulated ice cover and phenology was found to be dramatic for this lake. In the absence of this process, January snow was more than twice as deep as observed and March ice thickness was less than one-third of that observed. Without snow-ice production, a reasonable simulation of ice cover could only be restored if 62% of snowfall was removed ad hoc (e.g., through blowing snow redistribution)—an excessive amount for a small, sheltered boreal lake.