High-resolution hydrometeorological modelling of the June 2013
flood in southern Alberta, Canada

Vionnet, Vincent; Fortin, Vincent; Gaborit, Étienne; Roy, Guy; Abrahamowicz, Maria; Gasset, Nicolas; Pomeroy, John W.

doi:10.5194/hess-2019-152

Cited by 3 publications

(5 citation statements)

References 46 publications

(97 reference statements)

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“…One such example is the coupled atmosphere, land surface, and hydrodynamic models currently being developed for the Great Lakes of North America (Gronewold & Fortin, 2012). The ability of these coupled models to forecast compound flooding has yet to be reported in the literature, however, there have been numerous research and technical challenges in the process of coupling and parameterizing models (Gaborit et al, 2017;Gronewold et al, 2018;Pietroniro et al, 2007;Vionnet et al, 2019).…”

Section: Catering For Different Flood Eventsmentioning

confidence: 99%

Ensemble flood forecasting: Current status and future opportunities

et al. 2020

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Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

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Section: Catering For Different Flood Eventsmentioning

confidence: 99%

Ensemble flood forecasting: Current status and future opportunities

et al. 2020

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“…Radar data were not included in the different QPE products, since they under- estimated precipitation amounts in this mountainous region for this event (Kochtubajda et al, 2016), in agreement with earlier studies in other mountainous regions (e.g., Germann et al, 2006). The data from each CaPA experiment are available on the Federated Research Data Repository (Vionnet et al, 2019).…”

Section: Precipitation Analysismentioning

confidence: 61%

“…Model variables were stored at an hourly frequency. GEM forecasts at different resolutions are available on the Federated Research Data Repository (Vionnet et al, 2019).…”

Section: Atmospheric Simulationsmentioning

confidence: 99%

“…Data availability. The CaPA and GEM data at different resolutions are publicly available on the Federated Research Data Repository (Vionnet et al, 2019). Ground station data from the ABE, CRHO and COOP networks are available through dedicated web-portals: (i) ABE at https://agriculture.alberta.ca/acis/alberta-weather-data-viewer.jsp (last access: 27 April 2020), (ii) CRHO at http://giws.usask.…”

Section: Appendix B: Sensitivity Metricsmentioning

confidence: 99%

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Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins

Vionnet

Fortin

Gaborit

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. 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.

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“…In a weather prediction context, SVS improved ISBA offline simulations of soil moisture and brightness temperature during the warm season over the North American domain [43,44]. In a hydrologic context, SVS has been shown to produce satisfactory runoff simulations within the large and partly ungauged Lake Ontario basin [6], and in hindcasts of the June 2013 flood in Alberta (Canada) [45]. More recently, Maheu et al [46] evaluated the point scale soil water transport component of SVS by using precipitation and latent heat fluxes as input data.…”

Section: Introductionmentioning

confidence: 96%

Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

et al. 2020

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The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.

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High-resolution hydrometeorological modelling of the June 2013 flood in southern Alberta, Canada

Cited by 3 publications

References 46 publications

Ensemble flood forecasting: Current status and future opportunities

Ensemble flood forecasting: Current status and future opportunities

Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins

Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

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