The 2013 flood event in the South Saskatchewan and Elk River basins: Causes, assessment and damages

Pomeroy, John W.; Stewart, Ronald E.; Whitfield, Paul H.

doi:10.1080/07011784.2015.1089190

Cited by 120 publications

(120 citation statements)

References 9 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…The results of this analysis show that strong positive water vapour advection occurred over southwestern Alberta during the event (upper left panel). This strong advection is balanced by large-scale condensation (lower right panel), with convection playing a minor role (lower left panel), which is consistent with low CAPE (Milrad et al 2015) and the lack of lightning over regions receiving heavy precipitation (Pomeroy et al 2016). The location and the magnitude of large-scale condensation are very similar to those of the modelled rainfall (Fig.…”

Section: Meteorological and Hydrological Evolution From Observationssupporting

confidence: 57%

“…From a historical viewpoint, the water levels along the Bow River were the highest in the past 60 years. According to Pomeroy et al (2016), the return time of the streamflows is around 40 years for the Bow River at Banff and at Calgary, based on gauged and estimated streamflows at these two locations.…”

mentioning

confidence: 99%

“…On the hydrological side, soils in high elevation areas were saturated and/or frozen, offering little capacity to store additional water, thus contributing to the magnitude of the flooding. Additionally, the rain fell on snow over the upper elevations of the Bow River basin (BRB), which is estimated to have contributed up to an additional 100 mm of runoff derived from snowmelt (Pomeroy et al 2016).…”

mentioning

confidence: 99%

“…Although many of the factors that contributed to this flood event have been identified (Milrad et al 2015;Pomeroy et al 2016), the relative importance of each of these factors remains unclear. Thus, the goal of this paper is to investigate the flood from weather and climate perspectives, through targeted experiments with the fifth generation Canadian Regional Climate Model (CRCM5; Martynov et al 2013).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Investigation of the 2013 Alberta flood from weather and climate perspectives

et al. 2016

View full text Add to dashboard Cite

Section: Meteorological and Hydrological Evolution From Observationssupporting

confidence: 57%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Investigation of the 2013 Alberta flood from weather and climate perspectives

et al. 2016

View full text Add to dashboard Cite

“…The Oldman, Bow and Red Deer river basins, all located at the foothills of the Canadian Rocky mountain range, are subjected to extreme precipitation events. In June 2013 a major flood occurred in this region resulting from the combined effect of heavy rainfall during mountain snowpack melt over partially frozen ground (Pomeroy et al, 2016;Teufel et al, 2016). Some river basins received 1.5 times 1:100 year rainfall, estimated to be 250 mm rain in 24 hours.…”

Section: Study Area and Datasets 25mentioning

confidence: 99%

Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

Jha¹,

Shrestha²,

Stadnyk³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for 10 streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effect over diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall-post processing (RPP), developed in Australia Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global ensemble forecasting system (GEFS) Reforecast 2 project from National Centers for Environmental Protection (NCEP), and 15 Global deterministic forecast system (GDPS) from Environment and Climate Change Canada (ECCC) are used in this study.The study period from Jan 2013 to Dec 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias, and reduce the continuous ranked probability score of both GEFS and GDPS forecasts. Ensembles generated from the RPP better depict the forecast uncertainty.

show abstract

The Spatial and Temporal Variability of Meltwater Flow Paths: Insights From a Grid of Over 100 Snow Lysimeters

Webb

Williams

Erickson

2018

Water Resources Research

View full text Add to dashboard Cite

Snowmelt is an important part of the hydrologic cycle and ecosystem dynamics for headwater systems. However, the physical process of water flow through snow is a poorly understood aspect of snow hydrology as meltwater flow paths tend to be highly complex. Meltwater flow paths diverge and converge as percolating meltwater reaches stratigraphic layer interfaces creating high spatial variability. Additionally, a snowpack is temporally heterogeneous due to rapid localized metamorphism that occurs during melt. This study uses a snowmelt lysimeter array at tree line in the Niwot Ridge study area of northern Colorado. The array is designed to address the issue of spatial and temporal variability of basal discharge at 105 locations over an area of 1,300 m2. Observed coefficients of variation ranged from 0 to almost 10 indicating more variability than previously observed, though this variability decreased throughout each melt season. Snowmelt basal discharge also significantly increases as snow depth decreases displaying a cluster pattern that peaks during weeks 3–5 of the snowmelt season. These results are explained by the flow of meltwater along snow layer interfaces. As the snowpack becomes less stratified through the melt season, the pattern transforms from preferential flow paths to uniform matrix flow. Correlation ranges of the observed basal discharge correspond to a mean representative elementary area of 100 m2, or a characteristic length of 10 m. Snowmelt models representing processes at scales less than this will need to explicitly incorporate the spatial variability of snowmelt discharge and meltwater flow paths through snow between model pixels.

show abstract

The 2013 flood event in the South Saskatchewan and Elk River basins: Causes, assessment and damages

Cited by 120 publications

References 9 publications

Investigation of the 2013 Alberta flood from weather and climate perspectives

Investigation of the 2013 Alberta flood from weather and climate perspectives

Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

The Spatial and Temporal Variability of Meltwater Flow Paths: Insights From a Grid of Over 100 Snow Lysimeters

Contact Info

Product

Resources

About