Field monitoring of secondary consolidation events and ice cover progression during freeze-up on the Lower Dauphin River, Manitoba

Wazney, Lucas; Clark, Shawn P.; Wall, Alexander J.

doi:10.1016/j.coldregions.2018.01.014

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…prior years lower flows were noted. This resulted in notably thinner ice covers, more thermal ice growth, and less extensive ice jamming than was reported by Wazney et al (2018). The average values of observed ice thickness reduced from 2.9 m at site DRLL05 and DRLL06 in 2017-2018 to 1.8 m and 0.8 m in 2018-2019 and 2019-2020 respectively.…”

Section: Rpa Performancementioning

confidence: 54%

“…Pool sections were observed to be silt bottomed. During winter ice formation, dramatic ice consolidation events, jams, and flooding have been reported by Wazney et al (2018) on the Lower Dauphin River. Lake Winnipeg water levels can have a significant effect on the most downstream 2 km of this reach which is typically where the largest toe of the ice jam would form.…”

mentioning

confidence: 99%

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Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry

Ehrman¹,

Clark²,

Wall³

2021

Preprint

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Abstract. Structure-from-Motion Photogrammetry conducted with images obtained via Remotely Piloted Aircraft (RPA) has revolutionized the field of land surface monitoring. RPA-Photogrammetry can quickly and easily capture a full 3D representation of a study area. The result of this process is a high-definition Digital Elevation Model (DEM) representing the land surface of a given study area. It is particularly useful in applications where land surface data collection would otherwise be expensive or dangerous. The monitoring of fluvial ice covers can be time-intensive, dangerous, and costly, if detailed data are required. Fluvial ice roughness is a sensitive parameter in hydraulic models and is incredibly difficult to measure directly using traditional field methods. This research hypothesized that the surface roughness of a newly-frozen fluvial ice cover is indicative of subsurface roughness. The hypothesis was tested through a comparison of ice roughness determined through the statistical analysis of RPA-photogrammetry DEMs to ice roughness values predicted by the Nezhikhovskiy equation. The Nezhikhovskiy equation is a widely used empirical method for estimating ice roughness based on observed ice thickness. Hydraulic and topographic data were collected over two years of field research on the Dauphin River in Manitoba, Canada. Various statistical metrics were used to represent the roughness of the DEMs. Strong trends were identified in the comparison of ice cover roughness values determined through RPA-photogrammetry and those calculated via the Nezhikhovskiy equation, as well as with ice thickness. The inter-quartile range of observed roughness heights was determined to be the most representative roughness metric. The maximum peak value performed better in some cases, but the fact that this metric would be heavily influenced by outliers led to it being rejected as a representative metric. Three distinct forms of surface ice roughness were noted: rough, smooth, and ridged. Statistical properties of the DEMs of fluvial ice covers were calculated. No DEMs were found to be normally distributed. k-means clustering analysis was used to group sampled data into two categories, which were interpreted as rough and smooth ice. The inter-quartile range of the smooth and rough categories were found to be 0.01–0.05 meters and 0.07–0.12 meters, respectively. RPA-photogrammetry was concluded to be a suitable method for the monitoring of fluvial ice covers. Other applications of RPA-photogrammetry for the characterization of fluvial ice covers are proposed.

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Section: Rpa Performancementioning

confidence: 54%

mentioning

confidence: 99%

Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry

Ehrman¹,

Clark²,

Wall³

2021

Preprint

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“…The Dauphin River flows out of Lake St. Martin and discharges into Lake Winnipeg approximately 52 km downstream and has a gross drainage area of 82 300 km 2 (Water Survey of Canada, 2021). The river width ranges from approximately 110 to 160 m with a mean discharge of about 100 m 3 /s during freeze‐up, and a percentile analysis of the historical flow data during the ice‐affected period is presented in Wazney, Clark, and Wall (2018). For the first 40 km the river is characterized by a mild average slope of 0.00029; however, the slope sharply increases to 0.0016 at a point ~11.2 km upstream of the outlet (Wazney et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The river width ranges from approximately 110 to 160 m with a mean discharge of about 100 m 3 /s during freeze-up, and a percentile analysis of the historical flow data during the ice-affected period is presented in Wazney, Clark, and Wall (2018). For the first 40 km the river is characterized by a mild average slope of 0.00029; however, the slope sharply increases to 0.0016 at a point $11.2 km upstream of the outlet (Wazney et al, 2018). The surrounding vegetation is mature boreal forest, consisting largely of coniferous trees including spruce and tamarack, which leads to some shading throughout the day along the right (south) bank.…”

Section: Introductionmentioning

confidence: 99%

A detailed energy budget analysis of river supercooling and the importance of accurately quantifying net radiation to predict ice formation

McFarlane

Clark

2021

Hydrological Processes

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River supercooling and ice formation is a regular occurrence throughout the winter in northern countries. The resulting frazil ice production can obstruct the flow through intakes along the river, causing major problems for hydropower and water treatment facilities, among others. Therefore, river ice modellers attempt to calculate the river energy budget and predict when supercooling will occur in order to anticipate and mitigate the effects of potential intake blockages. Despite this, very few energy budget studies have taken place during freeze-up, and none have specifically analysed individual supercooling events. To improve our understanding of the freeze-up energy budget detailed measurements of air temperature, relative humidity, barometric pressure, wind speed and direction, short-and longwave radiation, and water temperature were made on the Dauphin River in Manitoba. During the river freeze-up period of late October to early November 2019, a total of six supercooling events were recorded. Analysis of the energy budget throughout the supercooling period revealed that the most significant heat source was net shortwave radiation, reaching up to 298 W/m 2 , while the most significant heat loss was net longwave radiation, accounting for losses of up to 135 W/m 2. Longwave radiation was also the most significant heat flux overall during the individual supercooling events, accounting for up to 84% of the total heat flux irrespective of flux direction, highlighting the importance of properly quantifying this flux during energy budget calculations. Five different sensible (Q h) and latent (Q e) heat flux calculations were also compared, using the bulk aerodynamic method as the baseline. It was found that the Priestley and Taylor method most-closely matched the bulk aerodynamic method on a daily timescale with an average offset of 8.5 W/m 2 for Q h and 10.1 W/m 2 for Q e , while a Daltontype equation provided by Webb and Zhang was the most similar on a sub-daily timescale with average offsets of 20.0 and 14.7 W/m 2 for Q h and Q e , respectively.

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“…8 depicting one month between the two peak stages. It is possible that rising water levels after H F are caused by secondary consolidation events (Andres, 1999, Andres et al, 2003, Wazney et al, 2018 however, the daily resolution may be too coarse to capture this short-lived occurrence. An H F2 is also reported (Beltaos, unpublished data) to occasionally occur on the regulated Peace River at Peace Point (07KC001) when midwinter flow releases cause increasing water levels but the ice cover remains stable.…”

Section: Ice Cover: Hf2 Hf2 Max Hlw2 Hlq2mentioning

confidence: 99%

A Canadian River Ice Database from National Hydrometric Program Archives

Rham¹,

Dibike²,

Beltaos³

et al. 2020

Preprint

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Abstract. River ice is a common occurrence in cold climate hydrological systems. The annual cycle of river ice formation, growth, decay and clearance can include low flows and ice jams, as well as mid-winter and spring break-up events. Reports and associated data on river ice occurrence are often limited to site and season-specific studies. Within Canada, the National Hydrometric Program (NHP) operates a network of gauging stations with water level as the primary measured variable to derive discharge. In the late 1990s, the Water Science and Technology Directorate of Environment and Climate Change Canada initiated a long-term effort to compile, archive and extract river ice related information from NHP hydrometric records. This data article describes the original research data set produced by this near 20-year effort: the Canadian River Ice Database (CRID). The CRID holds almost 73,000 variables from a network of 196 NHP stations throughout Canada that were in operation within the period 1894 to 2015. Over 100,000 paper and digital files were reviewed representing 10,378 station-years of active operation. The task of compiling this database involved manual extraction and input of more than 460,000 data entries on water level, discharge, date, time and data quality rating. Guidelines on the data extraction, rating procedure and challenges are provided. At each location, a time series of up to 15 variables specific to the occurrence of freeze-up and winter-low events, mid-winter break-up, ice thickness, spring break-up and maximum open-water level were compiled. This database follows up on several earlier efforts to compile information on river ice, which are summarized herein, and expands the scope and detail for use in Canadian river ice research and applications. Following the Government of Canada Open Data initiative, this original river ice data set is available at: https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 (de Rham et al., 2020).

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Field monitoring of secondary consolidation events and ice cover progression during freeze-up on the Lower Dauphin River, Manitoba

Cited by 16 publications

References 7 publications

Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry

Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry

A detailed energy budget analysis of river supercooling and the importance of accurately quantifying net radiation to predict ice formation

A Canadian River Ice Database from National Hydrometric Program Archives

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