Signal processing for in situ detection of effective heat pulse probe spacing radius as the basis of a self-calibrating heat pulse probe

Nicholas Kinar, John Pomeroy and Bing Si
Published July 16, 2020
Geoscientific Instrumentation, Methods and Data Systems
volume 9, issue 2, pages 293–315
DOI: https://doi.org/10.5194/gi-9-293-2020

Abstract
A sensor comprised of an electronic circuit and a hybrid single and dual heat pulse probe was constructed and tested along with a novel signal processing procedure to determine changes in the effective dual-probe spacing radius over the time of measurement. The circuit utilized a proportional–integral–derivative (PID) controller to control heat inputs into the soil medium in lieu of a variable resistor. The system was designed for onboard signal processing and implemented USB, RS-232, and SDI-12 interfaces for machine-to-machine (M2M) exchange of data, thereby enabling heat inputs to be adjusted to soil conditions and data availability shortly after the time of experiment. Signal processing was introduced to provide a simplified single-probe model to determine thermal conductivity instead of reliance on late-time logarithmic curve fitting. Homomorphic and derivative filters were used with a dual-probe model to detect changes in the effective probe spacing radius over the time of experiment to compensate for physical changes in radius as well as model and experimental error. Theoretical constraints were developed for an efficient inverse of the exponential integral on an embedded system. Application of the signal processing to experiments on sand and peat improved the estimates of soil water content and bulk density compared to methods of curve fitting nominally used for heat pulse probe experiments. Applications of the technology may be especially useful for soil and environmental conditions under which effective changes in probe spacing radius need to be detected and compensated for over the time of experiment.

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Sask.’s $4B irrigation plan must address changing climate, Indigenous rights: professor

CBC News, July 5th, 2020

“The Saskatchewan government has announced a $4-billion plan to expand irrigation out of the Lake Diefenbaker reservoir. Work is set to begin immediately, and will be completed in three phases over the next decade.

CBC reporter Jason Warick spoke Friday with John Pomeroy, a Canada Research chair and director of the University of Saskatchewan’s Global Water Futures program.”

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Canada’s flooding crisis is spilling over our shores. Only urgent action can dam the breach

Globe and Mail, June 19, 2020
Thomas Axworthy and John Pomeroy

“Calgary didn’t need this.

Amidst all its other woes, the city was lashed recently by an extreme rain and hailstorm that closed Deerfoot Trail, destroyed scores of homes and flooded streets. Fire crews had to rescue stranded motorists from the highways by boat. Estimates of the financial costs go as high as $1-billion.

Calgary’s plight has been endured by many other Canadian cities. Only six weeks earlier, warming temperatures and rapid snowmelt resulted in ice jams in northern Alberta, which caused record-high flooding on the Athabasca River in Fort McMurray; more than 14,000 Albertans had to flee their homes. Premier Jason Kenney put it well when he said that “the devastation caused by the flooding … has impacted thousands of lives, washing away memories and losing the security of your home.”

And Alberta is not alone among provinces hit by extreme flooding while trying to cope with a pandemic. The story is the same in southern Manitoba and Saskatchewan, where local municipalities have declared states of emergency to evacuate residents from the rising Roseau River.”

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Alberta wrestles with its most critical resource: water
The Narwhal, June 18, 2020
Sarah Lawryniuk

“A severe thunderstorm in Calgary in mid-June delivered a salvo of hail that shattered windshields and stripped siding off houses. The accompanying rain flooded streets to the point of closure — though not before submerging dozens of cars.

In a press conference on June 15, Mayor Naheed Nenshi said the magnitude of damages within the city exceeds even those of 2013, when the Bow River swelled to precipitate the largest flood Calgary had seen since 1932.

“Over the last 15 years, Alberta has had the most severe environmental disasters associated with water of any part of Canada,” says John Pomeroy, director of the Global Water Futures program, which is based at the University of Saskatchewan.”

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Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques

Phillip Harder, John Pomeroy, Warren Helgason
Published June 15th, 2020
The Cryosphere, volume 14, issue 6, pages 1919–1935
DOI: https://doi.org/10.5194/tc-14-1919-2020

Abstract
Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

For the full article, go here.