Freeze–Thaw Changes of Seasonally Frozen Ground on the Tibetan Plateau from 1960 to 2014

Siqiong Luo; Jingyuan Wang; John W. Pomeroy; Shihua Lyu

American Meteorological Society Journal of Climate,
Volume 33, Issue 2, pages 9427–9446.
October 2, 2020
https://doi.org/10.1175/JCLI-D-19-0923.1

Abstract
The freeze–thaw changes of seasonally frozen ground (SFG) are an important indicator of climate change. Based on observed daily freeze depth of SFG from meteorological stations on the Tibetan Plateau (TP) from 1960 to 2014, the spatial–temporal characteristics and trends in SFG were analyzed, and the relationships between them and climatic and geographical factors were explored. Freeze–thaw changes of SFG on a regional scale were assessed by multiple regression functions. Results showed multiyear mean maximum freeze depth, freeze–thaw duration, freeze start date, and thaw end date that demonstrate obvious distribution characteristics of climatic zones. A decreasing trend in maximum freeze depth and freeze–thaw duration occurred on the TP from 1960 to 2014. The freeze start date has been later, and the thaw end date has been significantly earlier. The freeze–thaw changes of SFG significantly affected by soil hydrothermal conditions on the TP could be assessed by elevation and latitude or by air temperature and precipitation, due to their high correlations. The regional average of maximum freeze depth and freeze–thaw duration caused by climatic and geographical factors were larger than those averaged using meteorological station data because most stations are located at lower altitudes. Maximum freeze depth and freeze–thaw duration have decreased sharply since 2000 on the entire TP. Warming and wetting conditions of the soil resulted in a significant decrease in maximum freeze depth and freeze–thaw duration in the most area of the TP, while drying soil results in a slight increase of them in the southeast of the TP.

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Canada Water Agency will help protect and sustain Canada’s water resources, say USask water scientists

USask News
September 29, 2020

The new Canada Water Agency—announced in the recent federal Throne Speech—is the first critical step toward transforming how water is managed across the country, said John Pomeroy, director of the University of Saskatchewan (USask)-led Global Water Futures (GWF) program.

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Scientific and human errors in a snow model intercomparison

Cecile B. Menard; Richard Essery; Gerhard Krinner; Gabriele Arduini; Paul Bartlett; Aaron Boone; Claire Brutel-Vuilmet; Eleanor Burke; Matthias Cuntz; Yongjiu Dai Bertrand Decharme; Emanuel Dutra; Xing Fang; Charles Fierz; Yeugeniy Gusev; Stefan Hagemann; Vanessa Haverd; Hyungjun Kim; Matthieu Lafaysse; Thomas Marke; Olga Nasonova; Tomoko Nitta; Masashi Niwano; John Pomeroy; Gerd Schädler; Vladimir Semenov; Tatiana Smirnova; Ulrich Strasser; Sean Swenson; Dmitry Turkov; Nander Wever; Hua Yuan

Bulletin of the American Meteorological Society 1-46
September 9, 2020
DOI: https://doi.org/10.1175/BAMS-D-19-0329.1

Abstract
Twenty-seven models participated in the Earth System Model – Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modelling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables, and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modelling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parametrizations are problematic and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behaviour and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

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Simulation of Preferential Flow in Snow With a 2‐D Non‐Equilibrium Richards Model and Evaluation Against Laboratory Data

Nicolas R. Leroux, Christopher B. Marsh, John W. Pomeroy
Published August 10, 2020
Water Resources Research, Volume 56. Issue 9, Pages 1-11
DOI: https://doi.org/10.1029/2020WR027466

Abstract

Recent studies of water flow through dry porous media have shown progress in simulating preferential flow propagation. However, current methods applied to snowpacks have neglected the dynamic nature of the capillary pressure, such as conditions for capillary pressure overshoot, resulting in a rather limited representation of the water flow patterns through snowpacks observed in laboratory and field experiments. Indeed, previous snowmelt models using a water entry pressure to simulate preferential flow paths do not work for natural snowpack conditions where snow densities are less than 380 kg m−3. Because preferential flow in snowpacks greatly alters the flow velocity and the timing of delivery of meltwater to the base of a snowpack early in the melt season, a better understanding of this process would aid hydrological predictions. This study presents a 2‐D water flow through snow model that solves the non‐equilibrium Richards equation. This model, coupled with random perturbations of snow properties, can represent realistic preferential flow patterns. Using 1‐D laboratory data, two model parameters were linked to snow properties and model boundary conditions. Parameterizations of these model parameters were evaluated against 2‐D snowpack observations from a laboratory experiment, and the resulting model sensitivity to varying inputs and boundary conditions was calculated. The model advances both the physical understanding of and ability to simulate water flow through snowpacks and can be used in the future to parameterize 1‐D snowmelt models to incorporate flow variations due to preferential flow path formation.

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Nikolas O. Aksamit and John W. Pomeroy
Published: September 1, 2020
The Cryosphere, volume14, issue 9, pages 2795–2807
DOI: https://doi.org/10.5194/tc-14-2795-2020

Abstract:

Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modeled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies nonstationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if specific turbulent motions are responsible for warm- and dry-air advection during blowing snow events, quadrant analysis and variable interval time averaging was used to investigate turbulent time series from the Fortress Mountain Snow Laboratory alpine study site in the Canadian Rockies, Alberta, Canada, during the winter of 2015–2016. By analyzing wind velocity and sonic temperature time series with concurrent blowing snow, such turbulent motions were found to supply substantial sensible heat to near-surface wind flows. These motions were responsible for temperature fluctuations of up to 1 ∘C, a considerable change for energy balance estimation. A simple scaling relationship was derived that related the frequency of dominant downdraft and updraft events to their duration and local variance. This allows for the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The downdraft and updraft scaling relationship described herein provides a significant step towards a more physically based blowing snow sublimation model with more realistic mixing of atmospheric heat. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of nonstationarity impacts on sublimation rates.

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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.