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Record W7039810972

New Estimates of Snow Water Availability in the Northern Regions of North America.

2024· other· en· W7039810972 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2024
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSnowSnowpackSnow fieldSnow coverSnowmeltSnow lineClimate changeWater equivalent
DOInot available

Abstract

fetched live from OpenAlex

Seasonal snow has a crucial role on freshwater supply in mountainous regions and high latitudes. The advent of remote sensing data and Earth System reanalysis products has opened enormous opportunities for estimating snow water availability at larger scales. Despite these technological advancements, still estimating the water stored in snow and determining its variability in space and time pose major challenges. One major issue is limitations in the benchmarking studies and the fact that while several new datasets are introduced, little is known about their accuracy, reliability and robustness. The second issue is related to the way that water stored in the snowpack is assessed using the concept of Snow Water Equivalent (SWE). Most importantly, maximum annual SWE does not reflect the losses of snow water during winter melts– a phenomenon that has become widespread due to the rising temperature and more frequent winter rain as a result of climate change. In addition, SWE does not take into account snow cover extent, and therefore cannot distinguish whether changes in stored water in the snow correspond to changes in snow depth or snow cover. To address the first challenge, a formal benchmarking is performed to test three key snow fields of a newly released reanalysis product, ERA5-Land, over the area of Canada and Alaska, ~9% of global land in which snow processes have a critical role on water supply. The considered snow variables are snow depth, snow cover and SWE, from which snow density can be also retrieved. The ERA5-Land’s snow depth and SWE fields are intercompared with Canadian Meteorological Centre’s (CMC’s) snow depth and SWE, whereas snow cover field is tested against MODIS satellite observations as the reference. Special care is made to assess how spatial and temporal patterns of change and persistence are reconstructed using ERA5-Land’s snow field over 21 ecological regions that cover the domain. In addition, the spatial patterns discrepancies between ERA5-Land’s snow fields and corresponding reference products are explored to inspect whether they entail there is any significant dependence with latitude, longitude and elevation, which points to a systematic bias in ERA5-Land data. Based on this benchmarking attempt, it is advised against the use of ERA5-Land’s snow depth and SWE estimates in Canada and Alaska, while estimates of snow cover and snow density can be still used although with cautions, particularly for local assessments, which may require bias-correction. To address the second challenge, a new and more physically-appealing metric, Snow Water Availability (SWA), is defined that take into account snow cover extent in conjunction with snow depth and snow density. Based on the findings of the benchmarking attempt, four monthly estimates of SWA are established over Canada and Alaska by integrating CMC snow depth fields with ERA5-Lands’s and CMC’s snow density as well as MODIS’s and ERA5-Land’s snow cover during the water years of 2000 to 2020 at 25×25 km2 spatial resolution. Using these SWA estimates, the implications on water availability over 25 drainage regions in Canada and Alaska are explored and discussed. It is concluded that while Canada and Alaska as a whole has gained substantial amount of SWA during the study period, the strategically important drainage regions in western Canada have lost substantial amount of SWA since the beginning of the century. This can jeopardize regional water resource management in some of the world’s most important food baskets in Canadian Prairies, revealing the urgency for regional adaptation to maintain the water, food and energy security in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.260
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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