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Record W4380087167 · doi:10.5194/hess-27-2173-2023

Hydrological, meteorological, and watershed controls on the water balance of thermokarst lakes between Inuvik and Tuktoyaktuk, Northwest Territories, Canada

2023· article· en· W4380087167 on OpenAlexafffundabout
Evan J. Wilcox, Brent B. Wolfe, Philip Marsh

Bibliographic record

VenueHydrology and earth system sciences · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsArcticNetAurora Research Institute
KeywordsThermokarstPermafrostEnvironmental scienceHydrology (agriculture)TundraSnowmeltWater balanceSnowWatershedSurface runoffMeltwaterPrecipitationArcticSoil waterGeologySoil scienceOceanographyEcologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Thermokarst lake water balances are becoming increasingly vulnerable to change in the Arctic as air temperature increases and precipitation patterns shift. In the tundra uplands east of the Mackenzie Delta in the Northwest Territories, Canada, previous research has found that lakes responded non-uniformly to year-to-year changes in precipitation, suggesting that lake and watershed properties mediate the response of lakes to climate change. To investigate how lake and watershed properties and meteorological conditions influence the water balance of thermokarst lakes in this region, we sampled 25 lakes for isotope analysis five times in 2018, beginning before snowmelt on 1 May and sampling throughout the remainder of the ice-free season. Water isotope data were used to calculate the average isotope composition of lake source water ( δ I ) and the ratio of evaporation to inflow (E/I). We identified four distinct water balance phases as lakes responded to seasonal shifts in meteorological conditions and hydrological processes. During the freshet phase from 1 May to 15 June, the median E/I ratio of lakes decreased from 0.20 to 0.13 in response to freshet runoff and limited evaporation due to lake ice presence that persisted for the duration of this phase. During the following warm, dry, and ice-free period from 15 June to 26 July, designated the evaporation phase, the median E/I ratio increased to 0.19. During the brief soil wetting phase, E/I ratios did not respond to rainfall between 26 July and 2 August, likely because watershed soils absorbed most of the precipitation which resulted in minimal runoff to lakes. The median E/I ratio decreased to 0.11 after a cool and rainy August, identified as the recharge phase. Throughout the sampling period, δ I remained relatively stable and most lakes contained a greater amount of rainfall-sourced water than snow-sourced water, even after the freshet phase, due to snowmelt bypass. The range of average E/I ratios that we observed at lakes (0.00–0.43) was relatively narrow and low compared with thermokarst lakes in other regions, likely owing to the large ratio of watershed area to lake area (WA/LA), efficient preferential flow pathways for runoff, and a shorter ice-free season. Lakes with smaller WA/LA tended to have higher E/I ratios ( R 2 = 0.74). An empirical relationship between WA/LA and E/I was derived and used to predict the average E/I ratio of 7340 lakes in the region, which identified that these lakes are not vulnerable to desiccation, given that E/I ratios were < 0.33. If future permafrost thaw and warming cause less runoff to flow into lakes, we expect that lakes with a smaller WA/LA will be more influenced by increasing evaporation, while lakes with a larger WA/LA will be more resistant to lake-level drawdown. However under wetter conditions, lakes with a larger WA/LA will likely experience a greater increases in lake level and could be more susceptible to rapid drainage.

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 score0.025
Threshold uncertainty score0.084

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.212
Teacher spread0.179 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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