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Record W4224278054 · doi:10.3390/meteorology1020009

Trends in the Frequency of Water and Heat Stress in Mid-Latitude North America since 1980

2022· article· en· W4224278054 on OpenAlexaboutno aff
Arik Tashie

Bibliographic record

VenueMeteorology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEvapotranspirationStreamflowForcing (mathematics)Environmental scienceClimate changeClimatologyWater balanceLatitudeWatershedGlobal warmingGeographyDrainage basinEcologyGeology

Abstract

fetched live from OpenAlex

The water-energy balance of many mid-latitude watersheds has been changing in recent decades due to global warming. These changes manifest themselves over both long timescales (e.g., hydrologic drought) and short timescales (e.g., agricultural drought) and may be ameliorated or exacerbated by vegetative response. We apply a Budyko framework to assess short-term response to long-term trends in water and heat stress (HS) across mid-latitude North America. Using high-resolution meteorological data and streamflow records, we calculate the frequency of HS every year since 1980 for every gaged watershed with adequate data (n = 1528). We find that HS has become more frequent in most watersheds in the western US, New England, and southeastern Canada. However, we find that HS has become less frequent in the Midwest and the relatively humid eastern US. By assessing the relationship between trends in HS frequency and proximate forcing variables (annual PPT, annual streamflow, minimum and maximum daily temperatures, actual evapotranspiration, and potential evapotranspiration), we find that these trends in HS frequency are primarily driven by meteorological forcings rather than vegetative response. Finally, we contextualize our findings within the Budyko framework, which assumes a landscape in equilibrium with its climate, with the implication that these trends in HS are only likely to be realized after local vegetation has adapted to new meteorological norms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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