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Record W2889653743 · doi:10.1029/2018wr023087

Climate Controls on Runoff and Low Flows in Mountain Catchments of Western North America

2018· article· en· W2889653743 on OpenAlexafffund
J.R. Dierauer, Paul H. Whitfield, D. M. Allen

Bibliographic record

VenueWater Resources Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsEnvironment and Climate Change CanadaUniversity of SaskatchewanSimon Fraser University
FundersSimon Fraser UniversityPacific Institute for Climate Solutions
KeywordsSnowmeltSurface runoffPrecipitationEnvironmental scienceClimate changeHydrology (agriculture)Water yearSnowStreamflowClimatologyDrainage basinGeographyGeologyMeteorologyEcology

Abstract

fetched live from OpenAlex

Abstract In the mountainous regions of western North America, snowmelt recharges groundwater and provides ecosystem‐sustaining base flow during low‐flow periods. Continued warming is expected to have large impacts on snowmelt hydrology and on low‐flow regimes, but the relative impact of temperature and precipitation on low flows is unclear. To address this knowledge gap, the dominant climate controls on summer and winter season low flows in 63 near‐natural catchments in mountainous ecoregions of western North America are identified with correlation analysis, and low‐flow sensitivity to temperature and precipitation is quantified with multiple linear regression analysis. Results show that precipitation is the dominant control on the interannual variability of annual runoff and on the duration and severity of summer and winter low flows. The temperature sensitivity of low flows, however, can be as much as twice that of annual runoff. Warm winters correspond to significantly lower runoff; significantly longer, more severe summer low flows; and significantly shorter winter low flows. This highlights the importance of winter climate conditions for runoff and low flows in these mountain catchments and provides another line of evidence regarding the impacts of climate change on snowmelt hydrology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.294
Teacher spread0.274 · 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.

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

Citations115
Published2018
Admission routes2
Has abstractyes

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