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Record W4249191732 · doi:10.5194/hess-2019-676-rc3

Reviewer Comments for HESS-2019-676

2020· preprint· en· W4249191732 on OpenAlexaboutno aff
Matthew G. Cooper

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Dierauer et al. 2020 present an analysis of snow and streamflow drought in four catchments in British Columbia, Canada.They use an uncalibrated process-based hydrologic model forced with downscaled climate data from four general circulation models archived in the CMIP-5 climate model database.The processbased hydrologic model includes physical representations of unsaturated zone and saturated zone groundwater flow, physical representations of overland flow/channel routing, and conceptual representations of snow accumulation and snowmelt.This allows the authors to draw inferences about possible linkages between changes in snow C1 HESSD Interactive comment Printer-friendly version Discussion paper accumulation and melt, groundwater storage, and summer and winter low streamflow, including the lowest 20% of all summer and winter flows, referred to as "streamflow drought".The authors justify their study by identifying two knowledge gaps: 1) no studies have explicitly quantified the impact of different snow drought types, i.e. dry, warm, or warm and dry, on the severity of summer/winter streamflow droughts, and 2) no studies have completed a combined analysis of snow drought and streamflow drought regimes in the context of climate change.Thereafter, the authors offer two hypotheses: 1) the snow-streamflow drought response depends on baseline mean winter temperature, and 2) the snow-streamflow drought response depends on changes in groundwater recharge/storage.Only the former is explicitly stated in the introduction, the latter is implicitly stated by the study design, reported results, and discussion.The authors use four catchments that span a range of climatic conditions, from -0.6-5.9 oC average annual temperature, and 46-235 cm average annual total precipitation.This is appropriate with respect to the first hypothesis.The catchments also differ in terms of consumptive water use demands, which ends up dominating much of the discussion despite not being related to the study hypotheses.The catchment slopes range from 2-35o.Unsaturated zone and saturated zone depths, bulk soil densities, and hydraulic conductivity values are assigned based on published data sources.The combination of physically-based groundwater modeling, realistic soil properties, and variations in catchment slope are appropriate with respect to the second hypothesis.The authors find that the distribution of future "snow droughts" into warm, dry, and warm and dry differ depending on the baseline mean annual air temperature and the future changes in air temperature and precipitation.The distribution of future low flows shifts toward less severe in winter and more severe in summer.Precipitation and temperature both increase under all future scenarios.The frequency and severity of dry and warm/dry snow droughts therefore decreases while warm snow droughts increase.Total annual runoff increases for all catchments because winter runoff increases more

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)0.006
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.4470.154

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 head0.038
GPT teacher head0.280
Teacher spread0.242 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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