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Record W4293074898 · doi:10.11159/rtese22.003

Linking Climate Change to Environmental Impact and Adaptation Studies: Recent Advances and Shortcomings in Modeling of Extreme Hydrologic Processes

2022· article· en· W4293074898 on OpenAlexaffabout
Van‐Thanh‐Van Nguyen

Bibliographic record

VenueProceedings of the International Conference of Recent Trends in Environmental Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdaptation (eye)Climate changeComputer scienceEnvironmental changeEnvironmental scienceEnvironmental resource managementGeologyOceanographyPsychology

Abstract

fetched live from OpenAlex

Climate change has been recognized as having a profound impact on hydrologic (water quantity) and environmental (water quality) processes for a local site or over an urban catchment area. Hence, global/regional climate models have been extensively used in climate change impact and adaptation studies. However, due to the current limitations on detailed physical modelling and computational capability, outputs from these models are provided at resolutions that are too coarse and not suitable for these impact studies. Hence, different downscaling methods have been proposed for linking these coarsescale climate change projections to hydrologic and environmental processes at the required relevant space and time scales. Of particular importance for environmental engineering applications are those procedures dealing with the linkage of the large-scale climate variability to the historical observations of the precipitation and temperature extreme processes at a location of interest. If this linkage could be established, then the projected change of climate conditions given by climate models could be used to predict the resulting changes of the precipitation and temperature characteristics at the given local site. Therefore, the main focus of the present keynote lecture is to provide an overview of recent advances and shortcomings in the modeling of extreme rainfall and temperature processes in the climate change context from both theoretical and practical viewpoints. In particular, another focus of this lecture is on the recently published technical guide by the Canadian Standards Association entitled "Development, interpretation, and use of rainfall intensity-duration-frequency (IDF) information: Guideline for Canadian water resources practitioners" (CSA PLUS 4013:19) to provide some guidance to water professionals in Canada on how to consider the climate change information in the design of urban water infrastructure.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.170
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0030.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.069
GPT teacher head0.269
Teacher spread0.200 · 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 designSimulation or modeling
Domainnot available
GenreReview

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
Published2022
Admission routes2
Has abstractyes

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