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Record W3126682988 · doi:10.1002/joc.7036

Will land use land cover change drive atmospheric conditions to become more conducive to wildfires in the United States?

2021· article· en· W3126682988 on OpenAlexaboutno aff
Shiyuan Zhong, Ting Wang, Pietro Sciusco, Meicheng Shen, Lisi Pei, Jovanka Nikolic, Kevin McKeehan, Herve B. Kashongwe, Pouyan Hatami‐Bahman‐Beiglou, Ken Camacho, Donald Akanga, Joseph J. Charney, Xindi Bian

Bibliographic record

VenueInternational Journal of Climatology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersU.S. Forest ServiceNational Institute of Food and AgricultureNational Science Foundation
KeywordsEnvironmental scienceShrublandLand coverClimate changePrecipitationClimatologyLand usePhysical geographyGeographyMeteorologyEcosystemEcologyGeology

Abstract

fetched live from OpenAlex

Abstract The increase in wildfire risk in the United States in recent decades has been linked to rapid growth of the wildland‐urban interface and to changing climate. While there have been numerous studies on wildfires and climate change, few have separately assessed the impact of climate response to land‐use‐land‐cover change (LULCC) on wildfires. In this study, we analyse two 10‐year regional climate simulations driven by the current (2011) and future (2100) land‐use‐land‐cover patterns to assess modifications by the projected LULCC to the frequency and severity of fire‐prone atmospheric conditions described by two fire weather indices, the Canadian Forest Fire Weather Index and the Hot‐Dry‐Windy Index. The simulation corresponding to future land‐use‐land‐cover pattern yields higher surface temperature and vapour pressure deficit and lower precipitation compared to the simulation with the current pattern in areas where urbanized landscapes replace forests and grasslands, such as along the Piedmont and outside the Chicagoland region, while in areas where croplands replace forests, such as the southeast Coastal Plains, the results are reversed. These changes to local and regional atmospheric conditions lead to longer fire seasons and more extreme fire‐weather conditions in much of the eastern United States, specifically in the Southeast and Ohio River Valley where significant urban expansion is projected by the end of the century. Whereas in Southern California where some highly flammable shrublands will be replaced by urban or crop lands, fire‐prone atmospheric conditions are likely to be less frequent and less extreme in the future. However, much of California moves towards a year‐round fire season under the projected LULCC. The results suggest that by altering atmospheric conditions, LULCC may play an important role in determining fire regime, but the effects are highly heterogeneous and regionalized.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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
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

Citations18
Published2021
Admission routes1
Has abstractyes

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