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Record W4292195826 · doi:10.3390/w14162502

Spatiotemporal Variations in Reference Evapotranspiration and Its Contributing Climatic Variables at Various Spatial Scales across China for 1984–2019

2022· article· en· W4292195826 on OpenAlexaff
Xiaohui Yan, Abdolmajid Mohammadian, Ruigui Ao, Jianwei Liu, Xin Chen

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Ottawa
FundersFundamental Research Funds for the Central Universities
KeywordsSunshine durationEvapotranspirationEnvironmental scienceWind speedHydrometeorologyClimatologyRelative humiditySpatial variabilityWater cycleAtmospheric sciencesMeteorologyPrecipitationGeographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Reference evapotranspiration (ET0) is of great significance in studies of hydrological cycle, agricultural water resources, and hydrometeorology. The present study collected daily meteorological data at 536 meteorological stations in China from 1984 to 2019, calculated daily ET0 using the FAO Penman–Monteith equation, analyzed the spatial distribution and temporal variation characteristics of ET0 and meteorological variables at four different spatial scales (continental, regional, provincial, and local), and discussed the sensitivity of ET0 to the meteorological variables and the contribution rates of the meteorological variables to the ET0 variations. The results showed that ET0 increased at 406 out of the 536 stations (75.7%), with the trends being significant at 65 stations at the 5% significance level, and 147 at the 1% significance level. The slope calculated using Sen’s method and linear trend method showed that the annual ET0 at the continental scale increased by approximately 12 mm/decade. Most of the stations showed decreasing trends in relative humidity (Hm), sunshine duration (SD), and wind speed at 2 m height (U2) while increasing trends in the maximum air temperature (Tmax) and minimum air temperature (Tmin). ET0 was most sensitive to Hm (sensitivity coefficient, St = −0.66), followed by Tmax (St = 0.29), SD (St = 0.18), U2 (St = 0.16), and Tmin (St = 0.07). Most of the stations showed increasing trends in St for Hm (56.16%), Tmax (72.95%), Tmin (87.31%), and U2 (90.49%), and decreasing trends for SD (69.78%). The variations in Hm, Tmax, and Tmin increased the ET0 at most of the stations (82.28%, 98.13%, and 69.03%, respectively). The variations in SD and U2 decreased ET0 at most of the stations (66.04% and 56.34%, respectively). Some ET0 characteristics in a few regions can be well described using a single spatial scale. However, most regions exhibited significantly different ET0 characteristics across spatial scales. The results of this project can provide reference for hydrological analysis and agricultural water management under climate change conditions and provide data and information for other hydrology-related applications.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.226
Teacher spread0.216 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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