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Record W4409800125 · doi:10.1016/j.fecs.2025.100337

Quantifying spatiotemporal inconsistencies in runoff responses to forest logging in a subtropical watershed, China

2025· article· en· W4409800125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForest Ecosystems · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of British Columbia, Okanagan Campus
FundersNational Natural Science Foundation of China
KeywordsWatershedLoggingSurface runoffEnvironmental scienceSubtropicsTropical and subtropical moist broadleaf forestsChinaAgroforestryHydrology (agriculture)GeographyEcologyForestryGeologyComputer science

Abstract

fetched live from OpenAlex

Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances, profoundly impacting hydrological processes. However, the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales, challenging comprehensive assessment of logging impacts at the watershed scale. Here, we developed multiple forest logging scenarios using the soil and water assessment tool (SWAT) model for the Le'an River watershed, a 5,837 ​km 2 subtropical watershed in China, to quantify the hydrological effects of forest logging across different spatiotemporal scales. Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity. However, sensitivity varied across spatiotemporal scales, with the rainy season (15.30%–15.81%) showing higher sensitivity than annual (11.56%–12.07%) and dry season (3.38%–5.57%) periods. Additionally, the ecohydrological sensitivity of logging varied significantly across the watershed, with midstream areas exhibiting the highest sensitivity (13.13%–13.25%), followed by downstream (11.87%–11.98%) and upstream regions (9.96%–10.05%). Furthermore, the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas, with attenuated runoff changes due to scale effects. Scale effects were more pronounced during dry seasons ((−8.13 to −42.13) ​× ​10 4 ​m 3 ⋅month −1 ) than in the rainy season ((−11.11 to −26.65) ​× ​10 4 ​m 3 ⋅month −1 ). These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions, providing valuable insights for forest management under increasing anthropogenic activities and climate change. • Increasing forest logging ratios consistently amplified ecohydrological sensitivity. • Ecohydrological sensitivity of logging varied depending on the spatial location within the watershed. • The whole watershed exhibited greater hydrological resilience to forest logging compared to the upstream areas.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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