Evidence map›Paper›PMID 42853915›Full record

ArticleScience advances2026

Wildfires diminish vegetation control on surface runoff.

Yuanyuan Peng, Kun Shi, Shun Bi, Qi Guan

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuanyuan PengState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210000, China.ORCID 0009-0006-5089-3865
Kun ShiState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210000, China.ORCID 0000-0002-6124-7512
Shun BiState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210000, China.ORCID 0000-0002-2668-5808
Qi GuanState Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210000, China.ORCID 0000-0002-6064-134X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wildfires are increasingly reshaping global terrestrial ecosystems, yet their impacts on the fundamental coupling between vegetation and the hydrological cycle remain poorly understood. Here, we present a global-scale assessment of how wildfire disturbances alter the sensitivity of surface runoff to vegetation dynamics using multidecadal satellite observations and reanalysis datasets. We developed a spatially explicit framework to quantify pre- and postfire runoff-vegetation sensitivity across climatic gradients and fire regimes. We show a widespread and systematic weakening of vegetation control on runoff after wildfires, indicating a fundamental decoupling of hydrology-vegetation interactions. This attenuation is notably modulated by aridity and fire intensity, with the most pronounced sensitivity losses occurring in fragile drylands. These findings expose a critical blind spot in traditional hydrological models that overlook disturbance-driven vegetation shifts. Our study provides a mechanistic understanding of how compound climate-fire perturbations accelerate terrestrial water flux instability, suggesting that increasing fire frequency will exacerbate ecosystem vulnerability and threaten water security in a warming world.

Identifiers

PMID42853915
PMCPMC13651861

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.