Evidence map›Paper›PMID 42111222›Full record

ArticleiScience2026

Assessing the impacts of extreme precipitation on groundwater recharge under diverse hydrogeological conditions via the theis equation.

Qingtao Ma, Xinghang Zhang, Zhijie Bai, Sishan Pang, Guofei Shang, Zhaowang Chen, Ruohan Chen, Xiaoxia Hou, Yue Liu, Zhixian Wang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

14 authors.

Qingtao MaSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Xinghang ZhangSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Zhijie BaiSatellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China.
Sishan PangSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Guofei ShangSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Zhaowang ChenCollege of Life Sciences, Hebei University, Baoding 071002, China.
Ruohan ChenSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Xiaoxia HouSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Yue LiuSchool of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China.
Zhixian WangThe Third Geological Team of Hebei Provincial Bureau of Geology and Mineral Resources Exploration and Development, Zhangjiakou City, Hebei Province, China.
Shumin HanCenter for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China.
Dandan RenSchool of Resources and Environment (College of Carbon Neutrality), Linyi University, Linyi, China.
Xiaojing LiuCenter for Geophysical Survey, China Geological Survey, Lang Fang 065000, China.
Zhitong MaCollege of Geology and Environment, Xi'an University of Science and Technology, Xi'an 710054, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change has amplified the frequency of extreme high-intensity precipitation events, altering groundwater recharge across diverse hydrogeological settings. Using observed precipitation, groundwater level (GL), hydrogeological drilling data, and the Theis equation, we quantified how precipitation affects groundwater in the Baiyangdian-Basin Plain. We found that GL declined in 80% of counties, with a milder decrease in piedmont regions. During rainy and irrigation periods, the cumulative rise and fall of GL accounted for 55.3% and 57.5% of the annual total, respectively. Short-duration intense precipitation dominates annual water gain in wet years and amplifies inter- and intra-annual GL fluctuations. When precipitation exceeds 600 mm/year (intensity >8 mm/day), both GL recovery and well flow rate decline rapidly, implying reduced groundwater recharge. In the piedmont, precipitation is strongly positively correlated with GL and well flow rate, indicating little impact of extreme precipitation on groundwater recharge. These findings prevent the overestimation of groundwater recharge from extreme precipitation.

Indexed as

Environmental scienceHydrologyMeteorology

Identifiers

PMID42111222
PMCPMC13156614

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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.