Evidence map›Paper›PMID 41093823›Full record

ArticleNature communications2025

Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning.

Haoyu Ji, Yalan Song, Tadd Bindas, Chaopeng Shen, Yuan Yang, Ming Pan, Jiangtao Liu, Farshid Rahmani, Ather Abbas, Hylke Beck and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Haoyu JiDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Yalan SongDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Tadd BindasDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Chaopeng ShenDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA. cshen@engr.psu.edu.ORCID http://orcid.org/0000-0002-0685-1901
Yuan YangCenter for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA.
Ming PanCenter for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA.
Jiangtao LiuDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0002-9219-8354
Farshid RahmaniDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0001-9241-7206
Ather AbbasPhysical Science and Engineering Department, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Hylke BeckPhysical Science and Engineering Department, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID http://orcid.org/0000-0003-2553-9566
Kathryn LawsonDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0003-0075-7911
Yoshihide WadaBiological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID http://orcid.org/0000-0003-4770-2539

Funding

National Aeronautics and Space Administration (NASA) 80NSSC24K1646National Science Foundation (NSF) DE-SC0021979National Science Foundation (NSF) EAR-2221880United States Department of Commerce | National Oceanic and Atmospheric Administration (NOAA) NA22NWS4320003United States Department of Defense | United States Army | U.S. Army Corps of Engineers (US Army Corps of Engineers) W912HZ-24-2-0001U.S. Department of Energy (DOE) DE-SC0021979
6 · The paper itself

Abstract

To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as the baseflow fraction of streamflow - but are hindered by their limited ability to learn from data. Here we introduce a high-resolution, physics-embedded, big-data-trained model to reliably capture characteristic hydrologic response patterns (signatures) and their shifts. By realistically representing the long-term water balance, the model revealed widespread shifts - in some cases, more than 20% over 20 years - in fundamental green-blue-water partitioning and baseflow ratios worldwide. Shifts in these previously-assumed-static response patterns contributed to increasing flood risks in northern mid-latitudes, heightening water supply stresses in southern subtropical regions, and declining freshwater inputs to many European estuaries, all with ecological implications. With substantially more accurate simulations at monthly and daily scales than current operational systems, this next-generation model resolves large, nonlinear, seasonal runoff responses to rainfall (elasticity) and streamflow flashiness in semi-arid and arid regions. Our results highlight regions with management challenges due to large water supply variability and high climate sensitivity, and demonstrate an advanced tool to forecast seasonal water availability. This capability enables global-scale models to deliver reliable and locally-relevant insights for water management.

Identifiers

PMID41093823
PMCPMC12528480

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.