Evidence map›Paper›PMID 40624027›Full record

ArticleScientific data2025

A High-Resolution Gridded Dataset for China's Monthly Sectoral Water Use.

Yuqian Zhang, Yunhe Yin, Mijia Yin, Xufang Zhang

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Yuqian ZhangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Yunhe YinKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. yinyh@igsnrr.ac.cn.
Mijia YinKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Xufang ZhangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-quality water use datasets are essential for advancing water resources research in changing environment. However, existing Chinese water use data, typically aggregated by administrative boundaries or watersheds, lack sufficient spatial and temporal resolution. This limitation hinders detailed analysis of human water use patterns and spatiotemporal variations. Here, we present the High-resolution Sectoral Water Use Dataset (HSWUD) for China mainland, covering the period from 1965 to 2022 and including sectors such as irrigation, manufacturing, thermal power cooling, and domestic water use. By integrating remote sensing land use data, population density maps, reanalysis meteorological data, geospatial information on thermal power plants and micro-level survey data from industrial enterprises, we developed a downscaling algorithm incorporating multiple covariates. This algorithm disaggregates provincial annual water use data to monthly grid cells at a 0.1° × 0.1° resolution, enhancing the spatial detail and seasonal distribution of water use across China. HSWUD shows a strong correlation with prefecture-level statistical data (R

Identifiers

PMID40624027
PMCPMC12234809

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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.