Viewing China's escalating heatwaves through the lens of complex networks

Zhang, Peng; Dai, Erfu; Wu, Chunsheng; Hu, Jun; Liu, Fang

doi:10.1016/j.ecolind.2023.111248

Ecological Indicators

2023

DOI: 10.1016/j.ecolind.2023.111248

|View full text |Cite

Viewing China's escalating heatwaves through the lens of complex networks

Peng Zhang,

Erfu Dai,

Chunsheng Wu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

Wang,

Wei,

Wang

et al. 2024

Sci Data

View full text Add to dashboard Cite

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (T max ), minimum (T min ), and mean (T mean ) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily T max , T min , and T mean . The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations.

show abstract

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

Wang,

Wei,

Wang

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Viewing China's escalating heatwaves through the lens of complex networks

Cited by 1 publication

References 54 publications

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

Contact Info

Product

Resources

About