Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 10
Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-2931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 10
Research article
 | 
18 May 2026
Research article |  | 18 May 2026

Regionalization of IDF curves for mainland China: a comparative evaluation of machine learning versus spatial interpolation techniques

Yuantian Jiang, Wenting Wang, Andrew T. Fullhart, Bofu Yu, and Bo Chen

Viewed

Total article views: 7,939 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
6,177 1,452 310 7,939 523 248 296
  • HTML: 6,177
  • PDF: 1,452
  • XML: 310
  • Total: 7,939
  • Supplement: 523
  • BibTeX: 248
  • EndNote: 296
Views and downloads (calculated since 23 Jul 2025)
Cumulative views and downloads (calculated since 23 Jul 2025)

Viewed (geographical distribution)

Total article views: 7,939 (including HTML, PDF, and XML) Thereof 7,889 with geography defined and 50 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 15 Oct 2026
Download
Short summary
Intensity-Duration-Frequency (IDF) curves is important for designing infrastructure that can withstand floods. We compared traditional interpolation methods with machine learning to map these curves across mainland China. ML using widely available daily gridded data can estimate sub-daily intensity as accurately as methods needing rarer hourly site data. This study provides a valuable understanding for IDF in data-limited regions and generates a new IDF dataset for mainland China.
Read more
Share