Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 14
Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3305-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3305-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 14
Research article
 | 
25 Jul 2024
Research article |  | 25 Jul 2024

Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China

Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak

Viewed

Total article views: 2,024 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,541 412 71 2,024 244 58 70
  • HTML: 1,541
  • PDF: 412
  • XML: 71
  • Total: 2,024
  • Supplement: 244
  • BibTeX: 58
  • EndNote: 70
Views and downloads (calculated since 06 Sep 2023)
Cumulative views and downloads (calculated since 06 Sep 2023)

Viewed (geographical distribution)

Total article views: 2,024 (including HTML, PDF, and XML) Thereof 1,931 with geography defined and 93 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Read more
Share