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

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

doi:https://doi.org/10.5194/hess-28-3305-2024

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

Supplement

https://doi.org/10.5194/hess-28-3305-2024-supplement

Data sets

ISIMIP3b bias-adjusted atmospheric climate input data (v1.1) S. Lange and M. Büchner https://doi.org/10.48364/ISIMIP.842396.1

ERA5-Land hourly data from 1950 to present J. Muñoz Sabater https://doi.org/10.24381/cds.e2161bac

The streamflow data simulated by 10 hybrid terrestrial models under CMIP6, and TWSA trends data R. Liu https://doi.org/10.17605/OSF.IO/FVYSE

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.