Scholarlink

<p>The historical and future spatiotemporal responses of terrestrial water storage (TWS), a crucial water cycle component, to climate warming are poorly understood due to the lack of globally observational TWS data and the high uncertainty of climate projection studies, especially in China with diverse climate types. This study develops a new framework for comprehensive TWS projection attribution by integrating the data from CMIP6 Global Climate Models (GCMs), Variable Infiltration Capability (VIC) hydrological model, machine learning algorithms, and hierarchical sensitivity analysis, based on which the spatiotemporal TWS changes in response to climate change in China are analyzed under the SSP1-2.6, SSP2-4.5, SSP5-8.5 scenarios. Climate warming is expected to intensify the water cycle in China, leading to an increase of 5.59 ~ 21.09 mm/10a in precipitation and 2.48 ~ 11.61 mm/10a in evapotranspiration during 2030–2099, respectively. The greater water cycle intensification rate is projected in some parts of western China, with precipitation and evapotranspiration respectively increasing by 50% and 70% by the end of this century. TWS shows a significant increasing trend (2030–2099) under SSP1-2.6 (~1.32 mm/10a) and SSP2-4.5 (~1.81 mm/10a), but not under SSP5-8.5. However, the western China is expected to experience a declining TWS trend (2030–2099) under all SSP scenarios with the most pronounced trend under SSP5-8.5, suggesting more severe future water scarcity and challenges for this region. Attribution analysis indicates that precipitation dominates the future TWS variability, followed by soil moisture and temperature, while snow water equivalent shows the least impact. However, the precipitation contribution to TWS variability is projected to decrease under all SSP scenarios relative to the historical period.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing the response of terrestrial water storage to climate warming in China by coupling CMIP6 multi-model ensembles, hydrological model, and machine learning algorithms

  • 期刊论文

  • 发表日期：

    2026年1月29日

查看全文
  • Xiaoyan Cao,
  • Jiali Ju,
  • Chuanhao Wu,
  • Pat J.-F. Yeh,
  • Min Shi,
  • Ashraf Dewan,
  • Yongze Song,
  • Xueyuan Zhang,
  • Tian Yao,
  • Yufei Jiao,
  • Qiongfang Li,
  • Shanshui Yuan,
  • Xiaolei Fu,
  • Bill X. Hu

摘要

The historical and future spatiotemporal responses of terrestrial water storage (TWS), a crucial water cycle component, to climate warming are poorly understood due to the lack of globally observational TWS data and the high uncertainty of climate projection studies, especially in China with diverse climate types. This study develops a new framework for comprehensive TWS projection attribution by integrating the data from CMIP6 Global Climate Models (GCMs), Variable Infiltration Capability (VIC) hydrological model, machine learning algorithms, and hierarchical sensitivity analysis, based on which the spatiotemporal TWS changes in response to climate change in China are analyzed under the SSP1-2.6, SSP2-4.5, SSP5-8.5 scenarios. Climate warming is expected to intensify the water cycle in China, leading to an increase of 5.59 ~ 21.09 mm/10a in precipitation and 2.48 ~ 11.61 mm/10a in evapotranspiration during 2030–2099, respectively. The greater water cycle intensification rate is projected in some parts of western China, with precipitation and evapotranspiration respectively increasing by 50% and 70% by the end of this century. TWS shows a significant increasing trend (2030–2099) under SSP1-2.6 (~1.32 mm/10a) and SSP2-4.5 (~1.81 mm/10a), but not under SSP5-8.5. However, the western China is expected to experience a declining TWS trend (2030–2099) under all SSP scenarios with the most pronounced trend under SSP5-8.5, suggesting more severe future water scarcity and challenges for this region. Attribution analysis indicates that precipitation dominates the future TWS variability, followed by soil moisture and temperature, while snow water equivalent shows the least impact. However, the precipitation contribution to TWS variability is projected to decrease under all SSP scenarios relative to the historical period.