Scholarlink

<p>Accurate representation of terrestrial water storage is essential for water resource management and climate adaptation, yet model‑observation inconsistencies hinder global water cycle assessment. Here we show, using a unified multi‑scale framework, that seven model products with diverse modeling strategies and varying structural completeness exhibit scale‑dependent performance against Gravity Recovery and Climate Experiment observations across global, climate-zone, and basin scales. Globally, hydrological models show the highest temporal correlation (0.94) but underperform spatially (0.32), whereas the assimilation system balances both dimensions with higher spatial agreement (0.73). Across climate zones, the assimilation system maintains robust performance, while physical models degrade in polar regions (−0.40). From large to small basin scales, hydrological model performance deteriorates systematically, whereas the assimilation system retains higher consistency and directional accuracy. These findings demonstrate that satellite observation-constrained assimilation substantially reduces inconsistencies relative to model‑driven products, offering essential guidance for climate adaptation and water management strategies.</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

Scale-dependent model-observation inconsistencies in global terrestrial water storage models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

查看全文
  • Gangqiang Zhang,
  • Tongren Xu,
  • Shaomin Liu,
  • Liqiang Zhang,
  • Sayed M. Bateni,
  • Jianzhi Dong,
  • Wenjie Yin,
  • Qingqing Wang

摘要

Accurate representation of terrestrial water storage is essential for water resource management and climate adaptation, yet model‑observation inconsistencies hinder global water cycle assessment. Here we show, using a unified multi‑scale framework, that seven model products with diverse modeling strategies and varying structural completeness exhibit scale‑dependent performance against Gravity Recovery and Climate Experiment observations across global, climate-zone, and basin scales. Globally, hydrological models show the highest temporal correlation (0.94) but underperform spatially (0.32), whereas the assimilation system balances both dimensions with higher spatial agreement (0.73). Across climate zones, the assimilation system maintains robust performance, while physical models degrade in polar regions (−0.40). From large to small basin scales, hydrological model performance deteriorates systematically, whereas the assimilation system retains higher consistency and directional accuracy. These findings demonstrate that satellite observation-constrained assimilation substantially reduces inconsistencies relative to model‑driven products, offering essential guidance for climate adaptation and water management strategies.