Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Wang, Xiaoyan, Song, Chunqiao, Yang, Tao, Gu, Huanghe, Liu, Gang, and Zhan, Pengfei, 2025. How well do the CMIP6 climate models capture terrestrial water storage variations in data-scarce basins originating from the high mountains of Asia?. Journal of Hydrology, 661:133677, doi:10.1016/j.jhydrol.2025.133677.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025JHyd..66133677W,
author = {{Wang}, Xiaoyan and {Song}, Chunqiao and {Yang}, Tao and {Gu}, Huanghe and {Liu}, Gang and {Zhan}, Pengfei},
title = "{How well do the CMIP6 climate models capture terrestrial water storage variations in data-scarce basins originating from the high mountains of Asia?}",
journal = {Journal of Hydrology},
keywords = {Terrestrial water storage, High-mountain Asian Basins with scarce data, CMIP6 climate models, The Bayesian model averaging method, GRACE},
year = 2025,
month = nov,
volume = {661},
eid = {133677},
pages = {133677},
abstract = "{Understanding the terrestrial water storage (TWS) change across high-
mountain Asian (HMA) basins is critical to enhancing our
capability to predict and adopt to future climate change impacts
on water resources. Meanwhile, it is critically important to
accurately represent the dynamics of the terrestrial water
storage for global climate models. This study, for the first
time, explored the modeling and prediction skill in TWS change
across HMA basins with scarce data for CMIP6 climate models. TWS
was generally overestimated in the south and underestimated in
the northwest of the study area. Nonetheless, high positive
correlation coefficients (CC, above 0.6) between most of model
simulations and monthly GRACE observations were detected over
the above regions. Climate models reproduced well the seasonal
variation of the observed TWS in most basins. However, it was
difficult to capture interannual variability in TWS for the
individual model, with CC lower than 0.6 in most basins. Then a
Bayesian model averaging (BMA)-based multi-model ensemble
framework was constructed to predict TWS change across HMA
basins with scarce data by 2060 under three scenarios (SSP1-2.6,
SSP2-4.5 and SSP5-8.5). Our BMA-based TWS change estimation
decreased the areal-mean normalized root mean square errors by
0.35-0.77 and increased the areal-mean CC by 0.32-0.44 across
HMA basins with scarce data for 2002-2020. Future projections of
TWS under most scenarios show decreasing trends in two thirds of
HMA basins with scarce data, where consistent sign of trends for
TWS in the historical period and future scenarios was detected
except for the Yangtze River basin. By contrast, consistent
increases of TWS are projected for all seasons in basins of
Qaidam, Inner Tibetan Plateau and Yellow River under future
scenarios, where significantly increasing trends of projected
TWS are also detected. The decreasing trend in projected TWS
over a majority of the HMA basins with scarce data suggests the
risk of water shortage is likely to be aggravated and adaptive
water resources management is needed. This study enriches the
information for TWS change over HMA basins and offers a helpful
direction for local water resource protection.}",
doi = {10.1016/j.jhydrol.2025.133677},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..66133677W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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