Understanding the impact of climatic variability on terrestrial water storage in the Qinghai-Tibet Plateau of China

ABSTRACT

Monitoring terrestrial water storage anomalies (TWSA) is essential for better understanding the influences of climatic variability on hydrological cycles. Here we use Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on satellite data and meteorological observations to analyse the inter-annual trends of TWSA across the Qinghai-Tibet Plateau (QTP) during 2003–2020 and investigate the relationships between climatic variability and annual changes in TWSA. Results indicate that TWSA across the QTP generally decreased, at a rate of −0.5 ± 1.4 mm/year during 2003–2020, which mainly arises from the coupled effects of Potential evapotranspiration (PET) and precipitation. In terms of the effects of climatic variability, annual changes in TWSA show a negative correlation coefficient (r = −0.76) with PET, which is greater than that with precipitation (r = 0.67). The cross-wavelet transformation analysis revealed that the evolution of TWSA across the QTP is closely associated with the Atlantic Multidecadal Oscillation. Our conclusions can help decision makers to formulate appropriate policies for the assessment and management of water resources over the QTP.

KEYWORDS:

• terrestrial water storage
• Gravity Recovery and Climate Experiment (GRACE)
• climatic variability
• teleconnections
• Qinghai-Tibet Plateau

Editor A. Fiori Associate editor M. Ionita

Editor A. Fiori Associate editor M. Ionita

Acknowledgements

The authors thank the China Meteorological Administration for providing the necessary meteorological data used in this study. We also sincerely thank the following three processing centres, (1) Center for Space Research at the University of Texas at Austin; (2) the Jet Propulsion Laboratory at NASA and the California Institute of Technology, California; and (3) the German Research Centre for Geosciences, for providing GRACE/GRACE Follow-on datasets. The GLDAS-derived data including the Noah and CLSM models used in this study were acquired as part of the mission of NASA’s Earth Science Division and are archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The dataset on lakes, glaciers and permafrost across the QTP was provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn). Furthermore, we are very grateful to all the researchers who provided the necessary datasets to calculate the variations in groundwater in this study. Finally, editors and two reviewers are greatly acknowledged for their constructive comments to improve the quality of our manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here

Data availability statement

All data that support the findings of this study are openly available. The GRACE/GRACE Follow-on data used in this study are available online at https://grace.jpl.nasa.gov/data/get-data/; the meteorological data are available from the National Climatic Center of the China Meteorological Administration (http://data.cma.cn/); the teleconnection factors can be acquired from the National Centers for Environmental Information of the US National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/teleconnections/); the soil moisture storage and the snow water equivalent come from the Global Land Data Assimilation System version 2.1 Noah land surface model (https://earthdata.nasa.gov/search?q=noah); the groundwater data come from the Global Land Data Assimilation System version 2.1 CLSM land surface model (https://earthdata.nasa.gov/search?q=clsm); the water storage changes for different lakes in the study region are from Li et al. (2019); the annual rates of glacial and permafrost retreat during the study period are from Brun et al. (2017) and Xiang et al. (2016), respectively; the distribution of lakes, glaciers and permafrost across the study region are provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/); the TRMM_3B43V7 data are available from the National Aeronautics and Space Administration at https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_L3/ TRMM_3B43.7; and the GPCP data are available from the National Aeronautics and Space Administration at https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [52109037, 91547106]; and the National Key Research and Development Program of China [2021YFC3201100].