Assessing groundwater storage anomalies in Beijing based on the new multifactor-quantitative joint prediction model

Abstract

Water shortages and groundwater depletion are critical issues in the world, leading to unsustainable agricultural production and adverse ecological impacts. Here, the new Multifactor-Quantitative joint Prediction Model (MQPM) is developed to quantitatively predict the Groundwater Storage Anomalies (GWSA), which includes an annual multifactor module and a monthly quantitative module. The correlative coefficients from two modules between simulated GWSA and observed GWSA reach up to 0.98 and 0.87, respectively. Taking Beijing as an example, results show that GWSA trends before South-to-North Water Diversion (SNWD) (2005–2014) and after SNWD (2015–2018) are at a rate of −3.00 × 10⁸ m³/yr and 1.95 × 10⁸ m³/yr, respectively, which reflects the effectiveness of water diversion. Additionally, the predicted results show that GWSA from the multifactor module will increase to 45.97 × 10⁸ m³ by 2028. The quantitative module designs four scenarios under different climate changes and policies, from which the predicted GWSA with values ranging from 28.49 × 10⁸ m³ to 63.06 × 10⁸ m³. For the latter module, the groundwater level will recover to ∼8.9 m up to 2028, combining multiple favorable conditions. Finally, factors consisting of water diversion, climate change, and water-saving policies have a vital influence on groundwater variations, and contributions of these factors to the GWSA account for 46%, 27%, and 27%, respectively.

KEYWORDS:

• Multifactor-quantitative joint prediction model
• groundwater storage anomalies
• Beijing
• South-to-North Water Diversion
• climate change

Acknowledgments

The editor and reviewers are sincerely acknowledged for their instructive and detailed comments on the early versions of this manuscript. The authors appreciate all centres for supplying open-source data sets used in the present research. Qingqing Wang, Wei Zheng, and Wenjie Yin contributed equally to this paper.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant (42274119), in part by the Liaoning Revitalization Talents Program under Grant (XLYC2002082), in part by National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration (2022YFF1400500), and in part by the Key Project of Science and Technology Commission of the Central Military Commission.