The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

Wanqiu Li; Lifeng Bao; Guobiao Yao; Fengwei Wang; Qiuying Guo; Jie Zhu; Jinjie Zhu; Zhiwei Wang; Jingxue Bi; Chengcheng Zhu; Yulong Zhong; Shanbo Lu

doi:10.1038/s41598-024-55588-3

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

Sci Rep. 2024 Mar 9;14(1):5819. doi: 10.1038/s41598-024-55588-3.

Authors

Wanqiu Li¹, Lifeng Bao², Guobiao Yao³, Fengwei Wang⁴, Qiuying Guo³, Jie Zhu⁵, Jinjie Zhu⁶, Zhiwei Wang⁷, Jingxue Bi³, Chengcheng Zhu³, Yulong Zhong⁸, Shanbo Lu³

Affiliations

¹ School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China. 1404855692@qq.com.
² State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan, 430077, China.
³ School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
⁴ College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China.
⁵ China Earthquake Networks Center, Beijing, 100045, China.
⁶ 801 Institute of Hydrogeology and Engineering Geology, Shandong Provincial Bureau of Geology & Mineral Resources, Jinan, 250014, China.
⁷ School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China. wangzhiwei18@sdjzu.edu.cn.
⁸ School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China.

Abstract

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate - 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is - 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

Keywords: GRACE; GWS; ICA; Influencing factors; Prediction; SVM; Shandong Province.

Abstract

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