Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Li, Wanqiu, Bao, Lifeng, Yao, Guobiao, Wang, Fengwei, Guo, Qiuying, Zhu, Jie, Zhu, Jinjie, Wang, Zhiwei, Bi, Jingxue, Zhu, Chengcheng, Zhong, Yulong, and Lu, Shanbo, 2024. The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China. Scientific Reports, 14:5819, doi:10.1038/s41598-024-55588-3.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024NatSR..14.5819L,
author = {{Li}, Wanqiu and {Bao}, Lifeng and {Yao}, Guobiao and {Wang}, Fengwei and {Guo}, Qiuying and {Zhu}, Jie and {Zhu}, Jinjie and {Wang}, Zhiwei and {Bi}, Jingxue and {Zhu}, Chengcheng and {Zhong}, Yulong and {Lu}, Shanbo},
title = "{The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China}",
journal = {Scientific Reports},
keywords = {GRACE, GWS, ICA, Influencing factors, SVM, Prediction, Shandong Province},
year = 2024,
month = mar,
volume = {14},
eid = {5819},
pages = {5819},
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
{\ensuremath{-}} 5.80 {\ensuremath{\pm}} 2.28 mm/a of GWS; the
linear trend of GWS change is {\ensuremath{-}} 5.39
{\ensuremath{\pm}} 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.}",
doi = {10.1038/s41598-024-55588-3},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024NatSR..14.5819L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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