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

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

“…Moreover, certain variables exhibit nonlinear and intricate relationships, rendering them challenging to comprehend using conventional means. In response to these challenges, conventional statistical techniques, such as autoregressive (AR), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA), have been consistently applied to model groundwater resources [6][7][8]. ARIMA is advantageous for reducing the impact of extreme values on prediction accuracy.…”

The Extraction of Terrestrial Water Storage Anomaly from GRACE in the Region with Medium Scale and Adjacent Weak Signal Area: A Case for the Dnieper River Basin

“…Many approaches have been proposed for precise inversion of TWS anomaly on a regional scale. However, most research concentrated on regions or river basins like the Amazon, Greenland, Indus, Antarctica, Yangtze River, Tibetan Plateau, Congo, Nile, and so on [22][23][24][25][26][27][28][29][30][31]. Those regions are characteristic of larger areas (>10 6 km 2 ) or stronger signals attributed to mass changes.…”