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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.
摘要：
监测和预测区域地下水储量（GWS）波动是有效管理水资源的重要支持。因此,以山东省为例，重力恢复和气候实验（GRACE）和GRACE后续（GRACE-FO）的数据用于反演2003年1月至2022年12月的GWS波动以及水隙全球水文模型（WGHM），原位地下水量和水位数据。使用独立成分分析(ICA)分解时空特征，以及影响因素，如降水和人类活动，也进行了分析。为了预测GWS的短时间变化，支持向量机(SVM)与三种常用的长短期记忆方法(LSTM)结合使用，奇异谱分析(SSA),自回归移动平均模型(ARMA)，作为比较。结果表明：（1）西部GWS的损失强度明显大于沿海地区。2003-2006年GWS急剧增加,2007-2014年GWS损失率为-5.80±2.28mm/a,2015-2022年GWS变化线性趋势为-5.39±3.65mm/a,可能主要受南水北调工程影响。GRACE与WGHM的相关系数为0.67，与原位地下水量和水位一致。（2）考虑移动平均线后的时间延迟，GWS与每月全球降水气候项目（GPCP）具有较高的正相关性。根据连续小波变换(CWT)方法具有相似的能量谱。此外,分析了影响GWS年度波动的因素，GWS与包括地下水开采消耗在内的原位数据之间的相关系数，农田灌溉量分别为0.80、0.71。(3)对于GWS预测，采用SVM方法进行分析,建立了三个训练样本，分别为180、204和228个月，拟合优度均高于0.97。相关系数分别为0.56、0.75、0.68；RMSE分别为5.26、4.42、5.65mm；NSE分别为0.28、0.43、0.36。SVM模型的短期预测性能优于其他方法。
