Hu Linbing, Wang Linsong, Peng Zhenran, Zhang Hao
Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China.
Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China.
J Environ Manage. 2025 Feb;375:124322. doi: 10.1016/j.jenvman.2025.124322. Epub 2025 Jan 29.
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.25° resolution using multi-source datasets, including in-situ wells, the Gravity Recovery and Climate Experiment (GRACE), land surface models and hydrogeological parameters. The results indicated the ANN algorithm outperformed the RF algorithm in predicting the spatiotemporal variations of the shallow and deep GWLA in the Middle and Lower Yangtze River Basin (MLYRB). Hence, the ANN algorithm was used to construct a model for predicting the GWSA over the 2005-2017 period. The GWSA exhibited an increasing linear rate of about 0.77 ± 0.30 mm/yr in almost the entire area, except in the Han River Basin (HRB), where GWSA decreased by -1.18 ± 0.38 mm/yr due to decreased precipitation amounts. The occurrence of seasonal variations in the deep GWSA showed lead time effects compared with those in the shallow GWSA, ranging from 0 to 1 month and 1 to 2 months in the humid and relatively dry areas, respectively. It was found that the ANN-based model results showed pronounced responses of the GWSA to the drought events. The standard groundwater drought index (SGDI) was further calculated to assess the spatiotemporal characteristics of the groundwater drought events. The results revealed the occurrence of a severe drought event in 2011, as well as pronounced impacts of the El Niño-Southern Oscillation (ENSO) events on the GWSA. This study demonstrated the effectiveness of in-situ groundwater measurements in enhancing the reliability of ML-based GWSA prediction. Furthermore, the constructed high-resolution groundwater variation features in this study can provide water resource managers with enhanced information and valuable insights into climate-induced groundwater changes.
地下水在水循环中起着关键作用，被用于满足工业、农业和生活用水需求。由于观测技术和数学方法的限制，对地下水储量进行高分辨率建模往往具有挑战性。在本研究中，采用了两种机器学习（ML）算法，即随机森林（RF）和人工神经网络（ANN），利用包括原位井、重力恢复与气候实验（GRACE）、陆面模型和水文地质参数在内的多源数据集，以0.25°分辨率估算地下水位异常（GWLA）和地下水储量异常（GWSA）。结果表明，在预测长江中下游流域（MLYRB）浅层和深层GWLA的时空变化方面，ANN算法优于RF算法。因此，使用ANN算法构建了一个预测2005 - 2017年期间GWSA的模型。除汉江流域（HRB）外，几乎整个地区的GWSA呈现出约0.77±0.30毫米/年的线性增加率，在汉江流域，由于降水量减少，GWSA下降了-1.18±0.38毫米/年。深层GWSA的季节变化出现时间比浅层GWSA提前，在湿润和相对干燥地区分别为0至1个月和1至2个月。研究发现，基于ANN的模型结果显示GWSA对干旱事件有明显响应。进一步计算了标准地下水干旱指数（SGDI）以评估地下水干旱事件的时空特征。结果揭示了2011年发生的一次严重干旱事件，以及厄尔尼诺 - 南方涛动（ENSO）事件对GWSA的显著影响。本研究证明了原位地下水测量在提高基于ML的GWSA预测可靠性方面的有效性。此外，本研究构建的高分辨率地下水变化特征可为水资源管理者提供有关气候引起的地下水变化的更多信息和有价值的见解。
