Properly understanding the evolution mechanisms of groundwater storage anomaly (GWSA) is the basis of making effective groundwater management strategies. However, current analysis methods cannot objectively capture the spatiotemporal evolution characteristics of GWSA, which might lead to erroneous inferences of the evolution mechanisms. Here, we developed a new framework to address the challenge of spatiotemporal heterogeneity in the GWSA evolution analysis. It is achieved by integrating the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and the Optimal Parameters-based Geographical Detector (OPGD). In the case study of the North China Plain (NCP), the GWSA time series is divided into four stages by three trend change points in BEAST. An increasing trend of GWSA is observed at Stage IV, and the third trend change point occurs before the third seasonal change point. This distinguishes the positive feedback of anthropogenic interventions and the effects of seasonal precipitations for the first time. Moreover, the spatial distribution of GWSA in the NCP is classified into two clusters by BIRCH in each stage. The differences in GWSA trends and responses to environmental changes between Cluster-1 and Cluster-2 are significant. Then the driving effects of 16 factors on the evolution of GWSA are identified using OPGD, in which the contributions of topographic and aquifer characteristics are highlighted by quantitative analysis. This framework provides a novel method for examining the spatiotemporal heterogeneity of GWSA, which can be extended to analyze spatiotemporal trends in GWSA at diverse scales.

中文翻译：

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地下水储量时空演变中的异质性分解与机理推理新框架——以华北平原为例


正确理解地下水储量异常 （GWSA） 的演变机制是制定有效地下水管理策略的基础。然而，目前的分析方法无法客观地捕捉GWSA的时空演化特征，这可能导致对演化机制的错误推断。在这里，我们开发了一个新的框架来应对 GWSA 进化分析中时空异质性的挑战。它是通过集成突变、季节性变化和趋势的贝叶斯估计器 （BEAST）、使用层次结构的平衡迭代缩减和聚类 （BIRCH） 以及基于最优参数的地理检测器 （OPGD） 来实现的。在华北平原 （NCP） 的案例研究中，GWSA 时间序列在 BEAST 中被 3 个趋势变化点分为四个阶段。GWSA 在第 IV 阶段呈上升趋势，第三个趋势变化点出现在第三个季节变化点之前。这首次区分了人为干预的正反馈和季节性降水的影响。此外，GWSA 在 NCP 中的空间分布在每个阶段通过 BIRCH 分为两个聚类。Cluster-1 和 Cluster-2 之间 GWSA 趋势和对环境变化的响应差异很大。然后，利用 OPGD 确定了 16 个因素对 GWSA 演变的驱动效应，其中通过定量分析突出了地形和含水层特征的贡献。该框架为检查 GWSA 的时空异质性提供了一种新方法，可以扩展到分析不同尺度上 GWSA 的时空趋势。