A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain

Abstract 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.