Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhao, Xiaowei, Yu, Ying, Cheng, Jianmei, Ding, Kuiyuan, Luo, Yiming, Zheng, Kun, Xian, Yang, and Lin, Yihang, 2024. A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain. Water Resources Research, 60(12):2023WR036102, doi:10.1029/2023WR036102.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024WRR....6036102Z,
author = {{Zhao}, Xiaowei and {Yu}, Ying and {Cheng}, Jianmei and {Ding}, Kuiyuan and {Luo}, Yiming and {Zheng}, Kun and {Xian}, Yang and {Lin}, Yihang},
title = "{A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain}",
journal = {Water Resources Research},
keywords = {GRACE, groundwater storage, spatiotemporal evolution, heterogeneity decomposition, driving factors, North China Plain},
year = 2024,
month = dec,
volume = {60},
number = {12},
pages = {2023WR036102},
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.}",
doi = {10.1029/2023WR036102},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024WRR....6036102Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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