Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models

Abstract

Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities. Daily groundwater depth (GWD) data from 43 wells (2018–2022) were collected in three coastal cities in Jiangsu Province, China. Seasonal and Trend decomposition using Loess (STL) together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis (HCA). Machine learning models were developed to predict GWD, then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations (SHAP) method. Results showed that eXtreme Gradient Boosting (XGB) was superior to other models in terms of prediction performance and computational efficiency (R² > 0.95). GWD in Yancheng and southern Lianyungang were greater than those in Nantong, exhibiting larger fluctuations. Groundwater within 5 km of the coastline was affected by tides, with more pronounced effects in agricultural areas compared to urban areas. Shallow groundwater (3–7 m depth) responded immediately (0–1 day) to rainfall, primarily influenced by farmland and topography (slope and distance from rivers). Rainfall recharge to groundwater peaked at 50% farmland coverage, but this effect was suppressed by high temperatures (>30 °C) which intensified as distance from rivers increased, especially in forest and grassland. Deep groundwater (>10 m) showed delayed responses to rainfall (1–4 days) and temperature (10–15 days), with GDP as the primary influence, followed by agricultural irrigation and population density. Farmland helped to maintain stable GWD in low population density regions, while excessive farmland coverage (>90%) led to overexploitation. In the early stages of GDP development, increased industrial and agricultural water demand led to GWD decline, but as GDP levels significantly improved, groundwater consumption pressure gradually eased. This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.

Keywords

Groundwater depth / Spatial heterogeneity / Multiple influence factors / Coastal cities / Machine Learning models / SHAP values

CRediT authorship contribution statement

Yuming Mo: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Jing Xu: Funding acquisition, Methodology, Software, Investigation, Writing – review & editing. Senlin Zhu: Validation, Writing – review & editing. Beibei Xu: Methodology, Software. Jinran Wu: Methodology, Software. Guangqiu Jin: Writing – review & editing. You-Gan Wang: Methodology, Software. Ling Li: Supervision, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by the Natural Science Foundation of Jiangsu province, China (BK20240937); the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention (2022491411, 2021491811); and the Basal Research Fund of Central Public Welfare Scientific Institution of Nanjing Hydraulic Research Institute (Y223006). The rainfall datasets were provided by National Tibetan Plateau/Third Pole Environment Data Center (http://data.tpdc.ac.cn).

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