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Analysis of driving factors for land subsidence in typical cities of the North China Plain based on geodetector technology
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Abstract: The North China Plain is vital hub for agricultural production and urban development. However, decades of excessive groundwater extraction have resulted on significant land subsidence, posing severe threats to the region's socio-economic stability and sustainable development. The relationship between land deformation and groundwater storage Anomalies in this region remains insufficiently understood, and the driving factors behind land subsidence require further exploration. This study employs downscaled GRACE and SBAS InSAR technologies to monitor and analyze land subsidence and groundwater storage Anomalies in four representative cities of the North China Plain: Beijing, Tianjin, Cangzhou, and Hengshui. Using geodetector methods, the study investigates the driving factors of land subsidence, incorporating both natural environmental and human activity factors. The results indicate that: (1) Groundwater storage in the North China Plain generally exhibited an overall declining trend from 2002 to 2022, with the rate of decrease weakening from southwest to northeast, showing a clear spatial clustering pattern. (2) While, land subsidence rates in the main urban areas of each city were relatively low, severe subsidence persisted in the surrounding suburban and rural areas. (3) The temporal trends of land subsidence were consistent with changes in groundwater storage across all cities. (4) Groundwater storage Anomalies emerged as the most significant factor influencing the spatial distribution of land subsidence, with a q-value of 0.387, followed by factors such as DEM, evapotranspiration, and rainfall. Seasonal characteristics were evident in land deformation corresponding to groundwater storage Anomalies: During the spring and summer irrigation periods, land subsidence occurred due to groundwater depletion, while in autumn and winter, the surface uplifted with increased groundwater storage. In Cangzhou and Hengshui, excessive deep groundwater extraction caused a lagged response in land subsidence relative to groundwater storage Anomalies. Furthermore, interaction among various factors significantly amplified their influence on land subsidence. The interaction between groundwater storage Anomalies and rainfall had the strongest combined effect, underscoring its critical role in shaping land subsidence in the study area. The findings offer valuable insights for the scientific prevention and management of land subsidence in the North China Plain.
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Key words:
- Land subsidence /
- InSAR /
- groundwater storage Anomalies /
- Geodetector /
- GRACE
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Figure 2. Validation of GRACE downscaled groundwater storage
Note: (a) Relationship of shallow groundwater storage Anomalies in the Haihe River Basin based on the Haihe River Basin Water Resources Bulletin; (b) GWSA relationship in the Beijing plain area based on the Beijing Water Resources Bulletin; (c) Comparison of characteristics between GRACE downscaled GWSA and Haihe River Basin Water Resources Bulletin data.
Figure 4. Time series of TWSA, SWSA, and GWSA changes in the North China Plain and monthly precipitation variations
Figure 6. Spatial distribution of annual average land subsidence rates and standard deviations in different cities
Note: (a, b, c, d) represent the spatial distribution of subsidence rates in Beijing, Tianjin, Cangzhou, and Hengshui, respectively; (a1, b1, c1, d1) represent the standard deviation distribution of subsidence rates in Beijing, Tianjin, Cangzhou, and Hengshui, respectively.
Figure 7. Relationship between GWSA and land deformation in different cities
Note: a, b, c, d represent Beijing, Tianjin, Hengshui, and Cangzhou, respectively.
Figure 9. Changes in the q-value of each influencing factor at different sample ratios
Figure 10. q-values of each influencing factor for ground subsidence in the study area
Table 1. Description of different driving factors
Data name Data description Data source North China Plain GRACE GWSA spatial distribution Using GRACE downscaled results, the GWSA change rate for the North China Plain from 2002 to 2022 was obtained. Spatial resolution: 1 km. —— Fault scale index Based on fault line data, the length of fault lines within a 1 km grid is calculated. Spatial resolution: 1 km. —— Annual rainfall spatial distribution The 2019 monthly precipitation data is accumulated annually. Spatial resolution: 1 km. National Earth System Science Data Center Evapotranspiration spatial distribution Based on the 2019 GLASS and MODIS products, inversion results are used. Spatial resolution: 1 km. Spatio-Temporal Environment Big Data Platform NDVI spatial distribution Inversion based on 2019 SPOT data. Spatial resolution: 1 km. National Earth System Science Data Center DEM spatial distribution SRTM3 DEM data, spatial resolution: 90 m. Geospatial Data Cloud Building density spatial distribution Based on the 2019 land use data for the North China Plain, building land area within a 1 km grid is calculated. Spatial resolution: 1 km. Zenodo Population spatial distribution 2019 population spatial distribution data for China, in 1 km grid. Spatial resolution: 1 km. Resource and Environment Science GDP spatial distribution 2019 GDP spatial distribution data for China, in 1 km grid. Spatial resolution: 1 km. Resource and Environment Science 
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