A physics-informed neural network approach to predicting land subsidence-rebound in Dezhou City under different climate scenarios

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies Pub Date : 2025-09-16 DOI:10.1016/j.ejrh.2025.102773

Haotong Wang , Huili Gong , Beibei Chen , Chaofan Zhou , Yabin Yang , Xiaoxiao Sun

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引用次数: 0

Abstract

Study region

Dezhou, China, is one of the typical areas of land subsidence in the North China Plain.

Study focus

This study focuses on the changes in subsidence patterns following groundwater level (GWL) recovery in Dezhou City, developing a dualistic water cycle framework that integrates both climate and human factors. Two Physics-Informed Neural Network (PINN) models are constructed to simulate: (1) the relationship between precipitation, evapotranspiration, groundwater (GW) extraction, and GWLs (2) the coupling between GWLs and land subsidence. Trained with meteorological, hydrogeological, and Interferometric Synthetic Aperture Radar (InSAR) deformation data, the models use Shared Socioeconomic Pathway–Representative Concentration Pathway (SSP-RCP) scenario data and simulated GW extraction data to predict future GWLs and subsidence under different scenarios.

New hydrological insights for the region

Shallow GWLs are highly sensitive to climate change, showing significant seasonal fluctuations under the SSP5-RCP8.5 scenario, with a maximum amplitude of 2.79 m. In contrast, deep GWLs have a slower response, though long-term trends gradually emerge under the SSP5-RCP8.5 scenario, up to 0.872 m/yr. Groundwater extraction directly drives GWL decline, suppressing seasonal fluctuations and extending the response time to precipitation, with a maximum lag of 8 months. Precipitation indirectly affects subsidence through the multi-aquifer system, with subsidence-rebound variations mainly influenced by groundwater extraction and GWL fluctuations. Overall, climate change affects subsidence fluctuations, while groundwater extraction remains the primary factor for long-term subsidence trends.

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不同气候情景下德州市土地沉降-反弹的物理信息神经网络预测方法

研究区中国德州是华北平原地面沉降的典型地区之一。本研究以德州市地下水位恢复后沉降模式的变化为研究对象，构建了气候与人为因素相结合的二元水循环框架。建立了两个物理信息神经网络（PINN）模型来模拟：(1)降水、蒸散发、地下水（GW）提取与地表沉降的关系(2)地表沉降与地表沉降的耦合关系。该模型采用气象、水文地质和干涉合成孔径雷达（InSAR）变形数据进行训练，利用共享社会经济路径-代表性浓度路径（SSP-RCP）情景数据和模拟GW提取数据，预测不同情景下未来的gwl和沉降。浅层gwl对气候变化高度敏感，在SSP5-RCP8.5情景下表现出显著的季节波动，最大振幅为2.79 m。相比之下，深层gwl的响应较慢，但在SSP5-RCP8.5情景下，长期趋势逐渐显现，最高可达0.872 m/yr。地下水开采直接驱动GWL下降，抑制了季节波动，延长了对降水的响应时间，最大滞后8个月。降水通过多含水层系统间接影响沉降，沉降回弹变化主要受地下水开采和GWL波动的影响。总体而言，气候变化影响沉降波动，而地下水开采仍然是长期沉降趋势的主要因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)

CiteScore

6.70

自引率

8.50%

发文量

284

审稿时长

60 days

期刊介绍： Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.