Analysis of subsidence factors and modeling of susceptibility under coupled geohydrological conditions - A case study of Jiangsu Yangtze River section

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Wen-Jiang Long , Xue-Xiang Yu , Ming-Fei Zhu
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引用次数: 0

Abstract

Ground subsidence along the riverbanks near the Yangtze River Delta has been accelerating due to human activities and other factors, seriously impacting various aspects of social development. Mapping susceptibility patterns and analyzing subsidence factors are crucial for effective management. This study focused on the Yangtze River riparian perimeter in Jiangsu Province, our study area. We assessed the importance of different factors using the random forest regression (RFR) model and the temporal convolution network (TCN). Additionally, we used GeoDetector to analyze the spatial relationship between sedimentation and potential drivers. Finally, we utilized the RFR and Maxent model to map susceptibility to sedimentation patterns in different risk zones. The study results show that the method effectively depicts the susceptibility to subsidence in each risk zone (44.18% and 32.56% for high and average risk zones, respectively). Anthropogenic factors mainly drive the subsidence-prone areas around the Yangtze River in Jiangsu. Groundwater extraction and soft soil thickness are the primary drivers of subsidence patterns in high-risk areas. In contrast, the main drivers of subsidence in other risk areas vary. These differences reflect the delayed effects of natural and anthropogenic factors on subsidence and the significant differences in how anthropogenic drivers affect the marginal effects of subsidence. Through susceptibility modeling and driver evaluation, this study reveals that establishing risk zones has improved our understanding of the impact of regional variations in environmental variables on subsidence. This understanding will facilitate the development of subsidence management strategies tailored to different regions.
耦合地质水文条件下沉降因子分析及敏感性模拟——以江苏长江段为例
由于人类活动等因素的影响,长三角沿岸地区地面沉降加速,严重影响了社会发展的各个方面。绘制敏感性模式和分析沉降因素是有效管理的关键。本研究以江苏省长江岸线周长为研究对象。我们使用随机森林回归(RFR)模型和时间卷积网络(TCN)来评估不同因素的重要性。此外,我们还利用GeoDetector分析了沉积与潜在驱动因素之间的空间关系。最后,我们利用RFR和Maxent模型绘制了不同风险区对沉积模式的易感性。研究结果表明,该方法能较好地描述各风险区的沉降敏感性(高风险区为44.18%,平均风险区为32.56%)。江苏长江周边地区的沉降主要受人为因素驱动。地下水开采和软土厚度是高风险地区沉降模式的主要驱动因素。相比之下,其他风险地区下沉的主要驱动因素各不相同。这些差异反映了自然和人为因素对沉降的延迟效应,以及人为驱动因素对沉降边际效应的影响差异显著。通过敏感性建模和驱动因素评价,研究表明,建立风险区提高了我们对区域环境变量变化对沉降影响的认识。这种理解将有助于制定适合不同地区的沉降管理策略。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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