Land Subsidence Overview

Principles

Definition

Land subsidence is the research field and academic concept dedicated to the study of the vertical downward displacement of the Earth's surface. This multidisciplinary area investigates the natural and anthropogenic causes, mechanisms, rates, spatial patterns, and impacts of this phenomenon, utilizing various geological, hydrological, and remote sensing techniques to monitor, model, and predict surface deformation and inform mitigation strategies.

Ontological type

Causative Processes

Monitoring Techniques

Mitigation Strategies

Key developments

Key Figures

Integrated Quantitative Modeling era

W. E. Dietrich [1] is associated with Stanford University [3] and the University of Washington [4] during this era. He advanced a physically based model for the topographic control on shallow landsliding [5], articulated source areas and drainage density [6], and explored erosion thresholds and land-surface morphology [7], providing a quantitative basis for translating subsurface processes into surface deformation and supporting backstripping and poroelastic modeling. David R. Montgomery [2] is also tied to Stanford University [3] and the University of Washington [4] in this period. Montgomery's work on erosion thresholds and land surface morphology [7], source areas, drainage density, and channel initiation [6], and topographic controls on landsliding [5] helped unify landscape evolution concepts within the integrated quantitative modeling framework, enabling more reproducible, risk-aware subsidence reconstructions.

InSAR Time-Series Monitoring era

Shimon Wdowinski [1] emerges as a leading figure in land subsidence research during the InSAR Time-Series Monitoring era, affiliated with Tel Aviv University [3] and the University of Miami [4]. His contributions include deploying InSAR time-series to quantify subsidence and tie deformation to groundwater pumping, as reflected in Land subsidence in central Mexico detected by ALOS InSAR time-series [7], Subsidence and flooding in New Orleans [8], and Monitoring land subsidence and its induced geological hazard with Synthetic Aperture Radar Interferometry: A case study in Morelia, Mexico [9], which collectively enabled high-resolution mapping and mechanistic attribution essential for this era. Thomas J. Burbey [2] is a pivotal figure associated with Virginia Tech [5] and Weatherford College [6] during this period. His work synthesizes regional subsidence with groundwater extraction and illustrates ground failure mechanisms, as shown in Review: Regional land subsidence accompanying groundwater extraction [10] and Land subsidence and ground failure associated to groundwater exploitation in the Aguascalientes Valley, México [11], providing a framework for risk assessment and mitigation that guided practice in the era.

Multisensor InSAR and ML era

Huili Gong [1] is a leading scholar in land subsidence within the multisensor InSAR and ML era, with affiliations spanning Jilin University [3] and Jilin Medical University [4]. Her key contributions include quantifying long-term groundwater storage changes and land subsidence in the North China Plain (1971–2015) [7], imaging land subsidence induced by groundwater extraction in Beijing using satellite radar interferometry (2016) [8], and analyzing land subsidence and its relation with groundwater aquifers in the Beijing Plain (2020) [9], advancing large-scale signal discrimination and informing resilience planning. Chaofan Zhou [2] is a prominent figure in this era, with affiliations at Fudan University [5] and Peking University [6]. His contributions include characterizing and explaining the causes of land subsidence in Beijing, China (2016) [10], examining land subsidence and its relation to groundwater resources in Beijing Plain (2020) [9], and analyzing long-term groundwater storage changes and land subsidence development in the North China Plain (2018) [7], illustrating how multisensor InSAR data can be integrated with ML-based susceptibility models to support hazard mapping and policy-relevant resilience.