Over half a billion people live in coastal plains and deltas threatened by anthropogenically induced subsidence, and this number is expected to increase in the foreseeable future (Neumann et al., 2015;Schmidt, 2015). Many anthropogenic subsurface activities in coastal areas and delta plains result in subsidence, thereby amplifying relative sea-level rise and flood risks, inflicting damage to infrastructure, and overall, reducing the viability of these low-lying areas (
This research targets disentangling shallow causes of anthropogenically-induced subsidence in a reclaimed and urbanized coastal plain. The study area is around the city of Almere, in the South Flevoland polder, the Netherlands, which is among the countries’ fastest subsiding areas. The procedure consists of integrating synthetic Interferometric Synthetic Aperture Radar (InSAR) data with high-resolution phreatic groundwater and lithoclass models, and a database containing construction details. The two main parts of the workflow are isolation of the InSAR points of structures without a pile foundation and a data assimilation procedure by Ensemble Smoothing with Multiple Data Assimilation. The shrinkage of surficial clay beds by phreatic groundwater level lowering is identified to be the main cause of shallow subsidence in the area, with an average contribution of 6 mm/year. The history-matched physics-based model predicts that one meter drop in phreatic groundwater level now translates into 10 millimeter of subsidence in the next five years. Also, this study showed that a groundwater deficiency due to severe dry periods should be considered as an accelerator of subsidence in both the short- and long-term planning. To ensure a robust network to estimate future subsidence, we advise on a consistent monitoring strategy of the phreatic groundwater level.
<p>The coastal plains of the Netherlands are subject to anthropogenic and natural subsidence with rates which are an order of magnitude higher than sea-level rise. Because one-third of the Netherlands lies below mean sea level, subsidence may threaten the country&#8217;s subsistence with major socio-economic consequences. Subsidence is a normal natural process but is overprinted and accelerated by anthropogenic activities which drives deep or shallow processes. Deep processes are caused by the extraction of hydrocarbons and salt, whereas shallow processes are primarily caused by lowering of phreatic groundwater levels. At present, the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies.</p><p>We present the outline of a hybrid Artificial Intelligence (AI) big data and model workflow to disentangle different subsidence forcing and we report on preliminary results for an area covering a gas field in the peat-rich Friesland coastal plain. The proposed workflow is a hybrid approach between a knowledge-based physical model and machine-learning techniques. The big input data comprises a suite of static (structural model) and time-dependent subsurface data (phreatic groundwater level, reservoir pressure), and geodetic measurements. Geomechanical models provide the connection between the drivers (groundwater levels and reservoir pressures) and the surface movement.</p>
<p>Reclaimed coastal plains often experience significant subsidence as a result of phreatic water level lowering, which induces oxidation of organic material, shrinkage of clay, and sediment compaction. A primary example in the center of the Netherlands, is the &#8216;South Flevopolder&#8217; which was reclaimed in 1968 and transformed into an area for residential, industrial and agricultural use. The area subsided over 1.5 m since its reclamation and its surface is still lowering.</p><p>The city of Almere, with roughly 200.000 inhabitants and a surface area of c. 250 km<sup>2</sup>, is situated in the South Flevopolder. Most buildings in the city are founded in deeper Pleistocene sand, whilst objects such as parking lots, sport fields and playgrounds are often unfounded and are directly situated on the younger Holocene coastal deposits. Currently, the unfounded objects show subsidence rates as high as 5 mm per year for which the different subsidence rates &#160;may be related to subsurface heterogeneities. The upper layers in the area are dominated by clay and sand, up to a few meters in thickness, which overly peat and highly organic layers. The lowering of the phreatic surface results in an erratic pattern of subsidence over the area.</p><p>We present a workflow to disentangle and parameterize the different contributions of shallow subsidence from Interferometric synthetic-aperture radar (InSAR) measurements. InSAR measurements from founded and unfounded scatterers are separated with a dimensionality reduction technique, t-Distributed Stochastic Neighbor Embedding (t-SNE), followed by an automatic detection of clusters with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). We have limited ourselves to structures with a construction date of >10 years with respect to the first InSAR measurement date to reduce the effect of construction-related consolidation and isolate the effect of shallow subsidence related to reclamation and phreatic surface level changes. The filtered dataset represents the surface response of unfounded structures in the urbanized reclaimed coastal area.</p><p>The subsidence processes are disentangled and parameterized with an Ensemble Smoother with Multiple Data Assimilation (ES-MDA). Additional input data for this method is provided by a phreatic groundwater level model and a voxelized lithological model from the surface towards the top of the Pleistocene sand layers.</p><p>We show that the automated data selection method prevents bias by selecting unfounded objects and the proposed workflow can be of aid when studying shallow subsidence in urbanized areas, where most objects are founded below the level at which shallow subsidence takes place. The results of this study quantify the rate of the different subsidence processes on a spatiotemporal scale and thus provide insights for tailored decision making to mitigate subsidence.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.