Towards regionally forecasting shallow subsidence in the Netherlands

• Published in: Proceedings of the International Association of Hydrological Sciences Vol. 382; pp. 427 - 431
• Main Authors: Candela, Thibault, Koster, Kay, Stafleu, Jan, Visser, Wilfred, Fokker, Peter
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 22.04.2020; Copernicus Publications
• Subjects: Anthropogenic factors; Clay; Confidence; Data assimilation; Data collection; Forecasting; Geological surveys; Geology; Groundwater; Groundwater levels; Hydrocarbons; Integrated approach; Mathematical models; Modelling; Oxidation; Parameter uncertainty; Peat; Salt mines; Subsidence; Surveying; Three dimensional models; Netherlands
• ISSN: 2199-899X; 2199-8981; 2199-899X
• DOI: 10.5194/piahs-382-427-2020

The Netherlands is subject to anthropogenically induced deep-source and shallow subsidence. Deep sources are related to the extraction of hydrocarbons or salt mining activities, whereas shallow subsidence comprise compaction, shrinkage and oxidation of clay and peat under progressive lowering groundwater levels. At TNO – Geological Survey of the Netherlands, deep-source and shallow subsidence are presently investigated separately. Forward and inverse modelling techniques are generally deployed to forecast subsidence caused by deep sources, whereas shallow subsidence is predicted using the high-resolution geological 3-D subsurface model GeoTOP. A new approach is proposed which encompasses forward and inverse modelling techniques and GeoTOP. Such combination will yield a powerful shallow subsidence forecasting model, which would be a critical step forward in analyzing shallow subsidence in the Netherlands on a regional scale. In the present contribution, we sketch the setup of this new approach that combines subsidence measurements, GeoTOP subsurface data, and data assimilation of subsidence with the help of state-of-the-art forward and inverse modelling techniques. The setup uses ensemble technology to catch uncertainties of parameters, different model choices, and implicit correlations. With such a setup, forecasts can be faithfully accompanied with a quality measure that enables to judge its relevance and confidence range.