This work package is on tracking subsidence rates at local and regional scales: on providing essential subsidence values for use in NWA-LOSS causal analysis, measure design, subsidence forecasting and mitigation policy scenarios.
While SAR satellites today can measure ongoing subsidence quite well in urban areas and with some effort also over agricultural areas, this is measurement of total subsidence, known to bear components due to both natural and human-induced processes. WP1 focuses on making remote sensing data on total subsidence useful for human-induced soft-soil shallow subsidence analysis and forecasting, by optimizing its processing and by filtering for subsidence due to other factors: background natural ones (tectonic and hydroglacio-isostatic) and human-induced processed in the deeper substrate (drinking water from aquifers at intermediate depth; salt mining and gas field exploitation at kilometers depth).
Work package 1 (Measuring and Monitoring: see above) feeds data products to work packages 2 (Mechanism and GHG emissions: plot scale investigations and model parameterization) and 3 (Impact Analysis: spatial forecasting).
Land subsidence due to soft soil oxidation and consolidation causes in that sum is the spatially and temporally most variable component, meaning it does not have a constant value in the measurement time series and that it requires high resolution analysis (ideally: point clouds from InSAR data processed to continuous grids with cell-sizes of 1x1 to 25x25 meters). InSAR data yields data series on the subsidence of reflective surfaces of many types (e.g. Hanssen, 2001). Often analysis is restricted to data points on build structures and roads for which solving the interferometry of shifts in synthetic-aperture radar reflectance is relatively straight forward. In WP1 we will also use technologies that tease out a meaningful InSAR-based signal of land subsidence in the dairy farming meadows, agricultural fields and renaturalized wetlands in the polder landscape. These are the type of challenges taken up in WP1.1 at TUDelft, with strong links to https://ncgeo.nl/ and their previous initiatives such as and https://bodemdalingskaart.nl
InSAR based land subsidence data products before they are valuable for soft soil subsidence analysis also have to be undone from the part of subsidence that is attributable to other causes, rooted in processes in play in the deeper subsurface, in part of natural cause and in part anthropogenic (e.g. Fokker et al., 2018). To disentangle total ground movement into shallow and deep and anthropogenic and natural contributions, including sea-level rise related subsidence components, WP1 incorporates geophysical modelling results. In WP1.2, 3D models of the deep subsurface developed in mapping programs of TNO Geological Survey of the Netherlands and data series on groundwater, salt and hydrocarbon extractions at various depths will be subjected to inverse modelling to generate time-series of these forms of anthropogenic subsidence.
In WP1.3 (at Utrecht University), the subsidence signal that is related to relative sea-level rise in the North Sea region will be queried from from geophysical scenario models in use in sea-level research, which make use of input such as past ice-volume reconstructions, spatial models of crustal rheology and mantle viscosity, gravimetric knowledge and datasets on past sea-level rise. For multiple existing glacio-isostatic adjustment models for the Netherlands (GIA-models, in collaboration with NIOZ and TUDelft), a library of their respective input data and settings will be set up, while their output background land subsidence estimates will be intercompared and cross-validated against geologically and geodetically derived bench mark data (e.g. Koster et al., 2017; Vermeersen et al., 2018).
WP 1.2 lead: Dr. P.A. (Peter) Fokker, TNO Geological Survey of the Netherlands
WP 1.2 Postdoc:
WP 1.3 lead: Dr. K.M. (Kim) Cohen, Utrecht University
WP 1.3 PhD candidate: Kim de Wit, MSc, Utrecht University
WP 1.3 collaborators: Dr. P. (Paolo) Stocchi (NIOZ, GIA modelling), Dr.ir. W. (Wouter) van der Wal (TUDelft, GIA modelling), Prof. R.W. van de Wal (UU, promotor PhD candidate)
Fokker, P. A., van Leijen, F. J., Orlic, B., van der Marel, H., & Hanssen, R. F. (2018). Subsidence in the Dutch Wadden Sea. Netherlands Journal of Geosciences, 97(3), 129-181.
Hanssen, R.F. (2001). Radar interferometry: data interpretation and error analysis 2. Springer Science & Business Media.
Koster, K., Stafleu, J., & Cohen, K. M. (2017). Generic 3D interpolation of Holocene base‐level rise and provision of accommodation space, developed for the Netherlands coastal plain and infilled palaeovalleys. Basin Research, 29(6), 775-797.
Vermeersen, B. L., Slangen, A. B., Gerkema, T., Baart, F., Cohen, K. M., Dangendorf, S., ... & Jevrejeva, S. (2018). Sea-level change in the Dutch Wadden Sea. Netherlands Journal of Geosciences, 97(3), 79-127.