Subside down: improving global patterns of vertical land motion

Vertical Land Motion (VLM) plays a critical role in the evolving risk of coastal sea-level change (Nicholls et al. 2021; Dodman et al. 2022). Mega-cities across the world, such as Jakarta and Bangkok, are experiencing subsidence (negative VLM) that is faster than oceanic sea-level rise leading to enhanced exposure, reduced resilience, and accelerating risk (e.g., Erkens et al. 2015). Conversely, areas such as Hudson Bay and Scandinavia are experiencing uplift (positive VLM) that is faster than oceanic sea-level rise leading to net sea-level fall.
The causes of VLM are numerous and vary in time and space from earthquakes (local, fast) to isostasy (global, slow). Most observations of VLM come from a global network of Global Navigation Satellite System (GNSS) stations (e.g., Hammond & Kreemer, 2018). However, additional insight can be gained by combining GNSS observations with information from tide gauges or model predictions of glacial isostatic adjustment (GIA).
To date, projections of local sea-level change (such as the IPCC 6th Assessment Report) remain of limited use to decision-makers due partly to the low spatial resolution of modern coastal VLM, and the uncertainty of future VLM evolution.
To address these issues, the overall aim of this project is to use the global network of coastal observations and selected numerical model output to enhance spatial resolution of VLM (e.g., Pfeffer & Allemand, 2016) for sea level projections and decouple natural from anthropogenic contributions.

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Image Captions

Views of inundated areas of New Orleans following Hurricane Katrina (2005) – subsidence of levees following their construction led to a lowering of coastal protection levels. Image Credit: NOAA / NOAA. Photographer name: NOAA. Licence: CC by 2.0


Evaluate existing literature and develop understanding of VLM data requirements for sea-level analysis, including projections. Build an understanding of the techniques used to estimate VLM rates and local sea level change (GNSS, GIA models, Interferometric Synthetic Aperture Radar (InSAR), geological data, tide gauges, coastal altimetry).
Construct a quality-controlled global database of instrumental VLM rates from published literature and online data archives.
Develop a methodology that combines multiple data sets and interpolates coastal VLM rates on a grid that optimises data-availability and highlights local-to-regional variability.
Using the interpolated field of coastal VLM, separate into natural and anthropogenic components and test the results against independent data sources (e.g., InSAR) for specific locations.

Project Timeline

Year 1

Data-handling and geostatistical analysis training from supervisory team.
Literature review
Begin compiling instrumental and modelled VLM rates database.
Submit first year progress report

Year 2

Complete quality-controlled instrumental/modelled VLM database.
Develop methodology to synthesise and interpolate VLM information along the global coastline.
International conference attendance/presentation
Draft paper for journal

Year 3

Inter-comparison of modelled and data-constrained estimates of VLM and sea-level change at case study locations.
Develop approach to partition natural/anthropogenic signals
International conference attendance/presentation
Engage with external stakeholder community (via supervisory connections to industry) to assess/refine utility of outputs.
Draft thesis chapters and/or journal submission

Year 3.5

Finalise thesis chapters
Thesis submission

& Skills

The student will receive both generic and bespoke training in GNSS, sea level (tide gauges and satellite altimetry) and GIA analysis from the supervision team. This will include software training in Linux and associated scripting, Matlab, Python or R, and geospatial mapping via ArcGIS.
Additional numerical modelling and data-manipulation skills will be provided via online training workshops via ESRI, and national/international workshops with this focus (e.g., CLIVAR, Delft Sea-Level Summer School).
Broader transferable skills will be developed through various training events at Durham University offered by IAPETUS (e.g., communicating science, thesis writing, writing for publication, presentation skills).

References & further reading

Dodman, D., et al., 2022: Cities, Settlements and Key Infrastructure. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, et al., (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 907–1040, doi:10.1017/9781009325844.008
Erkens, G., Bucx, T., Dam, R., de Lange, G., and Lambert, J., 2015. Sinking coastal cities, Proc. IAHS, 372, 189–198, https://doi.org/10.5194/piahs-372-189-2015
Hammond, W.C. and Kreemer, C., 2018. Harnessing the GPS data explosion for interdisciplinary science. Eos, 99(10.1029), p.485. https://doi.org/10.1029/2018EO104623
Nicholls, R.J., Lincke, D., Hinkel, J., Brown, S., Vafeidis, A.T., Meyssignac, B., Hanson, S.E., Merkens, J.L. and Fang, J., 2021. A global analysis of subsidence, relative sea-level change and coastal flood exposure. Nature Climate Change, 11(4), pp.338-342. https://rdcu.be/czInN
Pfeffer, J. and Allemand, P., 2016. The key role of vertical land motions in coastal sea level variations: A global synthesis of multisatellite altimetry, tide gauge data and GPS measurements. Earth and Planetary Science Letters, 439, 39-47. https://doi.org/10.1016/j.epsl.2016.01.027

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