TimingAFs

TimingAFs is a novel framework for projecting the timing of frequency amplifications of extreme sea levels due to sea-level rise. The framework allows the user to compute when a certain amplification of a benchmark frequency will occur, instead of how much it will amplify by 2100 or another arbitrary future year. Instead of using the historical centennial event as a benchmark for amplification factors, estimated flood protection standard from Tiggeloven et al. (2020) are used as a benchmark frequency. The projections therefore indicate the timing of decreases in the degree of coastal flood protection at a given location. For the projections, the sea-level projections of IPCC AR6 are used. The extreme sea level distributions are derived from GESLA3 tide gauge data using a peak-over-threshold analysis with an automatic threshold selection based on Solari et al. (2017).

The code in this repository was used for the manuscript:

Hermans, T.H.J., Malagón-Santos, V., Katsman, C.A. et al. The timing of decreasing coastal flood protection due to sea-level rise. Nat. Clim. Chang. (2023). https://doi.org/10.1038/s41558-023-01616-5

Please cite it when using this repository for your own studies.

(back to top)

Required input data

• GESLA3 data (download here)
• FLOPROS data from Tiggeloven et al. (2020) (download here)
• AR6 Sea-level projections at GESLA3 locations (uploaded here)

(back to top)

Workflow

1. Derive daily maxima from GESLA3 data and write them to CSV files using the script daily_max_GESLA3_to_csv.py.

2. Load the CSV files and apply the method of Solari et al. (2017) to the daily maxima using gpdfit_solaris_thres_gesla3.R. This script automatically selects the extreme threshold and outputs central-estimate general Pareto distribution parameters and their uncertainty to NetCDF files. A script to compute distribution parameters for a constant threshold of 98.8% is also included (get_gpd_parameters988.R). The script ats_output_to_dataset.py is used to merge the NetCDF files containing the automatic threshold selection output for each location into a single NetCDF file for all locations, keeping only the data needed for the timing projections.

3. To project the timing of AFs, project_AF_timing_ar6wfs.py takes the GPD parameter estimates and fetches the FLOPROS estimates nearest to the GESLA3 locations. Using the GPD fits, the return curves and required SLR are computed. The timing of AFs is then projected by interpolating the timing of SLR projected by the IPCC AR6 onto the required SLR derived from the return curves, and the resutls are stored in a NetCDF file containing the projections for all locations.

4. (optional) Scripts to reproduce Figures 4 and 5 of the main manuscript can be found here. The timing projections used for these plots are available at this Zenodo repository.

(back to top)

Prerequisites and built with

• Python
  • numpy
  • matplotlib
  • xarray
  • scipy
  • os
  • cartopy
  • cmocean
  • shapely
  • gesla
• R
  • MultiHazard
  • texmex
  • ncdf4

(back to top)

Acknowledgements

We thank S. Solari for sharing his automatic threshold selection code and T. Tiggeloven for elucidating the FLOPROS estimates. R.E.K. and M.O. were supported by the National Science Foundation (NSF) as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. T.H.J.H., V.M.-S. and A.B.A.S. were supported by PROTECT. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869304, PROTECT contribution number 58. T.H.J.H. also received funding from the NPP programme of NWO.

(back to top)

Contact

Tim Hermans t.h.j.hermans@uu.nl

(back to top)