Dataset
Input data for: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
Usage on Zenodo
View on Zenodo- Downloads
- 444
- Views
- 700
Unique downloads and views across all versions, as counted by Zenodo on 11 October 2026. This version: 444 downloads, 695 views.
- Authors
- Muis, Sanne Dangendorf, Sönke Irazoqui Apecechea, Maialen Dullaart, Job de Lima Rego, Joao Madsen, Kristine Skovgaard Su, Jian Yan, Kun Verlaan, Martin Hay, Carling Calafat, Francisco Mir Marcos, Marta Piecuch, Christopher Berk, Kevin Jensen, Jurgen Treu, Simon
- Research Domain
- RD3 - Transformation Pathways
- Research Topic
- Sea-level Rise
- Model/Method
- ISIMIP
- Further keywords
- Coastal Water Levels Counterfactuals Impact Attribution CoDEC Hybrid Reconstructions
- License
- cc-by-4.0
- Files
- 2 (46.8 GB)
Description
This Zenodo archive contains essential input datasets utilized in our research study titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution".
This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the ISIMIP repository.
Datasets Included:
CoDEC (Coastal Dataset for the Evaluation of Climate Impact):
- This dataset is described in Muis et al. (2020)
- cf_esl folder: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point.
- cf_tides folder: This folder holds data related to tidal elevation.
- coor_coastal.nc: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.
- HR (Hybrid Reconstructions):
- HybridRec_Upd0422.mat: This file contains data from the Hybrid Reconstructions dataset (Dangendorf et al 2019), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our source code named split_hr_dataset_to_stations.py.
We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.