Model Data for 'Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding' (TC-FF)

doi: 10.4121/a5174397-3489-4f5d-b220-6749f3750942.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/a5174397-3489-4f5d-b220-6749f3750942
Datacite citation style:
Nederhoff, Kees; van Ormondt, Maarten; Veeramony, Jay; Ap Van Dongeren; AntolĂ­nez, Jose et. al. (2024): Model Data for 'Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding' (TC-FF). Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

This dataset is an integral part of the research presented in the paper titled "Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding" (TC-FF). It encompasses a comprehensive collection of data and model setups used in our study, to facilitate further research and understanding in this area.

The contents of this dataset include:

  1. SFINCS Model Setup: The SFINCS (Super-Fast INundation of CoastS) model is a critical component of our research. It was employed for simulating the hydrodynamic processes. More information about the SFINCS model can be found on Deltares' official website at Deltares SFINCS.
  2. Tidal Validation Data: As illustrated in our paper, this section includes detailed tidal validation data, supporting the accuracy and reliability of our model predictions in tidal scenarios.
  3. Validation of Event Idai: This section contains specific validation data for Tropical Cyclone Idai, which is a key case study in our research. It demonstrates the model's effectiveness in predicting the impacts of this particular event.
  4. TC-FF Generated Ensemble Members: This critical component of our dataset includes the ensemble members generated for the TC-FF model, offering predictions from 1 to 5 days before landfall. These ensemble members are essential for understanding the range of potential outcomes and uncertainties associated with tropical cyclone-induced flooding.

This dataset is intended to complement the findings and discussions presented in our paper, offering a deeper insight into the methodologies and analyses employed. We believe it will be a valuable resource for researchers and practitioners working in the field of meteorology, hydrology, and disaster risk management.

  • 2024-01-30 first online, published, posted
NetCDF output and SFINCS input
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Hydraulic Engineering


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