Data underlying the publication: Incorporating Forecast Uncertainty into Anticipatory Flood Management using a Bayesian Decision Support Framework

DOI:10.4121/0ee90a1c-bb09-4c3a-968c-7fced78c8bea.v1
The DOI displayed 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: 10.4121/0ee90a1c-bb09-4c3a-968c-7fced78c8bea

Datacite citation style

Young, Adele; Bhattacharya, Biswa; Daniëls, Emma; Zevenbergen, Chris (2025): Data underlying the publication: Incorporating Forecast Uncertainty into Anticipatory Flood Management using a Bayesian Decision Support Framework. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0ee90a1c-bb09-4c3a-968c-7fced78c8bea.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Geolocation

Alexandria, Egypt
lat (N): 31.20499
lon (E): 29.92535
view on openstreetmap

Time coverage

2015, 2018-2020

Licence

CC BY 4.0

Interoperability

This research proposes to demonstrate how a Bayesian decision model can be used to identify the optimal anticipatory actions that minimise the expected losses associated with different flood vulnerability characterisations for selected neighbourhoods (Shyahkas) across Alexandria. The dataset includes output files of the Weather Research Forecasting model. The model is initialised with Global Ensemble Forecast System (GEFS) data over the Alexandria area, Egypt.The original NetCDF files have been converted to .npy files to manage the size. The ensemble rainfall data provided in this dataset is used to develop the probability distribution functions used in the Bayesian decision model, as detailed in the publication and thesis. The scripts for the Bayesian Decision model are also included.

History

  • 2025-11-07 first online, published, posted

Publisher

4TU.ResearchData

Format

numpy arrays/.nyp , netcdf/.nc,scripts/.py NCAR Command Language/.ncl, , Unix shell script/.sh, Unix shell scripts/.csh

Funding

  • This work used the Dutch national e-infrastructure with the support of the SURF Cooperative (grant code EINF-7584) Dutch Research Council (NWO)

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Hydraulic Engineering
IHE Delft Institute for Water Education, Department of Coastal and Urban Risk Resilience

DATA

Files (2)