CLASSIFIED STORM SURGE HYDROGRAPHS DATASET
Dutch historical storm surge events recorded between 1950 and 2018. 
The events are catalogued per station and classified by event type

DATE OF RELEASE
11 November 2024

CONTACT INFORMATION
Mia Pupic Vurilj
Delft University of Technology, Department of Hydraulic Engineering
M.PupicVurilj@tudelft.nl

OVERVIEW
This dataset documents Dutch historical storm surge events recorded between 
1950 and 2018. The events are catalogued per station and classified by event 
type. For more details on the derivation and background of the data, the 
reader is refered to the accompanying publication (to be added once published).

REFERENCES
To be added

CONTENTS
- stations_info.csv:
   Contains station names, latitude, longitude, 70th percentile value, 
   and 99th percentile value
- p70_events.json: 
   Contains storm surge events per station, extracted using the 70th 
   percentile
- p99_events.json: 
   Contains storm surge events per station, extracted using the 99th 
   percentile
- hydrographs.h5:
   Contains the storm surge hydrographs per station
- event_types.h5:
   Contains event types and associated hydrographs


DATA FORMAT
- CSV (*.csv)
- JSON (*.json)
- HDF5 (*.h5)

DATA STRUCTURE:
- stations_info.csv
   This file contains tabular data with the following columns:
    station: station name
    lon: longitude of the station
    lat: latitude of the station
    p70: 70th percentile surge value
    p99: 99th percentile surge value
- p70_events.json and p99_events.json
   These JSON files contain storm surge events for each station. 
   The data for each event includes:
    time_max: time of surge maximum (ISO string)
    max: maximum surge value (m)
    duration: duration of the event (hours)
    start: start time of the event (ISO string)
    end: end time of the event (ISO string)

- hydrographs.h5:
   Contains the storm surge hydrographs per station with the following 
   simplified structure:

	{
    	'Station_A': {
      	  '0': np.array([ ... ]),  # hydrograph of the first event
      	  '1': np.array([ ... ]),  # hydrograph of the second event
      	  ...
  	  },

   	 'Station_B': {
      	  '0': np.array([ ... ]),  # hydrograph of the first event
	  '1': np.array([ ... ]),  # hydrograph of the second event
      	  ...
   	 },

   	 ...
	}

- event_types.h5:
   Contains the event types with their corresponding hydrographs per station, 
   structured as:

	{
    	"type_1": {
        	"Station_A": {
            		"0": np.array([...]),
            		"1": np.array([...]),
            		 ...
        	},
        	"Station_B": {
            		"50": np.array([...]),
            		 ...
        	},
        	...
    	},
    	"type_2": {
        	"Station_C": {
            		"21": np.array([...]),
            		 ...
        	},
        	"Station_D": {
            		"67": np.array([...]),
            		 ...
        	},
        	...
    	},
    	...
	}
   
   The numbered key of each hydrograph represents its position in a concatenated 
   matrix of all hydrographs, without station information.


USAGE
The dataset can be analyzed using any software or programming language that 
supports CSV, JSON, and HDF5 file formats (e.g., Python, R, C++, MATLAB). We have used 
Python for all processing and operations on the data.

LICENSE
CC BY 4.0