Selkie GIS Techno-Economic Tool input datasets

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the
Irish and Welsh maritime areas. Read more here:
https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

********************
File Formats
********************
Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS)
csv Human-readable text format, which can also be opened in Excel
png Image files that can be viewed in standard desktop software and give a spatial view of results

******************
Input Data
******************
All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

- Wind -
Copernicus ERA5 dataset
17 by 27.5 km grid  
10m wind speed

- Wave -
Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset
3 by 5 km grid

*********************
Accessibility
*********************
The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.  
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if  
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.  
Wind hourly data is from the ERA 5 dataset.  

********************
Availability
********************
A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather  
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the  
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between
accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the  
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.  
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined  
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total  
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship
between the two. A mature technology reliability was assumed.

**********************
Weather Window
**********************
The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the  
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset  
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.  
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather  
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain  
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable  
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available  
at any given point in the month.

*****************************
Extreme Wind and Wave
*****************************
The Extreme wave layers show the highest significant wave height expected to occur during the given return period.
The Extreme wind layers show the highest wind speed expected to occur during the given return period.  

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced  
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model  
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values  
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The
second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected  
extremes and used to calculate the extreme value for the selected return period.