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            <gco:CharacterString>This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed).

The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling.
 
Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore.

Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.</gco:CharacterString>
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                  <gmd:LocalisedCharacterString locale="#fra">Cet ensemble de données comprend cinq couches d'indice d'exposition aux ondes relatives atténuées en profondeur en format raster. Les valeurs de l'indice d'exposition relative (REI) sont calculées en fonction de l'analyse efficace (dérivée des valeurs d'extraction) combinée à des données modélisées sur le vent. Les couches de REI de sortie sont atténuées par la profondeur, ce qui donne des valeurs plus élevées dans les zones peu profondes près des côtes (Bekkby et al., 2008). Les valeurs des cellules représentent une estimation de l'exposition aux vagues à la profondeur du fond normalisée entre les régions de 0 (protégée) à 1 (exposée).

L'objectif de cet ensemble de données est de fournir une estimation de l'exposition aux vagues à la profondeur du fond, principalement à des fins de modélisation de la répartition des espèces.
 
Chaque raster monobande correspond à une région marine, qui coïncide généralement avec les couches suivantes du jeu de données Limites de la modélisation de la distribution des espèces (https://www.gis-hub.ca/dataset/sdm-boundaries) : Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS et Shelf_Salishsea. Ces couches s'étendent jusqu'à 50 m de profondeur et jusqu'à 5 km du rivage.

Des données tabulaires (fichiers csv) sont également incluses dans le dossier de données. Ces données sont les valeurs calculées de l'indice d'exposition relative (REI) avec des champs pour les informations de position. Les valeurs d'extraction à partir de la recherche côtière quadrillée (https://gis-hub.ca/dataset/gridded-nearshore-fetch) sont utilisées comme ensemble de données source et les emplacements dans le REI sont les mêmes que ceux de la recherche quadrillée.</gmd:LocalisedCharacterString>
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            <gco:CharacterString>Fetch is the unimpeded distance over which wind-driven waves can build. The methods used to create fetch are described here: https://gis-hub.ca/dataset/gridded-nearshore-fetch, but generally they are the distance in metres from a point on the ocean to the first land barrier along a given heading (in our case, 72 headings, every 5 degrees).

Effective fetch was computed from the gridded nearshore fetch source data in R code (developed by John O'Brien https://github.com/obrienjm25/REI-WaveExp/tree/main/Code) for each site and 8 wind directions (N, NE, E, SE, S, SW, W, NW). For a given compass heading (e.g., 0 degrees), all fetch lengths within 45 degrees on either side are considered. In our case, this means that the fetch length for the given bearing, along with 9 lines on either side (every 5 degrees) are used by averaging the product of those lines multiplied by the cosine of the angle of departure from the heading. The weighting of many fetch values is meant to address shoreline geometry irregularities and follows the work of Fonseca and Bell (1998).

Modelled wind data (2015-2022) obtained from the High Resolution Deterministic Prediction System (HRDPS) - nested limited-area model (LAM) forecast grids from the Global Environmental Multiscale (GEM) model were processed using Climate Data Operators (CDO) software, R, and Python. CDO was used to calculate wind speed and direction from the zonal and meridional components of the wind (u and v). The data were binned into wind directions and subset geographically and filtered to remove data where wind speed is below the 95th percentile for a given site. Wind frequency was then determined for each of the binned wind directions. Finally, the grand mean (mean of monthly means of daily maximum values) was calculated. The end result is 8 records (binned wind directions) per location with the grand mean and frequency at that side. The frequency and grand mean values were then interpolated and resampled to raster surfaces that were used to calculate the REI values.

For each fetch/REI location, the interpolated grand mean and frequency values were extracted. Then, the relative exposure index (REI) was calculated by summing the product of the wind frequency, maximum wind speed (grand mean), and effective fetch for each direction. The resulting REI values and site geometries were saved as a REI.csv file (provided in the data package).

The REI values were converted to raster format and resampled to 20 m resolution before having a 5 cell circular focal mean applied. Next, bathymetric data was used to attenuate the REI layers by depth following the methods described by Bekkby et al. (2008). Essentially, the depth attenuation is applied by using an exponential decay function to the REI value, based on the depth value at the site location. Finally, the layers were normalized between regions from 0 to 1.</gco:CharacterString>
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                  <gmd:LocalisedCharacterString locale="#fra">Fetch est la distance sans entrave sur laquelle les vagues entraînées par le vent peuvent s'accumuler. Les méthodes utilisées pour créer le fetch sont décrites ici : https://gis-hub.ca/dataset/gridded-nearshore-fetch, mais il s'agit généralement de la distance en mètres entre un point de l'océan et la première barrière terrestre le long d'un cap donné (dans notre cas, 72 rubriques, tous les 5 degrés).

Le calcul de la valeur de prélèvement efficace a été calculé à partir des données quadrillées de sources de prélèvement côtières en code R (élaborées par John O'Brien https://github.com/obrienjm25/REI-WaveExp/tree/main/Code) pour chaque site et 8 directions du vent (N, NE, E, SE, S, SW, W, NW). Pour un cap de boussole donné (p. ex. 0 degré), toutes les longueurs de ramassage à moins de 45 degrés de chaque côté sont prises en compte. Dans notre cas, cela signifie que la longueur de sortie pour le roulement donné, ainsi que 9 lignes de chaque côté (tous les 5 degrés) sont utilisées en faisant la moyenne du produit de ces lignes multiplié par le cosinus de l'angle de départ par rapport au cap. La pondération de nombreuses valeurs de prélèvement vise à corriger les irrégularités géométriques du littoral et suit les travaux de Fonseca et Bell (1998).

Les données modélisées sur le vent (2015-2022) obtenues à partir du Système de prédiction déterministe à haute résolution (SRDPS) - les grilles de prévision du modèle à aire limitée imbriquées (LAM) du modèle Global Environmental Multiscale (GEM) ont été traitées à l'aide du logiciel des opérateurs de données climatiques (CDO), R et Python. La CDO a été utilisée pour calculer la vitesse et la direction du vent à partir des composantes zonale et méridionale du vent (u et v). Les données ont été regroupées selon les directions du vent et les sous-ensembles géographiques et filtrées pour éliminer les données lorsque la vitesse du vent est inférieure au 95e percentile pour un site donné. La fréquence du vent a ensuite été déterminée pour chacune des directions de vent combinées. Enfin, la moyenne générale (moyenne des moyennes mensuelles des valeurs maximales quotidiennes) a été calculée. Le résultat final est de 8 enregistrements (directions de vent groupées) par emplacement avec la moyenne générale et la fréquence de ce côté. Les valeurs de fréquence et de moyenne générale ont ensuite été interpolées et rééchantillonnées en surfaces raster qui ont été utilisées pour calculer les valeurs REI.

Pour chaque emplacement Fetch/REI, la moyenne générale interpolée et les valeurs de fréquence ont été extraites. Ensuite, l'indice d'exposition relative (REI) a été calculé en additionnant le produit de la fréquence du vent, de la vitesse maximale du vent (moyenne générale) et de la valeur de calcul effective pour chaque direction. Les valeurs REI et les géométries de site résultantes ont été enregistrées dans un fichier REI.csv (fourni dans le package de données).

Les valeurs REI ont été converties en format raster et rééchantillonnés à une résolution de 20 m avant d'appliquer une moyenne focale circulaire à 5 cellules. Ensuite, les données bathymétriques ont été utilisées pour atténuer les couches REI en profondeur selon les méthodes décrites par Bekkby et al. (2008). Essentiellement, l'atténuation de la profondeur est appliquée à l'aide d'une fonction de désintégration exponentielle à la valeur REI, basée sur la valeur de profondeur à l'emplacement du site. Enfin, les couches ont été normalisées entre les régions de 0 à 1.</gmd:LocalisedCharacterString>
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