## 2 Data and Methods
### Methods
#### 2.3.5 Sensitivity Test of the Energy Resource Parameters  
The classification indices used in Equations (5) and (6) were calculated for each grid pixel (k) by means of an expression that can be generalized as:  
\[C^{k}=\sum_{i=1}^{N}\langle W_{i}^{k}\rangle\times I_{i}^{k} \tag{8}\]  
where \(N\) is the number of indices, \(I_{i}^{k}\) is the normalized value of each index (i) calculated for every pixel (k), and \(\langle W_{i}^{k}\rangle\) is the mean weight of each index obtained from Tables 2-6. Note that each weight in Tables 4 and 6 is described as a mean and a standard deviation. Although the mean weight value of the weight coefficients (\(\langle W_{i}^{k}\rangle\)) will be considered throughout most of the study (Equations (5) and (6)), a sensitivity test was carried out following a Monte Carlo approach to analyze the dependence of the classification indices (\(C^{k}\)) on the variability of the weight coefficients. The procedure applied to each pixel can be summarized as follows:  
1. Random variation. Each weight was allowed to vary randomly \[W_{i}^{k}=\langle W_{i}^{k}\rangle+R^{k}\,\sigma\big{(}W_{i}^{k}\big{)}\] (9)  
where \(R^{k}\) is a random number with equal probability between \(\pm 1\), and \(\sigma\) is the standard deviation provided by Tables 4 and 6 for wind and waves, respectively. A different random number was used for each pixel (k).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Richness**} & \multicolumn{2}{c}{**Variability**} & **Risk** & \multicolumn{2}{c}{**Cost**} \\ \hline
**WP** & **DWNT** & **Cv** & **Mv** & **EWH** & **WD** & **DC** \\ \hline
0.44 \(\pm\) 0.05 & 0.10 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 & 0.05 \(\pm\) 0.01 & 0.14 \(\pm\) 0.01 & 0.07 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weight coefficient in the wave energy classification. Factors averaged to the fifteen wave energy experts and engineers consulted. The standard deviation was calculated as \(\sigma=\sqrt{\sum_{i=1}^{N}\lvert A_{i}-\overline{A}\rvert^{2}/N}\), where \(A\) is the weight coefficient, \(\overline{A}\) is the mean value of \(A\) and N is the number of experts (15).  
2. Renormalization. Before the random variation, weights were normalized in such a way that \[\sum_{i=1}^{N}\langle W_{i}^{k}\rangle=1\] (10)  
However, that normalization cannot be guaranteed after random variation. Consequently, weights must be renormalized by  
\[Wn_{i}^{k}=\frac{W_{i}^{k}}{\sum_{i=1}^{N}\langle W_{i}^{k}\rangle} \tag{11}\]
3. The classification indices were recalculated using the new weights \[C_{j}^{k}=\sum_{i=1}^{N}Wn_{i}^{k}\times I_{i}^{k}\] (12)  
where the subscript (\(j\)) refers to the different realizations of the Monte Carlo approach. In particular, \(j\) = 100,000 realizations will be considered in the present analysis.
4. The RMSE\({}^{k}\) associated to each pixel was calculated between the reference index (\(C^{k}\)) and the different realizations, \(C_{j}^{k}\). \[\text{RMSE}^{k}=\sqrt{\frac{\sum_{j=1}^{J}\left(C^{k}-C_{j}^{k}\right)^{2}}{ J}}\] (13)
5. In a similar way, the \(\text{RMSE}_{j}\) associated to each realization was calculated as \[\text{RMSE}_{j}=\sqrt{\frac{\sum_{k=1}^{N_{p}}\left(C^{k}-C_{j}^{k}\right)^{2 }}{N_{p}}}\] (14)  
where \(N_{p}\) is the number of pixels.
6. The mean value of the classification index was calculated as the average of all realizations by using the following expression \[\langle C_{j}^{k}\rangle=\frac{1}{J}\sum_{j=1}^{J}C_{j}^{k}\] (15)