Implement dynamic carbon pool naming in SDA_downscale function

Key Changes in this commit are as follows : 

- Replace fixed "carbon_data" column with dynamic naming (paste0(C_pool, "_ens", i))
- Update CNN model training to use specific carbon pool column names
- Modify metrics calculation to use dynamic column names for each ensemble
- Adjust ensemble processing to handle variable carbon pool names

This change allows for more flexible handling of different carbon pools.
Core functionality remains the same, but now supports multiple named carbon pools.

modules/assim.sequential/R/downscale_function.R

@@ -94,7 +94,7 @@ SDA_downscale <- function(preprocessed, date, C_pool, covariates_path) {
 
   # Rename the carbon_data column for each ensemble member
   for (i in 1:length(ensembles)) {
-    colnames(ensembles[[i]])[1] <- "carbon_data"
+    colnames(ensembles[[i]])[1] <- paste0(C_pool, "_ens", i)
   }
 
   # Split the observations in each data frame into two data frames based on the proportion of 3/4
@@ -111,9 +111,9 @@ SDA_downscale <- function(preprocessed, date, C_pool, covariates_path) {
   for (i in 1:length(ensembles)) {
     # Prepare data for CNN
     x_train <- as.matrix(ensembles[[i]]$training[, c("tavg", "prec", "srad", "vapr")])
-    y_train <- as.matrix(ensembles[[i]]$training$carbon_data)
+    y_train <- as.matrix(ensembles[[i]]$training[[paste0(C_pool, "_ens", i)]])
     x_test <- as.matrix(ensembles[[i]]$testing[, c("tavg", "prec", "srad", "vapr")])
-    y_test <- as.matrix(ensembles[[i]]$testing$carbon_data)
+    y_test <- as.matrix(ensembles[[i]]$testing[[paste0(C_pool, "_ens", i)]])
 
     # Normalize the data
     x_train <- scale(x_train)
@@ -179,7 +179,7 @@ SDA_downscale <- function(preprocessed, date, C_pool, covariates_path) {
   # Calculate performance metrics for each ensemble member
   metrics <- list()
   for (i in 1:length(predictions)) {
-    actual <- ensembles[[i]]$testing$carbon_data
+    actual <- ensembles[[i]]$testing[[paste0(C_pool, "_ens", i)]]
     predicted <- predictions[[i]]
     mse <- mean((actual - predicted)^2)
     mae <- mean(abs(actual - predicted))