diff --git a/functions/weatherFunctions.R b/functions/weatherFunctions.R
index 37497a8..d344de2 100644
--- a/functions/weatherFunctions.R
+++ b/functions/weatherFunctions.R
@@ -152,6 +152,7 @@ runFutureSWwithAnomalies <- function(sw_in0, wdata, SoilsDF,
   generatedAnomData <- generateAnomalyData(monthlyWdata, TempAnoms, PPTAnoms,
                                            leads = seq_len(Nleads), Nleads = Nleads,
                                            n = n)
+  which(is.nan(generatedAnomData), arr.ind = TRUE)
   #saveRDS(generatedAnomData,  'ExampleData/generatedAnomData')
   ## in generatedAnomData, the rows are for each lead (12 rows), and the columns (30) are each for a different multivariate sample
   
diff --git a/main/implementation/01.2_input-values-into-ncdfs.R b/main/implementation/01.2_input-values-into-ncdfs.R
index 7c65541..488eee2 100644
--- a/main/implementation/01.2_input-values-into-ncdfs.R
+++ b/main/implementation/01.2_input-values-into-ncdfs.R
@@ -27,60 +27,117 @@ for(n in seq_along(netCDFnames)){
   # indicating the count of values to write along each dimension, order is x-y-t)
   co <- c(1, 1, tdim[n])
   st_n <- st
-  #AES get values for last 180 days? 
-  # get the date for 180 previous from today
-  if (tdim[n] == 180) {
-    vals <- AllVarData[1:183,valueName[n]] 
-  } else if (tdim[n] == 549){
-    vals <- as.vector(AllVarData[ ,valueName[n]]) 
-  } else if (tdim[n] == 352) {
-    vals <- as.vector(AllVarData[(nrow(AllVarData)-351):nrow(AllVarData),valueName[n]])
-  } else if (tdim[n] == 31) { # for Oconnor variables
-    if (valueName_short[n] == "oconnor-swp_mean") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean)
-    }
-    if (valueName_short[n] == "oconnor-swp_95CI_lower") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) - c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
-    }
-     if (valueName_short[n] == "oconnor-swp_95CI_upper") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) + c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
-     }
-    if (valueName_short[n] == "oconnor-stemp_mean") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean)
+  
+  ## add something to deal w/ the fact that there may not be a "deep" soil layer
+  #value if the soil isn't deep enough in a given location write!
+  # if there is NOT Deep soils data...
+  if (sum(SoilsDF$Depth == "Deep") == 0) {
+    # if the layer in question (this 'n') is a "deep soils layer", then put NA values (-9999)
+    if (n %in% grep("Deep", valueName) ) {
+      vals <- rep_len(-9999, length.out = co[3])
+    } else if (tdim[n] == 180) {
+      vals <- AllVarData[1:183,valueName[n]] 
+    } else if (tdim[n] == 549){
+      vals <- as.vector(AllVarData[ ,valueName[n]]) 
+    } else if (tdim[n] == 352) {
+      vals <- as.vector(AllVarData[(nrow(AllVarData)-351):nrow(AllVarData),valueName[n]])
+    } else if (tdim[n] == 31) { # for Oconnor variables
+      if (valueName_short[n] == "oconnor-swp_mean") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean)
+      }
+      if (valueName_short[n] == "oconnor-swp_95CI_lower") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) - c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
+      }
+      if (valueName_short[n] == "oconnor-swp_95CI_upper") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) + c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
+      }
+      if (valueName_short[n] == "oconnor-stemp_mean") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean)
+      }
+      if (valueName_short[n] == "oconnor-stemp_95CI_lower") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) - c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+      }
+      if (valueName_short[n] == "oconnor-stemp_95CI_upper") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) + c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+      }
+    } 
+    if (valueName_short[n] == "shriver_historical") vals <- c(Shriver_Stats[TP == 'Historical', 'Prob'])[["Prob"]]
+    if (valueName_short[n] == "shriver_prediction") {
+      vals <- c(Shriver_Stats[TP != 'Historical', 'Prob'])$Prob
+      # redefine tdim to have the number of time steps for this run 
+      # the 'count' that will be fed into nc_put, which is a "vector of integers 
+      # indicating the count of values to write along each dimension, order is x-y-z-t)
+      co <- c(1, 1, 30, tdim[n]) 
+      # ammend the 'st' vecotr, which tells the ncvar_put function where to start writing the data 
+      st_n <- c(st, 1)
     }
-    if (valueName_short[n] == "oconnor-stemp_95CI_lower") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) - c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+    
+    if (valueName_short[n] == "GISSM_historical") vals <- as.numeric(c(NA, Hist_GISSM$SeedlingSurvival_1stSeason))
+    if (valueName_short[n] == "GISSM_prediction") {
+      vals <-  Future_GISSM$Prob
+      # redefine tdim to have the number of time steps for this run 
+      # the 'count' that will be fed into nc_put, which is a "vector of integers 
+      # indicating the count of values to write along each dimension, order is x-y-z-t)
+      co <- c(1, 1, 30, tdim[n]) 
+      # ammend the 'st' vecotr, which tells the ncvar_put function where to start writing the data 
+      st_n <- c(st, 1)
     }
-    if (valueName_short[n] == "oconnor-stemp_95CI_upper") {
-      vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) + c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+  } else {
+    # if there IS Deep soils data
+    #AES get values for last 180 days? 
+    # get the date for 180 previous from today
+    if (tdim[n] == 180) {
+      vals <- AllVarData[1:183,valueName[n]] 
+    } else if (tdim[n] == 549){
+      vals <- as.vector(AllVarData[ ,valueName[n]]) 
+    } else if (tdim[n] == 352) {
+      vals <- as.vector(AllVarData[(nrow(AllVarData)-351):nrow(AllVarData),valueName[n]])
+    } else if (tdim[n] == 31) { # for Oconnor variables
+      if (valueName_short[n] == "oconnor-swp_mean") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean)
+      }
+      if (valueName_short[n] == "oconnor-swp_95CI_lower") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) - c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
+      }
+      if (valueName_short[n] == "oconnor-swp_95CI_upper") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$SWP_mean) + c(Oconnor_Stats[TP == "Forecast",]$SWP_CI95)
+      }
+      if (valueName_short[n] == "oconnor-stemp_mean") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean)
+      }
+      if (valueName_short[n] == "oconnor-stemp_95CI_lower") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) - c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+      }
+      if (valueName_short[n] == "oconnor-stemp_95CI_upper") {
+        vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) + c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
+      }
+    } 
+    if (valueName_short[n] == "shriver_historical") vals <- c(Shriver_Stats[TP == 'Historical', 'Prob'])[["Prob"]]
+    if (valueName_short[n] == "shriver_prediction") {
+      vals <- c(Shriver_Stats[TP != 'Historical', 'Prob'])$Prob
+      # redefine tdim to have the number of time steps for this run 
+      # the 'count' that will be fed into nc_put, which is a "vector of integers 
+      # indicating the count of values to write along each dimension, order is x-y-z-t)
+      co <- c(1, 1, 30, tdim[n]) 
+      # ammend the 'st' vecotr, which tells the ncvar_put function where to start writing the data 
+      st_n <- c(st, 1)
     }
-  } 
-  if (valueName_short[n] == "shriver_historical") vals <- c(Shriver_Stats[TP == 'Historical', 'Prob'])[["Prob"]]
-  if (valueName_short[n] == "shriver_prediction") {
-    vals <- c(Shriver_Stats[TP != 'Historical', 'Prob'])$Prob
-    # redefine tdim to have the number of time steps for this run 
-    # the 'count' that will be fed into nc_put, which is a "vector of integers 
-    # indicating the count of values to write along each dimension, order is x-y-z-t)
-    co <- c(1, 1, 30, tdim[n]) 
-    # ammend the 'st' vecotr, which tells the ncvar_put function where to start writing the data 
-    st_n <- c(st, 1)
+    
+    if (valueName_short[n] == "GISSM_historical") vals <- as.numeric(c(NA, Hist_GISSM$SeedlingSurvival_1stSeason))
+    if (valueName_short[n] == "GISSM_prediction") {
+      vals <-  Future_GISSM$Prob
+      # redefine tdim to have the number of time steps for this run 
+      # the 'count' that will be fed into nc_put, which is a "vector of integers 
+      # indicating the count of values to write along each dimension, order is x-y-z-t)
+      co <- c(1, 1, 30, tdim[n]) 
+      # ammend the 'st' vector, which tells the ncvar_put function where to start writing the data 
+      st_n <- c(st, 1)
     }
-  
-  if (valueName_short[n] == "GISSM_historical") vals <- as.numeric(c(NA, Hist_GISSM$SeedlingSurvival_1stSeason))
-  if (valueName_short[n] == "GISSM_prediction") {
-    vals <-  Future_GISSM$Prob
-    # redefine tdim to have the number of time steps for this run 
-    # the 'count' that will be fed into nc_put, which is a "vector of integers 
-    # indicating the count of values to write along each dimension, order is x-y-z-t)
-    co <- c(1, 1, 30, tdim[n]) 
-    # ammend the 'st' vecotr, which tells the ncvar_put function where to start writing the data 
-    st_n <- c(st, 1)
-  }
-  ## add something to deal w/ the fact that there may not be a "deep" soil layer
-  #value if the soil isn't deep enough in a given location write!
-  if (n %in% grep("Deep", valueName)) {
-    vals <- rep_len(-9999, length.out = co[3])
   }
+  
+  
+  
+
   RNetCDF::var.put.nc(ncfile = get(netCDFnames[n]),
                       variable = varName[n], 
                       data = vals,
diff --git a/projects/07_TestOutputForFRESC/GenerateOutputForFRESC.R b/projects/07_TestOutputForFRESC/GenerateOutputForFRESC.R
index 1e0d4ae..11c154d 100644
--- a/projects/07_TestOutputForFRESC/GenerateOutputForFRESC.R
+++ b/projects/07_TestOutputForFRESC/GenerateOutputForFRESC.R
@@ -324,12 +324,13 @@ for (j in 1:alljid){#1:alljid) { # TO DO: use "while" not "for"
   PPTAnoms <- PPTAnoms[1:Nleads,]
   
   # function in "weatherFunctions.R"
-  AnomalyData1 <- runFutureSWwithAnomalies(sw_in0 = sw_in, wdata, SoilsDF, 
+AnomalyData1 <- runFutureSWwithAnomalies(sw_in0 = sw_in, wdata, SoilsDF, 
                                            TempAnoms, PPTAnoms,
                                            Nleads, n = nRuns,
                                            currDOY, currMonth, currYear, currDate)
   
   
+  
   ################ -------------------------------------------------------------
   # FORMAT OUTPUTS  --- Get 18 month median, 10, and 90 for all !!! ---
   ################ -------------------------------------------------------------