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BurnArea_GAM_Obs_Forecasting_Ziter_validation_SWEImod_PCA_SummerVars.R
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#load in libraries
library(splines)
library(foreach)
library(nlme)
library(mgcv)
library(Metrics)
library(MASS)
library(sm)
library(mgcv.helper)
library(tibble)
library(dplyr)
library(fmsb)
library(randomForest)
library(KRLS)
#remove variables from prior run
rm(list = ls())
##=========================pick best model based on BurnArea_GAM_v0*.R
#read in data
#define model concurvity threshold:
concurvity_threshold = 0.4
#0-1100m:
Climate_Fire_Data = read.table("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Annual_Fire_Climate_Obs_forecast_Ziter1.csv",sep=",")
#define variables:
WYs = Climate_Fire_Data[1:37,1]
BAs = Climate_Fire_Data[1:37,2]
BAs_Z1 = (BAs*0.000247105) /(10^6);
SWEIs_Z1 = Climate_Fire_Data[1:37,3]
WinPRCP_Z1 = Climate_Fire_Data[1:37,4]
WinTMP_Z1 = Climate_Fire_Data[1:37,5]
WinterPDSI_Z1 = Climate_Fire_Data[1:37,6]
Spring_PRCP_Z1 = Climate_Fire_Data[1:37,7]
Spring_TMP_Z1 = Climate_Fire_Data[1:37,8]
Spring_PDSI_Z1 = Climate_Fire_Data[1:37,9]
Spring_SWEI_Z1 = Climate_Fire_Data[1:37,10]
Spring_VPD_Z1 = Climate_Fire_Data[1:37,11]
Winter_VPD_Z1 = Climate_Fire_Data[1:37,12]
MODIS_BA_Z1 = Climate_Fire_Data[1:37,13]
Spring_ET_Z1 = Climate_Fire_Data[1:37,14]
Spring_PET_Z1 = Climate_Fire_Data[1:37,15]
Spring_PETminusET_Z1 = Climate_Fire_Data[1:37,16]
Winter_ET_Z1 = Climate_Fire_Data[1:37,17]
Winter_PET_Z1 = Climate_Fire_Data[1:37,18]
Winter_PETminusET_Z1 = Climate_Fire_Data[1:37,19]
Summer_VPD_Z1 = Climate_Fire_Data[1:37,20]
Summer_PDSI_Z1 = Climate_Fire_Data[1:37,21]
Summer_PRCP_Z1 = Climate_Fire_Data[1:37,22]
Summer_Temp_Z1 = Climate_Fire_Data[1:37,23]
Summer_ET_Z1 = Climate_Fire_Data[1:37,24]
Summer_PET_Z1 = Climate_Fire_Data[1:37,25]
Summer_PETminusET_Z1 = Climate_Fire_Data[1:37,26]
Summer_SM_Z1 = Climate_Fire_Data[1:37,27]
Winter_DroughtArea_Z1 = Climate_Fire_Data[1:37,31]
Spring_DroughtArea_Z1 = Climate_Fire_Data[1:37,32]
Summer_DroughtArea_Z1 = Climate_Fire_Data[1:37,30]
Z1_ID = rep(1,37)
#relate MODIS and MTBS BA linearly to allow MODIS to gap fill MTBS 2020 BA:
BA_MTBS <- BAs_Z1[18:36]
BA_MODIS <- MODIS_BA_Z1[18:36]
BA_DF = data.frame(BA_MTBS=BA_MTBS,BA_MODIS=BA_MODIS)
BA_lm <- lm(data=BA_DF,BA_MTBS~BA_MODIS)
data_2020 = data.frame(BA_MODIS = MODIS_BA_Z1[37])
BA_2020_MTBS=predict(BA_lm,newdata=data_2020)
BAs_Z1[37] = BA_2020_MTBS
if (BAs_Z1[37] < 0){
BAs_Z1[37] = 0
}
#1100-2200:
Climate_Fire_Data = read.table("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Annual_Fire_Climate_Obs_forecast_Ziter2.csv",sep=",")
#define variables:
#define variables:
WYs = Climate_Fire_Data[1:37,1]
BAs = Climate_Fire_Data[1:37,2]
BAs_Z2 = (BAs*0.000247105) /(10^6);
SWEIs_Z2 = Climate_Fire_Data[1:37,3]
WinPRCP_Z2 = Climate_Fire_Data[1:37,4]
WinTMP_Z2 = Climate_Fire_Data[1:37,5]
WinterPDSI_Z2 = Climate_Fire_Data[1:37,6]
Spring_PRCP_Z2 = Climate_Fire_Data[1:37,7]
Spring_TMP_Z2 = Climate_Fire_Data[1:37,8]
Spring_PDSI_Z2 = Climate_Fire_Data[1:37,9]
Spring_SWEI_Z2 = Climate_Fire_Data[1:37,10]
Spring_VPD_Z2 = Climate_Fire_Data[1:37,11]
Winter_VPD_Z2 = Climate_Fire_Data[1:37,12]
MODIS_BA_Z2 = Climate_Fire_Data[1:37,13]
Spring_ET_Z2 = Climate_Fire_Data[1:37,14]
Spring_PET_Z2 = Climate_Fire_Data[1:37,15]
Spring_PETminusET_Z2 = Climate_Fire_Data[1:37,16]
Winter_ET_Z2 = Climate_Fire_Data[1:37,17]
Winter_PET_Z2 = Climate_Fire_Data[1:37,18]
Winter_PETminusET_Z2 = Climate_Fire_Data[1:37,19]
Summer_VPD_Z2 = Climate_Fire_Data[1:37,20]
Summer_PDSI_Z2 = Climate_Fire_Data[1:37,21]
Summer_PRCP_Z2 = Climate_Fire_Data[1:37,22]
Summer_Temp_Z2 = Climate_Fire_Data[1:37,23]
Summer_ET_Z2 = Climate_Fire_Data[1:37,24]
Summer_PET_Z2 = Climate_Fire_Data[1:37,25]
Summer_PETminusET_Z2 = Climate_Fire_Data[1:37,26]
Summer_SM_Z2 = Climate_Fire_Data[1:37,27]
Winter_DroughtArea_Z2 = Climate_Fire_Data[1:37,31]
Spring_DroughtArea_Z2 = Climate_Fire_Data[1:37,32]
Summer_DroughtArea_Z2 = Climate_Fire_Data[1:37,30]
Z2_ID = rep(2,37)
#relate MODIS and MTBS BA linearly to allow MODIS to gap fill MTBS 2020 BA:
BA_MTBS <- BAs_Z2[18:36]
BA_MODIS <- MODIS_BA_Z2[18:36]
BA_DF = data.frame(BA_MTBS=BA_MTBS,BA_MODIS=BA_MODIS)
BA_lm <- lm(data=BA_DF,BA_MTBS~BA_MODIS)
data_2020 = data.frame(BA_MODIS = MODIS_BA_Z2[37])
BA_2020_MTBS=predict(BA_lm,newdata=data_2020)
BAs_Z2[37] = BA_2020_MTBS
if (BAs_Z2[37] < 0){
BAs_Z2[37] = 0
}
#2200-3300:
Climate_Fire_Data = read.table("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Annual_Fire_Climate_Obs_forecast_Ziter3.csv",sep=",")
#define variables:
#define variables:
WYs = Climate_Fire_Data[1:37,1]
BAs = Climate_Fire_Data[1:37,2]
BAs_Z3 = (BAs*0.000247105) /(10^6);
SWEIs_Z3 = Climate_Fire_Data[1:37,3]
WinPRCP_Z3 = Climate_Fire_Data[1:37,4]
WinTMP_Z3 = Climate_Fire_Data[1:37,5]
WinterPDSI_Z3 = Climate_Fire_Data[1:37,6]
Spring_PRCP_Z3 = Climate_Fire_Data[1:37,7]
Spring_TMP_Z3 = Climate_Fire_Data[1:37,8]
Spring_PDSI_Z3 = Climate_Fire_Data[1:37,9]
Spring_SWEI_Z3 = Climate_Fire_Data[1:37,10]
Spring_VPD_Z3 = Climate_Fire_Data[1:37,11]
Winter_VPD_Z3 = Climate_Fire_Data[1:37,12]
MODIS_BA_Z3 = Climate_Fire_Data[1:37,13]
Spring_ET_Z3 = Climate_Fire_Data[1:37,14]
Spring_PET_Z3 = Climate_Fire_Data[1:37,15]
Spring_PETminusET_Z3 = Climate_Fire_Data[1:37,16]
Winter_ET_Z3 = Climate_Fire_Data[1:37,17]
Winter_PET_Z3 = Climate_Fire_Data[1:37,18]
Winter_PETminusET_Z3 = Climate_Fire_Data[1:37,19]
Summer_VPD_Z3 = Climate_Fire_Data[1:37,20]
Summer_PDSI_Z3 = Climate_Fire_Data[1:37,21]
Summer_PRCP_Z3 = Climate_Fire_Data[1:37,22]
Summer_Temp_Z3 = Climate_Fire_Data[1:37,23]
Summer_ET_Z3 = Climate_Fire_Data[1:37,24]
Summer_PET_Z3 = Climate_Fire_Data[1:37,25]
Summer_PETminusET_Z3 = Climate_Fire_Data[1:37,26]
Summer_SM_Z3 = Climate_Fire_Data[1:37,27]
Winter_DroughtArea_Z3 = Climate_Fire_Data[1:37,31]
Spring_DroughtArea_Z3 = Climate_Fire_Data[1:37,32]
Summer_DroughtArea_Z3 = Climate_Fire_Data[1:37,30]
Z3_ID = rep(3,37)
#relate MODIS and MTBS BA linearly to allow MODIS to gap fill MTBS 2020 BA:
BA_MTBS <- BAs_Z3[18:36]
BA_MODIS <- MODIS_BA_Z3[18:36]
BA_DF = data.frame(BA_MTBS=BA_MTBS,BA_MODIS=BA_MODIS)
BA_lm <- lm(data=BA_DF,BA_MTBS~BA_MODIS)
data_2020 = data.frame(BA_MODIS = MODIS_BA_Z3[37])
BA_2020_MTBS=predict(BA_lm,newdata=data_2020)
BAs_Z3[37] = BA_2020_MTBS
if (BAs_Z3[37] < 0){
BAs_Z3[37] = 0
}
idx<-c(1:37)
WYs = rep(WYs[idx],3)
BAs = cbind(t(BAs_Z1[idx]),t(BAs_Z2[idx]),t(BAs_Z3[idx]))
SWEIs = cbind(t(SWEIs_Z1[idx]),t(SWEIs_Z2[idx]),t(SWEIs_Z3[idx]))
WinPRCP = cbind(t(WinPRCP_Z1[idx]),t(WinPRCP_Z2[idx]),t(WinPRCP_Z3[idx]))
WinTMP = cbind(t(WinTMP_Z1[idx]),t(WinTMP_Z2[idx]),t(WinTMP_Z3[idx]))
WinterPDSI = cbind(t(WinterPDSI_Z1[idx]),t(WinterPDSI_Z2[idx]),t(WinterPDSI_Z3[idx]))
Spring_PRCP = cbind(t(Spring_PRCP_Z1[idx]),t(Spring_PRCP_Z2[idx]),t(Spring_PRCP_Z3[idx]))
Spring_TMP = cbind(t(Spring_TMP_Z1[idx]),t(Spring_TMP_Z2[idx]),t(Spring_TMP_Z3[idx]))
Spring_PDSI = cbind(t(Spring_PDSI_Z1[idx]),t(Spring_PDSI_Z2[idx]),t(Spring_PDSI_Z3[idx]))
Spring_SWEI = cbind(t(Spring_SWEI_Z1[idx]),t(Spring_SWEI_Z2[idx]),t(Spring_SWEI_Z3[idx]))
Spring_VPD = cbind(t(Spring_VPD_Z1[idx]),t(Spring_VPD_Z2[idx]),t(Spring_VPD_Z3[idx]))
Winter_VPD = cbind(t(Winter_VPD_Z1[idx]),t(Winter_VPD_Z2[idx]),t(Winter_VPD_Z3[idx]))
Spring_ET = cbind(t(Spring_ET_Z1[idx]),t(Spring_ET_Z2[idx]),t(Spring_ET_Z3[idx]))
Spring_PET = cbind(t(Spring_PET_Z1[idx]),t(Spring_PET_Z2[idx]),t(Spring_PET_Z3[idx]))
Spring_PETminusET = cbind(t(Spring_PETminusET_Z1[idx]),t(Spring_PETminusET_Z2[idx]),t(Spring_PETminusET_Z3[idx]))
Winter_ET = cbind(t(Winter_ET_Z1[idx]),t(Winter_ET_Z2[idx]),t(Winter_ET_Z3[idx]))
Winter_PET = cbind(t(Winter_PET_Z1[idx]),t(Winter_PET_Z2[idx]),t(Winter_PET_Z3[idx]))
Winter_PETminusET = cbind(t(Winter_PETminusET_Z1[idx]),t(Winter_PETminusET_Z2[idx]),t(Winter_PETminusET_Z3[idx]))
Winter_DroughtArea = cbind(t(Winter_DroughtArea_Z1[idx]),t(Winter_DroughtArea_Z2[idx]),t(Winter_DroughtArea_Z3[idx]))
Spring_DroughtArea = cbind(t(Spring_DroughtArea_Z1[idx]),t(Spring_DroughtArea_Z2[idx]),t(Spring_DroughtArea_Z3[idx]))
Winter_Spring_Temp = (Spring_TMP+WinTMP)/2
Winter_Spring_Precip = (Spring_PRCP+WinPRCP)/2
Summer_VPD = cbind(t(Summer_VPD_Z1[idx]),t(Summer_VPD_Z2[idx]),t(Summer_VPD_Z3[idx]))
Summer_PDSI = cbind(t(Summer_PDSI_Z1[idx]),t(Summer_PDSI_Z2[idx]),t(Summer_PDSI_Z3[idx]))
Summer_PRCP = cbind(t(Summer_PRCP_Z1[idx]),t(Summer_PRCP_Z2[idx]),t(Summer_PRCP_Z3[idx]))
Summer_Temp = cbind(t(Summer_Temp_Z1[idx]),t(Summer_Temp_Z2[idx]),t(Summer_Temp_Z3[idx]))
Summer_ET = cbind(t(Summer_ET_Z1[idx]),t(Summer_ET_Z2[idx]),t(Summer_ET_Z3[idx]))
Summer_PET = cbind(t(Summer_PET_Z1[idx]),t(Summer_PET_Z2[idx]),t(Summer_PET_Z3[idx]))
Summer_PETminusET = cbind(t(Summer_PETminusET_Z1[idx]),t(Summer_PETminusET_Z2[idx]),t(Summer_PETminusET_Z3[idx]))
Summer_SM = cbind(t(Summer_SM_Z1[idx]),t(Summer_SM_Z2[idx]),t(Summer_SM_Z3[idx]))
Winter_DroughtArea = cbind(t(Winter_DroughtArea_Z1[idx]),t(Winter_DroughtArea_Z2[idx]),t(Winter_DroughtArea_Z3[idx]))
Spring_DroughtArea = cbind(t(Spring_DroughtArea_Z1[idx]),t(Spring_DroughtArea_Z2[idx]),t(Spring_DroughtArea_Z3[idx]))
Summer_DroughtArea = cbind(t(Summer_DroughtArea_Z1[idx]),t(Summer_DroughtArea_Z2[idx]),t(Summer_DroughtArea_Z3[idx]))
Winter_Spring_Temp = (Spring_TMP+WinTMP)/2
Winter_Spring_Precip = (Spring_PRCP+WinPRCP)/2
Winter_Spring_VPD = (Winter_VPD+Spring_VPD)/2
Winter_Spring_PET = (Winter_PET+Spring_PET)/2
Winter_Spring_ET = (Winter_ET+Spring_ET)/2
Zs = cbind(t(Z1_ID[idx]),t(Z2_ID[idx]),t(Z3_ID[idx]))
Climate_Fire_DF = data.frame(Spring_SWEI = t(Spring_SWEI), WinPRCP = t(WinPRCP), WinTMP = t(WinTMP),Spring_PRCP = t(Spring_PRCP),Spring_TMP=t(Spring_TMP),Spring_VPD=t(Spring_VPD),Winter_VPD=t(Winter_VPD),Spring_ET=t(Spring_ET),Spring_PET=t(Spring_PET),Winter_ET=t(Winter_ET),Winter_PET=t(Winter_PET),Spring_DroughtArea=t(Spring_DroughtArea),Winter_Spring_Temp=t(Winter_Spring_Temp),Winter_Spring_Precip=t(Winter_Spring_Precip),Winter_Spring_VPD=t(Winter_Spring_VPD),Winter_Spring_PET=t(Winter_Spring_PET),Winter_Spring_ET=t(Winter_Spring_ET),Summer_VPD=t(Summer_VPD),Summer_PRCP=t(Summer_PRCP),Summer_Temp=t(Summer_Temp),Summer_ET=t(Summer_ET),Summer_PET=t(Summer_PET),Summer_SM=t(Summer_SM))
#loop through all combinations of covariates:
Y = as.vector(BAs)
nvars_antecedent <- 17
nvars_summer <- 6
nvars_total <- nvars_antecedent+nvars_summer
best_antedcedent_mods_idx = read.csv("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Best_Obs_Forecast_outputs/BAs_1984_2020_Obs_Forecast_SE_Zbins_SWEImod_PCA_bestmods_5vars.csv",sep=",",header=TRUE)
best_antedcedent_mods_idx=as.numeric(best_antedcedent_mods_idx)
best_summer_mods_idx = read.csv("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Best_Obs_Forecast_outputs/BAs_1984_2020_Obs_Forecast_SE_Zbins_SWEImod_PCA_bestmods_SummerVars.csv",sep=",",header=TRUE)
best_summer_mods_idx = t(best_summer_mods_idx)
best_summer_mods_idx=as.numeric(best_summer_mods_idx)
nmods = length(best_antedcedent_mods_idx)
#define model combination IDs
n_antecedent_predictors <- 5
ncombos = factorial(nvars_antecedent)/(factorial(n_antecedent_predictors)*factorial(nvars_antecedent-n_antecedent_predictors))
x=1:nvars_antecedent
combo_IDs_antecedent<-combn(x,n_antecedent_predictors)
SWEI_iter=0 #count which of the best mods include SWEI as predictor
good_mod_iter=0
Good_mods=c()
store_R = c()
n=length(BAs)
index = 1:n
for (m in 1:nmods){
i <- best_antedcedent_mods_idx[m]
Y = as.vector(BAs)
#define antecedent predictors to consider:
col1 <- combo_IDs_antecedent[1,i]
col2 <- combo_IDs_antecedent[2,i]
col3 <- combo_IDs_antecedent[3,i]
col4 <- combo_IDs_antecedent[4,i]
col5 <- combo_IDs_antecedent[5,i]
predictor1 <-Climate_Fire_DF[,col1]
predictor2 <-Climate_Fire_DF[,col2]
predictor3 <-Climate_Fire_DF[,col3]
predictor4 <-Climate_Fire_DF[,col4]
predictor5 <-Climate_Fire_DF[,col5]
#define summer predictors to consider:
predictor6 <-Climate_Fire_DF[,18]
predictor7 <-Climate_Fire_DF[,19]
predictor8 <-Climate_Fire_DF[,20]
predictor9 <-Climate_Fire_DF[,21]
predictor10 <-Climate_Fire_DF[,22]
predictor11 <-Climate_Fire_DF[,23]
Predictors_DF = data.frame(predictor1=predictor1,predictor2=predictor2,predictor3=predictor3,predictor4=predictor4,predictor5=predictor5,predictor6=predictor6,predictor7=predictor7,predictor8=predictor8,predictor9=predictor9,predictor10=predictor10,predictor11=predictor11)
#for combos of 5 variables:
n_predictors <- 5
nvars<- 11
ncombos = factorial(nvars)/(factorial(n_predictors)*factorial(nvars-n_predictors))
x=1:nvars
combo_IDs<-combn(x,n_predictors)
#define summer model ID:
j = best_summer_mods_idx[m]
#define best summer+antecedent model:
col1 <- combo_IDs[1,j]
col2 <- combo_IDs[2,j]
col3 <- combo_IDs[3,j]
col4 <- combo_IDs[4,j]
col5 <- combo_IDs[5,j]
predictor1 <-Predictors_DF[,col1]
predictor2 <-Predictors_DF[,col2]
predictor3 <-Predictors_DF[,col3]
predictor4 <-Predictors_DF[,col4]
predictor5 <-Predictors_DF[,col5]
#PCA combo model:
Covariates_DF = data.frame(predictor1=predictor1,predictor2=predictor2,predictor3=predictor3,predictor4=predictor4,predictor5=predictor5)
df.pca <- prcomp(Covariates_DF, center = TRUE,scale. = TRUE)
pcs = df.pca$x
pc1 = pcs[,1]
pc2 = pcs[,2]
pc3 = pcs[,3]
pc4 = pcs[,4]
pc5 = pcs[,5]
current_DF_pcs <- data.frame(Y=Y,predictor1=pc1,predictor2=pc2,predictor3=pc3,predictor4=pc4,predictor5=pc5,predictor6=as.vector(Zs))
mod <- gam(data=current_DF_pcs,Y~s(predictor1)+s(predictor2)+s(predictor3)+s(predictor4)+s(predictor5)+predictor6,family="gaussian")
#GAM drop 1 year
yest_gam=1
standar_error=1
Y_iter=0
for(Y in 1984:2020){
Y_iter = Y_iter+1
#drop 1 year
IDX_drop <-which(WYs == Y)
index1=index[-IDX_drop]
dropped_DF=Climate_Fire_DF[index1,]
#PCA combo model:
Zs_dropped=as.vector(Zs)
Zs_dropped = Zs_dropped[index1]
Y_dropped = as.vector(BAs)
Y_dropped = Y_dropped[index1]
dropped_DF_pcs <- data.frame(Y=Y_dropped,predictor1=pc1[index1],predictor2=pc2[index1],predictor3=pc3[index1],predictor4=pc4[index1],predictor5=pc5[index1],predictor6=as.vector(Zs_dropped))
S=dim(dropped_DF_pcs)
if(S[1]!=111-3){
S[1]
stop('wrong dim')
}
#model
fit_drop_gam = gam(mod$formula,data=dropped_DF_pcs,family="gaussian")
#now estimate at the point that was dropped
newdata = data.frame(predictor1=pc1[IDX_drop],predictor2=pc2[IDX_drop],predictor3=pc3[IDX_drop],predictor4=pc4[IDX_drop],predictor5=pc5[IDX_drop],predictor6=as.vector(Zs[IDX_drop]))
yest_gam[IDX_drop]=predict(fit_drop_gam,newdata=newdata)
#get the confidence interval:
yhat <- predict(fit_drop_gam,newdata=newdata,se.fit = TRUE)
standar_error[Y_iter] <- sum(yhat$se.fit)
}
IDX_neg <- which(yest_gam<0)
yest_gam[IDX_neg] <- min(yest_gam[-IDX_neg])
#sum BAs across all Z bins:
iter=0
store_BA_obs=0
store_BA_est=0
for (Y in 1984:2020){
iter <- iter+1
current_IDX <-which(WYs == Y)
store_BA_est[iter] = sum(yest_gam[current_IDX])
store_BA_obs[iter] = sum(BAs[current_IDX])
}
#record drop 1-year correlations:
Drop1_Ann_Cor <- cor(store_BA_est,store_BA_obs)
store_R = c(store_R,Drop1_Ann_Cor)
#export data for analysis in MATLAB from all models with R2>=0.0:
if (Drop1_Ann_Cor >= sqrt(0.0)) {
good_mod_iter = good_mod_iter+1
BAs_output<-data.frame(BA_obs=t(BAs),BA_est_drop1=yest_gam,BA_fit=predict(mod))
outfilename = sprintf("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Best_Obs_Forecast_outputs/BAs_1984_2020_Obs_Forecast_GAM_Zbins_mod%d_SWEImod_PCA_SummerVars.csv",i)
write.table(BAs_output,file=outfilename,sep=",",row.names = FALSE)
#record ID of good models to export and use in matlab analysis
Good_mods[good_mod_iter] = i
#record standard error:
outfilename = sprintf("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Best_Obs_Forecast_outputs/BAs_1984_2020_Obs_Forecast_SE_Zbins_mod%d_SWEImod_PCA_SummerVars.csv",i)
write.table(standar_error,file=outfilename,sep=",",row.names = FALSE)
}
}
outfilename = sprintf("/Volumes/Pruina_External_Elements/DroughtFireSnow/Data/AnlysisData/Best_Obs_Forecast_outputs/BAs_1984_2020_Obs_Good_Mods_SWEImod_PCA_SummerVars.csv")
write.table(Good_mods,file=outfilename,sep=",",row.names = FALSE)
store_R