# loading in collocated in situ nitrate concentration measurements and remote 
# sensing/reanalysis predictor variables to generate generalized additive
# models (GAMs) for the prediction of nitrate

# Clear all plots and workspace variables; set working directory
graphics.off(); rm(list = ls());
setwd("..."); # set directory here
library(mgcv); # load in mgcv package for GAMs

# ----------------------------- surface GAM ----------------------------------
# loading in data
xy_surf <- read.csv("RB2025_no3surf.csv");
# set parameters for GAM here
alpha1 <- 0.05; pf1 <- 1.3; kn1 <- 10; kn2 <- 5; kn3 <- 4;
data1 <- xy_surf; 
# make the model
model1 <- gam(no3_surf..uM. ~ s(sst_0_lead..deg.C., k = kn2) + s(tau_15_lead..Pa., k = kn3)
              + s(log_chl_5_lag..mg.m.3., k = kn1) + s(xoff..km., k = kn1) 
              + s(dx..km., k = kn1) + s(dy..km., k = kn1) + s(doy, k = kn2),
              family = Tweedie(p = pf1, link = "log"), data = data1)
# plot model (show partial dependence plots)
plot(model1)
# this model can be used to predict nitrate at the surface as a function of the 
# above predictor variables using the "predict.gam" function
# ----------------------------------------------------------------------------

# --------------------------------- MLD GAM ----------------------------------
# loading in data
xy_mld <- read.csv("RB2025_no3mld.csv");
# set parameters for GAM here
alpha1 <- 0.05; pf1 <- 1.3; kn1 <- 10; kn2 <- 5; kn3 <- 4;
data2 <- xy_mld; 
# make the model
model2 <- gam(no3_mld..uM. ~ s(sst_0_lead..deg.C., k = kn2) + s(tau_15_lead..Pa., k = kn3)
              + s(log_chl_5_lag..mg.m.3., k = kn1) + s(xoff..km., k = kn1) 
              + s(dx..km., k = kn1) + s(dy..km., k = kn1) + s(doy, k = kn2),
              family = Tweedie(p = pf1, link = "log"), data = data2)
# plot model (show partial dependence plots)
plot(model2)
# this model can be used to predict nitrate at the mixed layer depth as a 
# function of the above predictor variables using the "predict.gam" function
# ----------------------------------------------------------------------------