CART on TOC CART for TOC R 2 = 0.83 Random forests were used to model TOC and turbidity. Predictor variables included monthly average maximum temperature, minimum temperature, palmer drought severity index, and normalized difference vegetation index. Monthly sums of precipitation and the number of dry days in the month were also used for this analysis. All covariates for the current month of the water quality variables were used, as well as 1, 2, and 3 months prior. 500 decorrelation regression trees were built, where each tree uses a different bootstrap sample from the original data. At each node, a random subset of the variables are used to determine the splitting decision. Variable importance in random forests models is determined using the IncNodePurity, i.e. the total decrease in node impurity that results from splits over each individual variable averaged over all the trees. The IncNodePurities for each predictor of both the TOC and turbidity random forests indicated that the the three most important variables in the regression analyses were the same for both water quality parameters: NDVI 2 months prior, avg. max temp. 2 months prior, and avg. min temp 2 months prior. Random forest info: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

Random Forest on TOC Obs. vs Modeled from Random Forest R 2 = 0.78 Random forests were used to model TOC and turbidity. Predictor variables included monthly average maximum temperature, minimum temperature, palmer drought severity index, and normalized difference vegetation index. Monthly sums of precipitation and the number of dry days in the month were also used for this analysis. All covariates for the current month of the water quality variables were used, as well as 1, 2, and 3 months prior. 500 decorrelation regression trees were built, where each tree uses a different bootstrap sample from the original data. At each node, a random subset of the variables are used to determine the splitting decision. Variable importance in random forests models is determined using the IncNodePurity, i.e. the total decrease in node impurity that results from splits over each individual variable averaged over all the trees. The IncNodePurities for each predictor of both the TOC and turbidity random forests indicated that the the three most important variables in the regression analyses were the same for both water quality parameters: NDVI 2 months prior, avg. max temp. 2 months prior, and avg. min temp 2 months prior. Random forest info: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm