Thermocline Model

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Define the model.

In this case we will be using a logistic curve to model the rise and fall of the thermocline

thermoclineDist = function(t){
  maxThermDepth = 40
  if (t < (2190*2)) { #Winter + Spring
    n1 = .003
    n2 = -5
    x = t 
    dist = (maxThermDepth)/(1 + exp(-(n2 + n1*x)))   
  } else { #Summer+ fall
    n1 = .005
    n2 = -8
    x = t - (2190*2)
    dist = (-maxThermDepth)/(1 + exp(-(n2 + n1*x))) + maxThermDepth
  }
  return(dist)
}

Generate data

Now we initialize an hour list and run the thermocline function over it to get our data.

hours  = 0:(365*24) 
dist = NULL

for (t in hours){ dist = c(dist, thermoclineDist(t)) }

Plot and save

Now let’s check out the results and save to a csv file.

data = cbind(hours, dist) # Wrap the data. 

thermocline = as.data.frame(data)
ggplot(thermocline, aes(x = hours, y = dist)) + geom_line() + 
  labs(title = "Thermocline Depth", y = "depth")

write.csv(data, "/Users/Nick/mysisModeling/data/Depth_Thermocline_Hour.csv", row.names=FALSE)