twoStation_DOPredict: Predict a time series of dissolved oxygen concentration
In mtalluto/NSmetabolism: N-Station Stream Metabolism Modelling

Predict a time series of dissolved oxygen concentration

twoStation_DOPredict(
  ws,
  initial,
  times,
  params,
  data,
  dt = 1,
  method = c("euler", "lsoda")
)

`ws`	A `WatershedTools::Watershed()` object, describing ONLY the two sites
`initial`	initial dissolved oxygen concentration in each station
`times`	The times at which to solve the system
`params`	a named vector of parameters; see details
`data`	a list of data for the ode; see details
`dt`	Time step for integration; only used if `method == 'euler'`
`method`	The integration method to use; default is euler
`lateral`	Vector of length two giving the DO concentration of lateral input (useful) if discharge varies significantly between the sites, defaults to 0

Light and temperature time series will be approximated using linear interpolation at the desired time steps

params must be a vector. The indices of the vector must map to model parameters as follows; (where parameters with length two are fit per site):

1:2 – P1 $(W min g^-1 O_2)$ – inverse of the slope of a photosynthesis–irradiance curve at low light intensity
3:4 – P2 $(m^2 min g^-1 O_2)$ – inverse maximum photosynthesis rate; can be zero to assume GPP is linear with light intensity instead of saturating
5:6 – k600 – coefficient of gas exchange for a gas with a Schmidt number of 600
7:8 – ER24_20 – daily ecosystem respiration rate, standardized at 20 degrees C

data is a list of length 2 (one per site); each item is a list of (constant) data items, including the following:

PAR 2-column data frame; first column is light, second is time of observation
temp 2-column data frame; first column is temperature, second is time of observation
P pressure, in atmospheres
z Depth, in meters