Positive Estimating of ER

Hello,

I have a problem when modeling the ecosystem respiration, that I get positive values for a part of my timeseries.

The oxygen concetration at this period of time is very high and even at night higher than the oxygen saturation. The oxygen saturation drops only at night. And when I get this right, of course when the saturation is lower than the concentration, the modelled value fo the ecosystem respiration is positive. This may be also effected because of the slow river system when it comes to low water levels.
At #367 was a similar problem but I couldn't solve my problem with the comments so far.

Is there a way how to model the "real" negative ER-value with streamMetabolizer? (Maybe at short time period at night, when the oxygen concetration is lower?)

And when I get positive ER-values, is the modeled GPP value also wrong?

I hope I could shre my problem understandable .

Best regards
Jakob

[GPP_ER_preds.pdf](https://github.com/USGS-R/streamMetabolizer/files/5386478/GPP_ER_preds.pdf)
[Oxygen.pdf](https://github.com/USGS-R/streamMetabolizer/files/5386481/Oxygen.pdf)
[DO_preds.pdf](https://github.com/USGS-R/streamMetabolizer/files/5386486/DO_preds.pdf)

Model Structure and Specifications:

`bayes_name <- mm_name(type = "bayes", pool_K600 = "binned", err_obs_iid = T, err_proc_acor = F, err_proc_iid = T,
err_proc_GPP = F, ode_method = "trapezoid", GPP_fun = "linlight",
ER_fun = "constant", deficit_src = "DO_mod")`

`bayes_specs <- specs(bayes_name, keep_mcmcs = T, keep_mcmc_data = F,split_dates = F,
GPP_daily_mu = 3, n_chains = 4 , n_cores = 6,
burnin_steps = 1000, saved_steps = 500, thin_steps = 1, verbose = T )`