Hi 👋

To help analyze your case, could you provide 2D subplots of inputs vs output? Pairplot from eg. seaborn. That would help visually understand what could be happening.

Of course, thanks @tupui

We are working with four hydrodynamic models: FLake, GLM, GOTM and Simstrat for 70 lakes. Here is just pairplots for Lake Stechlin for each model. X-axis holds model parameters and y-axis have performance metrics. Pairplot title is model and lake name.

Was this what you had in mind?

And here is for a "warm lake", Lake Kivu, where the interaction for the model FLake is high.

Hi @TobiasKAndersen

Just wondering, how is mean error being calculated here for your whisker/barplot? Through a package or your own code?

As far as I can tell the barplots don't really align with what the pairplots are showing.

Minor general comment on the figures: I find minimising the white edge/stroke colour of each point and making the fill colour more transparent helps with legibility.

Hi @ConnectedSystems

Thanks for your feedback!

mean error was calculated in R (by colleagues) with the function from the R package LakeEnsemblR as
bias <- mean((P - O), na.rm = TRUE)

So model predicted values (P) against observed values (all water temperature).

Interaction term (S_inter) was calculated in Python by sum of all S1 per model, lake and performance metric (var) (following Borgonovo et al, 2017):
sa_inter = sa_clean.groupby(by=['model', 'lake', 'var'], as_index=False)['S1'].sum()
sa_inter['S_inter'] = 1- sa_inter['S1']

We include 71 lakes in total in this study (classified into the different lake "groups" e.g. warm lakes which incl 7 lakes).

Thanks, here is the revised pairplots ;)

X-axis holds model parameters and y-axis have performance metrics

So to be sure, you want to compute indices on the performance metrics? i.e. answer the question, which of my model parameters contribute to the variance of e.g. bias?

If this is the case, looking at the last plot only, the metrics seem to give a somewhat similar information besides some sign change. For R on turb and wind notably there is some heteroscedasticity.

Getting Sobol' indices on the figures, I would expect to see high values for wind (except for R) and swr. And low values for the rest.

Does that help?