## 27 December, 2020 (Jung, bg, FLUXCOM carbon)

Gervasio Piñeiro, Susana Perelman, Juan P. Guerschman, José M. Paruelo, How to evaluate models: Observed vs. predicted or predicted vs. observed?, Ecological Modelling, Volume 216, Issues 3–4, 2008, Pages 316-322, ISSN 0304-3800, https://doi.org/10.1016/j.ecolmodel.2008.05.006.

## Prediction (x-axis) vs. Observation (y-axis)

Martin added a comment to my proposal. He said that I should put prediction on the x-axis, referring to a paper:

I have just look over the **conclusion** of the paper. It says that:

Model evaluation based on linear regressions should be done

placing the observed values in the. Model evaluation based ony-axis and the predicted values in thex-axis (OP)the opposite regression leads to incorrect estimates of both the slope and the. Underestimation of the slope and overestimation of they-intercepty-intercept increases asr2 values decrease.

We strongly recommend scientists to evaluate their models by regressing OP values and to

test the significance of slope = 1 and intercept = 0.

RMSE should not be reported for the OP regression, but the RMSD adds important information to model evaluation.

Honestly, **I don’t like the conclusion**. For me, **PO method is more intuitive** as the larger (smaller) slope means overestimation (underestimation) from model’s point of view. But… I will try to get used to it.

## Don’t report RMSE for OP regression!

On the other hand, the last sentence about RMSE is also interesting. I don’t know why for now, but I also don’t like to use RMSE as it is not intuitive and hard to interpret.