"between model performance" is correct and usable in written English.
You can use it to discuss the differences between the performance of two different models. For example, "The comparison between model performance revealed that the new model was more efficient than the previous model."
As a result, a tradeoff was made between model performance and high spatial resolution output maps in actual simulations.
However, a compromise should be made between model performance, provision of spatial information details and computing time.
A correlation between model performance and both the number of datapoints and chemical diversity was established (see main text).
Keeping a balance between model performance and computational efficiency in the context of limited resources is a delicate problem in model development.
We investigate the relations between model performance and catchment characteristics and find that the climatic characteristic of a catchment is the most important factor impacting model performance.
Our task is to find the set of reservoir parameters that minimizes the difference between model performance and historical performance of the field.
We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model's performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model's performance in a real-world clinical setting where some individuals have missing predictors.
It is argued that procedures commonly used at present to compare the performance of rainfall-runoff models are unsatisfactory for several reasons, but principally because they provide no measure of the uncertainty (as measured by an estimate of residual variation between experimental units) in differences between measures of model performance.
The trade-off of model performance between runoff and NH4-N concentration was clear.
When models were locally calibrated there was a weak inverse correlation between catchment area and model performance.
The dramatic difference in model performance between the test and the validated training sets can be explained by structural dissimilarity of the sets.
