Realism  (of models) –   It is mainly concerned with reproducing nature behavior (though some philosophical argument can be made, see, for instance, Dietrich et al., 2003). A realistic model reproduces the variety and the behavior complexity of  the (hydrological) system it is intended to describe. In evaluating this characteristic, one cannot limit the analysis to the model itself, but must also look at other aspects such as the calibration tools or if the model is integrated with data assimilations techniques. 

Reliability (of models) – A model is always relatively reliable. Therefore in the present context we define a model reliable if it is possible to give, at least theoretically, an error of the estimated quantities in any of the circumstances it can be used. According to the scope a simulation is done, the reliability is enough if the error (estimate) is acceptable for the scope.

Replicability  (of models behavior) – Replicability refers to the fact that multiple runs of the same model with the same inputs and the same setup must produce always the same results. A model behavior is replicable if its workflow is recorded or appropriately documented and the workflow deployed verbatim (not forgetting the observations made in Ceola et al., 2015). If the model is stochastic, however, the replicability concept is transferred to the statistics of the behavior.  A special case is that of models that depend on parameter calibration. Because parameter fitness is usually performed with stochastic searches, in this case, the replicability of the whole running actions is impossible. However, once parameters are frozen, simulations must be replicable.

Reproducibility (of models)-  The results presented in a science context must be reproducible by third parties autonomously by following the same type of procedure. This is what science is about.  As well, models behavior should be reproducible by other codes whose implementation follows the information contained in the documentation of the original software. As a matter of fact the precise reproducibility of models is often difficult cause to hidden implementation details or behavior of some internally used algorithms. 

Robustness (of models) –  It is a property of the informatics and numerics of the models. A robust models works for the largest foreseen set of use cases without issuing exceptions or being able to manage them. Its algorithms and design are accurately for this purpose.  Besides, specifically in the DARTHs context, we expect that the model can be run on different operating systems without taking care of the different platform details.

Reusability (of models) –  A model is maximally reusable if any of its parts can be reused effortlessly inside other models that share the appropriate characteristics. The MBC paradigm aims also to enhance this property. Considered together with the Robustness, reusability quality allows to simulate a great set of physical (hydrological) situations, even those for which the model was not initially conceived. 

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