Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-1103-2021
https://doi.org/10.5194/hess-25-1103-2021
Technical note
 | 
03 Mar 2021
Technical note |  | 03 Mar 2021

Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance

Elnaz Azmi, Uwe Ehret, Steven V. Weijs, Benjamin L. Ruddell, and Rui A. P. Perdigão

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Cited articles

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. 
Arkesteijn, L. and Pande, S.: On hydrological model complexity, its geometrical interpretations and prediction uncertainty, Water Resour. Res., 49, 7048–7063, https://doi.org/10.1002/wrcr.20529, 2013. 
Atkinson, S. E., Woods, R. A., and Sivapalan, M.: Climate and landscape controls on water balance model complexity over changing timescales, Water Resour. Res., 38, 50-51–50-17, https://doi.org/10.1029/2002wr001487, 2002. 
Azmi, E.: KIT-HYD/model-evaluation: Release 1 (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.4485876, 2021. 
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Computer models should be as simple as possible but not simpler. Simplicity refers to the length of the model and the effort it takes the model to generate its output. Here we present a practical technique for measuring the latter by the number of memory visits during model execution by Strace, a troubleshooting and monitoring program. The advantage of this approach is that it can be applied to any computer-based model, which facilitates model intercomparison.
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