Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2591-2023
https://doi.org/10.5194/hess-27-2591-2023
Technical note
 | 
18 Jul 2023
Technical note |  | 18 Jul 2023

Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems

Uwe Ehret and Pankaj Dey

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Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. 
Azmi, E., Ehret, U., Weijs, S. V., Ruddell, B. L., and Perdigão, R. A. P.: Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance, Hydrol. Earth Syst. Sci., 25, 1103–1115, https://doi.org/10.5194/hess-25-1103-2021, 2021. 
Bossel, H.: Dynamics of forest dieback: Systems analysis and simulation, Ecol. Model., 34, 259–288, https://doi.org/10.1016/0304-3800(86)90008-6, 1986. 
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Short summary
We propose the c-u-curve method to characterize dynamical (time-variable) systems of all kinds. U is for uncertainty and expresses how well a system can be predicted in a given period of time. C is for complexity and expresses how predictability differs between different periods, i.e. how well predictability itself can be predicted. The method helps to better classify and compare dynamical systems across a wide range of disciplines, thus facilitating scientific collaboration.
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