Publication

Journal	Environ. Model. Softw.
Volume (Issue)	78(C)
Page(s)	54 - 67
Title of proceedings	Environ. Model. Softw.
DOI	10.1016/j.envsoft.2015.12.007

Gaussian process (GP) emulation is a data-driven method that substitutes a slow simulator with a stochastic approximation. It is then typically orders of magnitude faster than the simulator at the costs of introducing interpolation errors. Our approach, the mechanism-based GP emulator, uses knowledge of the simulator mechanisms in addition to the information gained from previous simulator runs, so called design data. In this study, we investigate how the degree of incorporating mechanisms into the design of the GP emulator influences emulation accuracy. Similarly to the previous results, we get a significant gain in accuracy already when using the simplest approximation of the mechanisms by a single linear reservoir. However, in this case, we again considerably improve emulation accuracy when using the next two approximations. This allows us to decreases the required number of design data to achieve a similar accuracy as a non-mechanistic emulator. We substitute a hydrological model with a faster mechanism-based GP emulator.We compare the gains in emulation accuracy by considering various mechanisms.Already the simplest mechanisms lead to an improvement of emulation accuracy.The emulator leads to a good accuracy already for very small design data sets.The suggested emulator can shorten, e.g., computation time for model calibration.