Earth-system and environmental models calibration is a complex, computationally intensive task. One of the challenges is the large number of parameters involved. For this reason, preliminary sensitivity analysis may be used to reduce this number and select the relevant parameters. Still, the computational load of sensitivity analysis and calibration is high.
In this work I used High-Performance Computing solutions to calibrate GEOtop, a complex, over parameterized hydrological model. I used the derivative-free optimization algorithms implemented in the Facebook Nevergrad Python library, and run them on the Ulysses v2 HPC cluster, thanks to the Dask framework.
The computational aspects of GEOtop calibration have been examined, and the important issue of robustness against model convergence failures has been addressed. Finally, the scaling of calibration time has been measured up to 1024 CPU cores.