This thesis addresses my final project for the 2019/20 edition of the Master in High-Performance
Computing at SISSA and ICTP.
Earth-system and environmental models calibration is a complex, computationally intensive
task. At present, there is no general theory of model calibration, but instead a large collection
of methods, algorithms and case studies. As a result, calibration is often more an art than a
science: one must make several discretionary choices, guided more by his own experience
and intuition than by the scientific method.
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
[RBO06][EGDallAmicoR14], a complex, over parameterized hydrological model. I used the
derivative-free optimization algorithms implemented in the Facebook Nevergrad Python library [RT18], and run them on the Ulysses v2 HPC cluster, thanks to the Dask framework
[Tea16].
GEOtop has been used to simulate the time evolution of variables as soil water content and
evapotranspiration of mountain agricultural sites in South Tyrol with different elevations, land
cover (pasture, meadow, orchard), and soil types. In these simulations GEOtop solved the
energy and water budget equations on a one-dimensional domain, i.e. on a thin column of
soil and neglecting the lateral fluxes. Even in the simplified case of homogeneous soil, one
has tens of parameters. These parameters control the soil and vegetation properties, but only
a few of them are experimentally available, hence the need for calibration.
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 cores.
The outline of the thesis is the following:
1. Introduction and motivations. Where I introduce relevant information about the
GEOtop model. I also discuss the problem of GEOtop calibration, and the need for
High-Performance Computing.
2. Problem, methodology and implementation. Where I state the problem in mathematical
terms, but without mathematical rigour. Afterwards, I discuss the tools and implementation details of calibration.
3. Results and conclusions. Finally, I present the results and scaling of calibration, focusing
on the HPC content
