EGU22-569
https://doi.org/10.5194/egusphere-egu22-569
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

hydroPASS: a newly developed R package to go through the regional calibration of distributed catchment models

Matteo Pesce1, Alberto Viglione1, Jost von Hardenberg1, Larisa Tarasova2, Stefano Basso2, and Ralf Merz2
Matteo Pesce et al.
  • 1Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
  • 2Department of Catchment Hydrology, Helmholtz Centre for Environmental Research, Halle (Saale), Germany

Large scale hydrological modelling requires the estimation of model parameters across a variety of different environments. To deal with this issue, robust parameter estimation procedures, able to exploit observed patterns of climate and geomorphological characteristics, must be considered. This contribution presents hydroPASS, a newly developed R package available in GitHub, which automatically implements the PArameter Set Shuffling (PASS) method in around 100 catchments over North-Western Italy. This was developed and tested for SALTO (SAme Like The Others) model, but in principle is valid for every distributed or semi-distributed catchment model. In particular, the package contains the function to run SALTO model (Fortran code), the function for PASS application (R code) and functions for data pre-processing and post-processing (R codes). To ease the repeatability and reproducibility of experiments, examples are provided with full documentation. An example of how to use PASS for the regional calibration of other models (e.g., TUW model), is also provided. From the source package, installation packages have been built for Windows, Linux and Mac operating systems and can be freely downloaded from GitHub.

How to cite: Pesce, M., Viglione, A., von Hardenberg, J., Tarasova, L., Basso, S., and Merz, R.: hydroPASS: a newly developed R package to go through the regional calibration of distributed catchment models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-569, https://doi.org/10.5194/egusphere-egu22-569, 2022.

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