Articles | Volume 27, issue 15
https://doi.org/10.5194/hess-27-2935-2023
https://doi.org/10.5194/hess-27-2935-2023
Research article
 | 
09 Aug 2023
Research article |  | 09 Aug 2023

Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model

Simone Ulzega and Carlo Albert

Viewed

Total article views: 5,646 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,434 2,034 178 5,646 149 189 288
  • HTML: 3,434
  • PDF: 2,034
  • XML: 178
  • Total: 5,646
  • Supplement: 149
  • BibTeX: 189
  • EndNote: 288
Views and downloads (calculated since 01 Sep 2022)
Cumulative views and downloads (calculated since 01 Sep 2022)

Viewed (geographical distribution)

Total article views: 5,646 (including HTML, PDF, and XML) Thereof 5,498 with geography defined and 148 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 18 Sep 2026
Download
Short summary
Embedding input uncertainties in hydrological modelling naturally leads to stochastic models, which render parameter calibration an often computationally intractable problem. We use a case study from urban hydrology based on a stochastic rain model, and we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a timescale separation to demonstrate that fully fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications.
Share