Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 15
Articles | Volume 27, issue 15
https://doi.org/10.5194/hess-27-2935-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2935-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 15
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: 2,043 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,584 398 61 2,043 64 41 43
  • HTML: 1,584
  • PDF: 398
  • XML: 61
  • Total: 2,043
  • Supplement: 64
  • BibTeX: 41
  • EndNote: 43
Views and downloads (calculated since 01 Sep 2022)
Cumulative views and downloads (calculated since 01 Sep 2022)

Viewed (geographical distribution)

Total article views: 2,043 (including HTML, PDF, and XML) Thereof 2,024 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2025
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.
Read more
Share