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

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Cited articles

Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: An overview of weather type models, J. Soc. Fr. Stat., 156, 101–113, 2015. a
Albert, C., Künsch, H. R., and Scheidegger, A.: A Simulated Annealing Approach to Approximate Bayes Computations, Stat. Comput., 25, 1217–1232, https://doi.org/10.1007/s11222-014-9507-8, 2015. a
Albert, C., Ulzega, S., and Stoop, R.: Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation, Phys. Rev. E, 93, 043313, https://doi.org/10.1103/PhysRevE.93.043313, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Andrieu, C., Doucet, A., and Holenstein, R.: Particle Markov chain Monte Carlo methods, J. Roy. Stat. Soc. B, 72, 269–342, https://doi.org/10.1111/j.1467-9868.2009.00736.x, 2010. a
Bacci, M., Dal Molin, M., Fenicia, F., Reichert, P., and Šukys, J.: Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models, J. Hydrol., 612, 128057, https://doi.org/10.1016/j.jhydrol.2022.128057, 2022. a
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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.
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