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

Ulzega, Simone; Albert, Carlo

doi:10.5194/egusphere-egu22-8729

Publication type:	Conference other
Type of review:	Peer review (abstract)
Title:	Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model
Authors:	Ulzega, Simone Albert, Carlo
et. al:	No
DOI:	10.5194/egusphere-egu22-8729
Conference details:	EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022
Issue Date:	2022
Language:	English
Subjects:	Hamiltonian Monte Carlo; Stochastic modelling
Subject (DDC):	510: Mathematics 551: Geology and hydrology
Abstract:	Conceptual models are indispensable tools for hydrology. In order to use them for making probabilistic predictions, they need to be equipped with an adequate error model, which, for ease of inference, is traditionally formulated as an additive error on the output (discharge). However, the main sources of uncertainty in hydrological modelling are typically not to be found on the output, but on the input (rain) and in the model structure. Therefore, more reliable error models and probabilistic predictions can be obtained by incorporating those uncertainties directly where they arise, that is, into the model. This, however, leads us to stochastic models, which render traditional inference algorithms such as the Metropolis algorithm infeasible due to their expensive likelihood functions. However, thanks to recent advancements in algorithms and computing power, full-fledged Bayesian inference with stochastic models is no longer off-limit for hydrological applications. We demonstrate this with a case study from urban hydrology, for which we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a time-scale separation.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25628
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project:	BISTOM - Bayesian Inference with Stochastic Models
Appears in collections:	Publikationen Life Sciences und Facility Management

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Ulzega, S., & Albert, C. (2022). Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model. EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022. https://doi.org/10.5194/egusphere-egu22-8729

Ulzega, S. and Albert, C. (2022) ‘Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model’, in EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022. Available at: https://doi.org/10.5194/egusphere-egu22-8729.

S. Ulzega and C. Albert, “Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model,” in EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022, 2022. doi: 10.5194/egusphere-egu22-8729.

ULZEGA, Simone und Carlo ALBERT, 2022. Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model. In: EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022. Conference presentation. 2022

Ulzega, Simone, and Carlo Albert. 2022. “Bayesian Parameter Inference in Hydrological Modelling Using a Hamiltonian Monte Carlo Approach with a Stochastic Rain Model.” Conference presentation. In EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022. https://doi.org/10.5194/egusphere-egu22-8729.

Ulzega, Simone, and Carlo Albert. “Bayesian Parameter Inference in Hydrological Modelling Using a Hamiltonian Monte Carlo Approach with a Stochastic Rain Model.” EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022, 2022, https://doi.org/10.5194/egusphere-egu22-8729.