Preprints
https://doi.org/10.5194/egusphere-2022-857
https://doi.org/10.5194/egusphere-2022-857
 
01 Sep 2022
01 Sep 2022
Status: this preprint is open for discussion.

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

Simone Ulzega1 and Carlo Albert2 Simone Ulzega and Carlo Albert
  • 1Institute of Computational Life Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland
  • 2Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

Abstract. Conceptual models of the rainfall-runoff behaviour of hydrological catchments have proven to be useful tools for making probabilistic predictions. However, model parameters need to be calibrated to measured data and their uncertainty quantified. Bayesian statistics is a consistent framework for learning from observed data, in which knowledge about model parameters is described through probability distributions. One of the dominant sources of uncertainty in rainfall-runoff modelling is the true rainfall over the catchment, which often needs to be inferred from a few rain-gauge and runoff measurements. modelling this uncertainty naturally leads to stochastic differential equation models, which render traditional inference algorithms such as the Metropolis algorithm infeasible due to their expensive likelihood functions. Therefore, in hydrology and other applied fields of research, error models are traditionally oversimplified for ease of inference as additive errors on the output, leading to biased parameter estimates and unreliable predictions. However, thanks to recent advancements in algorithms and computing power, full-fledged Bayesian inference with stochastic models is no longer off-limits 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.

Simone Ulzega and Carlo Albert

Status: open (until 29 Oct 2022)

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Simone Ulzega and Carlo Albert

Simone Ulzega and Carlo Albert

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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 time-scale separation, to demonstrate that full-fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications.