Signature‐Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Theory and Comparison to Existing Applications. Issue 6 (30th June 2018)

• Record Type: Journal Article
• Authors: Kavetski, Dmitri; Fenicia, Fabrizio; Reichert, Peter; Albert, Carlo
• Is Part Of: Water resources research. Volume 54:Issue 6(2018)
• Page Start: 4059
• Page End: 4083
• Publication Date: 2018-06-30
• Subjects: hydrological model calibration -- data signature -- uncertainty -- Bayesian inference -- Approximate Bayesian Computation (ABC) -- GLUE; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2017WR020528
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: This study considers Bayesian calibration of hydrological models using streamflow signatures and its implementation using Approximate Bayesian Computation (ABC). If the modeling objective is to predict streamflow time series and associated uncertainty, a probabilistic model of streamflow must be specified but the inference equations must be developed in the signature domain. However, even starting from simple probabilistic models of streamflow time series, working in the signature domain makes the likelihood function difficult or impractical to evaluate (in particular, as it is unavailable in closed form). This challenge can be tackled using ABC, a general class of numerical algorithms for sampling from conditional distributions, such as (but not limited to) Bayesian posteriors given any calibration data. Using ABC does not avoid the requirement of Bayesian inference to specify a probability model of the data, but rather exchanges the requirement to evaluate the pdf of this model (needed to evaluate the likelihood function) by the requirement to sample model output realizations. For this reason ABC is attractive for inference in the signature domain. We clarify poorly understood aspects of ABC in the hydrological literature, including similarities and differences between ABC and GLUE, and comment on previous applications of ABC in hydrology. An error analysis of ABC approximation errors and their dependence on the tolerance is presented. An empirical case study is used to illustrate the impact of omitting the specification of a probabilistic model (and instead using a deterministic model within the ABC algorithm), and the impact of a coarse ABC tolerance. Key Points: Signature‐domain calibration can be implemented using Approximate Bayesian Computation (ABC) ABC is a class of sampling techniques that does not require evaluation of the likelihood function (e.g., if unavailable in closed form) Applications that omit randomness in the hydrological model and/or use a coarse ABC tolerance do not achieve the full potential of ABC