Hydrological post-processing based on approximate Bayesian computation (ABC)

Abstract

This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: i) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and ii) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios.

Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows

“…This relative reduced performance of post-processing can be attributed to the satisfactory predictions of the GR4J model. This finding is consistent with previous results obtained by Romero-Cuellar et al (2019), who found that NSE increases by 25.84% post-processed poor predictions and 1.8% post-processed good predictions. Besides Ye et al (2014), Bogner et al (2016) and Woldemeskel et al (2018) confirmed that post-processing methods could improve forecast significantly when uncorrected predictions are exceptionally poor.…”

“…Bayesian computation and streamflow statistics. Romero-Cuellar et al (2019) developed the ABC post-processor for inferential problems with intractable likelihood (see chapter 2), previously unavailable in a closed form or by numerical derivation (Robert, 2016). As the predictive uncertainty of climate change projections cannot be assessed by observations, but by summary statistics of observations, e.g.…”

“…The main characteristic of the ABC post-processor used herein is to outline what follows. For additional details on the ABC post-processor method, refer to Romero-Cuellar et al (2019). The ABC post-processor is a Bayesian linear regression model that relates climate change projected streamflows (predictors) and observed streamflows (predictands):…”

“…6 List of scientific publication 6.1 Peer-reviewed journal publications Romero-Cuellar, J., Abbruzzo, A., Adelfio, G., Francés, F (2019) Hydrological post-processing based on approximate Bayesian computation (ABC), Stoch Environ Res Risk Assess, 33, 1361-1373, DOI: 10.1007/s00477-019-01694-y.…”

Plant science in the age of simulation intelligence

“…Approximate Bayesian Computation (ABC) is a widely utilized approach for simulation-based inference (Marin et al, 2012;Romero-Cuellar and Franceś, 2023). In ABC, the simulator generates synthetic data by sampling parameters from a prior distribution or proposal distribution and using these parameters to perform a simulation.…”