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Bayesian Uncertainty Analysis of the Distributed Hydrological Model HYDROTEL.

Bouda, Médard; Rousseau, Alain N.; Konan, Brou; Gagnon, Patrick; Gumière, Silvio José (2012). Bayesian Uncertainty Analysis of the Distributed Hydrological Model HYDROTEL. Journal of Hydrologic Engineering , vol. 17 , nº 9. p. 1021-1032. DOI: 10.1061/(ASCE)HE.1943-5584.0000550.

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Résumé

In this study, a Bayesian, inference-based, Markov chain Monte Carlo (MCMC) method coupled with an autoregressive moving average (ARMA) error model framework was used to assess the uncertainty of the process-based, continuous, distributed hydrological model HYDROTEL when simulating daily streamflows. The uncertainty analysis was performed, as a case study, in two distinct watersheds (Montmorency, Quebec, Canada, and Sassandra, Ivory Coast, West Africa). The MCMC uncertainty analysis showed to be effective, primarily with respect to the fulfillment of the statistical assumptions of the error model. The results of the uncertainty analyses demonstrated that almost 95% of the observed daily outlet flows were bracketed by the 95% prediction uncertainty bands. This indicates that the parameter uncertainty associated with the ARMA error model could reach the prediction uncertainty. It was possible to mimic the prediction uncertainty using only the most sensitive model parameters for the Montmorency and Sassandra watersheds. The uncertainty framework, as presented herein, may be applied to any distributed, continuous, hydrological model such as HYDROTEL. Given sufficient computational resources, such framework could become quite useful in providing uncertainty bands within the scope of predicting inflows to reservoirs for subsequent planning and management purposes.

Type de document: Article
Mots-clés libres: watersheds; uncertainty principles; autoregressive moving average models; simulation models; bayesian analysis; markov process; hydrologic models; case studies; Africa; Quebec; Canada
Centre: Centre Eau Terre Environnement
Date de dépôt: 19 nov. 2020 20:55
Dernière modification: 19 nov. 2020 20:55
URI: http://espace.inrs.ca/id/eprint/10565

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