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Parameter Uncertainties Assessment in a Conceptual Rainfall-Runoff Model Using Bayesian Paradigm

7 pagesPublished: September 20, 2018

Abstract

In this study, the calibration of the rain-flow conceptual model UFGModel1.1 is carried out, and the uncertainties in the predictions of flow rates associated with the parameter set estimates are evaluated by the Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM). The water catchment area of the Botafogo Stream, located in the city of Goiânia, Brazil, was selected as experimental for the development of the study, in which a more distributed spatial discretisation degree (thirteen planes and six channels) was adopted for this basin. The results showed that the various parameter sets were considered optimal, allowing high modelling efficiency, despite the loss of the quality of the simulations and uncertainty increase when using the GLUE.

Keyphrases: MC, MCMC, runoff model, statistical algorithm

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1624--1630

Links:
BibTeX entry
@inproceedings{HIC2018:Parameter_Uncertainties_Assessment_in,
  author    = {Tatiane Pereira and Guilherme Cruz and Klebber Formiga},
  title     = {Parameter Uncertainties Assessment in a Conceptual Rainfall-Runoff Model Using Bayesian Paradigm},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {1624--1630},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/x5xx},
  doi       = {10.29007/czjl}}
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