Parametric Inference in Large Water Quality River Systems

Antonio Moreno-Rodenas*, Jeroen Langeveld, Francois Clemens

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Environmental models often contain parameters, which are not measurable, yet conceptual descriptions of some physical process. The value of such parameters is often derived by measuring internal state model variables in the system and indirectly tuning/calibrating the value of the parameters so some degree of match is achieved. Bayesian inference is a widely used tool in which the modeller can transfer some prior beliefs about the parameter space, which is updated when additional knowledge on the system is acquired (e.g. more measurements are available). However, the amount of simulations required to perform a formal inference becomes prohibitive when using computationally expensive models. In this work the inference of the hydraulic and dissolved oxygen processes is presented for a large scale integrated catchment model. Two emulator structures were used to accelerate the sampling of the river flow and dissolved oxygen dynamics. Posterior parameter probability distributions were computed using one year of measured data in the river.
Original languageEnglish
Title of host publicationNew Trends in Urban Drainage Modelling - UDM 2018
EditorsGiorgio Mannina
Place of PublicationCham
Number of pages5
ISBN (Electronic)978-3-319-99867-1
ISBN (Print)978-3-319-99866-4
Publication statusPublished - 2019
Event11th International Conference on Urban Drainage Modelling, UDM 2018 - Palermo, Italy
Duration: 23 Sep 201826 Sep 2018

Publication series

NameGreen Energy and Technology
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537


Conference11th International Conference on Urban Drainage Modelling, UDM 2018


  • Emulation and water quality
  • Integrated catchment modelling

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