TY - GEN
T1 - Parametric Inference in Large Water Quality River Systems
AU - Moreno-Rodenas, Antonio
AU - Langeveld, Jeroen
AU - Clemens, Francois
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Emulation and water quality
KW - Integrated catchment modelling
UR - http://www.scopus.com/inward/record.url?scp=85071519580&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99867-1_51
DO - 10.1007/978-3-319-99867-1_51
M3 - Conference contribution
AN - SCOPUS:85071519580
SN - 978-3-319-99866-4
T3 - Green Energy and Technology
SP - 307
EP - 311
BT - New Trends in Urban Drainage Modelling - UDM 2018
A2 - Mannina, Giorgio
PB - Springer
CY - Cham
T2 - 11th International Conference on Urban Drainage Modelling, UDM 2018
Y2 - 23 September 2018 through 26 September 2018
ER -