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@article{e8a48e0e9936430d9857090752f193a5,
title = "Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification",
abstract = "Parameter estimation, using historical observed data, is an important part of the environmental modeling. The uncertainty in the parameter estimation limits the applications of environmental models. In this paper, the influence of limited and uncertain calibrated data on the performance of the parameter estimation are systematically investigated. For this purpose, synthetic observations with a given uncertainty and frequency are used to estimate the model parameters of a conceptual water quality (WQ) model of the River Zenne, Belgium. Bayesian inference using Markov Chain Monte Carlo sampling is adopted to simultaneously perform the automatic calibration and the uncertainty analysis. The results highlight the critical roles of measurement frequency and uncertainty in the model calibration. We found that the effect of the measurement uncertainty on the parameter estimation is significant when the calibrated data points are limited (e.g. monthly data). The research findings can be used to support measurement prioritization and resource allocation.",
keywords = "DREAM, Measurement frequency, Measurement uncertainty, Parameter estimation, Parameter uncertainty, Simulation uncertainty",
author = "{Khorashadi Zadeh}, Farkhondeh and Jiri Nossent and Woldegiorgis, {Befekadu Taddesse} and Willy Bauwens and {van Griensven}, Ann",
year = "2019",
month = "8",
day = "1",
doi = "10.1016/j.envsoft.2019.03.022",
language = "English",
volume = "118",
pages = "35--47",
journal = "Environmental Modelling & Software",
issn = "1364-8152",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification

AU - Khorashadi Zadeh, Farkhondeh

AU - Nossent, Jiri

AU - Woldegiorgis, Befekadu Taddesse

AU - Bauwens, Willy

AU - van Griensven, Ann

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Parameter estimation, using historical observed data, is an important part of the environmental modeling. The uncertainty in the parameter estimation limits the applications of environmental models. In this paper, the influence of limited and uncertain calibrated data on the performance of the parameter estimation are systematically investigated. For this purpose, synthetic observations with a given uncertainty and frequency are used to estimate the model parameters of a conceptual water quality (WQ) model of the River Zenne, Belgium. Bayesian inference using Markov Chain Monte Carlo sampling is adopted to simultaneously perform the automatic calibration and the uncertainty analysis. The results highlight the critical roles of measurement frequency and uncertainty in the model calibration. We found that the effect of the measurement uncertainty on the parameter estimation is significant when the calibrated data points are limited (e.g. monthly data). The research findings can be used to support measurement prioritization and resource allocation.

AB - Parameter estimation, using historical observed data, is an important part of the environmental modeling. The uncertainty in the parameter estimation limits the applications of environmental models. In this paper, the influence of limited and uncertain calibrated data on the performance of the parameter estimation are systematically investigated. For this purpose, synthetic observations with a given uncertainty and frequency are used to estimate the model parameters of a conceptual water quality (WQ) model of the River Zenne, Belgium. Bayesian inference using Markov Chain Monte Carlo sampling is adopted to simultaneously perform the automatic calibration and the uncertainty analysis. The results highlight the critical roles of measurement frequency and uncertainty in the model calibration. We found that the effect of the measurement uncertainty on the parameter estimation is significant when the calibrated data points are limited (e.g. monthly data). The research findings can be used to support measurement prioritization and resource allocation.

KW - DREAM

KW - Measurement frequency

KW - Measurement uncertainty

KW - Parameter estimation

KW - Parameter uncertainty

KW - Simulation uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85064178924&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2019.03.022

DO - 10.1016/j.envsoft.2019.03.022

M3 - Article

AN - SCOPUS:85064178924

VL - 118

SP - 35

EP - 47

JO - Environmental Modelling & Software

JF - Environmental Modelling & Software

SN - 1364-8152

ER -

ID: 45652998