Fannar Ö Thordarson

Technical University of Denmark, Lyngby, Capital Region, Denmark

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Publications (2)6.14 Total impact

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    ABSTRACT: Monitoring of flows in sewer systems is increasingly applied to calibrate urban drainage models used for long term simulation. However, most often models are calibrated without considering the uncertainties. The GLUE methodology is here applied to assess parameter and flow simulation uncertainty using a simplified lumped sewer model that accounts for three separate flow contributions: wastewater, fast runoff from paved areas, and slow infiltrating water from permeable areas. Recently the GLUE methodology has been critised for generating prediction limits without statistical coherence and consistency and for the subjectivity in the choice of a threshold value to distinguish "behavioral" from "non-behavioral" parameter sets. In this paper we examine how well the GLUE methodology performs when the behavioural parameter sets deduced from a calibration period are applied to generate prediction bounds in validation periods. By retaining an increasing number of parameter sets we aim at obtaining consistency between the GLUE generated 90% prediction limits and the actual containment ratio (CR) in calibration. Due to the large uncertainties related to spatio-temporal rain variability during heavy convective rain events, flow measurement errors, as well as model limitations, it was not possible to obtain an overall CR of more than 80%. However, the GLUE generated prediction limits still proved rather consistent, since the overall CRs obtained in calibration corresponded well with the overall CRs obtained in validation periods for all proportions of retained parameter sets evaluated. When focusing on wet and dry weather periods separately, some inconsistencies were however found between calibration and validation and we address here some of the reasons why we should not expect the coverage of the prediction limits to be identical in calibration and validation periods in real-world applications. The large uncertainties propagate to the parameters and result in wide posterior parameter limits, that cannot be used for interpretation of e.g. the relative size of paved area vs. the size of infiltrating area. From this study it seems crucial to obtain more representative rain inputs and more accurate flow observations to reduce parameter and model simulation uncertainty.
    Hydrology and Earth System Sciences 01/2013; 17(10):4159-4176. DOI:10.5194/hess-17-4159-2013 · 3.59 Impact Factor
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    F. Ö. Thordarson · H. Madsen · H. Aa. Nielsen · P. Pinson
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    ABSTRACT: The classical regression model for combined forecasting was reformulated in order to impose restrictions on the combination weights. This restricted linear combination model was then extended to the case where the weights were allowed to be a non-parametric function of some meteorological variables, yielding the so-called conditional weighted combination method. The weight functions are estimated with local regression techniques. The conditional weighted combination method was applied to a test case of a wind farm over a period of 10 months. Various combinations of two forecasts out of the three available ones were considered. Analysis of the data suggested that meteorological forecasts of air density and turbulent kinetic energy may be considered as relevant external variables in the combination scheme. A performance comparison showed that the conditional weighted combination method introduced in this paper outperformed the least-squares combination method for almost all prediction horizons, especially for larger ones. This indicates that further developments based on conditional combination methods, including adaptivity of the weight functions estimation, may significantly enhance forecast accuracy and dampen the risk of large prediction errors. Copyright © 2010 John Wiley & Sons, Ltd.
    Wind Energy 11/2010; 13(8):751 - 763. DOI:10.1002/we.395 · 2.56 Impact Factor