Literature DB >> 27858790

Lost in calibration: why people still do not calibrate their models, and why they still should - a case study from urban drainage modelling.

Franz Tscheikner-Gratl1, Peter Zeisl1, Carolina Kinzel1, Johannes Leimgruber2, Thomas Ertl3, Wolfgang Rauch1, Manfred Kleidorfer1.   

Abstract

From a scientific point of view, it is unquestioned that numerical models for technical systems need to be calibrated. However, in sufficiently calibrated models are still used in engineering practice. Case studies in the scientific literature that deal with urban water management are mostly large cities, while little attention is paid to the differing boundary conditions of smaller municipalities. Consequently, the aim of this paper is to discuss the calibration of a hydrodynamic model of a small municipality (15,000 inhabitants). To represent the spatial distribution of precipitation, three distributed rain gauges were used for model calibration. To show the uncertainties imminent to the calibration process, 17 scenarios, differing in assumptions for calibration, were distinguished. To compare the impact of the different calibration scenarios on actual design values, design rainfall events were applied. The comparison of the model results using the different typical design storm events from all the surrounding data points showed substantial differences for the assessment of the sewers regarding urban flooding, emphasizing the necessity of uncertainty analysis for hydrodynamic models. Furthermore, model calibration is of the utmost importance, because uncalibrated models tend to overestimate flooding volume and therefore result in larger diameters and retention volumes.

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Year:  2016        PMID: 27858790     DOI: 10.2166/wst.2016.395

Source DB:  PubMed          Journal:  Water Sci Technol        ISSN: 0273-1223            Impact factor:   1.915


  1 in total

1.  Predicting bleeding risk in a Chinese immune thrombocytopenia (ITP) population: development and assessment of a new predictive nomogram.

Authors:  Mingjing Wang; Weiyi Liu; Yonggang Xu; Hongzhi Wang; Xiaoqing Guo; Xiaoqing Ding; Richeng Quan; Haiyan Chen; Shirong Zhu; Teng Fan; Yujin Li; Xuebin Zhang; Yan Sun; Xiaomei Hu
Journal:  Sci Rep       Date:  2020-09-18       Impact factor: 4.379

  1 in total

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