Stormwater quality models: sensitivity to calibration data

Mourad, M.; Bertrand-Krajewski, Jean-Luc; Chebbo, Ghassan

doi:10.2166/wst.2005.0110

Cited by 18 publications

(12 citation statements)

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“…Much work has been done on calibration (e.g. Kanso et al 2003;Madsen 2003;Mourad et al 2005;Kavetski et al 2006;Thyer et al 2006;Frost et al 2007) and uncertainty of urban storm water models (e.g. Lei & Schilling 1996;Bertrand-Krajewski et al 2003;Kanso et al 2005;Korving & Clemens 2005;Kuczera et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Optimization of measurement campaigns for calibration of a conceptual sewer model

Kleidorfer

Möderl

Fach

et al. 2009

Water Science and Technology

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To simulate hydrological models of combined sewer systems an accurate calibration is indispensable. In addition to all sources of uncertainties in data collection due to the measurement methods itself, it is a key question which data has to be collected to calibrate a hydrological model, how long measurement campaigns should last and where that data has to be collected in a spatial distributed system as it is neither possible nor sensible to measure the complete system characteristics. In this paper we address this question by means of stochastic modelling. Using Monte Carlo Simulation different calibration strategies (selection of measurement sites, selection of rainfall-events) and different calibration parameters (overflow volume, number of overflows) are tested, in order to evaluate the influence on predicting the total overflow volume of the entire system. This methodology is applied in a case study with the aim to calculate the combined sewer overflow (CSO) efficiency. It can be shown that a distributed hydrological model can be calibrated sufficiently when calibration is done on 30% of all existing CSOs based on long-term observation. Event based calibration is limited possible to a limited extend when calibration events are selected carefully as wrong selection of calibration events can result in a complete failure of the calibration exercise.

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Section: Introductionmentioning

confidence: 99%

Optimization of measurement campaigns for calibration of a conceptual sewer model

Kleidorfer

Möderl

Fach

et al. 2009

Water Science and Technology

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“…Even the simplest regression models have an additional calibration coefficient to SMC estimates, implying they will not perform well at sites with limited sampling, potentially overfitting on smaller datasets. This is supported by Mourad et al (2005aMourad et al ( , 2005bMourad et al ( , 2005c and Obropta and Kardos (2007) who suggested that SMCs required fewer data than regression or build-up washoff models. Furthermore, the study by May and Sivakumar (2013) found that the flow-weighted mean began performing less accurately on smaller datasets, potentially due to outlying storm event(s), which dominated the calibration of SMC.…”

Section: Authors' Replymentioning

confidence: 69%

Discussion: Prediction of long-term urban stormwater loads at single sites

May

Sivakumar

Sarmadi

et al. 2014

Proceedings of the Institution of Civil Engineers - Water Manag

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May and Sivakumar (2013) have predicted the long-term urban stormwater loads at single sites, using five different measures of central tendency. They have stated that their motivation in using measures of central tendency is one in which, in contrast to regression and process-based methods, there is no need for correlating urban stormwater loads with other variables (e.g. discharge), as well as less complexity. Overall, the flow-weighted mean concentration has produced the most accurate predictions of long-term loads. However, the following points are worth discussing:The definite superiority of the flow-weighted mean method obviously shows that there is sufficient correlation between loads and runoff, and therefore it suggests that regression methods could derive more accurate results. Interestingly, the authors claim in a different study (May and Sivakumar, 2009a) that the comparison between different statistical models shows that regression models are more applicable than simple estimates of mean concentration in urban areas. Therefore, here, May and Sivakumar should compare different mean functions at least with a simple regression model to support their new claim.From an environmental point of view, evaluating the trends, extreme events, dispersion tendency characteristics and bias are usually more effective than using a single value (e.g. long-term mean) for analysing/mitigating natural or human-induced side effects. For instance, Colin (1995) reports that urban stormwater loads have bias, which means the mode and arithmetic mean of samples are not related to the same load value; hence, pollution management for an area can be adjusted depending on the side of bias. In a different example, Vogel et al. (2005) state that the natural characteristics of long-term stormwater loads, at least in their case studies, are more sophisticated than using simple statistical methods. Therefore, Vogel et al. (2005) introduce a multivariate probability distribution function to model loadconcentration-discharge relationships in order to predict short and long-term stormwater loads. Furthermore, they derive benefit from using L-moment statistical analysis to discover the complexities of this phenomenon.Overall, the current study presents valuable points; however, the title and concepts should clarify that the aim of the research is simply to compare the performance of different measures of central tendency in deriving site mean concentration, and not to predict long-term urban stormwater loads. Authors' replyThe goal of the study by May and Sivakumar (2013) was to derive estimates of long-term load at a given catchment using a variety of estimates of site mean concentration (SMC) based on different measures of central tendency. The methodology was chosen for its inherent simplicity, making it readily applicable at sites with limited sampling.Regression and/or process-based models are not solely limited by the absence of strong correlations between concentration and explanatory variables such as discharge, but also by a spars...

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“…Multi-purpose advice for optimal sensor placement in any kind of urban water system network are presented in the PREPARED project [17], where also detailed locations for pollutant measures are considered, e.g., the position within the cross-section in dependence to the flow distribution. The majority of these studies however concentrate on conceptual drainage models [1], pluvial flood models [16] or pollutant models [18][19][20], respectively. Focus is laid therein on the quantification of mass fluxes, either to the receiving water bodies via combined sewer overflows or to the wastewater treatment plants.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Method for Measurement Site Selection in Sewer Systems

Vonach

Tscheikner-Gratl

Rauch

et al. 2018

Water

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Although calibration of a hydrodynamic model depends on the availability of measurement data representing the system behavior, advice for the planning of necessary measurement campaigns for model calibration is scarce. This work tries to address this question of efficient measurement site selection on a network scale for the objective of calibrating a hydrodynamic model case study in Austria. For this, a model-based approach is chosen, as the method should be able to be used before measurement data is available. An existing model is assumed to represent the real system behavior. Based on this extended availability of "measurement data" in every point of the system, different approaches are established to heuristically assess the suitability of one or more pipes in combination as calibration point(s). These approaches intend to find suitable answers to the question of measurement site selection for this specific case study within a relatively short time and with a reasonable computational effort. As a result, the relevance of the spatial distribution of calibration points is highlighted. Furthermore, particular efficient calibration points are identified and further measurement sites in the underlying network are recommended.

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Stormwater quality models: sensitivity to calibration data

Cited by 18 publications

References 0 publications

Optimization of measurement campaigns for calibration of a conceptual sewer model

Optimization of measurement campaigns for calibration of a conceptual sewer model

Discussion: Prediction of long-term urban stormwater loads at single sites

A Heuristic Method for Measurement Site Selection in Sewer Systems

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