Optimization of measurement campaigns for calibration of a conceptual sewer model.

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.

[1]  Gwo-Fong Lin,et al.  Uncertainty Analysis of Rainfall-Runoff Modeling , 1995 .

[2]  F. Clemens,et al.  Impact of dimension uncertainty and model calibration on sewer system assessment. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  G Chebbo,et al.  A benchmark methodology for managing uncertainties in urban runoff quality models. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[4]  Wolfgang Rauch,et al.  Assessing the relationship between water level and combined sewer overflow with computational fluid dynamics , 2008 .

[5]  G Chebbo,et al.  Bayesian approach for the calibration of models: application to an urban stormwater pollution model. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[6]  P A Vanrolleghem,et al.  Deterministic modelling of integrated urban drainage systems. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  George Kuczera,et al.  A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data , 2007 .

[8]  Peter Steen Mikkelsen,et al.  Sewer system performance assessment – an indicators based methodology , 2008 .

[9]  M Mourad,et al.  Accounting for sensor calibration, data validation, measurement and sampling uncertainties in monitoring urban drainage systems. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[10]  W. Rauch,et al.  Suitability of CSO performance indicators for compliance with ambient water quality targets , 2008 .

[11]  Henrik Madsen,et al.  Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives , 2003 .

[12]  G. Kuczera,et al.  Parameter estimation and model identification for stochastic models of annual hydrological data: Is the observed record long enough? , 2006 .

[13]  H. Hoppe,et al.  Significance of uncertainties in the input data used in the integrated design of wastewater systems , 2007 .

[14]  W. Schilling,et al.  Preliminary uncertainty analysis — a prerequisite for assessing the predictive uncertainty of hydrologic models , 1996 .

[15]  M Mourad,et al.  Stormwater quality models: sensitivity to calibration data. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[16]  George Kuczera,et al.  Calibration of conceptual hydrological models revisited: 2. Improving optimisation and analysis , 2006 .

[17]  D. Kavetski,et al.  Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters , 2006 .

[18]  W. Rauch.  MÖDERL Michael Friedrich SUSTAINABLE WASTE WATER TREATMENT BY MEANS OF URINE SEPARATION DISCHARGE STRATEGIES OF CONTROLLED URINE FLOW FROM DOMESTIC SEWAGE , 2007 .

[19]  Stefan Achleitner,et al.  CITY DRAIN copyright - An open source approach for simulation of integrated urban drainage systems , 2007, Environ. Model. Softw..

[20]  Peter Krebs,et al.  Requirements for integrated wastewater models - driven by receiving water objectives , 1998 .