Optimization of measurement campaigns for calibration of a conceptual sewer model

Abstract

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.

Using data from monitoring combined sewer overflows to assess, improve, and maintain combined sewer systems

“…Thus, researchers and engineers have resorted to using mathematical sewer models, calibrating the models with flow or level measurements taken in the sewer system (e.g. Kleidorfer et al, 2009;Gamerith et al, 2011). The main drawback of this modelling approach is that the real behavior of CSSs may not be accurately represented if the model was not calibrated properly.…”

DREAM<sub>(D)</sub>: an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems

“…Discrete optimization problems are abundant in many fields of study, and have also started to begin to appear in the hydrologic literature (Furman et al, 2004;Harmancioglu et al, 2004;Perrin et al, 2008;Kleidorfer et al, 2008;Neuman et al, 2011). Figure 1 illustrates a simple discrete parameter estimation problem involving a tile puzzle.…”

A Heuristic Method for Measurement Site Selection in Sewer Systems

“…In modeling practice however, calibration is still often neglected. In particular, data availability and quality tend to be limiting factors [1,2] and the consequently required sampling campaigns for calibration can increase the economic costs of projects up to an unachievable level for many operators [3].…”

“…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.…”