Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.     

Towards quantification of uncertainty in predicting water quality failures in integrated catchment model studies

This paper describes the development and application of a method for estimating uncertainty in the prediction of sewer flow quantity and quality and how this may impact on the prediction of water quality failures in integrated catchment modelling (ICM) studies. The method is generic and readily adaptable for use with different flow quality prediction models that are used in ICM studies. Use is made of the elicitation concept, whereby expert knowledge combined with a limited amount of data are translated into probability distributions describing the level of uncertainty of various input and model variables. This type of approach can be used even if little or no site specific data is available. Integrated catchment modelling studies often use complex deterministic models. To apply the results of elicitation in a case study, a computational reduction method has been developed in order to determine levels of uncertainty in model outputs with a reasonably practical level of computational effort. This approach was applied to determine the level of uncertainty in the number of water quality failures predicted by an ICM study, due to uncertainty associated with input and model parameters of the urban drainage model component of the ICM. For a small case study catchment in the UK, it was shown that the predicted number of water quality failures in the receiving water could vary by around 45% of the number predicted without consideration of model uncertainty for dissolved oxygen and around 32% for unionised ammonia. It was concluded that the potential overall levels of uncertainty in the ICM outputs could be significant. Any solutions designed using modelling approaches that do not consider uncertainty associated with model input and model parameters may be significantly over-dimensioned or under-dimensioned. With changing external inputs, such as rainfall and river flows due to climate change, better accounting for uncertainty is required.     

Performance of stochastic approaches for forecasting river water quality

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways, i.e. multiplicative autoregressive integrated moving average (ARIMA) model, deseasonalised model and Thomas–Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas–Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.     

The USEPA's distribution system water quality modelling program: a historical perspective

Hydraulic and water quality models have become widely used to understand both the hydraulic behaviour, and the fate and transport of contaminants in drinking water distribution systems. Research conducted by the United States (US) Environmental Protection Agency (EPA) played a major role in the development and application of hydraulic/water quality modelling in the United States and throughout the world. Eventually this research led to the development of EPANET, an integrated hydraulic/water quality model, and had a major influence on the implementation of the United States Safe Drinking Water Act (SDWA). The modelling research conducted by the US EPA has helped many drinking water utilities throughout the world alleviate public health threats due to the deterioration of water quality in drinking water networks. The US EPA has provided over 100 000 downloads of the EPANET software over the last 2 years.     

The conceptual modelling of construction management information

The conceptual modelling of construction management information models the information for the activities performed at the post design stage. In this paper, such activities have been identified by a steering group of industrialists and representatives of the major professional institutions in the UK who are assisting the research team in the development of conceptual models for construction management information. The resulting conceptual models form the basis for developing construction management databases which can monitor the time, cost and quality aspects of a construction project. Proper information modelling is a key factor in realising these aspects. However, unless a sound methodology is used to structure the information required by the various disciplines involved at the construction stage, the critical factors of time, cost and quality may not be achievable.

This paper reports on a methodology used by the ICON project being developed at the University of Salford which is attempting to provide a framework of information into which the modelling of construction management information fits. This methodology is based on the coupling of two powerful information modelling and analysis techniques namely Information Engineering and Object Oriented Analysis and Design with the assistance of an object oriented CASE (Computer Aided Software/Systems Engineering) tool which automates the production of information models. The Information Engineering method was used for the strategic planning of information using activity decomposition/activity hierarchy whilst the Object Oriented Analysis and Design approach was performed at the analysis, design and implementation stages due to the incorporation of the notion of information perspectives. An Object Oriented CASE tool (Ptech) is being used to assist in producing the various information models required by the construction management disciplines and to generate executable computer code from these models.     

Cost-Effective Water-Quality Modelling of Potable Water Distribution Systems

Before privatization of the water authorities in England and Wales in 1989, future water companies were instructed to provide auditable proof of their investment requirements by the production of asset management plans.
With respect to distribution systems for potable water, asset management plan studies have established that unlined ferrous mains are often the main cause of complaints about discoloured water. It has also been found that the investment in water mains rehabilitation, which is driven by water quality problems, overshadows similar investment requirements driven by high burst rates and poor hydraulic performance.
This paper describes how certain techniques of water quality modelling have been developed and refined to provide a rapid, cost-effective method for identifying requirements for water-main rehabilitation.     

Assessment of the integrated urban water quality model complexity through identifiability analysis

Urban sources of water pollution have often been cited as the primary cause of poor water quality in receiving water bodies (RWB), and recently many studies have been conducted to investigate both continuous sources, such as wastewater-treatment plant (WWTP) effluents, and intermittent sources, such as combined sewer overflows (CSOs). An urban drainage system must be considered jointly, i.e., by means of an integrated approach. However, although the benefits of an integrated approach have been widely demonstrated, several aspects have prevented its wide application, such as the scarcity of field data for not only the input and output variables but also parameters that govern intermediate stages of the system, which are useful for robust calibration. These factors, along with the high complexity level of the currently adopted approaches, introduce uncertainties in the modelling process that are not always identifiable. In this study, the identifiability analysis was applied to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two WWTPs and has a small river as the RWB. The system was simulated by employing an integrated model developed in previous studies. The main goal of the study was to assess the right number of parameters that can be estimated on the basis of data-source availability. A preliminary sensitivity analysis was undertaken to reduce the model parameters to the most sensitive ones. Subsequently, the identifiability analysis was carried out by progressively considering new data sources and assessing the added value provided by each of them. In the process, several identifiability methods were compared and some new techniques were proposed for reducing subjectivity of the analysis. The study showed the potential of the identifiability analysis for selecting the most relevant parameters in the model, thus allowing for model simplification, and in assessing the impact of data sources for model reliability, thus guiding the analyst in the design of future monitoring campaigns. Further, the analysis showed some critical points in integrated urban drainage modelling, such as the interaction between water quality processes on the catchment and in the sewer, that can prevent the identifiability of some of the related parameters.     

Development of a Decision Support System for Drought Management within North West Water

Whilst droughts, by their nature, are not frequently occurring events, it is essential, for prudent management of water-resources systems, to have analytical tools available for the effective management of the risks to water supplies when such events do occur.
Within North West Water Limited, modelling techniques have been developed and applied over a number of years, which allow water-supply managers to be provided with information concerning risks to supply within the Company's water-resources systems.
The paper describes how these modelling techniques have been integrated within a decision support system for drought management. Two different approaches have been used. Firstly, enhancement by the implementation of a user-friendly interface and, secondly, the development of a knowledge-based system. The problems and benefits of each approach are discussed.     

The impact of new developments on river water quality from an integrated system modelling perspective

New housing areas are a ubiquitous feature of modern life in the developing and developed world alike built in response to rising social, demographic and economic pressures. Inevitably, these new developments will have an impact on the environment around them. Empirical evidence confirms the close relationship between urbanisation and ambient water quality. However, what is lacking so far is a detailed and more generalised analysis of environmental impact at a relatively small scale. The aim of this paper is to quantify the impact of new developments on river water quality within an integrated system modelling perspective. To conduct the impact analyses, an existing integrated urban wastewater model was used to predict water flow and quality in the sewer system, treatment plant and receiving water body. The impact on combined sewer overflow (CSO) discharges, treatment plant effluent, and within the river at various reaches is analysed by ‘locating’ a new development on a semi-hypothetical urban catchment. River water quality is used as feedback to constrain the scale of the new development within different thresholds in compliance with water quality standards. Further, the regional sensitivity analysis (RSA) method is applied to reveal the parameters with the greatest impact on water quality. These analyses will help to inform town planners and water specialists who advise them, how to minimise the impact of such developments given the specific context.     

Object-oriented modelling and integrated CAD

In integrated CAD different techniques are used to model various features (informations, activities, data and data flow) of the problem domain. Because they model the same real world topic, all aspects developed using different techniques must be compatible and able to cooperate. Since most modelling techniques were developed independently, co-operation is not encouraged. We propose an object oriented modelling approach borrowed from object oriented software design that offers methods and notations for both static and dynamic aspects of state and behaviour of models and yields smoother development into implementations with object-oriented languages and databases. The paper discusses the motives, the details of the OO modelling approaches and an example from the domain of standards representation. It has been found out that particularly the introduction of the mechanism concept enables and forces us to think about new features of our models.     

Geotechnical aspects of rock tunnelling in China

To meet the huge demands of transportation, energy and other infrastructure projects, a large volume of rock tunnelling is being carried out across the country as China embarks on her modernization plan. This paper summaries the geotechnical issues encountered on such projects, including the various modes of rock mass instability and geological hazards. Methods of evaluating rock mass quality and ground conditions as developed in the West are now widely used on tunnelling projects in China. As well Chinese engineers and researchers have also developed geomechanical systems for rock mass classification, failure modes and support requirements. These systems have been proven to be effective in dealing with the complex geologic and tunnelling conditions in China. Also described in the paper are analytical techniques currently used in China for tunnel performance prediction, including canonical analysis, expert systems, block vector analysis and advanced numerical modelling techniques.     

Uncertainty in urban stormwater quality modelling: the effect of acceptability threshold in the GLUE methodology

Uncertainty analysis in integrated urban drainage modelling is of growing importance in the field of water quality. However, only few studies deal with uncertainty quantification in urban drainage modelling; furthermore, the few existing studies mainly focus on quantitative sewer flow modelling rather than uncertainty in water quality aspects. In this context, the generalised likelihood uncertainty estimation (GLUE) methodology was applied for the evaluation of the uncertainty of an integrated urban drainage model and some of its subjective hypotheses have been explored. More specifically, the influence of the subjective choice of the acceptability threshold has been detected in order to gain insights regarding its effect on the model results. The model has been applied to the Savena case study (Bologna, Italy) where water quality and quantity data were available. The model results show a strong influence of the acceptability threshold selection and confirm the importance of modeller's experience in the application of GLUE uncertainty analysis.     

Development of a re‐aeration equation for the simulation of dissolved oxygen in degraded streams

Discharge of domestic and industrial wastewater into the water bodies is the prime source of degradation of the freshwater ecosystem. Re‐aeration of the river allows the rejuvenation of river water quality by absorbing oxygen from the atmosphere at the air‐water interface. An accurate estimation of the re‐aeration coefficient helps to determine the assimilative capacity of streams and wastewater management also. This paper aims to develop the re‐aeration equation and its validation by modelling dissolved oxygen of the Yamuna River. Predictive re‐aeration equations are used to identify their applicability to the study area and the new equation is designed using multivariate statistical regression techniques. QUAL2Kw model is used to validate the equation by modelling dissolved oxygen. Results indicate that the developed equation performs better than the predictive re‐aeration equations for the prediction of water quality.     

Mapping of the water quality of Lake Erken, Sweden, from imaging spectrometry and Landsat Thematic Mapper

Hyperspectral data have been collected by the Compact Airborne Spectrographic Imager (CASI) and multispectral data by the Landsat Thematic Mapper (TM) instrument for the purpose of mapping lake water quality. Field campaigns have been performed on Lake Erken in Sweden during the summer of 1997. Water samples have been collected and analysed in laboratory. Continuously measured variables from a boat have added a spatial dimension to the ground truth dataset. The data have been used to construct algorithms, based on remotely sensed data, for the retrieval of water quality parameters. The correlation between the continuous data and the collected CASI data has been investigated. Algorithms using both the point sampling results and the continuous data have been developed. Maps based on data from each instrument, showing the distribution of chlorophyll, are presented. Problems of having few water sampling stations, and the potential of using sub-water optics models are addressed as well. Tests were performed on MERIS bands and found useful for mapping chlorophyll and turbidity, and algorithms have been suggested for future use with MERIS.     

A dimensional analysis‐based model for the prediction of nitrogen concentrations in Laborec River,Slovakia

During the past decades, several models that predict the concentration profiles after a discharge of pollutants in a river have been developed. A model that predicts nitrogen concentrations in a river has been developed and is presented in this paper. The developed model that determines nitrogen concentrations in a water stream is based on a dimensional analysis. Fundamentals of the modelling of the pollutant predictions in a water stream consist of a derivation of function dependency from expressed non‐dimension arguments. Non‐dimension arguments are stated from variables, which influence the occurrence of pollutants. The model for the prediction of nitrogen concentrations in water streams has been developed for the Laborec River (eastern Slovakia). The differences between the nitrogen concentrations predicted from developed models and measured concentrations in the river are also discussed here.     

Comprehensive review of structural deterioration of water mains: physically based models

This paper provides a comprehensive (although not exhaustive) overview of the physical/mechanical models that have been developed to improve the understanding of the structural performance of water mains. Several components have to be considered in modelling this structural behaviour. The residual structural capacity of water mains is affected by material deterioration due to environmental and operational conditions as well as quality of manufacturing and installation. This residual structural capacity is subjected to external and internal loads exerted by the soil pressure, traffic loading, frost loads, operational pressure and third party interference. Some models address only one or a few of the numerous components of the physical process that lead to breakage, while others attempt to take a more comprehensive approach. Initial efforts were aimed mainly towards development of deterministic models, while more recent models use a probabilistic approach to deal with uncertainties in defining the deterioration and failure processes. The physical/mechanical models were classified into two classes: deterministic and probabilistic models. The effect of temperature on pipe breakage is discussed from three angles; the first deals with temperature effects on pipe-soil interaction, the second deals with frost load effects and the third provides a brief review of various attempts to statistically quantify influence of temperature on water main failure.This paper complements the companion paper “Comprehensive review of structural deterioration of water mains: statistical models”, which reviews statistical methods that explain, quantify and predict pipe breakage or structural failures of water mains.     

Calibrating and validating bacterial water quality models: A Bayesian approach

Water resource management decisions often depend on mechanistic or empirical models to predict water quality conditions under future pollutant loading scenarios. These decisions, such as whether or not to restrict public access to a water resource area, may therefore vary depending on how models reflect process, observation, and analytical uncertainty and variability. Nonetheless, few probabilistic modeling tools have been developed which explicitly propagate fecal indicator bacteria (FIB) analysis uncertainty into predictive bacterial water quality model parameters and response variables. Here, we compare three approaches to modeling variability in two different FIB water quality models. We first calibrate a well-known first-order bacterial decay model using approaches ranging from ordinary least squares (OLS) linear regression to Bayesian Markov chain Monte Carlo (MCMC) procedures. We then calibrate a less frequently used empirical bacterial die-off model using the same range of procedures (and the same data). Finally, we propose an innovative approach to evaluating the predictive performance of each calibrated model using a leave-one-out cross-validation procedure and assessing the probability distributions of the resulting Bayesian posterior predictive p-values. Our results suggest that different approaches to acknowledging uncertainty can lead to discrepancies between parameter mean and variance estimates and predictive performance for the same FIB water quality model. Our results also suggest that models without a bacterial kinetics parameter related to the rate of decay may more appropriately reflect FIB fate and transport processes, regardless of how variability and uncertainty are acknowledged.     

Using genetic algorithms to calibrate a water quality model

With the increasing concern over the impact of diffuse pollution on water bodies, many diffuse pollution models have been developed in the last two decades. A common obstacle in using such models is how to determine the values of the model parameters. This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time. Compared with conventional optimisation approaches, soft computing techniques often have a faster convergence speed and are more efficient for global optimum searches. This paper presents an attempt to calibrate a diffuse pollution model using a genetic algorithm (GA). Designed to simulate the export of phosphorus from diffuse sources (agricultural land) and point sources (human), the Phosphorus Indicators Tool (PIT) version 1.1, on which this paper is based, consisted of 78 parameters. Previous studies have indicated the difficulty of full range model calibration due to the number of parameters involved. In this paper, a GA was employed to carry out the model calibration in which all parameters were involved. A sensitivity analysis was also performed to investigate the impact of operators in the GA on its effectiveness in optimum searching. The calibration yielded satisfactory results and required reasonable computing time. The application of the PIT model to the Windrush catchment with optimum parameter values was demonstrated. The annual P loss was predicted as 4.4 kg P/ha/yr, which showed a good fitness to the observed value.