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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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Ecological hazards such as floods, drought and poor water quality remain notable disparaging natural catastrophes of climate variability in West Africa. Associated hazard manifestation is an amalgamation of various factors, which require enhanced knowledge of its spatio-temporal extent. This work aims at the identification and mapping of areas prone to ecological vulnerabilities in the Densu River Basin of Ghana. This research utilized a combination of Analytical Hierarchical Process (AHP) and multicriteria methods (MCM) using dataset such as land use land cover, type of soil, slope, drainage density, rainfall variability and the community distribution in the basin, using Geospatial Technology. Vulnerability mapping models were developed for flooding, drought and surface water quality. The resulting analysis revealed that about 15% of the basin was highly vulnerable to flooding, about 1% was prone to drought and 6% was prone to poor surface water quality. It was revealed that the southernmost part of the basin was susceptible to flooding whilst communities along the tributaries of the Densu river were predisposed to flooding and poor water quality. The study further displayed the extent of vulnerability of the communities within the Densu basin. Uniquely, a combination of AHP and MCM was successfully used to map the vulnerability of a river basin. Therefore, it is now possible to extend the procedures to other river basins for the development of effective mitigating strategies for future hazards. [ABSTRACT FROM AUTHOR]

This study investigates the effects of indiscriminate disposal of abattoir waste into water bodies in the Aregbe area abattoir of Osogbo the capital city of Osun state, Nigeria. The samples for the study were gotten from the point source into the streams and wells of close proximity to the abattoir facility. The physiochemical and bacteriological parameters of the water bodies were analyzed and compared to the World health organization limits for discharge and use. The observed physicochemical and bacteriological parameters were Color, Odor, Temperature, pH, Alkalinity, Turbidity, Total hardness, Appearance, Calcium Hardness, Chlorine ion, Magnesium ion, Dissolved Oxygen and Total Coliform bacteria using the standard procedure in the Standard Methods for the Examination of Water and Wastewater Part 1000. It was observed that the Color, Dissolved oxygen and Total coliform exceeded the limits set by the world health organization. The fairly poor performance of the water bodies is due to the carrying capacity which in coming years will be exceeded. The indicators of the given water quality parameters give rise to future studies on the state of the water bodies in Nigeria. [ABSTRACT FROM AUTHOR]

As sources of greenhouse gas (GHG) emissions, integrated urban drainage systems (IUDSs) (i.e., sewer systems, wastewater treatment plants and receiving water bodies) contribute to climate change. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, reviews the state-of-the-art and modelling tools developed recently to understand and manage GHG emissions from IUDS. Further, open problems and research gaps are discussed and a framework for handling GHG emissions from IUDSs is presented. The literature review reveals that there is a need to strengthen already available mathematical models for IUDS to take GHG into account. [ABSTRACT FROM AUTHOR]

Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important ( factor prioritisation ) and non-influential ( factor fixing ) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality–quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term “screening” is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity. [ABSTRACT FROM AUTHOR]

Climate is one of the most important factors leading to changes in the hydrologic and environmental characteristics of river basins. When considering water quality, the natural factors should be weighed against anthropogenic factors (such as urbanization, increased population, and higher water demand) that may increase or decrease the effect of climatic modifications. Any prediction of future climatic and anthropogenic scenarios is affected by uncertainty and the modelling tools that are used for evaluating their impact on receiving water bodies. The present paper is aimed at investigating the complexity of such analyses and the uncertainty related to future impact predictions based on limited databases. Specifically, the study is focused on quantifying the impact of urbanization and social changes coupled with climate on river water quality under uncertainty. The study was carried out at a small catchment scale (less than ), an analysis that has been lacking from the field so far. The analysis took into account both anthropogenic change and climate determined from observed data. Because of inherent uncertainty in the model caused by the complexity of the model and its calibration, the analysis was performed considering the effect of such uncertainty on reliability by means of Monte Carlo simulations. This analysis has been applied to a monitored semiurbanized basin in Sicily (Italy). The study showed that the proposed analyses can useful for planners and urban managers; however, the uncertainty connected with mathematical modeling and small available databases has to be taken into account because it can affect decision making. The impacts of anthropogenic changes and climate can interact, thus requiring that such factors be jointly analyzed. A larger effort in monitoring and characterization of natural and urban water systems is needed to increase the reliability of models and to improve the effectiveness of decision making based on such models. [ABSTRACT FROM AUTHOR]

Abstract: In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box–Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box–Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box–Cox transformation is not applied or non-calibrated parameter is used. [Copyright &y& Elsevier]

Abstract: Identifiability analysis enables the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology enables one to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for setting up measuring campaigns for integrated water quality modelling. Particularly, by means of the identifiability analysis, the information about the location and the number of the monitoring stations in the integrated system required for assessing a specific group of model parameters were gained. The analysis has been applied to a real, partially urbanised, catchment containing two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterised by different monitoring station locations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameters quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data. Further, a connection between the data set and the number of model parameters effectively assessable has been established leading to much more reliable model results. [Copyright &y& Elsevier]

Abstract: The paper presents the results of a study in which the uncertainty levels associated with a detailed and a simplified/parsimonious sewer sediment modelling approach have been compared. The detailed approach used an Infoworks CS sewer network model combined with a user developed sediment transport code and the simplified approach used a conceptual sewer flow and quality model. The two approaches have been applied to a single case study sewer network and the simulation results compared. The case study was selected as moderate storm events had occurred during a 2year rainfall and sewer flow monitoring period. Flooding had been observed and this was thought to be caused by significant solids accumulation in the sewer network. As a result sediment deposit measurements were carried out over a 6month period. Model simulations were made of this period and predictions obtained of sediment deposit location and depth. The uncertainty analysis of both modelling approaches was carried out using Monte Carlo based computational methods. This was a limitation for the detailed approach with regards to computational time. Use of the simplified model was not constrained by this issue and so a more conventional assessment of the uncertainty was possible. The simplified approach, due to its structure, only provided a temporal estimate of uncertainty at the final section of the catchment. The detailed approach enabled an assessment of uncertainty at an individual pipe scale but only at the end of the simulation period. A comparison of the uncertainty estimations from both methods at the final section of the catchment and the end of the simulation period indicated comparable values of predicted uncertainty. Therefore a complementary use of both approaches would allow reasonably comparable estimations of levels of uncertainty at both a spatial and temporal scale. The use of such modelling approaches may provide a useful decision-making tool for sewer system management. [Copyright &y& Elsevier]

In integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones depending on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which simulation output from upstream models is transferred to the downstream ones as input. The overall uncertainty can differ from the simple sum of uncertainties generated in each sub-model, depending on well-known uncertainty accumulation problems. The present paper aims to study the uncertainty propagation throughout an integrated urban water-quality model. At this scope, a parsimonious bespoke integrated model has been used allowing analysis of the combinative effect between different sub-models. Particularly, the data and parameter uncertainty have been assessed and compared by means of the variance decomposition concept. The integrated model and the methodology for the uncertainty decomposition have been applied to a complex integrated catchment: the Nocella basin (Italy). The results show that uncertainty contribution due to the model structure is higher with respect to the other sources of uncertainty. [ABSTRACT FROM AUTHOR]

Every model is, by definition, a simplification of the system under investigation. Although it would be desirable to reduce the gap between the simulated and the observed behaviors of the system to zero, this reduction is generally impossible owing to the unavoidable uncertainties inherent in any modeling procedure. Uncertainty analyses can provide useful insights into the best model approach to be used for obtaining results with a high level of significance and reliability. The evaluation of parameter uncertainties is necessary for calibration and for estimating the impact of these uncertainties on model performance. In this context, the uncertainty of a river water-quality model developed in previous studies is presented. The main goal is to gain insights into the modeling approaches concerning small rivers. Previous works generally focused on modeling the large river while neglecting the small one. Following a model calibration, the model uncertainty has been assessed by means of the generalized likelihood uncertainty estimation (GLUE). The results showed that the biological process related to the biological oxygen demand (BOD) removal influenced mainly by the parameters characterizing deoxygenation and nitrogen removal. On the other hand, the biological processes related to nitrogen removal were influenced not only by the parameters related to the nitrogen removal but also to oxygen concentration. The application of the GLUE methodology shows that the river quality model considered is suitable for simulating the important processes involved. Uncertainty bounds showed different amplitudes with respect to the pollutant species considered. In particular, the oxygen uncertainty bounds were narrower with respect to the other model components suggesting much attention must be paid to both model algorithms and quality data to be gathered. The study confirmed the suitability of the GLUE methodology as a powerful tool as a simplified screening methodology to assess uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Summary: Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which the simulation output from upstream models is transferred to the downstream ones as input. The Variance Decomposition Approach tracks uncertainty propagation commonly assuming that the correlation among error sources is negligible. In complex environmental models, the overall uncertainty can differ significantly from the simple sum of uncertainties generated in each sub-model, showing the well-known uncertainty accumulation problems due to non-linearity in the model and correlation among the sources of uncertainty. This work discusses the importance of such issues in the application of a complex integrated urban drainage model with the aim of evaluating the applicability of Variance Decomposition Approach. The integrated model and the methodology for the uncertainty decomposition were then applied to a complex integrated catchment: the Nocella basin (Italy). The results showed that the Variance Decomposition Approach can be a powerful tool for uncertainty analysis, but a possible correlation among uncertainty sources should be considered because it can greatly affect the analysis. [ABSTRACT FROM AUTHOR]

Urban water quality management often requires use of numerical models allowing the evaluation of the cause-effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment. [ABSTRACT FROM AUTHOR]

Summary: River water quality models can be valuable tools for the assessment and management of receiving water body quality. However, such water quality models require accurate model calibration in order to specify model parameters. Reliable model calibration requires an extensive array of water quality data that are generally rare and resource-intensive, both economically and in terms of human resources, to collect. In the case of small rivers, such data are scarce due to the fact that these rivers are generally considered too insignificant, from a practical and economic viewpoint, to justify the investment of such considerable time and resources. As a consequence, the literature contains very few studies on the water quality modelling for small rivers, and such studies as have been published are fairly limited in scope. In this paper, a simplified river water quality model is presented. The model is an extension of the Streeter–Phelps model and takes into account the physico-chemical and biological processes most relevant to modelling the quality of receiving water bodies (i.e., degradation of dissolved carbonaceous substances, ammonium oxidation, algal uptake and denitrification, dissolved oxygen balance, including depletion by degradation processes and supply by physical reaeration and photosynthetic production). The model has been applied to an Italian case study, the Oreto river (IT), which has been the object of an Italian research project aimed at assessing the river’s water quality. For this reason, several monitoring campaigns have been previously carried out in order to collect water quantity and quality data on this river system. In particular, twelve river cross sections were monitored, and both flow and water quality data were collected for each cross section. The results of the calibrated model show satisfactory agreement with the measured data and results reveal important differences between the parameters used to model small rivers as compared to those for large rivers. Additionally, these results appear related to the different roles played by physical–chemical–biological processes in small rivers as compared to larger systems. [Copyright &y& Elsevier]

The objective of this paper is the definition of a methodology to evaluate the impact of the temporal resolution of rainfall measurements in urban drainage modelling applications. More specifically the effect of the temporal resolution on urban water quality modelling is detected analysing the uncertainty of the response of rainfall-runoff modelling. Analyses have been carried out using historical rainfall-discharge data collected for the Fossolo catchment (Bologna, Italy). According to the methodology, the historical rainfall data are taken as a reference, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between 'true' and rescaled rainfall data has been carried out using a non-dimensional accuracy index. Monte Carlo simulations have been carried out applying a parsimonious urban water quality model, using the recorded data and the resampled events. The results of the simulations were used to derive the cumulative probabilities of quantity and quality model outputs (peak discharges, flow volume, peak concentrations and pollutant mass) conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. The results showed that when coarser rainfall information is available, the model calibration process is still efficient even if modelling uncertainty progressively increases especially with regards to water quality aspects. [ABSTRACT FROM AUTHOR]

In the past, emission standard indicators have been adopted by environmental regulation authorities in order to preserve the quality of a receiving water body. Such indicators are based on the frequency or magnitude of a polluted discharge that may be continuous or intermittent. In order to properly maintain the quality of receiving waters, the Water Framework Directive, following the basic ideas of British Urban Pollution Manual, has been established. The Directive has overtaken the emission-standard concept, substituting it with the stream-standard concept that fixes discharge limits for each polluting substance depending on the self-depurative characteristics of receiving waters. Stream-standard assessment requires the deployment of measurement campaigns that can be very expensive; furthermore, the measurement campaigns are usually not able to provide a link between the receiving water quality and the polluting sources. Therefore, it would be very useful to find a correlation between the quality status of the natural waters and the emission-based indicators. Thus, this study is aimed to finding a possible connection between the receiving water quality indicators drawn by environmental regulation authorities and emission-based indicators while considering both continuous (i.e. from the wastewater treatment plants) and intermittent pollution discharges (mainly from combined sewer overflows). Such research has been carried out by means of long-term analysis adopting a holistic modelling approach. The different parts of the integrated urban drainage system were modelled by a parsimonious integrated model. The analysis was applied to an ephemeral river bounding Bologna (Italy). The study concluded that the correlation between receiving water quality and polluting emissions cannot be generally stated. Nevertheless, specific analyses on polluting emissions were pointed out in the study highlighting cause-effect link between polluting sources and receiving water quality. [ABSTRACT FROM AUTHOR]

The continuous growth of urban areas and the increasing public awareness of the environmental impacts of storm water have raised interest on the quality of the receiving water bodies. In the past two decades, many efforts have been directed at improving urban drainage systems by introducing mitigation measures to limit the negative environmental impacts of storm water. These mitigation measures are generally called best management practices (BMPs), sustainable urban drainage systems, or low impact developments, and they include practices such as infiltration and storage tanks that reduce the peak flow and retain some of the polluting materials. Choosing the best mitigation measure is still a controversial topic. To gain insight on the best technique, this study compares different distributed and centralized urban storm-water management techniques, including infiltration and storage facilities. The main objective of this study is to use modeling to assess the effects of the different urban drainage techniques. To this end, a homemade model that was developed in previous studies is applied. This model enables us to simulate both combined sewer systems and ancillary structures such as storm tanks or infiltration trenches to determine water quantity and quality characteristics. A long-term simulation is employed to account for the effects of sediments in BMPs, which generally reduce the hydraulic capacity. The results allow us to draw some conclusions on the peculiarities of BMP techniques, on the possibility of integrating different techniques for improving efficiency, and on BMP maintenance planning. [ABSTRACT FROM AUTHOR]

Summary: The evaluation of urban stormwater quality is of relevant importance for urban drainage, and mathematical models may be of great interest in this respect. To date, several detailed mathematical models are available to predict stormwater quantity–quality characteristics in urban drainage systems. However, only a few models take sewer sediments into account, considering their cohesive-like properties that influence the build-up process of the pollutant load. Furthermore, the model data requirements, especially for the quality aspects, are extensive, which limit their applicability and affect model results with large uncertainty. Uncertainty analysis provides a measure or index regarding the significance and the accuracy of the results obtained by mathematical modelling and is therefore of high interest. Nevertheless, only few studies have been carried out in the urban drainage field, and very few deal with water quality issues. One of the main reasons for this lack of research is the computational burden required by detailed models that preserve this analysis and generally require several Monte Carlo simulation runs. A possible to this problem may be the adoption of simplified parsimonious models that generally require shorter computational times. In this context, this paper presents a parsimonious conceptual model for the evaluation of the pollutant load in-sewers. The model contains two modules: a hydrological and hydraulic module that calculates the hydrographs at the inlet and at the outlet of the sewer system, and a solid transfer module that calculates the pollutographs. The cohesive properties of sewer sediments were carefully considered. Further, the effectiveness of the innovative sewer sediment modelling approach has been verified by taking into account the uncertainty assessed according to the GLUE methodology. The model has been tested using experimental quantity–quality data gathered in two Italian catchments, Fossolo (Bologna) and Parco d’Orlèans (Palermo). [Copyright &y& Elsevier]

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management. [ABSTRACT FROM AUTHOR]

In the past three decades, scientific research has focused on the preservation of water resources, and in particular, on the polluting impact of urban areas on natural water bodies. One approach to this research has involved the development of tools to describe the phenomena that take place on the urban catchment during both wet and dry periods. Research has demonstrated the importance of the integrated analysis of all the transformation phases that characterise the delivery and treatment of urban water pollutants from source to outfall. With this aim, numerous integrated urban drainage models have been developed to analyse the fate of pollution from urban catchments to the final receiving waters, simulating several physical and chemical processes. Such modelling approaches require calibration, and for this reason, researchers have tried to address two opposing needs: the need for reliable representation of complex systems, and the need to employ parsimonious approaches to cope with the usually insufficient, especially for urban sources, water quality data. The present paper discusses the application of a bespoke model to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two wastewater treatment plants, and has a small river as the receiving water body. The paper describes the monitoring approach that was used for model calibration, presents some interesting considerations about the monitoring needs for integrated modelling applications, and provides initial results useful for identifying the most relevant polluting sources. [ABSTRACT FROM AUTHOR]

Abstract: In the last years, the attention on integrated analysis of sewer networks, wastewater treatment plants and receiving waters has been growing. However, the common lack of data in the urban water-quality field and the incomplete knowledge regarding the interpretation of the main phenomena taking part in integrated urban water systems draw attention to the necessity of evaluating the reliability of model results. Uncertainty analysis can provide useful hints and information regarding the best model approach to be used by assessing its degrees of significance and reliability. Few studies deal with uncertainty assessment in the integrated urban-drainage field. In order to fill this gap, there has been a general trend towards transferring the knowledge and the methodologies from other fields. In this respect, the Generalised Likelihood Uncertainty Evaluation (GLUE) methodology, which is widely applied in the field of hydrology, can be a possible candidate for providing a solution to the above problem. However, the methodology relies on several user-defined hypotheses in the selection of a specific formulation of the likelihood measure. This paper presents a survey aimed at evaluating the influence of the likelihood measure formulation in the assessment of uncertainty in integrated urban-drainage modelling. To accomplish this objective, a home-made integrated urban-drainage model was applied to the Savena case study (Bologna, IT). In particular, the integrated urban-drainage model uncertainty was evaluated employing different likelihood measures. The results demonstrate that the subjective selection of the likelihood measure greatly affects the GLUE uncertainty analysis. [Copyright &y& Elsevier]

Over the last few years there has been a growing attention regarding the receiving water body quality state. As a matter of a fact, the Directive 91/271 of the European Union (EU) replaced the "emission standard" concept that fixes discharge limits depending on polluting emission characteristics, with the "stream standard" concept that fixes discharge limits for each polluting substance depending on self-depurative characteristics of the RWB. In this context, several WWTPs need to be upgraded in order to meet stricter effluent limits. The need of WWTP upgrading was also emphasized by the growing urbanization that have led, in most cases, to get overloaded WWTP due to an overcoming of the maximum WWTP capacity. In order to upgrade existing WWTP basically two main possibilities can be chosen: building new tanks or modify the WWTP by introducing new technologies such as the HMBBR systems. In this paper, such latter possibility was explored and as a case study an existing Italian WWTP (Acqua dei Corsari) located in Palermo (IT) was analysed. The main goal was to test the effectiveness of HMBBR systems with respect to the WWTP upgrading. The survey was carried out by means of model simulation and an HMBBR pilot plant. This latter was employed for the evaluation of the model parameters as well as kinetic coefficients for the HMBBR. The model results are encouraging towards the WWTP upgrading by means of HMBBR. As a matter of fact, the model simulation results showed that the WWTP maximum capacity can be upgraded from 480,000 up to one million PE. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]