Modelling methods to support urban sewer system management

Abstract

In the context of developing countries, problems such as unplanned and unregulated urban developments, poor water quality in the water courses, lack of sanitation coverage and water treatment facilities, lack of institutional co-ordination, mismanagement of water resources, financial constraints, lack of wise expenditure on the required infrastructure, and a conventional fragmented wastewater management approach pose particular challenges. A sensible way forward in developing countries, which are initiating the building of treatment infrastructure, is to start integrated analysis and more efficient planning control measurements at an early stage. It is not feasible to build costly wastewater treatment plants which are not properly integrated to the sewer system and which are not solving the river pollution problems. As a part of integrated analysis, models can be used to gain better understanding of certain phenomena and to predict the spatial and temporal evolution of a system when looking towards an integrated management of urban drainage systems. Particularly, a key requirement for integrated sewer system models is data to quantify flows and pollution loads, ideally including uncertainty estimates. Because well-gauged systems are rare, particularly for large scale developing urban centres, there is a need for methods of synthesising these data sets. Furthermore, large urban centres also need appropriate modelling frameworks to support an optimised sewer system preventive maintenance scheme. The final goal is to determine a plan of action that achieves the best balance between proactive and reactive maintenance to minimize the overall cost of system operation. With this context as background and using Bogotá (Colombia) as main case study, this Thesis aims: (a) at an improved understanding of pollutant load contribution at the sewer sub-catchment level under dry weather conditions to be used to support the development of an appropriate modelling framework to assess and improve the interaction of the operation of the sewer system and the treatment facility; (b) to propose and critically assess a novel statistical model, supported by an exceptionally long and spatially detailed customer complaints data-base, to support proactive maintenance of the sewer system. This Thesis demonstrates the potential for using mixed deterministic/stochastic models to characterise ungauged sub-catchment outflows and water quality during dry weather. The novel contributions are a new empirical model for gauged and ungauged catchment applications, and the simultaneous inclusion of autocorrelations and cross-correlations for water quantity and quality determinants in the error model. Such models, if used to generate inputs to parsimonious sewer system models, can be useful to quantify discharges of untreated sewage into receiving watercourses, and their uncertainty, thus allowing these sources to be included in detailed dynamic river water quality models. Besides this, the impact of source control measures within the system can also be estimated. This is important, as source control is an essential complement to centralised wastewater treatment and stormwater storage. Additionally, it is also possible to quantify the transfer of pollutant loads from the wastewater system into the stormwater system due to wrong connections, thus helping water utilities with the prioritisation of corrective interventions. Furthermore, model-based research, using scientifically defensible tools such as the ones presented in this Thesis, is central to evaluating the performance of management practices such as proactive maintenance, water demand management and greywater recycling in Bogotá. Due to the wide scope of the aims of this Thesis, they reflect different but strongly interrelated areas in current need of further research. The contribution of this dissertation is, therefore, not only individual theses, but the recognition of the integrated application of advanced and state-of the art methods (i.e. models and analysis frameworks) to deal with different operational requirements in the context of large and complex sewer systems. More generally, the work also demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations