A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management

Water Resources Management - In the context of climate stress, urbanisation and population growth, design and planning tools that assist in decentralised and environmental infrastructural planning...     

Enhancement of limited water supply network data for deterioration modelling and determination of rehabilitation rate

In the past decades, the main focus of water supply management has moved from construction of new water supply networks to rehabilitation and adaptation of the existing infrastructure. The decision-making process for the rehabilitation management relies heavily on the quality of the applied deterioration model. A recurring problem in the application of such models relates to the quality and availability of network data. These data are often incomplete or unreliable because building measures and damages are only documented properly recently and the recovery of older data is difficult and expensive. A key point in rehabilitation planning is therefore data collection and data reconstruction. Consequently, the aim of this paper is to present a methodology for the enhancement of the available data of water supply networks and the prognosis of the necessary rehabilitation rates under limited data availability. Results indicate that the presented data reconstruction technique has advantages as compared to traditional data extrapolation. It also allows the reconstruction of fragmentary data about existing water supply and wastewater collection systems for the operating utilities. However, it cannot be used for reconstructing failure types as well as the whole information on pipes (e.g. more than two missing information).     

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.     

GIS-based applications of sensitivity analysis for sewer models

Sensitivity analysis (SA) evaluates the impact of changes in model parameters on model predictions. Such an analysis is commonly used when developing or applying environmental models to improve the understanding of underlying system behaviours and the impact and interactions of model parameters. The novelty of this paper is a geo-referenced visualization of sensitivity indices for model parameters in a combined sewer model using geographic information system (GIS) software. The result is a collection of maps for each analysis, where sensitivity indices (calculated for model parameters of interest) are illustrated according to a predefined symbology. In this paper, four types of maps (an uncertainty map, calibration map, vulnerability map, and design map) are created for an example case study. This article highlights the advantages and limitations of GIS-based SA of sewer models. The conclusion shows that for all analyzed applications, GIS-based SA is useful for analyzing, discussing and interpreting the model parameter sensitivity and its spatial dimension. The method can lead to a comprehensive view of the sewer system.     

Modelling Interactions Between Lot-Scale Decentralised Water Infrastructure and Urban Form – a Case Study on Infiltration Systems

Modelling the design and implementation of urban water infrastructure (particularly decentralised systems) for strategic planning and policymaking requires detailed information of the spatial environment and quantitative knowledge of social preferences. Currently available models, however, mostly use land use, population and impervious cover data without much regard for detailed urban form or society. This study develops an algorithm for determining urban form from minimal spatial data input by incorporating local planning regulations. The interaction between urban form and implementation of lot-scale infiltration systems under different social, biophysical and climate constraints is then investigated, firstly by looking at how this varies in different residential land uses and subsequently in a case study of a typical Melbourne residential subdivision of mixed land uses. Feasibility of infiltration and its downstream impact (runoff volume, frequency and pollution) were assessed for a range of social preferences (quantified as allowable garden space) and climate scenarios (30 % increase/decrease in rainfall and evapotranspiration). Performance indicators were determined through long-term simulation with the MUSIC software. Results show how different biophysical, planning, social and climate conditions affect infiltration feasibility as well as system performance. High infiltrating soils, for example, allow smaller, well-performing and socially less-imposing systems. Low infiltrating soils lead to larger system sizes, occupy much of the allotment’s garden space, but nevertheless provide the benefit of runoff frequency reduction. Overall, climate impact was not significant except for areas with poorly infiltrating soils. Joint consideration of social, planning, climate and water management aspects potentially allows more efficient policymaking, as an array of system configurations can be tested against different multi-faceted scenarios. Such models can help facilitate better participatory planning and policymaking.     

Case study on the use of a combined system as an intermediate solution in Brazil: cost estimate

As a developing country, Brazil has to deal with a lack of basic sanitation. One of the major challenges is the treatment of wastewater. While 94% of Brazilian municipalities have stormwater systems, only 55% are connected to a wastewater system. To help improve the current situation, this study estimates the costs for implementing an intermediate solution, which is designed to ease the transition towards a separate wastewater treatment system. This solution utilizes a combined sewer system approach in areas where stormwater systems have already been implemented. A case study was carried out assessing two possible alternatives for a wastewater treatment system with data from the city of Joinville. Both alternatives are separate systems but one has a combined system as an intermediate solution. The results show that the alternative with an intermediate combined system will reduce the short‐term financial burden on governments in comparison to the separate sewer system approach.     

Influence of characteristics on combined sewer performance

Elements of combined sewer systems are among others sub-catchments, junctions, conduits and weirs with or without storage units. The spatial distribution and attributes of all these elements influence both system characteristics and sewer performance. Until today, little work has been done to analyse the influence of such characteristics in a case unspecific approach. In this study, 250 virtual combined sewer systems are analysed by defining groups of systems, which are representative for their different characteristics. The set was created with a further development of the case study generator (CSG), a tool for automatic generation of branched sewer systems. Combined sewer overflow and flooding is evaluated using performance indicators based on hydrodynamic simulations. The analysis of system characteristics, like those presented in this paper, helps researchers to understand coherences and aids practitioners in designing combined sewers. For instance, it was found that characteristics that have a positive influence on emission reduction frequently have a negative influence on flooding avoidance and vice versa.     

Identifiability analysis in conceptual sewer modelling

For a sufficient calibration of an environmental model not only parameter sensitivity but also parameter identifiability is an important issue. In identifiability analysis it is possible to analyse whether changes in one parameter can be compensated by appropriate changes of the other ones within a given uncertainty range. Parameter identifiability is conditional to the information content of the calibration data and consequently conditional to a certain measurement layout (i.e. types of measurements, number and location of measurement sites, temporal resolution of measurements etc.). Hence the influence of number and location of measurement sites on the number of identifiable parameters can be investigated. In the present study identifiability analysis is applied to a conceptual model of a combined sewer system aiming to predict the combined sewer overflow emissions. Different measurement layouts are tested and it can be shown that only 13 of the most sensitive catchment areas (represented by the model parameter 'effective impervious area') can be identified when overflow measurements of the 20 highest overflows and the runoff to the waste water treatment plant are used for calibration. The main advantage of this method is very low computational costs as the number of required model runs equals the total number of model parameters. Hence, this method is a valuable tool when analysing large models with a long runtime and many parameters.