Next Article in Journal
Temporal Distribution Characteristics of Alpine Precipitation and Their Vertical Differentiation: A Case Study from the Upper Shule River
Next Article in Special Issue
LCA Methodology for the Quantification of the Carbon Footprint of the Integrated Urban Water System
Previous Article in Journal
Large Differences between Glaciers 3D Surface Extents and 2D Planar Areas in Central Tianshan
Previous Article in Special Issue
Where to Find Water Pipes and Sewers?—On the Correlation of Infrastructure Networks in the Urban Environment

Water 2017, 9(4), 285; https://doi.org/10.3390/w9040285

Review
Urban Water Cycle Simulation/Management Models: A Review
1
Environmental Engineering Program, Universidad Santo Tomas, 110231426 Bogotá, Colombia
2
Institute of Water and Environmental Sciences, Universidad de Alicante, San Vicente del Raspeig Route, s/n, 03690 Alicante, Spain
3
Civil Engineering Program, Pontificia Universidad Javeriana, 110231594 Bogotá, Colombia
4
Matanza Riachuelo Catchment Authority (ACUMAR), Esmeralda 255 PB, C1033AAJ Ciudad Autónoma de Buenos Aires, Argentina
*
Author to whom correspondence should be addressed.
Academic Editors: Manfred Kleidorfer and Robert Sitzenfrei
Received: 19 January 2017 / Accepted: 16 April 2017 / Published: 19 April 2017

Abstract

:
Urban water management is increasingly important given the need to maintain water resources that comply with global and local standards of quantity and quality. The effective management of water resources requires the optimization of financial resources without forsaking social requirements. A number of mathematical models have been developed for this task; such models account for all components of the Urban Water Cycle (UWC) and their interactions. The wide range of models entails the need to understand their differences in an effort to identify their applicability, so academic, state, and private sectors can employ them for environmental, economic, and social ends. This article presents a description of the UWC and relevant components, a literature review of different models developed between 1990 and 2015, and an analysis of several case studies (applications). It was found that most applications are focused on new supply sources, mainly rainwater. In brief, this article provides an overview of each model’s use (primarily within academia) and potential use as a decision-making tool.
Keywords:
urban water cycle; integrated management of urban water; computational models

1. Introduction

The continuous growth of urban areas across the globe is directly tied to rapid economic, population, and infrastructure growth [1,2]. Currently, urban areas account for more than half of the world’s population; more than 500 cities already have more than a million inhabitants [3,4]. It is estimated that in 1900, only 9% of the world’s population lived in urban areas; by the 1980s, urban population had increased to 40% globally. By 2000, this figure had reached 50%, and it is expected to reach 60% by 2025 [5].
For urban populations, the importance of water cannot be overestimated. Its management is a challenge in terms of sustainability and administration, for cities have short time frames in which to offer the best possible water administration, wastewater collection, rainwater harvesting, and effective water treatment without generating negative environmental, social, sanitary, or health effects [6].
Over the last two decades, computational models have gained recognition as effective tools for addressing the aforementioned challenges. These models allow for the achievement of policy objectives [7], evaluation of the feasibility of different solutions for specific problems [8,9], and proper decision-making for urban water management [10].
Historically, these models have encompassed a number of different approaches, from individual perspectives to more holistic visions, bringing together natural water flows, piping flows, and various subsystems (ecological, environmental, socioeconomic, and political) [11,12]. Initially developed approximately two decades ago, these models were primarily employed to understand the behavior and interactions of urban drainage, treatment systems, and bodies of water. Such models were presented in Denmark in 1992 at the INTERURBA conference “Interactions between Sewers, Treatment Plants and Receiving Waters in Urban Areas”, and they were geared toward identifying relations, impacts, and possible controls [13,14]. At INTERURBA II in Portugal (2001), these models were expanded to include rainwater management [15,16]. As the relations between model components and water supply began to be captured and expressed, researchers began to concentrate their efforts on the development of models that were more comprehensive in terms of elements and interactions [17]. In Austria, INTERURBA III took place in 2013. This iteration was titled “Modeling the urban water cycle as integral part of the city”, and its objective was to study the interactions between models used for management of urban water and socioeconomically feasible urban development [14].
The incorporation of all components of the Urban Water Cycle (UWC) has improved the management of urban water resources and the development of different component systems, including supply, treatment, distribution, consumption, wastewater collection, drainage, and quality and quantity control of surface and groundwater sources [12,15,18,19,20,21,22].
As per Renouf and Kenway [23], UWC modeling was previously based on the quantitative simulation of anthropogenic flows given by water use, along with the simulation of water flows. UWC models factored in balances of mass, energy, and flow [9,20,24,25,26,27,28,29,30,31,32,33,34] until artificial-intelligence models were implemented [35,36]. All UWC models studied in this review, however, have been adapted to different temporal and spatial scales.
As proposed by Mitchell et al. [37], there are more than 65 commercial or free models that rely on partial or total combinations of UWC elements. Bach et al. [15] classified them according to four levels, each of which reflects the degree of integration: Integrated Component-based Models, Integrated Urban Drainage Models (IUDMs) or Integrated Water Supply Models (IWSMs), Integrated Urban Water Cycle Models (IUWCMs), and Integrated Urban Water System Models (IUWSMs).
It is crucial for researchers, academics, administrators of urban water resources, and urban-infrastructure planners and designers to learn about these models and their varied applications because they can be used to devise integrated solutions for the different UWC components. The use of these models may help ensure the feasibility of solid economic investments—and establish technical arguments—for the creation of policies and guidelines geared towards sustainability.
This article presents a review of UWC software and models designed for integrated urban water management. This review was based on two parts: models developed between 1990 and 2015 that included all UWC components (IUWCMs and IUWSMs) and different applications of these models between 1990 and 2016. The paper is divided into three parts: an introduction to UWC, a description of the models reviewed, and case studies (applications) of these models.

2. Urban Water Cycle

Based on the literature review, a blanket definition of the UWC concept can be articulated as follows: The spatiotemporal interaction between water and hydrological processes, as well as supply, treatment, distribution, consumption, collection, provision, and reuse carried out in urban or partially urban areas.
This cycle has four main inputs: water, contaminants, energy, and chemicals, as can be observed in Figure 1. The first, and most essential, for this cycle is water, which comes from two primary sources: supply sources (e.g., surface water and/or groundwater), and precipitation. These inputs allow for the calculation of balances and hydric consumption within the UWC [26,29,38,39]. The second input refers to contaminants, which are closely linked to water flows, for these flows are the transportation medium and/or input to the cycle. Contaminants enter the cycle via surface water and/or groundwater flows, wastewater water flows from property-related uses, treatment of wastewater flows, and, lastly, rainwater flows associated with atmospheric water, different surfaces, and chemical use associated with these surfaces [40].
The third input, energy, is highly consequential within the UWC because of the costs and environmental effects attributable to greenhouse gases and the use of natural resources [41,42,43,44,45,46]. Energy use is principally related to the function of treatment systems, water supply, and thermal water heating [47,48,49,50,51,52,53,54,55]. Moreover, during wastewater treatment, biogas is produced by the digestion of organic compounds [48,56,57].
The fourth and final input refers to chemicals used to treat wastewater and drinking water; special emphasis is placed on the costs associated with chemicals in terms of the operation of these treatment processes as well as their potential environmental and health impacts [34,59,60].
However, the cycle’s behavior, along with that of inputs, is modified by external and internal factors involved in any process. These factors intervene both directly and indirectly within each input, thereby increasing the entire cycle’s complexity. See Table 1 for more information on this complexity.

3. Description of Models of UWC Processes

To implement models for UWC management, a comprehensive understanding of these models, their characteristics, and users’ needs must be established. Therefore, in this section, 17 models encountered in the literature review are presented with a presentation of the spatial and temporal characteristics, other characteristics, and simulated processes, among other aspects.
To complement the information regarding UWC models found in Table 2, important factors and components are described below. The aquacycle sequentially simulates the balances of the UWC processes of drinking-water supply, hydrology (precipitation and evapotranspiration), and wastewater; these balances are established via loops that cover the entire system on a daily timescale. The amount of “imported” water supply is the sum of the entire population’s water uses in addition to the water utilized for irrigation and water lost due to leaks in the system. Wastewater refers to all imported water and percolation and runoff flow rates. For its part, rainwater is runoff minus percolation and storage. Urban Volume Quality (UVQ) is an expansion of Aquacycle. UVQ is distinguished by its simulation of contaminants; it also assumes that there is no degradation or conversion of the evaluated contaminants and that the user must specify concentrations, loads, and performance of treatments [29,30]. UVQ and Aquacycle are models with grouped parameters that do not require a large amount of input data, simplifying the use of these models.
Originally, the Urban Water Optioneering Tool (UWOT) was proposed to improve WaND-OT1, which simulates the interactions between drinking-water supply, wastewater, and runoff. To create possible scenarios, UWOT has an Excel library with nine applications for microcomponents, two for intermediate levels, and four for the top level. The conditions, operations, and design of each of these applications can be modified [9,100].
WaterCress relies on the concept of nodes. Nodes represent the UWC’s functions, operations, processes, and infrastructure. In total, there are 18 basic nodes, with each having a database with quantity and quality variables [105]. In turn, these nodes are linked by flows, such as supply and drainage. For drainage, these nodes are subdivided depending on the type of function. They are assigned a predetermined color: pink for water diversions, blue for catchment runoff, green for runoff from a house or urban node, gray for gray water, and black for wastewater [25,103]. With respect to Hydro Planner, this software works with the model E2, which allows for the integrated simulation of various components such as runoff and nutrient and sediment contamination of a water body [107]. Hydro Planner has seven modules: (1) catchment (simulation of contaminant and runoff processes); (2) water supply; (3) consumption; (4) rainwater (contamination and water flow); (5) wastewater (contamination and water flow); (6) receiving water bodies (contamination and water flow); and (7) integration (networking of modules, input and output calculations, and graphic interface) [28,108]. WaterMet2 quantifies the UWC’s metabolism into four main subsystems: (1) water supply (sources, treatment, and supply); (2) water demand (consumption and water uses); (3) wastewater (separated and combined systems and wastewater treatment); and (4) water treatment (in situ or centralized treatment).
UrbanCycle’s software is characterized by the creation of precipitation and demand data, which can be entered by the user. The software allows for their creation via stochastic models. For rain, the model has the Disaggregated Rectangular Intensity Pulse (DRIP); for demand, it has the Probabilistic Demand Mode. Urban Developer is based on UrbanCycle, which presents four main characteristics: Adaptive time-stepping allows for the simulation of different timescales, primarily as a function of climate conditions in wet season for short periods, in dry season for an adapted period, and in transition for an intermediary period, leading to a computational benefit in terms of calculation time. Canvas represents the system’s graphic interface, for which a number of improvements have been proposed from a structural perspective. Each input node is a component of the UWC, not a system of the UWC. Furthermore, the connections or flow movements are represented by a color that characterizes the type of flow. The configuration of input parameters allows for the modification, copy, and elimination of these parameters (among others) by the user quickly and easily. Finally, it has network nesting, a characteristic that allows for the linking of different spatial scales of the system through subnetworks, which symbolize a node containing all previously established connections.
Dance4Water is based on the Virtual Infrastructure Benchmarking (VIBe) model presented by Sitzenfrei et al. [118,119]. This model creates virtual urban environments that include digital models, water bodies, land use, and urban infrastructure (e.g., sewage and drinking-water distribution systems) [123,124,130,131]. This model follows a stochastic approach, using multiple layers of cellular automata [118]. This software presents three linked modules to simulate the entire UWC. The first module, the Urban Development Module (UDM), encompasses actions that create territorial development resulting from population increase or urban-development plans at an annual scale. For this purpose, UrbanSim software was created [35]. The second is the biophysical module (BMP) [122], which represents UWC-infrastructure components and performance. Two submodules—city and water-system generator (infrastructure) and performance assessment (performance)—were created as part of the second module. The performance submodule uses SWMM (Storm Water Management Model) software for hydraulic and hydrodynamic calculations that are subsequently included in the model [117]. The third and final module is the Societal Transition Module (STM) designed for simulating the extent and impact of society on the UWC; the STM also serves as a tool for strategic planning [117,120,132].
DUWSiM integrates multiple models, namely climate (LARS-WG stochastic weather generator), land use (MOLAND—Monitoring Land Use/Cover Dynamics), and water balance in urban areas (DUWSiM WB—Dynamic Urban Water Simulation Model Water Balance). This integration is done via a database consisting of different input data, such as socioeconomic, geographical, physical-infrastructure, demand, and climate factors. This database provides data for the water-balance model, which simulates the daily flow of movement of UWC processes (drinking-water supply, rainwater, wastewater, evapotranspiration, percolation, etc.). DMM was developed using the MS-Excel platform, which facilitates user adaptation of the interface. Excel lets the user enter different input values and create scenarios by modifying sustainability indicators; in Excel, calculations are performed in intermediary files. The program has four files: (1) notes, assumptions, and guidelines; (2) user control via an input data file (entered or calculated), for which values display particular characteristics of the study area and concomitant consumption; (3) annual files (annual-scale calculations), which are input data consisting of nine independent spreadsheets that are components of the UWC; and (4) comparison of final results (this spreadsheet presents absolute or relative indicators of the performance of economic, social, environmental, and functional factors).
MIKE URBAN independently (in parallel) simulates water supply, drainage, and wastewater sewage; this software couples 1-D sewer modeling with 2-D overland-flow modeling. It is integrated with the ESRI ArcGIS platform using the “geo-database” concept [95]. This assembly uses valuable aspects of GIS, such as network topologies, global-reference coordinates, labels, spatial analysis, and graphical functions, resulting in layers and the ability to connect layers for optimal management [98]. Sobek-Urban is an integrated software package consisting of 1DFLOW Rural-Urban-River, Overland flow-2D, Rainfall-runoff (RR), 1DWAQ Water quality and Real-time-Control (RTC). The 1-D flow solves the Saint-Venant equations by means of a finite difference, and the 2-D flow uses a rectangular grid and finite difference framework [133].

4. Application of UWC Models

The application of these models has led to mixed results, perhaps a result of these models’ use for myriad purposes including: resource administration, management, and decision-making in the development of cities to manage and control hydric resources in terms of quality and quantity, contamination control with relation to natural resources, infrastructure planning, public-policy evaluation, financial management, and evaluation of socioeconomic development [134]. Case studies are discussed below.
Firstly, as can be seen in Table 3, the primary use of these tools is for calculating hydric balances because such balances allow researchers to determine flows and/or volumes of different types of water in the UWC. These flows are crucial insofar as they represent the cycle’s main inputs and outputs [38]. In turn, this facilitates an understanding of water dynamics in urban areas and facilitates the identification of the degree of interrelation during different UWC processes. In addition, the aforementioned balances are an essential source when determining modifications of the hydrological cycle, for the is the most disruptive force with respect to the cycle’s equilibrium [261]. In light of the realities of disruption, balances let decision-makers create scenarios for adequate hydric resource management. Mass balances, for their part, are used to estimate contaminant loads because of the direct relationship between water flows and contaminants [171].
Secondly, Table 3 demonstrates multiple model applications for the management, calculation, and determination of drinking-water supply and water demand. This is attributable to the effort to decentralize water supply and establish alternate or unconventional sources to meet populations’ water needs without ignoring environmental constraints [12]. Research has found that urban areas face problems related to inequality, in-home contamination, economy, and infrastructure [262]. Consequently, within the most commonly employed practices, new sources include: rainwater, reuse of gray water and wastewater, desalinization, and groundwater. To address the aforementioned aspects, many scenarios proposed in the literature include the harvesting of rainwater and the reuse of gray water. This allows for the conservation of hydric resources, the reduction of runoff volume, and the reduction of wastewater and corresponding contaminant loads [263,264,265,266,267].
Thirdly, there is the selection and evaluation of best management practices, which are structural and nonstructural actions aimed at minimizing the impact of urbanization on the natural hydrological cycle [268]. These practices offer significant potential for UWC management insofar as they can be applied to any part of the UWC, such as reduction of water demand, management of rainwater, reduction of flooding, control of contamination, mitigation of environmental damage, evaluation of ecological possibilities, and reduction of infrastructure investment [269]. Many other applications are affected by the kind of software and the needs proposed by the authors (e.g., hydraulic and flooding applications).
Finally, the software MIKE URBAN and Sobek Urban are primarily used for flooding control, evaluation, monitoring, and optimization of drainage/sewage systems—which is a function of their inclusion of hydraulic and hydrological calculations. It is important to add that both software programs (notably the former) have been heavily employed around the world, although a few applications have involved all UWC components.
Of the more infrequent approaches, social factors are salient. The primary reason for the infrequent inclusion of social factors stems from the fact that most software programs and approaches are focused on technical solutions. Additionally, the inclusion of social factors can be quite complex, a reflection of social dynamics, practices, behaviors, and expectations with regard to water use [270]. This complexity may serve as a barrier to the determination of effective water-management strategies [271]. That said, to some extent, failing to include social factors generates a disconnect, for there is an undeniable relationship between society and the UWC’s technical elements (Sociotechnical, see Sofoulis [272]). Thus, there is a glaring need to develop tools that encompass social and economic factors (i.e., a more comprehensive engagement with these factors would improve UWC planning). As posited by Koutiva and Makropoulos [273], the use of artificial intelligence has produced tools that can be used for social and economic factors as well as water management. According to the two authors, the most frequently employed artificial-intelligence tools include agent-based modeling, artificial neural networks, Bayesian belief networks, and systems dynamics modeling.
The most frequently used models are Urban Volume and Quality (UVQ), Aquacycle, and MIKE URBAN; each accounted for more than 15 experiments in the literature. In fact, these models were used in more than the 50% of all experiments. After these three, water-balance and life-cycle analyses are next, with each accounting for 10 to 14 experiments (22% of all experiments). UWOT, WaterCress, Urban Metabolism, UrbanCycle, Sobek-Urban and Dance4Water accounted for 5 to 9 experiments each (18% of all experiments). Finally, the remaining models accounted for 1 to 4 experiments each (10% of all experiments).
In terms of year ranges, 60% of reported experiments were conducted between 2012 and 2016. Between 2009 and 2011, this percentage was 20%. Between 2006 and 2008, the percentage was 11%, and between 2003 and 2005, the percentage was 9%. This progressive increase can be attributed to the advent of the concept of Integrated Urban Water Management (IUWM) in the mid-1990s, though IUWM was not widely discussed and adopted until 2000 [268].
Looking at the geographical distribution of these experiments (see Figure 2; darker shading represents more experiments and lighter shading less experiments), the applications of these tools or methodologies have been carried out in a variety of countries. However, Australia has the most applications (41 revealed in the literature review). In that country, Aquacycle, UVQ, WaterCress, Hydro Planner, Urban Cycle, and Dance4Water were the most commonly used software programs. These programs were developed by public and private entities. After Australia, the country with the most frequent use of relevant experiments was Denmark, which conducted experiments using MIKE URBAN, a tool developed and evaluated in the Scandinavian country. The United Kingdom (most commonly using UVQ) and Greece (using Aquacycle and, above all, UWOT, which was developed in Greece) each had six experiments. Other countries, such as South Korea and the United States, had four and five experiments, respectively. There were also 24 countries with less than three recorded experiments each (including countries from continents such as Africa, Latin America, Asia, and Europe).
Despite the potential for the management and administration of UWC, it is important to mention that, by and large, the use of these tools has been concentrated in academia rather than in decision-making environments. This is explained by a number of different factors—related to institutions, politics, society, economics, laws, and organizations—as well as the absence of institutionality, lack of knowledge regarding relevant tasks, and lack of long-term vision, among others [274,275]. However, academics’ role in this situation cannot be overlooked: Most projects related to these models are focused on obtaining data rather than constructing bridges between investigation and social application. For instance, there is scant direct application of these models in industrial sectors. As per Abbott [276], this is because the needs within academia and within industrial sectors do not always coalesce. That said, the failure to implement the solutions proposed by UWC models hinders possible determination of the level of population acceptance, which is crucial for establishing the success (or lack thereof) of strategies because many of the experiments discussed herein are based on the decentralization of services and applications in situ [277].

5. Conclusions

Urban water management is a globally urgent problem and entails a host of pertinent issues related to supplying drinking water, handling wastewater and rainwater, reducing environmental impact and waterborne diseases, and mitigating operational and infrastructure costs. Together, these issues pose a challenge to public administration. A deeper understanding would allow for a holistic view of the UWC as well as the development of appropriate management strategies for water in urban areas. In addition, this deeper understanding would help explain the dynamics and interactions of different processes in the UWC.
The use of innovative software and approaches has gained recognition as an effective means for meeting the aforementioned challenges. Combining software and other approaches allows one to create a decision-making tool that integrates technical, environmental, economic, and social concepts to quickly visualize different trends or possible scenarios. The reliance on these tools has increased notably over the last 15 years, with examples ranging from hydric balances to artificial intelligence, with spatial scales from individual properties to entire regions, and timescales from daily to annual. These advances have helped incorporate as many UWC processes as possible.
These software programs or models have facilitated different applications’ employment as a vehicle for determining new sources of supply, contamination control, reduction of the effects of urbanization on the hydrological cycle, and efficient water use, among others. In so doing, these tools have greatly enhanced the capacity for sustainable alternatives and boosted the ability to efficiently manage financial and hydric resources.
The vast majority of the experiments revealed by the literature review touched on technical solutions or regulations, for the models/approaches studied were shown to be geared towards calculating water balances and the concentration or contaminant loads as water passes through the cycle. Nevertheless, social and ecological factors should not be forsaken if models and their responses are to be more comprehensive, especially in light of the close link between these factors and the UWC [5,278].
Even though most of these tools are available online or by request from the authors, their application has been centered in Australia and Europe, which is primarily explained by two facts. First, model development, which is only done in these two regions, leads to their application in different fields within these regions. Second, research and policies in these regions related to water management in urban areas have gained traction due to the need to conserve water resources or due to the lack of such resources.
The results described in the articles and academic theses reviewed herein demonstrate a high potential for management of the UWC, though its greatest contribution currently is academic; the approaches have not been applied as a decision-making tool by public or governmental entities. This confinement to academia is a serious obstacle to the implementation of the models for economic, social, and environmental means.
Although there are direct relationships between energy systems and different UWC processes, several models and software programs do not include these systems, for the primary objective of these tools is to determine strategies for managing water volume. Yet, many of these tools, when not designed from a holistic perspective, may directly impact infrastructure-investment costs [50] and/or lead to deleterious environmental effects in the form of greenhouse gases or high energy consumption [52,279,280].
Moreover, it is crucial to reinforce the usefulness of these models to evaluate the acceptance of the results obtained and thereby solidify social appropriation of this knowledge by means of strategies that promote implementation in decision-making contexts.
Lastly, this review allows different public and private entities to identify opportunities to use the software and models discussed herein for the management of urban water resources. Doing so would pave the way for the optimization of cities’ economic investments and ensure efficiency of the systems comprising the UWC as well as an environmentally conscious urban development that is future-oriented.

Author Contributions

This article provides a foundation for identifying components and interactions within the Urban Water Cycle (UWC). Furthermore, this article explains the types of models and development approaches for managing urban hydric resources. To this end, the authors describe the potential use and requisite operating conditions of UWC models. In addition, all authors analyze case studies (model applications) to determine model contribution (as well as place and date of implementation). Taken together, this information allows readers to determine each model’s benefits and uses.

Conflicts of Interest

The authors declare no conflicts of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Elvidge, C.D.; Tuttle, B.T.; Sutton, P.C.; Baugh, K.E.; Howard, A.T.; Milesi, C.; Bhaduri, B.; Nemani, R. Global Distribution and Density of Constructed Impervious Surfaces. Sensors 2007, 7, 1962–1979. [Google Scholar] [CrossRef]
  2. Van Leeuwen, C.J.; Frijns, J.; van Wezel, A.; van de Ven, F.H.M. City Blueprints: 24 Indicators to Assess the Sustainability of the Urban Water Cycle. Water Resour. Manag. 2012, 26, 2177–2197. [Google Scholar] [CrossRef]
  3. Fletcher, T.D.; Andrieu, H.; Hamel, P. Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art. Adv. Water Resour. 2013, 51, 261–279. [Google Scholar] [CrossRef]
  4. Lee, T.R. Urban water management for better urban life in Latin America. Urban Water 2000, 2, 71–78. [Google Scholar] [CrossRef]
  5. McIntyre, N.E.; Knowles-Yánez, K.; Hope, D. Urban Ecology as an Interdisciplinary Field: Differences in the use of “Urban” Between the Social and Natural Sciences. In Urban Ecology; Marzluff, J.M., Shulenberger, E., Endlicher, W., Alberti, M., Bradley, G., Ryan, C., Simon, U., ZumBrunnen, C., Eds.; Springer: New York, NY, USA, 2008; pp. 49–65. [Google Scholar]
  6. Price, R.K.; Vojinović, Z. Urban Hydroinformatics: Data, Models, and Decision Support for Integrated Urban Water Management; IWA Publishing: London, UK, 2011. [Google Scholar]
  7. Blind, M.; Gregersen, J.B. Towards an open modelling interface (OpenMI) the HarmonIT project. Adv. Geosci. 2005, 4, 69–74. [Google Scholar] [CrossRef]
  8. Bach, P.M.; Deletic, A.; Urich, C.; Sitzenfrei, R.; Kleidorfer, M.; Rauch, W.; McCarthy, D.T. Modelling Interactions Between Lot-Scale Decentralised Water Infrastructure and Urban Form—A Case Study on Infiltration Systems. Water Resour. Manag. 2013, 27, 4845–4863. [Google Scholar] [CrossRef]
  9. Makropoulos, C.K.; Natsis, K.; Liu, S.; Mittas, K.; Butler, D. Decision support for sustainable option selection in integrated urban water management. Environ. Model. Softw. 2008, 23, 1448–1460. [Google Scholar] [CrossRef]
  10. Mackay, R.; Last, E. SWITCH city water balance: A scoping model for integrated urban water management. Rev. Environ. Sci. Biotechnol. 2010, 9, 291–296. [Google Scholar] [CrossRef]
  11. Mirchi, A.; Madani, K.; Watkins, D., Jr.; Ahmad, S. Synthesis of System Dynamics Tools for Holistic Conceptualization of Water Resources Problems. Water Resour. Manag. 2012, 26, 2421–2442. [Google Scholar] [CrossRef]
  12. Mitchell, V.G. Applying Integrated Urban Water Management Concepts: A Review of Australian Experience. Environ. Manag. 2006, 37, 589–605. [Google Scholar] [CrossRef] [PubMed]
  13. Lijklema, L.; Tyson, J.M.; Lesouef, A. Interactions between Sewers, Treatment Plants and Receiving Waters in Urban Areas: A Summary of the Interurba ‘92 Workshop Conclusions. Water Sci. Technol. 1993, 27, 1–29. [Google Scholar]
  14. Sitzenfrei, R.; Rauch, W.; Rogers, B.; Dawson, R.; Kleidorfer, M. Modeling the urban water cycle as part of the city. Water Sci. Technol. 2014, 70, 1717–1720. [Google Scholar] [CrossRef] [PubMed]
  15. Bach, P.M.; Rauch, W.; Mikkelsen, P.S.; McCarthy, D.T.; Deletic, A. A critical review of integrated urban water modelling—Urban drainage and beyond. Environ. Model. Softw. 2014, 54, 88–107. [Google Scholar] [CrossRef]
  16. Harremös, P. Integrated urban drainage, status and perspectives. Water Sci. Technol. 2002, 45, 1–10. [Google Scholar]
  17. Schütze, M.; Butler, D.; Beck, B.M. Modelling, Simulation and Control of Urban Wastewater Systems; Springer Science & Business Media: New York, NY, USA, 2011. [Google Scholar]
  18. Fletcher, T.; Deletic, A. Data Requirements for Integrated Urban Water Management: Urban Water Series—UNESCO-IHP; CRC Press: Boca Raton, FL, USA, 2008. [Google Scholar]
  19. Fratini, C.F.; Geldof, G.D.; Kluck, J.; Mikkelsen, P.S. Three Points Approach (3PA) for urban flood risk management: A tool to support climate change adaptation through transdisciplinarity and multifunctionality. Urban Water J. 2012, 9, 317–331. [Google Scholar] [CrossRef]
  20. Hardy, M.; Kuczera, G.; Coombes, P. Integrated urban water cycle management: The UrbanCycle model. Water Sci. Technol. 2005, 52, 1–9. [Google Scholar] [PubMed]
  21. Maksimović, Č.; Tejada-Guibert, J.A. Frontiers in Urban Water Management: Deadlock or Hope; IWA Publishing: London, UK, 2001. [Google Scholar]
  22. Rauch, W.; Seggelke, K.; Brown, R.; Krebs, P. Integrated Approaches in Urban Storm Drainage: Where Do We Stand? Environ. Manag. 2005, 35, 396–409. [Google Scholar] [CrossRef] [PubMed]
  23. Renouf, M.A.; Kenway, S.J. Evaluation Approaches for Advancing Urban Water Goals. J. Ind. Ecol. 2016. [Google Scholar] [CrossRef]
  24. Behzadian, K.; Kapelan, Z.; Venkatesh, G.; Brattebø, H.; Sægrov, S.; Rozos, E.; Makropoulos, C.; Ugarelli, R.; Milina, J.; Hem, L. Urban Water System Metabolism Assessment Using WaterMet2 Model. Proc. Eng. 2014, 70, 113–122. [Google Scholar] [CrossRef]
  25. Clark, R.; Pezzaniti, D.; Cresswell, D. Watercress—Community Resource Evaluation and Simulation System—A Tool for Innovative Urban Water Systems Planning and Design. In Water Challenge: Balancing the Risks: Hydrology and Water Resources Symposium 2002; Institution of Engineers: Barton, Australia, 2002; p. 870. [Google Scholar]
  26. Kenway, S.; Gregory, A.; McMahon, J. Urban Water Mass Balance Analysis. J. Ind. Ecol. 2011, 15, 693–706. [Google Scholar] [CrossRef]
  27. Loubet, P.; Roux, P.; Loiseau, E.; Bellon-Maurel, V. Life cycle assessments of urban water systems: A comparative analysis of selected peer-reviewed literature. Water Res. 2014, 67, 187–202. [Google Scholar] [CrossRef] [PubMed]
  28. Maheepala, S.; Leighton, B.; Mirza, F.; Rahilly, M.; Rahman, J. Hydro Planner-a linked modelling system for water quantity and quality simulation of total water cycle. In MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand; The Modelling and Simulation Society of Australia and New Zealand Inc.: Perth, WA, Australia, 2005; pp. 170–176. [Google Scholar]
  29. Mitchell, V.G.; Mein, R.G.; McMahon, T.A. Modelling the urban water cycle. Environ. Model. Softw. 2001, 16, 615–629. [Google Scholar] [CrossRef]
  30. Mitchell, V.G.; Diaper, C. Simulating the urban water and contaminant cycle. Environ. Model. Softw. 2006, 21, 129–134. [Google Scholar] [CrossRef]
  31. Niza, S.; Rosado, L.; Ferrão, P. Urban Metabolism. J. Ind. Ecol. 2009, 13, 384–405. [Google Scholar] [CrossRef]
  32. Sakellari, I.; Makropoulos, C.; Butler, D.; Memon, F.A. Modelling sustainable urban water management options. Proc.-Inst. Civ. Eng. Eng. Sustain. 2005, 158, 143. [Google Scholar] [CrossRef]
  33. Snowdon, D.; Hardy, M.J.; Rahman, J.M. Urban Developer: A model architecture for manageably building urban water cycle models spanning multiple scales. In Proceedings of the 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011; pp. 12–16. [Google Scholar]
  34. Venkatesh, G.; Sægrov, S.; Brattebø, H. Dynamic metabolism modelling of urban water services—Demonstrating effectiveness as a decision-support tool for Oslo, Norway. Water Res. 2014, 61, 19–33. [Google Scholar] [CrossRef] [PubMed]
  35. Urich, C.; Bach, P.M.; Sitzenfrei, R.; Kleidorfer, M.; McCarthy, D.T.; Deletic, A.; Rauch, W. Modelling of Evolving Cities and Urban Water Systems in DAnCE4Water. In Proceedings of the ESE2012 Symposium Organisers Gratefully Acknowledge Support from a Number of Organisations, Newcastle, UK, 3–5 July 2012; pp. 141–155. [Google Scholar]
  36. Willuweit, L.; O’Sullivan, J.J. A decision support tool for sustainable planning of urban water systems: Presenting the Dynamic Urban Water Simulation Model. Water Res. 2013, 47, 7206–7220. [Google Scholar] [CrossRef] [PubMed]
  37. Mitchell, V.G.; Duncan, H.; Inma, R.M.; Stewart, J.; Vieritz, A.; Holt, P.; Grant, A.; Fletcher, T.D.; Coleman, J.; Maheepala, S.; et al. State of the Art Review of Integrated Urban Water Models; Novatech: Lyon, France, 2007; pp. 1–8. [Google Scholar]
  38. Mitchell, V.; McMahon, T.; Mein, R. Components of the Total Water Balance of an Urban Catchment. Environ. Manag. 2003, 32, 735–746. [Google Scholar] [CrossRef] [PubMed]
  39. Mitchell, V.G.; Cleugh, H.A.; Grimmond, C.S.B.; Xu, J. Linking urban water balance and energy balance models to analyse urban design options. Hydrol. Process. 2008, 22, 2891–2900. [Google Scholar] [CrossRef]
  40. Gray, S.R.; Becker, N.S.C. Contaminant flows in urban residential water systems. Urban Water 2002, 4, 331–346. [Google Scholar] [CrossRef]
  41. Clauson-Kaas, J.; Poulsen, T.S.; Jacobsen, B.N.; Guildal, T.; Wenzel, H. Environmental accounting—A decision support tool in WWTP operation and management. Water Sci. Technol. 2001, 44, 25–30. [Google Scholar] [PubMed]
  42. Hillman, T.; Ramaswami, A. Greenhouse gas emission footprints and energy use benchmarks for eight US cities. Environ. Sci. Technol. 2010, 44, 1902–1910. [Google Scholar] [CrossRef] [PubMed]
  43. Keller, J.; Hartley, K. Greenhouse gas production in wastewater treatment: Process selection is the major factor. Water Sci. Technol. 2003, 47, 43–48. [Google Scholar] [PubMed]
  44. Racoviceanu, A.I.; Karney, B.W.; Kennedy, C.A.; Colombo, A.F. Life-Cycle Energy Use and Greenhouse Gas Emissions Inventory for Water Treatment Systems. J. Infrastruct. Syst. 2007, 13, 261–270. [Google Scholar] [CrossRef]
  45. Rothausen, S.G.S.A.; Conway, D. Greenhouse-gas emissions from energy use in the water sector. Nat. Clim. Chang. 2011, 1, 210–219. [Google Scholar] [CrossRef]
  46. Strutt, J.; Wilson, S.; Shorney-Darby, H.; Shaw, A.; Byers, A. Assessing the carbon footprint of water production. J. Am. Water Works Assoc. 2008, 100, 80–91. [Google Scholar]
  47. Chen, S.; Chen, B. Urban energy–water nexus: A network perspective. Appl. Energy 2016, 184, 905–914. [Google Scholar] [CrossRef]
  48. Elías-Maxil, J.A.; van der Hoek, J.P.; Hofman, J.; Rietveld, L. Energy in the urban water cycle: Actions to reduce the total expenditure of fossil fuels with emphasis on heat reclamation from urban water. Renew. Sustain. Energy Rev. 2014, 30, 808–820. [Google Scholar] [CrossRef]
  49. Gleick, P.H. Water and Energy. Annu. Rev. Energy Environ. 1994, 19, 267–299. [Google Scholar] [CrossRef]
  50. Kenway, S.J.; Lant, P.A.; Priestley, A.; Daniels, P. The connection between water and energy in cities: A review. Water Sci. Technol. 2011, 63, 1983–1990. [Google Scholar] [CrossRef] [PubMed]
  51. Kenway, S.J.; Priestley, A.; Cook, S.; Seo, S.; Inman, M.; Gregory, A.; Hall, M. Energy Use in the Provision and Consumption of Urban Water in Australia and New Zealand; CSIRO: Water for a Healthy Country National Research Flagship: Canberra, Australia, 2008.
  52. Olsson, D.G. Water and Energy Nexus water energy nexus. In Encyclopedia of Sustainability Science and Technology; Meyers, R.A., Ed.; Springer: New York, NY, USA, 2012; pp. 11932–11946. [Google Scholar]
  53. Plappally, A.K.; Lienhard V, J.H. Energy requirements for water production, treatment, end use, reclamation, and disposal. Renew. Sustain. Energy Rev. 2012, 16, 4818–4848. [Google Scholar] [CrossRef]
  54. Siddiqi, A.; Anadon, L.D. The water–energy nexus in Middle East and North Africa. Energy Policy 2011, 39, 4529–4540. [Google Scholar] [CrossRef]
  55. Stokes, J.R.; Horvath, A. Energy and air emission effects of water supply. Environ. Sci. Technol. 2009, 43, 2680–2687. [Google Scholar] [CrossRef] [PubMed]
  56. Hofman, J.; Hofman-Caris, R.; Nederlof, M.; Frijns, J.; Loosdrecht, M. van Water and energy as inseparable twins for sustainable solutions. Water Sci. Technol. 2011, 63, 88–92. [Google Scholar] [CrossRef] [PubMed]
  57. Remy, C.; Jekel, M. Sustainable wastewater management: Life cycle assessment of conventional and source-separating urban sanitation systems. Water Sci. Technol. 2008, 58, 1555–1562. [Google Scholar] [CrossRef] [PubMed]
  58. Wagner, I.; Breil, P. The role of ecohydrology in creating more resilient cities. Ecohydrol. Hydrobiol. 2013, 13, 113–134. [Google Scholar] [CrossRef]
  59. Venkatesh, G.; Brattebø, H. Analysis of chemicals and energy consumption in water and wastewater treatment, as cost components: Case study of Oslo, Norway. Urban Water J. 2011, 8, 189–202. [Google Scholar] [CrossRef]
  60. Venkatesh, G.; Brattebø, H. Environmental impact analysis of chemicals and energy consumption in wastewater treatment plants: Case study of Oslo, Norway. Water Sci. Technol. 2011, 63, 1018–1031. [Google Scholar] [CrossRef] [PubMed]
  61. Haider, H.; Sadiq, R.; Tesfamariam, S. Performance indicators for small- and medium-sized water supply systems: A review. Environ. Rev. 2013, 22, 1–40. [Google Scholar] [CrossRef]
  62. Organization World Health. Guidelines for Drinking-Water Quality: Recommendations; World Health Organization: Geneva, Switzerland, 2004. [Google Scholar]
  63. Vilanova, M.R.N.; Magalhães Filho, P.; Balestieri, J.A.P. Performance measurement and indicators for water supply management: Review and international cases. Renew. Sustain. Energy Rev. 2015, 43, 1–12. [Google Scholar] [CrossRef]
  64. Balfaqih, H.; Nopiah, Z.M. Performance measurement factors for water supply: A systematic review. In AIP Conference Proceedings; AIP Publishing: New York, NY, USA, 2015; Volume 1643, pp. 770–775. [Google Scholar]
  65. Der Bruggen, B.V.; Borghgraef, K.; Vinckier, C. Causes of Water Supply Problems in Urbanised Regions in Developing Countries. Water Resour. Manag. 2009, 24, 1885–1902. [Google Scholar] [CrossRef]
  66. Donkor, E.; Mazzuchi, T.; Soyer, R.; Roberson, A. Urban Water Demand Forecasting: Review of Methods and Models. J. Water Resour. Plan. Manag. 2014, 140, 146–159. [Google Scholar] [CrossRef]
  67. Herrera, M.; Torgo, L.; Izquierdo, J.; Pérez-García, R. Predictive models for forecasting hourly urban water demand. J. Hydrol. 2010, 387, 141–150. [Google Scholar] [CrossRef]
  68. Froukh, M.L. Decision-Support System for Domestic Water Demand Forecasting and Management. Water Resour. Manag. 2001, 15, 363–382. [Google Scholar] [CrossRef]
  69. Billings, R.B.; Jones, C.V. Forecasting Urban Water Demand; American Water Works Association: Denver, CO, USA, 2011. [Google Scholar]
  70. Mylopoulos, Y.A.; Mentes, A.K.; Theodossiou, I. Modeling Residential Water Demand Using Household Data: A Cubic Approach. Water Int. 2004, 29, 105–113. [Google Scholar] [CrossRef]
  71. Arbués, F.; Garcia-Valiñas, M.Á.; Martinez-Espiñeira, R. Estimation of residential water demand: A state-of-the-art review. J. Socio-Econ. 2003, 32, 81–102. [Google Scholar] [CrossRef]
  72. Rauch, W.; Bertrand-Krajewski, J.L.; Krebs, P.; Mark, O.; Schilling, W.; Schutze, M.; Vanrolleghem, P.A. Deterministic modelling of integrated urban drainage systems. Water Sci. Technol. 2002, 45, 81–94. [Google Scholar] [PubMed]
  73. Butler, D.; Parkinson, J. Towards sustainable urban drainage. Water Sci. Technol. 1997, 35, 53–63. [Google Scholar] [CrossRef]
  74. Delleur, J. The Evolution of Urban Hydrology: Past, Present, and Future. J. Hydraul. Eng. 2003, 129, 563–573. [Google Scholar] [CrossRef]
  75. Mitchell, V.G.; Mein, R.G.; McMahon, T.A. Utilising Stormwater and Wastewater Resources in Urban Areas. Aust. J. Water Resour. 2002, 6, 31–43. [Google Scholar]
  76. Kärrman, E. Strategies towards sustainable wastewater management. Urban Water 2001, 3, 63–72. [Google Scholar] [CrossRef]
  77. Yazdanfar, Z.; Sharma, A. Urban drainage system planning and design—Challenges with climate change and urbanization: A review. Water Sci. Technol. 2015, 72, 165–179. [Google Scholar] [CrossRef] [PubMed]
  78. Vanrolleghem, P.A.; Jeppsson, U.; Carstensen, J.; Carlsson, B.; Olsson, G. Integration of wastewater treatment plant design and operation—A systematic approach using cost functions. Water Sci. Technol. 1996, 34, 159–171. [Google Scholar] [CrossRef]
  79. Rivas, A.; Irizar, I.; Ayesa, E. Model-based optimisation of Wastewater Treatment Plants design. Environ. Model. Softw. 2008, 23, 435–450. [Google Scholar] [CrossRef]
  80. Dominguez, D.; Gujer, W. Evolution of a wastewater treatment plant challenges traditional design concepts. Water Res. 2006, 40, 1389–1396. [Google Scholar] [CrossRef] [PubMed]
  81. Alasino, N.; Mussati, M.C.; Scenna, N. Wastewater treatment plant synthesis and design. Ind. Eng. Chem. Res. 2007, 46, 7497–7512. [Google Scholar] [CrossRef]
  82. Niemczynowicz, J. Urban hydrology and water management—Present and future challenges. Urban Water 1999, 1, 1–14. [Google Scholar] [CrossRef]
  83. House, M.A.; Ellis, J.B.; Herricks, E.E.; Hvitved-Jacobsen, T.; Seager, J.; Lijklema, L.; Aalderink, H.; Clifforde, I.T. Urban drainage–impacts on receiving water quality. Water Sci. Technol. 1993, 27, 117–158. [Google Scholar]
  84. Gurnell, A.; Lee, M.; Souch, C. Urban Rivers: Hydrology, Geomorphology, Ecology and Opportunities for Change. Geogr. Compass 2007, 1, 1118–1137. [Google Scholar] [CrossRef]
  85. Candela, L.; Fabregat, S.; Josa, A.; Suriol, J.; Vigués, N.; Mas, J. Assessment of soil and groundwater impacts by treated urban wastewater reuse. A case study: Application in a golf course (Girona, Spain). Sci. Total Environ. 2007, 374, 26–35. [Google Scholar] [CrossRef] [PubMed]
  86. Gregory, K.J. The human role in changing river channels. Geomorphology 2006, 79, 172–191. [Google Scholar] [CrossRef]
  87. Mitchell, V.G. Aquacycle User Manual; CRC for Catchment Hydrology; Monash University: Clayton, Australia, 2000. [Google Scholar]
  88. Wolf, L.; Morris, B.L.; Burn, S.; Hötzl, H. The AISUWRS approach. In Urban Water Resources Toolbox—Integrating Groundwater into Urban Water Management; Wolf, L., Morris, B., Burn, S., Eds.; IWA Publishing: London, UK, 2006. [Google Scholar]
  89. Mitchell, V.G.; Diaper, C. UVQ: A tool for assessing the water and contaminant balance impacts of urban development scenarios. Water Sci. Technol. 2005, 52, 91–98. [Google Scholar] [PubMed]
  90. Mitchell, V.G.; Diaper, C. UVQ User Manual; CSIRO Urban Water, CMIT Report, No. 2005-282; CSIRO: Canberra, Australia, 2005. [Google Scholar]
  91. Burn, S.; DeSilva, D.; Ambrose, M.; Meddings, S.; Diaper, C.; Correll, R.; Miller, R.; Wolf, L. A decision support system for urban groundwater resource sustainability. Water Pract. Technol. 2006, 1, wpt2006010. [Google Scholar] [CrossRef]
  92. Liu, A. Influence of Rainfall and Catchment Characteristics on Urban Stormwater Quality; Doctor of Philosophy; Queensland University of Technology: Brisbane, Australia, 2011. [Google Scholar]
  93. Obropta, C.C.; Kardos, J.S. Review of Urban Stormwater Quality Models: Deterministic, Stochastic, and Hybrid Approaches1. JAWRA J. Am. Water Resour. Assoc. 2007, 43, 1508–1523. [Google Scholar] [CrossRef]
  94. Andersen, H.S.; Tamašauskas, H.; Mark, O. The full urban water cycle–modeling with MIKE URBAN. In Proceedings of the 7th International Conference on Urban Drainage Modelling, Dresden, Germany, 15–17 September 2004. [Google Scholar]
  95. Sitzenfrei, R.; Rauch, W. From water networks to a “Digital City”: A shift of paradigm in assessment of urban water systems. In Proceedings of the 12th International Conference on Urban Drainage, Porto Alegre, Brazil, 11–16 September 2011; pp. 11–16. [Google Scholar]
  96. Žibienė, G.; Žibas, A. Capability Assessment of Application of Software MIKE URBAN for Rural Water Distribution System Operation Optimization. Rural Dev. 2013, 6, 524–530. [Google Scholar]
  97. Ingeduld, P.; Pradhan, A.; Svitak, Z.; Terrai, A. Modelling Intermittent Water Supply Systems with EPANET. In Water Distribution Systems Analysis Symposium 2006; American Society of Civil Engineers: New York, NY, USA, 2008; pp. 1–8. [Google Scholar]
  98. Metelka, T. Integrated urban water cycle modeling. In Integrated Urban Water Resources Management; NATO Security through Science Series; Hlavinek, P., Kukharchyk, T., Marsalek, J., Mahrikova, I., Eds.; Springer: Dordrecht, The Netherlands, 2006; pp. 51–58. [Google Scholar]
  99. Makropoulos, C. Thinking platforms for smarter urban water systems: Fusing technical and socio-economic models and tools. Geol. Soc. Lond. Spec. Publ. 2014, 408, 201–219. [Google Scholar] [CrossRef]
  100. Rozos, E.; Makropoulos, C. Source to tap urban water cycle modelling. Environ. Model. Softw. 2013, 41, 139–150. [Google Scholar] [CrossRef]
  101. Kossieris, P.; Panayiotakis, A.; Tzouka, K.; Gerakopoulou, P.; Rozos, E.; Makropoulos, C. An eLearning Approach for Improving Household Water Efficiency. Proc. Eng. 2014, 89, 1113–1119. [Google Scholar] [CrossRef]
  102. Hunt, J.; Anda, M.; Mathew, K.; Ho, G. A water efficiency rating system for land developments implementing integrated urban water management. Water Sci. Technol. Water Supply 2006, 6, 1–7. [Google Scholar]
  103. Cresswell, D.; Piantadosi, J.; Rosenberg, K. Watercress User Manual, 2002.
  104. Maier, H.R.; Paton, F.L.; Dandy, G.C.; Connor, J.D. Impact of Drought on Adelaide’s Water Supply System: Past, Present, and Future. In Drought in Arid and Semi-Arid Regions; Schwabe, K., Albiac, J., Connor, J.D., Hassan, R.M., González, L.M., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 41–62. [Google Scholar]
  105. Paton, F.L.; Dandy, G.C.; Maier, H.R. Integrated framework for assessing urban water supply security of systems with non-traditional sources under climate change. Environ. Model. Softw. 2014, 60, 302–319. [Google Scholar] [CrossRef]
  106. Hydrodynamics, Rainfall Runoff and Real Time Control. User Manual; Daltares Sobek: Delft, The Netherlands, 2017.
  107. Feikema, P.M.; Sheridan, G.J.; Argent, R.M.; Lane, P.N.J.; Grayson, R.B. Using E2 to model the impacts of bushfires on water quality in South-Eastern Australia. In Proceedings of the MODSIM 2005 International Congress on Modelling and Simulation, Melbourne, Australia, December 2005; Modelling and Simulation Society of Australia and New Zealand: Melbourne, Australia; pp. 170–176. [Google Scholar]
  108. Grant, A.; Maheepala, S.; Mirza, F.; Leighton, B.; Rahilly, M.; Rahman, J.; Perraud, J.-M.; Sharma, A. Hydro Planner: Providing an Improved Process for Assessing Urban Water Supply-demand Balance. In Proceedings of the 30th Hydrology & Water Resources Symposium: Past, Present & Future, Tasmania, Australia, 4–7 December 2006; pp. 456–461. [Google Scholar]
  109. Nazari, S.; Mousavi, S.; Behzadian, K.; Kapelan, Z. Sustainable Urban Water Management: A Simulation Optimization Approach. In Proceedings of the 11th International Conference on Hydroinformatics, New York, NY, USA, 17–21 August 2014. [Google Scholar]
  110. Behzadian, K.; Kapelan, Z.; Govindarajan, V.; Brattebø, H.; Sægrov, S.; Rozos, E.; Makropoulos, C. Quantitative UWS Performance Model: WaterMet2. Transitions to the Urban Water Services of Tomorrow (TRUST) Project. 2014. Available online: https://riunet.upv.es/handle/10251/46620 (accessed on 18 April 2017).
  111. Behzadian, K.; Kapelan, Z. Advantages of integrated and sustainability based assessment for metabolism based strategic planning of urban water systems. Sci. Total Environ. 2015, 527–528, 220–231. [Google Scholar] [CrossRef] [PubMed]
  112. Ugarelli, R.; Almeida, M.C.; Behzadian, K.; Liserra, T.; Smeets, P.; Kapelan, Z.; Sægrov, S. Sustainability risk based assessment of the integrated urban water system: A case study of Oslo. In Proceedings of the 11th International Conference on Hydroinformatics, New York, NY, USA, 17–21 August 2014. [Google Scholar]
  113. Graddon, A.R.; Kuczera, G.; Hardy, M.J. A flexible modelling environment for integrated urban water harvesting and re-use. Water Sci. Technol. 2011, 63, 2268–2278. [Google Scholar] [CrossRef] [PubMed]
  114. Jefferson, C.; Hardy, M.; Kuczera, G. Integrated Urban Water Management: Combining Multi-Criterion Optimization and Decision Analysis. In Computing in Civil Engineering (2005); American Society of Civil Engineers: Cancun, Mexico, 2005; pp. 1–12. [Google Scholar]
  115. Hardy, M.J. Integrated Urban Water Cycle Management: New Tools for a New Perspective. Ph.D. Thesis, Newcastle University, Newcastle, Australia, 2007. [Google Scholar]
  116. eWaterCRC. Urban Developer: Technical Overview; e Water CRC: Canberra, Australia, 2011. [Google Scholar]
  117. Urich, C.; Bach, P.M.; Hellbach, C.; Sitzenfrei, R.; Kleidorfer, M.; McCarthy, D.T.; Deletic, A.; Rauch, W. Dynamics of cities and water infrastructure in the DAnCE4Water model. In Proceedings of the 12th International Conference on Urban Drainage, Porto Alegre, Brazil, 11–16 September 2011; pp. 10–15. [Google Scholar]
  118. Sitzenfrei, R.; Fach, S.; Kinzel, H.; Rauch, W. A multi-layer cellular automata approach for algorithmic generation of virtual case studies: VIBe. Water Sci. Technol. J. Int. Assoc. Water Pollut. Res. 2010, 61, 37–45. [Google Scholar] [CrossRef] [PubMed]
  119. Sitzenfrei, R.; Fach, S.; Kleidorfer, M.; Urich, C.; Rauch, W. Dynamic virtual infrastructure benchmarking: DynaVIBe. Water Sci. Technol. Water Supply 2010, 10, 600–609. [Google Scholar] [CrossRef]
  120. De Haan, J.; Ferguson, B.; Brown, R.; Deletic, A. A workbench for societal transitions in water sensitive cities. In Proceedings of the 12nd International Conference on Urban Drainage, Porto Alegre, Brazil, 11–16 September 2011. [Google Scholar]
  121. De Haan, F.J.; Ferguson, B.; Deletic, A.; Brown, R.R. Exploring scenarios for urban water systems using a socio-technical model. In Proceedings of the Ninth International Conference on Urban Drainage Modelling; Prodanovic, D., Plavšic, J., Eds.; Faculty of Civil Engineering, University of Belgrade: Belgrade, Serbia, 2012. [Google Scholar]
  122. Bach, P.M.; McCarthy, D.T.; Urich, C.; Sitzenfrei, R.; Kleidorfer, M.; Rauch, W.; Deletic, A. DAnCE4Water’s BPM: A planning algorithm for decentralised water management options. In Proceedings of the Ninth International Conference on Urban Drainage Modelling; Faculty of Civil Engineering, University of Belgrade: Belgrade, Serbia, 2012. [Google Scholar]
  123. Sitzenfrei, R.; Möderl, M.; Rauch, W. Automatic generation of water distribution systems based on GIS data. Environ. Model. Softw. 2013, 47, 138–147. [Google Scholar] [CrossRef] [PubMed]
  124. Sitzenfrei, R.; Möderl, M.; Rauch, W. Assessing the impact of transitions from centralised to decentralised water solutions on existing infrastructures—Integrated city-scale analysis with VIBe. Water Res. 2013, 47, 7251–7263. [Google Scholar] [CrossRef] [PubMed]
  125. Kennedy, C.; Pincetl, S.; Bunje, P. The study of urban metabolism and its applications to urban planning and design. Environ. Pollut. 2011, 159, 1965–1973. [Google Scholar] [CrossRef] [PubMed]
  126. Gandy, M. Rethinking urban metabolism: Water, space and the modern city. City 2004, 8, 363–379. [Google Scholar] [CrossRef]
  127. Brunner, P.H. Reshaping urban metabolism. J. Ind. Ecol. 2007, 11, 11–13. [Google Scholar] [CrossRef]
  128. International Organization for Standardization (ISO). Environmental Management: Life Cycle Assessment: Principles and Framework; ISO: Geneva, Switzerland, 2006; Volume 14040. [Google Scholar]
  129. International Organization for Standardization (ISO). Environmental Management: Life Cycle Assessment: Requirements and Guidelines; ISO: Geneva, Switzerland, 2006. [Google Scholar]
  130. Urich, C.; Sitzenfrei, R.; Möderl, M.; Rauch, W. An agent-based approach for generating virtual sewer systems. Water Sci. Technol. 2010, 62, 1090–1097. [Google Scholar] [CrossRef] [PubMed]
  131. Sitzenfrei, R.; Möderl, M.; Rauch, W. Graph-based approach for generating virtual water distribution systems in the software VIBe. Water Sci. Technol. Water Supply 2010, 10, 923–932. [Google Scholar] [CrossRef]
  132. De Haan, F.J.; Ferguson, B.C.; Deletic, A.; Brown, R.R. A socio-technical model to explore urban water systems scenarios. Water Sci. Technol. J. Int. Assoc. Water Pollut. Res. 2013, 68, 714–721. [Google Scholar] [CrossRef] [PubMed]
  133. Vanderkimpen, P.; Melger, E.; Peeters, P. Flood modeling for risk evaluation: A MIKE FLOOD vs. SOBEK 1D2D benchmark study. In Flood Risk Management: Research and Practice; Taylor & Francis Group: Boca Raton, FL, USA, 2009. [Google Scholar]
  134. Qin, H.-P.; Su, Q.; Khu, S.-T. An Integrated Model for Water Management in a Rapidly Urbanizing Catchment. Environ. Model. Softw. 2011, 26, 1502–1514. [Google Scholar] [CrossRef]
  135. Chenevey, B.; Buchberger, S. Impact of Urban Development on Local Water Balance. In World Environmental and Water Resources Congress 2013; American Society of Civil Engineers: New York, NY, USA, 2013; pp. 2625–2636. [Google Scholar]
  136. Donia, N.; Manoli, E.; Assimacopoulos, D. Modelling the urban water system of Alexandria using the Aquacycle model. J. Water Reuse Desalination 2013, 3, 69–84. [Google Scholar] [CrossRef]
  137. Duong, T.T.H.; Adin, A.; Jackman, D.; van der Steen, P.; Vairavamoorthy, K. Urban water management strategies based on a total urban water cycle model and energy aspects—Case study for Tel Aviv. Urban Water J. 2011, 8, 103–118. [Google Scholar] [CrossRef]
  138. Shukla, R.L.; Barron, O.; Turner, J.; Grant, A.; Sharma, A.; Bell, J.; Nikraz, H. Rural Towns-Liquid Assets: Analysis Using Water Balance Modelling for Water Resources Availability for Rural Towns in Western Australia. Eur. Water 2011, 36, 53–64. [Google Scholar]
  139. Schulz, M.; Short, M.D.; Peters, G.M. A streamlined sustainability assessment tool for improved decision making in the urban water industry. Integr. Environ. Assess. Manag. 2012, 8, 183–193. [Google Scholar] [CrossRef] [PubMed]
  140. Pak, G.; Lee, J.; Kim, H.; Yoo, C.; Yun, Z.; Choi, S.; Yoon, J. Applicability of Aquacycle model to urban water cycle analysis. Desalination Water Treat. 2010, 19, 80–85. [Google Scholar] [CrossRef]
  141. Lee, J.; Pak, G.; Yoo, C.; Kim, S.; Yoon, J. Effects of land use change and water reuse options on urban water cycle. J. Environ. Sci. 2010, 22, 923–928. [Google Scholar] [CrossRef]
  142. Zhang, Y.; Grant, A.; Sharma, A.; Donghui, C.; Liang, C. Assessment of rainwater use and greywater reuse in high-rise buildings in a brownfield site. Water Sci. Technol. 2009, 60, 575–581. [Google Scholar] [CrossRef] [PubMed]
  143. Steendam, R. The Effects of Urban Water Management Options on the Water Balance and Energy Use in a New Urban Development (Haulander Weg): A Field Research in Hamburg, Germany. Master’s Thesis, UNESCO-IHE, Delft, The Netherlands, 2009. [Google Scholar]
  144. Gires, A.; de Gouvello, B. Consequences to water suppliers of collecting rainwater on housing estates. Water Sci. Technol. 2009, 60, 543–553. [Google Scholar] [CrossRef] [PubMed]
  145. Situmorang, M.R. Modelling Urban Water Cycle: An Approach for Future Urban Water Supply Alternatives. Master’s Thesis, UNESCO-IHE, Delft, The Netherlands, 2008. [Google Scholar]
  146. Sharma, A.K.; Gray, S.; Diaper, C.; Liston, P.; Howe, C. Assessing integrated water management options for urban developments—Canberra case study. Urban Water J. 2008, 5, 147–159. [Google Scholar] [CrossRef]
  147. Lekkas, D.F.; Manoli, E.; Assimacopoulos, D. Integrated urban water modelling using the aquacycle model. Glob. NEST J. 2008, 10, 310–319. [Google Scholar]
  148. De SanSan Miguel Brinquis, M.d.l.P. Simulation of the Total Urban Water Cycle in a Neighbourhood of a Spanish City and Establishment of Urban Water Sustainable Indicators. Master’s Thesis, University of Wageningen, Dundee, UK, 2007. [Google Scholar]
  149. Lee, J.; Pak, G.; Yoo, C.; Yoon, J. Analysis of urban water cycle considering water reuse options. Water Sci. Technol. Water Supply 2007, 7, 101–107. [Google Scholar] [CrossRef]
  150. Cleugh, H.A.; Bui, E.; Simon, D.; Xu, J.; Mitchell, V.G. The impact of suburban design on water use and microclimate. In Proceedings of the MODSIM 2005 International Congress on Modelling and Simulation, Melbourne, Australia, December 2005; Modelling and Simulation Society of Australia and New Zealand: Melbourne, Australia; pp. 10–14. [Google Scholar]
  151. Cleugh, H.A.; Bui, E.N.; Mitchell, V.G.; Xu, J.; Grimmond, C.S.B.; Simon, D.A.P. Evapotranspiration in urban water balance models: A methodological framework. In Proceedings of the MODSIM 2005 International Congress on Modelling and Simulation, Melbourne, Australia, 12–15 December 2005; Modelling and Simulation Society of Australia and New Zealand: Melbourne, Australia, 2005; pp. 2012–2018. [Google Scholar]
  152. Marleni, N.; Gray, S.; Sharma, A.; Burn, S.; Muttil, N. Impact of water management practice scenarios on wastewater flow and contaminant concentration. J. Environ. Manag. 2015, 151, 461–471. [Google Scholar] [CrossRef] [PubMed]
  153. Gurung, T.R.; Stewart, R.A.; Beal, C.D.; Sharma, A.K. Smart meter enabled water end-use demand data: Platform for the enhanced infrastructure planning of contemporary urban water supply networks. J. Clean. Prod. 2015, 87, 642–654. [Google Scholar] [CrossRef]
  154. Gurung, T.R.; Sharma, A. Communal rainwater tank systems design and economies of scale. J. Clean. Prod. 2014, 67, 26–36. [Google Scholar] [CrossRef]
  155. Poustie, M.S.; Deletic, A. Modeling integrated urban water systems in developing countries: Case study of Port Vila, Vanuatu. AMBIO 2014, 43, 1093–1111. [Google Scholar] [CrossRef] [PubMed]
  156. Cook, S.; Sharma, A.; Chong, M. Performance Analysis of a Communal Residential Rainwater System for Potable Supply: A Case Study in Brisbane, Australia. Water Resour. Manag. 2013, 27, 4865–4876. [Google Scholar] [CrossRef]
  157. Leitner, K. Water Balance of Vienna as Framework for a Substance Flow Analysis of Copper. Master’s Thesis, University of Natural Resources and Life Sciences, Vienaa, Austria, 2013. [Google Scholar]
  158. Tjandraatmadja, G.; Sharma, A.K.; Grant, T.; Pamminger, F. A Decision Support Methodology for Integrated Urban Water Management in Remote Settlements. Water Resour. Manag. 2013, 27, 433–449. [Google Scholar] [CrossRef]
  159. Martinez, S.E.; Escolero, O.; Wolf, L. Total Urban Water Cycle Models in Semiarid Environments—Quantitative Scenario Analysis at the Area of San Luis Potosi, Mexico. Water Resour. Manag. 2011, 25, 239–263. [Google Scholar] [CrossRef]
  160. Shin, S.-M.; Choi, G.-E.; Lee, S.-E.; Park, H.-K. Study on decentralized options of the in-stream flow for restoring the Gyobang cheon: Application of the Urban Volume and Quality (UVQ) model to examine feasibilities in water quantity and quality. J. Korean Soc. Water Wastewater 2011, 25, 699–706. [Google Scholar]
  161. Cook, S.; Sharma, A.; Batten, D.; Burn, S. Matching alternative water services to industry type: An eco-industrial approach. Water Sci. Technol. Water Supply 2010, 10, 969–977. [Google Scholar] [CrossRef]
  162. Sharma, A.; Burn, S.; Gardner, T.; Gregory, A. Role of decentralised systems in the transition of urban water systems. Water Sci. Technol. Water Supply 2010, 10, 577–583. [Google Scholar] [CrossRef]
  163. Rueedi, J.; Cronin, A.A.; Morris, B.L. Estimation of sewer leakage to urban groundwater using depth-specific hydrochemistry. Water Environ. J. 2009, 23, 134–144. [Google Scholar] [CrossRef]
  164. Goonrey, C.M.; Perera, B.J. C.; Lechte, P.; Maheepala, S.; Mitchell, V.G. A technical decision-making framework: Stormwater as an alternative supply source. Urban Water J. 2009, 6, 417–429. [Google Scholar] [CrossRef]
  165. Vizintin, G.; Souvent, P.; Veselič, M.; Cencur Curk, B. Determination of urban groundwater pollution in alluvial aquifer using linked process models considering urban water cycle. J. Hydrol. 2009, 377, 261–273. [Google Scholar] [CrossRef]
  166. Zhang, Y.; Grant, A.; Sharma, A.; Chen, D.; Chen, L. Alternative Water Resources for Rural Residential Development in Western Australia. Water Resour. Manag. 2009, 24, 25–36. [Google Scholar] [CrossRef]
  167. Morris, B.; Rueedi, J.; Cronin, A.A.; Diaper, C.; DeSilva, D. Using linked process models to improve urban groundwater management: An example from Doncaster England. Water Environ. J. 2007, 21, 229–240. [Google Scholar] [CrossRef]
  168. Wolf, L.; Klinger, J.; Hoetzl, H.; Mohrlok, U. Quantifying Mass Fluxes from Urban Drainage Systems to the Urban Soil-Aquifer System (11 pp). J. Soils Sediments 2007, 7, 85–95. [Google Scholar] [CrossRef]
  169. Souvent, P.; Vižintin, G.; Curk, B.Č. Impact assessment of an urban pollution on the aquifer of Ljubljana, Slovenia. In Geophysical Research Abstracts; Copernicus Publications: Copernicus, Germany, 2006; Volume 8, p. 05552. [Google Scholar]
  170. Rueedi, J.; Cronin, A.A.; Moon, B.; Wolf, L.; Hoetzl, H. Effect of different water management strategies on water and contaminant fluxes in Doncaster, United Kingdom. Water Sci. Technol. 2005, 52, 115–123. [Google Scholar] [PubMed]
  171. Eiswirth, M.; Wolf, L.; Hötzl, H. Balancing the contaminant input into urban water resources. Environ. Geol. 2004, 46, 246–256. [Google Scholar] [CrossRef]
  172. Thorndahl, S.; Balling, J.D.; Larsen, U.B.B. Analysis and integrated modelling of groundwater infiltration to sewer networks. Hydrol. Process. 2016, 30, 3228–3238. [Google Scholar] [CrossRef]
  173. Bisht, D.S.; Chatterjee, C.; Kalakoti, S.; Upadhyay, P.; Sahoo, M.; Panda, A. Modeling urban floods and drainage using SWMM and MIKE URBAN: A case study. Nat. Hazards 2016, 84, 749–776. [Google Scholar] [CrossRef]
  174. Kidmose, J.; Troldborg, L.; Refsgaard, J.C.; Bischoff, N. Coupling of a distributed hydrological model with an urban storm water model for impact analysis of forced infiltration. J. Hydrol. 2015, 525, 506–520. [Google Scholar] [CrossRef]
  175. Olsen, A.S.; Zhou, Q.; Linde, J.J.; Arnbjerg-Nielsen, K. Comparing Methods of Calculating Expected Annual Damage in Urban Pluvial Flood Risk Assessments. Water 2015, 7, 255–270. [Google Scholar] [CrossRef]
  176. Locatelli, L.; Gabriel, S.; Mark, O.; Mikkelsen, P.S.; Arnbjerg-Nielsen, K.; Taylor, H.; Bockhorn, B.; Larsen, H.; Kjølby, M.J.; Blicher, A.S.; et al. Modelling the impact of retention–detention units on sewer surcharge and peak and annual runoff reduction. Water Sci. Technol. 2015, 71, 898–903. [Google Scholar] [CrossRef] [PubMed]
  177. Mark, O.; Jørgensen, C.; Hammond, M.; Khan, D.; Tjener, R.; Erichsen, A.; Helwigh, B. A new methodology for modelling of health risk from urban flooding exemplified by cholera—Case Dhaka, Bangladesh. J. Flood Risk Manag. 2015. [Google Scholar] [CrossRef]
  178. Locatelli, L.; Mark, O.; Mikkelsen, P.S.; Arnbjerg-Nielsen, K.; Bergen Jensen, M.; Binning, P.J. Modelling of green roof hydrological performance for urban drainage applications. J. Hydrol. 2014, 519, 3237–3248. [Google Scholar] [CrossRef]
  179. Vezzaro, L.; Löwe, R.; Madsen, H.; Grum, M.; Mikkelsen, P.S. Investigating the use of stochastic forecast for RTC of urban drainage systems. In Proceedings of the 8th International Conference on Planning and Technologies for Sustainable Urban Water Management, Lyon, France, 23–27 June 2013. [Google Scholar]
  180. Andersen, S.T.; Erichsen, A.C.; Mark, O.; Albrechtsen, H.-J. Effects of a 20 year rain event: A quantitative microbial risk assessment of a case of contaminated bathing water in Copenhagen, Denmark. J. Water Health 2013, 11, 636–646. [Google Scholar] [CrossRef] [PubMed]
  181. Siekmann, M.; Vomberg, N.; Mirgartz, M.; Pinnekamp, J.; Mühle, S. Multifunctional Land Use in Urban Spaces to Adapt Urban Infrastructure. In Climate Change and the Sustainable Use of Water Resources; Filho, W.L., Ed.; Climate Change Management; Springer: Berlin/Heidelberg, Germany, 2012; pp. 611–625. [Google Scholar]
  182. Zhou, Q.; Mikkelsen, P.S.; Halsnæs, K.; Arnbjerg-Nielsen, K. Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits. J. Hydrol. 2012, 414–415, 539–549. [Google Scholar] [CrossRef]
  183. Berggren, K.; Olofsson, M.; Viklander, M.; Svensson, M.; Gustafsson, A. Hydraulic Impacts on Urban Drainage Systems due to Changes in Rainfall Caused by Climatic Change. J. Hydrol. Eng. 2012, 17, 92–98. [Google Scholar] [CrossRef]
  184. Roldin, M.; Fryd, O.; Jeppesen, J.; Mark, O.; Binning, P.J.; Mikkelsen, P.S.; Jensen, M.B. Modelling the impact of soakaway retrofits on combined sewage overflows in a 3 km2 urban catchment in Copenhagen, Denmark. J. Hydrol. 2012, 452–453, 64–75. [Google Scholar] [CrossRef]
  185. Roldin, M.; Mark, O.; Kuczera, G.; Mikkelsen, P.S.; Binning, P.J. Representing soakaways in a physically distributed urban drainage model—Upscaling individual allotments to an aggregated scale. J. Hydrol. 2012, 414–415, 530–538. [Google Scholar] [CrossRef]
  186. Chen, A.S.; Hammond, M.J.; Djordjević, S.; Butler, D. Flood damage assessment for urban growth scenarios. In Proceedings of the International Conference on Flood Resilience: Experiences in Asia and Europe, Exeter, UK, 5–7 September 2013; pp. 5–7. [Google Scholar]
  187. Hammond, M.J.; Chen, A.S.; Djordjevic, S.; Butler, D.; Khan, D.M.; Rahman, S.M.M.; Haque, A.K.E. The Development of a Flood Damage Assessment Tool for Urban Areas. In Proceedings of the 9th International Joint IWA/IAHR Conference on Urban Drainage Modelling, Belgrade, Serbia, 3–6 September 2012. [Google Scholar]
  188. Morgan, M.C.; Hubbard, P.L.; Martz, R.J.; Moore, C.I.; Wittenberg, M.D.-I. A Collaborative Approach to Modeling the Hampton Roads Regional Wastewater Collection System. Proc. Water Environ. Fed. 2012, 305–326. [Google Scholar] [CrossRef]
  189. Nielsen, N.H.; Larsen, M.R.A.; Rasmussen, S.F. Development of a screening method to assess flood risk on Danish national roads and highway systems. Water Sci. Technol. 2011, 63, 2957–2966. [Google Scholar] [CrossRef] [PubMed]
  190. Borup, M.; Grum, M.; Mikkelsen, P.S. Real time adjustment of slow changing flow components in distributed urban runoff models. In Proceedings of the 12th International Conference on Urban Drainage, Porto Alegre, Brazil, 11–16 September 2011. [Google Scholar]
  191. Liu, A.; Egodawatta, P.; Kjolby, M.J.; Goonetilleke, A. Development of pollutant build-up parameters for MIKE URBAN for Southeast Queensland, Australia. In Proceedings of the International MIKE by DHI Conference, Copenhagen, Denmark, 6–8 September 2010; Danish Hydraulics Institute: Copenhagen, Denmark, 2010; pp. 024-1–024-15. [Google Scholar]
  192. Nagatani, T.; Yasuhara, K.; Murata, K.; Takeda, M.; Nakamura, T.; Fuchigami, T.; Terashima, K. Residual chlorine decay simulation in water distribution system. In The 7th International Symposium on Water Supply Technology; Citeseer: Yokohama, Japan, 2008; pp. 1–11. [Google Scholar]
  193. Koutiva, I.; Makropoulos, C. Modelling domestic water demand: An agent based approach. Environ. Model. Softw. 2016, 79, 35–54. [Google Scholar] [CrossRef]
  194. Universidad de Sevilla. Guía Para la Incorporación de la Gestión Sostenible del Agua en Áreas Urbanas; Universidad de Sevilla: Sevilla, Spain, 2015. [Google Scholar]
  195. Baki, S.; Makropoulos, C. Tools for Energy Footprint Assessment in Urban Water Systems. Proc. Eng. 2014, 89, 548–556. [Google Scholar] [CrossRef]
  196. Papariantafyllou, E.; Makropoulos, C. Developing Roadmaps for the Sustainable Management of the Urban Water Cycle: The Case of ww Reuse in Athens. In Proceedings of the 13th International Conference of Environmental Science and Technology, Athens, Greece, 5–7 September 2013. [Google Scholar]
  197. Rozos, E.; Makropoulos, C. Assessing the combined benefits of water recycling technologies by modelling the total urban water cycle. Urban Water J. 2012, 9, 1–10. [Google Scholar] [CrossRef]
  198. Koutiva, I.; Makropoulos, C. Linking Social Simulation and Urban Water Modelling Tools to Support Adaptive Urban Water Management; International Environmental Modelling and Software Society (iEMSs): Ottawa, ON, Canada, 2012. [Google Scholar]
  199. Rozos, E.; Baki, S.; Bouziotas, D.; Makropoulos, C. Exploring the link between urban development and water demand: The impact of water-aware technologies and options. In Proceedings of the Computing and Control for the Water Industry 2011, Exeter, UK, 5–7 September 2011. [Google Scholar]
  200. Bouziotas, D.; Rozos, E.; Makropoulos, C. Water and the city: Exploring links between urban growth and water demand management. J. Hydroinform. 2015, 17, 176–192. [Google Scholar] [CrossRef]
  201. Makropoulos, C.K.; Butler, D. Distributed Water Infrastructure for Sustainable Communities. Water Resour. Manag. 2010, 24, 2795–2816. [Google Scholar] [CrossRef]
  202. Rozos, E.; Makropoulos, C.K.; Butler, D. Design Robustness of Local Water-Recycling Schemes. J. Water Resour. Plan. Manag. 2010, 136, 531–538. [Google Scholar] [CrossRef]
  203. Beh, E.H.Y.; Maier, H.R.; Dandy, G.C. Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty. Water Resour. Res. 2015, 51, 1529–1551. [Google Scholar] [CrossRef]
  204. Beh, E.H.Y.; Maier, H.R.; Dandy, G.C. Scenario driven optimal sequencing under deep uncertainty. Environ. Model. Softw. 2015, 68, 181–195. [Google Scholar] [CrossRef]
  205. Clark, R.; Gonzalez, D.; Dillon, P.; Charles, S.; Cresswell, D.; Naumann, B. Reliability of water supply from stormwater harvesting and managed aquifer recharge with a brackish aquifer in an urbanising catchment and changing climate. Environ. Model. Softw. 2015, 72, 117–125. [Google Scholar] [CrossRef]
  206. Beh, E.H.Y.; Dandy, G.C.; Maier, H.R.; Paton, F.L. Optimal sequencing of water supply options at the regional scale incorporating alternative water supply sources and multiple objectives. Environ. Model. Softw. 2014, 53, 137–153. [Google Scholar] [CrossRef]
  207. Barton, A.B.; Argue, J.R. Integrated urban water management for residential areas: A reuse model. Water Sci. Technol. 2009, 60, 813–823. [Google Scholar] [CrossRef] [PubMed]
  208. Marks, R.; Clark, R.; Rooke, E.; Berzins, A. Meadows, South Australia: Development through integration of local water resources. Desalination 2006, 188, 149–161. [Google Scholar] [CrossRef]
  209. Schmitter, P.; Goedbloed, A.; Galelli, S.; Babovic, V. Effect of Catchment-Scale Green Roof Deployment on Stormwater Generation and Reuse in a Tropical City. J. Water Resour. Plan. Manag. 2016, 142, 05016002. [Google Scholar] [CrossRef]
  210. Sušnik, J.; Strehl, C.; Postmes, L.A.; Vamvakeridou-Lyroudia, L.S.; Mälzer, H.-J.; Savić, D.A.; Kapelan, Z. Assessing Financial Loss due to Pluvial Flooding and the Efficacy of Risk-Reduction Measures in the Residential Property Sector. Water Resour. Manag. 2015, 29, 161–179. [Google Scholar] [CrossRef]
  211. Faraji, Y. Water Quality Modelling with SOBEK in Dutch Polders Subject to Salinization and River Water Flushing Case Study in Anna Paulownapolder. Master’s Thesis, Utrecht University, Utrecht, The Netherlands, 2015. [Google Scholar]
  212. Van Dijk, E.; van der Meulen, J.; Kluck, J.; Straatman, J.H.M. Comparing modelling techniques for analysing urban pluvial flooding. Water Sci. Technol. 2014, 69, 305–311. [Google Scholar] [CrossRef] [PubMed]
  213. Vergroesen, T.; Verschelling, E.; Becker, B. Modelling of sustainable urban drainage measures. Rev. Ing. Innova 2014, 8, 1–16. [Google Scholar]
  214. Doan, C.D.; Liu, J.; Liong, S.-Y.; Verwey, A. Rainfall-runoff study for Singapore river catchment. In Proceedings of the 10th International Conference on Hydroinformatics, Hamburg, Germany, 14–18 July 2012. [Google Scholar]
  215. Hellmann, F.; Vermaat, J.E. Impact of climate change on water management in Dutch peat polders. Ecol. Model. 2012, 240, 74–83. [Google Scholar] [CrossRef]
  216. Mirza, F.; Maheepala, S.; Ashbolt, S.; Neumann, L.; Kinsman, D.; Coultas, E. HydroPlanner: A Prototype Modelling Tool to Aid Development of Integrated Urban Water Management Strategies; Urban Water Security Research Alliance Technical Report 108; Urban Water Security Research Alliance: City East, Australia, 2013. [Google Scholar]
  217. Kinsman, D.L.; Mirza, F.F.; Maheepala, S.; Neumann, L.E.; Coultas, E.H. Representing wastewater recycling in an integrated urban water modelling tool. Water Pract. Technol. 2012, 7, wpt2012002. [Google Scholar] [CrossRef]
  218. Mousavi, S.J.; Behzadian, K.; Kim, J.H.; Kapelan, Z. A Multi-objective Optimisation Approach to Optimising Water Allocation in Urban Water Systems. In Harmony Search Algorithm; Kim, J.H., Geem, Z.W., Eds.; Advances in Intelligent Systems and Computing; Springer: Berlin, Heidelberg, Germany, 2016; pp. 447–457. [Google Scholar]
  219. Liserra, T.; Benzedian, K.; Ugarelli, R.; Bertozzi, R.; Federico, V.D.; Kapelan, Z. Metabolism-based modelling for performance assessment of a water supply system: A case study of Reggio Emilia, Italy. Water Sci. Technol. Water Supply 2016, 16, 1221–1230. [Google Scholar] [CrossRef]
  220. Venkatesh, G.; Brattebø, H.; Sægrov, S.; Behzadian, K.; Kapelan, Z. Metabolism-modelling approaches to long-term sustainability assessment of urban water services. Urban Water J. 2017, 14, 11–22. [Google Scholar] [CrossRef]
  221. Morley, M.S.; Vitorino, D.; Behzadian, K.; Ugarelli, R.; Kapelan, Z.; Coelho, S.T.; Almeida, M.D.C. Decision support system for the long-term city metabolism planning problem. Water Sci. Technol. Water Supply 2015. [Google Scholar] [CrossRef]
  222. Behzadian, K.; Kapelan, Z. Modelling metabolism based performance of an urban water system using WaterMet2. Resour. Conserv. Recycl. 2015, 99, 84–99. [Google Scholar] [CrossRef]
  223. Nazari, S.; Mousavi, S.; Behzadian, K.; Kapelan, Z. Compromise Programming Based Scenario Analysis Of Urban Water Systems Management Options: Case Study Of Kerman City. In Proceedings of the International Conference on Hydroinformatics, New York, NY, USA, 16–21 August 2014. [Google Scholar]
  224. Thyer, M.; Hardy, M.; Coombes, P.; Patterson, C. The impact of end-use dynamics on urban water system design criteria. Aust. J. Water Resour. 2008, 12, 161–170. [Google Scholar]
  225. Hardy, M.; Kuczera, G.; Coombes, P.; Barbour, E.; Jurd, K. An evaluation of the performance of the application of the urbanCycle model to a gauged urban catchment. In Proceedings of the Rainwater and Urban Design 2007, Sydney, Australia, 21–23 August 2007; p. 340. [Google Scholar]
  226. Barton, A.; Coombes, P.; Rodriguez, J. Understanding ecological response in urban catchments. In Proceedings of the Rainwater and Urban Design 2007, Sydney, Australia, 21–23 August 2007; p. 61. [Google Scholar]
  227. Hardy, M.; Jefferson, C.; Coombes, P.; Kuczera, G. lntegrated Urban Water Cycle Management: Redefining the Boundaries. In Proceedings of the 28th International Hydrology and Water Resources Symposium: About Water, Symposium Proceedings. Wollongong, NSW, Australia, 10–13 November 2003; p. 1. [Google Scholar]
  228. Sapkota, M.; Arora, M.; Malano, H.; George, B.; Nawarathna, B.; Sharma, A.; Moglia, M. Development of a framework to evaluate the hybrid water supply systems. In Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide Australia, 10–13 November 2013; pp. 1–6. [Google Scholar]
  229. Urich, C.; Sitzenfrei, R.; Kleidorfer, M.; Rauch, W. Klimawandel und Urbanisierung—Wie soll die Wasserinfrastruktur angepasst werden? Österr. Wasser-Abfallwirtsch. 2013, 65, 82–88. [Google Scholar] [CrossRef]
  230. Ferguson, B.C.; de Haan, F.J.; Brown, R.R.; Deletic, A. Testing a strategic action framework: Melbourne’s transition to WSUD. In WSUD 2012: Water Sensitive Urban Design; Building the Water Sensiitve Community, Proceedings of the 7th International Conference on Water Sensitive Urban Design, Melbourne, Australia, 21–23 February 2012; Engineers Australia: Barton, Australia; pp. 236–243.
  231. Bach, P.M.; Urich, C.; McCarthy, D.T.; Sitzenfrei, R.; Kleidorfer, M.; Rauch, W.; Deletic, A. Characterising a city for integrated performance assessment of water infrastructure in the DAnCE4Water model. In Proceedings of the 12nd International Conference on Urban Drainage, Porto Alegre, Brazil, 10–15 September 2011. [Google Scholar]
  232. Venkatesh, G. Testing different rehabilitation options in the drinking water pipeline network in Oslo using Dynamic Metabolism Model (DMM). J. Water Manag. Res. 2014, 70, 215–223. [Google Scholar]
  233. Willuweit, L.; O’Sullivan, J.J.; Shahumyan, H. Simulating the effects of climate change, economic and urban planning scenarios on urban runoff patterns of a metropolitan region. Urban Water J. 2015, 0, 1–16. [Google Scholar] [CrossRef]
  234. Willuweita, L.; O’Sullivana, J.J.; Shahumyanb, H. Modelling the effects of urban Growth scenarios on water demand and runoff patterns in Dublin Ireland. In Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide, Australia, 10–13 November 2013; pp. 3162–3168. [Google Scholar]
  235. Farooqui, T.A.; Renouf, M.A.; Kenway, S.J. A metabolism perspective on alternative urban water servicing options using water mass balance. Water Res. 2016, 106, 415–428. [Google Scholar] [CrossRef] [PubMed]
  236. Marteleira, R.; Pinto, G.; Niza, S. Regional water flows—Assessing opportunities for sustainable management. Resour. Conserv. Recycl. 2014, 82, 63–74. [Google Scholar] [CrossRef]
  237. Chèvre, N.; Coutu, S.; Margot, J.; Wynn, H.K.; Bader, H.-P.; Scheidegger, R.; Rossi, L. Substance flow analysis as a tool for mitigating the impact of pharmaceuticals on the aquatic system. Water Res. 2013, 47, 2995–3005. [Google Scholar] [CrossRef] [PubMed]
  238. Bhaskar, A.S.; Welty, C. Water Balances along an Urban-to-Rural Gradient of Metropolitan Baltimore, 2001–2009. Environ. Eng. Geosci. 2012, 18, 37–50. [Google Scholar] [CrossRef]
  239. Charalambous, K.; Bruggeman, A.; Lange, M.A. Assessing the urban water balance: The Urban Water Flow Model and its application in Cyprus. Water Sci. Technol. 2012, 66, 635–643. [Google Scholar] [CrossRef] [PubMed]
  240. Chèvre, N.; Guignard, C.; Rossi, L.; Pfeifer, H.-R.; Bader, H.-P.; Scheidegger, R. Substance flow analysis as a tool for urban water management. Water Sci. Technol. 2011, 63, 1341–1348. [Google Scholar] [CrossRef] [PubMed]
  241. Järvi, L.; Grimmond, C.S.B.; Christen, A. The Surface Urban Energy and Water Balance Scheme (SUEWS): Evaluation in Los Angeles and Vancouver. J. Hydrol. 2011, 411, 219–237. [Google Scholar] [CrossRef]
  242. Haase, D. Effects of urbanisation on the water balance—A long-term trajectory. Environ. Impact Assess. Rev. 2009, 29, 211–219. [Google Scholar] [CrossRef]
  243. Van Rooijen, D.J.; Turral, H.; Wade Biggs, T. Sponge city: Water balance of mega-city water use and wastewater use in Hyderabad, India. Irrig. Drain. 2005, 54, S81–S91. [Google Scholar] [CrossRef]
  244. Binder, C.; Schertenleib, R.; Diaz, J.; Bader, H.-P.; Baccini, P. Regional Water Balance as a Tool for Water Management in Developing Countries. Int. J. Water Resour. Dev. 1997, 13, 5–20. [Google Scholar] [CrossRef]
  245. García, M.; Morales-Pinzón, T.; Guerrero Erazo, J. Análisis de flujos de agua en áreas metropolitanas desde la perspectiva del metabolismo urbano. Rev. Luna Azul. 2014, 234–249. [Google Scholar] [CrossRef]
  246. Chrysoulakis, N.; Lopes, M.; San José, R.; Grimmond, C.S.B.; Jones, M.B.; Magliulo, V.; Klostermann, J.E.M.; Synnefa, A.; Mitraka, Z.; Castro, E.A.; et al. Sustainable urban metabolism as a link between bio-physical sciences and urban planning: The BRIDGE project. Landsc. Urban Plan. 2013, 112, 100–117. [Google Scholar] [CrossRef]
  247. Liu, Y.; Wang, W.; Li, X.; Zhang, G. Eco-efficiency of urban material metabolism: A case study in Xiamen, China. Int. J. Sustain. Dev. World Ecol. 2010, 17, 142–148. [Google Scholar] [CrossRef]
  248. Thériault, J.; Laroche, A.-M. Evaluation of the Urban Hydrologic Metabolism of the Greater Moncton Region, New Brunswick. Can. Water Resour. J. Rev. Can. Ressour. Hydr. 2009, 34, 255–268. [Google Scholar] [CrossRef]
  249. Zhang, Y.; Yang, Z. Eco-efficiency of urban material metabolism: A case study in Shenzhen, China. Acta Ecol. Sin. 2007, 27, 3124–3131. [Google Scholar] [CrossRef]
  250. Sahely, H.R.; Dudding, S.; Kennedy, C.A. Estimating the urban metabolism of Canadian cities: Greater Toronto Area case study. Can. J. Civ. Eng. 2003, 30, 468–483. [Google Scholar] [CrossRef]
  251. Lee, S. Hydrological Metabolism and Water Resources Management of the Beijing Metropolitan Region in the Hai River Basin. Master’s Thesis, University of Toronto, Toronto, ON, Canada, 1998. [Google Scholar]
  252. Hermanowicz, S.W.; Asano, T. Abel Wolman’s “the metabolism of cities” revisited: A case for water recycling and reuse. Water Sci. Technol. 1999, 40, 29–36. [Google Scholar] [CrossRef]
  253. Loubet, P.; Roux, P.; Bellon-Maurel, V. WaLA, a versatile model for the life cycle assessment of urban water systems: Formalism and framework for a modular approach. Water Res. 2016, 88, 69–82. [Google Scholar] [CrossRef] [PubMed]
  254. Amores, M.J.; Meneses, M.; Pasqualino, J.; Antón, A.; Castells, F. Environmental assessment of urban water cycle on Mediterranean conditions by LCA approach. J. Clean. Prod. 2013, 43, 84–92. [Google Scholar] [CrossRef]
  255. Uche, J.; Martínez, A.; Castellano, C.; Subiela, V. Life cycle analysis of urban water cycle in two Spanish areas: Inland city and island area. Desalination Water Treat. 2013, 51, 280–291. [Google Scholar] [CrossRef]
  256. Godskesen, B.; Zambrano, K.C.; Trautner, A.; Johansen, N.-B.; Thiesson, L.; Andersen, L.; Clauson-Kaas, J.; Neidel, T.L.; Rygaard, M.; Kløverpris, N.H.; et al. Life cycle assessment of three water systems in Copenhagen–a management tool of the future. Water Sci. Technol. 2011, 63, 565–572. [Google Scholar] [CrossRef] [PubMed]
  257. Fagan, J.E.; Reuter, M.A.; Langford, K.J. Dynamic performance metrics to assess sustainability and cost effectiveness of integrated urban water systems. Resour. Conserv. Recycl. 2010, 54, 719–736. [Google Scholar] [CrossRef]
  258. El-Sayed Mohamed Mahgoub, M.; van der Steen, N.P.; Abu-Zeid, K.; Vairavamoorthy, K. Towards sustainability in urban water: A life cycle analysis of the urban water system of Alexandria City, Egypt. J. Clean. Prod. 2010, 18, 1100–1106. [Google Scholar] [CrossRef]
  259. Lane, J.; De Haas, D.; Lant, P. Life cycle impacts of the Gold Coast urban water cycle. In Proceedings of the Ozwater 2010, Brisbane, Australia, 8–10 March 2010. [Google Scholar]
  260. Jeppsson, U.; Hellström, D. Systems analysis for environmental assessment of urban water and wastewater systems. Water Sci. Technol. 2002, 46, 121–129. [Google Scholar] [PubMed]
  261. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.; Bai, X.; Briggs, J.M. Global Change and the Ecology of Cities. Science 2008, 319, 756–760. [Google Scholar] [CrossRef] [PubMed]
  262. Peter-Varbanets, M.; Zurbrügg, C.; Swartz, C.; Pronk, W. Decentralized systems for potable water and the potential of membrane technology. Water Res. 2009, 43, 245–265. [Google Scholar] [CrossRef] [PubMed]
  263. Al-Jayyousi, O.R. Greywater reuse: Towards sustainable water management. Desalination 2003, 156, 181–192. [Google Scholar] [CrossRef]
  264. Asano, T.; Levine, A.D. Wastewater reclamation, recycling and reuse: Past, present, and future. Water Sci. Technol. 1996, 33, 1–14. [Google Scholar] [CrossRef]
  265. Dixon, A.; Butler, D.; Fewkes, A. Water saving potential of domestic water reuse systems using greywater and rainwater in combination. Water Sci. Technol. 1999, 39, 25–32. [Google Scholar] [CrossRef]
  266. Hatt, B.E.; Deletic, A.; Fletcher, T.D. Integrated treatment and recycling of stormwater: A review of Australian practice. J. Environ. Manag. 2006, 79, 102–113. [Google Scholar] [CrossRef] [PubMed]
  267. Pidou, M.; Memon, F.A.; Stephenson, T.; Jefferson, B.; Jeffrey, P. Greywater recycling: A review of treatment options and applications. Inst. Civ. Eng. Proc. Eng. Sustain. 2007, 160, 119–131. [Google Scholar] [CrossRef]
  268. Fletcher, T.D.; Shuster, W.; Hunt, W.F.; Ashley, R.; Butler, D.; Arthur, S.; Trowsdale, S.; Barraud, S.; Semadeni-Davies, A.; Bertrand-Krajewski, J.-L.; et al. SUDS, LID, BMPs, WSUD and more—The evolution and application of terminology surrounding urban drainage. Urban Water J. 2014, 12, 525–542. [Google Scholar] [CrossRef]
  269. Shutes, B. A Design Manual Incorporating Best Practice Guidelines for Stormwater Management Options and Treatment under Extreme Conditions—Part B: The Potential of BMPs to Integrate with Existing Infrastructure (i.e., Retro-Fit/Hybrid Systems) and to Contribute to Other Sectors of the Urban Water Cycle; 018530—SWITCH WP2.1 Project; Middlesex University: London, UK, 2008. [Google Scholar]
  270. Macy, M.W.; Willer, R. From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annu. Rev. Sociol. 2002, 28, 143–166. [Google Scholar] [CrossRef]
  271. Pataki, D.E.; Boone, C.G.; Hogue, T.S.; Jenerette, G.D.; McFadden, J.P.; Pincetl, S. Socio-ecohydrology and the urban water challenge. Ecohydrology 2011, 4, 341–347. [Google Scholar] [CrossRef]
  272. Sofoulis, Z. Big Water, Everyday Water: A Sociotechnical Perspective. Continuum 2005, 19, 445–463. [Google Scholar] [CrossRef]
  273. Koutiva, I.; Makropoulos, C. Towards adaptive water resources management: Simulating the complete socio-technical system through computational intelligence. In Proceedings of the 12th International Conference on Environmental Science and Technology, Rhodes, Greece, 8–10 September 2011. [Google Scholar]
  274. Brown, R.R.; Sharp, L.; Ashley, R.M. Implementation impediments to institutionalising the practice of sustainable urban water management. Water Sci. Technol. 2006, 54, 415–422. [Google Scholar] [CrossRef] [PubMed]
  275. Brown, R.R.; Farrelly, M.A. Delivering sustainable urban water management: A review of the hurdles we face. Water Sci. Technol. 2009, 59, 839–846. [Google Scholar] [CrossRef] [PubMed]
  276. Abbott, M.B. Some Future Prospects in Hydroinformatics. In Practical Hydroinformatics; Abrahart, R.J., See, L.M., Solomatine, D.P., Eds.; Water Science and Technology Library; Springer: Berlin/Heidelberg, Germany, 2009; pp. 3–16. [Google Scholar]
  277. Mankad, A.; Tapsuwan, S. Review of socio-economic drivers of community acceptance and adoption of decentralised water systems. J. Environ. Manag. 2011, 92, 380–391. [Google Scholar] [CrossRef] [PubMed]
  278. Swyngedouw, E.; Kaïka, M.; Castro, E. Urban Water: A Political-Ecology Perspective. Built Environ. 1978 2002, 28, 124–137. [Google Scholar]
  279. Daigger, G.T. Evolving Urban Water and Residuals Management Paradigms: Water Reclamation and Reuse, Decentralization, and Resource Recovery. Water Environ. Res. 2009, 81, 809–823. [Google Scholar] [CrossRef] [PubMed]
  280. Nair, S.; George, B.; Malano, H.M.; Arora, M.; Nawarathna, B. Water–energy–greenhouse gas nexus of urban water systems: Review of concepts, state-of-art and methods. Resour. Conserv. Recycl. 2014, 89, 1–10. [Google Scholar] [CrossRef]
Figure 1. Urban Water Cycle (UWC). Adapted from [58].
Figure 1. Urban Water Cycle (UWC). Adapted from [58].
Water 09 00285 g001
Figure 2. Experiments reported by country.
Figure 2. Experiments reported by country.
Water 09 00285 g002
Table 1. Internal and external factors of the UWC.
Table 1. Internal and external factors of the UWC.
UWC PartUWC Component Internal FactorExternal FactorSource
Water supply subsystemRaw-water intakePopulation, availability, techniquesClimate, environment, economy, geography [61,62,63,64,65]
Water treatmentPopulation, techniques, quantity, quality, energyClimate, economy, regulations, geography
StoragePopulation, techniques, energyClimate, environment, economy, geography
Water supply distributionPopulation, techniques, quantity, quality, energyEconomy, geography, society, culture, environment, regulations
Water demand Water consumptionPopulation, weather, population density, land use, equipment, economy Education, territory growth, culture, regulations[66,67,68,69,70,71]
Wastewater and stormwater subsystemCollectionPopulation, weather, population density, land use, equipment geography, hydraulics, regulations, public health, environment, economySociety, culture, education[17,72,73,74,75,76,77]
TreatmentLand use, equipment, geography, regulations, public health, quality, quantity, environment, economy, energySociety, culture, education [52,53,78,79,80,81]
Receiving WaterEquipment, geography, regulations, public health, quality, quantity, ecology, environment, economy Territory growth, type of water-receiving body [58,82,83,84,85,86]
Table 2. Description of UWC models.
Table 2. Description of UWC models.
ModelType of ModelDevelopment Team or InstitutionCountrySpatial ScaleTime ScalePlatformSupport SoftwareSimulated ProcessesModel EmphasisSoftware LinkSource
HHdHyCS
AquacycleIUWCMsCRCCHAustraliaPr, Ne, GNe. DailyWindows X X XHydric balance.www.toolkit.net.au[29,87]
UVQIUWCMsCSIROAustraliaPr, Ne, GNe, Cit.DailyWindows X XXXHydric and contaminants balance. [30,88,89,90,91]
MIKE URBANIUWCMsDHIDenmarkNe, GNe, Cit.Hourly and DailyWindowsArcGIS, MOUSE, SWMM5, EPANET2 MIKENETXXXXXHydrological balance, hydraulic calculations.www.mikepoweredbydhi.com[92,93,94,95,96,97,98]
UWOTIUWCMsThe urban water management and hydroinformatics team of the School of Civil Engineering, NTUA GreeceIndD, Pr, Ne, GNe, Cit.10 min to monthlyWindows, Linux, Matlab (for optimization) and eLearning platform X XXXOptimization of the development of strategies for UWC managementwww.watershare.eu[9,32,99,100,101]
WaterCressIUWCMsRichard Clark and David CresswellAustraliaPr, Cat.DailyWindows X XXXHydric and contaminant balance.www.waterselect.com.au[25,102,103,104,105]
Sobek-UrbanIUWCMsDaltaresThe NetherlandsCat, Ne, GNe Cit.Minutes and secondsWindowsGISXXXXXHydrological balance, hydraulic calculations, real-time control, water quality.www.deltares.nl[106]
Hydro PlannerIUWCMsCSIROAustraliaNe, Cit, Cat.DailyWindowsREsource ALlocation Model (REALM), E2X XXXHydric balance. [28,107,108]
WaterMet2IUWCMsExeter University and NTUAGreece and UKPr, GNe, Ci.DailyWindows X XXXHydric and contaminants balance, energy, greenhouse gases, chemical material balance.www.emps.exeter.ac.uk[24,109,110,111,112]
UrbanCycleIUWCMsUniversity of NewcastleAustraliaPr, GNe, Cit.Hourly, dailyFORTRANDRIP, Probabilistic Demand ModelX X XHydric balance. [20,113,114,115]
Urban DeveloperIUWCMsCRCCHAustraliaPr, GNe, Cit.Hourly, dailyWindowsMUSICX X XHydric balance.www.ewater.org.au[33,116]
Dance4WaterIUWSMsMonash University, University of Innsbruck, Centre for Water Sensitive Cities and Melbourne WaterAustralia and AustriaPr, Ne, GNe, Cit.DailyVirtual, WebSWMM, UrbanSimXXX XHydrological balance, hydraulic calculation, UWC-related social factorswww.dance4water.org[117,118,119,120,121,122,123,124]
DUWSiMIUWCMsLars Willuweit and John J. O’Sullivan University College DublinIrelandNe, GNe, Cit.DailyMicrosoft ExcelLARS-WG, MOLANDX XXXHydric and contaminant balance. [36]
WaND-OT1IUWCMsUniversity of ExeterUKIndD, Pr, Ne.DailyMatlab Symulink, Microsoft Excel (VBA) X X XHydric balance. [32]
DMMIUWCMs or IUWSMsNorwegian University of Science and Technology NorwayNe, Cit.Hourly, daily, monthly, yearlyMicrosoft Excel X X XHydric balance energy, greenhouse gases. [34]
Water Balance *IUWCMs or IUWSMsN/AN/APr, Ne, GNe, Cit.Hourly, daily, monthly, yearly SIMBOX, Matlab, Phyton, R, Microsoft Excel (VBA), ABIMO, X XXXHydric and contaminant balance, energy, greenhouse gases, chemical and material balance. [26,38,39]
Urban Metabolism *IUWCMs or IUWSMsN/AN/APr, Ne, GNe, Cit.Hourly, daily, monthly, yearlyExcel (VBA), Matlab, Phyton, R. X XXXHydric and contaminant balance, energy, greenhouse gases, chemical and material balance. [31,125,126,127]
LCA *IUWCMs or IUWSMsN/AN/APr, Ne, GNe, Cit.Hourly, daily, monthly, yearlyMatlab, Phyton, R, Symulink, Microsoft Excel (VBA), SIMAPRO, GaBi4 X XXXHydric and contaminant balance, energy, greenhouse gases, chemical and material balance. [27,128,129]
Notes: H = hydrological, Hd = hydraulic, Hy = UWC hydric components, C = contaminants, S = strategies for structural management and/or nonstructural action, BMP = best management practices, DRIP = Disaggregated Rectangular Intensity Pulse, VIBe = Virtual Infrastructure Benchmarking, CRCCH = Cooperative Research Centre of Catchment Hydrology, CSIRO = Commonwealth Scientific Industrial and Research Organisation, DHI = Danish Hydraulic Institute, Pr = Property, Ne = neighborhood, GNe = group of neighborhoods, Cit = city, IndD = Individual-dwelling water uses, Cat = catchment, Dance4Water = Dynamic Adaptation for eNabling City Evolution for Water, DUWSiM = Dynamic Urban Water Simulation Model, DMM = Dynamic Metabolism Model, LCA = Life Cycle Assessment, WaterCress = Water-Community Resource Evaluation and Simulation System, UWOT = Urban Water Optioneering Tool, UVQ = Urban Volume and Quality, IUWCM = Integrated Urban Water Cycle Models, IUWSMs = Integrated Urban Water System Models, N/A =Not applicable, VBA = Visual Basic for Applications. *Approach does not refer to specific software.
Table 3. Case studies involving UWC software or approaches.
Table 3. Case studies involving UWC software or approaches.
ModelCase StudyCountryType of Application
WHBCSDDWEISWEIGWRCWWTBMPHFRTRRRGWEGGSF
Aquacycle[135]USAX X
[136]EgyptX XX X
[137]IsraelX X X XX
[138]AustraliaX X XX
[139]AustraliaX
[140]South KoreaX
[141]South KoreaX X XX
[142]AustraliaX X XX
[143]GermanyX X XX
[144]FranceX X X X
[145]GhanaXXX XX
[146]AustraliaX X
[147]GreeceX XX X XX
[39]AustraliaX X
[148]SpainXX X
[149]South KoreaX X XX
[150,151]AustraliaX
[29]AustraliaX X X X
Urban Volume Quality (UVQ)[152]AustraliaXXX X XX
[153]AustraliaXXX X X
[154]AustraliaXXX X X X
[155]VanuatuXXX XXX XX
[156]AustraliaXXX X XX
[157]AustriaXX
[158]AustraliaXX X
[159]MexicoXXX X X
[160]South KoreaXX X X
[161]AustraliaX X X X
[162]AustraliaXX X X
[163]UKXX X
[164]AustraliaX X
[142]AustraliaXXX XX XX
[165]SloveniaXX X
[166]AustraliaXXX XX
[167]UKXX X
[168]GermanyXX X
[169]SloveniaXX X
[170]UKXXXXXX X
[171]GermanyXXX X XX
MIKE URBAN[172]DenmarkX X
[173]India XX
[174]DenmarkX XX
[175]Denmark XX
[176]Denmark XX
[177]India X
[178]Denmark XX
[179]Denmark XX
[96]Lithuania X
[180]Denmark XX
[181]Germany X X
[182]Denmark XX
[183]Sweden X
[184,185]Denmark XX
[185]Denmark X
[186,187]Bangladesh XX
[188]USAX
[189]Denmark XX
[190]DenmarkX X
[191]Australia X X
[192]Japan X
Urban Water Optioneering Tool (UWOT)[193]GreeceX
[194]SpainX X X XX
[195]Greece X X
[196]GreeceX X XX
[197]HypotheticalX X XX XXX
[198]GreeceX X X XX
[199,200]GreeceX
[201]HypotheticalX X XX XXX
[202]HypotheticalX X XX XX
WaterCress[203]AustraliaX X
[204]AustraliaX X
[205]AustraliaX
[105]AustraliaX X X X
[206]AustraliaX
[104]AustraliaX X X
[207]AustraliaX XX
[208]AustraliaX
Sobek-Urban[209]Singapore X X
[210]The Netherlands X
[211]The Netherlands X X
[212]The Netherlands X
[213]The Netherlands X
[214]SingaporeX
[215]The NetherlandsX
Hydro Planner[216]AustraliaXXXX XX XXX
[217]AustraliaX X XX
[108]AustraliaX X
WaterMet2[218]IranXXX X XX
[219]ItaliaX X
[220]Norway X
[221]UnspecifiedX X
[222]UnspecifiedXXXX XX XX
[112]NorwayX
[223]IranX X
[24]NorwayX X X X XXX
UrbanCycle[113]HypotheticalX X X XX
[224]AustraliaX X
[225]AustraliaX X X
[226]AustraliaX X
[227]AustraliaX
Urban Developer[228]HypotheticalX
Dance4Water[229]AustriaX X X
[230]AustraliaX X X
[121]Australia X
[122]AustraliaX X
[231]AustraliaX X
WaND-OT1[32]UKX XX XX
Dynamic Metabolism Model (DMM)[220]Norway X
[34,232]NorwayX X X
DUWSiM[233,234]IrelandX
[235]AustraliaX X XXX
Water Balance[236]PortugalX X XX
[237]Switzerland X X
[238]USAX
[239]CyprusX X X
[240]Switzerland X X
[241]USA and CanadaX X
[26]AustraliaX
[10]UKXX XX
[242]GermanyX
[243]IndiaX X
[244]ColombiaXX XX X
Urban Metabolism[245]ColombiaX
[246]UKX X
[247]ChinaX
[248]CanadaX
[249]ChinaX
[250]CanadaX
[251]ChinaX
[252]USAX X
Life Cycle Assessment (LCA)[253]Hypothetical X X
[254]SpainX XX XX
[255]SpainX X
[256]Norway X X
[59]Norway X
[257]AustraliaX X XXX
[258]Egypt X
[259]Australia X X
[260]Sweden X X
Notes: WHB = Water or hydrology balance of UWC for managing rainwater or wastewater, C = UWC contaminants, SDDW = Supply and demand of drinking water, EISW = Environmental impact on surface hydric resources, EIGW = Environmental impact on groundwater resources, RC = Rainwater contamination, WWT = Wastewater treatment, BMP = Best management practices, H = Hydraulic, F = Flooding, RT = Rainwater treatment, RR = Rainwater reuse, RGW = Reuse of gray water and sewage, EGG = Energy and greenhouse gases, SF = Social factors.

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Back to TopTop