Modelling an urban water system on the edge of chaos

Moglia, Magnus; Perez, Pascal; Burn, Stewart

doi:10.1016/j.envsoft.2010.05.002

Cited by 36 publications

(17 citation statements)

References 27 publications

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“…Mathematical models with differential equations that represent certain changes of a certain quantity as a function of other quantities might be good in making predictions about the behavior of the system under different initial conditions, but their limitation is that they often capture only the average behavior of the system [16]. Experiments with the ABM allow users to quickly improve their intuition and understanding of the overall domestic water system [17].…”

Section: Methodsmentioning

confidence: 99%

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An Agent Based Model of Household Water Use

Linkola

Andrews

Schuetze

2013

Water

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Households consume a significant fraction of total potable water production. Strategies to improve the efficiency of water use tend to emphasize technological interventions to reduce or shift water demand. Behavioral water use reduction strategies can also play an important role, but a flexible framework for exploring the "what-ifs" has not been available. This paper introduces such a framework, presenting an agent-based model of household water-consuming behavior. The model simulates hourly water-using activities of household members within a rich technological and behavioral context, calibrated with appropriate data. Illustrative experiments compare the resulting water usage of U.S. and Dutch households and their associated water-using technologies, different household types (singles, families with children, and retired couples), different water metering regimes, and educational campaigns. All else equal, Dutch and metered households use less water. Retired households use more water because they are more often at home. Water-saving educational campaigns are effective for the part of the population that is receptive. Important interactions among these factors, both technological and behavioral, highlight the value of this framework for integrated analysis of the human-technologywater system.

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Section: Methodsmentioning

confidence: 99%

“…Agent based modeling has been successfully used in simulating consumer behavior and technological lock-ins as a cellular automaton [18] and modeling possible trade-offs resulting from urban water system governance [17]. Typical ABMs includes four elements: environment, decision making, interactions, and adaptation.…”

Section: Methodsmentioning

confidence: 99%

An Agent Based Model of Household Water Use

Linkola

Andrews

Schuetze

2013

Water

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“…These limitations notwithstanding, it is suggested that the 'thinking platform' presented in this chapter could be particularly important as a means to investigate uncertain impacts and potential side effects of new interventions (such as distributed technologies) in urban water management under different 'futures' (Makropoulos et al 2008b). This is because, as illustrated by Moglia et al (2010), a sustainable (in theory) intervention aiming to improve the state of a particular system may have unintended and undesirable effects on another. When diverse interventions such as pricing, new centralized infrastructure or rebates for low water using appliances need to be examined in combination for planning horizons of often more than a couple of decades, the examination of possible side effects (feedback loops) across systems becomes crucial and, it is argued, gives the toolbox its main reason for uptake by research and practice.…”

Section: Thinking Platforms For Urban Water 215mentioning

confidence: 99%

Thinking platforms for smarter urban water systems: fusing technical and socio-economic models and tools

Makropoulos

2014

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Engineering is currently expanding its conceptual boundaries by accepting the challenge of interdisciplinarity, while often adopting social and biological concepts in developing tools (e.g. evolutionary optimization or interactive autonomous agents) or even world views (e.g. co-evolution, resilience, adaptation). The emerging socio-technical knowledge domain is still very much restricted by partial knowledge associated with the lack of long-term transdisciplinary research effort and the unavailability of robust, integrated tools able to cover both the technical and the socio-economic domains and to act as 'thinking platforms' for long-term scenario planning and strategic decision making under (high-order) uncertainties. Here we present an example of a toolkit that attempts to bridge this gap focusing on urban water (UW) systems and their management. The toolkit consists of three tools: the UW Optioneering Tool (UWOT); the UW Agent Based Modelling Platform (UWABM); and the UW System Dynamic Environment (UWSDE). The tools are briefly presented and discussed, focusing on interactions and data flows between them and their typical results are illustrated through a case study example. A further tool (a Cellular Automata Based Urban Growth Model) is currently under development and an early coupling with the other tools is also discussed. It is argued that this type of extended model fusion, beyond what has traditionally been thought of as 'integrated modelling' in the engineering domain is a new frontier in the understanding of environmental systems and presents a promising, emerging field in modelling interactions between our societies and cities, and our environment.

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“…This contrasts with the large number of publications coupling ABMs with other biophysical models Aulinas et al, 2009;Balbi and Giupponi, 2009;Bousquet and Le Page, 2004;Gunkel, 2005;Heath et al, 2009;Kelly et al, 2013). Early work on GW-ABMs reports lumped aquifer models implemented on an ABM grid (Carlin et al, 2007;Dray et al, 2006;Feuillette et al, 2003;Guilfoos et al, 2013;Heckbert et al, 2006;Moglia et al, 2010;Perez et al, 2003;Smajgl et al, 2009). Recent work is based on linked GW-ABMs (Barthel et al, 2005;Miro, 2012;Mulligan et al, 2014;Reeves and Zellner, 2010), where an ABM generates groundwater stresses (i.e.…”

Section: Introductionmentioning

confidence: 99%