Sustainable and Resilient Design of Interdependent Water and Energy Systems: A Conceptual Modeling Framework for Tackling Complexities at the Infrastructure-Human-Resource Nexus

A modeling framework was conceptualized for capturing the complexities in resilience and sustainability associated with integration of centralized and decentralized water and energy systems under future demographic, climate, and technology scenarios. This framework integrates survey instruments for characterizing individual preferences (utility functions) related to decentralization of water and energy infrastructure systems. It also includes a spatial agent-based model to develop spatially explicit adoption trajectories and patterns in accordance with utility functions and characteristics of the major metropolitan case study locations as well as a system dynamics model that considers interactions among infrastructure systems, characterizes measures of resilience and sustainability, and feeds these back to the agent-based model. A cross-scale spatial optimization model for understanding and characterizing the possible best case outcomes and for informing the design of policies and incentive/disincentive programs is also included. This framework is able to provide a robust capacity for considering the ways in which future development of energy and water resources can be assessed.

[1]  Ximing Cai,et al.  A decentralized optimization algorithm for multiagent system–based watershed management , 2009 .

[2]  Weiwei Mo,et al.  Understanding the influence of climate change on the embodied energy of water supply. , 2016, Water research.

[3]  M. V. Vliet,et al.  Water constraints on European power supply under climate change: impacts on electricity prices , 2013 .

[4]  Arpad Horvath,et al.  Assessing Location and Scale of Urban Nonpotable Water Reuse Systems for Life-Cycle Energy Consumption and Greenhouse Gas Emissions. , 2016, Environmental science & technology.

[5]  Zhongming Lu,et al.  Use of impact fees to incentivize low-impact development and promote compact growth. , 2013, Environmental science & technology.

[6]  Anthony J. Jakeman,et al.  Selecting among five common modelling approaches for integrated environmental assessment and management , 2013, Environ. Model. Softw..

[7]  Reinhard Madlener,et al.  Homeowners' preferences for adopting innovative residential heating systems: A discrete choice analysis for Germany , 2012 .

[8]  Thomas Berger,et al.  Agent-based spatial models applied to agriculture: A simulation tool , 2001 .

[9]  K. Willis,et al.  Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies , 2010 .

[10]  Stephen Moysey,et al.  A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment , 2013 .

[11]  A. Hoekstra,et al.  The economic impact of restricted water supply: a computable general equilibrium analysis. , 2007, Water research.

[12]  C. Müller,et al.  Constraints and potentials of future irrigation water availability on agricultural production under climate change , 2013, Proceedings of the National Academy of Sciences.

[13]  Frank Southworth,et al.  Market potential for smart growth neighbourhoods in the USA: A latent class analysis on heterogeneous preference and choice , 2015 .

[14]  P. Gleick Water and Energy , 1994 .

[15]  Troy R. Hawkins,et al.  Life cycle assessment of domestic and agricultural rainwater harvesting systems. , 2014, Environmental science & technology.

[16]  Emily Zechman Berglund,et al.  Complex Adaptive Modeling Framework for Evaluating Adaptive Demand Management for Urban Water Resources Sustainability , 2015 .

[17]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[18]  Alex K. Jones,et al.  Dynamic life cycle assessment: framework and application to an institutional building , 2012, The International Journal of Life Cycle Assessment.

[19]  Aditi Mankad,et al.  Adapting to Less Water: Household Willingness to Pay for Decentralised Water Systems in Urban Australia , 2014, Water Resources Management.

[20]  Yohji Uchiyama,et al.  Life-cycle assessment of electricity generation options: The status of research in year 2001 , 2002 .

[21]  Qiong Zhang,et al.  Modeling the influence of various water stressors on regional water supply infrastructures and their embodied energy , 2016 .

[22]  A. Castelletti,et al.  Assessing the value of cooperation and information exchange in large water resources systems by agent‐based optimization , 2013 .

[23]  Weiwei Mo,et al.  Energy-water nexus analysis of enhanced water supply scenarios: a regional comparison of Tampa Bay, Florida, and San Diego, California. , 2014, Environmental science & technology.

[24]  K. Frenken,et al.  Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics , 2009 .

[25]  J. Levine,et al.  A Choice-Based Rationale for Land Use and Transportation Alternatives , 2005 .

[26]  Rahul B. Hiremath,et al.  Decentralized energy planning; modeling and application—a review , 2007 .

[27]  J. Mason,et al.  The technical, geographical, and economic feasibility for solar energy to supply the energy needs of the US , 2009 .