599
• Water–energy–food Nexus Tool 2.0
• Stockholm Environment Institute (SEI)—LEAP (Long Range Energy
Alternatives Planning System)
• United Nations Statistics Division—The System of Environmental-Economic
Accounting (SEEA)
• The WBCSD (World Business Council on Sustainable Development) Global
Water Tool
• UK DECC (Department of Energy & Climate Change) United Kingdom: 2050
Pathways Calculator
• MuSIASEM—Multi-Scale Integrated Analysis of Societal & Ecosystem
Metabolism—The Flow-Fund Model
• The International Atomic Energy Agency (IAEA)—Climate, Land-use, Energy,
and Water (CLEW)
• Stockholm Environment Institute (SEI)—REAP (Resource and Energy Analysis
Programme)
• Agriculture and Agri-food Canada—BIMAT (Biomass Inventory mapping and
Analysis Tool)
Such models illustrate opportunities for modeling, including simulation and
trade-offs of FEW systems, with a focus on one or more of these primary resources
at different scales.
21.3.6 Computing
In Chap. 16, we describe how the increasing volume, velocity, and variety of data
required to analyze FEW systems creates challenges for traditional computational
tools. Advances in experimental computer and software engineering and design
applied to experimental algorithms provide an array of tools that can be selectively
deployed on diverse models and data sets. The combination of systems modeling,
big data, and high-performance computing power is particularly powerful because
of its potential to unlock a new class of rapid interactive and exploratory immersive
decision-making processes that are informed by a complete set of systems connections. This is an intuitive way for decision-makers to immerse themselves in systems and explore the connections—and it is a fundamentally transformative
capability made possible by advanced computing power.
However, without careful integration of the different types of science involved
in FEW system analysis, these tools can often be used as “black boxes” without
looking into what is going on “under the hood.” Thus, future efforts should
focus on bringing the developers and domain scientists together to develop prescriptive solutions instead of over-the-counter ones that will improve algorithm
efficiencies as well as the understanding of the effects of various scenarios for specific use cases.
21 Opportunities at the Nexus
• Water–energy–food Nexus Tool 2.0
• Stockholm Environment Institute (SEI)—LEAP (Long Range Energy
Alternatives Planning System)
• United Nations Statistics Division—The System of Environmental-Economic
Accounting (SEEA)
• The WBCSD (World Business Council on Sustainable Development) Global
Water Tool
• UK DECC (Department of Energy & Climate Change) United Kingdom: 2050
Pathways Calculator
• MuSIASEM—Multi-Scale Integrated Analysis of Societal & Ecosystem
Metabolism—The Flow-Fund Model
• The International Atomic Energy Agency (IAEA)—Climate, Land-use, Energy,
and Water (CLEW)
• Stockholm Environment Institute (SEI)—REAP (Resource and Energy Analysis
Programme)
• Agriculture and Agri-food Canada—BIMAT (Biomass Inventory mapping and
Analysis Tool)
Such models illustrate opportunities for modeling, including simulation and
trade-offs of FEW systems, with a focus on one or more of these primary resources
at different scales.
21.3.6 Computing
In Chap. 16, we describe how the increasing volume, velocity, and variety of data
required to analyze FEW systems creates challenges for traditional computational
tools. Advances in experimental computer and software engineering and design
applied to experimental algorithms provide an array of tools that can be selectively
deployed on diverse models and data sets. The combination of systems modeling,
big data, and high-performance computing power is particularly powerful because
of its potential to unlock a new class of rapid interactive and exploratory immersive
decision-making processes that are informed by a complete set of systems connections. This is an intuitive way for decision-makers to immerse themselves in systems and explore the connections—and it is a fundamentally transformative
capability made possible by advanced computing power.
However, without careful integration of the different types of science involved
in FEW system analysis, these tools can often be used as “black boxes” without
looking into what is going on “under the hood.” Thus, future efforts should
focus on bringing the developers and domain scientists together to develop prescriptive solutions instead of over-the-counter ones that will improve algorithm
efficiencies as well as the understanding of the effects of various scenarios for specific use cases.
21 Opportunities at the Nexus