466
components of the terrestrial water cycle, including groundwater, soil moisture,
snow, surface runoff, and precipitation. A normalized index is employed to succinctly display the relative magnitude of each component of the terrestrial
water cycle.
This relatively simple example, when coupled to agricultural demand, thermoelectric water demand, and biofuel production, provides a holistic record of freshwater availability that can be linked and subsequently used as input to a FEW system
model across a range of spatial and temporal scales. Further, historical observational data sets enable the evaluation of model-generated estimates of terrestrial
water availability. By ensuring that model estimates of water availability agree with
historical observations, it provides more confidence in model projections of future
water availability.
17.3.2 Integrated Assessment Modeling of FEW Systems
Over the three decades since its inception, IAMs have made fundamental contributions to our understanding of human system dynamics and human-Earth system
interactions, providing critical insights into how energy technologies, land-use
practices, and other human activities interact with one another and influence greenhouse gas emissions and land-cover change. Of importance in this context, IAMs
are a primary means by which researchers at global and national levels have explored
possible pathways to climate stabilization or the achievement of global temperature goals.
A range of challenges remain, however, in the development and use of integrated
tools such as IAMs for FEW system decision-making across scales.
First, because integrated modeling requires representations of all three FEW systems, and across regional scales, the temporal and spatial resolution may frequently
be sacrificed in the name of integration. Yet, carefully chosen focus areas of higherresolution can substantially enhance the usefulness of FEW system modeling for
decision-making. Major scientific questions remain about when, where, and how to
create this higher resolution; when it matters and when it does not.
FEW systems interact at multiple scales from local to global. Although IAMs are
increasingly proving effective at exploring these interactions at global and regional
scales and over multi-year time periods, they are not designed for, nor applied at
smaller, local scales, which may be of interest to many stakeholders and decisionmakers. Important scientific questions remain about how to effectively transfer the
methods applied to understand larger-scale phenomena at more local levels while
retaining linkages to larger-scale phenomena such as agricultural or energy trade.
In most cases, exploration of issues at finer regional scales has involved softcoupling IAMs with regional sectoral models such as regional hydrology models
and power systems models. However, these approaches raise important computational and software challenges and have not taken full advantage of the integrated
character of IAMs. In contrast, the low computational intensity of IAMs makes
E. Eftelioglu et al.
components of the terrestrial water cycle, including groundwater, soil moisture,
snow, surface runoff, and precipitation. A normalized index is employed to succinctly display the relative magnitude of each component of the terrestrial
water cycle.
This relatively simple example, when coupled to agricultural demand, thermoelectric water demand, and biofuel production, provides a holistic record of freshwater availability that can be linked and subsequently used as input to a FEW system
model across a range of spatial and temporal scales. Further, historical observational data sets enable the evaluation of model-generated estimates of terrestrial
water availability. By ensuring that model estimates of water availability agree with
historical observations, it provides more confidence in model projections of future
water availability.
17.3.2 Integrated Assessment Modeling of FEW Systems
Over the three decades since its inception, IAMs have made fundamental contributions to our understanding of human system dynamics and human-Earth system
interactions, providing critical insights into how energy technologies, land-use
practices, and other human activities interact with one another and influence greenhouse gas emissions and land-cover change. Of importance in this context, IAMs
are a primary means by which researchers at global and national levels have explored
possible pathways to climate stabilization or the achievement of global temperature goals.
A range of challenges remain, however, in the development and use of integrated
tools such as IAMs for FEW system decision-making across scales.
First, because integrated modeling requires representations of all three FEW systems, and across regional scales, the temporal and spatial resolution may frequently
be sacrificed in the name of integration. Yet, carefully chosen focus areas of higherresolution can substantially enhance the usefulness of FEW system modeling for
decision-making. Major scientific questions remain about when, where, and how to
create this higher resolution; when it matters and when it does not.
FEW systems interact at multiple scales from local to global. Although IAMs are
increasingly proving effective at exploring these interactions at global and regional
scales and over multi-year time periods, they are not designed for, nor applied at
smaller, local scales, which may be of interest to many stakeholders and decisionmakers. Important scientific questions remain about how to effectively transfer the
methods applied to understand larger-scale phenomena at more local levels while
retaining linkages to larger-scale phenomena such as agricultural or energy trade.
In most cases, exploration of issues at finer regional scales has involved softcoupling IAMs with regional sectoral models such as regional hydrology models
and power systems models. However, these approaches raise important computational and software challenges and have not taken full advantage of the integrated
character of IAMs. In contrast, the low computational intensity of IAMs makes
E. Eftelioglu et al.