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component of the system, and the incorporation of feedback loops. The topic Limits to
Growth is a good example of this, as it was based on a system dynamics simulation of the
earth's population growth and resource use (Meadows et al., 1972). By design, they allow
the decision-makers to see the entire forest through the trees, instead of getting lost in the
details of each field and its specialized models. By including only what is important from
each component of the system, they make it easier to represent and gain understanding of
interactions among the different components of the system.
Everyone will agree this is a complex task. If a functional holistic and integrative model is to
be developed to support decision-making, it is likely that this model will draw from
findings and information from models specific to each system component. Regarding
natural processes from the physical system, it will benefit from more spatially explicit and
detailed models. Such a multi-resolution integrated modeling approach may be essential to
face multi-disciplinary research and management challenges. Models of different
resolutions will allow representation of different aspects of the problem and can be geared
to answer different research questions and inform different sets of decisions (Liu et al 2008).
For example, high resolution models (~100m grid cells) can represent in great detail the
processes in the physical environment such as the land-atmosphere partitioning of water,
the role of vegetation, the interactions between surface and groundwater hydrology or the
dynamics of the saline wedge in coastal systems. These fine resolution models provide the
state-of-the-art scientific understanding of the physical system. They allow us to extract the
key aspects regarding the functioning of the physical system to be included in the medium
resolution models (~1-12 km). Models at medium resolution combine (1) a less complex but
accurate representation of the natural environment and (2) the human interventions on the
environment such as land use management, engineering infrastructure and its operation in
terms of intercepting and moving water within the basin. These medium resolution models
allow us to represent the water allocations and re-distribution within the system and bridge
the gap with the coarse resolution models. The higher level (coarse) models are the best
attempt to represent the socio-economic and institutional aspects of water management over
a simplified representation of the natural and engineered system, with a resolution at the
scale of the sub-watershed.
Besides being able to answer different kinds of research questions, the benefit of a multiple
resolution modeling approach is that information and findings can be transferred - and used
to fine-tune - across models. While information regarding natural processes, impacts and
feedbacks in the natural system can be up-scaled from the fine resolution to higher-level
models, the behaviors and policies from the socio-economic and institutional models can be
used to drive lower resolution models and assess impacts on the natural system. This
approach has been formulated and described in detail by Wagener et al (2005) and Liu et al
(2008) based on the experience of the NSF Science and Technology Center SAHRA
(Sustainability of semi-Arid Hydrology and Riparian Areas) in conducting integrated
multidisciplinary research addressing water management challenges in the US southwest.
Ultimately, planners and decision-makers are likely to use the modeling tools that simulate
the overall behavior of the basin with a simplified but still accurate representation of all its
components. Such a tool will represent the relevant behaviors of the system to answer their
specific management questions. Because it draws from the findings of more complex
models, this DSS model will be more computationally efficient, allowing numerous model
runs in a short time. Roach and Tidwell (2009) and Kang and Lansey (2011) are excellent
examples. The possibility of comparing simulations of different management options and
decision alternatives through a user interface in a short time span makes system dynamics a
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