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interpretation of new data, and inclusion in conceptual and numerical models of the system
to help validate past interpretations and/or provide new working hypothesis of how the
system behaves.
To the present date, DSS models have mostly been viewed as a product that can be
developed to help answer management and planning questions at a given time. It is only
very recently that DSS models are starting to be perceived as evolving tools. Rather than
developing and using them once, they offer greater benefits when they are dynamically
changed over time to represent the evolving present, becoming a working tool that may
never be a
finished product
but a product to work along the years. In participatory planning
processes this allows the model to be a common representation of the system and the DSS
model and supporting documentation can be like an “accountability trail” of what has been
done in the past. In adaptive management practice, a DSS model will have to be updated as
ongoing policies and management actions are implemented. Model updates will reflect
modifications in the
engineered system layer
(canals, pipes, wells, dams, water re-allocations,
changes in use efficiencies, changes in land use cover, etc.) as well as new or modified
understanding gained through adaptive management on how the system works.
The issues of model updates and institutional flexibility can be well illustrated by the
worries of many stakeholders in the San Pedro Basin, collected in a study to evaluate the
contributions of the collaborative process in the basin. Being able to feed current, accurate
and updated data into the model was a concern for the future that relates well with
institutional limitations. A modeling team from the University of Arizona had ensured
model and data accuracy, along with technical people from different government and
state agencies involved in the process. The point was raised that if the modeling team left
the collaboration, no human capabilities existed within the basins' managing institutions
to easily take over and continue the modeling work. Local capacity building to update
and modify the model was necessary:
Otherwise, if [the main modeler] leaves the State and
stops working on it, nobody is able now to take care of things and move on from here.
A comment
by one top level policy person illustrates the precarious institutional integration and the
need for new flexible institutional arrangements:
“The model will help us a lot in our
planning and zoning, our municipalities and county entities, water districts, water planning, etc.
[...] my concern is how to keep it up to date with future science, options, and alternatives. If federal
funding fails to help [the process] …if no more money comes, all will be lost.
” (Serrat-Capdevila
et al., 2008).
The final important point to make here is that an integrative modeling approach in adaptive
management institutions will be essential in these types of contexts for many reasons.
Decision makers usually use (or benefit from the use of) medium or coarse resolution
models in system dynamics platforms (DSS models) that incorporate findings of more
refined models in a simplified but still accurate manner. As new information and
understanding becomes available, these DSS models are likely to be unsuited to the
assimilation of such information. Instead, the more detailed physical models that support
and inform system dynamics simulations, are more likely to accommodate new data
properly and help improve the understanding of that particular component of the system.
Once this is accomplished, the DSS model can be modified accordingly to accurately
represent new findings in a simplified way. The full potential of adaptive management can
only be reached when it is coupled with an integrative decision support systems modeling
approach and with continued research and observation.
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