Decision support experiments and evaluations using seasonal to ...

The U.S. Climate Change Science Program
While forecasts vary
in their skill, multiple
forecasts that examine
various factors are
most useful because
they provide decision
makers more access
to data that they can
manipulate themselves.
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4.4 SUMMARY FINDINGS AND
CONCLUSIONS
The decision-support experiments discussed
here and in Chapter 3, together with the analytical
discussion, have depicted several barriers to
use of decision-support experiment information
on SI climate conditions by water resource
managers. The discussion has also pinpointed a
number of ways to overcome these barriers and
ensure effective communication, transfer, dissemination,
and use of information. Our major
findings are as follows.
Effective integration of climate information
in decisions requires identifying topics of mutual
interest to sustain long-term collaborative
research and application of decision-support
outcomes: Identifying topics of mutual interest,
through forums and other means of formal collaboration,
can lead to information penetration
into agency (and stakeholder group) activities,
and produce self-sustaining, participant-managed
spin-off activities. Long-term engagement
also allows time for the evolution of scientist/
decision-maker collaborations, ranging from
understanding the roles of various players to
connecting climate to a range of decisions, issues,
and adaptation strategies—and building
trust.
Tools must engage a range of participants,
including those who generate them, those who
translate them into predictions for decisionmaker
use, and the decision makers who apply
the products. Forecast innovations might
combine climate factor observations, analyses
of climate dynamics, and SI forecasts. In turn,
users are concerned with varying problems and
issues such as planting times, instream flows
to support endangered species, and reservoir
operations. While forecasts vary in their skill,
multiple forecasts that examine various factors
(e.g., snow pack, precipitation, temperature
variability) are most useful because they provide
decision makers more access to data that
they can manipulate themselves.
A critical mass of scientists and decision makers
is needed for collaboration to succeed:
Development of successful collaborations requires
representation of multiple perspectives,
including diversity of disciplinary and agency-
group affiliation. For example, operations,
planning, and management personnel should all
be involved in activities related to integrating
climate information into decision systems; and
there should be sound institutional pathways for
information flow from researchers to decision
makers, including explicit responsibility for
information use. Cooperative relationships that
foster learning and capacity building within and
across organizations, including restructuring
organizational dynamics, are important, as is
training of “integrators” who can assist stakeholders
with using complex data and tools.
What makes a “critical mass” critical? Research
on water resource decision making suggests that
agencies and other organizations define problems
differently depending on whether they are
dedicated to managing single-issue problems
in particular sectors (e.g., irrigation, public
supply) or working in political jurisdictions
or watershed-based entities designed to comprehensively
manage and coordinate several
management objectives simultaneously (e.g.,
flood control and irrigation, power generation,
and in-stream flow). The latter entities face the
unusual challenge of trying to harmonize competing
objectives, are commonly accountable
to numerous users, and require “regionally and
locally tailored solutions” to problems (Water in
the West, 1998; also, Kenney and Lord, 1994;
Grigg, 1996). A lesson that appears to resonate
in our cases is that decision makers representing
the affected organizations should be incorporated
into collaborative efforts.
Forums and other means of engagement must be
adequately funded and supported. Discussions
that are sponsored by boundary organizations
and other collaborative institutions allow for coproduction
of knowledge, legitimate pathways
for climate information to enter assessment
processes, and a platform for building trust.
Collaborative products also give each community
something tangible that can be used within
its own system (i.e., information to support
decision making, climate service, or academic
research products). Experiments that effectively
incorporate seasonal forecasts into operations
generally have long-term financial support,
facilitated, in turn, by high public concern
over potential adverse environmental and/or
economic impacts. Such concern helps generate
Chapter 4