Decision support experiments and evaluations using seasonal to ...

obust communication in which each side informs
the other with regard to decisions, needs,
and products—this communication cannot be
intermittent; it must be robust and ongoing.
Second, some RISAs employ an “information
broker” approach. They produce little
new scientific information themselves, due to
resource limitations or lack of critical mass in
a particular scientific discipline. Rather, the
RISAs’ primary role is providing a conduit for
information and facilitating the development of
information networks.
Third, RISAs generally utilize a “participant/
advocacy” or “problem-based” approach, which
involves focusing on a particular problem or
issue and engaging directly in solving that problem.
They see themselves as part of a learning
system and promote the opportunity for joint
learning with a well-defined set of stakeholders
who share the RISA’s perspective on the
problem and desired outcomes.
Fourth, some RISAs utilize a “basic research”
approach in which the researchers recognize
particular gaps in the fundamental knowledge
needed in the production of context sensitive,
policy-relevant information. Any RISA may
utilize many or most of these approaches at different
times depending upon the particular context
of the problem. The more well-established
RISAs have more formal processes and procedures
in place to identify stakeholder needs
and design appropriate responses, as well as to
evaluate the effectiveness of decision-support
tools that are developed.
Finally, a critical lesson for climate science policy
from RISAs is that, despite knowing what
is needed to produce, package, and disseminate
useful climate information—and the wellrecognized
success of the regional partnerships
with stakeholders, RISAs continue to struggle
for funding while RISA-generated lessons are
widely acclaimed. To a large extent, they have
not influenced federal climate science policy
community outside of the RISAs themselves,
though progress has been made in recent years.
Improving feedback between RISA programs
and the larger research enterprise need to be
enhanced so lessons learned can inform broader
climate science policy decisions—not just those
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
decisions made on the local problem-solving
level (McNie et al., 2007).
In April 2002, the House Science Committee
held a hearing to explore the connections of climate
science and the needs of decision makers.
One question it posed was the following: “Are
our climate research efforts focused on the right
questions”? (). The Science Committee found that the
RISA program is a promising means to connect
decision-making needs with the research
prioritization process, because “(it) attempts to
build a regional-scale picture of the interaction
between climate change and the local environment
from the ground up. By funding research
on climate and environmental science focused
on a particular region, [the RISA] program currently
supports interdisciplinary research on
climate-sensitive issues in five selected regions
around the country. Each region has its own
distinct set of vulnerabilities to climate change,
e.g., water supply, fisheries, agriculture, etc.,
and RISA’s research is focused on questions
specific to each region”.
4.3.3 Developing Knowledge-
Action Systems—a Climate
for Inclusive Management
Research suggests that decision makers do not
always find seasonal-to-interannual forecast
products, and related climate information, to be
useful for the management of water resources—
this is a theme central to this entire Product (e.g.,
Weiner, 2004). As our case study experiments
suggest, in order to ensure that information is
useful, decision makers must be able to affect
the substance of climate information production
and the method of delivery so that information
producers know what are the key questions to
respond to in the broad and varied array of decisional
needs different constituencies require
(Sarewitz and Pielke, 2007; Callahan et al.,
1999; NRC, 1999). This is likely the most effective
process by which true decision-support
activities can be made useful.
Efforts to identify factors that improve the usability
of SI climate information have found that
effective “knowledge-action” systems focus
on promoting broad, user-driven risk management
objectives (Cash and Buizer, 2005). These
Decision makers
do not always
find seasonal-to-
interannual forecast
products, and related
climate information,
to be useful for
the management of
water resources.
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