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- All Subjects: Statistics
- All Subjects: Consensus
- All Subjects: Glen Canyon Dam
- All Subjects: Collaborative Planning
- Creators: Lanchier, Nicolas
- Resource Type: Text
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This brief article, written for a symposium on "Collaboration and the Colorado River," evaluates the U.S. Department of the Interior's Glen Canyon Dam Adaptive Management Program ("AMP"). The AMP has been advanced as a pioneering collaborative and adaptive approach for both decreasing scientific uncertainty in support of regulatory decision-making and helping manage contentious resource disputes -- in this case, the increasingly thorny conflict over the Colorado River's finite natural resources. Though encouraging in some respects, the AMP serves as a valuable illustration of the flaws of existing regulatory processes purporting to incorporate collaboration and regulatory adaptation into the decision-making process. Born in the shadow of the law and improvised with too little thought as to its structure, the AMP demonstrates the need to attend to the design of the regulatory process and integrate mechanisms that compel systematic program evaluation and adaptation. As such, the AMP provides vital information on how future collaborative experiments might be modified to enhance their prospects of success.
With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.
The Glen Canyon Dam Adaptive Management Program (AMP) has been identified as a model for natural resource management. We challenge that assertion, citing the lack of progress toward a long-term management plan for the dam, sustained extra-programmatic conflict, and a downriver ecology that is still in jeopardy, despite over ten years of meetings and an expensive research program. We have examined the primary and secondary sources available on the AMP’s design and operation in light of best practices identified in the literature on adaptive management and collaborative decision-making. We have identified six shortcomings: (1) an inadequate approach to identifying stakeholders; (2) a failure to provide clear goals and involve stakeholders in establishing the operating procedures that guide the collaborative process; (3) inappropriate use of professional neutrals and a failure to cultivate consensus; (4) a failure to establish and follow clear joint fact-finding procedures; (5) a failure to produce functional written agreements; and (6) a failure to manage the AMP adaptively and cultivate long-term problem-solving capacity.
Adaptive management can be an effective approach for addressing complex ecosystem-related processes like the operation of the Glen Canyon Dam, particularly in the face of substantial complexity, uncertainty, and political contentiousness. However, the Glen Canyon Dam AMP shows that a stated commitment to collaboration and adaptive management is insufficient. Effective management of natural resources can only be realized through careful attention to the collaborative design and implementation of appropriate problem-solving and adaptive-management procedures. It also requires the development of an appropriate organizational infrastructure that promotes stakeholder dialogue and agency learning. Though the experimental Glen Canyon Dam AMP is far from a success of collaborative adaptive management, the lessons from its shortcomings can foster more effective collaborative adaptive management in the future by Congress, federal agencies, and local and state authorities.
munity. With life insurance policies and annuity products as dominant financial
instruments which depend on future mortality rates, there is a risk that observed
human mortality experiences will differ from projected when they are sold. From an
insurer’s portfolio perspective, to curb this risk, it is imperative that models of hu
man survivorship are constantly being updated and equipped to accurately gauge and
forecast mortality rates. At present, the majority of actuarial research in mortality
modeling involves factor-based approaches which operate at a global scale, placing
little attention on the determinants and interpretable risk factors of mortality, specif
ically from a spatial perspective. With an abundance of research being performed
in the field of spatial statistics and greater accessibility to localized mortality data,
there is a clear opportunity to extend the existing body of mortality literature to
wards the spatial domain. It is the objective of this dissertation to introduce these
new statistical approaches to equip the field of actuarial science to include geographic
space into the mortality modeling context.
First, this dissertation evaluates the underlying spatial patterns of mortality across
the United States, and introduces a spatial filtering methodology to generate latent
spatial patterns which capture the essence of these mortality rates in space. Second,
local modeling techniques are illustrated, and a multiscale geographically weighted
regression (MGWR) model is generated to describe the variation of mortality rates
across space in an interpretable manner which allows for the investigation of the
presence of spatial variability in the determinants of mortality. Third, techniques for
updating traditional mortality models are introduced, culminating in the development
of a model which addresses the relationship between space, economic growth, and
mortality. It is through these applications that this dissertation demonstrates the
utility in updating actuarial mortality models from a spatial perspective.