SDM begins with a comprehensive understanding of the decision landscape (e.g., programmatic objectives, desired outcomes, possible implementation options, regulatory aspects of the decision). Stakeholders and decision-makers are critical to identifying and defining the decision landscape. They are the custodians of community values, defined as principles for evaluating the desirability of possible alternatives or consequences. Values-focused thinking focuses on the essential activities that must occur prior to determining how to solve a decision problem (Keeney 1994). The decision landscape for climate adaptation may also been seen through the lens of social, economic, and environmental causes-and-effects coupled across spatio-temporal scales modulated by policies at the appropriate level. Socio-ecological systems, or coupled human-environment systems (Chapin et al 2009), are a useful framework for analyzing complex climate resilience challenges. Neptune used SDM for a Coastal Community Resilience Planning and Decision Making project in Dania Beach, Florida. The project included quantitative assessments of how different combinations of environmental management options impacted objectives derived from stakeholder values and cost constraints. That information was captured with a web-based SDM software that linked objectives, implementation options, performance measures, models, and data into a cohesive structure. These deliverables may be easily assembled into an electronic "decision-management package". The increased emphasis on reproducibility, transparency, and traceability has changed the way science is done. Publications are increasingly released with accompanying data and code, often freely web accessible. The public would be well-served if the same paradigm is applied to climate resilience planning. If a resilience plan is released with an accompanying electronic decision-management package ("Package") that can be archived and distributed, the decision-provenance (a formalized, canonical representation of how decisions are made) embedded in a Package enables: Data-driven decision-management has its roots in "science-informed policy" and "data-intensive science". The latter is facilitated through a rapidly evolving open ecosystem of interoperable repositories for data, models, and information. Many US federal agencies have invested in technologies that enable better discoverability and accessibility of such repositories. Inter-agency bodies like the US Global Change Research Program and the US Group on Earth Observations have actively promulgated best data management practices through the US government. These practices range from adopting e-infrastructure technical standards to producing electronic scientific assessments that allow users to trace scientific findings back to computer models and repository data. In the data-intensive sciences, traceability facilitates reproducibility. In data-driven decision-management, traceability facilitates technical defensibility, accountability, transparency, and adaptive management. The rapid pace of large-scale environmental changes around the globe underscores the value of long-term data sets for understanding the context of scientific observations, projecting future conditions, and making informed decisions on how to adapt to these large-scale challenges. However, data and models that provide status and trend information are only as good as the human-mediated processes that utilize these information products for decisions. How do we formulate a stakeholder-driven set of climate resilience solutions that combine stakeholder values, data, and models to guide decisions that are technically defensible? How do we facilitate adaptive management by creating ?decision management products?, akin to scientific data products, where decision processes are reproducible and traceable? What are the best practices informed by decision science that lend structure to the co-creation of resilience solutions by stakeholders and subject matter experts? Structured decision making (SDM) (Gregory et al 2012) provides a transparent framework to develop solutions for climate resilience challenges. The diagram on the right depicts an abstract representation of SDM. The boxes below provide an overview of key concepts at the beginning, middle, and end of the SDM process. Data-Driven Decision-Management: A Values-focused Approach to Enable Traceable Decision Analytics for Adaptive Climate Resilience Option N1 Option N2 Objective 2 (e.g. meet cost constraints) Objective 1 (e.g. sustainable livelihoods) Option N3 Option N4 Objective 3 (e.g. responsive to env. change) Data & models are used to evaluate how the suite of options achieve the objectives N1 N2 N3 N4 Decision pathways are curated as traceable work products that inform an adaptation plan Modify implementation options using traceable work products Policy Constraints Expert Knowledge and Data Develop Options Evaluate Consequences, Uncertainties, and Tradeoffs Define Objectives & Measures Structured Decision Making (SDM) Take Action, Monitor, Adapt Understand Decision Landscape Costs Stakeholder Values Neptune and Company, Inc. | 1435 Garrison Street, Suite 201 | Lakewood, CO 80215 | www.neptuneinc.org The Challenge Decision Landscape for Climate Resilience Perform programmatic evaluation against baseline Data, Models, and Information Interoperability Integrating, Managing, and Disseminating Decisions Traceability Socio-ecological data from observations, experiments, and model runs Tools, models, collaboratories for transforming data into indicators and information Socio-ecological indicators derived from data and models Scientific publications, assessments, climate adaptation plans, policy Data for science, decisions, and policy Other communities can discover, re-use, re-purpose, and contribute Packages. An existing Package is used to structure and bootstrap a climate resilience plan. Stakeholder values, data, models, and other decision parameters are modified as appropriate. GitHub for Climate Resilience Decision-Management Package "What-if" scenarios are re-run against an existing Package using updated socio-ecological data. Programmatic evaluations are conducted on an existing project using the decision- provenance captured in a Package. Leverage points for improvements are identified. Programmatic Evaluation and Adaptive Management Authors: Brian Wee, Paul Black, Pat Billig, Kelly Black, Paul Duffy, Scott Rupp, Tom Stockton DOI: 10.6084/m9.figshare.4515722 Expert Knowledge and Data Stakeholder Values Large-scale environmental stressors and impacts Impact on state and federal disaster management funds Impacts of local stressors on environment Costs for community engagement, infrastructure upgrade Shared values on livelihoods and the environment Community health and well-being Costs Coupling across scales Large scale spatio-temporal processes, modulated by corresponding policies Local scale spatio-temporal scale processes, modulated by corresponding policies QR-Code for poster download: