Performing Department
Natural Resources & the Environment
Non Technical Summary
Many resource managers are actively working to address the profound impacts that climate has on resources, services, and communities in the US West, but they face many challenges in trying to plan for climate change. The scientific information about prospective climate changes and impacts is daunting, with ever?evolving research findings, datasets inconsistent with typical decision needs, gaps in understanding about changes and impacts related to thresholds and extreme events, and limited ability to control or predict changes in other stresses (e.g., changes in water demand, land use, wildfire characteristics) that affect resource management. Land and water managers in the Southwest, including Arizona, are facing particularly daunting challenges in dealing with climate and its impacts, as competition for water resources can be contentious, and management of agricultural and public trust resources are intertwined through complex land ownership, regulations, and relationships.Developing tools and services to support adaptation planning in the face of uncertainty is challenging because of the many different spatiotemporal scales, interconnected issues, and resources available to decision makers and stakeholders. Further, planning efforts are complicated by the need to support rapid institutional learning and adjustment of plans as novel conditions emerge, extreme event frequencies change, and scientific understanding changes about climate, impacts, and vulnerabilities. Stakeholders face multiple barriers to the use of advanced scientific information, including (1) interpreting uncertain and probabilistic information and dealing with uncertainty and probability concepts, (2) discerning good information from bad, (3) putting information into context (from the general to their specific situation; linking past, present, and future); (4) linking short, medium, and long-term issues; and (5) dealing with complex systems and behavior like cascading events, thresholds, non-linear effects, surprise. Further, science translators and decision support service providers have trouble in most of the same areas; for example, they need clear and consistent framing of messages related to extremes (e.g., talking about annual risks rather than return intervals). Operations refers to agency entities responsible for routine operations, whether routine short-term system adjustments or intermittent but recurring long-term planning. Not understanding this sector, or being willing to work with its challenges, has kept much research from achieving long-lasting or wide-scale impact.Addressing these barriers requires understanding how people interpret information, identifying product formats and elements (e.g., legends, captions) that enable easy and reliably correct interpretation, and development of tools and ancillary materials (e.g., training materials, case study applications) to facilitate independent learning by decision makers (Feldman et al, 2008). We define decision support tools broadly to include computerized tools for obtaining information, performing customized analyses, or evaluating policy and management alternatives, among other tasks. The rapidly-evolving information and communications technologies offer tremendous opportunities to address complex needs of stakeholders in ways previously unimagined or considered impractical. While development of decision support tools may be based on interactions with a small group of stakeholders, the real payoff for operational services is in synthesizing needs across stakeholders and applications to develop tools that are relevant for a much broader audience across previously unconsidered situations. In addition, tools that have proved successful in regional applications may be usefully extended to new regions. Rather than simply transfer the software, we develop partner capacities to implement collaborative software development protocols and processes.While the topic areas and sectoral applications connected with decision support tools and hydroclimatic uncertainty and change are wide-ranging, two areas have broad applicability and warrant special attention. In particular, guidance is needed to help decision makers and stakeholders: (1) use scenario planning to deal with irreducible uncertainties, particularly related to extremes and crossing critical thresholds at even short timescales, and impacts of unknowable emissions levels beyond mid-century, and (2) use seasonal, interannual, and decadal outlooks to test and refine adaptation and adaptive management plans (double loop learning) and assess how their institutions learn new approaches in the face of non-stationarity and irreducible uncertainty (triple loop learning) (Romme and van Witteloostuijn, 1999).
Animal Health Component
0%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
OBJECTIVES: Three objectives relate to the challenges of using information technology to support science-based management of resources in arid and semi-arid regions:(1) Engage with the research community, stakeholders, information intermediaries, and operational agencies to determine needs for decision support tools, training, and applications.(2) Employ advanced information technologies in the development of decision support tools, training, and applications.(3) Transfer decision support tools to sustainable operations.Two objectives relate to specific approaches for science-based management of resources in arid and semi-arid regions in the face of hydroclimatic uncertainty and change:(4) Using scenarios and scenario planning to characterize and embrace uncertainty in adaptation planning and management.(5) Using forecasts of weather and seasonal to interannual variability as a bridge to adaptation.
Project Methods
While specific tools, functionalities, and applications will be defined by stakeholder engagement and participation, we anticipate addressing the following research questions.1. How can we exploit Web 3.0 capabilities to support situational awareness, early warning, and adaptive management? Web 1.0 was characterized by access to information (e.g., HTML pages) and Web 2.0 by users interacting with each other (e.g., social networking). Web 3.0 uses automated agents to perform complex tasks on behalf of users by connecting distributed sources of information. An example is the linking of user-customized portfolios of analysis parameters for multiple decision support tools (e.g., drought indices, forecasts) within CLIDDSS with the automated monitoring of user-customized criteria, notification via email and cell phones, and event-based impact reporting with AHTAS. This would provide constant assessment of multiple lines of evidence for conditions of interest, defined by stakeholders, with rapid notification of personalized and relevant information, connected to the value-added interpretations, choices, and recommended actions from information intermediaries to their clients.2. How can we most effectively support shared learning and participatory engagement at the scale of regional and national climate services? Our development of decision support tools supports personalized access to relevant data and information and development of knowledge, but recognizes the wisdom of management practitioners and stakeholders. Efficient models of shared learning include the Carpe Diem West Academy (Hartmann and Morino, 2011), collaborative software development processes recently institutionalized at the NWS Climate Prediction Center (Hartmann et al, 2011b), and the use of webinars in remote participatory scenario planning (Hartmann, 2011; Weeks et al, 2011).3. What tools, training, and case study applications are needed to support the use of scenario planning in resources planning and management decisions that include the uncertainties introduced by consideration of climate projection and impact information? Scenarios are important tools for managing resources and risks in the face of high uncertainty. Scenarios can help practitioners improve decision making by considering alternative climate futures and impacts, identifying key vulnerabilities, and gauging adaptation and mitigation capacities. They have also proven useful in helping research scientists integrate multiple models and characterize system uncertainties. Leveraged projects will be used to develop tools and training to help boundary organizations and local analysts support regional and local decision makers who are developing climate change action plans or want to consider how climate change should be considered in their decision making processes. Prospective tools and decision resources include interactive spreadsheets, interactive GANNT/PERT charts for working with scenario outputs and prioritizing adaptation options, guidebooks, and training.4. What tools, training, and case study applications are needed to enable decision makers to use forecasts and seasonal to interannual outlooks to test and adjust their prospective implementation of adaptation options in ways that maximize rapid learning? Every resource management decision makes use of forecasts, whether through an explicit process or an unacknowledged assumption that the future will be like the past. Every forecast that is issued represents a synthesis of current scientific knowledge that has some level of uncertainty. Each forecast, then, provides an opportunity to engage about the state of the science, as well as to test decision processes. Each forecast can provide a bridge to climate change adaptation as it provides decision makers with repeated practice in dealing with uncertainties. We will build on our extensive prior CLIMAS work on forecast assessment and application (Hartmann, 2002a,b; Franz et al., 2003), and partnerships with the NWS CSD and Climate Prediction Center (CPC) in developing forecast-related tools that enable consideration of thresholds and extremes. For example, we have recently developed an interactive probability of exceedance (POE) application that will allow us to engage, in concrete terms, with stakeholders about any probability threshold (e.g., 5% risk) or past extremes.Our decision support products and tools emerge from a commitment to (1) interdisciplinary, interactive, and iterative research that has real-world impact and (2) better linkages among the research, operational forecasting, and resource management communities. Toward these ends, we have developed proficiency at direct engagement with stakeholders, decision makers, and climate services providers; research that supports real-world decision making, evidenced by its uptake into practice; and development of decision support processes and tools that can be transferred across regions and organizations, and scaled to accommodate many more new users in ways that can be supported by operational climate services.Sustainability of decision support tools is increased when they are integrated across applications and purpose, rather than "stove-piped" with each new project implemented independently. Other development strategies that foster sustainability include code reuse, developing shared code libraries, and integrating applications through web services or other tools (e.g., CLIDDSS, AHTAS). We also address policy issues related to information and decision support scientists, including the use of creative commons licenses to facilitate use of products by the private sector, project evaluation metrics that extend beyond traditional research and peer-reviewed publications, and joint development of collaboratively developed "community" code and open-source software (Hartmann, 2009). The latter contributes to shared learning and capacity development across organizations, continued engagement between the research and operational climate services communities, and clear standards and pathways for evolving research from distributed sources to synergistically be entrained into operational processes and applications.We consider a wide variety of techniques and metrics to be useful for evaluating decision support tools as they are in development (Hartmann, 2010b). Needs assessments; participatory processes that inspire and inform the purpose, functionality, and design of decision support tools; and effectiveness of tool applications for changing decision processes or advancing discussion among stakeholders, are adapted from Extension and RISA protocols (e.g., pre- and post-engagement). Products and application interfaces are evaluated via web usability protocols (e.g, Krug 2006) and field-testing for easy and reliably correct interpretation (Hartmann et al 2007a). Training and associated tracking is an essential component in the iterative development and application of decision support tools, providing feedback about the user and application experience. Evaluation of our achievement of project objectives will focus on measuring changes in action, because they can be objectively documented, and they de facto demonstrate changes in knowledge. We will document and count the number and proportion of our decision support tool software systems that are transferred to other organizations for their sustained maintenance and operation. We will document and count the number of organizations that are using scenarios and scenario planning methods as a direct result of our collaboration, the number of organizations that seek to develop joint scenario-based projects. We will document and count the number of organizations that are using scenarios and scenario planning methods as a direct result of our collaboration, the number of organizations that seek to develop joint scenario-based projects.