Decision support experiments and evaluations using seasonal to ...

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The U.S. Climate Change Science Program Chapter 2 64 communication effectiveness of the prototype products. Just as new types of forecasts should show (at least) no degradation in predictive skill, they should also show no degradation in their communication effectiveness.
CHAPTER 3 KEY FINDINGS Decision-Support Experiments and Evaluations using Seasonal to Interannual Forecasts and Observational Data: A Focus on Water Resources Decision-Support Experiments Within the Water Resource Management Sector Convening Lead Authors: David L. Feldman, Univ. of California, Irvine; Katharine L. Jacobs, Arizona Water Institute Lead Authors: Gregg Garfin, Univ. of Arizona; Aris Georgakakos, Georgia Inst. of Tech.; Barbara Morehouse, Univ. of Arizona; Robin Webb, NOAA; Brent Yarnal, Penn. State Univ. Contributing Authors: John Kochendorfer, Riverside Technology, Inc. & NOAA; Cynthia Rosenzweig, NASA; Michael Sale, ORNL; Brad Udall, NOAA; Connie Woodhouse, Univ. of Arizona Decision-support experiments that test the utility of seasonal-to-interannual (SI) information for use by water resource decision makers have resulted in a growing set of successful applications. However, there is significant opportunity for expansion of applications of climate-related data and decision-support tools, and for developing more regional and local tools that support management decisions within watersheds. Among the constraints that limit tool use are: • • • • The range and complexity of water resources decisions: This is compounded by the numerous organizations respon- sible for making these decisions, and the shared responsibility for implementing them. These organizations include water utility companies, irrigation management districts and other entities, and government agencies. Inflexible policies and organizational rules that inhibit innovation: Large institutions historically have been reluctant to change practices in part because of value differences; risk aversion; fragmentation; the primacy accorded water rights, which often vary from region to region, and among various users; and sharing of authority. This conservatism impacts how decisions are made as well as whether to use newer, scientifically generated information, including SI forecasts and observational data. Different spatial and temporal frames for decisions: Spatial scales for decision making range from local, state, and national levels to international. Temporal scales range from hours to multiple decades impacting policy, operational planning, operational management, and near real-time operational decisions. Resource managers often make multidimensional decisions spanning various spatial and temporal frames. Lack of appreciation of the magnitude of potential vulnerability to climate impacts: Communication of the risks differs among scientific, political, and mass media elites, each systematically selecting aspects of these issues that are most salient to their conception of risk, and thus, socially constructing and communicating its aspects most salient to a particular perspective. Decision-support systems are not often well integrated into planning and management activities, making it difficult to realize the full benefits of these tools. Because use of many climate products requires special training or access to data that are not easily available, decision-support products may not equitably reach all audiences. Moreover, over-specialization and narrow disciplinary perspectives make it difficult for information providers, decision makers, and the public to communicate with one another. Three lessons stem from this: 65
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Decision-Support Experiments and Ev
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TABLE OF CONTENTS Abstract ........
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TABLE OF CONTENTS 5................
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ACKNOWLEDGEMENTS The authors would
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ABSTRACT Faced with mounting pressu
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PREFACE Report Motivation and Guida
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EXECUTIVE SUMMARY ES.1 WHAT IS DECI
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• Improved bias corrections in ex
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• • ness with customized produc
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CHAPTER 1 1.1 INTRODUCTION Increasi
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western United States and is the ba
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Page 29 and 30: Study or Experiment Chapter CPC Sea
Page 31 and 32: Study or Experiment Chapter Interpr
Page 33 and 34: Study or Experiment Chapter Integra
Page 35 and 36: Study or Experiment Chapter Regiona
Page 37 and 38: Study or Experiment Chapter Murray-
Page 39 and 40: Study or Experiment Chapter Integra
Page 41 and 42: Study or Experiment Chapter Regiona
Page 43 and 44: Study or Experiment Chapter Murray-
Page 45 and 46: CHAPTER 2 KEY FINDINGS Decision-Sup
Page 47 and 48: 2.1 INTRODUCTION In the past, water
Page 49 and 50: data and forecast products that sup
Page 51 and 52: BOX 2.2: Forecast Approaches Decisi
Page 53 and 54: cal elements, including models, dat
Page 55 and 56: a sub-seasonal lead time, the singl
Page 57 and 58: The hydrologic and water resources
Page 59 and 60: forecasts link to CPC drought produ
Page 61 and 62: losses of snowpack and the tendenci
Page 63 and 64: At NCEP, the dynamical tool, CFS, i
Page 65 and 66: Decision-Support Experiments and Ev
Page 67 and 68: 2.4.1 Improving Seasonal-to- Intera
Page 69 and 70: climate variations. The rationale f
Page 71 and 72: 2.4.2.2 improvementS in hydrologic
Page 73 and 74: BOX 2.3: The CPC Seasonal Drought O
Page 75 and 76: Koch’s research to operational pr
Page 77 and 78: BOX 2.4: What Role Can a "Testbed"
Page 79: satisfy all users. The Advanced Hyd
Page 83 and 84: discuss impediments to forecast inf
Page 85 and 86: Commission (SRBC) to pave the way f
Page 87 and 88: BOX 3.1: Georgia Drought Decision-S
Page 89 and 90: Figure 3.1 Water resources decision
Page 91 and 92: frameworks for decision making are
Page 93 and 94: Adaptive capacity is the least expl
Page 95 and 96: tions, discussion, feedback, and re
Page 97 and 98: water quality in downstream reservo
Page 99 and 100: 25 years, as predicted by the Energ
Page 101 and 102: forecasting, however, is hampered b
Page 103 and 104: 3.2.5 Reliability and Trustworthine
Page 105 and 106: mental conditions (e.g., demands fo
Page 107 and 108: it must involve an extended group o
Page 109 and 110: egional population and economic gro
Page 113 and 114: to the gravity of its consequences.
Page 117 and 118: CHAPTER 4 KEY FINDINGS Decision-Sup
Page 119 and 120: including ecological restoration, r
Page 121 and 122: For example, observed global sea-le
Page 123 and 124: into the Pacific Ocean. The Norther
Page 125 and 126: leaders in considering climate chan
Page 127 and 128: and communities vulnerable to wildf
Page 129 and 130: tion patterns. There are concerns t
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forecasts in the region also enhanc
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knowledge, as well as development o
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obust communication in which each s
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Organizational measures to hasten,
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and, thus, able to span the domains
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in climate and weather (Garfin and
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local water supply sector: the grow
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investment” efforts and their eff
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time by the interest of groups to c
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Case Study F: Southeast Climate Con
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ect application of climate science
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a receptive audience for new tools
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strength offered by the case study
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CHAPTER 5 5.1 INTRODUCTION The futu
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poverty, and resources are required
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Fostering inclusive, equitable acce
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effective in managing problems, or
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5.3.1 A Better Understanding of Vul
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on climate variability and water re
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apid development of better decision
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APPENDIX A Decision-Support Experim
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APPENDIX B Decision-Support Experim
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GLOSSARY AND ACRONYMS adaptive capa
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ACRONYMS AND ABBREVIATIONS ACCAP Al
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REFERENCES References marked with (
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Atger, F., 2003: Spatial and intera
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Hughes, M.K. and P.M. Brown, 1992:
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of seasonal forecasts. Bulletin of
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Brandon, D., B. Udall, and J. Lowre
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Georgia Forestry Commission, 2007:
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Leatherman, S.P. and G. White, 2005
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Pringle, C.M., 2000: Threats to U.S
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Yarnal, B., A.L. Heasley, R.E. O’
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Gallaher, B.M. and R.J. Koch, 2004:
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McNie, E.C., 2007: Reconciling the
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* Trimble, P.J., E.R. Santee, and C
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Hartmann, H., 2001: Stakeholder Dri
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PHOTGRAPHY CREDITS Cover/Title Page
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