Decision support experiments and evaluations using seasonal to ...

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The U.S. Climate Change Science Program Chapter 3 98 Figure 3.4 El Niño precipitation anomalies in inches (Source: NOAA Earth System Research Laboratory) makers have this level of support for using climate forecasts and associated information. The absence of such support systems may increase managers’ exposure to risk, in turn generating a strong disincentive to use climate forecasts. 3.4 SUMMARY Decision-support systems are not often well integrated into policy networks to support planning and management, making it difficult to convey information. Among the reasons for this are a tendency toward institutional conservatism by water agencies, a decision-making climate that discourages innovation, lack of national-scale coordination of decisions, difficulties in providing support for decisions at varying spatial and temporal scales due to vast variability in “target audiences” for products, and growing recognition that rational choice models of information transfer are overly simplistic. The case of information use in response to Georgia’s recent drought brings to light problems that students of water decision making have long described about resistance to innovation. Ensuring information relevance requires overcoming the barriers of over-specialization by encouraging inter-disciplinary collaboration in product and tool development. Decision makers need to learn to appreciate the inevitability and desirability of forecast uncertainties at a regional scale on the one hand, and potential decreases in accuracy on the other. Scientists must understand both internal institutional impediments (agency rules and regulations) as well as external ones (e.g., political-level conflicts over water allocation as exemplified in the Southeast United States, asymmetries in information access in the case of Northeast Brazil) as factors constraining decision-support translation and decision transformation. While the nine cases discussed here have been useful and instructive, more generalizable findings are needed in order to develop a strong, theoretically-grounded understanding of processes that facilitate information dissemination, communication, use, and evaluation—and to predict effective methods of boundary spanning between decision makers and information generators. We discuss this set of problems in Chapter 4.
Decision-Support Experiments and Evaluations using Seasonal to Interannual Forecasts and Observational Data: A Focus on Water Resources Figure 3.5 La Niña precipitation anomalies in inches (Source: NOAA Earth System Research Laboratory) Figure 3.6 Southern Oscillation Index (SOI) June through November, versus Winter precipitation November through April for 1896 to 2001 for three phases of ENSO; El Niño, La Niña, and Neutral, for Arizona climate division 6. Note the greater variation in El Niño precipitation (blue) than in La Niña precipitation (red). 99
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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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Study or Experiment Chapter CPC Sea
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Study or Experiment Chapter Interpr
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Study or Experiment Chapter Integra
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Study or Experiment Chapter Regiona
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Study or Experiment Chapter Murray-
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Study or Experiment Chapter Integra
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Study or Experiment Chapter Regiona
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Study or Experiment Chapter Murray-
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CHAPTER 2 KEY FINDINGS Decision-Sup
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2.1 INTRODUCTION In the past, water
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data and forecast products that sup
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BOX 2.2: Forecast Approaches Decisi
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cal elements, including models, dat
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a sub-seasonal lead time, the singl
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The hydrologic and water resources
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forecasts link to CPC drought produ
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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 and 80: satisfy all users. The Advanced Hyd
Page 81 and 82: CHAPTER 3 KEY FINDINGS Decision-Sup
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 111 and 112: Decision-Support Experiments and Ev
Page 113: 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
Page 131 and 132: forecasts in the region also enhanc
Page 133 and 134: knowledge, as well as development o
Page 135 and 136: obust communication in which each s
Page 137 and 138: Organizational measures to hasten,
Page 139 and 140: and, thus, able to span the domains
Page 141 and 142: in climate and weather (Garfin and
Page 143 and 144: local water supply sector: the grow
Page 145 and 146: investment” efforts and their eff
Page 147 and 148: time by the interest of groups to c
Page 149 and 150: Case Study F: Southeast Climate Con
Page 151 and 152: ect application of climate science
Page 153 and 154: a receptive audience for new tools
Page 155 and 156: strength offered by the case study
Page 157 and 158: CHAPTER 5 5.1 INTRODUCTION The futu
Page 159 and 160: poverty, and resources are required
Page 161 and 162: Fostering inclusive, equitable acce
Page 163 and 164: 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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