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EXECUTIVE SUMMARY The U.S. Climate Change Science Program, in its effort to provide the best possible scientific information to support decision making on key climate-related issues, is producing 21 synthesis and assessment products (SAPs) that address its highest priority needs. This report reviews a draft of SAP 5.3, which attempts to synthesize lessons learned in decision-support efforts in the water resource management sector. The draft that was presented for review was, as the authors explained, in a fairly early stage of development, and the authors already had plans for significant revision and reorganization. This review examines the draft as given, with the understanding that it is not presented as near final. Thus, this review focuses on major issues of coverage and organization and other substantive comments but offers few comments at fine levels of detail, such as would be appropriate for a more polished draft. The panel concludes that the draft SAP is appropriately objective and policy neutral and that data, when relevant, are handled appropriately. Its summary mainly recapitulates the key findings from the chapters, so it raises the same issues as the full draft. This summary emphasizes the major issues we think deserve attention in the revision of the draft SAP. Other issues are raised throughout the report. A major issue is the need for some disconnects between different sections of the report to be resolved in revision. In particular, we suggest that the authors give explicit consideration to two assumptions we see as implicit in the report, or in sections of it, that we find problematic or inconsistent with assumptions implicit elsewhere. ASSUMPTION 1: More forecast skill implies more forecast value Parts of the document, particularly in Chapter 1, seem to assume implicitly that forecasts that have greater skill or higher resolution in time and space will necessarily be better for decision support, whereas other parts of the document do not make these assumptions. Assuming that skill improves usefulness leads to recommendations to invest in improved forecast skill and resolution; not assuming this leads to recommendations to invest in improving networks that link forecast producers and potential users. These sets of recommendations are likely to compete with each other in a tight-budget environment, but the draft does not note or address this issue. The sections emphasizing the need to improve networks are more consistent with the language in the Executive Summary, and also with available scientific evidence about the use of scientific information (National Research Council, 2007), than the sections assuming that more skill means more value. We suggest that the revised document discuss the evidence on the skill-value assumption and discuss its implications for meeting the objectives of making climate information more decision-relevant and more commonly used in the water resource management sector. ASSUMPTION 2: Most useful form of information Another assumption that is implicit, at least in Chapter 1, is that the most useful form of scientific information is a projected expected future value of some outcome parameter with a 1
distribution reflecting uncertainty. This assumption is not supported by sufficient scientific evidence. Other kinds of scientific outputs (e.g., sets of plausible scenarios; models that could simulate the implications of forecasts) might more closely fill the needs of some decision makers in the water management sector. Building knowledge-action networks, as the draft SAP recommends, is advisable in part as a way to learn what would be useful. Also, Chapter 3 presents a serial flow chart as a model of innovation. We urge the authors to consider a continuous improvement model that is circular in nature. Other important issues needing attention In terms of coverage, the draft explicitly addresses all but two of the questions raised in the prospectus. Some are addressed in more than one place, when issues are raised in particular chapters. However, we note that Chapter 3, which addresses innovation and offers useful insights, does not draw on the research literature on innovation processes. Incorporating concepts from this literature may help to better conceptualize the operational insights and their implications and strengthen the evidentiary base for findings. Chapter 4 includes discussions of climate change issues, although the focus of the report is mainly on climate variability, which can pose different issues for modeling and for decision support. The document should clarify what it does and does not cover, distinguish clearly between discussions of climate change and of variability, and better justify the inclusion of material on climate change if the authors wish to include it. Responses to questions in the prospectus related to communication of forecasts, operationalization of tools, and evaluation should be elaborated. The discussion of evaluation, a major area of interest for the policy maker audience, is quite limited. If this is due to a lack of published materials, this should be stated. In terms of the adequacy of evidentiary support for findings and recommendations, we note that very little evidence and analysis are available regarding decision-support efforts in the water sector, with the implications that findings must be based on the relatively weak grounding provided by case study evidence and that recommendations must be based largely on judgment. These points could be made more explicitly in the document. Although the research priorities and general recommendations are all reasonable and generally supported by argumentation, they are stated in vague language that is hard to contradict and that does not offer clear guidance about the relative importance of different objectives or activities. The arguments raised in the document allow for persuasive arguments to be made for giving some of these ideas higher priority than others and for making some of the recommendations more pointed. For example, the recommendations for improving forecast skill could target areas ripe for improvement, such as realistic land-atmosphere interaction and cryospheric processes. The recommendation to support dense hydrologic monitoring networks is far stronger than the supporting text, which may need to be strengthened in support of this recommendation. Chapter 2 claims several potential benefits for knowledge-action networks without providing the sources of research or illustrative examples to substantiate each claim. It should make a stronger case for wealth as a key variable affecting the use or nonuse of climate forecasts, or else revise this claim. It presents a relatively strong discussion of knowledge about âscience citizenshipâ but does not clearly link it to the idea of using climate information in decisions. In Chapter 4, some of the key findings seem to depend on an analysis of lessons 2
implicit in the case studies, but the case studies do not always include the type of information needed to support the findings. The appropriate balance of roles between governmental and private efforts deserves more careful consideration. The case has not generally been made that private-sector organizations or local and state governments will not undertake the research priorities, so that the federal government must. An exception is the argument that, although private organizations may provide tailored decision-support products to those who can afford them, the government should provide useful information for general use by those who may not be able to afford customized information and/or are not requiring it (e.g., smaller water districts, towns, rural areas). We suggest that the document give consideration to an approach to climate forecast development that includes public-private partnerships in funding and developing needed information. 3
