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Climate Impact Assessment (climate + impact_assessment)

Distribution by Scientific Domains

Humanities and Social Sciences	75%

Selected Abstracts

Climate Science and Decision Making

GEOGRAPHY COMPASS (ELECTRONIC), Issue 3 2007
Kirstin Dow
This article reviews progress in understanding climate variability and change and how such understanding might better contribute to decision processes and the design of decision support tools. We emphasize the value of collaborative engagement between climate information users and scientists to continue innovation in this area. Our assessment presents opportunities for geographic perspectives and insights that can increase understanding of the physical processes causing interannual variability and improve climate model output for climate impact assessment. As decision-makers' interests expand to address adaptation, nature-society research can also contribute significantly to understanding the diversity of climate information users, their evolving needs, and to the development of strategies for communicating risk and uncertainty. [source]

A review of climate risk information for adaptation and development planning

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 9 2009
R. L. Wilby
Abstract Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges. Copyright © 2009 Royal Meteorological Society [source]

The reliability of an ,off-the-shelf' conceptual rainfall runoff model for use in climate impact assessment: uncertainty quantification using Latin hypercube sampling

AREA, Issue 1 2006
Conor Murphy
Much uncertainty is derived from the application of conceptual rainfall runoff models. In this paper, HYSIM, an ,off-the-shelf' conceptual rainfall runoff model, is applied to a suite of catchments throughout Ireland in preparation for use in climate impact assessment. Parameter uncertainty is assessed using the GLUE methodology. Given the lack of source code available for the model, parameter sampling is carried out using Latin hypercube sampling. Uncertainty bounds are constructed for model output. These bounds will be used to quantify uncertainty in future simulations as they include error derived from data measurement, model structure and parameterization. [source]