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The need for data and information that can be used to enhance community resilience to coastal inundation and erosion has been highlighted by the devastating impacts of recent events such as Hurricane Katrina and the 2004 Indian Ocean tsunami. The physical systems causing coastal inundation and erosion are governed by a complex combination of oceanic, atmospheric, and terrestrial processes interacting across a broad range of temporal and spatial scales. Depending on time and place the expression of these processes may variously take the form of episodic storm-induced surge or wave overtopping and undercutting, chronic flooding and erosion associated with long-term relative sea level rise, or catastrophic inundation attributable to tsunami. Differences related to geographic setting, such as sea ice in Alaska or coral reefs in Hawai'i and the Pacific Islands, enhance this phenomenological variability. Anticipating the expression of these phenomena is also complicated by observed and projected changes in climate. Combined with these physical systems are social systems made up of diverse cultural, economic, and environmental conditions. Like the physical systems, the social systems are changing, largely because of increases in population and infrastructure along coastlines. These diverse conditions and systems reveal wide-ranging needs for the content, format, and timing of data and information to support decision-making. In addition, other considerations complicate these requirements for data and information: (1) the decentralized acquisition of information from a variety of platforms (e.g., tide gauges, wave buoys, satellites, radars); (2) data and models of varying complexity and spatial and temporal application; and (3) gaps and overlaps in agency, institutional, and organizational activity and authority. This systemic complexity presents a challenge to scientists, planners, managers, and others working to increase community resilience in the face of the risks associated with inundation and erosion. This paper describes a conceptual framework for an integrating architecture that would support program planning and product development toward hazard resilient communities. Central to this framework is a comprehensive, horizontally and vertically integrated view of the physical and social systems that shape the risks associated with coastal inundation and erosion, and the kinds of information needed to manage those risks. Equally important, the framework addresses the necessary connections among systems and scales. This integrated approach also emphasizes the needs of planners, managers, and decision-makers in a changing physical and social environment, as well as the necessity of an iterative, nested, collaborative, and participatory process.

Methods for improving the hydrological simulation through the use of multi-model ensembles (MME) are demonstrated. In recent years, the meteorological community has exploited several MME combination techniques as a means for improving short-term weather and seasonal climate forecasts. Within the hydrological community, little work has been carried out to explore the benefits of MMEs for streamflow simulations. This study examines the use of MMEs for improving streamflow simulation, including their potential benefits to flood simulation. Multimodel ensembles are generated using ten distinct model structures derived using a new hydrological modelling tool. These model structures were applied to a US NWS test catchment, the Blue River and evaluated using a split-sample procedure. The resulting ensemble can be used to make probabilistic simulations that characterise model structure uncertainty. Furthermore it is shown that the ensemble average of all 10 models performs better than any single model in split sample test. Using regression methods, improvements in ensemble simulations using linear combinations of the ensemble members were explored. The performance of the resulting weighted ensemble is similar to the simple ensemble average but uses a smaller ensemble. This may provide a means to identify which model structures provide significant contributions to accurate hydrological simulation.