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Observing Surface Waters for Global Change Applications

  • Richard G. Lawford
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Abstract

Water cycle observations play a major role in the detection, monitoring and prediction of global change. Furthermore, variations in the water cycle must be a central consideration when evaluating planning options to mitigate the impacts of climate variability and global change. Global change is used herein to describe the full range of impacts that humans have on the global water system. It is a continuing concern for society, since it plays a central role in the sustainability of the world’s resource base and security issues. The continuing increase in the atmospheric concentration of greenhouse gases in the atmosphere is leading to changes in the Earth’s climate including effects on the global water cycle (IPCC, 2001). Other human interactions in the Earth system are also causing significant changes in the water cycle (Hornberger etal., 2001). For example, the construction of dams and reservoirs and the drainage of wetlands over the last century have altered discharge patterns in many of the Earth’s largest river basins.

This chapter describes the types of observational data needed to define baseline conditions against which future change can be compared. Integrated systems are needed to monitor change and its impacts. In this chapter these issues are addressed by considering the importance of surface waters and their sensitivity to change: by reviewing specific information requirements within each resource sector affected by change, and by exploring the potential of science and technology to provide new observational systems for monitoring surface waters and programs that address gaps in our current observational support for the resource sectors.

Keywords

Water Cycle Tropical Rainfall Measure Mission Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer Global Precipitation Climatology Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science + Business Media B.V. 2008

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  • Richard G. Lawford

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