grated programs like the National Ecology Observatory Network (NEON) and the Water and Environmental Research Systems (WATERS) Network will offer additional research results showing the benefits of integrating observations and models.

Neither data nor models have value unless they are used. And they can only be used if they can be easily discovered, acquired, and understood in a timely and convenient manner by those who wish to apply them to practical issues such as flood forecasting, water availability modeling, and ecological flows, as inputs to decisionmaking. The communication and delivery of data and information (including their interpretation, quality, and uncertainties) to such end-users is the back-bone to a beneficial integrated system.

Therefore, efficient use by society of these new sources of data from land, air, and space requires concomitant improvements in the capture and archiving of these data, in the modeling of hydrologic systems, and in the communication of hydrologic data and information to researchers, water managers, and other users. However, achieving these goals requires a level of cyberinfrastructure not currently available or even designed. There are critical and extensive cyberinfrastructure needs if models are to routinely and efficiently take advantage of advances in measurements. It is critical that cyberinfrastructure evolve in concert with new developments in sensing capacity and hydrologic modeling.

Thus, this chapter first presents new opportunities in the merging of observations with models, followed by a discussion of the cyberinfrastructure needed to support these models and their application to societal needs.

APPROACHES FOR INTEGRATING OBSERVATIONS AND MODELS

Real-Time Environmental Observation and Forecasting Systems

The availability of tremendous computational power coupled with widespread communication connectivity has fueled the development of real-time environmental observation and forecasting systems. These systems offer the opportunity to couple real-time in-situ monitoring of physical processes with distribution networks that carry data to central processing sites. The processing sites run models of the physical processes, possibly in real-time, to predict trends or outcomes using on-line data for model tuning and verification. The forecasts can then be passed back into the physical monitoring network to adapt the monitoring with respect to expected conditions (Steere et al., 2000). Both wireless networks of sensors and sensors webs will enhance this development.

This approach is being successfully used in weather forecasting. Using protocols developed around the Global Telecommunication System (GTS), the National Weather Service has created an integrated network that interconnects me-



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