responsiveness to growing environmental issues, new process-based solutions grounded in scientific research are needed to generate the knowledge necessary to inform practical decisions. Addressing those challenges requires the synthesis of data and model projections that may routinely span length scales (from micro to global) and time scales (from a few tens of milliseconds to millennia). An SDI for Earth system science makes use of tools for data creation, curation, analysis, and archiving and leverages the Web as a platform for collection, analysis, reporting, and publication.
Understanding large-scale human-stressed environments over long time periods requires observing multiple variables on regional scales. This includes monitoring and measuring how the characteristics and functioning of environmental systems change and determining the cause and extent of such change. The resulting knowledge can lead to improved predictive models that inform decisions about more effective adaptive management policies and practices. Developing these predictive models requires not only establishing new environmental sensor networks, but also integrating data from existing sources and available sensors to provide high-resolution and integrated data. It requires a cyberinfrastructure capable of collecting, managing, and using integrated geospatial datasets. Having a robust data infrastructure would facilitate research investigations aimed at improving understanding of interacting environmental system processes.
The tools needed to conduct science and inform policy-making are changing. Meeting many of the challenges faced by the USGS Science Strategy (USGS, 2007) requires information from the basic sciences, but they also require new scientific approaches that focus on integrating physical, biogeochemical, engineering, and human processes. This cultural and organizational shift becomes more challenging as science becomes more computational and data-intensive. In this new research environment, scientific data are captured by instruments or generated by simulations and then processed by software into models that can be examined by scientists, policy-makers, and the public using the Web. As the Earth becomes increasingly instrumented with interconnected, low-cost, high-bandwidth sensors that are linked through the Web, scientists will be in a better position to sense the environment and predict possible environmental outcomes.
The USGS has multiple disciplinary data infrastructures, but recent efforts to develop a science SDI at the USGS have been conducted largely under the umbrella of The National Map (TNM). TNM is a collaborative effort with the USGS and federal, state, tribal, and local partners to create “a database of continuously maintained base geographic information for the United States and its territories that will serve as the Nation’s topographic map for the 21st century” (USGS, 2001). TNM provides a common set of base information for use by public and private stakeholders. The 2007 National Research Council report