Adding 'Real-Time' Data to the Black Rock Forest Digital Library to Enable Students to Engage in Predictive Investigations

9907689

Kastens

The project will (a) implement a system for delivering data from environmental sensors within an instrumented forest to classroom computers immediately after the data are collected (i.e. in near 'real-time'), and (b) demonstrate that the 'real-time-ness' of the data makes it possible to accomplish educational objectives that could not be accomplished with archived data. The instrumented forest is the Black Rock Forest (BRF) in the Hudson Highlands region of New York. Two terrestrial sensor stations and one stream sensor station automatically monitor and record environmental parameters such as air, water, and soil temperature, solar radiation, precipitation, relative humidity and stream discharge. The project will bring these environmental data into a professionally managed digital data library, onto the World Wide Web, into an easy-to-use data visualization tool, onto student computers, and into student imaginations-all within a few hours after it is collected. In addition, the project will develop and test an exemplary undergraduate investigation, which will showcase the value of real-time data for education, and exercise the real-time data link under realistic conditions. Students will examine archival precipitation and stream discharge data, develop a model linking the two, and then use their model to predict what the stream discharge will be a few days into the future. Most adult geoscientists have spent most of the questions that society is posing to the geoscience community have to do with the future. We need to find ways to train our students to think rigorously but boldly about the future. Predictive investigations are one way to do this.