Author: Clifford Astill at NOTE Date: 3/22/98 7:53 PM Priority: Normal TO: acoles at nsf18 Subject: Abstract: CMS-9733925, Kaye Brubaker, U. Maryland Message Contents Abstract: CMS-9733925, Kaye Brubaker, U. Maryland In regions with significant seasonal snowpacks, some of the most devastating floods are caused by sudden snowmelt or rain-on-snow events. At the other hydrologic extreme, a below-average winter snowpack is likely to translate into severe warm-season water shortages. In the United States, operational hydrology has made great strides in short-term and extended forecasting of these disastrous events, aided by sophisticated observations and models. Modern hydrologic techniques can incorporate information on uncertainty in the snow pack, precipitation and other weather factors to produce probabilistic forecasts. The aim of this CAREER project is to develop tools to determine mathematical expressions for physical variability and measurement uncertainty, for a variety of topographic climatic situations, and for different resolutions of measurement. The research objectives are: * Characterize patterns in snowpack/snow-cover depletion during the melt season using remote-sensing products at different spatial resolutions, and a Geographic Information System; * Relate these spatio-temporal patterns to topography, land cover, and climate; * Investigate the scaling properties of these patterns and their dependence on the resolution of the observed data; and * Test the use and value of these findings in short-term and extended streamflow forecasting models, both deterministic and probabilistic. It is expected that this research will result in: * Physically-based mathematical expressions for uncertainty in snow-water equivalent estimates used to update streamflow forecast models. * Model validation data sets of spatially-distributed time-series of multi-se nsor snow-cover and weather observations, and weather forecasts for testing and developing forecast models. The educational objectives are: * Develop a new hands-on seminar in hydrologic prediction, design and forecasting, incorporating the research results. * Incorporate the research findings into a modest number of new modules for an existing graduate hydrology class and an existing undergraduate probability and statistics class. The new modules will incorporate innovative classroom technology and pedagogy. * Perform objective and subjective assessments of the value of these innovations in aiding students' learning. It is expected that this educational objective for water-resource engineers will result in training in the use of sophisticated observational and analytical technology, including remotely-sensed Earth systems data, and Geographic Information Systems and spatial data manipulation; a solid understanding of natural variability in hydrologic variables, measurement uncertainty, mathematical expressions for that variability and uncertainty, and the implications for resource prediction and forecasting; and exposure to techniques and perspectives from related disciplines. +=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+= + Clifford J. Astill Ph:703-306-1362 Fax:703-306-0291 castill@nsf.gov Nat'l Science Fdn 4201 Wilson Blvd., Rm545 Arlington, VA 22230 <www.eng.nsf.gov/cms/castill.htm>
