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Water & Energy Cycles

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Traditional forecasting methods rely on watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature. With the advent of satellite remote sensing, the continuing development of more physically representative models, and the adoption of a more flexible operational forecast system by the National Weather Service, significant advances in process representation (such as snow depletion and evapotranspiration) in hydrologic forecasts can now be made. The proposed study will test the potential for satellite-derived data to meet current forecasting needs in the upper Midwest U.S. There is a need to develop new forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This area is highly flood prone but also sensitive to prolonged dry periods in late summer and early fall. The region is also characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. The application of MODIS snow albedo, snow covered area, cloud cover, land surface temperatures, and derived evapotranspiration values will be investigated as hydrologic model input for the subsequent improvement of streamflow forecasts. The analysis will cover annual streamflow variability, but also focus on two key time periods for the Midwest: the spring wetting and late summer drying periods. Models of varying complexities will be investigated to understand the relative impacts of using spatially explicit remotely sensed data in a range of hydrologic models (including the current operational system), with the goal of improving model simulations and the initial conditions prior to the start of a forecast. Hindcasting and forecast verification techniques will be applied to quantify the influence of the initial conditions on forecast skill for persistence and ensemble predictions. The education and outreach component of this project is designed to integrate with the research component by: 1) providing research opportunities for female undergraduate students through summer internships, and 2) educating operational hydrologic forecasters on the use of NASA products for hydrologic prediction.

Project PI: Kristie Franz/Iowa State University

3023 Agronomy Dept. of Geological and Atmospheric Sciences Iowa State University Ames, IA 50011

Phone: (515) 294-7454

Email: kfranz@iastate.edu

http://www.ge-at.iastate.edu/franz.shtml

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Started: Sep 29, 2010

Last Activity: Jan 05, 2011

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