Watching the Skies to Protect the Earth
DG Research Taps Satellite Network Data to Predict Crop-Damaging Hail
By Karen Heyman
For the DGRC

Ecological Forecasting via Satellite
	• Researcher profile: Geoffrey Henebry • Project profile: Spatio-temporal Models of Biogeophysical Fields for Ecological Forecasting: A Cross-Disciplinary Incubation Activity • Project Home Page
Storm in Phoenix, AZ

For those who don't live in the Midwest, nothing seems more terrifyingly destructive than a tornado. Surely no natural phenomenon of the Great Plains could be worse than a black funnel cloud turning everything in its path - tractor, silo, field and farmhouse - into spinning debris.

But there is one force of nature even more powerful. Though no more than a nuisance across the Eastern half of the country, and barely thought about west of the Rockies, hail can be as devastating as a locust plague for farmers and ranchers of the heartland. Where tornadoes cut a short and narrow path of destruction, a severe hail storm can flatten crops in a 5 mile wide swath that extends for more than 40 miles.

And there's still no good way to know far enough in the future when such severe weather will happen. There have been great advances. These days the data the local TV weathercaster receives from the National Weather Service, or other authoritative sources, is likely to predict with reasonable accuracy on a Tuesday that it might hail on Thursday. But long-range weather forecasting is still not what it needs to be for those whose livelihoods are dependent on weather, like farmers and ranchers. With all due respect to the 187-year-old Farmer's Almanac, even its editors admit on its Web site, "Weather forecasting still remains an inexact science. Therefore, our forecasts may sometimes be imperfect."

Digital Government researcher Geoffrey Henebry, a Research Associate Professor in the School of Natural Resources at the University of Nebraska-Lincoln, received an incubation grant under the NSF's Biodiversity and Ecosystem Informatics (BDEI) program to explore how to extract baseline conditions from time series of satellite images for use in ecological forecasting.

This initial grant funded research that, together with colleagues from Nebraska and resources from NASA and USGS, has provided a new look at how vegetation dynamics are observed from space and which will be the subject of an upcoming paper in Geophysical Research Letters. In addition, the Digital Government grant has led to a research partnership with the Risk Management Agency of the USDA on characterizing the year-to-year variations in grassland productivity.

Quantifying these uncertainties and identifying certain patterns can give livestock and forage producers an edge in minimizing risk.

The reason such a variety of agencies are interested in Henebry's work is that hopes are high it will ultimately be of economic value to the nation, since the results can potentially increase productivity for farmers and ranchers. BDEI has been identified as "a critical national priority" by several presidential advisory committees.

What makes Henebry's work especially significant is his emphasis on the "feedback loop" interaction between atmospheric conditions and vegetation in the creation of long-term climate effects. Most climatic modeling has focused on the results of atmospheric conditions on crops and other vegetation. Far less attention has been paid to how the spatial and temporal patterns of vegetation affect the lower bounds of atmosphere that skim the land surface. For example, the devegetated area left in the wake of a severe hail event may serve as a trigger for subsequent severe weather. The contrast between the hot, dry devegetated surface and the cooler surrounding croplands can generate a local circulation pattern that gives rise to convection and the formation of thunderheads.

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Farmers currently depend on sources ranging from the venerable Almanac to communal memory to the very latest government weather predictions. By looking at ecological cause and effect, Henebry hopes to add one more crucial piece of perspective to the puzzle.

"There's a mismatch in scale between models of atmospheric and land surface processes," he explains, "Models of atmospheric dynamics operate at a very fine temporal resolution, but a coarse spatial resolution. By contrast, the ecological processes across on the land surface, such as vegetation growth, occur at fine spatial resolution but longer temporal resolution."

In other words, the winds respond quickly but only to spatially big things such as mountain ranges, reservoirs, and urban areas; whereas, the growth and development of plants occurs through season long responses to the local environment. This scale mismatch poses a challenge for getting the models to interact effectively.

Henebry's work concentrates therefore on identifying broad patterns at the land surface to provide better linkages with atmospheric models. He works with the "macroscope" that the constellation of orbiting Earth-observing sensors creates.

"We're trying to establish something analogous to climate - to provide an expectation of land surface dynamics. If you have a baseline, then you can determine what constitutes an unusual event. We don't yet have those kinds of baselines defined for land surface phenology. But it's important in terms of understanding interactions between vegetated surface and weather." Such questions are essential if you are trying to detect and predict changes in the length of the growing season, as might be occurring in some locations due to climate change, he says.

"We seek patterns at these broader spatial and temporal scales because we can't do manipulative experiments," says Henebry, "We must also rely on the experiments of opportunity that nature provides through extreme events, such as floods, droughts, wildfires, hailstorms and hurricanes. It's exactly the responses to those events we want to track as early as possible."

When told his experimental method sounds similar to that of early astronomers who waited for eclipses to prove their models correct, he replies, "Celestial motion is far less subject to variation, it's a far easier problem."