The Development of a Geospatial Blowing Snow Susceptibility Index

1. University of North Dakota The Development of a Geospatial Blowing Snow Susceptibility Index Scott Kroeber and Damon Grabow RGIS – Great Plains Regional Weather Information Center University of North Dakota Grand Forks, North Dakota

2. The National Consortium forRuralGeospatial Innovations in America (RGIS)Consists of eight sites across the United States

3. Chesapeake, Pennsylvania State UniversityChesapeake,Wilkes University Great Lakes, University of Wisconsin-MadisonGreat Plains, University of North DakotaMid-South, University of ArkansasPacific Northwest, Central Washington UniversitySouth Georgia, South Georgia Regional Development CenterTribal Technical Center, Southwestern Indian Polytechnic InstituteWeb Sitehttp://www.ruralgis.org RGIS Sites:

4. Roadway Vegetation Outline • Roadway vegetation importance • Highlight test areas and methods • Example of data collected • Raw data • Processed data for model input • Blowing snow validation • Differences in blowing snow types • Video examples of collected data

5. Roadway Vegetation Mapping Data Assimilation Ensemble Forecasting Model Blowing Snow Model for Maintenance Decision Support System Roadway Vegetation Grid Blowing Snow Validation Blowing Snow and Roadway Vegetation Geospatial Comparison

6. Maintenance Decision Support System

7. Why the need for a geospatial blowing snow susceptibility index within surface transportation weather research?

8. Winter travel in the Snow Belt areas can be hazardous during times of blizzards, winter storms, and blowing snow events.

9. For surface transportation precipitation does not have to be falling in order for travel to become hazardous. Wind alone can transport snow, from previous snow events onto the roadway, which reduces visibility and can begin to accumulate.

10. Localized areas of blowing/drifting snow

11. Winter Maintenance Region of the United States: Approx. 33 States

12. What would be the benefits of improved forecasting of the spatial distribution of blowing and drifting snow to road maintenance? Reactive: “de-icing” vs. Proactive: “anti-icing” Currently done now but hopefully more spatially efficient

13. The areas of roadway accumulation depend upon some of the following… Orientation of the roadway with respect to the prevailing winds during the event The amount of snow mass present The surface roughness factors along the roadway. Topography?

14. Detailed 3D Digital Elevation Model of Eastern North Dakota

16. Roadway Orientation Example…

17. Selected Routes East-West North-South 25 Mile Segment 20 Mile Segment

18. Surface Roughness Identifying the surface roughness or vertical extent of the vegetation is a geospatial problem that can be accomplished with ground-based observations.

19. Method Chosen: The use of video mapping along a roadway and the process involves transferring that data to a GIS.

20. For This Project Red Hen Systems, Inc. Red Hen Systems GPS Unit Cost ~ $3500 Sony DVD Video Recorder Cost ~ $600+

21. Initial Obstruction Ratio x 5.7° 10x

22. Shelterbelts The Forest Service encouraged farmers in areas of the Great Plains to plant trees to slow the wind and reduce moisture evaporation from the soil. One forester reported that windbreaks were particularly important in the eastern portion of North and South Dakota where the need for trees was perhaps the greatest in the region. On June 7, 1924, Congress passed the Clarke-McNary Act which authorized the secretary of agriculture to cooperate with the states for the distribution of trees to establish windbreaks, shelterbelts, and woodlots on denuded or non-forested lands. FORESTRY ON THE GREAT PLAINS, 1902-1942 R. Douglas Hurt Iowa State University

24. Shelterbelt – road view

25. Not Just Trees! Corn Field Cattails

26. Not Just Trees! Brush ~ 8’

27. Roadway Vegetation Mapping “Side View” Segment between North Dakota mile reference markers 130 and 123 along Interstate 29

28. Roadway Vegetation Mapping Red=Obstructions Yellow=No Obstructions • Construct a dataset that better represents the spatial variability of roadside vegetation • Used as input dataset to provide for a more accurate blowing/drifting snow model • Localized areas of vegetative wind obstructions not picked up with current datasets that cause localized areas of blowing snow

31. Roadway Vegetation Mapping Minor vegetative obstructions: L. Blue = no, D. blue = yes Mature vegetative obstructions: Yellow = no, red = yes

32. Blowing Snow Validation Types of blowing snow • Rolling snow < 0.01 m • Saltating snow < 0.1 m • Suspended snow 0.1 – 100 m

33. Start Roadway Video

34. Blowing Snow ValidationRolling Snow Example Segment between North Dakota mile reference markers 130 and 123 along Interstate 29

35. Blowing Snow ValidationSaltating and Suspended Snow Example Segment between North Dakota mile reference markers 130 and 123 along Interstate 29

36. Field Season 2005-2006

37. Goals for Roadway Vegetation and Blowing Snow • Develop a better data set for more accurate blowing snow modeling • Find correlation between blowing snow and roadway vegetation variables • Wind speed and direction • Snow pack conditions • Available vegetation holding capacity • Terrain (LiDAR)

38. Acknowledgements • USDA funded Rural Geospatial InnovationS (RGIS, www.ruralgis.org) • USDA contract number 832G720 • Also supported by Federal Highway Administration and North Dakota DOT • contract number ITS – 9999(174)

39. RGIS – Rural Geospatial Innovations USDA – Cooperative State, Research, Education, and Extension Service (CSREES) North Dakota Department of Transportation US Department of Transportation, Federal Highway Administration University of North Dakota (Regional Weather Information Center)

40. Thank You! Scott Kroeber scottk@rwic.und.edu Damon Grabow dgrabow@rwic.und.edu RGIS – Great Plains Regional Weather Information Center University of North Dakota Grand Forks, North Dakota