STATISTICAL RUNOUT Field observations MODELS · Field observations Computer predictions Field β 30...

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STATISTICAL RUNOUT MODELS How well can computers predict β? Conclusions Models with < 20 m resolution should be comparable with field survey error Google Earth performed best Other DEM methods conservatively biased, perhaps due to ‘smoothed’ terrain More work needed to understand how model accuracy affects results. Problem Digital Elevation Models (DEM) are increasingly being used to analyze avalanche terrain. They can make things quicker, but how do they rate against a traditional field survey? Sponsors / Acknowledgments Chris Argue, Ryan Buhler, Jay Chrysafidis, Mike Conlan, Marc Deschenes, Doug Feely, Phil Hein, Simon Horton, Katherine Johnston, Alan Jones, Andrew Mason, Jon Neufeld, Cora Shea, Christine Sinickas, Scott Thumlert, Mark Vesely and Brad White. 1. What is the error of finding β in the field? Used five measures to quantify β point variation. See paper for more information. Chose ± 30 m as typical error range (shaded) Approach 2. Do any models predict β within this range? Used three models and topo map to predict β Judged each model’s performance by its ability to predict within ± 30 m error range +50 +100 -150 +150 +200 +250 -100 -50 -200 +300 +350 -250 +400 m Cross section of an avalanche path runout. Graph shows field observations of β variability and model predictions of β. Outliers excluded from graph. 23 m DEM (n=30) Google Earth (~30 m) (n=30) Topographic Map (1:20,000) (n=30) Repeatability recorded by observer. Represents range of β resulting from centerline and segment selection, amount of ‘benchy’ terrain and visibility. (n=44) Distance between β found in 2012 and β found in 2010 by different surveyors. (n=5) Distance between field β and ‘smoothed’ β. Obtained by finding 10º point from parabola fitted to path. (n=30) Distance between benches. Used for paths where β difficult to place due to benches at top and bottom of runout. (n=8) Continuous distance of survey segments with slopes between 9º and 11º. Represents error in inclinometer readings and uneven ground. (n=30) Field observations Computer predictions Field β 30 m DEM (n=30) Predicted β (Google Earth) Google Earth image (above) and large photo (left) of the same avalanche path, showing centerline (yellow), predicted β and field- determined β. Topographic map generated from DEM showing field-determined β points versus model- generated β points for six centerlines. Inset is close-up of grey box. Field beta Predicted betas Field survey 23 m DEM Fitted parabola Google Earth Topographic map 30 m DEM Alexandra Sinickas 1 , Bruce Jamieson 1,2 1 Department of Civil Engineering, University of Calgary, AB, Canada 2 Department of Geoscience, University of Calgary, AB, Canada

Transcript of STATISTICAL RUNOUT Field observations MODELS · Field observations Computer predictions Field β 30...

Page 1: STATISTICAL RUNOUT Field observations MODELS · Field observations Computer predictions Field β 30 m DEM (n=30) Predicted β (Google Earth) Google Earth image (above) and large photo

STATISTICAL RUNOUT MODELS

How well can computers predict β?

Conclusions• Models with < 20 m resolution should be comparable

with field survey error• Google Earth performed best• Other DEM methods conservatively biased, perhaps due

to ‘smoothed’ terrain• More work needed to understand how model accuracy

affects results.

ProblemDigital Elevation Models (DEM) are increasingly being used to analyze avalanche terrain. They can make things quicker,

but how do they rate against a traditional field survey?

Sponsors / Acknowledgments

Chris Argue, Ryan Buhler, Jay Chrysafidis, Mike Conlan, Marc Deschenes, Doug Feely, Phil Hein, Simon Horton, Katherine Johnston, Alan Jones, Andrew Mason, Jon Neufeld,

Cora Shea, Christine Sinickas, Scott Thumlert, Mark Vesely and Brad White.

1. What is the error of finding β in the field?• Used five measures to quantify β point variation.

See paper for more information. • Chose ± 30 m as typical error range (shaded)

Approach

2. Do any models predict β within this range?• Used three models and topo map to predict β• Judged each model’s performance by its ability to

predict within ± 30 m error range

+50 +100-150 +150 +200 +250-100 -50-200 +300 +350-250 +400 m

Cross section of an avalanche path runout. Graph shows field observations of β variability and model predictions of β. Outliers excluded from graph.

23 m DEM (n=30)

Google Earth (~30 m) (n=30)

Topographic Map (1:20,000) (n=30)

Repeatability recorded by observer. Represents range of β resulting from centerline and segment selection, amount of ‘benchy’ terrain and visibility. (n=44)

Distance between β found in 2012 and β found in 2010 by different surveyors. (n=5)

Distance between field β and ‘smoothed’ β. Obtained by finding 10º point from parabola fitted to path. (n=30)

Distance between benches. Used for paths where β difficult to place due to benches at top and bottom of runout. (n=8)

Continuous distance of survey segments with slopes between 9º and 11º. Represents error in inclinometer readings and uneven ground. (n=30)

Field observations

Computer predictions

Field β

30 m DEM (n=30)

Predicted β(Google Earth)

Google Earth image (above) and large photo (left) of the same avalanche path, showing centerline (yellow), predicted β and field-determined β.

Topographic map generated from DEM showing field-determined β points versus model-generated β points for six centerlines. Inset is close-up of grey box.

Field beta

Predicted betas

Field survey 23 m DEM

Fitted parabola

Google Earth

Topographic map

30 m DEM

Alexandra Sinickas1, Bruce Jamieson1,21Department of Civil Engineering, University of Calgary, AB, Canada

2Department of Geoscience, University of Calgary, AB, Canada