clustering historical wildfires from interpolated rates of spread

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Clustering Historical Rates of Spread Stephen Guerin, SimTable James Gattiker, LANL Statistical Sciences May 20, 2014

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Page 1: Clustering Historical Wildfires from Interpolated Rates of Spread

Clustering Historical Rates of Spread

Stephen Guerin, SimTable

James Gattiker, LANL Statistical Sciences

May 20, 2014

Page 2: Clustering Historical Wildfires from Interpolated Rates of Spread

What an Empirical Cluster @#$%!

Stephen Guerin, SimTable

James Gattiker, LANL Statistical Sciences

May 20, 2014

Page 3: Clustering Historical Wildfires from Interpolated Rates of Spread
Page 4: Clustering Historical Wildfires from Interpolated Rates of Spread
Page 5: Clustering Historical Wildfires from Interpolated Rates of Spread
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Page 15: Clustering Historical Wildfires from Interpolated Rates of Spread

Silver Fire ROS vs Time for 11 Fuel Types

prog

ress

ion

rate

time

Page 16: Clustering Historical Wildfires from Interpolated Rates of Spread

Dendrogram Clustering of 47 Fires

Page 17: Clustering Historical Wildfires from Interpolated Rates of Spread

Example: Progression Rate, Fuel and Fire

Clustering from last slide

Orders the fire rate empirical pdf:

Page 18: Clustering Historical Wildfires from Interpolated Rates of Spread

ROS Clustering Historical Fires

[email protected]

[email protected]

May 20, 2014

Page 19: Clustering Historical Wildfires from Interpolated Rates of Spread

End

Page 20: Clustering Historical Wildfires from Interpolated Rates of Spread
Page 21: Clustering Historical Wildfires from Interpolated Rates of Spread

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Cluster Ordering

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Cluster Ordering

Page 23: Clustering Historical Wildfires from Interpolated Rates of Spread
Page 24: Clustering Historical Wildfires from Interpolated Rates of Spread

Using Knowledge from Observed Wildfires

Historical analysis / fire-ography (pyronography :-)• Training and Education

– Use past fire data to explore fire characteristics and response– Demonstrate the variability of fire qualities and fire conditions

• Expectations of fire behavior• Community decisions on defensible spaces

• Fire situation awareness– Query historical database of fire observations for likely analogs during

an unfolding wildfire event– Create empirical models; aggregate characteristics of fire types

• Model Calibration– Resource for tuning model parameters, and analyzing the possible

failures (and quantified uncertainty) of model predictions

Page 25: Clustering Historical Wildfires from Interpolated Rates of Spread