A Closer Look at Drought Conditions and Wildland Fire ... · Incident Management Situation Report...

A Closer Look at Drought

Conditions and Wildland

Fire Suppression

Expenditures in California

Presentation for the Western Forest Economists Meeting

May 19, 2014

Charlotte Ham

North Carolina State University

Department of Forestry and Environmental Resources

Motivation

Improve Suppression Expenditure Forecasts Karen Abt and Jeff Prestemon

USDA Forest Service Southern Research Station

FLAME: annual data to forecast 1 year out by FS regions

Outyear: annual data to forecast 2 to 10 years out for FS total

Monthly: monthly data to forecast remaining expenditures

by month for FS East, West, and RFS aggregates

June

2013

0

200

400

600

800

1000

1200

1400

1600

1800

End o

f M

ay

End o

f Ju

ne

End o

f Ju

ly

End o

f A

ugust

End o

f

Sep

tem

ber

Exp

end

itu

res

(mil

lion

s)

Month

Total Forest Service Suppression Expenditures (2013$)

90% CI Upper

Median

90% CI Lower

Budget

July FLAME

forecast

July

2013

0

200

400

600

800

1000

1200

1400

1600 E

nd o

f Ju

ne

End o

f Ju

ly

End o

f A

ugust

End o

f S

epte

mber

Exp

en

dit

ure

s (m

illi

on

s)

Month

Total Forest Service Suppression Expenditures (2013$)

90% CI Upper

Median

90% CI Lower

Budget

July FLAME forecast

August

2013

0

200

400

600

800

1000

1200

1400

1600

1800

End o

f Ju

ne

End o

f Ju

ly

End o

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ugust

End o

f S

epte

mber

Exp

end

itu

res

(mil

lion

s)

Month

Total Forest Service Suppression Expenditures (2013$)

90% CI Upper

Median

90% CI Lower

Budget

July FLAME

forecast

ACTUAL 2013

Questions

How much do drought conditions

explain variation in suppression

expenditures over the summer in

California?

Does the relationship depend on the

measure used to represent drought?

E0 as a drought indicator Reference ET in CA, water year 2014 to March 31

Climatology: 1981-2010 Drought year: 2014

Mike Hobbins

NOAA-Earth System Research Laboratory-Physical Sciences

Division, National Integrated Drought Information System

April

Monthly

Mean

Dryness:

EDDI > 0

PDSI < 0

* Highest

Costs

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

EDDI -6

-4

-2

0

2

4

6

8

PDSI

YEAR EDDI

1987 0.28

2013 * 0.21

2014 0.16

2004 * 0.16

1985 0.16

YEAR PDSI

1977 -4.48

2014 -4.16

2013 * -3.41

2009 * -3.37

2007 * -3.37

California Fires and Acres to Date

0

2000

4000

6000

8000

10000

12000

14000

16000

BIA BLM FWS NPS ST/OT USFS TOTAL

FIRES

ACRES

National Interagency Coordination Center

Incident Management Situation Report

Thursday, May 15, 2014

Northern California

Fires and Acres to Date

National Interagency Coordination Center

Incident Management Situation Report

Thursday, May 15, 2014

0

500

1000

1500

2000

2500

BIA BLM FWS NPS ST/OT USFS TOTAL

FIRES

ACRES

Southern California

Fires and Acres to Date

National Interagency Coordination Center

Incident Management Situation Report

Thursday, May 15, 2014

0

2000

4000

6000

8000

10000

12000

14000

BIA BLM FWS NPS ST/OT USFS TOTAL

FIRES

ACRES

Forest Service Suppression Expenditures

in Totals by Month in California (2014 Dollars)

0

100,000,000

200,000,000

300,000,000

400,000,000

500,000,000

600,000,000

700,000,000

800,000,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

April

May

June

July

August

September

Source: FMMI

Spending

to date

FY2014 =

$47 million

Drought and Suppression

Expenditures • Palmer Drought Severity Index most often used measure

for forecasting acres burned and suppression spending

• Previous growing season drought decreases current

year suppression expenditures

• Current season drought increases suppression

expenditures

Collins et al. 2006; Crimmins and Comrie 2004;

Gedalof et al. 2005; Westerling et al. 2003; Westerling

et al. 2002

Drought Measures

• Palmer Drought Severity Index

PDSI-Hydrological monthly mean weighted by

Forest Service acres per climate division

• Evaporative Demand Drought Index

EDDI monthly mean by Forest Service region

FS Regions and Climate Divisions

EDDI Dec 2013

with FS Regions

Resolution is 1/8th

of a degree, which

across CONUS

runs ~ 12-13 kms

Background Types of evaporative demand E0

OBSERVED

Physically

integrates all

above drivers

• Epan – pan evaporation

• Ep – potential evaporation

• ETrc – reference ET Physically based ETrc:

• Air temperature

• SW radiation

• Humidity

• Wind speed

PDSI T-based Ep:

• Air temperature

• DOY

• Latitude

MODELED

MODELED

Temperature-based:

• T reflects radiative drivers, particularly net

radiation balance

• advective drivers unimportant

• convenience

• low data requirements:

• T, Tmax,Tmin, cloud cover data widely

available.

• lack rigorous physical underpinning:

• E0 not well characterized by T alone,

• most ignore radiative driver

• all ignore advective driver

• Thornthwaite – Ep

• Hargreaves – ETrc

• Hamon – Ep

• Blaney-Criddle – Ep

• …

Physically based:

• radiative and advective drivers

explicitly modeled

• physically sound

• match observations well

• international, scientific acceptance

• data requirements:

• Uz noisy

• Rd seldom observed

• Ld almost never observed

• Penman – Ep

• Penman-Monteith – Ep and ETrc

• PenPan – synthetic Epan

• Priestley-Taylor – Ew

• …

Advantages:

Concept:

Varieties:

Drawbacks:

Competing E0-modeling philosophies

PDSI trends

T-b

ased

E0 trends

Physic

ally

based,

P

Diffe

rence

T -

P

The dangers of poor E0 parameterizations Long-term trends and the PDSI Global long-term trends

Sheffield et al., 2012

PDSI

98% land area

0.56% area/yr

E0

58% land area

0.08% area/yr

Little change in global drought over the past 60 years

Drivers of temporal variability in ETrc Dominant drivers of daily ETrc variability, by month (1981-2010)

[Hobbins et al., ASCE (in press), 2014]

Mar

January

April

July

October

February

May

August

November

March

June

September

December

T, air temperature

U10, 10-m wind speed

SH, specific humidity

SWd, downwards SW

Simple Equation

Suppression Expenditures = f(Drought)

June Expenditures

Drought Month RMSE R2

EDDI March

September(-1) 12 42

PDSI April 13 31

July Expenditures

Drought Month RMSE R2

EDDI March

September(-1) 52 47

PDSI none

August Expenditures

Drought Month RMSE R2

EDDI March September(-1) 36 38

June 33 46

July September(-1) 28 63

PDSI May 38 32

June 35.3 40

July 34 44

September Expenditures

Drought Month RMSE R2

EDDI February 32 42

May 33 35

July 34 32

PDSI none

Conclusion

• EDDI explains from 32 to 63% of the

variation in summer monthly

suppression expenditures in

California for the Forest Service

• EDDI outperforms PDSI for

forecasting suppression

expenditures based on lowest RMSE

May Forecast

Jun=f(March September(-1))

Jul=f(March September(-1))

Aug=f(March September(-1))

Sep=f(February)

May Forecast

Lower

95% Mean Upper

95% Mean Max

June 24 59 93 19 69

July 101 250 398 60 327

August 94 196 300 88 194

September 42 126 211 89 172

TOTAL 309 679 1,049 295 695

FORECAST HISTORICAL

0

100,000,000

200,000,000

300,000,000

400,000,000

500,000,000

600,000,000

700,000,000

800,000,000

FORECAST

ACTUAL

June Forecast

Jul=f(March September(-1))

Aug=f(March September(-1))

Sep=f(February)

July Forecast

Aug=f(July September(-1))

Sep=f(February)

August Forecast

Sep=f(February)

Next Steps

• Similar analysis for other regions

• Change time frame (monthly to weekly)

&/or scale (region to national forest or

other aggregate)

Thank You

• Questions/Comments

Introduction