a closer look at drought conditions and wildland fire ... · incident management situation report...
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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
f A
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