direct carbon emissions from wildfires of siberia ... · climate. wildfires under climate change...

1 V.N. Sukachev Institute of Forest SB RAS, 2 Regional Center for Remote Sensing, Federal Research Center «Krasnoyarsk Science Center SB RAS»,

3 Siberian Federal University,4 S.S. Kutateladze Institute of Thermophysics SB RAS,

5 George Mason University

Direct carbon emissions from wildfires of Siberia

estimated based on remote sensing data

Evgenii Ponomarev (1,2,3), Kirill Litvintsev (4), Evgeny Shvetsov (1),

Viacheslav Kharuk (1,3) and Susan Conard (5)

This research was supported bythe Russian Foundation for Basic Research (# 18-05-00432),

the Government and Science Fund of the Krasnoyarsk region (# 17-41-240475),the NASA Land-Cover Land-Use Change (LCLUC) Science Program (08-LCLUC08-2-0003).

IBFRA-2018 “Cool forests at risk?”17-20 September 2018, Laxenburg, Austria.

Further development of technologies for remote monitoring of wildfires is an important component for monitoring and forecasting fire impacts on Siberian forests under changing climate.

Wildfires under climate change

The current dynamics of fire regimes in Siberia is determined by a complex of factors, such as temperature anomalies, change and redistribution of precipitation, as well as the frequency of periodic droughts.

The first decade of the 21st century was characterized by an increase in the frequency of fires and burned areas (Flannigan et al., 2009; Kharuk et al., 2013; Ponomarev, Kharuk, 2016). Up to 70-90% of fires in the Russian Federation occur in Siberia.

According to some forecasts, direct fire emissions of carbon, which currently amount to 120-140 Tg per year (Shvidenko et al., 2011), might increase to 230-240 Tg per year in the second half of the 21st century (Zamolodchikov et al., 2011).


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1900 1920 1940 1960 1980 2000 2020

Tem

pera

ture

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y, °

С

1901 - 1990

1990 - 2013

I

II

a)

-0,7

-0,2

0,3

0,8

1900 1920 1940 1960 1980 2000 2020

SP

EI

b)

Ponomarev E.I., Kharuk V.I. (2016) Wildfire Occurrence in Forests of the Altai–Sayan Region under Current Climate Changes // Contemporary Problems ofEcology. 2016. Vol. 9. № 1. P. 29–36. doi: 10.1134/S199542551601011X

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Long-term trends for temperature and The Standardised Precipitation-Evapotranspiration Index for Siberia

I - SiberiaII - Altai-Sayan RegionIII - Yenisey transect of Central SiberiaIV - Eurasia

III

I

II

Area of interest

1 2 3 4 5 6 7 8 9 10 11 12

IV

E u r a s i a


Satellite monitoring data on wildfires

Terra/MODIS2017

Active burning and post-fire pattern of territory.Terra/MODIS. 2016, 2017

Sentinel-22016

Landsat-8/OLI2017

AQUA/Modis SNPP/VIIRS

Raw satellite dataA Wildfire GIS databaseС

Database time: 1996–2018;Data volume: ~2106 records;Data format: polygonal GIS layers, and joint attribute information for each records

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Pre-processing

B

D Analysis


methods for estimating wildfire energy characteristics, adapted for burning in Siberian forests; identification of high intensity/crown fires;

assessment of direct fire emissions of carbon based on real-time satellite data

Characterizing fire in Siberia

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geospatial distribution and fire danger scenarios for the subregions of Siberia

monitoring effects of post-fire thermal anomalies, modeling of anomalies in the seasonal thaw layer


Kharuk V.I., Ponomarev E.I. (2017) Spatiotemporal Characteristics of WildfireFrequency and Relative Area Burned in Larch-dominated Forests of CentralSiberia // Russian Journal of Ecology. Vol. 48, No 6, p. 507–512. doi:10.1134/S1067413617060042

Ponomarev E.I., Skorobogatova A.S., Ponomareva T.V. (2018) WildfireOccurrence in Siberia and Seasonal Variations in Heat and Moisture Supply //Russian Meteorology and Hydrology. Vol. 43, No. 7, p. 456–463. DOI:10.3103/S1068373918070051.

Relative burned area (RBA)per year, % in Siberia

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Wildfires in Siberian forests

Fire numbers, burned areas, fire danger period and fire recurrence

within South-North transect

Relative burned area per year is in range of

0.1% — 14.5%Averaged for Siberia — 1.19%, for western Canada — 0.56% (deGroot et al., 2013).

ScenarioProbability

P{E} (Min–max)Period,

yeasRBA, %

(Min–Max)

I (extreme) 0.18–0.20 8 ± 3 4.5–14.5

IIa (moderate/

spring)0.24–0.57 4 ± 1 0.5–1.5

IIb (moderate/summer)

0.24–0.38 3 ± 1 1.0–4.0

III (low) 0.19–0.48 4 ± 2 0.01–0.3

Fire season scenarios in Siberia


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Fire Radiative Power and intensity estimations

To estimate the heat radiation power from the active fire zone used data of Terra/MODIS standard

product MOD14 (Kaufman et al., 1998; Justice et al., 2002).

Adapted for burning conditions in Siberia;A threshold technique is implemented for classification of fires in term of energy and for registration of high-intensity burning stages as well as crown phase of burning (with probability ~60%).

It is shown that registered FRP is ~ 15% of the total radiated energy of fire. Varying (at the level of 10-30%) provides a scenario of burning (burnup rate and fire front velocity).


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Extreme and crown wildfires of Siberia

Sporadic peaks of FRP corresponded to the extreme

burning phase

Ponomarev E.I., Shvetsov E.G., Usataya Yu.O. (2017) Registration ofwildfire energy characteristics in Siberian forests using remote sensing //Issledovanie Zemli iz kosmosa. 2017. # 4. P. 3-11.doi:10.7868/S0205961417040017. [in Russian]

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Number of fire observation

FR

P, M

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Extreme burning areas of Siberian wildfires were classified from total database by analysing FRP data for each active pixel detected.

In Siberia the average annual percent of fires with extreme heat radiation is 5.51.2% of the total, that is about 8.5% of total area burned annually.

FRP maxima


Thermal anomalies from Terra/MODIS

imagery of an active fire

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Assessment of fire impact and monitoring of post-fire changes can be carried out not only on the basis of "traditional" methods for analyzing vegetation indices, but also by characterizing the energy of the fire (FRP) during the process of burning.

This approach can be used as an method for qualitative and quantitative diagnostics of the post-fire vegetation cover state.

Also it could be used for remote estimating of the burned fuel amount.

Vegetation cover classification results for the post-fire scar:a) initial Landsat imageb) uncontrolled classificationc) NDVI classifyingd) dNBR classifyinge) Classification on the base of Fire Radiative Power estimates

Different data classification on the fire impact


a b c

d e

Fire impact “strongness”

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Classifying the fire intensity over whole Siberia

The ratio of fire areas of variable

intensity.

Fractiles of Intensity : I) < FRPmean–σ, II) от FRP mean–σ до FRP mean+σ,

III) > FRP mean +σσ - is standard deviation

Ponomarev E.I., Shvetsov E.G., Kharuk V.I. (2018) Intensity of wildfires

for direct fire emissions estimating // Ecology. №6. P. 1–8.doi:10.1134/S0367059718060094 (currently in press) [in Russian]

The energy release from fire is linearly related to the amount of burnt biomass (Wooster et al., 2002).

Fire Radiative Power data are the initial information for differential accounting of the biomass burned during different stages of each wildfire.

11.72

44.6043.67

0

25

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75

I II IIIКвантиль FRP/Интенсивность

Доля

пло

щад

и, %

.

б)

11.68

46.0442.28

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75

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Пло

щад

ь, %

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а)

43.64 42.92

13.44

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Доля

пло

щад

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г)

/FRP

10.94

41.74

47.32

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Пло

щад

ь, %

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в)

Квантиль интенсивности


Larch Pine

Dark Decid. /Mixed

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Variation of the registered radiation power (FRP) in relation to fire parameters in model equations

Ponomarev E.I., Shvetsov E.G., Litvintsev K.Y. (2018) Calibration of

estimates on direct wildfire emissions based on remote sensing data //Issledovanie Zemli iz kosmosa. (Currently in press) [in Russian]

Wildfire area (m2)

Combustion completeness ()Fuel load (kg/m2)

Burned biomass (Seiler, Crutzen, 1980 )

Wildfire polygon classifying / intensity category in terms of FRP

Adopted estimations of fuels burned amount (kg);Adopted method for direct carbon emission estimates (Tg С/year)

Wildfire emissions evaluating

StandS, mlnha peryear

“Standard” season Extreme season % of emission

(min–max)

TgС/year

t С/ha

Tg С/year

t С/ha

Larch 2.765 42.9 15.5 52.0 18.8 51.6–62.4

Pine 0.656 11.0 16.7 11.8 18.0 13.2–14.2

Dark coniferous

0.153 1.9 20.4 3.1 20.4 2.3–3.7

Decidious/mixed

0.275 3.8 13.7 4.7 17.24 4.5–5.7

а) FRP vs burnup rate (kg/m2sec) for different sub-pixel active burning area: 1) 1000 m2, 2) 500 m2, 3) 250 m2;

b) FRP vs fire front velocity and fuel load (kg/m2): 1) for 1.5 kg/m2 and β = 0.55, 2) for 1.5 kg/m2 and β = 0.4, 3) for 2.5 kg/m2 and β = 0.55, 4) for 0.7 kg/m2 and β = 0.4


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0 0,05 0,1Front velocity, m/sec

FRP,

МВт

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b)

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Burnup rate, kg/m2sec

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MW

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Direct emission is 83 ± 21 Tg С/year. In compare to assessment for Siberia 112 ± 25 Tg С/year, according standard method (Soja et al., 2004).

Emission values per year vary from minima 20–40 Tg С/year (during 2004, 2005, 2007, 2009, 2010) up to230 Tg С/year during extreme fire season of 2012. This is lower than the extreme estimates obtained for Siberia (>500 Tg С/year) (Soja et al., 2004), as well as for Western Canada forests (>300 Tg С/year) (de Groot et al., 2013).

The total emission statistics includes 33–37%, 47–49% and 14–17% from wildfires of low-, moderate- andhigh intensity in terms of FRP. And specific emission values were 8.7, 12.0 and 15.4 tonne С/ha correspondingly.

Method

Burned biomass (М) Direct emissions (C) Mrel и Сrel

1012 kg σε

for

α= 0.1

Tg С / year

σ ε % σε

for α= 0.1

“Standard” (Seiler, Crutzen,

1980 )0.192 0.131 0.067 111.9 68.4 25.4

17.3 1.6 0.8Standart+

FRP

classification

of fires

0.159 0.108 0.055 83.1 56.5 21.0

Long-term emission estimates

Ponomarev E.I., Shvetsov E.G., Litvintsev K.Y., Bezkorovaynaya I.N.,Ponomareva T.V., Klimchenko A.V., Ponomarev O.I., Yakimov N.D., PanovA.V. (2018) Remote Sensing Data for Calibrated Assessment of WildfireEmissions in Siberian Forests // Proceedings. 2018. Vol.2. № 7 (348). 7 p.DOI: 10.3390/ecrs-2-05161.


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Estimated direct emission from Siberian fires

Variations of direct carbon emissions from Siberian fires in the time interval 2002-2016:

a) trend based on the multi-year series (p <0.05);

b) In relation with air temperature anomalies for

Siberia

The updated estimates of direct fire emissions in Siberia during 2002–2016 were 83 ± 21 Tg C/year in average. Current linear trend (R2=0.56) was evaluated for the last 15 years.

A significant trend of the increase in direct fire emissions is observed. According to the trend the fire emissions in Siberia in the end of 21 century will rising up to 220, 700 и 2300 Tg C/year in RCP2.6, RCP4.0 and RCP8.5 scenarios, correspondingly.

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Ponomarev E.I., Shvetsov E.G., Kharuk V.I. (2018) Intensity of wildfires

for direct fire emissions estimating // Ecology. №6. P. 1–8.doi:10.1134/S0367059718060094 (currently in press) [in Russian]

R2 = 0.56

0

100

200

2002 2006 2010 2014

Year

С, Tg/year

a)

y = 32.7exp(0.59x)

r = 0.51

10

100

1000

0,0 0,5 1,0 1,5 2,0 2,5Temperature anomaly, °С

b)

Thank you!


This research was supported bythe Russian Foundation for Basic Research (# 18-05-00432),

the Government and Science Fund of the Krasnoyarsk region (# 17-41-240475),the NASA Land-Cover Land-Use Change (LCLUC) Science Program (08-LCLUC08-2-0003).

Direct carbon emissions from wildfires of Siberia

estimated based on remote sensing data

Evgenii Ponomarev, Kirill Litvintsev,

Evgeny Shvetsov, Viacheslav Kharuk and Susan Conard

direct carbon emissions from wildfires of siberia ... · climate. wildfires under climate change...

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