Climate Information for the Changing Forest Fire Regimes in...

© C-DAC, 2018

Climate Information for the Changing Forest Fire Regimes in India

Manish P. Kale Emerging Solutions and e-Governance Group

Centre for Development of Advanced Computing (C-DAC)

Pune

Twelfth Session of South Asian Climate Outlook Forum (SASCOF-12) Pune, India, 19-20 April 2018

© C-DAC, 2018

Fire affects all the forests on earth1……Some history….

• Black-dragon fire in China (1987) burned about 18 million acres of pristine conifer forest. It blackened the area as large as New England and destroyed timberlands the size of Scotland. At least 15 million acres burned on Soviet side. About 200 people dead2

• In Indonesian (1997) around 0.81 to 2.57 GT of carbon-di-oxide was released into the atmosphere as a result of burning peat and vegetation 3,4,5

• Many other Asian countries have witnessed forest fires from time to time; Some of the noteworthy wild fires occurred in Israel (1989,1995,2010,2016); Japan (1971), South Korea (2000, 2013)6

• Among European countries noteworthy wildfires were reported in Croatia (2007), Germany

(1975), Greece (2000,2005,2007,2009,2012); France (1979); Poland (1992), Portugal (2003, 2016,2017); Russia (1921,1935,2010,2015); Spain (2005), Sweden (2014); UK (2011)6

• Canada and US are among the countries worst affected by the wildfire. Innumerable wildfires

have occurred in these courtiers since 1825 till date6 • India witnessed significant forest-fire in Utterakhand state in 2016, affecting around 2000ha

land area7. Very recently (2018) 20 Trekkers died in Karungani forest fire in Tamilnadu8

1. FAO (2007) Fire management global assessment 2006. FAO Forestry Paper. FAO, Rome, p 119 2.. http://www.nytimes.com/1988/10/01/opinion/the-breath-of-the-black-dragon-in-russia-and-china.html

3. The global impact of Indonesian forest fires, Mark E Harrison, Susan E Page, and Suwido H Limin, Biologist, Volume 56 Number 3, August 2009

4. Page, S.E., Siegert, F., Riley, J.O., Boehm, H.V., Jaya, A., Limin, S. (2002). Nature, 420:7, 61-65

5. Indonesia Rainforest Fires Doubled CO2 Levels Globally, Cat Lazaroff, Albion Monitor, 5 November 2002 6. https://en.wikipedia.org/wiki/List_of_wildfires 7. http://www.thehindu.com/news/national/other-states/forest-fire-claims-five-lives-ndrf-forces-deployed-to-control-flames/article8538875.ece 8. http://www.thehindu.com/news/cities/Madurai/death-toll-in-forest-fire-rises-to-20/article23339879.ece

http://www.nytimes.com/1988/10/01/opinion/the-breath-of-the-black-dragon-in-russia-and-china.html

http://climate.ncas.ac.uk/people/allan/Seasonal_Forecasting_papers/harrison_09_global_impact_indonesian_forest_fires.pdf

http://www.monitor.net/monitor/0211a/rainfire2.html

http://www.thehindu.com/news/national/other-states/forest-fire-claims-five-lives-ndrf-forces-deployed-to-control-flames/article8538875.ece

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• It is accepted that more than 80% of forest fires are

anthropogenic1

• Although the forest fires are primarily anthropogenic, climate variability has its strong influence2

• Increased temperature during extreme climate events, enhances the chance of forests catching the fire3,4

• At regional and global scales, fire regimes may be related to

climatic phenomena such as the El Nino Southern Oscillation (ENSO)5,6,7

1. FAO (2007) Fire management global assessment 2006. FAO Forestry Paper. FAO, Rome, p 119 2. Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DM (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun.

https://doi.org/10.1038/ncomms8537 3. N’Datchoh ET, Konare A, Diedhiou A, Diawra A, Quansah E, Assamoi P (2015) Effects of climate variability on Savannah fire regimes in West Africa. Earth Syst Dyn. https://doi.org/

10.5194/esd-6-161-2015 4. Devischer T, Anderson LO, Aragao LEOC, Galvan L, Malhi Y (2016) Increased wildfire risk driven by climate and development interactions in the Bolivian Chiquitania, Southern Amazonia. PLoS

ONE. https://doi.org/10.1371/journal.pone.0161323 5. Siegert F, Hoffmann AA (2000) The 1998 forest fires in East Kalimantan (Indonesia): a quantitative evaluation using high resolution, multitemporal ERS-2 SAR images and NOAAAVHRR hotspot

data. Remote Sens Environ 72(1):64–77 6. Siegert F, Ruecker G, Hinrichs A, Hoffmann AA (2001) Increased damage from fires in logged forests during droughts caused by El Nin ˜o. Nature 414(6862):437–440 7. Patra PK, Ishizawa M, Maksyutov S, Nakazawa T, Inoue G (2005) Role of biomass burning and climate anomalies for land– atmosphere carbon fluxes based on inverse modelling of

atmospheric CO2. Global Biogeochem Cycles. https://doi.org/10. 1029/2004GB002258

Forest fire and changing climate….

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100 Vegetation types; from sea level (Mangroves) to 6500 mt (Dry Alpine Pasture); mean annual average precipitation ranging from 25 mm (Dry Alpine Scrub) to around 11000 mm (Sub tropical Broad Leaved Evergreen Forests); and mean annual average temperature from -29.15◦C (Moist Alpine Scrub) to Desert dune scrub (41.18◦C)

Major formations include: 1. Mixed Forest formation 2. Gregarious Forest Formations 3. Locale Specific Formations 4. Plantation 5. Degraded Formation 6. Woodland 7. Scrub/Shrub land 8. Grassland 9. Managed Ecosystems 10. Others

Vegetation type map of India1

Salient features • Collaborative efforts of 21 Institutes across

India • 16518 geo-referenced 0.04 ha plots were laid

down • 7761 species were recorded

1. Roy P S….. Manish Kale et al. (2015). New Vegetation type map of India prepared using Satellite Remote Sensing: its comparison with global vegetation maps and utilities. International Journal of Applied Earth Observations and Geoinformation 39,142-159

© C-DAC, 2018

Evergreen forest Special ecosystems (Shola forests)

Dry deciduous (Growing Phase) Dry deciduous (Senescent Phase)

Some vegetation types of India

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MODIS fire distribution1 (FMAMJ 2009)

1. MODIS active fire product. http://modis-fire.umd.edu/. Accessed 15 June 2017

2

1

4

3 1 2

2

8

5

10

9

6

7

10

2.Rodgers WA, Panwar HS (1988) Biogeographical classification of India. Wildlife Institute of India, Dehradun 3. Rodgers, W. A., Panwar, H.S., and Mathur, V.B. 2000. Wildlife Protected Area Network in India: A review, Executive Summary. Wildlife Institute of India, Dehradun.

Ref2,3

© C-DAC, 2018

Bio

Geographic

zone 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total

Deccan

Peninsula 7461 7499 7291 5546 9769 7508 12761 9414 8131 12492 7311 95183

Desert 0 0 0 0 1 0 1 3 1 1 12 19

Gangetic Plain 147 176 143 98 72 203 100 233 108 163 103 1546

Himalaya 1447 1770 1091 1578 1837 1764 3232 1500 931 2993 1312 19455

North-East 12366 15278 12239 19436 15761 10418 19738 17894 9920 14833 12507 160390

Semi-Arid 33 373 165 192 197 324 313 222 254 302 154 2529

Trans-

Himalaya 1 3 0 0 1 0 1 0 0 0 3 9

Western Ghats 1034 2467 1207 926 1259 651 1749 997 1129 1524 1228 14171

Biogeographic zone-wise occurrence of fire in fire season (FMAMJ)

Kale Manish, Reshma M Ramchandran, Satish N Pardeashi, Manoj Chavan, Ashok Karumuri, P K Joshi, D S Pai, Bhavani Yadav P, P S Roy (2017). Are climate extremities changing forestfire regimes in India? An analysis using MODIS fire locations of 2003-2013 and gridded climate data of India Meteorological Department, accepted in Proceedings of National Academy of Science (PNAS), India

© C-DAC, 2018

Vegetation type-wise yearly occurrence of fire

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Year Normalized Nino3 (FMAMJ)

2003 -0.449

2004 0.327

2005 0.931

2006 -0.387

2007 -0.952

2008 -1.372

2009 0.506

2010 1.788

2011 -0.919

2012 0.911

2013 -0.798

NINO 3 index was used to investigate the impact of extreme events on occurrence of forest fire

The normalised NINO3 index values for FMAMJ were used to investigate the relationship with fire occurrence1

Acknowledgement: Ms. Jeba Francis, University of Hyderabad for providing FMAMJ, NINO 3 time series

1. Trenberth KE, Stepaniak DP (2001) Indices of El Niño evolution. Journal of Climate. doi:10.1175/1520-0442(2001)014<1697:LIOEN O>2.0.CO;2

© C-DAC, 2018

-0.449

0.327

0.931

-0.387

-0.952 -1.372

0.506 1.788

-0.919

0.911

-0.798

Fire occurrence in degraded forests of India from 2003-2013 in fire season


© C-DAC, 2018

0.25˚ × 0.25˚ grid1 was used as base for analysis of different climatic, fire, anthropogenic, topographic, forests and fuel datasets1

2

1

4

3 1 2 2

8

5

10

9

6

7

10

1. Pai DS, Sridhar Latha, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25 9 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18

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Fire count (Fire season-2009), MODIS Elevation (90m SRTM) Distance from road

Distance from settlement Mean NDVI (MODIS) Population density (2011), Census of India

Low High

0.25˚ × 0.25˚ grid has been found suitable to accommodate the variability of different socioeconomic, climatic and topographic features at national scale

© C-DAC, 2018

*Zone Fire occurrence

Average dry- days

Mean NDVI Average total rainfall

Maximum average

temperature

Nino3 (FMAMJ)

Fire occurrence

1 1 0.75 -0.06 -0.46 0.76 0.54

2 1 0.49 0.25 -0.47 0.39 0.28 3 1 0.18 0.24 -0.08 0.10 0.13 4 1 -0.01 -0.15 -0.17 0.17 0.33 5 1 0.82 -0.08 -0.62 0.61 0.40

6 1 0.27 -0.33 0.03 0.36 0.46 7 1 0.33 -0.11 0.15 0.37 0.48

Average dry-days

1 1 0.15 -0.56 0.57 0.31 2 1 0.59 -0.66 -0.59 -0.06

3 1 -0.02 -0.75 0.62 0.75

4 1 -0.16 -0.53 0.74 0.29

5 1 0.00 -0.83 0.79 0.77

6 1 -0.46 -0.38 0.56 0.53 7 1 -0.11 -0.50 0.60 -0.09

Mean NDVI

1 1 0.00 -0.52 -0.36 2 1 -0.68 0.15 -0.50 3 1 0.36 -0.27 -0.32 4 1 -0.29 -0.05 0.13 5 1 0.36 -0.31 -0.02 6 1 0.09 -0.15 -0.10 7 1 -0.05 -0.64 -0.20

Average total rainfall

1 1 -0.23 0.16 2 1 -0.25 0.39 3 1 -0.70 -0.84 4 1 -0.66 -0.50 5 1 -0.9 -0.74 6 1 -0.41 -0.41 7 1 -0.40 0.65

Maximum average

temperature

1 1 0.59 2 1 0.11 3 1 0.74 4 1 0.72 5 1 0.86 6 1 0.88 7 1 -0.04

Nino3 (FMAMJ)

1 1 2 1 3 1 4 1 5 1 6 1 7 1

*1.India 2.Himalaya 3.Semi-Arid 4.Western Ghats 5. Deccan Peninsula 6.Gangetic Plain 7.North-East

Correlation among different fire, climate and fuel parameters (based on yearly values from 2003-2013)

Kale et al. 2017

© C-DAC, 2018

Per

cen

t fi

re

Distance from settlement (scaled values)

0m 5000m

Effect of Elevation on fire

Effect of proximity to road on fire occurrence


© C-DAC, 2018

Data used for estimation of Forest Fire Frequency Ratio Data Source Active Fire location - MODIS Vegetation type map- Roy et al. 2015 Elevation- SRTM NDVI- MODIS Population- Census of India Rainfall- IMD 0.25˚ × 0.25˚ grid Temperature- IMD 1˚ × 1˚ grid Biogeographic zones- WII Drainage network- DCW Major Roads- Diva GIS Settlements- Geofabrik

© C-DAC, 2018

2003 2004 2005

2006 2007 2008


Forest fire frequency ratio (2003-2013)

Year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

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Fire forecasting

• Since the forest fires are mostly anthropogenic to know the exact location where fire will initiate is challenging

• The index based forecasting may be important at regional and national level

• As the dry-days and temperature are well correlated with forest fire occurrence, it forms a strong basis to develop multiple regression based model to be incorporated in national level FFR investigations

• Different researches have taken into the consideration NDVI as a surrogate to fuel, however considering the heterogeneity and seasonality of forests in India more research needs to be carried out in this direction

© C-DAC, 2018

Fire Danger rating/alert systems

Fire forecasting systems

Country/Agency Based on Predicts what? Predictability

Reference

National Fire Danger Rating System (NFDRS)

U.S. forest Service

Fuel type, topography and weather (temperature, relative humidity and precipitation), cloud cover

Burning Index, Ignition component

Daily

(https://www.wfas.net/images/firedanger/)

Fire Weather Index Rating System (FWI)

Canadian Forest Service

Temperature, humidity, precipitation, wind, cloud cover and fuel. FWI is calibrated based on fire behavior of typical Jack pine stand (characteristic of Canada)

Rate of fire spread, fuel available for combustion, general fire intensity, Fire weather index

Daily http://cwfis.cfs.nrcan.gc.ca/maps/fw?type=fdr&year=2018&month=4&day=18

The European Forest Fire Information System (EFFIS)

At present 40 countries are involved in EFFIS network

NFDRS, FWI and Mark 5 (Australia) indices

Fire danger rating indices

Daily (1-10 days)

http://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html, Geiuseppe et al. 2005

© C-DAC, 2018

Conclusion

Although in India forest fires are mostly anthropogenic, extreme climate events creates conducive conditions for fire to enhance. Thus even a fragile fire catalyst may result in higher fire spread

Temperature and dry days are the main drivers of forest fires

If during the fire season the NINO 3 values are negative, fuels are preserved and are available for next fire seasons. If the next fire season has strong Positive NINO3 values, the possibility of occurrence of fire is high

If two extreme temperature events(positive NINO3) occur one after the other following an year with negative NINO3 values, the possibility of forest fire is generally higher in the first

IMD 0.25˚× 0.25˚ rainfall grids forms a strong basis to carry out fire

forecasting at national scale as it can be aptly accommodated with different socioeconomic, topographic, anthropogenic datasets available in the country

Since forest-fires are highly seasonal the scenario based forecasting using time series data sets can prove extremely beneficial

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