Improving the Representation of Fire Disturbance in Dynamic Vegetation Models by Assimilating Satellite

Data

E.Kantzas, S.Quegan & M.LomasSchool of Maths and StatisticsUniversity of Sheffield

EuRuCAS

European-Russian Centre for Cooperation in the Arctic and Sub-Arctic environmental and Climate Research

• Nansen Centre, Russia• Nansen Centre, Norway• Global Climate Forum, Germany• Max Planck, Germany• Stockholm University, Sweden• University of Jena, Germany• IFREMER, France• CLS, France• FH Joanneum, Austria• University of Sheffield, UK• Finnish Meteorological Institute, Finland• University of Helsinki, Finland

Partners• Researchers from partnering institutions

visit Nansen Centre, St. Petersburg.

• Collaborate with Russian colleagues on environmental, climate and socio-economic research in the Arctic and sub-Arctic.

• Establish links between Russian and EU research institutions

Scope

Dynamic Vegetation Models

Fire

Land Cover

Biomass Litter Carbon

Permafrost

Vegetation

SoilGPP

AtmosphereClimate & CO2

Snow Properties

Snow Dynamics

Soil Surface

Ground Water

Runoff

Runoff

Net Biome Productivity

River C Transport

EVT

Atmosphere

NPP

Fires at High Latitudes

Carbon Balance (gC yr-1) 20-year Average, 50º-70ºN, Pan-Boreal

• Carbon fluxes to the atmosphere• Trigger Vegetation Succession• Alter Hydrology• Remove Soil Carbon• Thaw Permafrost

Carbon emissions from fires have a significant effect on the magnitude of Net Biome Production (NBP).

Fire Effects

The Problem

Dynamic Vegetation Models• Gridcell by Gridcell Treatment

• No Lateral Fluxes

Every gridcell experiences very small fire disturbance each year.

Even if area average is accurateMinimal removal of• Vegetation• Soil litter

Effects on ecosystem insignificant

Kantzas et al., 2013

Connected-Component Labeling

Univ. of Washington, Computer Vision, 2000

Dewar, M., 2013

• Labelling (grouping) connected pixels in a binary image.

• Pixels can be connected through a common side or a common vertex.

CCL in 2D

• CCL has numerous applications in image analysis, computer vision.

• Also referred to as blob detection.

Connected-Component Labeling

CCL in 3D

• GFED4.0: Daily, global 0.25° spatial resolution burned area/fire emissions product. (2001-2012)

1) Acquire set of daily GFED4.0 images of burned area for the region of interest (Russia & Canada).

2) Convert to binary. Fire pixels become 1’s, rest become 0’s.

3) Stack (overlay) daily binary images. Latitude and longitude provide 2 dimensions and day the third.

4) Apply the 3 dimensional CCL algorithm. As fire propagates through space and time, CCL will link continuous fire pixels.

Method

Data

Results: CCL

Assessing CCL Results

• Canadian Large Fire Database (CLFD): Ground data of forest fires in Canada (1959-1999).

• No temporal overlap between GFED4.0 and CLFD.

• No similar database for fires in Russia.

• PFs obtained from 6-connected CCL and CLFD were largely similar in all categories.

• Best results between CLFD and CCL-6 on Canadian forests.

• Worst results between CLFD and CCL-6 on Russian non-forest cover.

Assimilating CCL results in DVMs

Method is model-independent!1) Run model once. Obtain fire output (fraction of

area burned each year).2) Run the assimilation algorithm with inputs the

CCL fires and the fire output from the model. Obtain the new fires.

3) Re-run the model using the new fires obtained from Step 2.

2 model runs are NOT required each time

Method

• LPJ-WM: Dynamic Vegetation Model tailored for High Latitudes.

• Includes organic soils and permafrost dynamics

Model

0.15 0.02 0.15 0.05

0.1 0.15 0.21 0.14

0.14 0.22 0.25 0.31

0.21 0.17 0.22 0.17

0.01 0.02 0.01 0.03

0.01 0.03 0.01 0.02

0.03 0.01 0.01 0.02

0.01 0.01 0.02 0.02

0.16 0.04 0.16 0.08

0.11 0.18 0.22 0.16

0.17 0.23 0.26 0.33

0.22 0.18 0.24 0.19

0.01

0.11

0.09 0.06

0.16 0.03 0.16 0.08

0.11 0.07 0.22 0.16

0.17 0.14 0.2 0.33

0.22 0.18 0.24 0.19

Assimilating CCL results in DVMs

Potential Fraction

(Year N-1)

Fraction of Area Burned

(Year N)

Potential Fraction(Year N)

Fires(Year N)

Potential Fraction(Year N)

Potential Fraction(Year N)

0.27

Randomly chosen CCL Fires

Randomly placed on grid

(Year N+1)0.16 0.04 0.16 0.07

0.11 0.18 0.22 0.16

0.17 0.23 0.26 0.33

0.22 0.18 0.24 0.19

Results: Assimilation

• Fire Return Interval remained largely unchanged in the boreal region bar sub-region boundaries.

• Fire Size Distribution now follows CCL fires.

Case Study

• Cover was re-established after 5 years.• Natural Vegetation advantageous over prescribed

cover to describe vegetation succession. • Biomass required 50 years to recover.• Region remained a strong sink for decades. No canopy interception/radiation balance in LPJ-WM!

Conclusions

• Kantzas E., Quegan S., Lomas M., Improving the representation of fire disturbance in Dynamic Vegetation Models by assimilating satellite data, Geoscientific Model Development, 2015 (under review)

• Kantzas E., Lomas M., Quegan S., Fire at high latitudes: Data-model comparisons and their consequences, Global Biochemical Cycles, 27, 677-691, 2013

• Dewar, M., Characterization and Evaluation of Aged 20Cr32Ni1Nb Stainless Steels, 2013

• Univ. of Washington, Computer Vision, 2000

1) CCL approach creates pool of individual fire events to be assimilated in Dynamic Vegetation Models.

2) The assimilation approach produced Fire Return Interval largely similar to the one the model originally produces.

3) Fire Sizes in the model becomes realistic.4) Method is model-independent.

Key Points

1) Evaluate model post-fire dynamics considering radiation effects and compare against field data.

2) Run future scenarios and evaluate fire effects on vegetation succession, soil temperatures and permafrost in a changing climate.

Next Steps

References