Victor Gornyy, Sergei Kritsuk, Iscander Latypov

Scientific Research Centre for Ecological Safety by Russian Academy of Sciences

Saint-Petersburg, Russia

E-mail: v.i.gornyy@mail.ru

V.I.Gornyy, S.G.Kritsuk, I.Sh.Latypov, O.V. Brovkina

Global Change Research Centre, Academy of Sciences

of the Czech Republic, Beˇlidla 986/4a,

603 00 Brno

Titta Majasalmi

Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki,

Finland

2016

Mapping of

Taiga Ecosystems Evapotranspiration

by Using Results of

EOS and Flux Tower observations

1. Evaporation is linearly related to the carbon deposition in biomass.

(Jorgensen S. E., Svirezhev Yu.M. Towards a Thermodynamic Theory

for Ecological Systems // Oxford: Elsevier, 2004. - 366 p.)

2. Evaporation map uses for the thermodynamic index of ecosystem

health disturbance (TIEHD) compilation.

(Victor I. Gornyy, Sergei G. Kritsuk, Iscander Sh. Latypov. Remote

Mapping of Thermodynamic Index of Ecosystem Health Disturbance //

Journal of Environmental Protection, 2010, 1, pp. 242-250).

Why Evaporation?

State of Art

Helen A. Cleugh, Ray Leuning, Qiaozhen Mu, Steven W. Running. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment 106 (2007) 285–304.

Maps of the Monthly Evapotranspiration of Australia

On the base of satellite

IR-thermal survey & flux tower

On the base of 30-year

climatology (1961 to 1990)

Australian Method

Time series of measured and simulated:

heat (H) and evaporation (λE) fluxes

Data are smoothed 8-day averages

from 2001 to 2005 and are for the

period 10:00–11:00 AM only

Major Relations

Main idea: - to use flux towers to measure aerodynamic resistance and for the moment

of satellite flight to extend the results to the total investigating territory with help of

Terra/Aqua(MODIS) satellite IR-thermal data.

Shortcomings of the Australian method

1. The method is attractive, but the significant portion of Russia and Finlandsituated in the another geographical zone - Taiga Biome.

2. The traditional theory of boundary layer heat transfer is correct for arid and semi-arid regions and incorrect for the forested territories, because vegetation, asan alive object, regulates it’s own temperature by an evapotranspiration.

3. It is not possible to map precisely a daily averaged evaporation, because the evaporation regime of forest is different during day and night. More over, each windflaw distorts results.

4. The heat flux into the soil must be determined empirically.

Conclusions:

1) The traditional equations, based on the theory of boundary layer heat transferof homogeneouse semi-infinite space, has to be modified for the Taiga Biome.

2) It is better to apply the thermal inertia approach, because it provides possibility to measure the daily averaged evaporation. This approach more conservativeto windflaws and “soil” heat flux can be obtained as solution via the thermal inertia.

Target: the method of daily averaged evaporation rate mapping by using

EOS and flux tower data, applied for forest ecosystems

Subject of investigation: a surface energy budget of the Taiga Biome ecosystems

Objectives:

• To develop new equations of energy exchange, suitable for a forest cover.

• To simulate elements of the surface energy budget of forest ecosystems by using

new equations and flux tower data.

• To verify the forest cover temperatures, retrieved from satellite data by outgoing IR-

thermal irradiance, measured by the flux tower.

• To map daily averaged evaporation of territory of Leningrad Oblast’ and Finland on

the base of flux towers and EOS data.

Project

The Thermal Inertia Approach for Daily Averaged Evaporation Mapping

Algorithm of remote mapping

of the heat flow, the thermal inertia,

and the evaporation

Initial data:

* multiple IR-thermal

satellite flown survey;

* round o’clock meteorological

observations;

Resulting maps:

•the heat flow, W/m2;

•the thermal inertia, UTI;

•evaporation rate, mm/day

•etc.

Watson K. Geothermal reconnaissance from quantitative thermal infrared images.

9-th Symp. Remote Sensing of Environment, Univ. of Michigan, 1974. pp. 1919 - 1932.

P=(lcr)1/2 - thermal inertia, UTI;

l - thermal conductivity, W/(m*K);

c - thermal capacity, J/(kg*K);

r - density, kg/m3.

Diurnal surface temperature

variationsThe thermal inertia – a physical variable

describing the impedance to heating.

Low

thermal

inertia

High

thermal

inertia

Methods

Data

Satellite surveys of land surface

daily temperature variations

Terra/Aqua(MODIS) visible (albedo) & IR-thermal spectral band data

Satellite Date,

dd.mm.yyyy

Local

Time,

hh:mm

Type of orbit

Aqua

03.07.2010

03:25 Nadir

Terra 13:04 Nadir

Terra 22:30 Neighboring

Aqua

04.07.2010

02:34 Neighboring

Terra 11:55 Neighboring

Terra 21:34 Nadir

Aqua

05.07.2010

03:15 Nadir

Aqua 12:40 Nadir

Terra 22:15 Neighboring

Aqua 06.07.2010 02:19 Neighboring

Satellite data

The standard product (MOD11_L2;

MYD11_L2; MOD05_L2) were used for

geometrical corrections.

1 2

3

4

Flux towers:

1 – Siikaneva

2 – Hyytiala

3 – Kumpula

4 - Torni

Object of investigation: Taiga Biome ecosystems of Leningrad Oblast’ of

Russia and Finland

Saint-Petersburg

Finland

Russia

Estonia

Gulf of Finland

Data

Investigating territory

Data

Marshland

Siikaneva flux tower

Forest

Hyytiala flux tower 2

1

2

DT = Tsurf – Tair, - temperature difference

between the surface and air on the height

Dh;

k – heat exchange (turbulent) coefficient ;

r, c – air density and heat capacity;

De = esurf– eair; vapor partial pressure

difference between the surface and

air on the height Dh;

D – mass exchange (turbulent

diffusion) coefficient;

pa – air pressure

L – specific latent heat;

r – air density;

LE = D*r*L*De/paDhH = k * r * c * DT/Dh

Surface Energy Budget

Major ralations

S * (1-A) = H + LE + G + R

S – shortwave solar radiation;

А – surface albedo;

Н – sensible heat flux;

LE – latent heat flux;

G – heat flux to the soil;

R – far IR radiation budget;

where:

where:

Elements (fluxes) of the Surface Energy Budget Hyytiala Flux Tower (Finland)

- Shortwave solar radiation.

- Sensible heat flux.

- Latent heat flux.

-100

0

100

200

300

400

500

600

700

800

900

01.07.10 0:00 02.07.10 1:00 03.07.10 2:00 04.07.10 3:00 05.07.10 4:00 06.07.10 5:00

Time

Sh

ort

wave s

ola

r ra

dia

tio

n,

W/m

2

-50

0

50

100

150

200

250

300

350

400

450

Heat,

W/m

2

1 2 3

Flu

x, W

/m2

Correlations of Sensible Heat Flux and

Meteorological Characteristics

Characteristics

Flux Tower

(Hyytiala)

Shortwave solar radiation 0.95

Air temperature 0.63

Surface and air temperature difference 0.86

Air temperature standard deviation 0.82

Wind speed 0.44

Wind speed standard deviation 0.76

Vapor partial pressure -0.16

Conclusion:

• the shortwave solar radiation and the wind speed was chosen for heuristic

equation design.

• a wind speed provides possibility to separate the sensible heat flux from the

latent heat flux during night time.

DT = Tsurf – Tair, - temperature difference

between the surface and air on the height Dh; r

and c – air density and heat capacity;

De = esurf– eair - vapor partial pressure

difference between the surface and air on

the height Dh; pa – air pressure; L – specific

latent heat;

LE = D*r*L*De/paDhH = k * r * c * DT/Dh

Modified Equations

where:

g – heat exchange factor

(model parameter)

W – wind speed; S – shortwave solar radiation.

c – water saturation

(model parameter)

k = g * (S + W) - heat exchange

coefficient;

D = c * (S + W) mass exchange

coefficient;

Gt(P) = St * (1-A) +Ht (g) + LEt (c) + Rt

P – thermal inertia; t – time.

Solution of this system of equations regarding P, c, G was done by using method

of impulse response function (Jaeger J.C. Pulsed surface heating of a semi-infinite

solid. Quat. of Applied Mathematics. Vol. XI, 1953, pp. 132-137.

0

50

100

150

200

250

300

350

6.26.10 12:00 6.27.10 0:00 6.27.10 12:00 6.28.10 0:00 6.28.10 12:00 6.29.10 0:00 6.29.10 12:00 6.30.10 0:00 6.30.10 12:00

Time

Late

nt

heat

flu

x, W

/m2

Measured on Hyytiala flux tower

Modeled data for Hyytiala

Forest

Latent Heat Flux

Hyytiala flux tower

- measured

- simulated

Measured latent heat flux, W/m2

Sim

ula

ted

late

nt

he

at

flu

x,

W/m

2

y = 0.6877x - 16.673

R2 = 0.9247-100

-50

0

50

100

150

200

250

300

-100 -50 0 50 100 150 200 250 300 350 400

Sensible heat flux, measured on Hyytiala flux

tower, W/m2

Sen

sib

le h

eat

flu

x, m

od

ele

d f

or

Hyyti

ala

, W

/m2

-100

-50

0

50

100

150

200

250

300

350

400

6.26.10 12:00 6.27.10 0:00 6.27.10 12:00 6.28.10 0:00 6.28.10 12:00 6.29.10 0:00 6.29.10 12:00 6.30.10 0:00 6.30.10 12:00

Time

Sen

sib

le h

eat

flu

x, W

m2

Measured on Hyytiala flux tower

Modeled data for Hyytiala

Forest

Sensible Heat Flux

- measured- simulated

Measured sensible heat flux,

W/m2

Sim

ula

ted

se

nsib

le h

ea

t flu

x,

W/m

2

Hyytiala flux tower

-50

0

50

100

150

200

250

300

350

6.26.10 12:00 6.27.10 0:00 6.27.10 12:00 6.28.10 0:00 6.28.10 12:00 6.29.10 0:00 6.29.10 12:00 6.30.10 0:00 6.30.10 12:00

Time

Late

nt

heat

flu

x, W

/m2

Measured on Siikaneva flux tower

Modeled data for Siikaneva

Marshland

Latent Heat Flux

y = 1.1255x - 0.4326

R2 = 0.9035

-50

0

50

100

150

200

250

300

350

400

-50 0 50 100 150 200 250 300 350

Latent heat flux, measured on Siikaneva flux

tower, W/m2

Late

nt

heat

flu

x, m

od

ele

d f

or

Siikan

eva,

W/m

2

Siikaneva flux tower

- measured

- simulated

Sim

ula

ted

late

nt

he

at

flu

x,

W/m

2

Measured latent heat flux, W/m2

Marshland

Sensible Heat Flux

-50

-30

-10

10

30

50

70

90

110

130

150

6.26.10 12:00 6.27.10 0:00 6.27.10 12:00 6.28.10 0:00 6.28.10 12:00 6.29.10 0:00 6.29.10 12:00 6.30.10 0:00 6.30.10 12:00

Time

Sen

sib

le h

eat

flu

x, W

/m2

Measured on Siikaneva flux tower

Modeled data for Siikaneva

y = 0.7621x - 3.002

R2 = 0.7801

-60

-40

-20

0

20

40

60

80

100

120

140

-100 -50 0 50 100 150

Sensible heat flux, measured on Siikaneva

flux tower, W/m2

Se

ns

ible

hea

t fl

ux

, m

od

ele

d f

or

Siik

an

ev

a, W

/m2

Siikaneva flux tower

Measured sensible heat flux,

W/m2

Sim

ula

ted

sensib

le h

eat

flux,

W/m

2

- measured- simulated

Accuracy of Land Surface Temperature Simulation

D = c * (S + W)

The same mass

exchange coefficient:

was used for the

forest and

the marshland !!!

Wet/dry tropical

savanna

Eucalyptus wet

sclerophyll forest

Map of Water Saturation27-29, June 2010

0 0.5 1

Normalized saturation

Gulf of Finland

Saint-

PetersburgHelsinki

Russia&Estonia

border

Russia&Finland

border

-24 -12 0

Rate of the latent heat flux

change, W/m2/day

Map of Latent Heat Flux Degradation 27-29 June 2010

Gulf of FinlandHelsinki

Russia&Estonia

border

Russia&Finland

border

Saint-

Petersburg

Conclusions

• The new heuristic formulas, based on flux time series analyses, provide

possibility to simulate surface temperature of Taiga Biome ecosystems more

precisely than the traditional equations in desert conditions of Australia.

• The same heuristic mass exchange coefficient can applied for the scrubland &

marshlands.

• A water saturation map reflects differences of evapotranspiration

between ecosystem types such as forest, marsh, agricultural and urban areas.

• Scrublands, located near the border of Finland and Russia, as well as

Russia and Estonia are characterized by the different level of evapotranspiration.

This phenomenon may be caused by different forest management systems in these

countries.