Mapping of Taiga Ecosystems Evapotranspiration by Using Results of EOS and Flux Tower observations

Transcription

1 Mapping of Taiga Ecosystems Evapotranspiration by Using Results of EOS and Flux Tower observations Victor Gornyy, Sergei Kritsuk, Iscander Latypov Scientific Research Centre for Ecological Safety by Russian Academy of Sciences Saint-Petersburg, Russia 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, Brno Titta Majasalmi Department of Forest Sciences, P.O. Box 27, FI University of Helsinki, Finland 2016

2 Why Evaporation? 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, 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 ).

3 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) 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)

4 Australian Method Major Relations 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 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.

5 Shortcomings of the Australian method 1. The method is attractive, but the significant portion of Russia and Finland situated 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, as an 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 transfer of 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 conservative to windflaws and soil heat flux can be obtained as solution via the thermal inertia.

6 Project 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 IRthermal 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.

7 Methods The Thermal Inertia Approach for Daily Averaged Evaporation Mapping Algorithm of remote mapping of the heat flow, the thermal inertia, and the evaporation The thermal inertia a physical variable describing the impedance to heating. P=(lcr) 1/2 - thermal inertia, UTI; l - thermal conductivity, W/(m*K); c - thermal capacity, J/(kg*K); r - density, kg/m 3. Initial data: * multiple IR-thermal satellite flown survey; * round o clock meteorological observations; Resulting maps: the heat flow, W/m 2 ; the thermal inertia, UTI; evaporation rate, mm/day etc. Diurnal surface temperature variations High thermal inertia Low thermal inertia Watson K. Geothermal reconnaissance from quantitative thermal infrared images. 9-th Symp. Remote Sensing of Environment, Univ. of Michigan, pp

8 Data Terra/Aqua(MODIS) visible (albedo) & IR-thermal spectral band data Satellite data Satellite surveys of land surface daily temperature variations Satellite Date, dd.mm.yyyy Local Time, hh:mm Type of orbit Aqua 03:25 Nadir Terra 13:04 Nadir Terra 22:30 Neighboring Aqua 02:34 Neighboring Terra :55 Neighboring Terra 21:34 Nadir Aqua 03:15 Nadir Aqua :40 Nadir Terra 22:15 Neighboring Aqua :19 Neighboring The standard product (MOD11_L2; MYD11_L2; MOD05_L2) were used for geometrical corrections.

9 Data Object of investigation: Taiga Biome ecosystems of Leningrad Oblast of Russia and Finland Investigating territory Flux towers: 1 Siikaneva 2 Hyytiala 3 Kumpula 4 - Torni Finland Russia 3 Gulf of Finland Saint-Petersburg Estonia

10 Hyytiala flux tower 2 Data Siikaneva flux tower Marshland 1 Forest 2

11 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; where: G heat flux to the soil; R far IR radiation budget; H = k * r * c * DT/Dh where: DT = T surf T air, - temperature difference between the surface and air on the height Dh; k heat exchange (turbulent) coefficient ; r, c air density and heat capacity; LE = D*r*L*De/p a Dh De = e surf e air ; vapor partial pressure difference between the surface and air on the height Dh; D mass exchange (turbulent diffusion) coefficient; p a air pressure L specific latent heat; r air density;

12 Shortwave solar radiation, W/m 2 Heat, W/m 2 Elements (fluxes) of the Surface Energy Budget Hyytiala Flux Tower (Finland) Flux, W/m : : : : : :00 Time Shortwave solar radiation. - Sensible heat flux. - Latent heat flux.

13 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 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.

14 H = k * r * c * DT/Dh Modified Equations where: DT = T surf T air, - temperature difference between the surface and air on the height Dh; r and c air density and heat capacity; LE = D*r*L*De/p a Dh De = e surf e air - vapor partial pressure difference between the surface and air on the height Dh; p a air pressure; L specific latent heat; k = g * (S + W) - heat exchange coefficient; D = c * (S + W) mass exchange coefficient; g heat exchange factor (model parameter) c water saturation (model parameter) W wind speed; S shortwave solar radiation. G t (P) = S t * (1-A) +H t (g) + LE t (c) + R t 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

15 Latent heat flux, W/m 2 Simulated Forest Latent Heat Flux Hyytiala flux tower latent heat flux, W/m 2 Measured latent heat flux, W/m 2 - measured - simulated Measured on Hyytiala flux tower Modeled data for Hyytiala : : : : : : : : :00 Time

16 Sensible heat flux, Wm 2 Simulated Forest Sensible Heat Flux Hyytiala flux tower sensible heat flux, W/m 2 Sensible heat flux, modeled for Hyytiala, W/m y = x R 2 = Measured sensible heat flux, tower, W/m W/m 2 2 Sensible heat flux, measured on Hyytiala flux - measured - simulated Measured on Hyytiala flux tower Modeled data for Hyytiala : : : : : : : : :00 Time

17 Latent heat flux, W/m 2 Marshland Latent Heat Flux Latent heat flux, modeled for Siikaneva, latent heat W/m flux, 2 W/m 2 Simulated Siikaneva flux tower y = x R 2 = Measured Latent heat flux, latent measured heat on flux, Siikaneva W/m flux 2 tower, W/m measured - simulated Measured on Siikaneva flux tower Modeled data for Siikaneva : : : : : : : : :00-50 Time

18 Sensible heat flux, W/m 2 Simulated Marshland Sensible Heat Flux Siikaneva flux tower Sensible heat flux, modeled for Siikaneva, W/m 2 sensible heat flux, W/m Measured on Siikaneva flux tower Modeled data for Siikaneva y = x R 2 = Measured sensible heat flux, W/m 2 Sensible heat flux, measured on Siikaneva flux tower, W/m 2 - measured - simulated : : : : : : : : :00 Time

19 Accuracy of Land Surface Temperature Simulation The same mass exchange coefficient: D = c * (S + W) was used for the forest and the marshland!!! Eucalyptus wet sclerophyll forest Wet/dry tropical savanna

20 Map of Water Saturation 27-29, June 2010 Russia&Finland border Helsinki Gulf of Finland Saint- Petersburg Normalized saturation Russia&Estonia border

21 Map of Latent Heat Flux Degradation June 2010 Russia&Finland border Helsinki Gulf of Finland Saint- Petersburg Rate of the latent heat flux change, W/m 2 /day Russia&Estonia border

22 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.