Eddy covariance CO2 flux above a Gmelin larch forest on continuous permafrost in Central Siberia during a growing season

2008 ◽  
Vol 93 (3-4) ◽  
pp. 133-147 ◽  
Author(s):  
Y. Nakai ◽  
Y. Matsuura ◽  
T. Kajimoto ◽  
A. P. Abaimov ◽  
S. Yamamoto ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Co2 Flux ◽  
Growing Season ◽  
Larch Forest ◽  
Central Siberia ◽  
Continuous Permafrost

scholarly journals CH4 and N2O dynamics of a Larix gmelinii forest in a continuous permafrost region of central Siberia during the growing season

Polar Science ◽  
2014 ◽  
Vol 8 (2) ◽  
pp. 156-165 ◽  
Author(s):  
Tomoaki Morishita ◽  
Yojiro Matsuura ◽  
Takuya Kajimoto ◽  
Akira Osawa ◽  
Olga A. Zyryanova ◽  
...  
Keyword(s):  
Growing Season ◽  
Larix Gmelinii ◽  
Permafrost Region ◽  
Central Siberia ◽  
Continuous Permafrost ◽  
Ch4 And N2o

Exploring the variation in ecosystem scale methane fluxes and its drivers within a boreal mire complex

2021 ◽  
Author(s):  
Koffi Dodji Noumonvi ◽  
Joshua L. Ratcliffe ◽  
Mats Öquist ◽  
Mats B. Nilsson ◽  
Matthias Peichl
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Chemical Properties ◽  
Growing Season ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Footprint ◽  
Growing Season Length ◽  
Methane Fluxes ◽  
Northern Peatlands

<p>Northern peatlands cover a small fraction of the earth’s land surface, and yet they are one of the most important natural sources of atmospheric methane. With climate change causing rising temperatures, changes in water balance and increased growing season length, peatland contribution to atmospheric methane concentration is likely to increase, justifying the increased attention given to northern peatland methane dynamics. Northern peatlands often occur as heterogeneous complexes characterized by hydromorphologically distinct features from < 1 m² to tens of km², with differing physical, hydrological and chemical properties. The more commonly understood small-scale variation between hummocks, lawns and hollows has been well explored using chamber measurements. Single tower eddy covariance measurements, with a typical 95% flux footprint of < 0.5 km², have been used to assess the ecosystem scale methane exchange. However, how representative single tower flux measurements are of an entire mire complex is not well understood. To address this knowledge gap, the present study takes advantage of a network of four eddy covariance towers located less than 3 km apart at four mires within a typical boreal mire complex in northern Sweden. The variation of methane fluxes and its drivers between the four sites will be explored at different temporal scales, i.e. half-hourly, daily and at a growing-season scale.</p>

scholarly journals Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

2019 ◽  
Vol 11 (6) ◽  
pp. 649 ◽  
Author(s):  
Koffi Noumonvi ◽  
Mitja Ferlan ◽  
Klemen Eler ◽  
Giorgio Alberti ◽  
Alessandro Peressotti ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Information Criterion ◽  
Growing Season ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Eddy Covariance Method

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

Seasonal and spatial variability of elemental concentrations in boreal forest larch foliage of Central Siberia on continuous permafrost

2012 ◽  
Vol 113 (1-3) ◽  
pp. 435-449 ◽  
Author(s):  
J. Viers ◽  
A. S. Prokushkin ◽  
O. S. Pokrovsky ◽  
Y. Auda ◽  
A. V. Kirdyanov ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Boreal Forest ◽  
Central Siberia ◽  
Elemental Concentrations ◽  
Continuous Permafrost
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