Assessing CO₂ sink/source strength of a degraded temperate peatland: atmospheric and hydrological drivers and responses to extreme events

Abstract

Monthly mean sink/source strength pattern of a degraded temperate peatland in northwestern Turkey was quantified using the eddy covariance technique between July 2010 and February 2014. Atmospheric and hydrological drivers and responses to extreme events of sink/source strength were analysed using multiple linear regression models, support vector machine, multilayer perceptron with principal component analysis and multiple comparisons following general linear models. On the basis of the three full‐year measurements, mean annual, growing season and maximum estimates of net ecosystem CO2 exchange (NEE) were −0.22 mg CO2 m−2 s−1, −0.28 mg CO2 m−2 s−1 and −0.11 mg CO2 m−2 s−1 in July, respectively. The main driver of changes in monthly mean NEE pattern was found to be latent heat flux. Independent validation results showed a similar predictive performance (r2 = 74%) of multiple linear regression and support vector machine models as a function of month, total rainfall, water table depth, relative humidity, latent heat, atmospheric water vapour, wind direction and downwelling shortwave radiation. A shift in NEE from a net sink to a net source was observed in response to the deepest and shallowest water table depths due to drought and flooding, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

The Estimation of Actual Evapotranspiration by the SEBAL Model Using the Landsat Satellite Images: A Case Study of Yenicaga, Bolu

“…Yeniçağa torf yatakları bakımından zengindir ve esas olarak nemli, soğuk ve besin açısından fakir koşullar altında, yeraltı suyunun etkisi olmaksızın oluşan bitkilerin lifli kalıntılarından oluşur. Akı kulesi 2010 yılında 2x2 km²lik bir turbalık üzerine kurulmuştur ve burada faaliyetlerini 2014 yılına kadar sürdürmüştür (Evrendilek, 2015). Şekil 2'de akı kulesinin çalışma alanı içerisindeki konumu yer almaktadır.…”

“…Akı kulesi karbon akısı toplamak amacıyla kurulduğu için toprak plakası içermemektedir. Bundan dolayı akı kulesinde Denklem (1)'de verilen terimlerden H, LE ve Rnet değişkenleri için gözlem toplanmasına rağmen G değişkeni için gözlem toplanmamıştır (Evrendilek, 2015). Ancak, ECOR sisteminde G, H, LE ve Rnet farklı sensörler ile birbirinden bağımsız toplanmaktadır.…”

Sphagnum mosses, the impact of disturbances and anthropogenic management actions on their ecological role in CO2 fluxes generated in peatland ecosystems

“…Despite the significant ecosystem services provided by peatlands, it is estimated that 15-20% of their global extent has experienced degradation processes due to anthropogenic disturbances (Joosten, 2009;Joosten & Clarke, 2002;Ma et al, 2022;Parish et al, 2008;Urák et al, 2017;Waddington et al, 2015), generating 5-10% of annual global anthropogenic CO 2 emissions (Friedlingstein et al, 2014;Loisel & Gallego-Sala, 2022). Human pressures have transformed the biogeochemical functionality of peatlands from a C sink in the long term to CO 2 emitters in the short term (Evrendilek, 2014;Munir et al, 2015;Page et al, 2022). Anthropogenic disturbances that directly affect Sphagnum mosses are related to drainage, commercial extraction, or removal of the moss as donor plant material for restoration of degraded peatlands, which have the potential to affect their ecological functionality and modify C dynamics.…”

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

“…Dou et al [22] used the ANN method to map different carbon and water fluxes in three Douglas-fir Stands in the Pacific Northwest based on long-term measurements and found that the use of ANN could be effective to determine the responses of three different carbon fluxes (GPP, R and NEE), evapotranspiration and water use efficiency to nitrogen fertilization. Evrendilek [23] compared the three different data-driven methods, including multiple linear regression, ANN and SVM, to estimate the influences of both atmospheric and hydrological drivers on monthly NEE using three years of measurements at a peatland site in northwestern Turkey and obtained satisfactory predictive accuracy.…”