2012
DOI: 10.1111/j.1365-2486.2012.02720.x
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Carbon dioxide exchange over multiple temporal scales in an arid shrub ecosystem near La Paz, Baja California Sur, Mexico

Abstract: Arid environments represent 30% of the global terrestrial surface, but are largely under-represented in studies of ecosystem carbon flux. Less than 2% of all FLUXNET eddy covariance sites exist in a hot desert climate. Long-term datasets of these regions are vital for capturing the seasonal and interannual variability that occur due to episodic precipitation events and climate change, which drive fluctuations in soil moisture and temperature patterns. The objectives of this study were to determine the meteorol… Show more

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Cited by 40 publications
(34 citation statements)
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References 58 publications
(78 reference statements)
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“…The NEE data were gap‐filled using meteorological data available during the time period when these values were missing or unusable. The missing values were calculated by averaging data under similar meteorological conditions using a moving look‐up table with flexible window sizes (Bell et al, 2012). Meteorological conditions were considered similar when the air temperature ( T air ), vapor pressure deficit (VPD), and irradiance did not deviate by more than 2.5°C, 0.5 kPa, and 50 W m –2 , respectively.…”
Section: Methodsmentioning
confidence: 99%
“…The NEE data were gap‐filled using meteorological data available during the time period when these values were missing or unusable. The missing values were calculated by averaging data under similar meteorological conditions using a moving look‐up table with flexible window sizes (Bell et al, 2012). Meteorological conditions were considered similar when the air temperature ( T air ), vapor pressure deficit (VPD), and irradiance did not deviate by more than 2.5°C, 0.5 kPa, and 50 W m –2 , respectively.…”
Section: Methodsmentioning
confidence: 99%
“…The increasing number of eddy-covariance sites across the globe has encouraged the application of data-driven models by machine learning (ML) methods such as artificial neural networks (ANNs, Papale and Valentini, 2003), random forest (RF, Tramontana et al, 2015), model trees ensemble (MTE, Jung et al, 2009;Xiao et al, 2008Xiao et al, , 2010 or support vector regression (SVR, Yang et al, 2006Yang et al, , 2007 to estimate land surface-atmosphere fluxes from site level to regional or global scales (e.g., Beer et al, 2010Kondo et al, 2015;Schwalm et al, 2010Schwalm et al, , 2012Yang et al, 2007;Xiao et al, 2008Xiao et al, , 2010. The ML upscaled outputs are also increasingly used to evaluate process-based land surface models (e.g., Anav et al, 2013;Bonan et al, 2011;Ichii et al, 2009;Piao et al, 2013).…”
Section: Introductionmentioning
confidence: 99%
“…Ecosystem flux results quantifying net ecosystem production of carbon dioxide (NEP) point to a more rapid and less lagged response in semiarid grasslands than shrublands or savannas to fluctuations in water availability, possibly due to a faster turnover of herbaceous biomass pools [ Emmerich and Verdugo , ; Kurc and Small , ; Scott et al ., , ]. Moreover, the NEP of many different semiarid ecosystem types such as shrublands [ Luo et al ., ; Petrie et al ., ], grasslands [ Ma et al ., ; Scott et al ., ], savannas [ Scott et al ., ], and desert scrub [ Bell et al ., ] throughout southwestern North America show high sensitivity to precipitation fluctuations with a tendency for being sources of carbon dioxide to the atmosphere during the dry years and carbon sinks in wet years. This implies a precipitation “pivot point” where the net carbon exchange for semiarid ecosystems may pivot between source and sink behavior.…”
Section: Introductionmentioning
confidence: 99%