Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

Hagen, Stephen; Braswell, B. H.; Linder, Ernst; Frolking, S. E.; Richardson, Andrew D.; Hollinger, David Y.

doi:10.1029/2005jd006154

Cited by 91 publications

(77 citation statements)

References 25 publications

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“…As shown in Tables 1 and 2, the number of EC stations becomes very large if it is necessary to detect very small seepage rates at large amplification factors () above background fluxes. Note that the number of EC towers in Tables 1 and 2 as low as 2.7 mol CO 2 m -2 s -1 (10 mol C m -2 s -1 ) above background respiration CO 2 exchanges (Miles et al, 2005;Hagen et al, 2006). Finally, the numbers derived in this analysis should be considered valid only in an order-of-magnitude sense and subject to change depending on local site-specific properties and processes.…”

Section: Spatial Supportmentioning

confidence: 99%

“…In recent years, ANNs have found widespread application in the hydrological literature, especially as regression tools (Govindaraju and Rao, 2000). ANNs have also been used to build a model for water vapor and carbon exchange in a forest ecosystem, which does not require a detailed knowledge of tree physiology (Hagen et al, 2006).…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

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The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Cortis

Oldenburg

Benson

2008

International Journal of Greenhouse Gas Control

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Storage of large amounts of carbon dioxide (CO 2 ) in deep geological formations for greenhousegas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this work we explore various optimization strategies for characterizing surface leakage (seepage) using near-surface measurement approaches such as accumulation chambers and eddy covariance towers. Seepage characterization objectives and limitations need to be defined carefully from the outset especially in light of large natural background variations that can mask seepage. The cost and sensitivity of seepage detection are related to four critical length scales pertaining to the size of the: (1) region that needs to be monitored; (2) footprint of the measurement approach, and (3) main seepage zone; and (4) region in which concentrations or fluxes are influenced by seepage. Seepage characterization objectives may include one or all of the tasks of detecting, locating, and quantifying seepage. Each of these tasks has its own optimal strategy. Detecting and locating seepage in a region in which there is no expected or preferred location for seepage nor existing evidence for seepage requires monitoring on a fixed grid, e.g., using eddy covariance towers. The fixed-grid approaches needed to detect seepage are expected to require large numbers of eddy covariance towers for large-scale geologic CO 2 storage. Once seepage has been detected and roughly located, seepage zones and features can be optimally pinpointed through a dynamic search strategy, e.g., employing accumulation chambers and/or soil-gas sampling. Quantification of seepage rates can be done through measurements on a localized fixed grid once the seepage is pinpointed. Background measurements are essential for seepage detection in natural ecosystems. Artificial neural networks are considered as regression models useful for distinguishing natural system behavior from anomalous behavior suggestive of CO 2 seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO 2 fluxes and concentrations in

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Section: Spatial Supportmentioning

confidence: 99%

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Cortis

Oldenburg

Benson

2008

International Journal of Greenhouse Gas Control

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“…The simulation was repeated 2000 times and the uncertainty of the measured annual NEP or ET were estimated by calculating the 90% prediction limits or standard deviation of all simulated annual flux values. Similarly, the random uncertainties for annual GEP and TER were evaluated following a Monte Carlo algorithm detailed by Hagen et al (2006). The algorithm infers the statistical properties of the random error from the residuals of the model for gap-filling and flux partitioning.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Dynamics and biophysical controls of carbon, water and energy exchange over a semiarid shrubland in northern China

Jia¹

2017

Diss. For.

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To be presented, with the permission of the Faculty of Science and Forestry of the University of Eastern Finland, for public examination in auditorium BOR 100 in borealis building on Joensuu campus of the University of Eastern Finland, Yliopistokatu 7, Joensuu on 23 May 2017 at 12 o' clock noon. ABSTRACTThe main aim of this study was to investigate the dynamics and biophysical controls of carbon, water and energy exchange over a semiarid shrub ecosystem in the Mu Us desert, northern China, using continuous eddy-covariance (EC) measurements. Specific objectives were as follows: (1) To examine intra-annual variations in net ecosystem CO 2 exchange (NEE) and its biophysical controls (Paper I); (2) To quantify the diurnal and seasonal variations in surface energy-balance components, and to examine the partitioning of net radiation (R n ) among different energy components at diurnal and seasonal timescales (Paper II); and (3) To examine how ecosystem production and water use efficiency (WUE) vary inter-annually with contrasting precipitation (PPT) and soil moisture patterns (Paper III).The results showed that, soil water content (i.e. at 30 cm depth, SWC_30), or water deficit, imposed a major control on the seasonal dynamics of carbon assimilation and energy partitioning. Water deficit (i.e. SWC_30 < 0.10 m 3 m -3 ) was a major constraint over daytime NEE, and also interacted with other stresses, e.g. heat stress and photoinhibition (Paper I). Low soil moisture reduced the temperature sensitivity (Q 10 ) of total ecosystem respiration (TER). Rain events triggered immediate pulses of carbon release from the ecosystem, followed by peaks of CO 2 uptake 1-2 days later. Leaf area index (LAI) accounted for 45 and 65% of the seasonal variation in NEE and gross ecosystem production (GEP), respectively. On the other hand, sensible heat flux (H) exceeded latent heat flux (λE) during most time of the year (Paper II). The evaporative fraction (EF, i.e. λE/R n ), Priestley-Taylor coefficient (α), surface conductance (g s ) and decoupling coefficient (Ω) all correlated positively with SWC_30 and LAI. The direct enhancement of λE by high vapor pressure deficit (VPD) was buffered by a concurrent suppression of g s , which controlled EF and α by mediating the effects of LAI, SWC_30 and VPD.At the annual scale, net ecosystem production (NEP, here defined as −NEE) indicated a rapid shift from an annual sink of carbon in 2012 (NEP = 77 ± 10 g C m -2 yr -1 ) to a source of carbon in 2014 (NEP = -22 ± 5 g C m -2 yr -1 ), with the year 2013 being close to carbon neutral (NEP = -4 ± 10 g C m -2 yr -1 ) (Paper III). GEP, TER and evapotranspiration (ET) also declined over the three years. Suppressed annual carbon and water fluxes were observed in years with low spring soil moisture. GEP declined more than TER and ET, leading to reduced carbon sequestration and WUE (i.e. GEP/ET). Neither annual nor growing-season PPT amount could explain the year-to-year variation in carbon fluxes. ET was a better proxy for water available to ecosystem carbon...

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“…Five eddy flux sites with high quality of data are selected in this study, covering the major representative PFTs over the conterminous U.S. (Table 1). Specifically, Howland Forest main (45.20N, 68.74W) (Hagen et al 2006.17 W) (Urbanski et al 2007, van Gorsel et al 2009.95 W) (Baldocchi et al 2004, Baldocchi et al 2005, Lost Creek (46.08 N, 89.98 W) (Yuan et al 2007, Sulman et al 2009) and UCI_1850 (55.88 N, 98.48 W) (Bond-Lamberty et al 2005, Goulden et al 2011) are used to parameterize for temperate coniferous forest, temperate deciduous forest, grassland, shrub land, and boreal forest, respectively. The optimal parameters are extrapolated to the conterminous United States.…”

Section: Terrestrial Ecosystem Model (Tem) and Datamentioning

confidence: 99%

“…(1) Using site-level Ameriflux data (Baldocchi et al 2004, Bond-Lamberty et al 2005, Hagen et al 2006, Urbanski et al 2007, Sulman et al 2009) to parameterize TEM for each PFT and extrapolate the optimal parameters to the region; (2) Using MODIS GPP product (Running et al 2004, Zhao et al 2005, Heinsch et al 2006, Zhao et al 2006, Mu et al 2007) to parameterize TEM for each grid cell (0.58 by 0.58) and use parameters to simulate the carbon budget over the conterminous U.S. Both methods are based on a welldeveloped adjoint version of TEM (Q. Zhu and Q. Zhuang, unpublished manuscript).…”

Section: Introductionmentioning

confidence: 99%

Improving the quantification of terrestrial ecosystem carbon dynamics over the United States using an adjoint method

Zhu

Zhuang

2013

Ecosphere

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Citation: Zhu, Q., and Q. Zhuang. 2013. Improving the quantification of terrestrial ecosystem carbon dynamics over the United States using an adjoint method. Ecosphere 4(10):118. http://dx.doi.org/10.1890/ES13-00058.1Abstract. We used an adjoint version of the process-based Terrestrial Ecosystem Model (TEM) to optimize the model parameters in a spatially explicit manner by assimilating satellite-based estimates of gross primary production (GPP) in the conterminous United States. Traditionally, terrestrial ecosystem model parameterization is conducted at site-level for various plant functional types (PFTs). The optimal parameters are then extrapolated to regions that have the same plant functional types as parameterized. However, site-level parameterization might be only valid within a few kilometers in the footprint of the individual site. Extrapolation of the optimal parameters to a region with an assumption that the parameters are the same for all pixels for the same ecosystem type in the region may introduce significant errors in quantification of regional carbon dynamics. This study used Moderate Resolution Imaging Spectroradiometer GPP to optimize parameters for each pixel using adjoint method in a spatially explicit manner. The spatially explicit parameters were then used to quantify the regional carbon fluxes from 2000 to 2005. The estimated net ecosystem production (NEP) was used to drive a global atmospheric transport model, GEOSChem, to estimate the near surface atmospheric CO 2 concentrations, which were then compared with flask measurements. We found that (1) the site-level TEM parameterization method provided good estimates of carbon fluxes at site levels, but had a large uncertainty in the regional simulations; (2) the spatially explicit parameterization improved the estimates of the spatial distribution and seasonal variation of regional carbon dynamics; and (3) when driven with the NEP estimated with the spatially explicit model, the GEOSChem captured the seasonal trend of near-surface CO 2 concentrations better than that was driven with the estimates based on the site-level parameterized model. This study suggested that future quantification of regional carbon dynamics should consider the spatial variation of parameters, which could be optimized using spatially explicit carbon flux data.

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Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

Cited by 91 publications

References 25 publications

The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Dynamics and biophysical controls of carbon, water and energy exchange over a semiarid shrubland in northern China

Improving the quantification of terrestrial ecosystem carbon dynamics over the United States using an adjoint method

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