Carbon flux was measured via EC methods at the Joseph Jones Ecological Research Center (JJERC), located in southeast Georgia (USA). ⢠JJERC has 3 EC ...
Preserving the variance of long-term eddy-covariance measurements using parameter prediction in gap filling Sujit
1 Kunwor ,
Christina L.
1 Staudhammer ,
Gregory
1 Starr ,
Henry W.
2 Loescher
1University
of Alabama, Tuscaloosa, AL 1National Ecological Observatory Network (NEON), Boulder, CO
Background
Table 1. Gap-filling Methods used
Objectives • To understand variation of environmental variables and variation of model parameters in light and temperature response curves • To improve existing methods of gap-filling eddy flux data by preserving variance structures inherent in the heteroscadastic flux data
#
Methods
Description
1. Traditional(TRAD) 2. Moving Window (MW) 3. Parameter Prediction (PP)
Model parameters calculated using data from each month individually Model parameters calculated using moving window using data 15 days before and after any day of interest Moving window and linearized combination of environmental variables used as model parameters
• Artificial gaps varying lengths were introduced to a base dataset : ½ hour; 5 consecutive hours; 12 consecutive hours; 24 consecutive hours; Combination • Three gap filling approaches (Table 1) were used to impute missing measurements • Variance and first-degree autocorrelation coefficient (AR(1)) were calculated at longer gap lengths (5hr and 12hr) for observed and gap-filling methods • Root mean square error (RMSE) was calculated at longer gap lengths for all gap-filling methods
Results
Methods
• Temperature response curve: NEEnight Reco R0 e bTair
A
Pmax (µmol CO2 m-2 s-1)
(mol CO2 mol quanta-1)
• where: is the apparent quantum efficiency (-µmol CO2 µmol quanta-1), is PAR (µmol quanta), Rd is ecosystem respiration (µmol CO2 m-2 s-1), Amax is the maximum ecosystem CO2 uptake rate (µmol CO2 m-2 s-1), R0 is the base respiration rate when air temperature is 0C, and b is an empirical coefficient3 • Multiple regression models were used to analyze and quantify the relationship between estimated light and temperature response parameters and 30- day running mean values of the environmental variables
C
Fig. 5 Nighttime observed net ecosystem exchange of CO2 , NEEnight (black), and predicted NEEnight using different gap-filling methdos for days of year (DOY) 175-250 (Summer).
B
PAR (µmol m-2 s-1) Fig. 2. Observed and predicted daytime net ecosystem exchange of CO2 NEEday using gap-filling methods as function of photosynthetically active radiation (PAR).
VPD (kPa) D
• Parameter prediction method had lower error and higher variance • The autocorrelation coefficient is higher versus the observed data in all three methods across different gap lengths • Varying the length of moving window, investigating parameter-environmental variables relationship and adding/removing some variables will potentially reduce autocorrelation of predicted values
Future Directions
Tair (oC)
• Further investigating response pattern of parameters to VPD, VWC, Tair as well as roles of additional variables such as wind speed, friction velocity and precipitation in the parameter prediction models • Investigating potential factors causing lower variance and higher autocorrelation of predicted values • Using orthonormal wavelets to analyze variability of flux and meteorological variables at different time scales
Tair (oC)
Fig 1. Apparent quantum efficiency (a) as a function of vapor pressure deficit (VPD), (A); maximum photosynthetic capacity (Pmax) as a function of vapor pressure deficit (VPD), (B), daytime respiration (Rd) as a function of air temperature (Tair),(C) and base respiration (Ro) as a function of air temperature (Tair), (D).
References
• A linearized combination of these environmental variables was used to replace the parameters in light response and temperature response curves • Selected models:
NEEday
Table 2. Averages of RMSE, variance and AR(1) coefficient of observed and gap-filled predictions at different gap lengths Gap length RMSE variance AR(1) TRAD MW PP observed TRAD MW PP observed TRAD MW PP 5hr 2.65 2.56 2.32 12.56 6.43 6.67 7.52 0.20 0.50 0.51 0.51 12hr 2.57 2.46 2.44 28.79 21.06 20.47 22.69 0.62 0.79 0.78 0.79
Conclusions
R0 (µmol CO2 m-2 s-1)
Rd (µmol CO2 m-2 s-1)
VPD (kPa)
NEEday (µmol CO2 m-2 s-1)
* Pmax * Rd * Pmax
Tair (oC) Fig. 4 Nighttime net ecosystem exchange of CO2 NEEnight (Observed) and predictions by different gap-filling methods as function of air temperature (Tair).
• Carbon flux was measured via EC methods at the Joseph Jones Ecological Research Center (JJERC), located in southeast Georgia (USA) • JJERC has 3 EC towers located along an edaphic moisture gradient (mesic, intermediate and xeric) • The dataset we chose for the project was from intermediate site (based on least number of missing measurements) • Light and temperature response curves or their derivatives were used to predict missing measurements • Light response curve: NEEday
NEEnight (µmol CO2 m-2 s-1)
• Eddy covariance (EC) data inherently have missing measurements ranging from 30-min to days, weeks and even months at times • Flux measurements have a seasonal pattern and the variance structure differs at different temporal scales • Gap-filling methods to account for such pattern and variance at different temporal scales are crucial to reduce uncertainty introduced while imputing missing measurements, and when used to estimate non-linear functional relationships (e.g., light response, Q10)1 • Gap-filling has been traditionally performed via non-linear methods with e.g., light and temperature response curves2 and previous research has shown additional seasonal and diurnal variation maybe controlled by other environmental variables such as VPD and VWC3
( 0 1 *VPD 2 *VWC )( Pmax 0 Pmax1 *VPD )
( 0 1 *VPD 2 *VWC ) ( Pmax 0 Pmax1 *VPD )
( Rd 0 Rd1 * Tair )
1Sierra,
Fig. 3. Observed daytime net ecosystem exchange of CO2, NEEday (black), and predicted NEEday using different gap-filling methods for days of year (DOY) 175-250 (Summer).
NEEnight Reco ( R0 R01 *VPD )e bTair
C. A., M. E. Harmon, E. Thomann, S. S. Perakis, and H. W. Loescher, 2011. Amplification and dampening of soil respiration by changes in temperature variability. Biogeosciences 8, 951-961, doi:10.5194/bgd-8-951-2011 2Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., … Wofsy, S. (2001). Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107(1), 43–69. 3Stoy, P. C., Richardson, a. D., Baldocchi, D. D., Katul, G. G., Stanovick, J., Mahecha, M. D., … Williams, M. (2009). Biosphere-atmosphere exchange of CO in 2 relation to climate: a cross-biome analysis across multiple time scales. Biogeosciences Discussions, 6, 4095–4141. doi:10.5194/bgd-6-4095-2009 4Loescher, H. W., Law, B. E., Mahrt, L., Hollinger, D. Y., Campbell, J., & Wofsy, S. C. (2006). Uncertainties in, and interpretation of, carbon flux estimates using the eddy covariance technique. Journal of Geophysical Research: Atmospheres, 111, 1–19. doi:10.1029/2005JD006932
Acknowledgements This research was funded by the University of Alabama and the Joseph W. Jones Ecological Research Center (JJERC). We would like to acknowledge JJERC for their logistic support. HWL acknowledges the National Science Foundation (NSF), EF-102980, for their ongoing support. The National Ecological Observatory Network (NEON) is a project sponsored by the NSF and managed under cooperative agreement by NEON, Inc.
