TY - JOUR
T1 - Recovery of arctic tundra from thermal erosion disturbance is constrained by nutrient accumulation: a modeling analysis
JF - Ecological Applications
Y1 - 2015
A1 - Pearce, A. R.
A1 - Rastetter, E. B.
A1 - Kwiatkowski, B. L.
A1 - Bowden, W. B.
A1 - Mack, M. C.
A1 - Jiang, Y.
KW - Alaska
KW - arctic
KW - biogeochemistry
KW - disturbance
KW - ecosystem model
KW - global climate change
KW - nutrient cycles
KW - permafrost
KW - thermokarst
KW - tundra
AB - We calibrated the Multiple Element Limitation (MEL) model to Alaskan arctic tundra to simulate recovery of thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could significantly alter regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as the climate warms. We simulated recovery following TEF stabilization and did not address initial, short-term losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we modeled a range of post-stabilization conditions by varying the initial size of SOM stocks and nutrient supply rates. Simulations indicate that nitrogen (N) losses after the TEF stabilizes are small, but phosphorus (P) losses continue. Vegetation biomass recovered 90% of its undisturbed C, N, and P stocks in 100 years using nutrients mineralized from SOM. Because of low litter inputs but continued decomposition, younger SOM continued to be lost for 10 years after the TEF began to recover, but recovered to about 84% of its undisturbed amount in 100 years. The older recalcitrant SOM in mineral soil continued to be lost throughout the 100-year simulation. Simulations suggest that biomass recovery depended on the amount of SOM remaining after disturbance. Recovery was initially limited by the photosynthetic capacity of vegetation, but became co-limited by N and P once a plant canopy developed. Biomass and SOM recovery was enhanced by increasing nutrient supplies, but the magnitude, source, and controls on these supplies are poorly understood. Faster mineralization of nutrients from SOM (e.g., by warming) enhanced vegetation recovery but delayed recovery of SOM. Taken together, these results suggest that although vegetation and surface SOM on TEFs recovered quickly (25 and 100 years, respectively), the recovery of deep, mineral soil SOM took centuries and represented a major ecosystem C loss.
VL - 25
SN - 1939-5582
UR - http://dx.doi.org/10.1890/14-1323.1
IS - 5
ER -
TY - JOUR
T1 - Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter
JF - Ecological ApplicationsEcological ApplicationsEcological Applications
Y1 - 2010
A1 - Rastetter, E. B.
A1 - Williams, M.
A1 - Griffin, K. L.
A1 - Kwiatkowski, B. L.
A1 - Tomasky, G.
A1 - Potosnak, M. J.
A1 - Stoy, P. C.
A1 - Shaver, G. R.
A1 - Stieglitz, M.
A1 - Hobbie, J. E.
A1 - Kling, G. W.
KW - alaska, USA
KW - algorithms
KW - canopy
KW - co2 flux
KW - conductance
KW - covariance technique
KW - data assimilation
KW - ecosystem carbon balance
KW - ecosystem models
KW - eddy covariance
KW - forest
KW - inversion
KW - kalman filter
KW - Net ecosystem carbon exchange
KW - nitrogen
KW - uncertainty
AB - Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions.

We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data.

We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually.

The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.
VL - 20
SN - 1051-0761
N1 - 614HW

Times Cited:11

Cited References Count:44
JO - Ecol ApplEcol Appl
ER -