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| Titel |
Reducing uncertainty in model estimates of high-latitude net ecosystem exchange by incorporating remote sensing observations of snow cover area |
| VerfasserIn |
Kristina Luus, John Lin, Richard Kelly |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250074900
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| Zusammenfassung |
| Recent high-latitude studies have indicated that the seasonal timing of initial snow
accumulation and final snow melt each year substantially influence net ecosystem exchange
(NEE). Previous terrestrial biogeochemical models have either not simulated the
influence of snow season processes on NEE, or have used process-based estimates of
snow depth or subnivean temperature to estimate snow season NEE. As predictions
indicate that the northern carbon balance is likely to be altered by cumulative and
interconnected changes in Arctic air temperature, precipitation, and snowpack dynamics,
uncertainty in estimates of NEE may be reduced by incorporating independent
remote sensing observations of fractional snow cover into terrestrial biogeochemical
models.
The objective of this study was to examine whether uncertainty in Vegetation
Photosynthesis and Respiration Model (VPRM) estimates of North American NEE north of
55°N could be reduced by using remote sensing observations to explicitly represent the
influence of fractional snow cover on NEE. VPRM is a biospheric carbon flux model that
generates high resolution estimates of NEE from remote sensing observations of air
temperature, shortwave radiation and the normalized difference vegetation index (NDVI). In
the standard VPRM (VPRM0) formulation, photosynthesis is limited during the cold season
by low air temperatures, diminished shortwave radiation and low NDVI values, and
respiration is assumed to be constant below a threshold air temperature. Conversely, in
the new VRPMsnow formulation, moderate resolution imaging spectroradiometer
(MODIS) observations of fractional snow cover are used to simulate the effects
snow has on suppressing photosynthetic uptake by vegetation and decoupling soil
and air temperatures. Therefore, when MODIS observations indicate that snow is
present at a location, the rate of photosynthetic uptake by vegetation is diminished as
a function of the fractional snow cover area, and when a region is deemed to be
snow-covered, the rate of soil respiration is estimated as a linear function of soil
temperature.
Uncertainty in VPRM0 and VPRMsnow estimates of daily average NEE was assessed by
calculating model mean absolute error (MAE) and root mean squared error (RMSE)
according to raw observations of NEE collected at high-latitude sites using the eddy
covariance technique. When comparing estimates of daily average NEE over the portion of
the year when snow was present, VPRMsnow showed diminished error values (mean
RMSE=0.9μmol/m2/s and mean MAE=0.2μmol/m2/s) across all paired calibration
and validation sites relative to VPRM0 (mean RMSE=1.2μmol/m2/s and mean
MAE=0.5μmol/m2/s). Further analysis consisted of assessing systematic and random errors
in VPRM0 and VPRMsnow estimates of NEE according to eddy covariance observations
from Alaskan AmeriFlux sites. Systematic errors were then attributed to model
parameters and remote sensing inputs. Results indicated excellent agreement between
local observations of snow onset/melt relative to Landsat and MODIS observations
of fractional snow cover, and showed that assimilating MODIS observations of
fractional snow cover reduced uncertainty in model estimates of high-latitude NEE. |
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