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| Titel |
Uncertainty Analysis of Gross Primary Production Separated from Net Ecosystem Exchange Measurements at Speulderbos Forest, The Netherlands |
| VerfasserIn |
Rahul Raj, Nicholas Alexander Samuel Hamm, Christiaan van der Tol, Alfred Stein |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250101575
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| Publikation (Nr.) |
 EGU/EGU2015-742.pdf |
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| Zusammenfassung |
| Gross primary production (GPP), separated from the flux tower measurements of net
ecosystem exchange (NEE) of CO2, is used increasingly to validate process-based simulators
and remote sensing-derived estimates of simulated GPP at various time scales. Proper
implementation of validation requires knowledge of the uncertainty associated with the
separated GPP at different time scales so that the propagated uncertainty can be
determined. We estimate the uncertainty in GPP at half-hourly to yearly time scales. Flux
tower measurements of NEE results from two major fluxes GPP and ecosystem
respiration (Reco) as NEE = GPP – Reco and therefore GPP can be separated from NEE.
We used a non-rectangular hyperbola (NRH) model to separate half-hourly GPP
from the three years of continuous flux tower measurements of half-hourly NEE
at the Speulderbos forest site, The Netherlands. NRH includes the variables that
influence GPP, in particular radiation, vapor pressure deficit, and temperature. In
addition, NRH model provides a robust empirical relationship between radiation
and GPP by including the degree of curvature of light response curve. NRH was
fitted to the measured NEE data on a daily basis. Variation in the parameters of this
model was studied within each year. We did not obtain a single optimized value of
each parameter of NRH model, instead we defined the prior distribution of each
parameters based on literature search. We adopted a Bayesian approach, which was
implemented using Markov chain Monte Carlo (MCMC) simulation to update the prior
distribution of each parameter on a daily basis. This allowed us to estimate the
uncertainty in the separated GPP at the half-hourly time scale. The results of this
approach generated the empirical distribution of GPP at each half-hour, which are a
measure of uncertainty. The time series of empirical distributions of half-hourly GPP
values also allowed us to estimate the uncertainty at daily, monthly and yearly time
scales.
Our research provided a robust integration of numerically efficient NRH model and
MCMC method to estimate uncertainty in GPP at different time scales. This will provide
relevant and important information for the validation of process-based simulators. |
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