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Robust Inverse Modeling of Growing Season Net Ecosystem Exchange in a Mountainous Peatland : Influence of Distributional Assumptions on Estimated Parameters and Total Carbon Fluxes

GND
1149770023
Affiliation/Institute
Division of Soil Science and Soil Physics, Institute of Geoecology, Technische Universität Braunschweig
Weber, Tobias Karl David; Gerling, Lars; Reineke, Daniela;
GND
1166154475
Affiliation/Institute
Division of Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig
Weber, Stephan;
ORCID
0000-0002-9543-1318
Affiliation/Institute
Division of Soil Science and Soil Physics, Institute of Geoecology, Technische Universität Braunschweig
Durner, Wolfgang;
ORCID
0000-0001-8292-9048
Affiliation/Institute
Division of Soil Science and Soil Physics, Institute of Geoecology, Technische Universität Braunschweig
Iden, Sascha Christian

While boreal lowland bogs have been extensively studied using the eddy‐covariance (EC) technique, less knowledge exists on mountainous peatlands. Hence, half‐hourly CO2 fluxes of an ombrotrophic peat bog in the Harz Mountains, Germany, were measured with the EC technique during a growing season with exceptionally dry weather spells. A common biophysical process model for net ecosystem exchange was used to describe measured CO2 fluxes and to fill data gaps. Model parameters and uncertainties were estimated by robust inverse modelling in a Bayesian framework using a population‐based Markov Chain Monte Carlo sampler. The focus of this study was on the correct statistical description of error, i.e. the differences between the measured and simulated carbon fluxes, and the influence of distributional assumptions on parameter estimates, cumulative carbon fluxes, and uncertainties. We tested the Gaussian, Laplace, and Student's t distribution as error models. The t‐distribution was identified as best error model by the deviance information criterion. Its use led to markedly different parameter estimates, a reduction of parameter uncertainty by about 40%, and, most importantly, to a 5% higher estimated cumulative CO2 uptake as compared to the commonly assumed Gaussian error distribution. As open‐path measurement systems have larger measurement error at high humidity, the standard deviation of the error was modeled as a function of measured vapor pressure deficit. Overall, this paper demonstrates the importance of critically assessing the influence of distributional assumptions on estimated model parameters and cumulative carbon fluxes between the land surface and the atmosphere.

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