Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion
نویسندگان
چکیده
منابع مشابه
Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion
Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Th...
متن کاملProbabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction
[1] The Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique were applied to a terrestrial ecosystem model to analyze uncertainties of estimated carbon (C) transfer coefficients and simulated C pool sizes. This study used six data sets of soil respiration, woody biomass, foliage biomass, litterfall, C content in the litter layers, and C content in mineral soil measured...
متن کامل2 3 Probabilistic Inversion of a Terrestrial Ecosystem Model : 4 Analysis of Uncertainty in Parameter Estimation and Model Prediction
1 The Bayesian probability inversion and a Markov Chain Monte Carlo (MCMC) technique were 2 applied to a terrestrial ecosystem model to analyze uncertainties of estimated carbon (C) transfer 3 coefficients and simulated C pool sizes. This study used six data sets of soil respiration, woody 4 biomass, foliage biomass, litterfall, C content in the litter layers, C content in mineral soil 5 measur...
متن کاملUsing Model-Data Fusion to Interpret Past Trends, and Quantify Uncertainties in Future Projections, of Terrestrial Ecosystem Carbon Cycling
Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network...
متن کاملThe REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
Fox, A., Willaims, M., Richardson, A. D., Cameron, D., Gove, J. H., Quaife, T., Ricciuto, D., Reichstein, M., Tomelleri, E., Trudinger, C. M. and Van Wijk, M. T. (2009) The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data. Agricultural and Forest Meteorology, 149 (10). pp. 1597-1615. ISSN 0168-1923...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Advances in Modeling Earth Systems
سال: 2017
ISSN: 1942-2466
DOI: 10.1002/2016ms000687