Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models
暂无分享,去创建一个
[1] Andreas Huth,et al. Connecting dynamic vegetation models to data – an inverse perspective , 2012 .
[2] Ron Smith,et al. Bayesian calibration of process-based forest models: bridging the gap between models and data. , 2005, Tree physiology.
[3] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[4] D. Cameron,et al. Process-based simulation of growth and overwintering of grassland using the BASGRA model , 2016 .
[5] A. O'Hagan,et al. Bayesian calibration of computer models , 2001 .
[6] Jonathan Rougier,et al. Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations , 2007 .
[7] Ke Zhang,et al. Variation in stem mortality rates determines patterns of above‐ground biomass in Amazonian forests: implications for dynamic global vegetation models , 2016, Global change biology.
[8] Ranga B. Myneni,et al. Impact of droughts on the carbon cycle in European vegetation: a probabilistic risk analysis using six vegetation models , 2014 .
[9] R. Lark,et al. Communicating the uncertainty in estimated greenhouse gas emissions from agriculture , 2015, Journal of environmental management.
[10] Ivan A. Janssens,et al. Bayesian comparison of six different temperature-based budburst models for four temperate tree species , 2012 .
[11] P. Levy,et al. Estimation of cumulative fluxes of nitrous oxide: uncertainty in temporal upscaling and emission factors , 2017 .
[12] James S. Clark,et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States , 2016, Global change biology.
[13] S. Kellomäki,et al. Evaluation of past and future changes in European forest growth by means of four process-based models , 2008 .
[14] Francesco Minunno,et al. Selecting Parameters for Bayesian Calibration of a Process-Based Model: A Methodology Based on Canonical Correlation Analysis , 2013, SIAM/ASA J. Uncertain. Quantification.
[15] A. Rammig,et al. Modelling CO2 Impacts on Forest Productivity , 2015, Current Forestry Reports.
[16] M. Salam,et al. Comparing Simulated and Measured Values Using Mean Squared Deviation and its Components , 2000 .
[17] Mark A. Sutton,et al. Uncertainties in the relationship between atmospheric nitrogen deposition and forest carbon sequestration , 2008 .
[18] C. Reyer. Forest Productivity Under Environmental Change—a Review of Stand-Scale Modeling Studies , 2015, Current Forestry Reports.
[19] Margarida Tomé,et al. Using stand-scale forest models for estimating indicators of sustainable forest management , 2012 .
[20] Margarida Tomé,et al. Using a Bayesian framework and global sensitivity analysis to identify strengths and weaknesses of two process-based models differing in representation of autotrophic respiration , 2013, Environ. Model. Softw..
[21] T. Bayes. An essay towards solving a problem in the doctrine of chances , 2003 .
[22] Catherine A Calder,et al. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. , 2009, Ecological applications : a publication of the Ecological Society of America.
[23] S. Richardson,et al. Soil–climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests , 2016 .
[24] Thomas Rötzer,et al. Models for supporting forest management in a changing environment , 2011 .
[25] J. Yeluripati,et al. A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest , 2011 .
[26] Rob Kooper,et al. On improving the communication between models and data. , 2013, Plant, cell & environment.
[27] M. G. Ryan,et al. The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. , 2007, The New phytologist.
[28] M. Flechsig,et al. Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity , 2016, Climatic Change.
[29] R. B. Jackson,et al. Forest biogeochemistry in response to drought , 2016, Global change biology.
[30] Richard E Chandler,et al. Exploiting strength, discounting weakness: combining information from multiple climate simulators , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[31] Kiona Ogle,et al. Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology , 2008 .
[32] Gerard B. M. Heuvelink,et al. Environmental change impacts on the C- and N-cycle of European forests: a model comparison study , 2012 .
[33] Martin Wattenbach,et al. Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models , 2009 .
[34] P. Levy,et al. The Effect of Nitrogen Enrichment on the Carbon Sink in Coniferous Forests: Uncertainty and Sensitivity Analyses of Three Ecosystem Models , 2004 .
[35] Kiona Ogle,et al. Hierarchical bayesian statistics: merging experimental and modeling approaches in ecology. , 2009, Ecological applications : a publication of the Ecological Society of America.
[36] R. Milne,et al. Integrating remote sensing datasets into ecological modelling: a Bayesian approach , 2008 .
[37] Rognvald I. Smith,et al. Extending a Bayesian Belief Network for ecosystem evaluation , 2012 .
[38] David Cameron,et al. Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models , 2017, Environ. Model. Softw..
[39] James S. Clark,et al. Capturing diversity and interspecific variability in allometries: A hierarchical approach , 2008 .
[40] Benjamin Poulter,et al. Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America , 2017, Global change biology.
[41] George Kuczera,et al. Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory , 2006 .
[42] Amanda M. Thomson,et al. Toward Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry , 2010 .
[43] Mike Pearson,et al. Visualizing Uncertainty About the Future , 2022 .
[44] Noel A Cressie,et al. Statistics for Spatio-Temporal Data , 2011 .
[45] Atul K. Jain,et al. Using ecosystem experiments to improve vegetation models , 2015 .
[46] A. Mäkelä,et al. Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe , 2013 .