Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory

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Minunno , F , Peltoniemi , M , Harkonen , S , Kalliokoski , T , Makinen , H & Makela , A 2019 , ' Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory ' , Forest Ecology and Management , vol. 440 , pp. 208-257 . https://doi.org/10.1016/j.foreco.2019.02.041

Title: Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory
Author: Minunno, Francesco; Peltoniemi, Mikko; Harkonen, Sanna; Kalliokoski, Tuomo; Makinen, Harri; Makela, Annikki
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Department of Forest Sciences
Date: 2019-05-15
Language: eng
Number of pages: 50
Belongs to series: Forest Ecology and Management
ISSN: 0378-1127
URI: http://hdl.handle.net/10138/310559
Abstract: Policy-relevant forest models must be environment and management sensitive and provide unbiased estimates of predicted variables over their intended areas of application. While empirical models derive their structure and parameters from representative data sets, process-based model (PBM) parameters should be evaluated in ranges that have a biological meaning independently of output data. At the same time PBMs should be calibrated against observations in order to obtain unbiased estimates and an understanding of their predictive capability. By means of model data assimilation, we Bayesian calibrated a forest model (PREBAS) using an extensive dataset that covered a wide range of climatic conditions, species composition and management practices. PREBAS was calibrated for three species in Finland: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies [L.] H. Karst.) and Silver birch (Betula pendula L.). Data assimilation was strongly effective in reducing the uncertainty of PREBAS parameters and predictions. A country-generic calibration showed robust performances in predicting forest variables and the results were consistent with yield tables and national forest statistics. The posterior predictive uncertainty of the model was mainly influenced by the uncertainty of the structural and measurement error.
Subject: Process-based model
Data assimilation
Bayesian calibration
Forest carbon cycle
Forest inventory data
Permanent growth experiments
NORWAY SPRUCE
SITE PRODUCTIVITY
DATA ASSIMILATION
ECOSYSTEM MODEL
BIOMASS
STAND
PHOTOSYNTHESIS
UNCERTAINTY
RESPIRATION
SENSITIVITY
4112 Forestry
1172 Environmental sciences
511 Economics
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