On the use of the bayesian approach for the calibration, evaluation and comparison of process-based forest models

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaForest ecosystems have been experiencing fast and abrupt changes in the environmental conditions, that can increase their vulnerability to extreme events such as drought, heat waves, storms, fire. Process-based models can draw inferences about future environmental dynamics, but the reliability and robustness of vegetation models are conditional on their structure and their parametrisation. The main objective of the PhD was to implement and apply modern computational techniques, mainly based on Bayesian statistics, in the context of forest modelling. A variety of case studies was presented, spanning from growth predictions models to soil respiration models and process-based models. The great potential of the Bayesian method for reducing uncertainty in parameters and outputs and model evaluation was shown. Furthermore, a new methodology based on a combination of a Bayesian framework and a global sensitivity analysis was developed, with the aim of identifying strengths and weaknesses of process-based models and to test modifications in model structure. Finally, part of the PhD research focused on reducing the computational load to take full advantage of Bayesian statistics. It was shown how parameter screening impacts model performances and a new methodology for parameter screening, based on canonical correlation analysis, was presente

Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)

Tree structure equations derived from pipe model theory (PMT) are well-suited to estimate biomass allocation in Scots pine (Pinus sylvestrisL.) and Norway spruce (Picea abies[L.] Karst.). However, age dependence of parameters should be accounted for when applying the equations.Peer reviewe

Decomposing sources of uncertainty in climate change projections of boreal forest primary production

We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (C-a), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term C-a fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (C-a), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term C-a fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.Peer reviewe

A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models

Background and Aims Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees. Methods This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison. Key Results Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97-57.45 % and CCC 0.43-0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly. Conclusions The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.Peer reviewe

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

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.Peer reviewe

Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites

Forest ecosystems are already responding to changing environmental conditions that are driven by increased atmospheric CO2 concentrations. These developments affect how societies can utilise and benefit from the woodland areas in the future, be it for example climate change mitigation as carbon sinks, lumber for wood industry, or preserved for nature tourism and recreational activities. We assess the effect and the relative magnitude of different uncertainty sources in ecosystem model simulations from the year 1980 to 2100 for two Finnish boreal forest sites. The models used in this study are the land ecosystem model JSBACH and the forest growth model PREBAS. The considered uncertainty sources for both models are model parameters and four prescribed climates with two RCP (representative concentration pathway) scenarios. Usually, model parameter uncertainty is not included in these types of uncertainty studies. PREBAS simulations also include two forest management scenarios. We assess the effect of these sources of variation at four different points in time on several ecosystem indicators, e.g. gross primary production (GPP), ecosystem respiration, soil moisture, recurrence of drought, length of the vegetation active period (VAP), length of the snow melting period and the stand volume. The uncertainty induced by the climate models remains roughly the same throughout the simulations and is overtaken by the RCP scenario impact halfway through the experiment. The management actions are the most dominant uncertainty factors for Hyytiala and as important as RCP scenarios at the end of the simulations, but they contribute only half as much for Sodankyla. The parameter uncertainty is the least influential of the examined uncertainty sources, but it is also the most elusive to estimate due to non-linear and adverse effects on the simulated ecosystem indicators. Our analysis underlines the importance of carefully considering the implementation of forest use when simulating future ecosystem conditions, as human impact is evident and even increasing in boreal forested regions.Peer reviewe

Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland

The challenges of applying process-based models to uneven-aged forests are the difficulties in simulating the interactions between trees and resource allocation between size classes. In this study, we focused on a process-based forest growth model PREBAS which is a mean tree model with Reineke self-thinning mortality and was originally developed for even-aged forests. The primary aim was to test the application of PREBAS model to uneven-aged forests by introducing different diameter at breast height (DBH) size classes to better represent the forest structure. Additionally, we introduced a new mortality model MORnew to PREBAS which is developed for uneven-aged stands and compared with the current PREBAS version in which a modification Reineke rule is used. The tests were conducted in 26 old Norway spruce dominated stands in southern and central Finland with three consecutive measurements (on average a 25-year study period). To evaluate the model performance, we compared the estimations of stand averaged diameter at breast height (D), stand averaged tree height (H), stand averaged crown base height (Hc), stand basal area (B) and density (N) with measurements. Moreover, biomass estimations of each tree component (foliage, branch and stem) were compared to estimations from empirical models. Results showed that introducing size distributions can represent better stand structure and improve the model predictions compared with data. Moreover, the new mortality model MORnew showed promise with qualitatively more realistic results especially among the largest tree size classes. However, model bias still existed in the simulation although the predictions were improved. It revealed that further calibration of the PREBAS model with size classes should be done to better extend the model applicability to uneven-aged forests

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

Quantification of forest carbon flux and stock uncertainties under climate change and their use in regionally explicit decision making: Case study in Finland

Uncertainties are essential, yet often neglected, information for evaluating the reliability in forest carbon balance projections used in national and regional policy planning. We analysed uncertainties in the forest net biome exchange (NBE) and carbon stocks under multiple management and climate scenarios with a process-based ecosystem model. Sampled forest initial state values, model parameters, harvest levels and global climate models (GCMs) served as inputs in Monte Carlo simulations, which covered forests of the 18 regions of mainland Finland over the period 2015–2050. Under individual scenarios, the results revealed time- and region-dependent variability in the magnitude of uncertainty and mean values of the NBE projections. The main sources of uncertainty varied with time, by region and by the amount of harvested wood. Combinations of uncertainties in the representative concentration pathways scenarios, GCMs, forest initial values and model parameters were the main sources of uncertainty at the beginning, while the harvest scenarios dominated by the end of the simulation period, combined with GCMs and climate scenarios especially in the north. Our regionally explicit uncertainty analysis was found a useful approach to reveal the variability in the regional potentials to reach a policy related, future target level of NBE, which is important information when planning realistic and regionally fair national policy actions. A Correction to this article was published on 12 September 2023

A methodology for implementing a digital twin of the earth’s forests to match the requirements of different user groups

Publisher Copyright: © 2021 GI_Forum.Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.Peer reviewe