Spatial occurrence of drought-associated damages in Finnish boreal forests : results from forest condition monitoring and GIS analysis

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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

Estimating forest leaf area index using satellite images : comparison of k-NN based Landsat-NFI LAI with MODIS-RSR based LAI product for Finland

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Ikä vaikuttaa kuusen kuolleisuuteen

Ikääntyminen ja kilpailu heikentävät puiden veden ja ravinteiden saantia, joka altistaa ne kuolemiselle. Joissakin tutkimuksissa kuusen eloonjäämisen on havaittu laskevan suurissa läpimittaluokissa, mutta iän vaikutusta ei ole otettu huomioon. Tässä tutkimuksessa tarkasteltiin kuusen ikääntymisen vaikutusta eloonjäämiseen sovittamalla useita vaihtoehtoisia eloonjäämismalleja

User guide for PRELES, a simple model for the assessment of gross primary production and water balance of forests

Climforisk EU Life+ (EU/ENV/FI/00571) http://www.metla.fi/life/climforisk. Deliverable of the Action 3 of the Climforisk project: Modelling software and documentation , 30.6.2012.Simple models of ecosystem processes are useful tools for various kind of ecosystem impact studies. We built a simple model of ecosystem gross primary production, evapotranspiration and soil water content, which requires minimal input data, and which is efficient to run. In this report, we briefly describe the model equations, document the model program and provide user guide for the current version of the model. We also use the model to run a few example simulations that describe how the model responds to the environment, and test the model predictions of soil water in reference conditions with ICP level II data on soil water. The model is intended to be used in large scale prediction of GPP, ET, and drought in the Climforisk EU Life+ project

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

A model bridging waterlogging, stomatal behavior and water use in trees in drained peatland

Waterlogging causes hypoxic or anoxic conditions in soils, which lead to decreases in root and stomatal hydraulic conductance. Although these effects have been observed in a variety of plant species, they have not been quantified continuously over a range of water table depths (WTD) or soil water contents (SWC). To provide a quantitative theoretical framework for tackling this issue, we hypothesized similar mathematical descriptions of waterlogging and drought effects on whole-tree hydraulics and constructed a hierarchical model by connecting optimal stomata and soil-to-leaf hydraulic conductance models. In the model, the soil-to-root conductance is non-monotonic with WTD to reflect both the limitations by water under low SWC and by hypoxic effects associated with inhibited oxygen diffusion under high SWC. The model was parameterized using priors from literature and data collected over four growing seasons from Scots pine (Pinus sylvestris L.) trees grown in a drained peatland in Finland. Two reference models (RMs) were compared with the new model, RM1 with no belowground hydraulics and RM2 with no waterlogging effects. The new model was more accurate than the RMs in predicting transpiration rate (fitted slope of measured against modeled transpiration rate = 0.991 vs 0.979 (RM1) and 0.984 (RM2), R-2 = 0.801 vs 0.665 (RM1) and 0.776 (RM2)). Particularly, RM2's overestimation of transpiration rate under shallow water table conditions (fitted slope = 0.908, R-2 = 0.697) was considerably reduced by the new model (fitted slope = 0.956, R-2 = 0.711). The limits and potential improvements of the model are discussed.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

A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water — calibration and sensitivity analysis

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Consistent estimates of gross primary production of Finnish forests : comparison of estimates of two process models

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