Estimation of litter input in hemi-boreal forests with drained organic soils for improvement of GHG inventories

Assessments of net greenhouse gas (GHG) emissions in forest land with drained organic soils conducted within the scope of National GHG inventories requires reliable data on litter production and carbon (C) input to soil information. To estimate C input through tree above-ground litter, sampling of above-ground litter was done in 36 research sites in Latvia representing typical forests with drained organic soils in hemiboreal region. To estimate C input through tree below-ground litter and litter from ground vegetation, modelling approach based on literature review and data on characteristics of forest stands with drained organic soils in Latvia provided by National Forest Inventory (NFI) was used. The study highlighted dependence of C input to soil through litter production from the stand characteristics and thus significant differences in the C input with litter between young and middle age stands. The study also proves that drained organic soils in middle age forests dominated by Silver birch, Scots pine and Norway spruce may not be the source of net GHG emissions due to offset by C input through litter production. However, there is still high uncertainty of C input with tree below-ground litter and ground vegetation, particularly, mosses, herbs and grasses which may have crucial role in C balance in forests with drained organic soils. Key words: forests, drained organic soils, litter production, carbon input, National GHG inventory

IFC-CENTRIC VEGETATION MODELLING FOR BIM

There has been a growing interest in integrating vegetation into the built environment in order to ameliorate the negative effects of increasing urbanisation. In Singapore, government policies encourage the inclusion of skyrise greenery into new and existing buildings. To further streamline workflows, statutory BIM (Building Information Modelling) submissions in architecture, engineering and construction (AEC) industries have been mandated. However, landscape plans are still excluded from these BIM submissions due to the lack of a centralised vegetation database and the absence of a standardised BIM format for landscape architectural submissions. This paper presents a streamlined methodology for creating and using a centralised vegetation library for landscape architects. The workflow leverages off the Industry Foundation Classes (IFC) standard for data exchange regardless of the BIM authoring software used and provides a framework of four operational modules: an expandable and low-maintenance species-level vegetation library, a BIM authoring workflow that allows inclusion of vegetation objects, an IFC interface, and a lightweight 3D vegetation model generator. This paper also showcases a use-case of embedding information-enriched 3D vegetation objects into a simulated landscape plan. The proposed workflow, when adopted in AEC industries, will enable governing agencies to track diverse greening efforts by the industry and to potentially include other measurements such as cooling performance or maintainability.

An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling

Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.

Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season

Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

Plant phenology evaluation of CRESCENDO land surface models – Part I: start and end of growing season

Plant phenology plays a fundamental role in land-atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of beginning and end of the growing season, simulated by seven state-of-the-art European land surface models, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently-developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. Difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broad-leaf deciduous trees, while high variability is noted in regions dominated by broad-leaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

Sensitivity of tropical tree growth to climatic variation: a global meta-analysis of tree-ring data

<p>Tropical forests are a crucial component of the global carbon cycle and importantly contribute to the global carbon land sink. Stem growth of tropical trees is a key component of carbon dynamics in tropical forests, but our understanding of how this is driven by climatic variation is poor. Such understanding is needed for predictive vegetation modelling of climate change effects.</p><p>Here, we help to fill this knowledge gap by conducting a meta-analysis of published tropical tree-ring width chronologies. We compiled >350 tropical chronologies (30°N - 30°S) from all tropical climate zones. We used this data set to explore i) common patterns in the tree-growth responses to monthly rainfall and temperature (Tmax) patterns (cluster analysis), ii) the relative importance of temperature and rainfall in determining tropical tree growth (glm), iii) how these climatic drivers shift along gradients of temperature and precipitation.</p><p>Our cluster analysis revealed 6-8 primary types of responses to monthly climate variables. These clusters are associated with mean climate, elevation, or geographic location. The seasonality of growth responses to temperature and rainfall differed clearly among clusters, but the signs of responses were consistent: higher Tmax reduces growth, more precipitation increases growth. Multiple regression analyses of growth responses to seasonal climate further confirmed the negative effects of temperature and positive effects of rainfall. Rainfall during the dry season had the strongest relative importance. Finally, we found that seasonal drivers of tropical tree growth are modified by mean climate. In drier regions, growth sensitivity to temperature increases; in warmer regions, growth sensitivity to rainfall increases. The latter may imply that global warming leads to stronger drought effects on tree growth and possibly enhances mortality risks of tropical trees.</p><p>Our meta-analysis shows that tree-ring studies help to improve understanding of climate-driven carbon dynamics in tropical forests. Insights from this study can be used to benchmark global vegetation modelling and to better understand responses of tropical tree species to climate change.</p>

Does acclimation of plant traits improve dynamic vegetation modelling of a tree species?

<p>Dynamic vegetation modelling is intensively used with plant functional types which limits the range of interest of obtained outputs for other fields of knowledge like conservation science. An alternative approach is to simulate plant species. This however requires additional data, i.e. morphological and physiological traits values characterizing the species and determining their functional properties. However, not only many traits vary among the species belonging to the same plant functional type but also the traits vary broadly according to climate factors.</p><p>Since most of the traits are functional, their values may be critical for dynamic vegetation model outputs. We measured several traits (specific leaf area, leaf and sapwood C:N) of Cedrus atlantica in its native range, the Rif and Middle Atlas Mountains of Morocco, as well as in some plantations in western Europe. Trait values exhibit significant variations between the sampled sites. It is possible to predict these trait values using multiple regression with climate factors as explanatory variables. Using regression equations, we produced spatial- and time-varying traits over the study area. We implemented these equations in the CARAIB dynamic vegetation model and tested whether they improve the simulation of C. atlantica in the Rif and Middle Atlas Mountains, by comparing the net primary productivities and biomasses computed with and without trait variation, with those retrieved from measurements on the sampled sites. We then performed simulations of the future using climate projections of the regional climate model RCA4 nested in HadGEM2 general circulation model under the RCP8.5 scenario, in order to test the influence of trait acclimation on the predicted future changes in the range and productivity of the species.</p>