Adapting a growth equation to model tree regeneration in mountain forests

Management and risk analysis of protection forests depend on a reliable estimation of regeneration processes and tree growth under different site conditions. While the growth of forest stands and thus the average growth of larger trees is well studied and published in yield tables as well as embodied in numerous simulation models, there is still a lack of information about the crucial initial stages of tree growth. Thus, we evaluated juvenile tree growth for different site conditions in the Swiss Alps and developed an approach to model both the early and later stages of growth based on the Bertalanffy equation. This equation is physiologically well founded and requires only two parameter estimates: a maximum tree height and a growth parameter. Data for the parameter estimation were available from studies of tree regeneration at a range of sites in Switzerland: growth patterns of larch (Larix decidua) were available from a high-elevation afforestation experiment. For spruce (Picea abies), data were obtained from a blowdown area in the Alps. The growth equation was fitted to the observed data and we found a good correlation of the fitted curves with the observed data. The parameter estimates were validated with independent data sets. The extrapolated growth curves, calculated with the estimated growth rates, correspond well to the validation data. Thus, it is possible to use the Bertalanffy equation to model both the early and later stages of growth. With this approach, we provide a basis for modelling the growth of juvenile and mature trees of different tree species in mountain forests of the European Alp

Changes in alpine plant growth under future climate conditions

Alpine shrub- and grasslands are shaped by extreme climatic conditions such as a long-lasting snow cover and a short vegetation period. Such ecosystems are expected to be highly sensitive to global environmental change. Prolonged growing seasons and shifts in temperature and precipitation are likely to affect plant phenology and growth. In a unique experiment, climatology and plant growth was monitored for almost a decade at 17 snow meteorological stations in different alpine regions along the Swiss Alps. Regression analyses revealed highly significant correlations between mean air temperature in May/June and snow melt out, onset of plant growth, and plant height. These correlations were used to project plant growth phenology for future climate conditions based on the gridded output of a set of regional climate models runs. Melt out and onset of growth were projected to occur on average 17 days earlier by the end of the century than in the control period from 1971–2000 under the future climate conditions of the low resolution climate model ensemble. Plant height and biomass production were expected to increase by 77% and 45%, respectively. The earlier melt out and onset of growth will probably cause a considerable shift towards higher growing plants and thus increased biomass. Our results represent the first quantitative and spatially explicit estimates of climate change impacts on future growing season length and the respective productivity of alpine plant communities in the Swiss Alps

Changes in alpine plant growth under future climate conditions

Alpine shrub- and grasslands are shaped by extreme climatic conditions such as a long-lasting snow cover and a short vegetation period. Such ecosystems are expected to be highly sensitive to global environmental change. Prolonged growing seasons and shifts in temperature and precipitation are likely to affect plant phenology and growth. In a unique experiment, climatology and plant growth was monitored for almost a decade at 17 snow meteorological stations in different alpine regions along the Swiss Alps. Regression analyses revealed highly significant correlations between mean air temperature in May/June and snow melt out, onset of plant growth, and plant height. These correlations were used to project plant growth phenology for future climate conditions based on the gridded output of a set of regional climate models runs. Melt out and onset of growth were projected to occur on average 17 days earlier by the end of the century than in the control period from 1971–2000 under the future climate conditions of the low resolution climate model ensemble. Plant height and biomass production were expected to increase by 77% and 45%, respectively. The earlier melt out and onset of growth will probably cause a considerable shift towards higher growing plants and thus increased biomass. Our results represent the first quantitative and spatially explicit estimates of climate change impacts on future growing season length and the respective productivity of alpine plant communities in the Swiss Alps

The importance of forest structure for carbon fluxes of the Amazon rainforest

Precise descriptions of forest productivity, biomass, and structure are essential for understanding ecosystem responses to climatic and anthropogenic changes. However, relations between these components are complex, in particular for tropical forests. We developed an approach to simulate carbon dynamics in the Amazon rainforest including around 410 billion individual trees within 7.8 million km2. We integrated canopy height observations from space-borne LIDAR in order to quantify spatial variations in forest state and structure reflecting small-scale to large-scale natural and anthropogenic disturbances. Under current conditions, we identified the Amazon rainforest as a carbon sink, gaining 0.56 GtC per year. This carbon sink is driven by an estimated mean gross primary productivity (GPP) of 25.1 tC ha−1 a−1, and a mean woody aboveground net primary productivity (wANPP) of 4.2 tC ha−1 a−1. We found that successional states play an important role for the relations between productivity and biomass. Forests in early to intermediate successional states are the most productive, and woody above-ground carbon use efficiencies are non-linear. Simulated values can be compared to observed carbon fluxes at various spatial resolutions (>40 m). Notably, we found that our GPP corresponds to the values derived from MODIS. For NPP, spatial differences can be observed due to the consideration of forest successional states in our approach. We conclude that forest structure has a substantial impact on productivity and biomass. It is an essential factor that should be taken into account when estimating current carbon budgets or analyzing climate change scenarios for the Amazon rainforest

Climate change increases riverine carbon outgassing, while export to the ocean remains uncertain

Any regular interaction of land and river during flooding affects carbon pools within the terrestrial system, riverine carbon and carbon exported from the system. In the Amazon basin carbon fluxes are considerably influenced by annual flooding, during which terrigenous organic material is imported to the river. The Amazon basin therefore represents an excellent example of a tightly coupled terrestrial–riverine system. The processes of generation, conversion and transport of organic carbon in such a coupled terrigenous–riverine system strongly interact and are climate-sensitive, yet their functioning is rarely considered in Earth system models and their response to climate change is still largely unknown. To quantify regional and global carbon budgets and climate change effects on carbon pools and carbon fluxes, it is important to account for the coupling between the land, the river, the ocean and the atmosphere. We developed the RIVerine Carbon Model (RivCM), which is directly coupled to the well-established dynamic vegetation and hydrology model LPJmL, in order to account for this large-scale coupling. We evaluate RivCM with observational data and show that some of the values are reproduced quite well by the model, while we see large deviations for other variables. This is mainly caused by some simplifications we assumed. Our evaluation shows that it is possible to reproduce large-scale carbon transport across a river system but that this involves large uncertainties. Acknowledging these uncertainties, we estimate the potential changes in riverine carbon by applying RivCM for climate forcing from five climate models and three CO2 emission scenarios (Special Report on Emissions Scenarios, SRES). We find that climate change causes a doubling of riverine organic carbon in the southern and western basin while reducing it by 20% in the eastern and northern parts. In contrast, the amount of riverine inorganic carbon shows a 2- to 3-fold increase in the entire basin, independent of the SRES scenario. The export of carbon to the atmosphere increases as well, with an average of about 30%. In contrast, changes in future export of organic carbon to the Atlantic Ocean depend on the SRES scenario and are projected to either decrease by about 8.9% (SRES A1B) or increase by about 9.1% (SRES A2). Such changes in the terrigenous–riverine system could have local and regional impacts on the carbon budget of the whole Amazon basin and parts of the Atlantic Ocean. Changes in riverine carbon could lead to a shift in the riverine nutrient supply and pH, while changes in the exported carbon to the ocean lead to changes in the supply of organic material that acts as a food source in the Atlantic. On larger scales the increased outgassing of CO2 could turn the Amazon basin from a sink of carbon to a considerable source. Therefore, we propose that the coupling of terrestrial and riverine carbon budgets should be included in subsequent analysis of the future regional carbon budget

Multimodel Analysis of Future Land Use and Climate Change Impacts on Ecosystem Functioning

"Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to ?0.021 ± 0.007 and ?0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered. © 2019. The Authors.

A novel probabilistic risk analysis to determine the vulnerability of ecosystems to extreme climatic events

We present a simple method of probabilistic risk analysis for ecosystems. The only requirements are time series—modelled or measured—of environment and ecosystem variables. Risk is defined as the product of hazard probability and ecosystem vulnerability. Vulnerability is the expected difference in ecosystem performance between years with and without hazardous conditions. We show an application to drought risk for net primary productivity of coniferous forests across Europe, for both recent and future climatic conditions