Process-based Vegetation Model Research Articles

Machine-learning modeling on tree mortality and growth reduction of temperate forests with climatic and ecophysiological parameters

Massive tree diebacks and abnormal growth reduction have been more reported across continents, which calls an attention for methods that can assess the physiological stress of tree growth. This study aims to develop machine learning models (MLM) to explain tree mortality and growth reduction in terms of the key parameters of climate and tree ecophysiology in temperate forests of Korea. For this, we produced various ecophysiological parameters with a process-based vegetation model that was used as inputs to multiple machine learning algorithms and the conventional multiple linear regression (MLR). As a result, the MLMs outperformed the MLR. Among the MLMs, random forest (RF) showed the highest overall accuracy and AUC for both tree mortality (79% and 0.84±0.09, respectively) and growth reduction (61% and 0.63 ± 0.09). Winter temperature along with previous-year autumn soluble sugar content and precipitation were the most important in determining the tree mortality, while the growth reduction was largely regulated by current-year conditions such spring and autumn precipitation and summer starch content. Also, in the proportion of variance (communalities) of PCA, the precipitation and soluble sugar showed low values 0.15 and 0.29, respectively, which low communalities (<0.3) indicate that these variables have limited shared variance with other variables in the analysis. This study indicates the combined use of vegetation model and MLM can give reciprocal benefits to each other by providing ecophysiological parameters for MLM, otherwise hard to get, and coping with convoluted process of tree vitality undescribed yet in the vegetation model, respectively.

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least 2 consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module named the Permafrost and Organic LayEr module for Forest Models (POLE-FM) that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61 000 ha) and over temporal scales of days to centuries. The coupled model generated intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) generally consistent with independent observations in 17 instrumented forest stands. The model also represented the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 39.3 % of forested area in the landscape as underlain by permafrost, compared to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision-making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model's utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.

Aridity-dependent sequence of water potentials for stomatal closure and hydraulic dysfunctions in woody plants.

The sequence of physiological events during drought strongly impacts plants' overall performance. Here, we synthesized the global data of stomatal and hydraulic traits in leaves and stems of 202 woody species to evaluate variations in the water potentials for key physiological events and their sequence along the climatic gradient. We found that the seasonal minimum water potential, turgor loss point, stomatal closure point, and leaf and stem xylem vulnerability to embolism were intercorrelated and decreased with aridity, indicating that water stress drives trait co-selection. In xeric regions, the seasonal minimum water potential occurred at lower water potential than turgor loss point, and the subsequent stomatal closure delayed embolism formation. In mesic regions, however, the seasonal minimum water potential did not pose a threat to the physiological functions, and stomatal closure occurred even at slightly more negative water potential than embolism. Our study demonstrates that the sequence of water potentials for physiological dysfunctions of woody plants varies with aridity, that is, xeric species adopt a more conservative sequence to prevent severe tissue damage through tighter stomatal regulation (isohydric strategy) and higher embolism resistance, while mesic species adopt a riskier sequence via looser stomatal regulation (anisohydric strategy) to maximize carbon uptake at the cost of hydraulic safety. Integrating both aridity-dependent sequence of water potentials for physiological dysfunctions and gap between these key traits into the hydraulic framework of process-based vegetation models would improve the prediction of woody plants' responses to drought under global climate change.

Climate change during the last deglaciation at central equatorial African mountains

The “inverse modelling through iterative forward modelling” (IMIFM) approach is used to reconstruct the last deglaciation climates from central equatorial African mountains. Unlike other approaches that use statistical models, it directly incorporates the effects of changes in atmospheric CO2 concentrations on vegetation through a process-based vegetation model, but the results strongly depend on the model used. Therefore, two generally related but different equilibrium vegetation models are used for evaluating the dependence of the results on the choice of vegetation model. Our climate reconstructions show that long-term temperature increases during the last deglaciation, as seen in Greenland, were absent from many of the target sites. Moreover, there are few common long-term trends of climatic responses during the last deglaciation among central equatorial African mountains’ sites despite the relatively close of those sites. No systematic climate responses were also found between the eastern and western sides of the Congo basin. While there is consistency in the long-term trends and their amplitudes regarding temperature reconstructions with both models, there are large differences between the sites in the long-term hydroclimatic reconstructions, particularly during arid periods. This difference may be attributed to the carbon-water use efficiency in the vegetation models. This study implies that the IMIFM approach is effective for climate reconstructions in equatorial African mountains, where modern pollen samples are not plentiful. Although low atmospheric CO2 concentrations do not largely influence temperature reconstructions for the last deglaciation, they significantly affect the hydroclimate reconstructions, especially when drought-tolerant biomes are dominant.

Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity

Increasing tree growth and mortality rates in Europe are still poorly understood and have been attributed to a variety of drivers. This study explored the role of climate drivers, management and age structure in driving changes in tree mortality rates in six Central European countries from 1985 to 2010, using the process-based vegetation model LPJ-GUESS. Simulations show a strong positive trend in canopy mortality rates in Central Europe, consistent with satellite observations. This trend was explained by an assumed increase in managed thinning in response to a modelled increase in forest productivity caused by climate change and rising atmospheric CO2 concentration. Simulated rates of canopy mortality were highly sensitive to the minimum tree size threshold applied for inclusion in the rate calculation, agreeing with satellite observations that are likely to only capture the loss of relatively large trees. The calculated trends in mortality rate also differed substantially depending on the metric used (i.e. carbon, stem or canopy mortality), highlighting the challenge of comparing tree mortality trends from different observation types. We conclude that changes in forest productivity and management in combination can profoundly affect regional-scale patterns of tree mortality. Our findings underscore the fact that increasing forest mortality can occur without reductions in forest growth when mediated by management responses to increasing productivity.

Regional and seasonal partitioning of water and temperature controls on global land carbon uptake variability

Global fluctuations in annual land carbon uptake (NEEIAV) depend on water and temperature variability, yet debate remains about local and seasonal controls of the global dependences. Here, we quantify regional and seasonal contributions to the correlations of globally-averaged NEEIAV against terrestrial water storage (TWS) and temperature, and respective uncertainties, using three approaches: atmospheric inversions, process-based vegetation models, and data-driven models. The three approaches agree that the tropics contribute over 63% of the global correlations, but differ on the dominant driver of the global NEEIAV, because they disagree on seasonal temperature effects in the Northern Hemisphere (NH, >25°N). In the NH, inversions and process-based models show inter-seasonal compensation of temperature effects, inducing a global TWS dominance supported by observations. Data-driven models show weaker seasonal compensation, thereby estimating a global temperature dominance. We provide a roadmap to fully understand drivers of global NEEIAV and discuss their implications for future carbon–climate feedbacks.

Estimating relative sunshine duration from commonly available meteorological variables for simulating biome distribution in the Carpathian Region

Bright sunshine duration (BSD) data are required for simulating biomes using process-based vegetation models. However, monthly global paleoclimate datasets that can be used in paleo data–model comparisons do not necessarily contain BSD or radiation data. Considering the theoretical and practical aspects, the scheme of Yin, X. (1999) is here recommended to estimate monthly time series of relative BSD using only monthly climate and location data. As a case study for the Carpathian Region, the efficiency of both the original and a variant of that scheme is analysed in this paper. The alternative scheme has high applicability in paleoenvironmental studies. Comparison of the estimated and observed BSD data shows that from May to August, the value of relative root mean squared error in more than 90 percent of the study area does not exceed the threshold of 20 percent, indicating an excellent performance of the original estimation scheme. It is also found that though the magnitude of overestimation for the alternative algorithm is significant in the winter period, the proposed method performs similarly well in the growing season as the original. Furthermore, concerning modelling the distribution of biomes, simulation experiments are performed to assess the effects of modifying some configuration settings: (a) the generation of relative BSD data, and (b) the algorithm used to create quasi-daily weather data from the monthly values. Under both the recent humidity conditions of the study region and the spatial resolution of the climate dataset used, the results can be considered sufficiently robust, regardless of the configuration settings tested. Thus, using monthly temperature and precipitation climatologies, the spatial distribution of biomes can be properly simulated with the configuration settings proposed here.

Assessing vulnerability of agriculture system to climate change in the SAARC region

Agriculture production is primarily influenced by human-induced management interventions whereas climate plays an important role. Climate change with increasing global carbon dioxide concentration and changes in temperature, precipitation and other climatic parameters play a significant role in influencing agriculture productivity. The assessment of the vulnerability of agriculture production under the influence of climate change can be done using process-based vegetation models. We assessed the vulnerability of agriculture production for the South Asian Association for Regional Cooperation (SAARC) nations comprising of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka using the Joint UK Land Environment Simulator (JULES) model. Net Primary Productivity (NPP) was simulated under the forcing of climatic variables of the climate change scenarios of representative concentration pathways (RCP2.6, 4.5 & 8.5) for the year 2050 using JULES. JULES is a process-based model that simulates fluxes of carbon, energy, and momentum between the atmosphere, land surface, and water to estimate the productivity of a system. The sensitivity was calculated as the inter-annual dispersion from the average NPP of assessment period 2016 to 2050, whereas adaptive capacity was calculated as the slope of productivity regressed over the assessment period. The vulnerability was calculated by subtracting the values of adaptability from sensitivity. The JULES simulated NPP was also compared with Moderate Resolution Imaging Spectrometer (MODIS 17A) derived NPP for the period 2000–2014. The simulated NPP had a decreasing trend under projected climate change scenarios of 2050 for all the SAARC nations compared to recent years average of 2000–2014. The analysis reveals that one-third of the SAARC region is highly sensitive under low to moderate emission scenarios, while it was observed that under high emission scenarios the sensitivity would reduce with increased vulnerability. We observed that the vulnerability of the system is not alone influenced by sensitivity and any change in adaptive capacity or resilience would also impact the overall vulnerability of the system.

Integrating plant physiology and community ecology across scales through trait-based models to predict drought mortality.

Forests are a critical carbon sink and widespread tree mortality resulting from climate-induced drought stress has the potential to alter forests from a carbon sink to a source, causing a positive feedback on climate change. Process-based vegetation models aim to represent the current understanding of the underlying mechanisms governing plant physiological and ecological responses to climate. Yet model accuracy varies across scales, and regional-scale model predictive skill is frequently poor when compared with observations of drought-driven mortality. I propose a framework that leverages differences in model predictive skill across spatial scales, mismatches between model predictions and observations, and differences in the mechanisms included and absent across models to advance the understanding of the physiological and ecological processes driving observed patterns drought-driven mortality.

Risk and vulnerability of Mongolian grasslands under climate change

Climate change is projected to increase the aridity of semi-arid ecosystems, including Mongolian grasslands (MG), which provide ecosystem services that support food supply and pastoralist lifestyle. Here, we conducted a grid-scale (0.5° × 0.5°) probabilistic risk assessment of MG under climate change for 40 years (1976–2015) based on probability theory. We evaluated changes of risk (impacts) and vulnerability of MG to drought between the recent two decades R20 = 1996–2015 and the previous two decades P20 = 1976–1995. The risk is quantified as the product of the probability of hazardous drought and ecosystem vulnerability. The probability of hazardous drought is defined from the Standardized Precipitation–Evapotranspiration Index. Vulnerability is defined as the expected differences of key ecosystem variables between years with and without hazardous conditions. The ecosystem variables are productivity (peak aboveground biomass, net primary productivity, and leaf area index) and root-zone plant-available soil moisture, simulated with a process-based vegetation model Organizing Carbon and Hydrology in Dynamic Ecosystems-Grassland Management validated with field observations of biomass and soil moisture. Results reveal that MG experienced more frequent hazardous droughts with rapid warming and slight drying during R20 aggravated by ever-increasing grazing intensity (34% compared to P20), which resulted in a reduction in soil water availability and grassland productivity, particularly in northeastern areas (20%–65%). The risk of drought to productivity increased by 10% between P20 and R20 over extended areas, particularly in northcentral and northeast Mongolia. The increase in the risk to MG was mainly caused by climate change-induced increase in the probability of hazardous drought and, to a lesser extent, by the increasing vulnerability. Recent droughts modify the risk to grasslands, particularly in northcentral and northeast Mongolia, suggesting that these regions need strategic management for both adaptation and ecosystem conservation to cope with climate change impacts.

Predicting the Future Distribution of Ara rubrogenys, an Endemic Endangered Bird Species of the Andes, Taking into Account Trophic Interactions

Species distribution models (SDMs) are commonly used with climate only to predict animal distribution changes. This approach however neglects the evolution of other components of the niche, like food resource availability. SDMs are also commonly used with plants. This also suffers limitations, notably an inability to capture the fertilizing effect of the rising CO2 concentration strengthening resilience to water stress. Alternatively, process-based dynamic vegetation models (DVMs) respond to CO2 concentration. To test the impact of the plant modelling method to model plant resources of animals, we studied the distribution of a Bolivian macaw, assuming that, under future climate, DVMs produce more conservative results than SDMs. We modelled the bird with an SDM driven by climate. For the plant, we used SDMs or a DVM. Under future climates, the macaw SDM showed increased probabilities of presence over the area of distribution and connected range extensions. For plants, SDMs did not forecast overall response. By contrast, the DVM produced increases of productivity, occupancy and diversity, also towards higher altitudes. The results offered positive perspectives for the macaw, more optimistic with the DVM than with the SDMs, than initially assumed. Nevertheless, major common threats remain, challenging the short-term survival of the macaw.

Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis.

Despite their low contribution to forest carbon stocks, lianas (woody vines) play an important role in the carbon dynamics of tropical forests. As structural parasites, they hinder tree survival, growth and fecundity; hence, they negatively impact net ecosystem productivity and long‐term carbon sequestration.Competition (for water and light) drives various forest processes and depends on the local abundance of resources over time. However, evaluating the relative role of resource availability on the interactions between lianas and trees from empirical observations is particularly challenging. Previous approaches have used labour‐intensive and ecosystem‐scale manipulation experiments, which are infeasible in most situations.We propose to circumvent this challenge by evaluating the uncertainty of water and light capture processes of a process‐based vegetation model (ED2) including the liana growth form. We further developed the liana plant functional type in ED2 to mechanistically simulate water uptake and transport from roots to leaves, and start the model from prescribed initial conditions. We then used the PEcAn bioinformatics platform to constrain liana parameters and run uncertainty analyses.Baseline runs successfully reproduced ecosystem gas exchange fluxes (gross primary productivity and latent heat) and forest structural features (leaf area index, aboveground biomass) in two sites (Barro Colorado Island, Panama and Paracou, French Guiana) characterized by different rainfall regimes and levels of liana abundance.Model uncertainty analyses revealed that water limitation was the factor driving the competition between trees and lianas at the drier site (BCI), and during the relatively short dry season of the wetter site (Paracou). In young patches, light competition dominated in Paracou but alternated with water competition between the wet and the dry season on BCI according to the model simulations.The modelling workflow also identified key liana traits (photosynthetic quantum efficiency, stomatal regulation parameters, allometric relationships) and processes (water use, respiration, climbing) driving the model uncertainty. They should be considered as priorities for future data acquisition and model development to improve predictions of the carbon dynamics of liana‐infested forests. Synthesis. Competition for water plays a larger role in the interaction between lianas and trees than previously hypothesized, as demonstrated by simulations from a process‐based vegetation model.

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models.

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.

Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change.

Plant functional traits provide a link in process-based vegetation models between plant-level physiology and ecosystem-level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large-scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re-evaluation of trait-based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type-specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models.

Arboreal Epiphytes in the Soil-Atmosphere Interface: How Often Are the Biggest “Buckets” in the Canopy Empty?

Arboreal epiphytes (plants residing in forest canopies) are present across all major climate zones and play important roles in forest biogeochemistry. The substantial water storage capacity per unit area of the epiphyte “bucket” is a key attribute underlying their capability to influence forest hydrological processes and their related mass and energy flows. It is commonly assumed that the epiphyte bucket remains saturated, or near-saturated, most of the time; thus, epiphytes (particularly vascular epiphytes) can store little precipitation, limiting their impact on the forest canopy water budget. We present evidence that contradicts this common assumption from (i) an examination of past research; (ii) new datasets on vascular epiphyte and epi-soil water relations at a tropical montane cloud forest (Monteverde, Costa Rica); and (iii) a global evaluation of non-vascular epiphyte saturation state using a process-based vegetation model, LiBry. All analyses found that the external and internal water storage capacity of epiphyte communities is highly dynamic and frequently available to intercept precipitation. Globally, non-vascular epiphytes spend &lt;20% of their time near saturation and regionally, including the humid tropics, model results found that non-vascular epiphytes spend ~1/3 of their time in the dry state (0–10% of water storage capacity). Even data from Costa Rican cloud forest sites found the epiphyte community was saturated only 1/3 of the time and that internal leaf water storage was temporally dynamic enough to aid in precipitation interception. Analysis of the epi-soils associated with epiphytes further revealed the extent to which the epiphyte bucket emptied—as even the canopy soils were often &lt;50% saturated (29–53% of all days observed). Results clearly show that the epiphyte bucket is more dynamic than currently assumed, meriting further research on epiphyte roles in precipitation interception, redistribution to the surface and chemical composition of “net” precipitation waters reaching the surface.

Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

AbstractProcess-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBACC, and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.

Risky future for Mediterranean forests unless they undergo extreme carbon fertilization.

Forest performance is challenged by climate change but higher atmospheric [CO2 ] (ca ) could help trees mitigate the negative effect of enhanced water stress. Forest projections using data assimilation with mechanistic models are a valuable tool to assess forest performance. Firstly, we used dendrochronological data from 12 Mediterranean tree species (six conifers and six broadleaves) to calibrate a process-based vegetation model at 77 sites. Secondly, we conducted simulations of gross primary production (GPP) and radial growth using an ensemble of climate projections for the period 2010-2100 for the high-emission RCP8.5 and low-emission RCP2.6 scenarios. GPP and growth projections were simulated using climatic data from the two RCPs combined with (i) expected ca ; (ii) constant ca =390ppm, to test a purely climate-driven performance excluding compensation from carbon fertilization. The model accurately mimicked the growth trends since the 1950s when, despite increasing ca , enhanced evaporative demands precluded a global net positive effect on growth. Modeled annual growth and GPP showed similar long-term trends. Under RCP2.6 (i.e., temperatures below +2°C with respect to preindustrial values), the forests showed resistance to future climate (as expressed by non-negative trends in growth and GPP) except for some coniferous sites. Using exponentially growing ca and climate as from RCP8.5, carbon fertilization overrode the negative effect of the highly constraining climatic conditions under that scenario. This effect was particularly evident above 500ppm (which is already over +2°C), which seems unrealistic and likely reflects model miss-performance at high ca above the calibration range. Thus, forest projections under RCP8.5 preventing carbon fertilization displayed very negative forest performance at the regional scale. This suggests that most of western Mediterranean forests would successfully acclimate to the coldest climate change scenario but be vulnerable to a climate warmer than +2°C unless the trees developed an exaggerated fertilization response to [CO2 ].

Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management

Abstract. Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grass-biomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5° by 0.5°. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901–2012. The grass-biomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, rising CO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 × 106 km2 in 1901 to 12.3 × 106 km2 in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and interannual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.

Implementation of a Marauding Insect Module (MIM, version 1.0) in the Integrated BIosphere Simulator (IBIS, version 2.6b4) dynamic vegetation–land surface model

Abstract. Insects defoliate and kill plants in many ecosystems worldwide. The consequences of these natural processes on terrestrial ecology and nutrient cycling are well established, and their potential climatic effects resulting from modified land–atmosphere exchanges of carbon, energy, and water are increasingly being recognized. We developed a Marauding Insect Module (MIM) to quantify, in the Integrated BIosphere Simulator (IBIS), the consequences of insect activity on biogeochemical and biogeophysical fluxes, also accounting for the effects of altered vegetation dynamics. MIM can simulate damage from three different insect functional types: (1) defoliators on broadleaf deciduous trees, (2) defoliators on needleleaf evergreen trees, and (3) bark beetles on needleleaf evergreen trees, with the resulting impacts being estimated by IBIS based on the new, insect-modified state of the vegetation. MIM further accounts for the physical presence and gradual fall of insect-killed dead standing trees. The design of MIM should facilitate the addition of other insect types besides the ones already included and could guide the development of similar modules for other process-based vegetation models. After describing IBIS–MIM, we illustrate the usefulness of the model by presenting results spanning daily to centennial timescales for vegetation dynamics and cycling of carbon, energy, and water in a simplified setting and for bark beetles only. More precisely, we simulated 100 % mortality events from the mountain pine beetle for three locations in western Canada. We then show that these simulated impacts agree with many previous studies based on field measurements, satellite data, or modelling. MIM and similar tools should therefore be of great value in assessing the wide array of impacts resulting from insect-induced plant damage in the Earth system.