Global Vegetation Model Research Articles

Changes in the seasonal amplitude of northern ecosystem productivity under future global warming

Observations have shown a largely enhanced seasonal amplitude of northern atmospheric CO2 in the past several decades, and this enhancement is attributable to the increased seasonal amplitude of northern net ecosystem productivity (NEP amplitude). In the future, however, the changes in NEP amplitude are not clear, because of the uncertainties in climate change and vegetation dynamics. This study investigated the changes in NEP amplitude north of 45°N under future global warming by using a dynamic global vegetation model (DGVM). The authors conducted two sets of simulations: a present-day simulation (1981–2000) and future simulations (2081–2100) forced by RCP8.5 outputs from CMIP5. The results showed an overall enhanced northern NEP amplitude under the RCP8.5 scenario because of the increased maximum NEP and the decreased minimum NEP. The increases (decreases) in the maximum (minimum) NEP resulted from stronger (weaker) positive changes in gross primary production (GPP) than ecosystem respiration (ER). Changes in GPP and ER are both dominantly driven by surface air temperature and vegetation dynamics. This work highlights the key role of vegetation dynamics in regulating the northern terrestrial carbon cycle and the importance of including a DGVM in Earth system models.摘要观测显示过去几十年北半球大气二氧化碳季节幅度大幅增加, 这主要是由北半球陆地净生态系统生产力季节幅度的增加所致. 但是, 因为气候变化和植被动态的不确定性, 未来陆地净生态系统生产力季节幅度的变化还很不清楚. 本工作利用全球植被动力学模式研究了全球变暖背景下北纬45°以北陆地净生态系统生产力季节幅度的变化. 作者做了两大类试验: 当代试验 (1981−2000) 和CMIP5 RCP8.5 变暖情景驱动的未来试验 (2081−2100) . 结果显示, 在RCP8.5变暖情景下北半球中高纬陆地净生态系统生产力季节幅度整体增加, 这是因为陆地净生态系统生产力的月最大值增加且月最小值减小. 最大 (最小) 陆地净生态系统生产力的增加 (减小) 是由于总初级生产力的增加强 (弱) 于生态系统总呼吸. 总初级生产力和生态系统总呼吸的变化都主要受地表气温和植被动态的驱动. 本工作强调了植被动态对北半球中高纬陆地生态系统碳循环的关键调制作用, 也强调了在地球系统模式中包含全球植被动力学模式的重要性.

Global Carbon Budget 2022

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Interannual global carbon cycle variations linked to atmospheric circulation variability

Abstract. One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p≥800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n<60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n<30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.

Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment

Abstract. Extreme drought events in Amazon forests are expected to become more frequent and more intense with climate change, threatening ecosystem function and carbon balance. Yet large uncertainties exist on the resilience of this ecosystem to drought. A better quantification of tree hydraulics and mortality processes is needed to anticipate future drought effects on Amazon forests. Most state-of-the-art dynamic global vegetation models are relatively poor in their mechanistic description of these complex processes. Here, we implement a mechanistic plant hydraulic module within the ORCHIDEE-CAN-NHA r7236 land surface model to simulate the percentage loss of conductance (PLC) and changes in water storage among organs via a representation of the water potentials and vertical water flows along the continuum from soil to roots, stems and leaves. The model was evaluated against observed seasonal variability in stand-scale sap flow, soil moisture and productivity under both control and drought setups at the Caxiuanã throughfall exclusion field experiment in eastern Amazonia between 2001 and 2008. A relationship between PLC and tree mortality is built in the model from two empirical parameters, the cumulated duration of drought exposure that triggers mortality, and the mortality fraction in each day exceeding the exposure. Our model captures the large biomass drop in the year 2005 observed 4 years after throughfall reduction, and produces comparable annual tree mortality rates with observation over the study period. Our hydraulic architecture module provides promising avenues for future research in assimilating experimental data to parameterize mortality due to drought-induced xylem dysfunction. We also highlight that species-based (isohydric or anisohydric) hydraulic traits should be further tested to generalize the model performance in predicting the drought risks.

Combined impacts of future climate-driven vegetation changes and socioeconomic pressures on protected areas in Africa.

Africa's protected areas (PAs) are the last stronghold of the continent's unique biodiversity, but they appear increasingly threatened by climate change, substantial human population growth, and land-use change. Conservation planning is challenged by uncertainty about how strongly and where these drivers will interact over the next few decades. We investigated the combined future impacts of climate-driven vegetation changes inside African PAs and human population densities and land use in their surroundings for 2 scenarios until the end of the 21st century. We used the following 2 combinations of the shared socioeconomic pathways (SSPs) and representative greenhouse gas concentration pathways (RCPs): the "middle-of-the-road" scenario SSP2-RCP4.5 and the resource-intensive "fossil-fueled development" scenario SSP5-RCP8.5. Climate change impacts on tree cover and biome type (i.e., desert, grassland, savanna, and forest) were simulated with the adaptive dynamic global vegetation model (aDGVM). Under both scenarios, most PAs were adversely affected by at least 1 of the drivers, but the co-occurrence of drivers was largely region and scenario specific. The aDGVM projections suggest considerable climate-driven tree cover increases in PAs in today's grasslands and savannas. For PAs in West Africa, the analyses revealed climate-driven vegetation changes combined with hotspots of high future population and land-use pressure. Except for many PAs in North Africa, future decreases in population and land-use pressures were rare. At the continental scale, SSP5-RCP8.5 led to higher climate-driven changes in tree cover and higher land-use pressure, whereas SSP2-RCP4.5 was characterized by higher future population pressure. Both SSP-RCP scenarios implied increasing challenges for conserving Africa's biodiversity in PAs. Our findings underline the importance of developing and implementing region-specific conservation responses. Strong mitigation of future climate change and equitable development scenarios would reduce ecosystem impacts and sustain the effectiveness of conservation in Africa.

Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia

Abstract. Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover

Weakly-supervised learning has recently emerged in the classification context where true labels are often scarce or unreliable. However, this learning setting has not yet been extensively analyzed for regression problems, which are typical in macroecology. We further define a novel computational setting of structurally noisy and incomplete target labels, which arises, for example, when the multi-output regression task defines a distribution such that outputs must sum up to unity. We propose an algorithmic approach to reduce noise in the target labels and improve predictions. We evaluate this setting with a case study in global vegetation modelling, which involves building a model to predict the distribution of vegetation cover from climatic conditions based on global remote sensing data. We compare the performance of the proposed approach to several incomplete target baselines. The results indicate that the error in the targets can be reduced by our proposed partial-imputation algorithm. We conclude that handling structural incompleteness in the target labels instead of using only complete observations for training helps to better capture global associations between vegetation and climate.

Simulating the potential forest ranges in an old land: the case for Turkey’s forests

The central aim of this study is to estimate the current potential distribution of Turkey’s forests and their composition in absence of anthropogenic landcover change, and in this manner to contribute to past studies on the distribution of European forest taxa by filling an important spatial gap. For our simulations, we used high resolution climate data from ERA5-Land and a process based dynamic regional to global vegetation model (LPJ-GUESS), for which we modified the original European tree species list to match the climatic sensitivities that the species in turn exhibit throughout their natural ranges in Turkey, alternating between subspecies where necessary. This is the first application of LPJ-GUESS for the entire country using high resolution climate data. We defined the climatic tolerances for the model of three conifer species with wide distribution in Turkey: Pinus nigra, Pinus brutia, and Cedrus libani. We first identified relative bioclimatic tolerances of species for Turkey by way of comparing their current recorded distributions to ERA5-Land reanalysis data using observation data from the General Directorate of Forestry of Turkey, and later through ensemble runs with LPJ-GUESS we finetuned their parameters. Finally, we compared simulated net primary production to MODIS remote sensing observation estimates. In general, LPJ-GUESS captured the natural distribution ranges of different forest taxa, especially after the introduction of three additional species native to Turkey, highlighting competition for resources. Due to probable regional acclimatization of species, our final set of parameters for the different plant functional types offers a suitable alternative for future studies.

Water use efficiency-based assessment of risk to terrestrial ecosystems in China under global warming targets of 1.5 °C and 2.0 °C

Assessing ecosystem risk is crucial to ecosystem management and to achieving the Paris Agreement’s 1.5 °C or 2.0 °C global warming targets, but the spatial pattern and drivers of ecosystem risk are highly controversial. By simulating China’s ecosystem water use efficiency using the Lund–Potsdam–Jena global vegetation model, the ecosystem risk at the eco-geographical and vegetation-type scales was assessed in this study to identify the regions at risk under global warming, and the impacts of the species richness (ASR) and micro-meteorological factors on the ecosystem risk were investigated in the identified regions using the structural equation model (SEM). Our results indicate that about 30.5% and 32.2% of the ecosystems in China will face risk under global warming targets of 1.5 °C and 2.0 °C, respectively. The ecosystem risk is spatially heterogeneous and biome-specific. The ecosystem risk will mainly occur in the mid-temperate humid/sub-humid region, warm temperate humid/sub-humid region, northern subtropical humid region, mid-subtropical humid region, and the Tibetan Plateau region in China, of which cropland, grassland, and deciduous broadleaf forests will account for 39%, 17%, and 14%, respectively. The results of the SEM analysis indicate that the micro-meteorological factors and ASR explain 48% of the variation in the ecosystem risk, and the micro-meteorological factors and ASR have similar but opposite influences on the ecosystem risk. For the ecosystems identified as at risk under global warming, the negative total effect of the micro-meteorological factors on the ecosystem risk is mainly due to the dominant role of the shortwave radiation in regulating the ecosystem risk; and the positive total effect of the ASR on the ecosystem risk indicates that the ecosystems with higher species richness may face more severe damage when the risk occurs. Therefore, we should carefully balance the effect of the ASR on the ecosystems’ abilities to adapt to changes and ecosystems’ abilities to recover from damage. Our results contribute to precise ecosystem management in China and provide evidence to support the idea that properly optimizing biodiversity can prevent and reduce the damage to ecosystems caused by global warming.

Respiratory loss during late-growing season determines the net carbon dioxide sink in northern permafrost regions.

Warming of northern high latitude regions (NHL, > 50 °N) has increased both photosynthesis and respiration which results in considerable uncertainty regarding the net carbon dioxide (CO2) balance of NHL ecosystems. Using estimates constrained from atmospheric observations from 1980 to 2017, we find that the increasing trends of net CO2 uptake in the early-growing season are of similar magnitude across the tree cover gradient in the NHL. However, the trend of respiratory CO2 loss during late-growing season increases significantly with increasing tree cover, offsetting a larger fraction of photosynthetic CO2 uptake, and thus resulting in a slower rate of increasing annual net CO2 uptake in areas with higher tree cover, especially in central and southern boreal forest regions. The magnitude of this seasonal compensation effect explains the difference in net CO2 uptake trends along the NHL vegetation- permafrost gradient. Such seasonal compensation dynamics are not captured by dynamic global vegetation models, which simulate weaker respiration control on carbon exchange during the late-growing season, and thus calls into question projections of increasing net CO2 uptake as high latitude ecosystems respond to warming climate conditions.

Costs of forest carbon sequestration in the presence of climate change impacts

Forests play a critical role in mitigating climate change, and, at the same time, are predicted to experience large-scale impacts of climate change that will affect the efficiency of forests in mitigation efforts. Projections of future carbon sequestration potential typically do not account for the changing economic costs of timber and agricultural production and land use change. We integrated a dynamic forward-looking economic optimization model of global land use with results from a dynamic global vegetation model and meta-analysis of climate impacts on crop yields to project future carbon sequestration in forests. We find that the direct impacts of climate change on forests, represented by changes in dieback and forest growth, and indirect effects due to lost crop productivity, together result in a net gain of 17 Gt C in aboveground forest carbon storage from 2000 to 2100. Increases in climate-driven forest growth rates will result in an 81%–99% reduction in costs of reaching a range of global forest carbon stock targets in 2100, while the increases in dieback rates are projected to raise the costs by 57%–132%. When combined, these two direct impacts are expected to reduce the global costs of climate change mitigation in forests by more than 70%. Inclusion of the third, indirect impact of climate change on forests through reduction in crop yields, and the resulting expansion of cropland, raises the costs by 11%–38% and widens the uncertainty range. While we cannot rule out the possibility of climate change increasing mitigation costs, the central outcomes of the simultaneous impacts of climate change on forests and agriculture are 64%–86% reductions in the mitigation costs. Overall, the results suggest that concerns about climate driven dieback in forests should not inhibit the ambitions of policy makers in expanding forest-based climate solutions.

Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02

Abstract. Documenting year-to-year variations in carbon storage potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO2) emissions. However, the magnitude, pattern, and inner biomass partitioning of carbon storage potential and the effect of the changes in climate and CO2 on inner carbon stocks remain poorly quantified. Herein, we use a spatially explicit individual-based dynamic global vegetation model to investigate the influences of the changes in climate and CO2 on the enhanced carbon storage potential of vegetation. The modelling included a series of factorial simulations using the Climatic Research Unit (CRU) dataset from 1916 to 2015. The results show that CO2 predominantly leads to a persistent and widespread increase in light-gathering vegetation biomass carbon stocks (LVBC) and water-gathering vegetation biomass carbon stocks (WVBC). Climate change appears to play a secondary role in carbon storage potential. Importantly, with the intensification of water stress, the magnitude of the light- and water-gathering responses in vegetation carbon stocks gradually decreases. Plants adjust carbon allocation to decrease the ratio between LVBC and WVBC for capturing more water. Changes in the pattern of vegetation carbon storage were linked to zonal limitations in water, which directly weaken and indirectly regulate the response of potential vegetation carbon stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrate that the long-term trend in increased vegetation biomass carbon stocks is driven by CO2 fertilization and temperature effects that are controlled by water limitations.

LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism

Abstract. Land biosphere processes are of central importance to the climate system. Specifically, ecosystems interact with the atmosphere through a variety of feedback loops that modulate energy, water, and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land–atmosphere interactions. Dynamic global vegetation models (DGVMs) attempt to capture land ecosystem processes and are increasingly incorporated into Earth system models (ESMs), which makes it possible to study the coupled dynamics of the land biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water, and CO2 between the land ecosystem and the atmosphere and thus provide surface boundary conditions to an atmospheric model over land. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modelled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types. We find that the emerging ecosystem composition and carbon fluxes are sensitive to both the choice of stomatal conductance model and the response of plant water uptake to soil moisture. The new implementation described in this work lays the foundation for using the well-established LPJ-GUESS DGVM as an alternative land surface model (LSM) in coupled land–biosphere–climate studies, where an accurate representation of ecosystem processes is essential.

Dynamic Global Vegetation Models: Searching for the balance between demographic process representation and computational tractability

Vegetation is subject to multiple pressures in the 21st century, including changes in climate, atmospheric composition and human land-use. Changes in vegetation type, structure, and function also feed back to the climate through their impact on the surface-atmosphere fluxes of carbon and water. Dynamic Global Vegetation Models (DGVMs), are therefore key component of the latest Earth System Models (ESMs). Model projections for the future land carbon sink still span a wide range, in part due to the difficulty of representing complex ecosystem and biogeochemical processes at large scales (i.e. grid lengths ≈ 100km). The challenge for developers of DGVMs is therefore to find an optimal balance between detailed process representation and the ability to scale-up. We categorise DGVMs into four groups; Individual, Average Area, Two Dimensional Cohort and One Dimensional Cohort models. From this we review popular methods used to represent dynamic vegetation within the context of Earth System modelling. We argue that the minimum level of complexity required to effectively model changes in carbon storage under changing climate and disturbance regimes, requires a representation of tree size distributions within forests. Furthermore, we find that observed size distributions are consistent with Demographic Equilibrium Theory, suggesting that One Dimensional Cohort models with a focus on tree size, offer the best balance between computational tractability and realism for ESM applications.

A Process‐Model Perspective on Recent Changes in the Carbon Cycle of North America

AbstractContinental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state‐of‐the‐art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year−1 for the period 2000–2019 (0.31 and 0.44 PgC year−1 in each decade); split into 0.18 ± 0.12 PgC year−1 in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year−1 in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year−1). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental‐scale land C cycle along with the spatial distribution of local sink‐source dynamics.

Modeling the effects of alternative crop–livestock management scenarios on important ecosystem services for smallholder farming from a landscape perspective

Abstract. Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop–livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions (ESFs) and ecosystem services (ESSs). Such settings require a detailed system understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatiotemporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM (Agricultural Production Systems sIMulator) for cropland and aDGVM2 (Adaptive Dynamic Global Vegetation Model) for rangeland to investigate the effects of (i) current management practices (minimum input crop–livestock agriculture), (ii) an SI scenario for crop production (with dry-season cropland grazing), and (iii) a scenario with separated rangeland and cropland management (livestock exclusion from cropland) in two representative villages of the Limpopo Province, South Africa, for the period from 2000 to 2010. We focused on the following ESFs and ESSs provided by cropland and rangeland: yield and feed provision, soil carbon storage, cropland leaf area index (LAI), and soil water. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (small fertilizer quantities, weeding, and crop rotation) led to moderate yield increases of between a factor of 1.2 and 1.6 and reduced soil carbon loss, but they sometimes caused increased growing-season water limitation effects. Thus, SI effects strongly varied between years. Dry-season crop residue grazing reduced feed deficits by approximately a factor of 2 compared with the rangeland-only scenario, but it could not fully compensate for the deficits during the dry-to-wet season transition. We expect that targeted deficit irrigation or measures to improve water retention and the soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and decrease rangeland grazing pressure during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies.

Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)

Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)

Global Spectrum of Vegetation Light‐Use Efficiency

AbstractVegetation light‐use efficiency (LUE) is a critical parameter of vegetation photosynthesis, but its global variations and the drivers remain poorly understood. Here, we used data from 172 flux tower sites to characterize the global distribution of ecosystem‐level LUE spanning various vegetation and climate. The global growing season average LUE was 1.30 ± 0.55 g C MJ−1 for 2000–2014. The main factors driving LUE variations included environmental conditions (e.g., temperature), plant traits (e.g., foliar nitrogen concentration and isohydricity) and their interactions, which together accounted for about 76% of the observed global variations in LUE. However, an ensemble of 11 dynamic global vegetation models largely underestimated LUE, and hardly captured the key drivers. Our results highlight that a paradigm shift from vegetation functional type‐based scheme to trait‐climate spectrum‐based scheme is necessary for the next‐generation models to accurately simulate the response of vegetation carbon uptake to climate change.

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

How more sophisticated leaf biomass simulations can increase the realism of modelled animal populations

Animal biodiversity, and its key roles in ecosystem state and functioning, is facing critical challenges in the wake of anthropogenic activities. It is urgently necessary to improve understanding of the interconnections between animals and the vegetation within ecosystems. Process-based modelling has shown to be a mighty tool in making assessments on ecological processes. We assess the effect of different vegetation models on simulated animal biodiversity by replacing the vegetation module within Madingley, a multi-trophic model of functional diversity with LPJ-GUESS, a dynamic global vegetation model. We compare the output metrics of the model system to Madingley’s default version for four ecosystem types around the globe and analyse whether the realism of the simulation results increased as a result of the coupling between Madingley and LPJ-GUESS. Simulated animal populations react to the coupling by shifting towards smaller individuals with a higher abundance. General shifts in body mass and animal distributions can be traced back to ecological processes, allowing in-depth analysis of heterotrophic responses to changes in leaf biomass. We also derive power-law relationships for herbivory to NPP and herbivore biomass to NPP and conclude that the coupled model system simulates animal populations that follow reasonable power-laws which are similar to power-laws derived from empirical data. Our results indicate that developing process-based model systems is a viable way to assess multi-trophic interconnections between animal populations and the ecosystems vegetation.