Dynamic Vegetation Model Research Articles

Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro

Abstract. As temperatures decrease in autumn, vegetation of temperate and boreal ecosystems increases its tolerance to freezing. This process, known as hardening, results in a set of physiological changes at the molecular level that initiate modifications of cell membrane composition and the synthesis of anti-freeze proteins. Together with the freezing of extracellular water, anti-freeze proteins reduce plant water potentials and xylem conductivity. To represent the responses of vegetation to climate change, land surface schemes increasingly employ “hydrodynamic” models that represent the explicit fluxes of water from soil and through plants. The functioning of such schemes under frozen soil conditions, however, is poorly understood. Nonetheless, hydraulic processes are of major importance in the dynamics of these systems, which can suffer from, e.g., winter “frost drought” events. In this study, we implement a scheme that represents hardening into CLM5.0-FATES-Hydro. FATES-Hydro is a plant hydrodynamics module in FATES, a cohort model of vegetation physiology, growth, and dynamics hosted in CLM5.0. We find that, in frozen systems, it is necessary to introduce reductions in plant water loss associated with hardening to prevent winter desiccation. This work makes it possible to use CLM5.0-FATES-Hydro to model realistic impacts from frost droughts on vegetation growth and photosynthesis, leading to more reliable projections of how northern ecosystems respond to climate change.

A global-drive analysis of ecosystem respiration in the Arctic and Third Pole

Terrestrial ecosystem respiration is an important component of the global terrestrial carbon cycle. The special environment of low temperatures in the Arctic and Third Pole makes ecosystem respiration extremely sensitive to the response of climate change. Therefore, understanding the response of Arctic and Third Pole (Tibetan Plateau) ecosystem respiration to individual global environmental drivers is critical for predicting future climate change caused by global warming. However, current studies have not systematically reported on the relative contributions of rising CO2 concentrations, nitrogen deposition, land-use change, and climate change, which are global change factors with multi-year trends and interannual variability, to ecosystem respiration in the Arctic and Third Pole. Here, we used multiple multi-scenario models of vegetation dynamic process model from 1901 to 2010, and divided each environmental driver, each season into long-term trend and interannual variability components by using a novel variance decomposition method. Our results showed that (1) ecosystems autotrophic respiration and heterotrophic respiration in Arctic and Third Pole are relatively low, which remains within 110 g C m−2 in both the Arctic and the Tibetan Plateau; (2) ecosystem respiration tends to increase in all scenarios, and the increase rate has accelerated significantly since 1980. The interannual variability of climate change contributes the most to the variability of ecosystem respiration, up to 52 % and 63 % in the Arctic and Tibetan Plateau, respectively. Both the multi-year trend and interannual variability of increasing atmospheric CO2 concentration contribute to the increase in ecosystem respiration, with relative contributions of 28 % and 32 %, respectively. (3) In the Arctic and the Tibetan Plateau, which are sensitive to temperature and short-wave radiation, summer is the key seasonal component controlling ecosystem respiration, and the contribution of this season (including long-term trends and interannual variability) to annual ecosystem respiration in the Arctic and the Tibetan Plateau is 64 % and 45 %, respectively. Our study bridged the gap in understanding the response of cold ecosystems to global changes at high altitudes and high latitudes, which provides a methodological reference for future accurate prediction of global environmental drivers of cold ecosystem respiration.

Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)

Abstract. We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.

Historical and Future Vegetation Changes in the Degraded Frozen Soil and the Entire Tibetan Plateau and Climate Drivers

AbstractAbout 99% of the Tibetan Plateau (TP) is covered by frozen soils and degradation of frozen soils will certainly impact TP's ecosystems. Here, we investigate decadal changes of frozen soils and net primary productivity (NPP, representing vegetation) in the degraded frozen soil zones and the TP during 1982–2014 and 2015–2100 using a dynamic vegetation model, historical records and the latest CMIP6 projections, as well as observation‐based soil temperature thresholds. In 1982–2014, degraded permafrost soil zones were in the range of 316,975–455,402 km2, with mean annual NPP staying around 84.9 gCm−2and annual NPP showing a significant reduction at −1.71 gCm−2/year due primarily to warming air. Seasonally frozen soil also degraded by 15,636 km2in the southeast TP, with mean annual NPP staying around 620.0 gCm−2and annual NPP showing a significant increase at 11.00 gCm−2/year. In the future, frozen soil continues to degrade and the degradation accelerates toward the end of the century such that only 2.7% of permafrost soil in 2014 is left by 2080–2100 under the shared socioeconomic pathway SSP5‐8.5. Mean annual NPP in the permafrost soil degraded zones in 2015–2100 is about half of that for 1982–2014, with spatially mixed decrease and increase trends in the near‐, mid‐, and long‐term periods. Over the seasonally frozen soil degraded zones and the entire TP, more positive than negative annual NPP changes are seen in 2015–2100, especially in the southeast of the TP, due to improved growing conditions and the expansion of primarily subtropical and temperate scrubland.

Comparison of the green-to-desert Sahara transitions between the Holocene and the last interglacial

Abstract. The desertification and vegetation feedbacks of the “green Sahara” during the last interglacial (LIG) and the Holocene have been investigated by many studies. Yet the abruptness of climate and vegetation changes and their interactions are still under discussion. In this study, we apply an Earth system model of intermediate complexity (iLOVECLIM) in combination with two dynamical vegetation models (VECODE and LPJ-GUESS) to simulate climate–vegetation changes during the Holocene and the LIG to compare the patterns of North African vegetation evolutions and mechanisms of their feedbacks during these two interglacials. Our results confirmed the existence of the green Sahara during the early LIG, which is as an analogue to the green Sahara during the Holocene. During both interglacials, an overall consistent transition from vegetated Sahara to desert is shown in our results, but the amplitudes of these transitions vary. These simulated Sahara vegetation transitions are nearly linearly related to the summer insolation declines at 20∘ N, resulting in faster declines of vegetation cover during the LIG than in the Holocene. The decline of vegetation cover peaks at 25% kyr−1 at around 122 ka, while during the Holocene the steepest vegetation cover decline is 10 % kyr−1 at around 6 ka. Our results suggest net positive vegetation feedbacks to climate during the two interglacials. During the early LIG and Holocene, vegetation strengthens precipitation by a factor of 2 to 3 through the vegetation–albedo feedback when the vegetation cover is greater than 60 %. Vegetation cover decreases with declines of the incoming moisture transport by the atmosphere due to the reduced summer insolation at 20∘ N, weakening the summer monsoon during both interglacials. This desertification is accelerated when the positive vegetation–albedo feedback cannot offset the reduction of precipitation due to a weaker summer monsoon. The impacts of this positive vegetation feedback on precipitation decrease with decreased vegetation cover, during which the impacts of negative vegetation–evaporation feedbacks increase, accelerating the loss of soil moisture and vegetation cover. Overall, the net positive vegetation feedback is strong during the early phases of both interglacials, but the vegetation transition is more abrupt during the LIG than during the Holocene due to the more rapid changes in summer insolation during the LIG. The main difference between the two interglacials is the rate of precipitation change, which is relatively gradual during the Holocene, leading to a more gradual vegetation transition in comparison to the LIG.

Forest cover resilience to climate change over India using the MC2 dynamic vegetation model.

It is imperative to understand the climate change impact on the forest ecosystem to develop appropriate mitigation and management strategies. We have employed a process-based dynamic vegetation modeling (MAPSS-CENTURY: MC) approach to project change in vegetation life forms under projected climate conditions that attained 81% overall accuracy. The present and projected climate conditions suggested highly resilient/stable forest covers in wet climate regimes and moderately resilient in dry semi-arid regions. Several forested grids in the seasonally dry tropical forest in the Eastern Ghats and dry Deccan peninsula regions are estimated to be less resilient, which may experience a regime shift toward scrub and grassland. The future prediction demonstrated an upward temperature shift in the Western Himalayas and trans-Himalaya, which may facilitate forest spread at higher elevations. Although the forest cover resilience may increase in future climate conditions, the disturbances in several regions in the Deccan Peninsula and the Eastern Ghats may trigger forest to scrub and grassland transition. The inaccuracy in model simulation in the Western Himalayas could be attributed to coarse resolution grids (0.5°) failing to resolve the narrow climate niches. The spatially explicit model simulation provides opportunities to develop long-term climate change adaptation and conservation strategies.

Improving Dynamic Vegetation Modeling in Noah‐MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau

AbstractAccurate modeling of vegetation dynamics is needed to improve our understanding of and ability to predict the impacts of vegetation changes on terrestrial water‐energy‐carbon cycles. Parameter optimization (PO) and data assimilation (DA) are widely used to improve the performance of dynamic vegetation modules in land surface models (LSMs). However, their effectiveness is unclear. Here we analyze their impacts on the performance of the dynamic vegetation module of the Noah with multiparameterization options (Noah‐MP) LSM over the Chinese Loess Plateau, which is an ideal study case because it is a large region that has undergone dramatic vegetation change. We first optimize these parameters that strongly affect the predicted vegetation dynamics based on the results of sensitivity analysis using PO. In addition, we evaluate the effect of DA by assimilating leaf area index (LAI) remote sensing data into Noah‐MP without PO. Finally, we investigate the effect of applying PO and DA together. PO increases the predicted rates of carbon assimilation and turnover and thus reduces the underestimation of LAI and the lag in vegetation seasonality. DA has a weaker impact than PO: it only reduces the root mean squared error (RMSE) of the predicted LAI in around 49.76% of the studied region and is mainly beneficial in the growing phase. Combining PO and DA compensate the limitations of each other, and gives the most significant reduction in RMSE (median: −0.24 m2/m2) and increase in R2 (+0.44). The improved vegetation dynamics with different optimization methods thus improve the modeling of water and carbon cycle processes.

Climate Change Impacts on the Water Resources and Vegetation Dynamics of a Forested Sardinian Basin through a Distributed Ecohydrological Model

Climate change is impacting Mediterranean basins, bringing warmer climate conditions. The Marganai forest is a natural forest protected under the European Site of Community Importance (Natura 2000), located in Sardinia, an island in the western Mediterranean basin, which is part of the Fluminimaggiore basin. Recent droughts have strained the forest′s resilience. A long-term hydrological database collected from 1922 to 2021 shows that the Sardinian forested basin has been affected by climate change since the middle of the last century, associated with a decrease in winter precipitation and annual runoff, reduced by half in the last century, and an increase of ~1 °C in the mean annual air temperature. A simplified model that couples a hydrological model and a vegetation dynamics model for long-term ecohydrological predictions in water-limited basins is proposed. The model well predicted almost one century of runoff observations. Trees have suffered from the recent warmer climate conditions, with a tree leaf area index (LAI) decreasing systematically due to the air temperature and a vapor pressure deficit (VPD) rise at a rate of 0.1 hPa per decade. Future climate scenarios of the HadGEM2-AO climate model are predicting even warmer conditions in the Sardinian forested basin, with less annual precipitation and higher air temperatures and VPD. Using these climate scenarios, we predicted a further decrease in runoff and tree transpiration and LAI in the basin, with a reduction of tree LAI by half in the next century. Although the annual runoff decreases drastically in the worst scenarios (up to 26%), runoff extremes will increase in severity, outlining future scenarios that are drier and warmer but, at the same time, with an increased flood frequency. The future climate conditions undermine the forest’s sustainability and need to be properly considered in water resources and forest management plans.

Ecosystem productivity affected the spatiotemporal disappearance of Neanderthals in Iberia

What role did fluctuations play in biomass availability for secondary consumers in the disappearance of Neanderthals and the survival of modern humans? To answer this, we quantify the effects of stadial and interstadial conditions on ecosystem productivity and human spatiotemporal distribution patterns during the Middle to Upper Palaeolithic transition (50,000–30,000 calibrated years before the present) in Iberia. First, we used summed probability distribution, optimal linear estimation and Bayesian age modelling to reconstruct an updated timescale for the transition. Next, we executed a generalized dynamic vegetation model to estimate the net primary productivity. Finally, we developed a macroecological model validated with present-day observations to calculate herbivore abundance. The results indicate that, in the Eurosiberian region, the disappearance of Neanderthal groups was contemporaneous with a significant decrease in the available biomass for secondary consumers, and the arrival of the first Homo sapiens populations coincided with an increase in herbivore carrying capacity. During stadials, the Mediterranean region had the most stable conditions and the highest biomass of medium and medium–large herbivores. These outcomes support an ecological cause for the hiatus between the Mousterian and Aurignacian technocomplexes in Northern Iberia and the longer persistence of Neanderthals in southern latitudes.

Development and long-term dynamics of old-growth beech-fir forests in the Pyrenees: Evidence from dendroecology and dynamic vegetation modelling

Ecological knowledge on long-term forest dynamics and development has been primarily derived from the study of old-growth forests. Centuries of forest management have decreased the extent of temperate old-growth forests in Europe and altered managed forests. Disentangling the effects of past human disturbances and climate on current species composition is crucial for understanding the long-term development of forests under global change. In this study, we investigated disturbance and recruitment dynamics in two forests in the Western Pyrenees (Spain) with contrasting management history: an old-growth forest and a long-untouched forest, both dominated by the two shade-tolerant species Fagus sylvatica (European beech) and Abies alba (Silver fir). We used dendroecological methods in seven plots to analyse forest structure, growth patterns and disturbance histories in these forests. We benchmarked these data with the dynamic vegetation model ForClim to examine the effects of natural and human-induced disturbances on forest development, structure and species composition. Disturbance regimes differed between the study forests, but none showed evidence of stand replacing disturbances, either natural or human induced. Low disturbance rates and continuous recruitment of beech and fir dominated the old-growth forest over the last 400 years. In contrast, the long-untouched forest was intensively disturbed in 1700–1780, probably by logging, with lower natural disturbance rates thereafter. Beech and fir recruitment preferentially occurred after more intense disturbances, despite the high shade tolerance of both beech and fir. Higher fir abundance in the long-untouched forest than in the old-growth forest appeared to be related to its human-induced disturbances. ForClim closely simulated forest potential natural vegetation with a dominance of beech over fir, but overestimated the presence of less shade-tolerant species. Previously observed local fir decline may result from natural forest successional processes after logging. Within ∼200 years after logging cessation, some long-untouched forest structural attributes converged towards old-growth forest, but legacy effects still affected species composition and structure. Natural disturbance regimes in beech-fir forests of the Western Pyrenees induce temporal fluctuations between beech and fir abundance, with a natural tendency for beech dominance in advanced developmental stages with low disturbance rates.

What Are Contemporary Mexican Conifers Telling Us? A Perspective Offered from Tree Rings Linked to Climate and the NDVI along a Spatial Gradient

Forest structure and composition have changed rapidly worldwide, presenting tendencies towards an increasing proportion of younger trees. From chronologies of tree-ring indices (TRI) and the reconstruction of the basal area increment (BAI), a dendroecological study was conducted from the perspective of the radial growth of twelve contemporary conifer species in a highly diverse region of the planet. From an elevational perspective, the TRI were associated with climate and the NDVI, while the BAI was also modeled as a potential proxy for forest productivity. Climate affects the species differently according to elevation: at 1900 m asl, Pinus caribaea, P. oocarpa and P. jeffreyi presented the lowest sensitivities to climate and drought. For their part, species occupying the intermediate part of the gradient (1901–3000 m asl), such as P. engelmannii, P. patula, P. johannis and P. maximartinezii, were very sensitive to maximum temperature (TMax), precipitation (PP) and drought during the winter–spring period. Finally, of the species distributed on the upper part of the gradient (>3000 m asl), only Abies religiosa was associated with TMax and drought; Juniperus deppeana, A. hickelii and P. hartwegii did not seem to be vulnerable to drought. Complementarily, we found significant differences in the BAI as a function of elevation, with the sites at 1001–1500 m asl presenting higher BAI. The results suggest that the growth in these forests is impacted by droughts and follows a distinct spatial pattern, with greater restriction found in mid-elevation forests. Consistent implications are also observed in BAI trends. For its part, the NDVI demonstrated a decreasing tendency in greenness from south to north, although no elevation pattern was evident. The combined proxies utilized here produced parameters that improve our understanding of forest growth and should be considered in vegetation dynamics models in order to reduce their uncertainty in the face of climate vulnerability. These forests must be sustainably managed, and it is therefore crucial to determine the influence of ecological variables on their growth.

Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)

Abstract. Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while predictive uncertainties are dominantly induced by environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental drivers not only as contributors to predictive uncertainty in their own right but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development.

Impact of the numerical solution approach of a plant hydrodynamic model (v0.1) on vegetation dynamics

Abstract. Numerous plant hydrodynamic models have started to be implemented in vegetation dynamics models, reflecting the central role of plant hydraulic traits in driving water, energy, and carbon cycles, as well as plant adaptation to climate change. Different numerical approximations of the governing equations of the hydrodynamic models have been documented, but the numerical accuracy of these models and its subsequent effects on the simulated vegetation function and dynamics have rarely been evaluated. Using different numerical solution methods (including implicit and explicit approaches) and vertical discrete grid resolutions, we evaluated the numerical performance of a plant hydrodynamic module in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES-HYDRO version 0.1) based on single-point and global simulations. Our simulation results showed that when near-surface vertical grid spacing is coarsened (grid size >10 cm), the model significantly overestimates aboveground biomass (AGB) in most of the temperate forest locations and underestimates AGB in the boreal forest locations, as compared to a simulation with finer vertical grid spacing. Grid coarsening has a small effect on AGB in the tropical zones of Asia and South America. In particular, coarse surface grid resolution should not be used when there are large and prolonged water content differences among soil layers at depths due to long dry-season duration and/or well-drained soil or when soil evaporation is a dominant fraction of evapotranspiration. Similarly, coarse surface grid resolution should not be used when there is lithologic discontinuity along the soil depth. This information is useful for uncertainty quantification, sensitivity analysis, or the training of surrogate models to design the simulations when computational cost limits the use of ensemble simulations.

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Northeast China is an important commodity grain base in China and plays an important role in maintaining national and local food security. The intensifying climate variability and strengthening of extreme weather events threaten crop growth and yields in this region. The dynamic crop model coupled into the land surface models showed advances in simulating crop phenology and crop growth response to climate change, however, which was mainly validated for the United States. By comparing with the satellite observed crop growth cycles, we firstly calibrated the parameters for the Weather Research and Forecasting (WRF) coupled Noah-MP-Crop model. Then, we evaluated the performance of the calibrated WRF-Noah-MP-Crop model with the default crop models and the dynamic vegetation models coupled with WRF by comparing our simulations with corresponding satellite measurements and meteorological observations. Our results indicated the augmentation of the Noah-MP-Crop model in WRF significantly improved the crop phenology simulation at the beginning and ending time of the growing season compared with the dynamic vegetation model using unmanaged crops. The satellite-calibrated crop parameters substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%. The coupled WRF-Noah-MP-Crop model with flexible resolutions holds unmatched advantages in estimating and projecting crop growth and yield under future climate change scenarios, which would be critical in ensuring sustainable agricultural development and maintaining future food security.

Climate windows of opportunity for plant expansion during the Phanerozoic

Earth’s long-term climate may have profoundly influenced plant evolution. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth, and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic has not yet been established. Linking climate and dynamic vegetation modelling, we identify two key ‘windows of opportunity’ during the Ordovician and Jurassic-Paleogene capable of supporting dramatic expansions of potential plant biomass. These conditions are driven by continental dispersion, paleolatitude of continental area and a lack of glaciation, allowing for an intense hydrological cycle and greater water availability. These windows coincide with the initial expansion of land plants and the later angiosperm radiation. Our findings suggest that the timing and expansion of habitable space for plants played an important role in plant evolution and diversification.

Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method

The simulation of vegetation dynamics is essential for guiding regional ecological remediation and environmental management. Recent progress in deep learning methods has provided possible solutions to vegetation simulations. The gated recurrent unit (GRU) is one of the latest deep learning algorithms that can effectively process dynamic data. However, static and dynamic data, which typically coexist in the datasets of vegetation dynamic changes, are typically processed indistinguishably. To efficiently extract spatiotemporal patterns and improve our ability to simulate potential vegetation changes, we introduced GRU into vegetation simulation and further amended the original structure of GRU according to the characteristics of the simulation dataset. The new model, the vegetation dynamics model (VDM), can independently process static and dynamic data using a more appropriate algorithm, thereby improving the simulation accuracy. Moreover, we presented a model test applied in the Luntai Desert-Oasis Ecotone in Northwest China and compared the performance of the VDM with baseline models. The results showed that the VDM produced a 7.51% higher coefficient of determination (R2) value, 7.51% higher adjusted R2 value, 16.67% lower mean squared error, and 10.78% lower mean absolute error than those of the GRU, which is the best baseline model. The proposed VDM is the first GRU-based simulation model of vegetation dynamics that has the potential to detect the time-order characteristics of dynamic factors by comprehensively considering the static information that affects vegetation changes. Moreover, the flexibility of the VDM, in combination with the wide availability of data from different data sources, aids the broader application of the VDM.

A Hierarchical Ecosystem Approach to Evaluate Global Warming Impacts in Three Global Ecoregions

Global warming has substantial effects on terrestrial ecosystems in the different ecoregions. A hierarchical ecosystem approach was conducted to analyze global warming influences with global warming impacts on the three distinct global ecoregions. The ecosystem classification of land (ECL) has been developed and integrated into a hierarchical system. Recently, the hierarchical ecosystem classifications in the 300 Dry Domain of the United States, 100 Polar Domain of Canada, and 500 Plateau Domain of China were demonstrated and explored in studying the environmental system changes and global warming impacts. This article presents the distinctive dissimilarity in each ecoregion and demonstrates the ecosystem responses linked to the hierarchical ecosystem structure and ecological function level. 1) In the Dry Domain, the warmer and wetter climate of Utah gave rise to Rocky Mountain subalpine conifer forests and Great Basin pinyon and juniper woodlands suitable for growth, which corresponds to Utah’s Climate life zone and is affiliated with the middle levels of ECL, scale ranging from U7 up to U4. Conversely, in a warmer and drier climate in Utah, annual plant species and invaded species shifted and expanded at the lower levels of ECL, scale ranging from U10 up to U9. 2) In the Polar Domain, a warmer and wetter winter of the Yukon climate influences the Spruce treeline moving northward and to higher elevations, as well as for the Arctic tundra and alpine tundra. Arboreal species grow fast to reach fructification. These typically appeared in the middle levels of ECL with ranging from Y8 up to Y5, and changed the carbon budget to a carbon sink, with a scale ranging from Y4 up to Y2. With a warmer and drier summer, shrubification in Yukon occurs rapidly, in a range from Y6 to Y5. Potentilla shrub and Salix shrub expand to the Arctic tundra region. 3) In the Plateau Domain, the annual air temperature Increased by 0.5o C/10 y over the last 45 years, and the temperature fluctuations have significantly affected the essential changes in the global energy balance and carbon budget in the upper levels of ECL, scale ranging from Q4 up to Q1. However, the precipitation showed no noticeable difference. The alpine tundra vegetation was simulated by the Vegetation Dynamic Simulation Model (VDSM) integrated with scenarios of a global temperature increase of 1 to 3°C. This illustrated the vegetation biomass changes in the lower levels to middle levels of ECL with ranging from Q8 up to Q6, and the vegetation distribution dynamics appeared in upper levels of ECL with ranging from Q4 up to Q1.

Prediction of tree sapwood and heartwood profiles using pipe model and branch thinning theory.

Estimates of tree heartwood and sapwood profiles are important in the pulp industry and for dynamic vegetation models, in which they determine tree biomechanical stability and hydraulic conductivity. Several phenomenological models of stem profiles have been developed for this purpose, based on assumptions on how tree crown and foliage distributions change over time. Here, we derive estimates of tree profiles by synthesizing a simple pipe model theory of plant form with a recently developed theory of branch thinning that from simple assumptions quantifies discarded branches and leaves. This allows us to develop a new trunk model of tree profiles from breast height up to the top of the tree. We postulate that leaves that are currently on the tree are connected by sapwood pipes, while pipes that previously connected discarded leaves or branches form the heartwood. By assuming that a fixed fraction of all pipes remain on the trunk after a branching event, as the trunk is traversed from the root system to the tips, this allows us to quantify trunk heartwood and sapwood profiles. We test the trunk model performance on empirical data from five tree species across three continents. We find that the trunk model accurately describes heartwood and sapwood profiles of all tested tree species (calibration; R2: 84-99%). Furthermore, once calibrated to a tree species, the trunk model predicts heartwood and sapwood profiles of conspecific trees in similar growing environments based only on the age and height of a tree (cross-validation/prediction; R2: 68-98%). The fewer and often contrasting parameters needed for the trunk model make it a potentially useful complementary tool for biologists and foresters.

Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China

Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.

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.