Global Vegetation Research Articles

Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

A global behavioural model of human fire use and management: WHAM! v1.0

Abstract. Fire is an integral ecosystem process and a major natural source of vegetation disturbance globally. Yet at the same time, humans use and manage fire in diverse ways and for a huge range of purposes. Therefore, it is perhaps unsurprising that a central finding of the first Fire Model Intercomparison Project was simplistic representation of humans is a substantial shortcoming in the fire modules of dynamic global vegetation models (DGVMs). In response to this challenge, we present a novel, global geospatial model that seeks to capture the diversity of human–fire interactions. Empirically grounded with a global database of anthropogenic fire impacts, WHAM! (the Wildfire Human Agency Model) represents the underlying behavioural and land system drivers of human approaches to fire management and their impact on fire regimes. WHAM! is designed to be coupled with DGVMs (JULES-INFERNO in the current instance), such that human and biophysical drivers of fire on Earth, and their interactions, can be captured in process-based models for the first time. Initial outputs from WHAM! presented here are in line with previous evidence suggesting managed anthropogenic fire use is decreasing globally and point to land use intensification as the underlying reason for this phenomenon.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Climate change critically affects the status of the land-system change planetary boundary

The planetary boundaries framework defines a safe operating space for humanity. To date, these boundaries have mostly been investigated separately, and it is unclear whether breaching one boundary can lead to the transgression of another. By employing a dynamic global vegetation model, we systematically simulate the strength and direction of the effects of different transgression levels of the climate change boundary (using climate output from ten phase 6 of the Coupled Model Intercomparison Project models for CO2 levels ranging from 350 ppm to 1000 ppm). We focus on climate change-induced shifts of Earth’s major forest biomes, the control variable for the land-system change boundary, both by the end of this century and, to account for the long-term legacy effect, by the end of the millennium. Our simulations show that while staying within the 350 ppm climate change boundary co-stabilizes the land-system change boundary, breaching it (>450 ppm) leads to critical transgression of the latter, with greater severity the higher the ppm level rises and the more time passes. Specifically, this involves a poleward treeline shift, boreal forest dieback (nearly completely within its current area under extreme climate scenarios), competitive expansion of temperate forest into today’s boreal zone, and a slight tropical forest extension. These interacting changes also affect other planetary boundaries (freshwater change and biosphere integrity) and provide feedback to the climate change boundary itself. Our quantitative process-based study highlights the need for interactions to be studied for a systemic operationalization of the planetary boundaries framework.

CO2 enrichment accelerates alpine plant growth via increasing water-use efficiency

Phenological changes in global vegetation are often attributed to climate warming. However, climate warming and elevated atmospheric CO2 concentration (eCO2) are two co-occurring global change factors, and how eCO2 would affect vegetation phenology has received less attention. The partial pressure of atmospheric CO2 on the Tibetan Plateau (TP) is lower than that in regions of lower altitudes. Consequently, the growth and phenology of alpine plants in this region could be more sensitive to eCO2, but this hypothesis is not yet supported by empirical evidence. Here we explored the effect of eCO2 on plant phenology (including phenophases of green-up, budding, and flowering) through a 5-year field manipulation experiment in a high-altitude (4600 m above sea level) alpine grassland on the TP. Our results showed that eCO2 significantly advanced the spring phenology of an early-flowering species (Kobresia pygmaea), while it had no impact on the phenology of two mid-flowering species (Potentilla saundersiana and Potentilla cuneata). Compared to other low-altitude regions, plant phenology on the TP underwent greater alterations under eCO2, which supports our hypothesis that the growth of high-altitude plants is more sensitive to eCO2. Furthermore, we found that eCO2 significantly reduced the overlapping of flowering between contrasting plant species, mainly due to the phenological advancement of the K. pygmaea induced by eCO2. The observed advancement of the spring phenology in K. pygmaea under eCO2 was associated with increasing ecosystem water-use efficiency (WUE), thereby advancing its subsequent phenological development, such as budding and flowering. Our findings provide experimental evidence that atmospheric CO2 enrichment can accelerate plant growth processes in high-altitude regions, and suggest that large-scale model simulations should consider the effects of elevated atmospheric CO2 concentration on plant growth and phenology.

A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10 m resolution

ABSTRACT The multi-angle method in the pixel dichotomy model (PDM) is used to produce fine spatial resolution (FSR) products of fractional vegetation cover (FVC), which involves numerous iterative computations. This phenomenon causes a low computational efficiency for global FVC. Here, we established the multi-angle equations, and simplified the two-stream approximation method to obtain multi-angle reflectance, which calculated the KDM parameters possible. Moreover, to accelerate the calculation of parameters in the multi-angle equations, land cover data were utilized as priority knowledge, which aims to decrease iterative computations to speed up the computational speed. Finally, we proposed an accelerated pixel dichotomy coupled linear kernel-driven model (PDKDM-A). By validating PDKDM-A using field measurements and FVC products, we found that the global FVC computed for various land covers exhibited strong agreement with the field measurements. The root mean square errors (RMSEs) were consistently below 0.150, except for grasslands, croplands, and cropland/natural vegetation mosaics. When comparing FVC products on the same scale, we discovered that the RMSE was less than 0.103, except for deciduous broadleaf forests. Our study demonstrates that PDKDM-A is an efficient algorithm, with the potential to quickly generate accurate global FVC products with fine spatial resolution.

Drought threat to terrestrial gross primary production exacerbated by wildfires

Frequent droughts have aggravated the occurrence of wildfires and led to substantial losses in terrestrial ecosystems. However, our understanding of compound drought-wildfire events, including the hotspots, spatiotemporal patterns, trends, and their impacts on global vegetation growth, remains unclear. Utilizing satellite data on terrestrial water storage, burned areas, and gross primary production (GPP) from 2002 to 2020, we identified a positive correlation between droughts and wildfires and mapped the global patterns of compound drought-wildfire events. Approximately 38.6% of vegetated areas across the globe witnessed rise in probability of compound drought-wildfire events ( < 0.016 events/10a). This increasing trend is spatially asymmetric, and greater amplification is observed across the Northern hemisphere due to frequent droughts. Furthermore, the GPP reductions induced by compound drought-wildfire events are more than twice as high as that caused by isolated droughts. These findings identify hotspots for compound drought-wildfire events and offer quantitative evidence of their greater impacts on ecosystems, aiding in the assessment of compound event risks and the implementation of future climate actions.

Conflicting Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface

AbstractChanges in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.

Biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)

Abstract. Ecosystems are under multiple stressors, and impacts can be measured with multiple variables. Humans have altered mass and energy flows of basically all ecosystems on Earth towards dangerous levels. However, integrating the data and synthesizing conclusions is becoming more and more complicated. Here we present an automated and easy-to-apply R package to assess terrestrial biosphere integrity that combines two complementary metrics. (i) The BioCol metric that quantifies the human colonization pressure exerted on the biosphere through alteration and extraction (appropriation) of net primary productivity.(ii) The EcoRisk metric that quantifies biogeochemical and vegetation structural changes as a proxy for the risk of ecosystem destabilization. Applied to simulations with the dynamic global vegetation model LPJmL5 for 1500–2016, we find that large regions presently (period 2007–2016) show modification and extraction of &gt;20 % of the preindustrial potential net primary production. The modification (degradation) of net primary production (NPP) as a result of land use change and extraction in terms of biomass removal (e.g., from harvest) leads to drastic alterations in key ecosystem properties, which suggests a high risk of ecosystem destabilization. As a consequence of these dynamics, EcoRisk shows particularly high values in regions with intense land use and deforestation and in regions prone to impacts of climate change, such as the Arctic and boreal zone. The metrics presented here enable spatially explicit global-scale evaluation of historical and future states of the biosphere and are designed for use by the wider scientific community, being applicable not only to assessing biosphere integrity but also to benchmarking model performance. The package will be maintained on GitHub and through that we encourage its future application to other models and data sets.

Regional Responses of Vegetation Productivity to the Two Phases of ENSO

AbstractThe two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO.

CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests

Abstract. The Climate-responsive Land Allocation model with carbon Storage and Harvests (CLASH) is a global, biophysical land-use model that can be embedded into integrated assessment models (IAMs). CLASH represents vegetation growth, terrestrial carbon stocks, and production from agriculture and forestry for different land uses in a changing climate. Connecting CLASH to an IAM would allow the consideration of terrestrial carbon stocks, agriculture and forestry in global climate policy analyses. All terrestrial ecosystems and their carbon dynamics are comprehensively described at a coarse resolution. Special emphasis is placed on representing the world's forests. Vegetation growth, soil carbon stocks, agricultural yields and natural disturbance frequencies react to changing climatic conditions, emulating the dynamic global vegetation model LPJ-GUESS. Land is divided into 10 biomes with six land-use classes (including forests and agricultural classes). Secondary forests are age structured. The timing of forest harvests affects forest carbon stocks, and, hence, carbon storage per forest area can be increased through forest management. In addition to secondary forests, CLASH also includes primary ecosystems, cropland and pastures. The comprehensive inclusion of all land-use classes and their main functions allows representing the global land-use competition. In this article, we present, calibrate and validate the model; demonstrate its use; and discuss how it can be integrated into IAMs.

Insight into the imbalance of forest cover change at county level in mainland China during 2000–2020: From the perspective of subdividing forest cover change into forest gain and loss

The significant growth of China's forest resources presents a remarkable case in global vegetation dynamics. However, there is spatial imbalance in forest cover changes, which is not only reflected in regional imbalances, but also between total and net changes. There is still a lack of knowledge in this imbalance. In our comprehensive study, we subdivided forest cover change into forest gain and loss, and calculated the total and net forest change across 2850 county-level administrative units in mainland China, utilizing the Global Forest Change Dataset. The univariate linear regression and the Sen + Mann-Kendall test is utilized to explore the temporal trends in forest loss. Additionally, spatial autocorrelation models were employed to examine the spatial differentiation of forest cover change. Our findings unveiled a wide spectrum of forest loss rates ranging from 0% to 27%, and gain rates ranging from 0% to 10%, with the highest rates observed in the southern counties. We observed that the forest loss trends in southeastern counties showed significant and slight increases, while the northwestern counties experienced significant and slight decreases, with the Huhuanyong Line serving as the demarcation. In terms of spatial patterning, quite a number of the southern counties displayed a “large net change, large total change” state, which contrasted with diverse patterns observed in certain western regions. Spatial autocorrelation analysis showed that the High-high clusters of both forest gain and loss were primarily observed in southern counties, with the Low-low clusters in the northwest. This detailed analysis provides valuable insights into the dynamics of forest cover changes, offering a robust scientific foundation for developing informed and region-specific conservation policies.

Analysis of Vegetation Environmental Stress and the Lag Effect in Countries along the “Six Economic Corridors”

Climate conditions have a significant impact on the growth of vegetation in terrestrial ecosystems, and the response of vegetation to climate shows different lag effects with the change in spatial pattern and category of the ecosystem. Exploring the interaction mechanism between climate and vegetation growth is helpful to promote the sustainable development of the regional ecological environment. Using normalized vegetation index (NDVI) and meteorological data, based on univariate linear regression and partial correlation analysis, this study explores the temporal and spatial pattern and change trend of vegetation cover in regions and node cities along the “six economic corridors”, and analyzes the environmental stress of vegetation growth and the lag effect of climate response. This study shows that there are great differences in the overall vegetation coverage along the “six economic corridors”. The vegetation coverage in Southeast Asia is the best and that in central and West Asia is the worst. The vegetation coverage in the study area shows an improvement trend, accounting for 39.6% of the total area. There are significant differences in the lag effect of vegetation response and the main climate factors affecting vegetation growth, which is related to the diversity of vegetation and climate characteristics. In this study, we selected regions along the “six economic corridors” that exhibit large latitude and altitude gradients, diverse climate types, and significant seasonal changes and spatial differences in climate conditions as our research areas. Additionally, we considered the impact of different regions and various types of vegetation on their response to climate change. This is of great significance for gaining a deeper understanding of the response mechanism of global climate change and vegetation ecology. Furthermore, our research can provide valuable information to support the ecological environment protection of different typical vegetation against extreme climates, ultimately contributing to the sustainable development of “the Belt and Road”.

Development of long‐term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

AbstractBackgroundThe time‐series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long‐term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time‐series data sets.MethodsIn this study, a long time‐series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High‐Resolution Radiometer) time‐series NDVI products using the Random Forest (RF) downscaling model.ResultsCompared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7.ConclusionsThe long time‐series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long‐term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function

Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the “rock moisture” reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM; LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soil databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a temperate mixed broadleaf–needleleaf evergreen forest, whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

Spatiotemporal coverage capability of moon-based SAR for water dynamic monitoring in global vegetation tipping elements

ABSTRACT Changes in water content dynamics can serve as predictive indicators for catastrophic transitions in vegetation tipping elements. Vegetation water content is a rapidly changing parameter with sub-daily fluctuations, and current space-borne sensors fall short of providing adequate spatiotemporal coverage for effective analysis. The Moon-based Earth observation, i.e. Synthetic Aperture Radar has recently showcased prolonged durations, rapid revisiting rates, and extensive monitoring capabilities with a decent spatial resolution (10-100 m). This opens up new possibilities for vegetation water content monitoring which has not yet been discussed. The study has assessed its monitoring capability in four critical vegetation tipping elements and considered both overall temporal observation availability fraction and diurnal variation acquisition. The results indicate that Amazon and Sahelian regions can be observed for ∼30% of a nutation period (∼18.6 years). Diurnal variations in water content can be captured with a 1-hour interval every 16–23 days for the Amazon and Sahelian regions. For polar tundra and boreal regions, continuous observation spanning 7–12 h can be achieved. These outstanding characteristics offer a deeper understanding of water content dynamics in vegetation tipping elements, which will enhance our prediction for their critical transitions.

A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes

Abstract. Vegetation phenological shifts impact the terrestrial carbon and water cycle and affect the local climate system through biophysical and biochemical processes. Dynamic global vegetation models (DGVMs), serving as pivotal simulation tools for investigating climate impacts on terrestrial ecosystem processes, incorporate representations of vegetation phenological processes. Nevertheless, it is still a challenge to achieve an accurate simulation of vegetation phenology in the DGVMs. Here, we developed and implemented spring and autumn phenology algorithms into one of the DGVMs, LPJ-GUESS. The new phenology modules are driven by temperature and photoperiod and are parameterized for deciduous trees and shrubs by using remotely sensed phenological observations and the reanalysis data from ERA5. The results show that the LPJ-GUESS with the new phenology modules substantially improved the accuracy in capturing the start and end dates of growing seasons. For the start of the growing season, the simulated RMSE for deciduous trees and shrubs decreased by 8.04 and 17.34 d, respectively. For the autumn phenology, the simulated RMSE for deciduous trees and shrubs decreased by 22.61 and 17.60 d, respectively. Interestingly, we have also found that differences in the simulated start and end of the growing season also alter the simulated ecological niches and competitive relationships among different plant functional types (PFTs) and subsequentially influence the terrestrial carbon and water cycles. Hence, our study highlights the importance of accurate phenology estimation to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.

A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model

AbstractThe global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS), using a broad range of the latest available global observation‐based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ‐GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ‐GUESS accurately captures both inter‐ and intra‐annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ‐GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML‐V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ‐GUESS aligns with ESA‐CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

Estimating the aboveground biomass of the Hulunbuir Grassland and exploring its spatial and temporal variations over the past ten years

In the past 10 years, extreme weather phenomena have increased, and global warming has markedly advanced; moreover, the intensity of human activity has gradually increased. These have an impact on the growth of global vegetation. Related studies have focused on the Tibetan Plateau and some northern provinces of China to estimate grassland AGB at a large scale but a low resolution. Hulunbuir Grassland is an important supplier of livestock products, and therefore, it is important to precisely map AGB and explore the response of Hulunbuir Grassland AGB to climate change and human activities at a high resolution to identify complex spatial details. In this study, we selected vegetation indices from Landsat 8 OLI and topographic indices and used multiple linear regression and machine learning algorithms to estimate the spatial distribution of AGB from 2013 to 2022. Then, we analyzed the correlations between AGB and cumulative precipitation and daily average temperature in summer and between population density and livestock density at the pixel level. Our results demonstrated that the RF model performed well, with an RMSE of 28.23 and R2 value of 0.74; the AGB was positively correlated with the cumulative precipitation in summer in 94.45 % of the area and negatively correlated with the daily average temperature in summer in 96.32 % of the area. We suggest that it is necessary to reduce grazing activities in future warm and drought years and to adjust sources of income to adapt to the decrease in AGB under global warming conditions. This study will provide reference for the countries or regions that depend on temperate grasslands for husbandry.

Night-time decline in plant respiration is consistent with substrate depletion

Understanding the response of plant respiration to climate change is key to determining whether the global land carbon sink continues into the future or declines. Most global vegetation models use a classical growth-maintenance approach, which predicts that nocturnal plant respiration is controlled by temperature only. However, recently published observations of plant respiration show a decline through the night even at constant temperature, which these global models cannot reproduce. Here we assess the role of respiratory substrates in this observed decline by evaluating an alternative model of plant respiration, in which the rate of respiration at constant temperature is instead dependent on the size of available substrate pools. We find that the observed decline in nocturnal respiration is reproduced by a model with just two substrate pools, one fast and one slow. These results demonstrate a need to change the way that plant respiration is represented in global vegetation models, moving to models based on labile pools which represent only a fraction of total plant biomass. These models naturally represent plant acclimation via changing pool-sizes and may have a significant impact on the long-term predictions of the global land carbon sink.