Vegetation Dynamics Research Articles

Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning

Rare earth mining, essential for modern industries and economic growth, often leads to severe environmental degradation. Previous research has explored the ecological impacts of rare earth mining but has not fully investigated the intricate interplay and subdivisions of environmental and anthropogenic factors driving vegetation changes over extended periods. This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. Using the kNDVI derived from Landsat data, we identified three distinct vegetation trajectory types: pro-environment, des-environment, and res-environment. An ensemble machine learning model combined with SHAP analysis revealed the cropland area proportion, PM10 levels, and shrubland area proportion as the most influential factors affecting vegetation across all mining types. Additionally, after 2012, the palmer drought severity index and downward surface shortwave radiation emerged as positive contributors to vegetation health, while population pressure had a more substantial negative influence in des-environment areas. Our findings highlight spatial heterogeneity in vegetation recovery patterns and highlight the complex interactions among land cover changes, air quality, climate factors, and human activities in shaping vegetation dynamics. This study provides valuable insights for developing targeted, context-specific restoration strategies in rare earth mining areas, contributing to more sustainable mining practices and global environmental management.

Responses of vegetation low-growth to Extreme Climate Events on the Mongolian Plateau

Since the 21st century began, the frequency and intensity of extreme climate events have significantly increased globally, becoming a widely recognized phenomenon of global change. These extreme events, including droughts and heatwaves, have profound impacts on the structure and function of ecosystems. This study focuses on the Mongolian Plateau. It utilizes meteorological and remote sensing vegetation data, combined with consistency and sensitivity analyses. These approaches aim to provide an in-depth understanding of the relationship between various extreme climate events and vegetation low-growth. The study found that extreme drought and extreme heat events are the primary drivers affecting vegetation low-growth on the Mongolian Plateau. The analysis results indicate that the sensitivity of vegetation to these extreme climate events is regulated by regional hydrothermal conditions, with vegetation in long-term drought areas being more susceptible to the suppression of extreme drought, while humid areas exhibit some resistance. As the temperature gradient increases, the sensitivity of vegetation to extreme high temperatures increases, while sensitivity to extreme low temperatures decreases. Furthermore, the study also revealed differences in the responses of different vegetation types to extreme events under the same climatic conditions, highlighting the ecological basis of ecosystem resilience and adaptability. This research not only enhances our understanding of vegetation dynamics under the influence of extreme climate events but also provides scientific evidence for ecological management and climate adaptation in the Mongolian Plateau region.

Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor

Studying the influence of vegetation dynamics on water storage is fundamental for efficiently managing ecosystems in dryland areas. Therefore, this study was designed to investigate the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage (TWS) trends across the China-Pakistan Economic Corridor (CPEC). Exploring vegetation growth's effect on TWS utilizes remote sensing and statistical methods such as generalized additive model (GAM), random forest, Mann-Kendall test, Sen's slope, and Partial correlation coefficient. Our results revealed a consistent increase in vegetation cover in semi-arid and dry sub-humid regions, especially in croplands, from 1986 to 2020. However, a noticeable finding was that TWS has declined by approximately 39.65 % in the study area. On the other hand, forests have displayed resilience by mitigating the effects of climate change and human activities through hydraulic memory effects. Additionally, soil moisture has decreased in various land cover types, with croplands experiencing the most significant decrease. Similarly, increased vegetation growth in greening drylands significantly negatively impacts terrestrial water storage, with correlations of −0.31 for terrestrial water storage anomaly (TWSA), −0.26 for root zone soil moisture (RZSM), and −0.29 for surface soil moisture (SSM). Meanwhile, evapotranspiration (ET) also had negative correlations with TWSA, RZSM, and SSM, with standardized coefficients of −0.23, −0.16, and −0.11, respectively. These findings exposed the study area's vegetation interaction with land cover and hydrological dynamics. Addressing these hydrological imbalances will help ensure sustainable ecological management in the study area.

Integrating Mixed Livestock Systems to Optimize Forage Utilization and Modify Woody Species Composition in Semi-Arid Communal Rangelands

Communally owned rangelands serve as critical grazing areas for mixed livestock species such as cattle and goats, particularly in the arid and semi-arid regions of Southern Africa. This study aimed to evaluate the nutritional composition and woody species composition of communal rangelands where cattle and goat flocks graze together and to investigate the influence of grazing intensity on vegetation dynamics. Vegetation surveys were conducted across varying grazing intensities to assess species richness, biomass, and dietary preferences, while soil properties were analyzed to determine their interaction with vegetation attributes. Stepwise regression and path analyses were used to explore the relationships between soil characteristics, vegetation structure, and livestock dietary choices. The results revealed that high grazing pressure significantly reduced grass biomass (p = 0.003) and woody species density (p = 0.007) while increasing shrub cover (p = 0.018). Nutritional analysis indicated that goats preferred woody shrubs, which contributed 42.1% of their diet compared to 27.8% for cattle (p = 0.008). Regression analysis further showed that soil organic carbon (p = 0.002) and tree height (p = 0.041) were strong predictors of shrub cover. Seasonal variation significantly affected forage availability and nutritional content, with higher crude protein levels recorded during the wet season (p = 0.007). These findings suggest that grazing management strategies should be tailored to the distinct forage needs of cattle and goats to maintain the productivity and ecological stability of communal rangelands. A holistic approach that considers livestock dietary preferences, vegetation composition, and soil health is essential for sustainable rangeland management in mixed-species grazing systems.

The key role of forest disturbance in reconciling estimates of the northern carbon sink

AbstractNorthern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

A systematic review of nurse objects as safe sites for seedling establishment and implications for restoration.

Direct human activity and global climatic changes are threatening the existence of many vegetated habitats. Seedling establishment, one of the riskiest plant life stages, must be successful for such habitats to persist. The establishment of seedlings is known to be enhanced by nurse effects, but most studies to date have looked at the nursing effects of plants while sidelining inanimate objects. Nevertheless, nurse objects can support seedling establishment via diverse mechanisms such as moderating abiotic stresses like extreme temperatures and drought, reducing negative biological interactions such as herbivory while enhancing positive processes like seed dispersal, and providing protection from physical disturbances such as trampling and fire. The robust nature of nurse objects highlights their potential in habitat restoration. The addition of nurse objects allows a simple, single-effort rehabilitation strategy that can later draw on natural seed dispersal and establishment. By achieving a better understanding of the processes in which nurse objects are involved we should be able to better predict vegetation dynamics and manipulate them to minimize adverse processes and support regeneration in natural habitats.

Machine learning-based crop type detection and vegetation mon-itoring using Sentinel-1 SAR and Landsat 8

This study explores the use of machine learning for crop type detection and vegetation monitoring in arid and semi-arid regions, specifically in Biskra and Khenchela, Algeria. Traditional field-based agricultural monitoring is labor-intensive and limited in spatial coverage, prompting the need for more efficient and scalable methods. The research integrates Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Landsat 8 to enhance the classification of different crop types and track vegetation dynamics. Key metrics such as radar backscatter and Normalized Difference Vegetation Index (NDVI) are employed to distinguish between irrigated and rainfed crops. The Random Forest algorithm is utilized for classification, with training data derived from field surveys, high-resolution satellite imagery, and existing land cover maps. The Google Earth Engine (GEE) platform facilitates large-scale data processing, providing real-time agricultural analysis. Over the 12-month study period (January to December 2020), the integrated approach demonstrated high accuracy, achieving over 85% precision in classifying key crops such as date palms, cereals, and vegetables. Results indicate that SAR and NDVI data provide complementary insights into vegetation health, phenology, and agricultural practices in different climatic zones. This methodology offers a promising solution for improving agricultural monitoring and resource management in arid and semi-arid regions, enhancing food security and optimizing water usage. The study’s findings also underscore the value of satellite-based remote sensing for large-scale, real-time agricultural analysis.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka

Abstract Climate variations during the Holocene significantly impacted vegetation dynamics in the eastern Qinghai-Tibet Plateau (QTP). However, vegetation evolution in response to regional climatic trends and events during this interval remains controversial. Here, we present well-dated decadal-resolution loss on ignition (LOI) and grain size records from the Xing Co Lake on the eastern QTP. The records show an overall drying trend since 10 thousand years ago (ka), with multiple extreme precipitation events observed during 10 to 7 ka. An extreme drought event occurred at around 5.5 ka, after which the climate was drier and unstable with several drought events. In comparison with the hydroclimate, insolation, and El Niño Southern Oscillation records, our data show a close correspondence with the summer insolation differential between 30°N and 30°S and El Niño events on orbital-millennium timescales. This suggested that the increased rainfall during the early Holocene on the eastern QTP can be attributed to the high insolation differential between 30°N and 30°S and low El Niño events. Conversely, the drying trend in the late Holocene appears to correlate with a low insolation differential and high El Niño events. Whenever ice-rafted debris events occurred in the North Atlantic, there was a corresponding occurrence of drying events in the late Holocene in the Zoige Basin. This suggested that teleconnection between the precipitation on the eastern QTP and the North Atlantic climate exists in the Holocene. When compared to independent hydroclimatic and arboreal pollen (AP%) records on the eastern QTP, the evolutionary trends and events of AP% align closely with local hydroclimate changes. This suggested that arboreal coverage could rapidly respond to climate change during the Holocene, but further studies are needed.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.

Greening of India and revival of the South Asian summer monsoon in a warmer world

While it is accepted that the tropical hydrological cycle has intensified during past interglacial periods due to changes in insolation, greenhouse gases and ice volume, their respective influences are uncertain. Here we present a pollen record from Bengal Bay to reconstruct vegetation changes in India’s core monsoon zone during two warm periods, the current and last interglacial, comparing the data with numerical model simulations to assess the influence of different forcing mechanisms. Results show tropical forest expansion between 11.7-5 ka and 127-120 ka, defining two Indian humid periods, with the last interglacial showing the strongest monsoon activity, consistent with salinity reconstructions. Model-data comparison highlights boreal summer insolation as the primary driver of vegetation dynamics and monsoon intensity during interglacial periods, with CO2 and ice-sheets having a limited effect. Vegetation remains unaffected by pre-industrial CO2 variations above 250 ppmv, a threshold value that characterizes most interglacials of the last million years.

Vegetation changes of Lushan, China between 1959 and 2020 based on pollen data

Both climatic change and human activity are currently driving the dynamic of vegetation, which may vary from region to region. On Lushan, located in the eastern China, the subtropical secondary forests during the natural succession endure the pressure from ongoing climate warming on the one hand. They are influenced by anthropogenic disturbance on the other hand for recent decades. Previously, palynological research has discovered that the pollen assemblages from surface soil sample on Lushan can basically reflect the characteristics of local vegetation. Here, we did a comparative study by using the pollen analysis to investigate the vegetation changes of Lushan from 1959 to 2020. We compared the composition of pollen assemblages obtained in 1959 with that in 2020 along different altitudes. Further, the biomisation method was employed to discover the changes of different vegetation types, which were reflected by the pollen-based reconstructed biomes. Meanwhile, the temporal beta diversity analysis was used to extract the sites experienced the significant vegetation shift and the most important taxa change. Results showed that the pollen composition over the past 60 years on Lushan had altered remarkably. The vegetation shifted at all altitudes. The current vegetation pattern of Lushan: evergreen broadleaved forest in low elevation, temperate deciduous broadleaved forest in middle elevation and cool mixed forest in high elevation, is the successional consequence of the past 60 years. The forces driving the vegetation dynamics might be from multiple sources. However, the human disturbance seems to play quite an important role during forest succession. Our study is potentially useful both for paleoecological reconstructions and wider understanding of current climate change that are relevant to subtropical forests of Asia.

Satellite-Observed Hydrothermal Conditions Control the Effects of Soil and Atmospheric Drought on Peak Vegetation Growth on the Tibetan Plateau

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change.

Exploring the relationships between ground observations and remotely sensed hazelnut spring phenology.

Crop phenology is very important in regular crop monitoring. Generally, phenology is monitored through field observation surveys or satellite data. The relationships between ground observations and remotely sensed derived phenological data can enable near-real-time monitoring over large areas, which has never been attempted on hazelnuts. In this study, we extracted phenological metrics derived from MODIS Enhanced Vegetation Index (EVI) in hazelnut production regions and compared them with the spring ground phenological data (BBCH scale) from orchards located in the same area of Turkey over the period from 2019 to 2022. We observed a specific temporal dynamic between remote sensing phenometrics and ground observations. The metrics Greenup, Upturning Date, and Threshold 20% metrics corresponded to the early of EVI growth and were synchronous with the female flowering of hazelnut and ending before bud break. The metrics Threshold 50% and Start of season were associated with the steepest portion of the EVI curve, i.e., canopy greening and thickening, and occurred between ovaries enlargement and leaves unfolding. The metrics Peak of Season, Stabilization Date, and Maturity corresponded to the end of spring vegetative growth. The main outcomes are that (i) female flowering occurred before 20% of vegetation development (BBCH 64P occurred about one month before Threshold 20%), (ii) phenometrics from satellite remote sensing (i.e., Upturning Date and Threshold 20%) well-reflected leaf emergence (rs = 0.30 and rs = 0.32, respectively; p < 0.05) and unfolding (rs = 0.35 and rs = 0.39, respectively; p < 0.05), and (iii) cluster appearance temporally aligned with the peak of the EVI curve (Stabilization Date and BBCH 71P differed by around 4 days). Our method is transferable to operational phenology monitoring, and future applications will consider the senescence season and the effect of environmental variability on the comprehension of vegetation dynamics.

Mechanisms of rapid plant community change from the Miocene Succor Creek flora, Oregon and Idaho (USA).

The fossil record of the U.S. Pacific Northwest preserves many Middle Miocene floras with potential for revealing long-term climate-vegetation dynamics during the Miocene Climatic Optimum. However, the possibility of strong, eccentricity-paced climate oscillations and concurrent, intense volcanism may obscure the signature of prevailing, long-term Miocene climate change. To test the hypothesis that volcanic disturbance drove Middle Miocene vegetation dynamics, high-resolution, stratigraphic pollen records and other paleobotanical data from nine localities of the Sucker Creek Formation were combined with sedimentological and geochemical evidence of disturbance within an updated chronostratigraphic framework based on new U-Pb zircon ages from tuffs. The new ages establish a refined, minimum temporal extent of the Sucker Creek Formation, ~15.8 to ~14.8 Ma, and greatly revise the local and regional chronostratigraphic correlations of its dispersed outcrop belt. Our paleoecological analysis at one ~15.52 Ma locality reveals two abrupt shifts in pollen spectra coinciding with the deposition of thick ash-flow tuffs, wherein vegetation dominated by Cupressaceae/Taxaceae, probably representing a Glyptostrobus oregonensis swamp, and upland conifers was supplanted by early-successional forests with abundant Alnus and Betula. Another ephemeral shift from Cupressaceae/Taxaceae swamp taxa in favor of upland conifers Pinus and Tsuga correlates with a shift from low-Ti shale to high-Ti claystone, suggesting a link between altered surface hydrology and vegetation. In total, three rapid vegetation shifts coincide with ash-flow tuffs and are attributed to volcanic disturbance. Longer-term variability between localities, spanning ~1 Myr of the Miocene Climatic Optimum, is chiefly attributed to eccentricity-paced climate change. Overall, Succor Creek plant associations changed frequently over ≤105 years timespans, reminiscent of Quaternary vegetation records. Succor Creek stratigraphic palynology suggests that numerous and extensive collection of stratigraphically controlled samples is necessary to understand broader vegetation trends through time.

Beyond MRV: Combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA

Abstract Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984-2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr-1, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from -0.65 - 2.77 Tg C yr-1. At the county scale, the average annual net above ground flux ranged spatially from 0.01 - 0.13 Tg C yr-1 and spatiotemporally from -0.43 - 0.24 Tg C yr -1. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO2 to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region

The Arctic-Boreal region is projected to experience spatially divergent trends in snow depth following climate change. However, the impact of these spatial trends has remained largely unexplored, despite potentially large consequences for the carbon cycle. To address this knowledge gap, we forced a customised arctic version of the dynamic vegetation model LPJ-GUESS with daily CMIP6 outputs from a global climate model (MRI-ESM2-0) under three climate scenarios. We find that snow depths increased the most in the coldest, northernmost regions, insulating the soil, which led to increased heterotrophic respiration and reduced carbon residence times. We emphasise the need for improved projections of future snow depth - in particular diverging trends across landscapes - to more accurately simulate the strength of Arctic-Boreal carbon feedbacks and their impact on global climate.

Evaluation of Relationship of Vegetation Dynamics with Seasonal Climate Evolution in Sheikh Badin National Park, Pakistan

Evaluation of Relationship of Vegetation Dynamics with Seasonal Climate Evolution in Sheikh Badin National Park, Pakistan

Simulated sensitivity of the Amazon rainforest to extreme drought

Abstract The Amazon rainforest is highly biodiverse and the largest intact, tropical forest in the world. Undisturbed tropical forests act as a carbon sink by taking up about 15% of anthropogenic carbon emissions per year. In the past decades, a declining trend in the carbon sink capacity in the Amazon rainforest has been observed due to increased carbon losses and tree mortality. The causes are disputed, but increasing temperatures and more frequent severe droughts are potentially major drivers. We employ a novel modelling framework and hypothesize that previously rare, extreme droughts in the Amazon, such as the ones in 2005 and 2010, constitute the main cause behind the decline of the net carbon sink in aboveground biomass. Our dynamic vegetation model simulates process-based plant hydraulics and drought-induced mortality, and accounts for the diversity of strategies in plant responses to drought based on observed hydraulic vulnerability curves. The simulated impact of the 2005 drought event temporarily turned the annual Amazon net carbon sink to a carbon source of about 0.25 MgC ha-1. In contrast to other dynamic vegetation models our model simulated an increasing trend in carbon losses and a declining trend in the Amazon carbon sink over the past 25 years (net sink rate of -0.018 Mg C ha-1 year-1 or -0.22 Mg C ha-1 per decade) which corresponds well with long-term forest monitoring data (net sink rate of -0.016 Mg C ha-1 year-1). We show that this trend is entirely attributable to drought-induced forest mortality during extreme years. The simulations show a threshold-like behaviour between drought intensity and biomass loss, which is due to xylem vulnerability, indicating the potentially high sensitivity of Amazon forests to extreme drought. Further increases in the severity and frequency of droughts might thus lead to greater carbon release and tree mortality than previously assumed.

Factors influencing carbon and water use efficiency in changing environments

IntroductionVegetation plays a crucial role in terrestrial ecosystems, acting as a vital link connecting the lithosphere, hydrosphere, and atmosphere in terms of energy flow and material cycling. Changes in surface vegetation significantly regulate the water cycle, energy flow within terrestrial surfaces, and global carbon balance.MethodsThis study focuses on nine major river basins in China to quantitatively investigate the impacts of climate factors, vegetation dynamics, and land use changes on carbon use efficiency (CUE) and water use efficiency (WUE).ResultsThe primary controlling factors of WUE trends are NDVI (average contribution: 33.75% ± 6.90%) and VPD (average contribution: 28.04% ± 3.98%). NDVI predominates in the Haihe, Yellow River, Yangtze River, Pearl River, and Songliao River basins, while shortwave radiation (Srad) dominates in the southeastern rivers and inland river areas, and humidity (Shum) in the southwestern river basins. For CUE trends, the main controlling factors are Srad (average contribution: 36.46% ± 3.40%) and precipitation (Pre) (average contribution: 26.72% ± 5.20%). NDVI negatively influences the Huaihe River and southeastern river basins, while Pre negatively influences the Songliao River and Yellow River basins, and Srad negatively influences the Huaihe and southwestern river basins. Pre predominates in the Huaihe, Songliao, Haihe, southwestern river basins, and inland river areas, while Srad predominates in the Pearl River, Yangtze River, and Yellow River basins.DiscussionClimate factors and vegetation dynamics have significant regional impacts on WUE and CUE across different river basins, especially the roles of NDVI and VPD on WUE, and Srad and precipitation on CUE. These differences underscore the importance of developing region-specific management strategies to optimize ecosystem services in each basin.