• Energy Research

AbstractSpring vegetation phenology (green‐up onset date, GUD) exhibits notable sensitivity to climate change, serving as a critical indicator of ecosystem dynamics. However, long‐term changes and drivers of GUD remain unclear. Here we showed that satellite‐derived GUD averaged over China forests and grasslands advanced by −1.3 ± 0.4 (mean ± SD) days decade−1 during 1982–2022, but with contrasting trends between forests (−5.0 ± 0.6 days decade−1) and grasslands (2.8 ± 0.6 days decade−1), despite similarly increasing temperature and precipitation. Such contrasting trends were caused by different responses to higher preseason mean temperature and more total precipitation. Moreover, sensitivities of GUD to temperature and precipitation showed different patterns with respect to spatial gradient of background climate conditions between forests and grasslands. Our study elucidates different mechanisms behind forests and grasslands responding to climate change, which could help optimize land‐management strategies and anticipate vegetation distribution under climate change.

Aunque el cultivo de arroz es una de las fuentes agrícolas más importantes de metano (CH4) y contribuye con ~8% del total de las emisiones antropogénicas globales, persisten grandes discrepancias entre las estimaciones de las emisiones globales de CH4 del cultivo de arroz (que van de 18 a 115 Tg CH4 año−1) debido a la falta de limitaciones observables. La distribución espacial de las emisiones de arrozales se ha evaluado a escalas regionales a globales mediante inventarios ascendentes y modelos de superficie terrestre sobre resolución espacial gruesa (por ejemplo, > 0,5°) o unidades espaciales (por ejemplo, zonas agroecológicas). Sin embargo, las estimaciones de flujo de CH4 de alta resolución capaces de capturar los efectos del clima local y las prácticas de gestión sobre las emisiones, así como la replicación de datos in situ, siguen siendo difíciles de producir debido a la escasez de mapas de arroz de alta resolución y a la insuficiente comprensión de los predictores de CH4. Aquí, combinamos datos de flujo de metano de arroz con arroz de 23 sitios de covarianza de remolinos globales y datos de teledetección MODIS con aprendizaje automático para 1) evaluar el rendimiento del modelo basado en datos y la importancia variable para predecir los flujos de CH4 de arroz; y 2) producir estimaciones cuadriculadas de aumento de escala de las emisiones de CH4 de arroz a una resolución de 5000 m en toda Asia monzónica, donde se cultiva ~87% del área mundial de arroz y se produce ~ 90% de la producción mundial de arroz. Nuestro modelo de bosque aleatorio logró valores de eficiencia de Nash-Sutcliffe de 0,59 y 0,69 para flujos de CH4 de 8 días y flujos de CH4 medios del sitio, respectivamente, con índices relacionados con la temperatura de la superficie terrestre, la biomasa y la disponibilidad de agua como los predictores más importantes. Estimamos que las emisiones anuales promedio de arroz con cáscara CH4 (excluida la temporada de barbecho invernal) en toda Asia monzónica son de 20.6 ± 1.1 Tg año−1 para 2001–2015, que se encuentra en el rango más bajo de las estimaciones anteriores basadas en el inventario (20–32 CH4 Tg año−1). Nuestras estimaciones también sugieren que las emisiones de CH4 del arroz con cáscara en esta región han estado disminuyendo desde 2007 hasta 2015 después de las disminuciones tanto en el área de cultivo de arroz con cáscara como en las tasas de emisión por unidad de área, lo que sugiere que las emisiones de CH4 del arroz con cáscara en el monzón de Asia probablemente no hayan contribuido al renovado crecimiento del CH4 atmosférico en los últimos años. Bien que la riziculture soit l'une des plus importantes sources agricoles de méthane (CH4) et contribue à environ8% des émissions anthropiques mondiales totales, de grands écarts subsistent entre les estimations des émissions mondiales de CH4 provenant de la riziculture (allant de 18 à 115 Tg de CH4 par an) en raison d'un manque de contraintes d'observation. La distribution spatiale des émissions de riz paddy a été évaluée à l'échelle régionale et mondiale par des inventaires ascendants et des modèles de surface terrestre sur une résolution spatiale grossière (par exemple, > 0,5°) ou des unités spatiales (par exemple, des zones agro-écologiques). Cependant, les estimations de flux de CH4 à haute résolution capables de capturer les effets du climat local et des pratiques de gestion sur les émissions, ainsi que de reproduire les données in situ, restent difficiles à produire en raison de la rareté des cartes à haute résolution du riz paddy et d'une compréhension insuffisante des prédicteurs de CH4. Ici, nous combinons les données de flux de méthane de riz paddy provenant de 23 sites mondiaux de covariance des tourbillons et les données de télédétection MODIS avec l'apprentissage automatique pour 1) évaluer les performances du modèle basé sur les données et l'importance variable pour prédire les flux de CH4 du riz ; et 2) produire des estimations maillées des émissions de CH4 du riz à une résolution de 5000 m dans toute l'Asie de la mousson, où ∼87 % de la superficie mondiale du riz est cultivée et ∼ 90 % de la production mondiale de riz se produit. Notre modèle de forêt aléatoire a atteint des valeurs d'efficacité de Nash-Sutcliffe de 0,59 et 0,69 pour les flux de CH4 sur 8 jours et les flux de CH4 moyens du site, respectivement, la température de surface du sol, la biomasse et les indices liés à la disponibilité de l'eau étant les prédicteurs les plus importants. Nous estimons que les émissions annuelles moyennes de CH4 de riz paddy (hors saison de jachère hivernale) dans toute l'Asie de la mousson sont de 20,6 ± 1,1 Tgan-1 pour 2001–2015, ce qui se situe dans la fourchette inférieure des estimations antérieures basées sur les inventaires (20–32 Tgan-1 de CH4). Nos estimations suggèrent également que les émissions de CH4 du riz paddy dans cette région ont diminué de 2007 à 2015 à la suite de baisses à la fois de la superficie cultivée en riz paddy et des taux d'émission par unité de surface, ce qui suggère que les émissions de CH4 du riz paddy dans Monsoon Asia n'ont probablement pas contribué à la croissance renouvelée du CH4 atmosphérique ces dernières années. Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years. على الرغم من أن زراعة الأرز هي واحدة من أهم المصادر الزراعية للميثان (CH4) وتساهم بنسبة 8 ٪ من إجمالي الانبعاثات العالمية البشرية المنشأ، إلا أنه لا تزال هناك اختلافات كبيرة بين تقديرات انبعاثات الميثان العالمية من زراعة الأرز (التي تتراوح من 18 إلى 115 تيراغرام من الميثان في السنة−1) بسبب نقص قيود المراقبة. تم تقييم التوزيع المكاني لانبعاثات الأرز والأرز على المستويات الإقليمية إلى العالمية من خلال قوائم الجرد التصاعدية ونماذج سطح الأرض على الدقة المكانية الخشنة (على سبيل المثال، > 0.5درجة) أو الوحدات المكانية (على سبيل المثال، المناطق الزراعية الإيكولوجية). ومع ذلك، لا تزال تقديرات تدفق الميثان عالية الدقة القادرة على التقاط آثار المناخ المحلي وممارسات الإدارة على الانبعاثات، وكذلك تكرار البيانات في الموقع، صعبة الإنتاج بسبب ندرة الخرائط عالية الدقة لأرز الأرز وعدم كفاية فهم تنبؤات الميثان. هنا، نجمع بين بيانات تدفق الميثان من الأرز والأرز من 23 موقعًا عالميًا للتباين الدوامي وبيانات الاستشعار عن بعد MODIS مع التعلم الآلي من أجل 1) تقييم أداء النموذج القائم على البيانات والأهمية المتغيرة للتنبؤ بتدفقات CH4 للأرز ؛ و 2) إنتاج تقديرات شبكية لانبعاثات CH4 للأرز بدقة 5000 متر في جميع أنحاء آسيا الموسمية، حيث تتم زراعة 87 ٪ من مساحة الأرز العالمية و 90 ٪ من إنتاج الأرز العالمي. حقق نموذجنا للغابات العشوائية قيم كفاءة ناش- سوتكليف البالغة 0.59 و 0.69 لتدفقات الميثان لمدة 8 أيام ومتوسط تدفقات الميثان في الموقع على التوالي، مع مؤشرات درجة حرارة سطح الأرض والكتلة الحيوية وتوافر المياه كأهم المؤشرات. نقدر المتوسط السنوي (باستثناء موسم الإراحة الشتوية) لانبعاثات الميثان من الأرز في جميع أنحاء آسيا الموسمية بـ 20.6 ± 1.1 تيراغرام في السنة-1 للفترة 2001–2015، وهو في النطاق الأدنى للتقديرات السابقة القائمة على المخزون (20–32 تيراغرام في السنة-1). تشير تقديراتنا أيضًا إلى أن انبعاثات الميثان من أرز الأرز في هذه المنطقة قد انخفضت من عام 2007 حتى عام 2015 بعد الانخفاضات في كل من مساحة زراعة أرز الأرز ومعدلات الانبعاثات لكل وحدة مساحة، مما يشير إلى أن انبعاثات الميثان من أرز الأرز في الرياح الموسمية في آسيا من المحتمل ألا تساهم في النمو المتجدد للميثان في الغلاف الجوي في السنوات الأخيرة.

AbstractFlash drought is characterized by a period of rapid drought intensification with impacts on agriculture, water resources, ecosystems, and the human environment. Addressing these challenges requires a fundamental understanding of flash drought occurrence. This study identifies global hotspots for flash drought from 1980–2015 via anomalies in evaporative stress and the standardized evaporative stress ratio. Flash drought hotspots exist over Brazil, the Sahel, the Great Rift Valley, and India, with notable local hotspots over the central United States, southwestern Russia, and northeastern China. Six of the fifteen study regions experienced a statistically significant increase in flash drought during 1980–2015. In contrast, three study regions witnessed a significant decline in flash drought frequency. Finally, the results illustrate that multiple pathways of research are needed to further our understanding of the regional drivers of flash drought and the complex interactions between flash drought and socioeconomic impacts.

Against the backdrop of global climate change, climate change has become a major factor affecting the suitability of living environments. Therefore, in this study, based on multisource remote sensing data and geospatial analysis methods, we selected the annual land surface temperature (LST) and normalized difference vegetation index (NDVI) in 2022 of the Chengdu–Chongqing urban agglomeration in China as driving indicators. By extracting three meteorological indicators of meteorological stations throughout the year (temperature [T], humidity [U], and wind speed [K]) in study area to construct the comfortable index of human body (IBC) to quantify regional climatic comfort. The results were as follows: First, the IBC averages were 75.620 (summer) > 60.448 (autumn) > 59.922 (spring) > 42.743 (winter), showing a spatial pattern of high values in the middle and low values on the four sides. T was the primary factor influencing annual IBC. Second, we discussed the relationship between IBC and NDVI, air quality index (AQI), LST, and relief degree of land surface (RDLS) under ordinary least square method, geographically weighted regression (GWR), and multiscale GWR (MGWR) models. LST is the most important factor affecting the spatial distribution of annual climate comfort, and the other four factors have different effects on IBC in different seasons. Third, the climate comfort of natural-type local climate zone (LCZ) was high in spring and summer, while the IBC of LCZ corresponding to building type was high in autumn and winter. This study is crucial for understanding the suitability of urban living environments and provides a reference for urban development planning.

Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987–2017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82–0.96 and RMSE = 4.08–10.59 Mg/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 × 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions.

Spatial-temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate, and biodiversity in the Brazilian Amazon. Here we investigate inter-annual changes of AGB and forest area by analyzing satellite-based annual AGB and forest area datasets. We found the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, net AGB loss was three times smaller in 2019 than in 2015. During 2010-2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.

AbstractGlobal terrestrial vegetation dynamics have been rapidly altered by climate change. A widespread vegetation greenness over a large part of the planet from the 1980s to early this century has been reported, whereas weakening of CO2 fertilization effects and increasing climate extremes and the adverse impact of increasing rate of warming and severity of drought on vegetation growth were also reported. Earth system models project that the land carbon sink will decrease in size in response to an increase in warming during this century. How global vegetation is changing during this century in response to global warming and water availability across spatial and temporal scales remains uncertain. Our understanding of the widespread vegetation greening or browning processes and identifying the biogeochemical mechanisms remain incomplete. Here we use multiple long‐term satellite leaf area index (LAI) records to investigate vegetation growth trends from 1982 to 2018. We find that the widespread increase of growing‐season integrated LAI (greening) since 1980s was reversed (p‐value < 0.05) around the year 2000 over 90% of the global vegetated area, and continued in only 10% of the global vegetated area. The reversal of greening trend was largely explained by the inhibitive effects of excessive optimal temperature on photosynthesis in most of the tropics and low latitudes, and by increasing water limitation (increasing in atmospheric vapor pressure deficit and decreasing in soil water availability) in the northern high latitudes (>45°N). Overall, the reversal of greening trend since 2000 weakened the negative feedback of carbon sequestration on the climatic system and should be considered in the strategies for climate warming mitigation and adaptation. Our findings of the diversity of processes that drive browning across bioclimatic‐zones and ecosystems and of how those driving processes are changing would enhance our ability to project global future vegetation change and its climatic and abiotic consequences.