• Energy Research

AbstractThe relative greenness and wetness of Giant Sequoia (Sequoiadendron giganteum) groves and the surrounding Sierra Nevada, California forests were investigated using patterns in vegetation indices from Landsat imagery for the period 1985–2015. Vegetation greenness (normalized difference vegetation index) and thus forest biomass in groves increased by about 6% over that 30 year period, suggesting a 10% increase in evapotranspiration. No significant change in the surrounding nongrove forest was observed. In this period, local temperature measurements showed an increase of about 2.2°C. The wetness of groves (normalized difference wetness index) showed no overall long‐term trend but responded to changes in annual water‐year precipitation and temperature. The long‐term trends of grove greenness and wetness varied by elevation, with the lower rain‐snow transition elevation zone (1,700–2,100 m) marking a change from an increasing trend at lower elevations to a decreasing trend at higher elevations. The 2011–2015 drought brought an unprecedented drop in grove wetness, over 5 times the 1985–2010 standard deviation, and wetness in SEGI groves dropped 50% more than in nongrove areas. Overall, the wetness and greenness of SEGI groves showed a larger response to the warming climate and drought than nongrove areas. The influence of droughts on the wetness of SEGI groves reflected effects of both the multidecadal increase in forest biomass and the effects of warmer drought‐year temperatures on the evaporative demand of current grove vegetation, plus sufficient regolith water storage of rain and snowmelt to sustain that vegetation through seasonal and multiyear dry periods.

La complejidad de la comunidad de vegetación es un factor crítico que influye en la estabilidad del ecosistema terrestre. China, el país que lidera el mundo en el reverdecimiento de la vegetación como resultado de las actividades humanas, ha experimentado cambios dramáticos en la composición de la comunidad de vegetación durante los últimos 30 años. Sin embargo, la forma en que la complejidad de la comunidad de vegetación de China varía espacial y temporalmente sigue sin estar clara. Aquí, proporcionamos los conjuntos de datos y códigos utilizados para investigar este tema, según lo publicado en "Human-climate coupled changes in vegetation community complexity of China since 1980s" por Su et al. La complexité de la communauté végétale est un facteur critique influençant la stabilité de l'écosystème terrestre. La Chine, le pays leader mondial en matière de verdissement de la végétation résultant des activités humaines, a connu des changements spectaculaires dans la composition des communautés végétales au cours des 30 dernières années. Cependant, la façon dont la complexité de la communauté végétale chinoise varie spatialement et temporellement reste incertaine. Ici, nous avons fourni les ensembles de données et les codes utilisés pour étudier cette question, tels que publiés dans « Human-climate coupled changes in vegetation community complexity of China since 1980s » par Su et al. Vegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spatially and temporally remains unclear. Here, we provided the datasets and codes used to investigate this issue, as published in "Human-climate coupled changes in vegetation community complexity of China since 1980s" by Su et al. يعد تعقيد مجتمع الغطاء النباتي عاملاً حاسمًا يؤثر على استقرار النظام البيئي الأرضي. شهدت الصين، الدولة الرائدة في العالم في تخضير الغطاء النباتي الناتج عن الأنشطة البشرية، تغييرات جذرية في تكوين مجتمع الغطاء النباتي خلال الثلاثين عامًا الماضية. ومع ذلك، لا يزال من غير الواضح كيف يختلف تعقيد مجتمع الغطاء النباتي في الصين مكانيًا وزمنيًا. قدمنا هنا مجموعات البيانات والرموز المستخدمة للتحقيق في هذه المشكلة، كما نُشرت في "التغيرات المقترنة بالمناخ البشري في تعقيد مجتمع الغطاء النباتي في الصين منذ الثمانينيات" من قبل سو وآخرون.

AbstractVegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spatially and temporally remains unclear. Here, we examined the spatial pattern of China's vegetation community complexity and its temporal changes from the 1980s to 2015 using two vegetation maps of China as well as more than half a million field samples. Spatially, China's vegetation community complexity distribution is primarily dominated by elevation, although temperature and precipitation can be locally more influential than elevation when they become the factors limiting plant growth. Temporally, China's vegetation community complexity shows a significant decreasing trend during the past 30 years, despite the observed vegetation greening trend. Prevailing climate warming across China exhibits a significant negative correlation with the decrease in vegetation community complexity, but this correlation varies with biogeographical regions. The intensity of human activities have an overall negative influence on vegetation community complexity, but vegetation conservation and restoration efforts can have a positive effect on maintaining vegetation composition complexity, informing the critical role of vegetation management policies in achieving the sustainable development goal.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

AbstractChina’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

Abstract Background Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.

Water use efficiency (WUE; gross primary production [GPP]/evapotranspiration [ET]) estimates the tradeoff between carbon gain and water loss during photosynthesis and is an important link of the carbon and water cycles. Understanding the spatiotemporal patterns and drivers of WUE is helpful for projecting the responses of ecosystems to climate change. Here we examine the spatiotemporal patterns, trends, and drivers of WUE at the global scale from 2000 to 2013 using the gridded GPP and ET data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results show that the global WUE has an average value of 1.70 g C/kg H2O with large spatial variability during the 14‐year period. WUE exhibits large variability with latitude. WUE also varies much with elevation: it first remains relatively constant as the elevation varies from 0 to 1000 m and then decreases dramatically. WUE generally increases as precipitation and specific humidity increase; whereas it decreases after reaching maxima as temperature and solar radiation increases. In most land areas, the temporal trend of WUE is positively correlated with precipitation and specific humidity over the 14‐year period; while it has a negative relationship with temperature and solar radiation related to global warming and dimming. On average, WUE shows an increasing trend of 0.0025 g C·kg−1 H2O·yr−1 globally. Our global‐scale assessment of WUE has implications for improving our understanding of the linkages between the water and carbon cycles and for better projecting the responses of ecosystems to climate change.

AbstractWoody residence time (τw) is an important parameter that expresses the balance between mature forest recruitment/growth and mortality. Using field data collected from the literature, this study explored the global forest τw and investigated its influence on model simulations of aboveground biomass (AGB) at a global scale. Specifically, τw was found to be related to forest age, annual temperature, and precipitation at a global scale, but its determinants were different among various plant function types. The estimated global forest τw based on the filed data showed large spatial heterogeneity, which plays an important role in model simulation of AGB by a dynamic global vegetation model (DGVM). The τw could change the resulting AGB in tenfold based on a site‐level test using the Monte Carlo method. At the global level, different parameterization schemes of the Integrated Biosphere Simulator using the estimated τw resulted in a twofold change in the AGB simulation for 2100. Our results highlight the influences of various biotic and abiotic variables on forest τw. The estimation of τw in our study may help improve the model simulations and reduce the parameter's uncertainty over the projection of future AGB in the current DGVM or Earth System Models. A clearer understanding of the responses of τw to climate change and the corresponding sophisticated description of forest growth/mortality in model structure is also needed for the improvement of carbon stock prediction in future studies.