• Energy Research

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 With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency.

AbstractIncreasing drought frequency and severity in a warming climate threaten forest ecosystems with widespread tree deaths. Canopy structure is important in regulating tree mortality during drought, but how it functions remains controversial. Here, we show that the interplay between tree size and forest structure explains drought-induced tree mortality during the 2012-2016 California drought. Through an analysis of over one million trees, we find that tree mortality rate follows a “negative-positive-negative” piecewise relationship with tree height, and maintains a consistent negative relationship with neighborhood canopy structure (a measure of tree competition). Trees overshadowed by tall neighboring trees experienced lower mortality, likely due to reduced exposure to solar radiation load and lower water demand from evapotranspiration. Our findings demonstrate the significance of neighborhood canopy structure in influencing tree mortality and suggest that re-establishing heterogeneity in canopy structure could improve drought resiliency. Our study also indicates the potential of advances in remote-sensing technologies for silvicultural design, supporting the transition to multi-benefit forest management.

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.

Probability density forecast offers the whole distributions of forecasting targets, which brings greater flexibility and practicability than the other probabilistic forecast models such as prediction interval (PI) and quantile forecast. However, existing density forecast models have introduced various constraints on forecasted distributions, which has limited their ability to approximate real distributions and may result in suboptimality. In this paper, a distribution-free density forecast model based on deep learning is proposed, in which the real cumulative density functions (CDFs) of forecasting target are approximated by a large-capacity positive-weighted deep neural network (NN). Benefiting from the universal approximation ability of NNs, the range of forecasted distributions has been proven to contain all the distributions with continuous CDFs, which is superior to existing models' considering both width and accordance with reality. Three tests from different scenarios were implemented for evaluation, i.e., very-short-term wind power, wind speed, and day-ahead electricity price forecast, in which the proposed density forecast model has shown superior performance over the state of the art.

Abstract Accurate generation forecasting can effectively accelerate the use of renewable energy in hybrid energy systems, contributing significantly to the delivery of the net-zero emission target. Recently, neural-network-based quantile forecast models have shown superior performance on renewable energy generation forecasting, partially because they have subtly embedded quantile forecast evaluation metrics into their loss functions. However, the non-differentiability of involved metrics has rendered their metric-embedded loss functions not everywhere-derivable, resulting in inapplicability of gradient-based training approaches. Instead, they have resorted to heuristic searches for Neural Network (NN) training, bringing low training efficiency and a rigid restriction on the size of the resultant NN. In this paper, the Indicator Gradient Descent (IGD) is proposed to overcome the non-differentiability of involved metrics, and several metric-embedded loss functions are innovatively customized combining IGD, enabling NNs to be trained efficiently in a ‘gradient-descent-like’ manner. Moreover, the deep Bidirectional Long Short-Term Memory (BiLSTM) is adopted to capture the periodicity of renewable generation (diurnal and seasonal patterns), and the residual technique is used to improve the training efficiency of the deep BiLSTM. Finally, a Deep Quantile Forecast Network (DQFN) based on IGD and deep residual BiLSTM is developed for wind and solar power quantile forecasting. Practical experiments in four cases have verified the effectiveness and efficiency of DQFN and IGD, where DQFN has achieved the lowest average proportion deviations (all below 1.7%) and the highest skill scores.