• Energy Research

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Abstract China has realized a 56-fold increase in installed wind capacity, from 5.9 GW in 2007 to 328 GW in 2021. In addition to increasing installed capacity, plans to substantially increase wind energy production for climate change mitigation also depend on future wind speeds, which strongly influences the efficiencies of installed turbines within individual wind farms. A reversal in globally decreasing wind speeds over several decades has been reported previously. However, subsequent studies using other data sources reported only a slight increase or no reversal in China. These uncertainties regarding China’s wind energy production hamper estimates of wind energy production potential. Here, our analysis of quality-controlled wind speed measurements from in-situ stations shows that the wind speed decline in China reversed significantly since 2012 (P < 0.001), but with substantial spatio-temporal variability. We further estimated the capacity factor (CF) growth and the wind power gain solely associated with the changes in wind speed ranges from 31.6 to 56.5 TWh yr−1 based on the 2019 installed capacity. This estimate explains 22.0%–39.3% of the rapid increase in wind generation CF in China during 2012–2019. The result implies that the site selection of wind farms should consider both current wind situation and future wind speed trends. Further studies are needed to understand the driving factor of wind speed recovery in support of the wind energy industry.

AbstractGlobal reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.

Une estimation précise du stock de biomasse forestière C est essentielle pour comprendre les cycles du carbone. Cependant, les estimations actuelles de la biomasse forestière chinoise sont principalement basées sur les volumes de bois basés sur les inventaires et les facteurs de conversion empiriques à l'échelle provinciale, ce qui pourrait introduire de grandes incertitudes dans l'estimation de la biomasse forestière. Ici, nous fournissons une estimation basée sur les données de la biomasse aérienne forestière chinoise de 2001 à 2013 à une résolution spatiale de 1 km en intégrant une base de données de biomasse aérienne forestière mesurée au sol au niveau de la placette récemment examinée avec des informations géospatiales provenant d'un ensemble de données de spectroradiomètre d'imagerie à résolution modérée (MODIS) de 1 km dans un algorithme d'apprentissage automatique (l'ensemble d'arbres modèles, MTE). Nous montrons que la biomasse aérienne des forêts chinoises est de 8,56 Pg C, ce qui est principalement apporté par les forêts de feuilles d'aiguilles à feuilles persistantes et les forêts de feuillus à feuilles caduques. La densité moyenne de biomasse aérienne forestière est de 56,1 Mg C ha-1, avec des valeurs élevées observées dans les régions humides tempérées. Les réponses de la densité de la biomasse aérienne forestière à la température annuelle moyenne sont étroitement liées aux conditions de l'eau ; c'est-à-dire que les réponses négatives dominent les régions avec des précipitations annuelles moyennes inférieures à 1300 mm an-1 et les réponses positives prévalent dans les régions avec des précipitations annuelles moyennes supérieures à 2800 mm an-1. Au cours des années 2000, les forêts chinoises ont séquestré le carbone par 61,9 Tg C y-1, et ce puits de carbone est principalement réparti dans le nord de la Chine et peut être attribué au réchauffement climatique, à l'augmentation de la concentration de CO2, aux dépôts d'azote et à la croissance des jeunes forêts. La estimación precisa del stock de biomasa forestal C es esencial para comprender los ciclos del carbono. Sin embargo, las estimaciones actuales de la biomasa forestal china se basan principalmente en volúmenes de madera basados en inventarios y factores de conversión empíricos a escala provincial, lo que podría introducir grandes incertidumbres en la estimación de la biomasa forestal. Aquí proporcionamos una estimación basada en datos de la biomasa forestal sobre el suelo de China de 2001 a 2013 con una resolución espacial de 1 km mediante la integración de una base de datos de biomasa forestal sobre el suelo medida a nivel de parcela recientemente revisada con información geoespacial del conjunto de datos del espectrorradiómetro de imágenes de resolución moderada (MODIS) de 1 km en un algoritmo de aprendizaje automático (el conjunto de árboles modelo, mTe). Mostramos que la biomasa del bosque chino sobre el suelo es de 8,56 Pg C, que es aportada principalmente por los bosques de hoja de aguja de hoja perenne y los bosques de hoja ancha caduca. La densidad media de biomasa forestal sobre el suelo es de 56,1 Mg C ha-1, con altos valores observados en regiones húmedas templadas. Las respuestas de la densidad de biomasa forestal sobre el suelo a la temperatura media anual están estrechamente vinculadas a las condiciones del agua; es decir, las respuestas negativas dominan las regiones con una precipitación media anual inferior a 1300 mm y-1 y las respuestas positivas prevalecen en las regiones con una precipitación media anual superior a 2800 mm y-1. Durante la década de 2000, los bosques en China secuestraron C por 61.9 Tg C y-1, y este sumidero de C se distribuye principalmente en el norte de China y puede atribuirse al calentamiento del clima, el aumento de la concentración de CO2, la deposición de N y el crecimiento de bosques jóvenes. يعد التقدير الدقيق لمخزون الكتلة الحيوية للغابات C أمرًا ضروريًا لفهم دورات الكربون. ومع ذلك، فإن التقديرات الحالية للكتلة الحيوية للغابات الصينية تعتمد في الغالب على أحجام الأخشاب القائمة على الجرد وعوامل التحويل التجريبية على مستوى المقاطعة، مما قد يؤدي إلى شكوك كبيرة في تقدير الكتلة الحيوية للغابات. نقدم هنا تقديرًا قائمًا على البيانات للكتلة الحيوية للغابات الصينية فوق سطح الأرض من 2001 إلى 2013 بدقة مكانية تبلغ 1 كم من خلال دمج قاعدة بيانات الكتلة الحيوية للغابات فوق سطح الأرض على مستوى قطعة الأرض التي تمت مراجعتها مؤخرًا مع المعلومات الجغرافية المكانية من مجموعة بيانات مقياس الطيف التصويري متوسط الدقة (MODIS) لمسافة 1 كم في خوارزمية التعلم الآلي (مجموعة الشجرة النموذجية، MTE). نظهر أن الكتلة الحيوية للغابات الصينية فوق سطح الأرض هي 8.56 بيكوغرام سي، والتي تساهم بها بشكل أساسي الغابات دائمة الخضرة ذات الأوراق الإبرية والغابات ذات الأوراق العريضة المتساقطة. متوسط كثافة الكتلة الحيوية للغابات فوق سطح الأرض هو 56.1 ملغ C هكتار -1، مع قيم عالية لوحظت في المناطق الرطبة المعتدلة. ترتبط استجابات كثافة الكتلة الحيوية فوق سطح الأرض بمتوسط درجة الحرارة السنوية ارتباطًا وثيقًا بظروف المياه ؛ أي أن الاستجابات السلبية تهيمن على المناطق التي يقل متوسط هطول الأمطار السنوي فيها عن 1300 ملم y -1 وتسود الاستجابات الإيجابية في المناطق التي يزيد متوسط هطول الأمطار السنوي فيها عن 2800 ملم y -1. خلال العقد الأول من القرن الحادي والعشرين، عزلت الغابات في الصين C بمقدار 61.9 تيراغرام Cy -1، ويتم توزيع هذا الحوض C بشكل أساسي في شمال الصين ويمكن أن يعزى إلى ارتفاع درجة حرارة المناخ، وارتفاع تركيز ثاني أكسيد الكربون، وترسب N، ونمو الغابات الفتية. Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

Global warming-induced melting and thawing of the cryosphere are severely altering the volume and timing of water supplied from High Mountain Asia, adversely affecting downstream food and energy systems that are relied on by billions of people. The construction of more reservoirs designed to regulate streamflow and produce hydropower is a critical part of strategies for adapting to these changes. However, these projects are vulnerable to a complex set of interacting processes that are destabilizing landscapes throughout the region. Ranging in severity and the pace of change, these processes include glacial retreat and detachments, permafrost thaw and associated landslides, rock–ice avalanches, debris flows and outburst floods from glacial lakes and landslide-dammed lakes. The result is large amounts of sediment being mobilized that can fill up reservoirs, cause dam failure and degrade power turbines. Here we recommend forward-looking design and maintenance measures and sustainable sediment management solutions that can help transition towards climate change-resilient dams and reservoirs in High Mountain Asia, in large part based on improved monitoring and prediction of compound and cascading hazards.

AbstractWheat growth is sensitive to temperature, but the effect of future warming on yield is uncertain. Here, focusing on China, we compiled 46 observations of the sensitivity of wheat yield to temperature change (SY,T, yield change per °C) from field warming experiments and 102 SY,T estimates from local process-based and statistical models. The average SY,T from field warming experiments, local process-based models and statistical models is −0.7±7.8(±s.d.)% per °C, −5.7±6.5% per °C and 0.4±4.4% per °C, respectively. Moreover, SY,T is different across regions and warming experiments indicate positive SY,T values in regions where growing-season mean temperature is low, and water supply is not limiting, and negative values elsewhere. Gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project appear to capture the spatial pattern of SY,T deduced from warming observations. These results from local manipulative experiments could be used to improve crop models in the future.