• Energy Research

AbstractClimate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands (“alpine grasslines”) are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi‐source remote sensing data and determining their positions at 30‐m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph‐cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high‐spatial‐resolution images showed a high level of accuracy (R2, .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th–95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first‐time at large scale and lays a foundation for investigating their responses to climate change.

Vegetation activity on the Tibetan Plateau grassland has been substantially enhanced as a result of climate change, as revealed by satellite observations of vegetation greenness (i.e., the normalized difference vegetation index, NDVI). However, little is known about the temporal variations in the relationships between NDVI and temperature and precipitation, and understanding this is essential for predicting how future climate change would affect vegetation activity. Using NDVI data and meteorological records from 1982 to 2011, we found that the inter-annual partial correlation coefficient between growing season (May-September) NDVI and temperature (RNDVI-T) in a 15-year moving window for alpine meadow showed little change, likely caused by the increasing RNDVI-T in spring (May-June) and autumn (September) and decreasing RNDVI-T in summer (July-August). Growing season RNDVI-T for alpine steppe increased slightly, mainly due to increasing RNDVI-T in spring and autumn. The partial correlation coefficient between growing season NDVI and precipitation (RNDVI-P) for alpine meadow increased slightly, mainly in spring and summer, and RNDVI-P for alpine steppe increased, mainly in spring. Moreover, RNDVI-T for the growing season was significantly higher in those 15-year windows with more precipitation for alpine steppe. RNDVI-P for the growing season was significantly higher in those 15-year windows with higher temperature, and this tendency was stronger for alpine meadow than for alpine steppe. These results indicate that the impact of warming on vegetation activity of Tibetan Plateau grassland is more positive (or less negative) during periods with more precipitation and that the impact of increasing precipitation is more positive (or less negative) during periods with higher temperature. Such positive effects of the interactions between temperature and precipitation indicate that the projected warmer and wetter future climate will enhance vegetation activity of Tibetan Plateau grassland.

Abstract Climate warming has induced substantial advances in the onset of vegetation green-up in the northern hemisphere during recent decades. To date, however, the temporal changes in green-up date have not been adequately explained by the statistical relationships between green-up date and climatic factors, posing challenges in the attribution and prediction of phenological change. In this study, we thus turned to focus on autumn phenology, a critical biotic factor that is likely to affect the subsequent spring phenology of vegetation. Using satellite-retrieved start and end of growing season (SOS and EOS) over the period from 1982 to 2015, we examined the association between the EOS and the SOS in the following year in northern middle and high latitudes (north of 25°N). Interannual changes in SOS were significantly (P

AbstractThe uncertainties of China's gross primary productivity (GPP) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year2 from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.

AbstractTemperature sensitivity (TS) of the green‐up date (GUD) of plants is crucial for the prediction of grassland phenology that is important for animal husbandry and pasture management. Spatial variations in the TS are known to reflect interannual temperature variability and/or accumulated precipitation preceding the GUD (pre‐GUD). However, whether spatial TS variations are related to the interaction between pre‐GUD temperature variability and precipitation, which is a potential indicator of frost risk, remains unclear. Furthermore, because the interaction between interannual temperature variability and accumulated precipitation following the GUD (post‐GUD) can exert selection pressure on the plant life cycle, it may also be involved in shaping the spatial TS pattern. Using long‐term ground observations of GUD on the Tibetan Plateau, we show that TS is more negative (greater GUD advance per unit temperature increase) in areas with more pre‐GUD precipitation and low pre‐GUD interannual temperature variability, but less negative in areas with more pre‐GUD precipitation and high pre‐GUD interannual temperature variability. This result is likely because more pre‐GUD precipitation facilitates sprout and leaf development under stable temperature conditions, whereas it increases frost risk when the temperature variability is high. In contrast, TS magnitude decreases with increases in post‐GUD precipitation in areas where post‐GUD interannual temperature variability is low, but increases with post‐GUD precipitation in areas where post‐GUD interannual temperature variability is high. We speculate that because hydrothermal demands for leaf growth from the onset of green‐up to maturity are more easily fulfilled when interannual temperature variability is lower and precipitation is higher, green‐up need not be sensitive to pre‐GUD temperature. In contrast, high post‐GUD precipitation likely aggravates low‐temperature constraints on leaf growth when temperature variability is high, resulting in greater TS to maximize growing season length. These results suggest that spatial TS variations on the Tibetan Plateau are likely associated with adaptations of leaf‐out phenology to background pre‐GUD climatic conditions together with selection pressure from post‐GUD conditions.

Abstract Phenology studies the cycle of events in nature that are initiated and driven by an annually recurring environment. Plant phenology is expected to be one of the most sensitive and easily observable natural indicators of climate change. On the Tibetan Plateau (TP), an accelerated warming since the mid-1980s has resulted in significant environmental changes. These new conditions are accompanied by phenological changes that are characterized by considerable spatiotemporal heterogeneity. Satellite remote sensing observed widespread advance in the start of the plant growing season across the plateau during the 1980s and 1990s but substantial delay over 2000–2011 in the southwest although it continued to advance in the northeast regions of the TP. Both observational studies and controlled experiments have revealed, to some extent, the positive role of higher preseason temperature and even more precipitation in advancing the leaf onset and first flowering date of the TP. However, a number of rarely visited research issues that are essential for understanding the role of phenology in ecosystem responses and feedback processes to climate change remain to be solved. Our review recommends that addressing the following questions should be a high priority. How did other phenological events change, such as flowering and fruiting phenology? What are the influences from environmental changes other than temperature and precipitation, including human activities such as grazing? What are the genetic and physiological bases of plants phenological responses? How does phenological change influence ecosystem structure and function at different scales and feedback to the climate system? Investigating these research questions requires, first of all, new data of the associated environmental variables, and consistent and reliable phenological observation using different methodologies (i.e. in situ observations and remote sensing).

The phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) is critical to regional climate change through climate–vegetation feedback. Although many studies have examined QTP vegetation dynamics and their climate sensitivities, the interspecific difference in the phenology response to climate change between alpine species is poorly understood. Here, we used a 30-year (1989–2018) record of in situ phenological observation for five typical alpine herbs (Elymus nutans, Kobresia pygmaea, Plantago asiatica, Puccinellia tenuiflora, and Scirpus distigmaticus) and associated climatic records at Henan Station in the eastern QTP to examine the species-level difference in spring and autumn phenology and then quantify their climate sensitivities. Our results show that with significantly warming, the green-up dates of herbs were insignificantly shifted, while the brown-off dates in four out of the five herbs were significantly delayed. Meanwhile, the interspecific difference in brown-off dates significantly increased at a rate of 0.62 days/annual from 1989 to 2016, which was three times larger than that in green-up dates (0.20 days/annual). These diverse rates were attributed to the different climate controls on spring and autumn phenology. In particular, green-up dates in most herbs were sensitive to mean surface temperature, while brown-off dates were sensitive to the night surface temperature. Furthermore, brown-off dates are less sensitive to the warming in high ecological niche (with higher herb height and aboveground biomass) herbs than low niche herbs (with lower herb height and aboveground biomass). The increased phenology interspecific difference highlights the complex responses of herbs to future climate change even under the same alpine environment and indicates a potential alternation in the plants community of alpine QTP, which may further influence the regional climate–vegetation feedback.

Mt. Everest (Qomolangma or Sagarmatha), the highest mount on Earth and located in the central Himalayas between China and Nepal, is characterized by highly concentrated glaciers and diverse landscapes, and is considered to be one of the most sensitive area to climate change. In this paper, we comprehensively synthesized the climate and environmental changes in the Mt. Everest region, including changes in air temperature, precipitation, glaciers and glacial lakes, atmospheric environment, river and lake water quality, and vegetation phenology. Historical temperature reconstruction from ice cores and tree rings revealed the distinct features of 20th century warming in the Mt. Everest region. Meteorological observations further proved that the Mt. Everest region has been experiencing significant warming (approximately 0.33 °C/decade) but relatively stable precipitation during 1961−2018 AD. Projected results (during 2006−2099 AD) under different representative concentration pathway scenarios showed a general warming trend in the region, with larger warming occurring in winter than in summer. Meanwhile, the precipitation projections varied spatially with no significant trends over the region. Intensive glacier shrinkage was characterized by decreasing glacier areas, while glacier-fed river runoff increased. Glacial lakes expanded with increasing glacial lake areas and numbers. These findings indicated a clear regional hydrological response to climate warming. Owing to the remote location of Mt. Everest, the present atmospheric environment remained relatively clean; however, long-range transport of atmospheric pollutants from South Asia and West Asia may have substantially influenced the Mt. Everest region, resulting in increasing concentrations of pollutants since the Industrial Revolution. Anthropogenic activities have been shown to influence river and lake water quality in this remote region, especially in the downstream. The end of the vegetation growing season advanced in the northern slope and did not ...