Urban heat island (UHI) can be characterized and quantified to understand the modification of urban surfaces on the local and regional climate. This study examines UHI variation across three ...megacities that are located in rapid urbanization regions in eastern China (Beijing, Shanghai and Guangzhou). These cities are located within a warm temperate climate zone, north subtropical climate zone, and lower subtropical climate zone, respectively. Satellite-based land surface temperature (LST) data and air temperature records from 2003 to 2016 were used to identify surface urban heat island (SUHI) and canopy urban heat island (CUHI), respectively. Generally, the average annual SUHI is higher than the CUHI, with the greatest UHIs appearing in Beijing (SUHI: 2.33 ± 0.18 °C, CUHI: 1.45 ± 0.54 °C). UHI changes across latitudes were negatively related to humidity variation, with higher UHI in drier climates. Seasonal UHI analysis suggests that a lower SUHI would occur in winter and a higher UHI in spring and summer, except for Guangzhou. CUHI in dry season was higher than in wet season for all three megacities, and the largest CUHI (2.10 ± 0.33 °C) appeared in winter in Beijing. Various patterns of seasonal cycles of SUHI and CUHI were related to monthly precipitation and solar insolation. Annual average daytime SUHI was higher than the nighttime SUHI, and larger daytime SUHI appeared in Guangzhou, contrasting with Shanghai and Beijing. The difference between SUHI and CUHI for all seasons was also high in Guangzhou. UHI changes were considered to be altered by warm and wet conditions in mega-cities of eastern China, and heat transportation from urban surface to urban canopy provided some possible understanding of the UHI change.
•Sediment connectivity is influenced by vegetation, slope and watersheds area.•Anticlockwise loop is the most frequent hysteresis type and transport the most sediment.•Indices of connectivity showed ...a high correlation with sediment parameters and hysteresis index.
Sediment connectivity is an important property influencing landscape evolution. Understanding the link between sediment connectivity and hydrologic processes plays an important role in developing strategies for soil and water conservation measures. Based on 10 years of observations for 30 hydrologically monitored watersheds on the Loess Plateau, measurements for 926 hydrologic events were analyzed and the index of connectivity (IC) was calculated. The high-frequency runoff and sediment data were analyzed, and a normalized hysteresis index (HI) was calculated to assess the dominant hysteresis patterns for each watershed. Correlation analysis was conducted to determine the relationship between IC and HI. The results showed that the watersheds with low IC values were located in regions with dense vegetation and flat terrain and were dominated mainly by a clockwise hysteresis pattern, while the watersheds with high IC values were located in regions with sparse vegetation and steep terrain and were dominated by an anticlockwise hysteresis pattern. The anticlockwise hysteresis pattern was the most frequent and the most efficient type in terms of sediment transport among the 30 watersheds. The anticlockwise hysteresis type was more likely to occur as the IC values increased. A significant negative correlation was observed between the IC values and HI values during small and medium hydrologic events. The link is correlated with the notion that the separation into two components in connectivity indexes are similar to the two sediment sources controlling the hysteresis type. The outcomes from this study are helpful for determining the relationship between sediment connectivity and hydrologic behavior and thus for providing insight into the hydrologic processes that are essential to facilitate sustainable watershed management.
Boreal forests are facing profound changes in their growth environment, including warming‐induced water deficits, extended growing seasons, accelerated snowmelt, and permafrost thaw. The influence of ...warming on trees varies regionally, but in most boreal forests studied to date, tree growth has been found to be negatively affected by increasing temperatures. Here, we used a network of Pinus sylvestris tree‐ring collections spanning a wide climate gradient the southern end of the boreal forest in Asia to assess their response to climate change for the period 1958–2014. Contrary to findings in other boreal regions, we found that previously negative effects of temperature on tree growth turned positive in the northern portion of the study network after the onset of rapid warming. Trees in the drier portion did not show this reversal in their climatic response during the period of rapid warming. Abundant water availability during the growing season, particularly in the early to mid‐growing season (May–July), is key to the reversal of tree sensitivity to climate. Advancement in the onset of growth appears to allow trees to take advantage of snowmelt water, such that tree growth increases with increasing temperatures during the rapidly warming period. The region's monsoonal climate delivers limited precipitation during the early growing season, and thus snowmelt likely covers the water deficit so trees are less stressed from the onset of earlier growth. Our results indicate that the growth response of P. sylvestris to increasing temperatures strongly related to increased early season water availability. Hence, boreal forests with sufficient water available during crucial parts of the growing season might be more able to withstand or even increase growth during periods of rising temperatures. We suspect that other regions of the boreal forest may be affected by similar dynamics.
Pinus sylvestris growth reversed its response to temperature between the non‐warming period (1958–1986) and the warming period (1987–2014). The shifting of the growing season to April during rapid warming, the presence of snow cover during early growing season, and a consequent alleviation of water‐limitation during the early growing season contribute to the reversed correlation between temperature and growth for April and May since 1987.
•The distribution of Fe and Mn had the dominant longitudinal zonality.•The distribution of Cu and Zn had the prominent vertical zonality.•The scale and local effects on the micronutrients were much ...greater.•Multiscale analyzing methods could eliminate the collinearity among variables.
Soil bioavailable micronutrients have a significant impact on soil fertility, soil quality, maize production, and even environmental quality. However, minimal research has been conducted to characterize the spatial patterns of soil bioavailable micronutrients at the large spatial extent within the maize filed and to reveal the relative control of environmental factors on them at the global and local scale. In the study, topsoil samples (0–20 cm of plough depth, totaling 4,448) were collected from maize field to determine the spatial heterogeneity of soil bioavailable micronutrients including iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B), and to assess the relative effect of environmental indicators on them. Based on the 2-dimensional empirical mode decomposition (2D-EMD), the spatial pattern of micronutrients at the residues and their relationships with influencing indicators were explored. The results demonstrated that the distribution of Fe and Mn had the dominant longitudinal zonality, the distribution of Cu and Zn had the prominent vertical zonality, and the spatial characteristic of B did not exhibit any particular pattern at the large spatial extent. Based on the stepwise multivariate linear regression with the residues, environmental indicators had less global effect on the distribution of soil bioavailable micronutrients. However, based on the combination of geographically weighted regression (GWR) and 2D-EMD, environmental indicators had a good and significant interpretation on the dynamics of micronutrients bioavailability, which ranged from 53% to 75% in the study area. Specifically, the impact of environmental indicators on Fe, Mn, and Zn were greater, and the impact of human activities on B was greater. Our findings indicated that the local and scale effects on the soil available micronutrients should be integrated into the prediction methods, and the combined method of GWR and 2D-EMD would be a good choice for the high-quality digital mapping at large spatial extent.
Vegetation dynamics are sensitive to climatic warming and are affected by individual or combined climatic factors at different temporal scales with different intensities. Previous studies have ...unraveled the relationships between vegetation dynamics and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamics are dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherence (BWC), multivariate wavelet coherence (MWC), and multidimensional empirical mode decomposition (MEMD). The results indicated that significant vegetation dynamics were located mainly at scales of 1, 0.5, and 0.3 years. Vegetation variations were divided into seasonal (≤ 1 year), short-term (1–4 years), medium-term (4–8 years), and long-term (> 8 years) scales. The combined explanatory powers of seven climatic factors on the vegetation were greater at the short-term and long-term scales, whereas individual climatic factors, such as precipitation or temperature, might affect vegetation dynamics in some climatic regions at the seasonal and medium-term scales. The combined effect of climatic factors in the grassland of the Tibetan Plateau (TP) and the temperate grassland of Inner Mongolia (TGIM) were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate on crop dynamics in both temperate humid and subhumid Northeast China and the TP were around 47%, whereas the controls of climate on crops in both the TGIM and the temperate and warm-temperate desert of Northwest China were around 39%. Cropland farming practices could alleviate the spatial variation of the relationships between climate and vegetation while enhancing the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly at the medium-term scale. Collectively, the results might provide an alternative perspective for understanding vegetation evolution in response to climatic changes in China.
Forests play an important role in sequestrating atmospheric carbon dioxide (CO
2
). Therefore, in order to understand the spatial–temporal variations and controlling mechanisms of global forest ...carbon (C) storage under future climate change, an improved individual-based forest ecosystem carbon budget model and remote sensing outputs in this study were applied to investigate the spatial–temporal dynamics of global forest (vegetation+soil) C storage in the future climate change scenario. The results showed that in the future RCP4.5 (representative concentration pathways) climate scenario, the total C storage per unit area per year in vegetation and soil of the global forest ecosystem showed a trend of first decreasing and then increasing from 2006 to 2100, with an average of 22.77 kg C m
−2
year
−1
. However, the evolution trends of C storage changes in vegetation and soil were different. Moreover, the average soil C storage per unit area per year was 2.87 times higher than the average vegetation C storage. The impact of climate change on total C storage in vegetation and soil of the global forest ecosystems was positive, showing an obvious increase during 2006–2100. The total C storage varied significantly in spatial distribution. Spatially, the vegetation C storage and the soil organic C storage were projected to decrease significantly in most parts of South America and southern Africa in the Southern Hemisphere and increase in eastern North America, western Asia, and most areas of Europe in the Northern Hemisphere. Especially in the middle and high latitude regions of the Northern Hemisphere, the total forest C stock was projected to increase by 30–90% from 2046 to 2100. In the future, in these areas where forest C reserves were predicted to be reduced, it was suggested to increase afforestation, prohibit deforestation, and develop projects to increase forest C. Sustainable forest managements also offered opportunities for immediate mitigation and adaptation to climate change. Our findings provided not only a projection of C storage of global forest ecosystem responses to future climate change but also a useful methodology for estimating forest C storage at global levels.
•Satellite images captured the reasonable spatial pattern of global heat waves.•Seven hot spot regions identified by heat wave indices variation.•Australia and South Africa achieved the highest heat ...wave impacts.
The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.
Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and one of the key issues is how to identify the typical ...urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distributions. DTW algorithm could create more rigorous and reasonable classification for typical urban and rural stations in Beijing by comparing with remote sensing and cluster analysis method. We also found that some traditional rural stations did not represent temperature variation in rural areas because their surrounding environments have been highly modified by urbanization process in last decades, and this might lead to an underestimate of the urban climate effect by 1.24℃. The study suggests that DTW algorithm is a simple and efficient method for selecting appropriate temperature records and analyzing temperature sequences with a good potential in improving urban heat island estimation.
Key message
We identified the climate zones where the climate has highest variation similarity to aid to climate data selection.
The calculation of climate-growth correlations is the analytical ...foundation to study climate change influence on tree growth in dendrochronology. However, the majority of climate data used in climate-growth correlation analyses are not directly recorded on the sample sites, but obtained from nearby weather stations. We used a sample site in Saihanba region as a case study to address how correlation bias may occur if nearby climate products have no high correlation with the climate in the sample site. Temperatures in the sample site and from other data resources were highly correlated, suggesting that small potential bias in growth-temperature correlations when using temperatures from nearby climate stations. However, precipitation had large spatial variability, resulting in low correlation between precipitation of the sample site and precipitation from other resources. Large biases in growth-precipitation analysis would be expected when using precipitation from nearby stations, suggesting that precipitation records should be carefully chosen. To aid in this selection, we used a cluster analysis and multiple data-products across China to identify regions where station climate do and do not reflect accurately site conditions, and classified temperature and precipitation zones where climate has high correlation among grid cells of the same climate zone based on similarity of the macroclimate using a ~ 2.5 km resolution gridded climate dataset. Using climate stations located in the same cluster as the sample sites would help to prevent or reduce correlation biases in growth-climate analyses. The generated temperature and precipitation zones are freely available to download as GeoTIFF files in the online supplementary materials (Fig. 1S and Fig. 2S).
Abstract
Larch trees are widely used in afforestation and timber plantations. Yet, little is known on how planted larch trees cope with increasing drought. We used a tree-ring network of 818 trees ...from 31 plantations spanning most of the distribution of
Larix principis-rupprechtii
to investigate how extreme drought influences larch radial growth in northern China. We found that summer drought, rather than temperature or precipitation, had the strongest relationship with radial growth throughout the region. Drought increased in frequency in recent decades, leaving a strong imprint on the radial growth of larch, particularly in dry sites. Across its distribution, radial growth in larch trees that experienced extreme droughts more frequently displayed lower resistance to drought, but higher recovery after it, suggesting these populations were better adapted to extreme droughts. Radial growth decreased with increasing drought, with particularly severe declines below a threshold Palmer Drought Severity Index (PDSI) value of −3 to −3.5. Extreme droughts (PDSI < −4.5) caused a reduction of 62% of radial growth and chronic drought events caused around 20% reduction in total radial growth compared with mean growth on the driest sites. Given that current climate projections for northern China indicate a strong increase in the frequency and severity of extreme drought, trees in large portions of the largest afforestation project in the world, particularly those in the drier edge, are likely to experience severe growth reductions in the future.
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