Spatial-temporal Characteristics Research Articles

Analysis of the activities of high sea fishing vessels from China, Japan, and Korea via AIS data mining

In addition to offshore fishing, high sea fishing has become another important supply channel for seafood. The pelagic catches of China, Japan, and Korea (CJK) account for 60% of the global total, and an analysis of their activities adds essential clarity regarding global fishery resources. This paper develops a framework for analyzing the activities of HSF vessels based on satellite AIS data from 2019, which is used to study the spatial-temporal characteristics of the lengths, speeds, trajectories, and trajectory densities of HSF vessels, the numbers of active vessels, and their average fishing times. The analysis results show that: 1) Chinese HSF vessels are mostly small, relative to Japanese and Korean ships; 2) The speed of Japanese HSF vessels is consistently the highest; 3) All HSF vessels mainly fish in the Northwest Pacific, Western Central Pacific, Eastern Central Pacific, Southeast Pacific, Eastern Central Atlantic, Southeast Atlantic, and Western Indian Oceans; 4) The trajectory distribution of Chinese and Japanese vessels are similar, and both are more widely distributed than Korean vessels; 5) Chinese and Korean HSF vessels mostly enter the Atlantic through the Drake Passage, while Japanese ships mostly enter through the Panama Canal; 6) Chinese HSF vessels fish in Antarctic fisheries all year, while Korean ships aim to fish in Antarctic fisheries in the Spring, Summer, and Winter; 7) China has the largest number of HSF vessels in all fisheries, but their average fishing time is the shortest; 8) The average fishing time of all HSF vessels in Spring and Summer is short, increases during the Autumn, and continues to rise in some fisheries during Winter. This study is not only of great significance for the management and conservation of marine resources, but is also a valuable reference for the promotion of international cooperation in safeguarding marine fisheries.

Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China

The spatial-temporal distribution characteristics of carbon emissions under land use changes can fully reflect the impact of socio-economic development caused by human activities on terrestrial ecosystems. However, previous studies just focused on the traditional carbon emission coefficient method which was applied to calculate carbon emission amounts from different land use types at a large spatial scale over a long-time period. This approach did not consider the effects of spatial heterogeneity of socio-economic factors on carbon emissions, which can lead to overestimating and underestimating carbon emissions in intra-study areas. Therefore, it is urgent to build a corrected method integrating socio-economic factors into carbon emission calculation which can make up for this shortcoming. Firstly, this study calculated the carbon emissions under land use changes through the traditional method based on spatial maps of land uses and fossil energy consumption during 2000–2018 in 21 cities (states) in Sichuan Province. From 2000 to 2018, the overall carbon emissions increased by 43.14%, and the high and low carbon emission values occurred in the east and west of the study site, respectively. Chengdu had the largest carbon emissions, and its maximum value appeared in 2015. Only the Tibetan Autonomous Prefecture of Garz (Garz) had a negative carbon emission value. Furthermore, the total carbon emissions were significantly correlated with Gross Domestic Product (GDP) and population. This study then proposed a method to correct carbon emissions by considering the spatial heterogeneity of GDP and population. There were some obvious differences between uncorrected and corrected carbon emissions. From 2000 to 2018, the corrected carbon emissions also showed an increasing trend, but their values were much higher than uncorrected carbon emissions. The city (state) with the largest corrected carbon emissions was still in Chengdu but the maximum value occurred in 2018. The city (state) with negative corrected carbon emissions was still in Garz, but its corrected values were much lower than uncorrected carbon emissions. Additionally, the center of gravity of positive carbon emissions shifted from Ziyang before the correction to Chengdu after the correction during 2000–2018. In summary, the corrected carbon emissions proposed in this study by considering socio-economic driving factors can reflect an actual condition of carbon emissions from land use. The results can offer a scientific basis for the local government to construct low-carbon land use patterns in Sichuan Province. This approach can be promoted to calculate carbon emissions in other study sites at different spatial scales.

Spatio-temporal evolution of sodium aerosol and temperature distribution during sodium pool fire in a confined environment

In case of a Core Disruptive Accident in sodium-cooled fast reactor, sodium will be ejected from the reactor vessel into the Reactor Containment Building (RCB) and results in sodium fire. Liquid sodium combustion generates aerosol, which gets dispersed and remains suspended in RCB based on geometry of the building and degree of turbulence. The Environment Source Term (EST) primarily depends on the dynamics of sodium aerosols inside the containment. Hence, it is essential to carry out a safety analysis of SFR using computational tools and models shall be validated with experimental data, particularly aerosol dynamics inside the containment along with thermal hydraulics. Besides, all codes assume uniform aerosol concentration in large containment buildings and the evolution of suspended concentration was predicted. However, in actual case a non-uniform concentration profile exists, which leads to stratification of aerosol concentration inside the containment. Towards assessment of aerosol dynamics in realistic scenario, spatial–temporal characteristics of sodium aerosol and temperature distribution were studied by conducting sodium pool fire experiments in a large containment.The sodium fire experiment was conducted by using ∼2.0 kg of sodium in a pool area of ∼0.25 m2. The measured maximum pool and gas temperatures are 685 °C and 126 °C respectively, and the temperature gradient at the wall boundary layer was 7.5 °C/cm. The estimated average sodium burning rate and aerosol release rate are 17.3 kg/m2hr and 0.24 g/s, respectively. The spatial variation of aerosol characteristics was measured for both horizontal and vertical planes. The measured maximum aerosol concentration in the horizontal plane ranges from 2.76 to 5.32, 2.16–3.30 and 2.09–4.21 g/m3 for the bottom, middle and top elevations respectively. For the vertical plane, aerosol concentration varied from 2.16 to 3.65 g/m3 for central positions and 2.53–5.32 g/m3 for off-center positions. The trend of average suspended aerosol concentration at each elevation showed that the concentration was higher at the top elevation than at the middle and bottom for up to 15 min i.e., during aerosol generation period and after that concentration was found to be higher at the bottom elevation for the next 25 min. Based on the measurements, it is observed that the aerosol distribution is not well mixed within the experimental period. A significant difference in aerosol mass concentration was observed between each elevation, between estimation and observations based on a zero-dimensional model. The evolutions of aerosol median size by theoretical simulation capture the overall trend reasonably.

Spatial-Temporal Characteristics and Influencing Factors of Carbon Emissions from Land Use and Land Cover in Black Soil Region of Northeast China Based on LMDI Simulation

Land use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed traditional heavy industry and agriculture areas. In this study, we estimated the carbon emissions from land use in the Black Soil Region of Northeast China (BSRNC) from 1990 to 2020. We utilized seven periods of land use remote sensing image data spanning the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a 30-m grid resolution. Additionally, socio-economic data was incorporated into the analysis. The preprocessing of the remote sensing images involved several steps using ENVI 5.5, including radiometric correction, fusion, mosaic, and cropping. The land types were classified into six major categories: cropland, forest land, grassland, water area, construction land, and unused land, using the LUCC classification system. The IPCC coefficient method was used to calculate the trends in carbon emissions from land use, and the logarithmic mean Divisia index (LMDI) method was applied to analyze the influencing factors. The main conclusions are as follows: (1) From 1990 to 2020, the net carbon emissions from land use in the BSRNC increased from 11.91 × 104 t to 253.29 × 104 t, with an annual growth rate of 8.04%. (2) Spatially, land use carbon emissions exhibited an agglomeration pattern that gradually weakened and the regional emission differences gradually narrowed. (3) Income level was identified as the most important factor influencing land use carbon emissions in the BSRNC from 1990 to 2020. Land use efficiency had a inhibitory effect on net carbon emissions, reducing land use carbon emissions by 1730.63 × 104 t.

Fencing affects movement patterns of two large carnivores in Southern Africa

The erection of peripheral fences around protected areas has been shown to adversely affect the movement behavior, genetic connectivity and long-term viability of animal populations. However, fencing is also used extensively to reduce human encroachment, limit poaching activities, and mitigate human–wildlife conflicts. Limited information on the movement behavior of carnivores is available along the periphery of protected areas, particularly in semi-arid environments. To quantify the potential effects of the 824 km perimeter fence surrounding the Etosha National Park (Etosha, Namibia) on the movement and space use of carnivores, we collected GPS movement data from 36 lions (Panthera leo) and seven spotted hyenas (Crocuta crocuta) over a 14-year period. For these two apex carnivores, we measured the impact of the Etosha fence on their movements (step lengths and path straightness), calculated the likelihood of fence crossings, and evaluated the spatial–temporal characteristics of these crossings. Our results indicated that lions and hyenas moved faster (longer step lengths) when closer to the fence. Lions showed no variation in path straightness with respect to the fence but hyenas had straighter movement paths when closer to the fence. When moving within the vicinity of the fence, lions had a 9% likelihood of crossing the fence, while hyenas were much more likely (18%) to do so, which suggests that the Etosha fence was more permeable for hyenas than for lions. Fence crossings predominantly occurred at night for both species. Lions were more likely to cross the fence during the cold dry season (May–August) whereas hyenas crossed more often during the warm wet season (December–April). Female and adult lions had a higher probability of crossing the fence than males and subadults. These findings could be useful in the development of appropriate strategies to mitigate human–carnivore conflicts and thus promote the conservation of carnivores in multiple-use areas along the periphery of the park. Further research is needed to better elucidate the impact of perimeter fences on a broader suite of carnivores and other large mammals, including species-specific variation in the response to fences, as well as the economic benefits of fencing provided to local communities.

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries, with the impact effect values of 0.581, 0.647, −0.126, −0.729, −0.326, and −0.559, respectively. As a result, it is suggested that in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.

Characteristics of Spatial–Temporal Differences and Measurement of the Level of Forestry Industry Integration in China

The integration of the forestry industry can effectively resolve the conflict between ecological protection and socioeconomic development while bringing new vitality and growth to traditional forestry. In this study, the level of forestry industry integration in 31 provinces in China from 2005 to 2019 was measured using the Herfindahl index method. With ArcGIS and exploratory spatial data analysis methods, the spatial-temporal distribution characteristics, dynamic change trends, spatial correlation characteristics, and existing problems in China’s forestry industry integration development were analyzed. The results showed that the total output value of forestry integrated products and the output value of each product segment increased, but the proportion of product development was imbalanced, and it was concentrated in the understory planting and collection industry and wood processing and manufacturing industry, leaving substantial room for improvement and integration. The value of the forestry industry integration index also increased overall, but the level of integration was low or moderate. In terms of time, the integration index of most provinces trended upward but failed to break through 0.73, leaving a significant gap between it and deep integration. Spatially, the level of integration of the forestry industry varied across the northeast, central, west, and east, with the central and northeast showing a higher integration degree than the east and west. China’s forestry industry integration showed a significant positive spatial correlation, indicating that spatial factors had become an important factor affecting the development of the forestry industry in various regions. Therefore, it is necessary to strengthen the relevant mechanisms of cross-border cooperation and benefit sharing. Lastly, we identified problems with the integration development of the forestry industry, including insufficient and imbalanced integration, unreasonable structural layout of integration development, and insufficient driving capacity for integration. As a result, there were phased and regional differences in the evolution of forestry industry integration.

Comprehensive Analysis of the Global Zenith Tropospheric Delay Real-Time Correction Model Based GPT3

To obtain a higher accuracy for the real-time Zenith Tropospheric Delay (ZTD), a refined tropospheric delay correction model was constructed by combining the tropospheric delay correction model based on meteorological parameters and the GPT3 model. The meteorological parameters provided by the Global Geodetic Observing System (GGOS) Atmosphere and the zenith tropospheric delay data provided by Centre for Orbit Determination in Europe (CODE) were used as references, and the accuracy and spatial–temporal characteristics of the proposed model were compared and studied. The results show the following: (1) Compared with the UNB3m, GPT and GPT2w models, the accuracy and stability of the GPT3 model were significantly improved, especially the estimation accuracy of temperature, the deviation (Bias) of the estimated temperature was reduced by 90.60%, 32.44% and 0.30%, and the root mean square error (RMS) was reduced by 42.40%, 11.02% and 0.11%, respectively. (2) At different latitudes, the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models had great differences in accuracy and applicability. In the middle and high latitudes, the Biases of the GPT3 + Saastamoinen model and the GPT3 + Hopfield model were within 0.60 cm, and the RMS values were within 4 cm; the Bias of the UNB3m model was within 2 cm, and the RMS was within 5 cm; in low latitudes, the accuracy and stability of the GPT3 + Saastamoinen model were better than those of the GPT3 + Hopfield and UNB3m models; compared with the GPT3 + Hopfield model, the Bias was reduced by 22.56%, and the RMS was reduced by 5.67%. At different heights, the RMS values of the GPT3 + Saastamoinen model and GPT3 + Hopfield model were better than that of the UNB3m model. When the height was less than 500 m, the Biases of the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models were 3.46 cm, 3.59 cm and 4.54 cm, respectively. At more than 500 m, the Biases of the three models were within 4 cm. In different seasons, the Bias of the ZTD estimated by the UNB3m model had obvious global seasonal variation. The GPT3 + Saastamoinen model and the GPT3 + Hopfield model were more stable, and the values were within 5 cm. The research results can provide a useful reference for the ZTD correction accuracy and applicability of GNSS navigation and positioning at different latitudes, at different heights and in different seasons.

Research on the dynamic evolution and influence factors of industrial energy efficiency in China Yangtze River Economic Belt

Improving the Yangtze River Economic Belt's industrial energy efficiency is not only an important measure to alleviate China's energy shortages but also a drive to promote green economic development. The Super-EBM model, Malmquist productivity index, exploratory spatial data analysis, and Spatial Dubin model are used in this article to investigate the spatial-temporal dynamic development characteristics and influencing factors of industrial energy efficiency in 108 cities of the Yangtze River Economic Belt from 2011 to 2020. The findings demonstrate that the industrial energy efficiency of the Yangtze River Economic Belt and its three urban agglomerations went through three stages, including the “oscillation period,” “stability period,” and “enhancement period,” and decreases from east to west in the spatial dimension. The Yangtze River Delta urban agglomeration has the highest industrial energy efficiency, followed by the Middle-reach Yangtze River urban agglomeration, and the Chengdu-Chongqing urban agglomeration is the lowest. Further, this article identifies seven influencing factors including government intervention, industrial structure, degree of openness, R&D investment, urbanization, economic development, and environmental regulation. This article provides suggestions for industrial energy efficiency improvement.

Spatiotemporal Variation in Land Use and Ecosystem Services during the Urbanization of Xining City

Based on the five phases of land use data from Xining corresponding to 1980, 1990, 2000, 2010, and 2020, we used the equivalent factor method to analyze the values of spatial-temporal variation characteristics of land use and ecosystem services in Xining. The results showed that (1) farmland and developed areas were the most active types of land, and the continuous occupation of farmland and developed areas led to the formation of a kind of “cross shape” in the spatial pattern of conversion of land use types along the Huangshui River, Beichuan River, and Nanchuan River from south to north and from northwest to southeast, respectively, with the central urban area serving as a core of the pattern; (2) the transformation between different land use types mainly occurred in the land-slope range between 0–15° and altitudes between 2000–2750 m; and (3) the ESV of Xining increased by RMB 2165.26 × 106 in the past 40 years. The period from 2000 to 2020 was the main growth period of the ecosystem service value of Xining. Urbanization had a great impact on the variation in land use types and the evolution of ecosystem service values. In the middle and late stages of urbanization, different types of land use changed significantly within each county and district. The ecosystem service values of the central districts were low, with those of the marginal districts and counties being higher, forming a “core-periphery” trend and a phenomenon of hollowing ESV. (4) The spatial agglomeration effect of the ESV per unit area was continuously enhanced in Xining. The high-high (slope-altitude) type of area was distributed in the north and west of Xining, whereas the low-low type of area was distributed in the urban area at the intersection of major rivers in the southeast of Xining.

Spatial and temporal evolution of synergizing the reduction of pollution and carbon emissions and examination on comprehensive pilot effects–evidence from the national eco-industrial demonstration parks in China

Synergistic governance for reducing pollution and carbon emissions can generate savings in policy costs and respond to ecological and environmental change pressures effectively. For exploring the temporal-spatial evolution of pollution and carbon reduction, and probing the synergistic of promoting pollution and carbon reduction at the urban level, this study utilizes panel data from 275 Chinese cities between 2006 and 2019, we examine the spatial-temporal evolution characteristics of pollution and carbon reduction, the synergistic effects and spatial spillover effects of national eco-industrial demonstration parks (NEDP) on pollution and carbon reduction. And we do this work by using spatial analysis tools such as spatial autocorrelation, local cold-hot spot analysis, kernel density analysis, standard deviation ellipses, as well as multi-period double difference and spatial double difference models. We find that: (1) Carbon dioxide emissions and sulfur dioxide emissions display a geographical clustering trend during the study period, with high-density emission areas spreading from coastal regions to central and western ones. The center of emissions is located in Henan Province and shifting towards the northwest of China. (2) The development of NEDP in cities may greatly cut carbon dioxide and sulfur dioxide emissions. (3) National eco-industrial demonstration parks may significantly increase the synergistic impact of pollution and carbon reduction in the neighboring cities due to their significant spatial spillover effect. To enhance the synergistic effect of pollution and carbon reduction in the future, we need actively explore the construction of NEDP, and lay out these parks based on the level of resource endowment, industrial structure, and economic development.

Brain State Relays Self-Processing and Heartbeat-Evoked Cortical Responses.

The self has been proposed to be grounded in interoceptive processing, with heartbeat-evoked cortical activity as a neurophysiological marker of this processing. However, inconsistent findings have been reported on the relationship between heartbeat-evoked cortical responses and self-processing (including exteroceptive- and mental-self-processing). In this review, we examine previous research on the association between self-processing and heartbeat-evoked cortical responses and highlight the divergent temporal-spatial characteristics and brain regions involved. We propose that the brain state relays the interaction between self-processing and heartbeat-evoked cortical responses and thus accounts for the inconsistency. The brain state, spontaneous brain activity which highly and continuously changes in a nonrandom way, serves as the foundation upon which the brain functions and was proposed as a point in an extremely high-dimensional space. To elucidate our assumption, we provide reviews on the interactions between dimensions of brain state with both self-processing and heartbeat-evoked cortical responses. These interactions suggest the relay of self-processing and heartbeat-evoked cortical responses by brain state. Finally, we discuss possible approaches to investigate whether and how the brain state impacts the self-heart interaction.

Evaluating temporal-spatial variations of wetland ecosystem service value in China during 1990–2020 from the donor side based on cosmic exergy

China has pledged to strictly maintain wetland ecosystem services at the COP 14 of the Ramsar Convention on Wetlands that was recently held in Wuhan, highlighting the necessity of measuring wetland ecosystem service value (ESV) in China. Yet existing studies mainly focus on ecosystem service valuation of wetlands from the consumer side, while a donor-side evaluation of the temporal-spatial characteristics of the wetland ESV of China is lacking. This study introduces the thermodynamic concept of cosmic exergy into the evaluation of wetland ESV, which provides an objective evaluation index for the unified measurement and analysis of the ecological value and degradation of resources in the process of material, energy, and information transformation of the ecosystem. By constructing a cosmic-exergy-based accounting framework for wetland ecosystem valuation, this study comprehensively investigated the temporal-spatial variations of China's wetland ESV during 1990–2020 from the donor side. Results showed that from 1990 to 2020, the total wetland ESV in China first decreased and then increased notably, with an overall growth rate of 22%. Meanwhile, the ecological quality of wetlands, as indicated by cosmic exergy intensity, showed an upward trend in the middle and lower reaches of the Yangtze River Basin, but a downward trend in western China. Regions in the Yangtze River Basin, the Qinghai-Tibet Plateau, and Northeast China were demonstrated as the major contributors to the total wetland ESV. In addition, this study identified the vital role of marsh wetlands in ecosystem service supply, contributing nearly 30% to the total ESV, and microclimate regulation was the most prominent service type, accounting for up to 74%. The outcome of this study can offer a new biophysical perspective for the donor-side valuation of wetland ecosystem services and may provide a benchmark reference for policymakers to formulate supporting measures for wetland production and restoration.

Coupling and Coordination Relationship between Urbanization Quality and Ecosystem Services in the Upper Yellow River: A Case Study of the Lanzhou–Xining Urban Agglomeration, China

The study of the man–land relationship in the urbanization process is the current frontier and focus of international research. How to balance urban development and ecosystem conservation in the Upper Yellow River is a key issue for sustainable development in China. In this study, we evaluated the Lanzhou–Xining urban agglomeration (LXUA) by constructing a multi-dimensional assessment system for urbanization quality and ecosystem services. The efficacy function model, entropy weight method, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to quantitatively assess the subsystems’ state of development. Then, the coupling model (CD) and the coordination degree (CCD) model were used to explore the coupling coordination relationship and spatial-temporal change characteristics of the composite system. The findings indicate that: (1) In 2020, the quality of urbanization in the LXUA showed the pattern of “double core”. The development of urban centers in each city is insufficient, and the proportion of counties with a low level is too high. (2) Integrated ecosystem services showed an increasing distribution pattern from the northeast to the southwest. Water provision services, soil conservation services and carbon fixation services all showed growth trends. (3) Each county’s composite system was in the run-in stage or highly coupled stage. The subsystems were closely related to each other. (4) The CCD was decreased by 6% between two decades. The number of counties on the verge of disorder was the highest. About 80% of the counties were relatively lagging behind in ecosystem services.

Construction of carbon budget balance index and its application in the lake area

The imbalance of the carbon cycle in terrestrial ecosystems exacerbates global warming. Identifying the spatial–temporal characteristics and drivers of the carbon budget is important for the effective management of complex ecosystems and the achievement of the United Nations Sustainable Development Goals. Based on the complex ecosystem theory, this article constructs two carbon indicators, carbon carrying capacity–carbon footprint matching degree (Carbon-MD), and carbon carrying capacity–carbon footprint coupling coordination degree (Carbon-CCD). Taking a typical lake region—urban agglomeration around Poyang Lake as an example, the spatial–temporal characteristics and driving factors of the carbon budget from 2000 to 2020 are revealed by GIS technology and geographically–temporally weighted regression model. The results show that there is a significant spatial and temporal variability of carbon budget in Poyang Lake city agglomerations from 2000 to 2020, the Carbon-MD shows a gradually decreasing trend, and the Carbon-CCD shows a gradually increasing characteristic from moderate disorder to basic coordination development. From the spatial pattern, the Carbon-MD of the study area showed an increasing character from the core area to the peripheral area of the lake, and the low-value area of Carbon-MD shows the trend of spreading from point to surface and the Carbon-CCD changes from scattered to group type. In addition, land use type always has a significant effect on the carbon budget, while there is a spatial and temporal heterogeneity in the effects of natural and socioeconomic factors on the carbon budget. The research results show that it is reasonable and scientific to construct carbon budget indicators from the perspective of matching supply and demand and coupling and coordination, which provides a quantitative carbon budget analysis tool for local policymakers.

Study on the coupling thermal effect of thermokarst lake and high sunny slope on permafrost embankment

Sunny-shady slope and thermokarst lake are widely distributed along the Qinghai-Tibet highway (QTH), and their thermal influence on permafrost embankment can induce serious engineering diseases and affect its long-term service performance. This study aims to reveal the coupling thermal impact mechanism of roadside thermokarst lake and high sunny-slope on permafrost embankment. Firstly, an in-situ ground temperature monitoring site at K2952 + 300 section of QTH has been built and temperature distribution characteristic of observed embankment are investigated. Then, three numerical models are established and spatial–temporal characteristics of asymmetric temperature filed of permafrost embankment have also been analysed and compared. The results show that: (1) Permafrost artificial upper limit of observed embankment has a large fluctuation with the synergy of high sunny slope and thermokarst lake, and severe unilateral thermal erosion leads to the aggravation of subgrade disease despite heat pipes treatment has been adopted. (2) Thermalkarst lake has a certain degree of lateral thermal influence on permafrost embankment, while the magnitude of its thermal impact is related to the distance from embankment. (3) Significant endothermic effect of 5.3 m height sunny slope accelerates the melting depth of embankment underlying permafrost, and triggers off asymmetry morphology of permafrost temperature field. (4) Under the action of ipsilateral thermokarst lake and high sunny slope, the maximum melting depth and thawing plate deviation of embankment underlying permafrost can be exacerbated simultaneously, and the lateral thermal erosion caused by these two factors becomes more intensive as the thermokarst lake gradually approaches the roadbed.

Quantitative Analysis of Spatio-temporal Evolution Characteristics of Seasonal Average Maximum Temperature and Its Influence by Atmospheric Circulation in China from 1950 to 2019

Global warming and intensified human activities have led to regional climate instability with increasing frequency and the persistence of high-temperature climate events. Eco-environmental protection and socio-economic development have been faced with rigorous threats. Taking the monthly maximum temperatures from 1950 to 2019 as the basic data source, the spatial-temporal evolution characteristics of seasonal average maximum temperature (AMT) were discerned using the Mann-Kendall test and unary linear regression method in China from 1950 to 2019. Combined with linear correlation, partial linear correlation, and wavelet analysis, the correlation between seasonal AMT characteristics and atmospheric circulations was analyzed quantitatively. The results showed that:① the AMT in all seasons had a significant upward trend, with an increase of 1.21, 0.08, 1.81, and 0.25℃ in spring, summer, autumn, and winter, respectively. The abrupt change times of the AMT were concentrated in the 1990s to the early 21st century. ② In terms of spatial distribution, except for in summer, the average trend rates of AMT in other seasons increased gradually from south to north, although the increasing degrees were different. Among them, the AMT change rate in spring-winter was the fastest in northeast and northwest China. ③ There were complex correlations between the AMT of every season and atmospheric circulation factors, and the distribution of the interrelation energy varied significantly in different frequency domains. Specifically, the Pacific Decadal Oscillation had a significant negative correlation with AMT in summer. The North Atlantic Oscillation had an active effect on AMT changes in summer, autumn, and winter. The Arctic Oscillation had a significant positive driving effect on AMT in all seasons, and there were significant positive or negative influences on the short-or long-term changes of AMT in spring and summer due to the different EI Niño-Southern Oscillation years. These results could provide a theoretical basis and technical reference for China to formulate scientific and effective response plans of climate change.

Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

Numerical simulation of the grouting penetration process by developing a temperature‐dependent spatial–temporal rheological model

AbstractThis study aims to investigate the grout penetration process in rock fracture using a self‐developed grouting simulator, in which the spatial–temporal characteristics of the grout properties are adequately considered. To this end, time‐dependent Bingham models as rheological equations, which consider the time‐dependent characteristics of the cement‐sodium silicate grout properties induced by grout hydration, were proposed first by the experimental method. Then, the new grouting simulator was developed by integrating the time‐dependent Bingham model, modified stepwise algorithm, and fluid flow solver. To verify the developed model, two benchmark tests were conducted. Finally, to further explore the influences of the injection rates, ratios of cement to sodium silicate (C/S ratios) and temperatures on the grouting process, a series of numerical grouting examples were systematically conducted. The results indicate that the developed simulator can accurately model the penetration process of the grout with temperature‐dependent spatial–temporal rheological characteristics, especially the grouting pressure distribution in the grouted zone. With the increase of the C/S ratios and temperatures, the grout pressure in the grouted zone increases. The grout pressure distribution is greatly dependent on the injection rates and C/S ratios but is relatively less affected by the temperatures.