Spatial-temporal Characteristics Research Articles

Intra- and inter-host origin, evolution dynamics and spatial-temporal transmission characteristics of circoviruses

Intra- and inter-host origin, evolution dynamics and spatial-temporal transmission characteristics of circoviruses

Assessing Spatial–Temporal Characteristics of Land Desertification from 1990 to 2020 in the Heihe River Basin Using Landsat Series Imagery

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification.

Predicting the Deformation of a Slope Using a Random Coefficient Panel Data Model

Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

Spatial-Temporal Coupling Coordination Relationship between Urban Green Infrastructure Construction and Economic Development in China

Urban green infrastructure construction and economic growth are necessary ways and important supports to promote sustainable development. Exploring their coupling coordination relationship is important for achieving high-quality economic development. This study uses the entropy method, coupling coordination degree, kernel density estimation, the Dagum Gini coefficient, and spatial autocorrelation to explore the spatial-temporal pattern characteristics and coupling coordination relationship between green infrastructure construction and economic development for 273 cities in Chinese mainland in 2010–2020. The results show that the level of China’s green infrastructure construction and economic development gradually increased during 2010–2020. There were significant regional differences in space, exhibiting a decreasing spatial pattern from east to west. The coupling coordination degree was constantly improving. The overall Gini coefficient shows an upward trend. Among the four regions, eastern China has the greatest intraregional variation. The uneven level of coupled coordination is mainly from interregional variation. There was a significant positive spatial autocorrelation relationship, and cities that had a higher degree of coupling coordination tended to agglomeration development. Meanwhile, it also had certain spatial heterogeneity. China’s entire level of coupling coordination degree still has much room for improvement. The study is of great significance in reducing disparities between regions and strengthening regional spatial coordination development.

Spatial-temporal characteristics analysis of laser-induced shockwave pressure by reverse optimization with multi-island genetic algorithm

Spatial-temporal characteristics analysis of laser-induced shockwave pressure by reverse optimization with multi-island genetic algorithm

Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

Spatiotemporal evolution of carbon emissions and influencing factors in county-level based on nighttime lighting data: a case study in Huaihai economic zone core city

ABSTRACT Driven by China’s carbon peaking and carbon neutrality goals, this study constructs a carbon emission inversion model for 57 county-level in the core city cluster of the Huaihai Economic Zone. It analyzes the spatial-temporal characteristics of carbon emissions in these counties between 2001 and 2021. It also explores the impacts of different socio-economic factors on carbon emissions by adopting geographically and temporally weighted regression methods. The results showed that: (1) From 2001 to 2021, the total carbon emissions of counties increased from 63.42 Mt to 279.89 Mt, and the distribution pattern of the region is “high in the middle and low in the east and west.” (2) Carbon emissions have significant positive spatial correlations, with the global Moran’s I index showing a wave-like downward trend from 0.233 to 0.0954, indicating that the spatial concentration of carbon emissions in different counties is gradually decreasing, and the differences show a trend of narrowing. (3) Significant spatial and temporal heterogeneity is among the influencing factors. It is mainly manifested in population size and industrial structure, while economic scale and technological progress are the dominant factors in carbon emissions. The research results will inform county-level low-carbon development.

Water yield forecast in Shandong Province based on InVEST model

A better understanding of the effects of land use change and climate change on water yield is highly important for water resource planning and sustainable management. Land use changes in Shandong Province were analyzed over recent years. The PLUS Model was applied to forecast the land use layout for 2032, with adjustments made based on actual conditions and utilizing various CMIP6 scenarios for precipitation and actual evapotranspiration. The 2032 Water Yield was quantified using the InVEST Model at both grid and administrative scales, and its temporal-spatial characteristics and spatial correlation were analyzed. The findings revealed several key points: first, a decrease of 16.92% in arable land area, along with diminishing trends in grassland and unused land, and an increase of 33.92% in built-up land area, accompanied by growth in forest and water area. Second, the water yield decreases from southeast to northwest in Shandong Province, with significant inter-annual variations. Third, future predictions suggest that the annual water yield generally increases during flat water years, with offshore areas exhibiting greater water yield depth compared to inland areas. Rizhao is projected to have the highest water yield depth, while Binzhou and Dongying are expected to have the lowest depths, with a spatially positive correlation between them.

Study of dust events based on multi-source data in the North Slope of the Tianshan Mountains

To reveal the spatial-temporal characteristics of atmospheric pollution during dust events on the North Slope of the Tianshan Mountains (NSTM), this study conducted a joint detection experiment from April to June from 2019 to 2021 at Shihezi, using satellite remote sensing, a microwave radiometer, meteorological sensors, and environmental monitors. Using long-term detection data from the aforementioned equipment, this study analyzed the characteristics of meteorological elements, Aerosol Optical Depth (AOD), PM, and gaseous pollutants. The main findings are as follows: The dust particles from the two severe pollution dust events on April 9, 2020, and May 1, 2021, on the NSTM originated from the Gurbantünggüt Desert and were transported along the northwest direction, leading to a significant increase in AOD (averaging 1.36 g/m2) and dust column mass concentration (averaging 1.58 g/m2). During the two dust storms, the PM10 concentration peaks reached 2536.5 μg/m³ and 1804.5 μg/m³, respectively. The average relative humidity (RH) was less than 30%, the average wind speed was more than 6 m/s, and the average visibility (V) was less than 1000 m. Moreover, during the dust events from April to June, the temperature and relative humidity were higher in June, but the wind speed was lower. The vertical thermodynamic interactions of the dust storms were stronger than those of the blowing and floating dust storms. Finally, the relationship between PM10 and V was fitted using the following equation: V=11326.8166e−0.0015PM10.This study provides scientific support for the prediction of dust storms on the NSTM.

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction.

Temporal and Spatial Evolution and Prediction of Ecosystem Carbon Storage in Jiangxi Province Based on PLUS-InVEST Model

Land-use changes are an important factor affecting the change in carbon storage in terrestrial ecosystems. Exploring the relationship between land-use changes and carbon storage provides reliable data support for optimizing regional land-use structure and maintaining regional carbon balance. Taking Jiangxi Province as an example, we first analyzed the land-use changes; then simulated the land-use pattern under three scenarios (i.e., natural development, ecological priority, and economic development scenarios) in 2030 based on the PLUS model; and finally estimated the carbon storage change in the past (i.e., 1990-2020) and future periods (i.e., three scenarios in 2030) using the InVEST model, analyzed the spatial-temporal characteristics, and proposed the corresponding suggestions. The results showed:① The carbon storage in Jiangxi Province showed a downward trend from 1990 to 2020, with a total reduction of 4.58×107 t. The increase in the water bodies and construction land and the decrease in cultivated land, woodland, grassland, and unused land was the major cause. ② The carbon storage under natural development, ecological priority, and economic development scenarios in Jiangxi Province in 2030 were 2.20×109, 2.24×109 and 2.19×109 t, respectively. ③ The carbon storage under the three scenarios showed similar spatial characteristics, wherein the high carbon storage was distributed in northern, northwest, and western regions, and the low carbon storage was distributed near the central region. These results can provide data support for future land spatial planning and improving the carbon storage of terrestrial ecosystems in Jiangxi Province.

Regional differences, dynamic evolution, and obstacle factors of cultivated land ecological security in China

Under the influence of geopolitical conflicts, extreme weather, and other factors, global food security has been severely impacted. Cultivated land ecological security in China is vital for guaranteeing local food security and sustainable development. Therefore, using provincial data from 2003 to 2020, this research establishes an index system based on the pressure-state-response (PSR) model and assesses its cultivated land ecological security via the entropy weight method. A series of measurements such as Dagum's Gini coefficient and spatial Markov chain are then adopted to reveal changes in regional differences and spatial-temporal characteristics of cultivated land ecological security in China. The findings indicate that this ecological security has risen from insecure to critically secure, but the overall gap has gradually widened, mainly stemming from interregional differences. Cultivated land ecological security exhibits positive spatial clustering characteristics and the phenomenon of club convergence. The amount of water resources, the degree of soil erosion control, and the strength of local financial expenditure on agriculture are the main factors hindering its cultivated land ecological security, and the threats mainly come from the response criterion layer. Therefore, it is essential to strengthen measures for cropland ecological protection, to formulate targeted strategies by combining the regional characteristics, to narrow the intra-regional gap, and to bring into play the spatial radiation effect of high-level provinces.

Deep‐learning‐based downscaling of precipitation in the middle reaches of the Yellow River using residual‐based CNNs

AbstractThe middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi‐arid and semi‐humid climates. As one of the climate‐sensitive regions in China, MRYR has a fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses, and mudslides caused by extreme precipitation. However, scarceness of high‐resolution precipitation data over MRYR limits assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this article, we design a Residual‐in‐Residual Dense Block based Network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with a generalized linear regression model (GLM) and two popular deep‐learning‐based models. The multi‐level residuals and dense connectivity strategies introduced in RRDBNet help it to learn more abstract features and complex nonlinear relationships among climate variables to improve downscaling performance. The results show that the proposed RRDBNet has good performance in precipitation simulations, which can reproduce the spatial–temporal characteristics of high‐resolution precipitation well. RRDBNet reduces the root‐mean‐squared error (RMSE) by 19% and improves the Pearson correlation coefficient (CC) by 6% relative to GLM for climatology mean precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. It reduces RMSE by 58% (79%) and improves CC by 38% (145%) relative to GLM for R95P (R99P), where R95P and R99P represent extreme precipitation and very extreme precipitation, respectively. For the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.

Environmental Influences on Illex argentinus Trawling Grounds in the Southwest Atlantic High Seas

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI &gt; 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

Eulerian–Lagrangian multiscale numerical analysis of multimodal partial shedding dynamics

The objective of this paper is to investigate the multimodal partial shedding dynamics from a multiscale perspective of cloud cavitating flows under two distinct cavity shedding mechanisms, namely the re-entrant jet mechanism and the shock wave propagation mechanism. A two-way Eulerian–Lagrangian coupling algorithm is applied to capture the multiscale vapor topologies from microbubble to large-scale cavities. The large-scale cavity evolution is solved through large eddy simulations (LES) with the volume of fraction (VOF) method in Eulerian frame. The sub-grid microbubbles are tracked in Lagrangian frame based on the discrete bubble model (DBM) method. The predictions agree well with experimental observation of the periodical cavity evolution and microbubble dynamics under both the re-entrant jet mechanism and shock wave mechanism around a NACA66 hydrofoil. The numerical simulation provides detailed analysis of the cavitating turbulent flow on the microbubble behavior with emphasis on the spatial-temporal distribution characteristics of microbubbles. The results show that the number and mean size of microbubbles in the cavitation region increase gradually with the growth of attached sheet cavity, development of re-entrant jet and collapse of largescale cavity for both cavitation patterns. Meanwhile, microbubbles are mainly distributed on the largescale interfaces where have high value of vorticity and turbulent kinetic energy under the effect of re-entrant jet and vortex structures. And the probability density functions (PDFs) of microbubble exhibit gamma distributions with a dominant peak at approximately 50 μm for both shedding mechanisms. However, the shock wave formation and propagation process only occurs in the final stage of cavitating flow under shock wave mechanism causing the condensation of vapor and the decrease of the number and mean size of microbubbles. Moreover, the microbubbles are uniformly distributed along the streamwise and vertical directions behind shock wave front.

Research on spatial-temporal synergistic sensor fault diagnosis method for top-blowing furnace

Top-blowing furnace systems, characterized by a large number of sensors and harsh working environments, are prone to sensor failures due to factors like component aging and external interference. These failures can significantly impact the system's safe and reliable operation. However, traditional sensor fault diagnosis methods often neglect the exploration of spatial-temporal characteristics and focus solely on learning temporal relationships between sensors, failing to effectively consider their spatial relationships. In this study, we propose a spatial correlation model based on the maximal information-based graph convolutional network (MI-GCN) by constructing a sensor network knowledge graph using maximal mutual information. The MI-GCN leverages the graph convolution mechanism to extract multi-scale spatial features and capture the spatial relationships between sensors. Additionally, we develop a spatial-temporal graph-level prediction model, known as the spatial-temporal graph transformer (STGT), to extract temporal features. By combining the spatial features extracted by the MI-GCN with the temporal features captured by the STGT, accurate predictions can be achieved. Sensor fault diagnosis is conducted by analysing the normalized residuals between the predicted values and the ground truth. Finally, the feasibility and effectiveness of the proposed method are validated using test data from a top-blowing furnace system in the nickel smelting process.

Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou City

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.

Spatial-temporal characteristics of meteorological drought and its relationship with circulation indices over Songhua River Basin, China

ABSTRACT Global warming has increased the risk of meteorological drought and associated hazards. Based on daily data from 35 meteorological stations in the Songhua River Basin (SRB) collected from 1960 to 2019, this study applied the standardised precipitation evapotranspiration index (SPEI) and used the ensemble empirical mode decomposition (EEMD) method and Pettitt's mutation test to perform a partitioned segmentation analysis of the multi-temporal and spatial variations in the meteorological droughts affecting the basin in terms of frequency and intensity, and discussed the relationship between the atmospheric circulation index and meteorological drought. The results showed that the overall meteorological drought trends shifted from a drought to a humid trend from 1960 to 2019. The drought in the basin had periods of 3, 6.7, 15, and 30 years, with mutation points in 1968, 1980, 1997, and 2012. The drought trend in Regions I and IV of the entire basin was enhanced, while that in Regions II, III, and V was weakened. Drought in the basin was strongly influenced by the Arctic Oscillation (AO) and Southern Oscillation Index (SOI).