Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.

Spatial-temporal characteristics and influencing factors of relative humidity in arid region of Northwest China during 1966–2017

Playing an important role in global warming and plant growth,relative humidity(RH)has profound impacts on production and living,and can be used as an integrated indicator for evaluating the wet-dry conditions in the arid and semi-arid area.However,information on the spatial-temporal variation and the influencing factors of RH in these regions is still limited.This study attempted to use daily meteorological data during 1966–2017 to reveal the spatial-temporal characteristics of RH in the arid region of Northwest China through rotated empirical orthogonal function and statistical analysis method,and the path analysis was used to clarify the impact of temperature(T),precipitation(P),actual evapotranspiration(ETa),wind speed(W)and sunshine duration(S)on RH.The results demonstrated that climatic conditions in North Xinjiang(NXJ)was more humid than those in Hexi Corridor(HXC)and South Xinjiang(SXJ).RH had a less significant downtrend in NXJ than that in HXC,but an increasingly rising trend was observed in SXJ during the last five decades,implying that HXC and NXJ were under the process of droughts,while SXJ was getting wetter.There was a turning point for the trend of RH in Xinjiang,which occurred in 2000.Path analysis indicated that RH was negatively correlated to T,ETa,W and S,but it increased with increase of P.S,T and W had the greatest direct effects on RH in HXC,NXJ and SXJ,respectively.ETa was the factor which had the greatest indirect effect on RH in HXC and NXJ,while T was the dominant factor in SXJ.

Spatial-Temporal Characteristics of Regional Extreme Low Temperature Events in China during 1960-2009

An objective identification technique is used to detect regional extreme low temperature events （RELTE） in China during 1960-2009. Their spatial-temporal characteristics are analyzed. The results indicate that the lowest temperatures of RELTE, together with the frequency distribution of the geometric latitude center, exhibit a double-peak feature. The RELTE frequently happen near the geometric area of 30°N and 42°N before the mid-1980s, but shifted afterwards to 30°N. During 1960-2009, the frequency~ intensity, and the maximum impacted area of RELTE show overall decreasing trends. Due to the contribution of RELTE, with long duratioh and large spatial range, which account for 10% of the total RELTE, there is a significant turning point in the late 1980s. A change to a much more steady state after the late 1990s is identified. In addition, the integrated indices of RELTE are classified and analyzed.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Evolvement Characteristics of Climate Productivity for the Plants on Inner Mongolia Desert Steppe

Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.

Spatial-temporal characteristics of PM_(2.5) in China:A city-level perspective analysis

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM2.s makes a significant contribution to poor air quality. The spatio-temporal features of China＇s PM2.s concentrations should be investigated. This paper, based on ob- served data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification （ISIC）, reveals the spa- tio-temporal variations of PM2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PMg.g concentrations. Results： （1） The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. （2） The PM2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. （3） PM2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.

Spatial-temporal characteristics of lake area variations in Hoh Xil region from 1970 to 2011

As one of the areas with numerous lakes on the Tibetan Plateau, the Hoh Xil region plays an extremely important role in the fragile plateau eco-environment. Based on topographic maps in the 1970s and Landsat TM/ETM＋ remote sensing images iin the 1990s and the period from 2000 to 2011, the data of 83 lakes with an area above 10 km2 each were obtained by digitization method and artificial visual interpretation technology, and the causes for lake variations were also analyzed. Some conclusions can be drawn as follows. （1） From the 1970s to 2011, the lakes in the Hoh Xil region firstly shrank and then expanded, in particular, the area of lakes generally decreased during the 1970s-1990s. Then the lakes expanded from the 1990s to 2000 and the area was slightly higher than that in the 1970s. The area of lakes dramatically increased after 2000. （2） From 2000 to 2011, the lakes with different area ranks in the Hoh Xil region showed an overall expansion trend. Meanwhile, some regional differences were also discovered. Most of the lakes expanded and were widely distributed in the northern, central and western parts of the region. Some lakes were merged together or overflowed due to their rapid expansion. A small number of lakes with the trend of area decrease or strong fluctuation were scattered in the central and southern parts of the study area. And their variations were related to their own supply conditions or hydraulic connection with the downstream lakes or rivers. （3） The increase in precipitation was the dominant factor resulting in the expansion of lakes in the Hoh Xil region. The secondary factor was the increase in meltwater from glaciers and frozen soil due to climate warming.

A SPATIAL-TEMPORAL DISTRIBUTION CHARACTERISTICS STUDY ON THE ATMOSPHERIC CARBON DIOXIDE OBSERVED BY GOSAT SATELLITE REMOTE SENSING

The variation of the atmospheric Carbon Dioxide （CO2） concentration plays an important role in global cli- mate and agriculture. We analyzed the spatial-temporal characteristics of CO2 in the China region and around the globe with the CO2 column mixing ratios observed by the Japanese GOSAT satellite （Greenhouse Gases Observing Satellite）. In order to make sure that the accuracy of the CO2 data retrieved by the satellite meets the needs of the climate charac- teristics analyses, we ran a validation on the CO2 column mixing ratios retrieved by the satellite against the ground-based TCCON （Total Carbon Column Observing Network） observation data. The result shows that the two sets of data have a correlation coefficient of higher than 0.7, and a bias of within 2.2 ppmv. Therefore, the GOSAT CO2 da- ta can be used for the climate characteristics analysis of global CO2. Our analysis on the spatial-temporal characteristics of the CO2 column mixing ratios observed during the period of June 2009 through January 2014 proved that, with the impact of the natural emission of near ground CO2 and human activities, the global CO2 concentration has a significant latitudinal characteristics with its highest level averaging 390 oomv in the 0-40？N latitudinal zone in the Northern Hemisphere, and 387 ppmv in the Southern Hemisphere. China has a relatively higher CO2 concentration with the highest level exceeding 398 ppmv, and the eastern area higher than the western area. The variation of global CO2 concentration shows a seasonal pattern, i.e. the CO2 concen- tration reaches its highest in spring in the Northern Hemisphere averaging more than 392 ppmv, second highest in win- ter, and lowest in summer averaging less than 387 ppmv. It fluctuates the most in the Northern Hemisphere with an av- erage concentration of 392.5 ppmv in April, and 385.5 ppmv in July. While in the Southern Hemisphere, the seasonal fluctuation is smaller with the highest concentration occurring in July. Over the recent years, the global CO2 concentra- tion has shown an elevating trend with an average annual increase rate of 1.58 ppmv per year. It is a challenge that the human kind has to face to slow down the increase of the CO2 concentration.

Spatial-Temporal Distribution Characteristics and Limiting Factors of Medium-low Yield Farmland in Tianjin

[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.

Spatial-temporal characteristics of epidemic spread in-out flow——Using SARS epidemic in Beijing as a case study

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions,the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions.Three typical spatial information parameters including working unit/address,onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed.Furthermore,by the methods of spatial-temporal statistical analysis and network characteristic analysis,spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored,and spatial autocorrelation/heterogeneity,spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed.The results show that(1)The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces,but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong.And the control measurement should focus on the early and interim progress of SARS breakout.(2)The inner output cases had significant positive autocorrelative characteristics in the whole studied region,and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer.(3)The downtown districts were main high-risk hotspots of SARS epidemic in Beijing,the northwest suburban districts/counties were secondary high-risk hotspots,and northeast suburban areas were relatively safe.(4)The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity.The suburban Tongzhou and Changping districts were the underlying high-risk regions,and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow.The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic,and provide a more effective theoretical basis for emergency/control measurements and decision-making.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies.This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data.By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA)framework,this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013.The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units.The results show that,firstly,high accuracy was achieved by the model in simulating carbon emissions.Secondly,the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82%and 5.72%,respectively.The overall carbon footprints and carbon deficits were larger in the North than that in the South.There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units.Thirdly,the relative lengths of the Local Indicators of Spatial Association(LISA)time paths were longer in the North than that in the South,and they increased from the coastal to the central and western regions.Lastly,the overall decoupling index was mainly a weak decoupling type,but the number of cities with this weak decoupling continued to decrease.The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors

Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.