Cross-national Perspectives on Using Sustainable Development Goals(SDGs) Indicators for Monitoring Sustainable Development: A Database and Analysis

Sustainable development is the theme of the 21st century. To monitor the progress of sustainable development, the United Nations launched Sustainable Development Goals(SDGs) in 2015. Subsequently, nations of the world have drawn up a list of localized indicators regarding the United Nations SDGs as a paradigm. We established a database including SDGs indicator systems of 11 economies by collecting and determining a large number of materials. Based on this database, we analyzed SDGs indicators by designing a conceptual framework of comparative analysis that included three views. We found that the SDGs indicator systems of 11 economies are different between the number of indicators, the proportion of different categories, and the connotation of indicators. Although the SDGs indicator systems among economies regarded the United Nations SDGs as a framework and included the major social problems related to sustainability, the inconsistency between SDGs indicator systems is large. It is a major reason why scholars lack the systematic method for developing indicators. There are challenges faced in data accessibility. The framework for comparative analysis could be applied to different economies.

Visual analysis of the evolution and focus in landslide research field

This paper analysed the evolution of landslide research and research foci in different countries. The data comprise 3105 landslide SCI articles published between January 1977 and June 2015 from the Web of Science. The data are extracted under interaction constraints of the journal title, category, and keywords. The complex network method is used for the analysis. First, from the perspective of topics and methods, the evolution is systematically assessed by generating a co-citation network of the articles and a semantic cluster analysis. Second, from the perspective of topics and landsliderelated disasters, the focus in different countries is discussed by generating co-occurrence networks. These networks are the co-occurrence of the countries and keywords, and the co-occurrence of countries and landslide-related disaster phrases. The main conclusions are as follows:(1) landslide susceptibility analysis and methods of machine learning are popular research topics and methods, respectively. The topics change through time, and the article output is influenced by increasing landslide-related disasters, increasing economic losses and casualties, a desire for a more complete and accurate landslide inventory, and the use of effective methods, such as geographical information Science(GIS) and machine learning.(2) The research focus in each country is related with the country-specific disasters or economic costs caused by landslides to some degree. In addition to Italy and the USA, China is the country most commonly affected by landslides, and it should develop its own landslide database and complete in-depth studies of disaster mitigation.

Using an image segmentation and support vector machine method for identifying two locust species and instars

Locusts are agricultural pests around the world. To cognize how locust distribution density and community structure are related to the hydrothermal and vegetation growth conditions of their habitats and thereby providing rapid and accurate warning of locust invasions, it is important to develop efficient and accurate techniques for acquiring locust information. In this paper, by analyzing the differences between the morphological features of Locusta migratoria manilensis and Oedaleus decorus asiaticus, we proposed a semi-automatic locust species and instar information detection model based on locust image segmentation, locust feature variable extraction and support vector machine(SVM) classification. And we subsequently examined its applicability and accuracy based on sample image data acquired in the field. Locust image segmentation experiment showed that the proposed GrabCut-based interactive segmentation method can be used to rapidly extract images of various locust body parts and exhibits excellent operability. In a locust feature variable extraction experiment, the textural, color and morphological features of various locust body parts were calculated. Based on the results, eight feature variables were selected to identify locust species and instars using outlier detection, variable function calculation and principal component analysis. An SVM-based locust classification experiment achieved a semi-automatic detection accuracy of 96.16% when a polynomial kernel function with a penalty factor parameter c of 2 040 and a gamma parameter g of 0.5 was used. The proposed detection model exhibits advantages such as high applicability and accuracy when it is used to identify locust instars of L. migratoria manilensis and O. decorus asiaticus, and it can also be used to identify other species of locusts.

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.

Ecological-hydrological processes in arid environment： Past, present and future

Ecohydrology, aiming to study the interactions between terrestrial ecological systems and hydrological cycles as well as their impacts on water management, has been an emerging interdisciplinary research field since the 20th century. It hosts both natural and human regulated processes that are potentially coupled in complex ways. Understanding the ecological-hydrological processes, the fundamental mechanisms and the connections be- tween them is critical since these processes are not isolated but integrated to impact ba- sin-scale hydrological and biogeochemical functioning of a larger river system, especially in arid environment where water resources are considered to be the source of life. Thus, re- search on ecological-hydrological processes in arid environment is not only a scientific focus area but also important to sustainable development. Research projects and initiatives involved in observation, measurement, modeling and data assimilation have been well-developed for those purposes over the past 20 years. This review summarizes the historical development of ecohydrology science in China and the state-of-the-art tools available in the research framework. Five grand scientific challenges are listed as prospects and exciting opportunities for the scientific community. To advance the current ecological-hydrological processes research, scientists from multidisciplinary backgrounds （such as geography, geology, geomorphology, hydrology, geochemistry and ecology）, need to unite to tackle the many open problems in new dimensions.

Spatio-temporal differentiation of spring phenology in China driven by temperatures and photoperiod from 1979 to 2018

Large amounts of data accumulated in ecology and related environmental sciences arouses urgent need to explore useful patterns and information in it.Here we propose coclustering-based methods and a temperatures-photoperiod driven phenological model to explore spatio-temporal differentiation in long-term spring phenology in China.First,we created the first bloom date(FBD)dataset in China from 1979 to 2018 using the extended spring indices and China Meteorological Forcing Dataset.Then we analyzed the dataset using Bregman block average co-clustering algorithm with I-divergence(BBAC_I)and kmeans algorithm.Such analysis delineated the spatially-continuous phenoregions in China for the first time.Results showed three spatial patterns of FBD in China and their temporal dynamics for 40 years(1979–2018).More specifically,overall late spring onsets occur in 1979–1996,in which areas located in Jiangxi,northern Xinjiang and middle Inner Mongolia experienced constant changing spring onsets.Overall increasingly earlier spring onsets occur in 1997–2012,in which areas located in Fujian,Hunan and eastern Heilongjiang experienced the most variable spring onsets.Stable early spring onsets over China occur after 2012.Results also showed 15 temporal patterns of spring phenology over the study period and their spatial delineation in China.More specifically,most areas in China have the same FBD category for 40 years while northern Guizhou,Hunan and southern Hubei have the same category in 1979–1997 and then fluctuate between different categories.Finally,our results have certain directive significance on the design of existing observational sites in Chinese Phenological Network.

黑河流域NDVI与环境因子的空间关联性（英文）

The Heihe River Basin is located in the arid and semi-arid regions of Northwest China.Here,the terrestrial ecosystem is vulnerable,making it necessary to identify the factors that could affect the ecosystem.In this study,MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem.By combining these with environmental factors,we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000–2016.A geographical detector(Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution.The results indicate that:(1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north,showing spatial heterogeneity with a q-statistic of 0.38.The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016;(2) The results obtained by the Geodetector(as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins.Precipitation was the dominant factor for the overall basin.In the upper basin,elevation was found to be the dominant factor.The dominant factor in the middle basin was precipitation,closely followed by the soil type.In the lower basin,the dominant factor was soil type with a lower q-statistic of 0.13,and the dominant interaction between the elevation and soil type was nonlinearly enhanced(q-statistic = 0.22).

八宝河流域土壤温度波动的时空分异

The Babao River Basin is the "water tower" of the Heihe River Basin.The combination of vulnerable ecosystems and inhospitable natural environments substantially restricts the existence of humans and the sustainable development of society and environment in the Heihe River Basin.Soil temperature(ST) is a critical soil variable that could affect a series of physical,chemical and biological soil processes,which is the guarantee of water conservation and vegetation growth in this region.To measure the temporal variation and spatial pattern of ST fluctuation in the Babao River Basin,fluctuation of ST at various depths were analyzed with ST data at depths of 4,10 and 20 cm using classical statistical methods and permutation entropy.The study results show the following: 1) There are variations of ST at different depths,although ST followed an obvious seasonal law.ST at shallower depths is higher than at deeper depths in summer,and vice versa in winter.The difference of ST between different depths is close to zero when ST is near 5℃ in March or –5℃ in September.2) In spring,ST at the shallower depths becomes higher than at deeper depths as soon as ST is above –5℃;this is reversed in autumn when ST is below 5℃.ST at a soil depth of 4 cm is the first to change,followed by ST at 10 and 20 cm,and the time that ST reaches the same level is delayed for 10–15 days.In chilling and warming seasons,September and February are,respectively,the months when ST at various depths are similar.3) The average PE values of ST for 17 sites at 4 cm are 0.765 in spring > 0.764 in summer > 0.735 in autumn > 0.723 in winter,which implies the complicated degree of fluctuations of ST.4) For the variation of ST at different depths,it appears that Max,Ranges,Average and the Standard Deviation of ST decrease by depth increments in soil.Surface soil is more complicated because ST fluctuation at shallower depths is more pronounced and random.The average PE value of ST for 17sites are 0.863 at a depth of 4 cm > 0.818 at 10 cm > 0.744 at 20 cm.5) For the variation of ST at different elevations,it appears that Max,Ranges,Average,Standard Deviation and ST fluctuation decrease with increasing elevation at the same soil depth.And with the increase of elevation,the decrease rates of Max,Range,Average,Standard Deviation at 4 cm are –0.89℃/100 m,–0.94℃/100 m,–0.43℃/100 m,and –0.25℃/100 m,respectively.In addition,this correlation decreased with the increase of soil depth.6) Significant correlation between PE values of ST at depths of 4,10 and 20 cm can easily be found.This finding implies that temperature can easily be transmitted within soil at depths between 4 and 20 cm.7) For the variation of ST on shady slope and sunny slope sides,it appears that the PE values of ST at 4,10 and 20 cm for 8 sites located on shady slope side are 0.868,0.824 and 0.776,respectively,whereas they are 0.858,0.810 and 0.716 for 9 sites located on sunny slope side.

黑河流域地理观测分析——复杂性科学视角（英文）

Since 2005,dozens of geographical observational stations have been established in the Heihe River Basin(HRB),and by now a large amount of meteorological,hydrological,and ecological observations as well as data pertaining to water resources,soil and vegetation have been collected.To adequately analyze these available data and data to be further collected in future,we present a perspective from complexity theory.The concrete materials covered include a presentation of adaptive multiscale filter,which can readily determine arbitrary trends,maximally reduce noise,and reliably perform fractal and multifractal analysis,and a presentation of scale-dependent Lyapunov exponent(SDLE),which can reliably distinguish deterministic chaos from random processes,determine the error doubling time for prediction,and obtain the defining parameters of the process examined.The adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data,and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal properties.The SDLE is illustrated to determine intermittent chaos from river flow data.

黑河流域气温的稳定性与长程相关性（英文）

Air temperature(AT) is a subsystem of a complex climate.Long-range correlation(LRC) is an important feature of complexity.Our research attempt to evaluate AT’s complexity differences in different land-use types in the Heihe River Basin(HRB) based on the stability and LRC.The results show the following:(1) AT’s stability presents differences in different land-use types.In agricultural land,there is no obvious variation in the trend throughout the year.Whereas in a desert,the variation in the trend is obvious: the AT is more stable in summer than it is in winter,with Ta ranges of [8,20]°C and SD of the AT residual ranges of [0.2,0.7],respectively.Additionally,in mountainous areas,when the altitude is beyond a certain value,AT’s stability changes.(2) AT’s LRC presents differences in different land-use types.In agricultural land,the long-range correlation of AT is the most persistent throughout the year,showing the smallest difference between summer and winter,with the Hs range of [0.8,1].Vegetation could be an important factor.In a desert,the long-range correlation of AT is less persistent,showing the greatest difference between summer and winter,with the Hs range of [0.54,0.96].Solar insolation could be a dominant factor.In an alpine meadow,the long-range correlation of AT is the least persistent throughout the year,presenting a smaller difference between summer and winter,with the Hs range of [0.6,0.85].Altitude could be an important factor.(3) Usually,LRC is a combination of the Ta and SD of the AT residuals.A larger Ta and smaller SD of the AT residual would be conducive to a more persistent LRC,whereas a smaller Ta and larger SD of the AT residual would limit the persistence of LRC.A larger Ta and SD of the AT residual would create persistence to a degree between those of the first two cases,as would a smaller Ta and SD of the AT residual.In addition,the last two cases might show the same LRC.

中美俄合作与冲突互动——基于事件数据的定量分析

The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system.However,different conclusions about this question are generally made by researchers through qualitative analysis,and it is necessary to objectively and quantitatively investigate their interactions.Monthly-aggregated event data from the Global Data on Events,Location and Tone(GDELT)to measure cooperative and conflictual interactions between the three powers,and the complementary cumulative distribution function(CCDF)and the vector autoregression(VAR)method are utilized to investigate their interactions in two periods:January,1991 to September,2001,and October,2001 to December,2016.The results of frequencies and strengths analysis showed that:the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period.Although that cooperation prevailed in the three dyads in two periods,the conflictual interactions between the USA and Russia tended to be more intense in the second period,mainly related to the strategic contradiction between the USA and Russia,especially in Georgia,Ukraine and Syria.The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads,but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic,and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict.The USA was always an essential factor in affecting the interactions between Russia and China in both periods,but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad.Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.

不同辐射对土壤湿度长程相关性的影响——以黑河流域阿柔超级站为例（英文）

Analyses of the soil moisture evolution trend and the influence of different types of radiation on soil moisture are of great significance to the simulation and prediction of soil moisture.In this paper,soil moisture(2–60 cm) and various radiation data from 2014–2015 at the A’rou superstation were selected.The radiation data include the net radiation(NR),shortwave and longwave radiation(SR and LR).Using adaptive fractal analysis(AFA),the long-range correlation(LRC) of soil moisture and long-range cross correlation(LRCC) between moisture and three types of radiation were analyzed at different timescales and soil depths.The results show that:(1) Persistence of soil moisture and consistency between soil moisture and radiation mutate at 18-d and 6-d timescales,respectively.The timescale variation of soil moisture persistence is mainly related to the influence process of radiation on soil moisture;(2) Both the soil moisture persistence and soil moisture-radiation consistency vary substantially with soil depth.The soil depth variation of soil moisture persistence is related to the influence intensity of radiation;(3) From 2–6 day timescales,LR displays the strongest influence on soil moisture at depths of 2–10 cm through negative feedback of radiation on the soil temperature.The influence intensity decreases with depth from 2–15 cm.Therefore,the soil moisture persistence is weak and increases with depth from 2–15 cm;and(4) At more than 6 day timescales,SR and NR display a stronger influence on the soil moisture persistence at depths of 2–40 cm through positive feedback of radiation on the soil temperature,especially at depths of 2–10 cm.This influence also weakens with depth.The soil moisture persistence at depths of 2–10 cm is the weakest and increases with depth from 2–40 cm.The research results are instructive for determining timescales and soil depths related to soil water in hydrological models.

中国新冠肺炎疫情每日相对风险时空格局

The coronavirus disease 2019(COVID-19)pandemic continues to threaten lives and the economy around the world.Estimating the risk of COVID-19 can help in predicting spreading trends,identifying risk areas,and making public health decisions.In this study,we proposed a comparative risk assessment method to estimate comprehensive and dynamic COVID-19 risks by considering the pandemic severity and the healthcare system pressure and then employing the z-order curve and fractal theory.We took the COVID-19 cases from January 19-March 10,2020 in China as our research object.The results and analysis revealed that(1)the proposed method demonstrated its feasibility to assess and illustrate pandemic risk;(2)the temporal patterns of the daily relative risk indices of 31 provinces were clustered into four groups(high-value,fluctuating-increase,inverted U-shaped,and low-stable);(3)the spatial distribution of the relative pandemic risk indicated a significant circular pattern centered on Hubei Province;and(4)healthcare system capacity is the key to reducing relative pandemic risk,and cases imported from abroad should be given more attention.The methods and results of this study will provide a methodological basis and practical guidance for regional pandemic risk assessment and public health decision-making.