Against the deepening of China’s reform and opening up and high-speed economic growth, the number of transmigrants in China has soared over the last decade. Though most of the transmigrants tend to sojourn for less than five years in major metropolises such as Beijing, Shanghai and Guangzhou, their emergence gradually contributes to the reconstruction of urban social space in China. Now, due to transmigrants ’ preference for residing close to their compatriots, scattered immigrant enclaves in major metropolises are becoming characterized by different nationalities or nations. This topic has attracted widespread attention from scholars and the public. As such, this study sheds light on three typical immigrant enclaves as case study regions, including the South Korean enclave in Wangjing, Beijing, the Japanese enclave in Gubei, Shanghai and the African enclave in Xiaobei, Guangzhou, in order to discuss the following questions. 1) How and why have different ethnic groups chosen their residence city? 2) How do ethnic groups interact with residence cities, and what are the formation mechanism and characteristics of three immigrant enclaves? Based on the comparative study of the three immigrant enclaves in Beijing, Shanghai and Guangzhou, we found that: Firstly, many of the South Korean Transmigrants have expatriated to Beijing are enterprise-oriented, trade-oriented and education-oriented, the Japanese Transmigrants in Shanghai are enterprise-oriented, and the African Transmigrants in Guangzhou are trade-oriented. Secondly, the immigrant enclaves in three cities have been established under similar institutional backgrounds, such as global economic restructuring, bilateral relationship, and immigrant policy of China, which led to a policy-led or economy-led character of immigrant enclaves. Despite all this, due to distinct urban hard and soft environment, and ethnicity factors, the formation mechanisms of immigrant enclaves vary from city to city, from ethnic group to ethnic group. As a result, there are three immigrant enclave models: The Japanese enclave in Gubei is the special zone model, the South Korean enclave in Wangjing is the coupled modes, and the African enclave in Xiaobei is the ethnic model. Besides, the study also points out that immigrant enclaves in China are different from their counterparts in the west, where they are usually treated as a serious social problem brought by social deprivation and spatial differentiation. Against the contexts of China ’s unique situation and “sojourned” transmigrants, the immigrant enclaves in China are the results of transmigrants ’ efforts on adapting to China ’s immigrant policies, urban policies, and city’s public services. Now, the cities in China have entered a new stage, featured by both capital internationalization and immigrant internationalization in a completely distinct way. Therefore, we should put emphasis on the immigrant enclave governance and service, so as to build an exemplary role in the country and promote the in-depth opening and high-quality development in China.
Regional innovation cluster is an important part of the Yangtze River Delta high-quality integration strategy, but the current research on regional innovation cluster is relatively lack. There are differences in understanding the concept of regional innovation clusters at home and abroad. Based on the analysis of the related concepts of regional innovation cluster, this paper puts forward the concept of regional innovation cluster based on the Yangtze River Delta integration strategy. This paper obtains the authorized invention patents of the new material industry in the Yangtze River Delta from the IncoPat website in 2000-2017. On the basis of describing the characteristics of the internal innovation linkages of the Yangtze River Delta New Materials Regional Innovation Cluster, this paper uses the two-step System Generalized Method of Moments (GMM) model to measure the impact of the depth and breadth of integration into regional innovation cluster on local innovation cluster. We drew the following conclusions: Firstly, the development of regional innovation cluster is conforms to the viewpoint of the point-axis development theory. The Yangtze River Delta New Materials Regional Innovation Cluster take Shanghai, Nanjing, Hangzhou and other developed local innovation clusters as growth poles, and take the lead in forming a development axis with growth pole nodes in space. Developed local innovation clusters have become the core group within the Yangtze River Delta New Materials Regional Innovation Cluster. Secondly, the technological innovation linkage of the Yangtze River Delta New Materials Regional Innovation Cluster has the characteristics of various types of knowledge coupling, which is the same point of view described by the ‘global-local’ innovation network theory. The proportion of heterogeneous knowledge interaction in the cooperative research and development (R&D) network of the Yangtze River Delta New Materials Regional Innovation Cluster is as high as 49.08%, while the proportion of heterogeneous knowledge interaction in the patent transfer network is only 3.68%. On the basis of homogeneous knowledge interaction, heterogeneous knowledge interaction also plays an important role. Thirdly, in the current stage of the Yangtze River Delta New Materials Regional Innovation Cluster, the polarization effect of developed local innovation clusters as the core group is greater than the diffusion effect. The positive moderating effect of the knowledge base is obvious, and developed local innovation clusters are more likely to benefit from the construction of regional innovation cluster. It can be seen that regional innovation clusters not only have the common characteristics of local innovation clusters and cluster innovation networks, but also conform to the regional development law elaborated by the ‘point-axis’ development model and the ‘center-periphery’ theory in the evolution of spatial morphology. The research on regional innovation clusters is still in its infancy, and the comparative research on different industries and types needs to be further carried out by economic geographers.
Urban agglomeration characterized by compact regional cooperation and frequent human mobility has been an obvious direction of urban and socio-economic development in China. However, in the formation of urban agglomeration, rapid impervious expansion and tremendous anthropogenic heat sources making urban areas hotter, which generates the urban heat island (UHI) effect and associated extreme heat events, threatening public health and sustainable development. Although studies have documented the impact of UHI on the urban environment by using a single urban agglomeration as the research object, there is still a lack of knowledge of the driving mechanism of the spatiotemporal pattern of UHI on the national scale. In this study, we observed the urban expansion and the spatiotemporal pattern of UHI in 19 urban agglomerations of China from 2000 to 2015 by using long-term impervious data and MODIS surface temperature data. Furthermore, by using Redundancy analysis (RDA) and linear regression model with the datasets that represent the nature and socio-economic driving factors, we quantified the driving mechanism of spatiotemporal pattern of UHI for all urban agglomerations. Results show that the impervious surface expanded rapidly with its proportion increased from 2.08% to 5.33% during 2000-2015 in China’s urban agglomerations, concentrating in coastal urban agglomerations such as the Pearl River Delta. The proportion of heat island (PHI) was high and the intensity of heat island (SUHIIagg) was higher in summer nighttime than in daytime. The eastern and numerous of northern urban agglomerations such as Harbin-Changchun had strong cooling capacity in summer, however the SUHIIagg increased in nighttime to varying degrees. Besides, the PHI and the SUHIIagg in winter nighttime was higher than that in daytime. And the SUHIIagg in the north, northwest and east urban agglomerations increased in winter nighttime. We found vegetation was significantly reducing the nighttime PHI, while the precipitation was negatively affecting the SUHIIagg in summer nighttime (22%) and latitude was positively affecting the SUHIIagg in winter nighttime (56%). Meanwhile, the nighttime lights was negatively affecting the SUHIIagg(24%) and positively affecting the PHI (27%) while the population negatively affecting the SUHIIagg in summer nighttime (31%).These show that natural environmental factors dominantly contribute to the SUHIIagg, while the human disturbance factors dominantly contribute to the PHI. These show that the interaction between radiation changes and human activities has an important impact on the nighttime UHI effect in China. Since the urban expansion and immigration keep ongoing, the UHI effect is predicted to be more intense and of longer duration in China. Thus, the pathway to balance the development of urban agglomeration and the mitigation of the urban heat environment is a major challenge for government policymaking in China. This study expands the knowledge of the spatiotemporal UHI change at the national scale, which provides a scientific basis for urban planning, alleviating urban heat challenges, and achieving sustainable development.
Tourist flow is mostly regarded as homogeneous and its heterogeneity is ignored in the studies available. Based on the questionnaire data of a large sample of urban residents’ travel from Beijing, Xi'an and Wuhan of China in 2017, this paper divided the tourist flow into three types by different modes of transportation, which were, self-driving, train and plane, from the perspective of tourist origin, and investigated their spatial distribution characteristics based on the methods of mathematical statistics and map analysis. The results are as follows: 1) With the increase of traffic time between the tourist origin and destination, the self-driving tourist flow is exponentially decreasing in distribution, while the train tourist flow and plane tourist flow are in a positively skewed distribution that rises first and then falls. 2) The tourist flows of the three modes of transportation differ in their spatial distribution patterns. The scenic spots with high visiting rate by self-driving are around the tourist origin cities, characterized by continuity and gradual change; most of the scenic spots with high visiting rate by train or plane are far away from the tourist origin cities, characterized by wide dispersion and small contiguous areas. Scenic spots with high visiting rate by train are mostly well-known attractions in economically developed regions within the spherical distance of 400-1 200 km from the tourist origin, while scenic spots with high visiting rate by plane are mostly well-known attractions in economically developed regions beyond the spherical distance of 800 km. 3) The prime travel space by different modes of transportation is conjugate, the area with a spherical distance of 0-400 km from the tourist origin cities is the prime space for self-driving travelling, the area within 400-1 200 km that can be reached by train within 6 h is the prime space for travelling by train, and the area within 1 200-2 600 km is the prime space for travelling by plane. This paper puts forward a Conjugate model of the prime travel space for heterogeneous transportation modes. This model considers the proportional structure of tourist flows by different modes of transportation under different distance. Besides, the law of spatial distribution of different tourist flows and tiring time by travelling in different modes of transportation are also included. This model ascertains the locations of the prime travel space by different modes of transportation in a tourism system centered on the tourist origin, and reveals conjugate connection among the prime travel space for different modes of transportation, which is the macro-geographical expression of the spatial distribution of tourist flows by heterogeneous transportation modes.
In this paper, the theories and concepts of path dependence, path creation and agency in evolutionary economic geography are used to analyze the development mechanism of livestreaming e-commerce. Then, based on multiple open-source data such as DouYin platform, the regional differences, spatial distribution patterns and influencing factors of livestreaming e-commerce in 292 cities above prefecture level in China are empirically analyzed by Cartogram method, exploratory spatial data analysis tools, coefficient of variation, theil index and spatial econometric model. The results show that the spatial pattern of the number of streamers and the number of followers shows some similarity but with some spatially divergent characteristics. The most significant regional variability and spatial unevenness in the sales of livestreaming e-commerce are found in the core regions of the Yangtze River Delta and the Pearl River Delta, with significantly high and high clustering areas. In addition, the empirical measurement results show that the number of Taobao villages, the index of online e-commerce, the number of head streamers with goods, the number of other streamers with goods, the number of MCN and the level of express logistics services are the significant factors affecting the regional differences in the development level of livestreaming e-commerce. We found that the development of livestreaming e-commerce results from a combination of mechanisms and factors. Specifically, at first, the regional industry and the supply chain base are necessary guarantees for the development of livestreaming e-commerce. Secondly, MCN and streamers with goods play an essential role in integrating and linking relevant resources within and outside the region. In addition, government encouragement and support actions and the spatial effect of neighbouring regions positively impact the development of livestreaming e-commerce. In the future, the exploration of the interactions and relationships between supply and industry chains, actors, institutions and government actions in typical cities where livestreaming e-commerce is well developed will also be a key focus of EEG and Internet geography.
Based on media geography, this paper takes the net celebrity city Xi’an as the entry point, combining mathematical statistics, spatial analysis and geographic detectors, and selects the Baidu Index of Chinese 361 cities’ network attention data (excluding Hong Kong, Macao and Taiwan) to Xi’an in 2017-2019, and analyzes the net celebrities the spatial-temporal dynamic evolution and influencing factors of the virtual space construction of net celebrities in Xi’an. The research found that: 1) In terms of time, affected by the video of the bowl of wine, the network attention in Xi’an has been increasing year by year since 2017. The monthly change shows an obvious “precursor effect”. At the same time, affected by the broadcast of the TV series “The longest day in Chang’an”, the monthly network attentionvalue of Xi’an in 2019 is roughly higher than thaose in 2017 and 2018. From 2017 to 2019, the holiday changes of Xi’an’s network attention were characterized by high pre-holiday network attention and low post-holiday network attention. 2) In terms of space, the overall area of the country shows that Xi’an in the area east of the Heihe-Tengchong Line has significantly higher network attention than the area west of the boundary line, but this trend is gradually weakening. The provinces (municipalities, autonomous regions) regions show obvious characteristics of high network attention from the neighboring provinces and cities of Xi’an and coastal provinces and cities, and most of the eastern provinces (municipalities and autonomous regions) have always been in high attention areas. The city domain shows that the network attention of the provincial capital cities and the surrounding cities of Xi’an is generally higher than that of other cities in the province (except the provincial capital city). 3) In terms of influencing factors, climate comfort, leisure time and net celebrity image symbols are the main factors affecting the time distribution of network attention in Xi’an. Internet development, age structure, GDP scale, permanent resident population, education level and the geographic spatial distance of Xi’an are important factors affecting the spatial distribution, while the strength of the economic connection between the two places and the per capita GDP of the tourist-source places have weak influences on the spatial distribution of Xi’an’s network attention. It can be seen that through the spatial practice of urban residents in daily life, the media not only reconstructs the image of the city, but also shapes the dynamic virtual space of “people-media-city” interaction, which has become an important component of modern urban space production and practice.
Taking 326 administrative units at prefecture level in China in 2008-2018 as cases, this article comprehensively measures spatial-temporal evolution, dynamic coordinated, and interactive response relationships between geographical indication agricultural products and agricultural economy in China by using multi-index comprehensive evaluation method, coordinated development degree model, grey correlation degree model, and P-VAR model. The results are shown as follows: 1) The development level of geographical indication agricultural products and agricultural economy showed an overall upward trend. In terms of space, geographical indication agricultural products dispersed over an extensive area with localized concentrations, while agricultural economy showed the distribution characteristics of declining from coastal to inland. 2) The level of coordinated development of geographical indication agricultural products and agricultural economy increases with time, from primary coordination to high-level coordination, and the spatial differences have experienced the process of first increasing and then decreasing; there is a significant correlation between geographical indication agricultural products and agricultural economy, and the correlation has the characteristics of continuous strengthening. 3) Geographical indication agricultural products and agricultural economy have the characteristics of self-reinforcement and inertia growth; The influence of geographical indication agricultural products on agricultural economy is small, while the influence of agricultural economy on geographical indication agricultural products is great, which has a reverse forcing mechanism.
Taking the lead in modernizing agriculture is a major task for the revitalization of Northeast China, which is of great strategic significance for guaranteeing national food security, protecting black soil resources, and revitalizing the countryside. Northeast China, one of the four major economic sectors, is an important main grain-producing area and commodity grain base in China. The agricultural modernization development level of Northeast China has a direct relation to the national food security and profoundly affects regional economic and social development. According to the theoretical connotation and policy requirements of agricultural modernization in China, the agricultural modernization evaluation index system is constructed from three dimensions of input, output and social development, and the agricultural modernization level of 40 prefecture-level administrative units in Northeast China in 2008 and 2018 is measured. The results show that: 1) From the spatial pattern, the level of agricultural modernization in Northeast China shows a spatial pattern of “higher along the northeast-southwest axis and lower on the east and west sides of the axis”, with the Sanjiang Plain, the Songnen Plain, and the Bohai Sea Rim being the high-value clusters. 2) From the development speed, the level of agricultural modernization in Northeast China as a whole has improved, and the development speed in the northern region has increased. In terms of development speed, the overall level of agricultural modernization in Northeast China has improved, and the overall development speed in the northern region is greater than that in the south. 3) In terms of stage, the percentage of municipalities in the “initial realization” of agricultural modernization in 2018 is 86%, and the level of agricultural modernization in Northeast China is generally in the transition from “initial realization” to “basic realization”. 4) The three sub-dimensional indicators of agricultural modernization development level also show a certain pattern of geographical differentiation in geographic space. 5) Different regions should take the path of agricultural modernization following their characteristics given their development advantages and resource endowments. The research findings have important implications for policymakers formulating agricultural modernization policies following the local conditions of various cities. Firstly, the cities should improve the land scale economy and guide the land proper concentration, as well as improve the large-scale production capacity and management level. Secondly, the cities should make full use of the advantages of the industrial base in the Northeast China, and promote the agricultural mechanization production and the advanced agricultural technologies based in the Northeast China. Thirdly, the cities should speed up the agricultural technological transformation, and cultivate a group of professional farmers.
The location of expected apartment purchase of returned migrant workers reflects the future apartment purchase and residence expectations, and has an important impact on the development of local urbanization. Based on 1072 field survey data from 45 villages in Henan Province, descriptive statistical analysis and Multi-level Logistic Regression Analysis were used to explore the location and influencing factors of apartment purchase of returned migrant workers. It is found that the central location level has an important impact on the location choice of the expected apartment purchase of the returned migrant workers, but the intensity of the impact is related to the age of the returned migrant workers. The new generation of returned migrant workers prefers higher-level central places, while the first-generation prefer lower-level ones. On the whole, the county seat of household registration is the preferred location for returned migrant workers to purchase apartments, followed by the main urban areas and villages of prefecture-level cities, and fewer market towns and provincial capitals. Individual characteristics, capital accumulation, and marriage are the core factors that affect the location selection of expected apartment purchases, and the geographical environment of the village has an important influence on the location selection of expected apartment purchases. In each model, gender, age, length of work, marriage, skills, family economic status, type of return area, type of former work site, and village per capita net income, return ratio and terrain type reached significant levels. Returned migrant workers have high expectations of apartment purchase and residence in counties and cities. In the future, they will be an important force for new urbanization, especially in-situ urbanization. Urban apartment prices should be stabilized and lowered to realize the true urban integration of returning migrant workers.
Based on road network data and point of interest (POI) data, taking Qingdao in Qingdao main urban area as the research area, this article analyzes the centrality of road network and the spatial distribution characteristics of catering, and uses global and local regression models to explore the impact mechanism of road network centrality on the distribution of catering. The results show that: 1) The high value area of betweenness centrality is mainly concentrated in three main traffic trunk roads. The closeness centrality presents a circular distribution characteristic of decreasing from the center of the study area to the surrounding, and the spatial distribution of eigenvector centrality shows a multi-core structure. 2) Qingdao’s catering industry generally presents a spatial pattern of massive agglomeration and obvious regional differences. Chinese restaurants, Western restaurants and leisure catering have the characteristics of multi center structure. 3) The centrality index of road network has high spatial similarity with the kernel density value of catering. High-high and low-low aggregation areas constitute the main spatial correlation mode. 4) The distribution of catering in Qingdao is significantly affected by the centrality of road network. Among them, the betweenness centrality is mainly negative, and the positive effects of closeness centrality and eigenvector centrality are obvious. The impact of road network centrality on the distribution of catering has spatial heterogeneity. The imbalance of road network conditions leads to regional differences in economic activities, thus changing the spatial structure of catering. From the perspective of the impact of centrality on different types of catering, the western restaurant is most significantly affected by the road transit capacity and the importance of the road, the Chinese restaurant is the most sensitive to the improvement of road accessibility.
Wine tourists form a relationship network between wineries and scenic spots through their own movements in emerging wine tourism destinations. Studying the network structure and influencing factors of emerging wine tourism destinations from the demand-side perspective facilitates understanding of the development status, enhances competitiveness, promotes effective joint marketing and resists market competition. This paper took the enhances wine tourism destination on the east foothills of Helan Mountains in Ningxia as an example. We used a questionnaire survey to structure the spatial movement trajectory of tourists, constructed the spatial contact network of wineries and scenic spots by the network analysis method, and analyzed the factors influencing the spatial network structure of the destination. We found that the local hot scenic spots were in the center of the wine tourism network on the eastern foothills of Helan Mountains, not the winery. As for influencing factors, the region proximity, grade proximity, grade tenure proximity, and accessibility policy proximity significantly affected the network structure of emerging wine destinations. This study showed that in emerging wine tourism destinations, wineries located in the same area tend to share network relationships in attracting wine tourists because of their region proximity, grade proximity, and grade tenure proximity. However, the establishment's tenure proximity and type proximity had no significant effect. Our study also indicated that the winery in emerging wine destination had a short history, and tourists were mainly affected by the popularity of the node itself rather than its history. The brand image of the wine tourism destination was not distinctive and prominent, and tourists currently regarded the winery as an ordinary scenic spot. Therefore, the local government should formulate a strategic plan to promote and develop wine tourism so as to enhance the wine brand's image. The winery should strive to obtain a grade as high as possible and improve tourism facilities and reception capacity so as to accommodate more tourists and achieve a competitive advantage.
In order to reveal spatial variability of urban thermal environment in different functional regions, we identified seven types of functional regions for construction land based on multi-source data such as multi-source data such as the data of area of interest (AOI) in 2019. Landsat 8 OLI_TRIS remote sensing data was used to retrieve the land surface temperature. Logistic regression model and spatial autoregression model were applied to analyze the variability for influence of different functional regions on urban heat island effect. The multi-ring buffer and decay model were used to quantitatively analyze the variability for warming effect of functional regions. The results show the following: 1) The seven types of functional regions all have the phenomenon of high temperature. The phenomenon of high temperature in traffic region is significant, followed by storage region and industrial region, while public administration and service region, residential region and public facilities region are weak. 2) The proportion of seven types of functional regions all have a significant positive impact on the formation of urban heat island. The impact of the proportion of functional regions on the formation of urban heat island from high to low is: Industrial region>storage region>public administration and service region>residential region>commercial and service region>traffic region>public facilities region. 3) There is a positive correlation between urban heat island intensity (UHII) and the proportion of seven types of functional regions, and a spatial spillover effect is obvious. The increase of the proportion for each functional region in the local region may lead to the rise of UHII in the adjacent region. The proportion of traffic region and commercial and service region have significant positive effect on UHII, followed by storage region and industrial region, while the proportion of public administration and service region, residential region and public facilities region have weak positive effect. 4) The warming effect of industrial region, commercial and service region, storage region and traffic region are relatively prominent, while the warming effect of public administration and service region, residential region, public facilities region are inapparent. 5) There is a positively correlation between the area and the warming effect in functional regions. However, the warming effect in large functional region may be “saturated”. The warming effect of industrial region and traffic region may be more sensitive to the change for area, while the warming effect of public administration and service region, residential region and public facilities region are probably less affected by the change for area.The results of this study are conducive to provide a scientific guidance for layout of urban functional regions and the optimization of thermal environment in Xiamen City.
In this article, the importance of each band for predicting leaf character data was obtained by calculating variable importance of prediction (VIP) values. In the summer of 2020, the full-wavelength spectral information (350-2500 nm) of 12 species of typical wetland plants in Sanjiang National Nature Reserve was collected by ASD Lab spectrometer. Seven functional traits of plant leaves were tested in the laboratory. Through principal component analysis, the optimal principal component number of the spectrum required for modeling was obtained. The traits and spectrum models of 12 wetland plants were established by Partial Least Squares Regression (PLSR). The R2 and RMSE were used to test the fitting effect of the models. The VIP value is used to determine the most important spectral region. Finally, two main conclusions were drawn. Firstly, compared with studies on single species in other ecosystems, the traits-spectral general models for 12 typical wetland plants were satisfied except for starch. Among the seven traits, spectral model of nitrogen had the highest model accuracy. The fitting results of LMA, water content, leaf phosphorus, sugar, cellulose, and lignin were good, with R2 ranging from 0.38 to 0.55. The fitting result of starch content model was not ideal which R2 was only 0.29. Secondly, the VIP value showed the importance of each band in model construction. The first and second peaks of the VIP value are in the range of 400-405 nm and 708-719 nm. Therefore, for 7 traits, visible spectrum and near infrared edge are the most important for model construction. Inversion of plant traits through hyperspectral data has become one of the important means of ecological environment monitoring. But due to the complexity of wetland environment, spectral inversion of leaf traits of wetland plants had rarely studied. Based on previous modeling methods of grassland and forest ecosystems, this study established general models for 7 leaf functional traits for 12 typical wetland plants, it is feasible to monitor wetland plant traits by using spectrum. However, the fitting effect of starch content model was not ideal and needs to further improvement.
Flooding has taken a devastating societal and economic losses around the world, leading to numerous fatalities. Limted to the numbers and spatial distribution of hydrological stations, the flood prediction across the globe remains to be explored. Therefore, this study attributed seasonal flood magnitude/frequency to large-scale climate indices,including Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic (EA), East Atlantic/West Russia (EAWR), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Niño 3.4 SST and Niño 4 SST, by using hydrological data from 5839 gauging stations. The block maxima approach (BMA) and the peaks-over-threshold sampling technique (POT) were used to obtain flood magnitude and frequency based on these hydrological stations. Global assessmment indicated that flood magnitude and frequency are significantly correlated with large-scale climate indices at most stations (75%), and the influence of climate indices on flood generally has considerable hysteresis quality, with two-quarter lag for 31% of global stations. The climate indices showing statistically significantly correlated with floods are subsequently applied as predictors to build the global-scale season-ahead prediction models by Bayesian model averaging (BMA), with prediction performance evaluated by the mean squared error skill score (MSESS). The performance of simulation was qualified (0.6>MSESS>0.2) for 48% and 28% of the global hydrological stations for flood magnitude and frequency, respectively. Using the pre-season climate index, the flood forecasting models developed in this study perform well in forecast the flood anomalies over South China in 2020. The prediction based on Bayesian model averaging provides a new perspective for global flood prediction by identifying relevant climate indices, with valuable insights for flood risk management and mitigating flood hazards.
Based on the simulation data of the LMDZ (Laboratoire de Météorologie Dynamique-Zoom) model, the NCEP/NCAR grid meteorological data and the AOI (Arctic Oscillation Index), we verified the applicability of the LMDZ model in the Mongolian Plateau, analyzed the relationship between environmental factors and the LWML (Local Meteoric Water Line) as well as δ18O in precipitation, explored the temporal and spatial variations of δ18O and deuterium excess (d-excess) in precipitation, and simulated the transportation trajectory of air mass in the Mongolian Plateau using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model). The results show that among the two results of LMDZ, LMDZ-nudged has a better effect in simulating stable isotopes of precipitation in the Mongolian Plateau. The LMWL in Mongolian Plateau is δD=7.78δ18O+3.31 (R2=0.995), and its slope and intercept are less than those of GMWL (Global Meteoric Water Line), indicating the falling raindrops of this region are greatly affected by the below-cloud evaporation. The δ18O in precipitation is higher in summer than that in winter, with obvious latitudinal effect, and is positively correlated with temperature as well as AOI, but is negatively correlated with relative humidity. The d-excess in precipitation tends to be low in summer and high in winter. Westerly water vapor (about 88.39%), East Asian monsoon water vapor (about 5.73%) and Polar water vapor (about 5.88%) are three main water vapor paths in the study area by tracing the backward trajectory and clustering analysis, and westerly water vapor is the main control water vapor.
Because there are many mountainous areas and complex terrain in China, geological disasters are widely distributed. Frequent geological disasters not only cause great damage to the ecological environment, but also seriously threaten the life and property safety of local residents. Therefore, it is of great significance to evaluate the landslide disaster susceptibility by zoning. Taking Lixian County, which is prone to frequent geological disasters, as the study area, 11 affecting factors are selected from four aspects: landform, geological environment, hydrological conditions and human engineering activities. The correlation between each factor is studied and the landslide susceptibility evaluation index system is constructed through Pearson Correlation Coefficient. According to the number of landslides, the Information Value model is used to calculate the information value of each affecting factor and select the non-landslide samples. On this basis, two machine learning models, the method of Random Forest and Radial Basis Function Neural Network model, are applied to carry out landslide susceptibility evaluation and the accuracy of landslide hazard susceptibility evaluation results is verified by ROC curve. The results show that the area of high-prone areas and extremely high-prone areas predicted by the Random Forest model is small, but the number of disaster points is more and the disaster plot ratio is higher. The area of high-prone areas and extremely high-prone areas predicted by Neural Network model is larger than that predicted by Random Forest model. In the predicted results of Random Forest evaluation model, the distribution of high-prone areas and extremely high-prone areas is more concentrated, and there are more disaster points distributed per unit area. By verifying the accuracy of the two models and through the comparison of ROC curve, the AUC values of Random Forest Algorithm and Radial Basis Function Neural Network model are greater than 0.8. The AUC value of Random Forest model is higher than that of Radial Basis Function Neural Network model, which is more suitable for landslide susceptibility evaluation in this area. Using the existing landslide statistical data to quantitatively study the landslide susceptibility can effectively improve the prediction accuracy, explore the optimal prediction model and provide a scientific basis for the prevention and management of geological disasters in this area.
River terraces can act as a geomorphic record of tectonic uplift and have been used to characterize regional geomorphologic evolution. The terrain of the Guizhou Plateau has gradually developed a pattern of higher and lower elevation in the west and east, respectively since the Cenozoic. River terraces have shown positive and sensitive responses to this process. The Qingshuijiang River in eastern Guizhou is a tributary of the Yuanjiang River. A two-strath terrace has generally formed on both banks of the Qingshuijiang River, characterized by well-preserved fluvial facies. This study comprehensively analyzed the formation ages of the terrace morphology of four sections along the river. The results showed that the formation age of the T2 terrace in the Mazhai and Wengdong sections in the upstream area is 51-57 ka B.P., whereas that of the T1 terrace is 25 ka B.P. The formation age of the T2 terrace of the Sanjiang and Shidong sections in the downstream area is 122-102 ka B.P., whereas that of the T1 terrace is 78 ka B.P. The bedrock height of the T2 terrace of each profile was selected to calculate the river incision rate. The results showed similar river incision rates between the two profiles in the upstream area (Mazhai and Wengdong) of 0.41-0.34 m/ka respectively, significantly higher than that of the two profiles in the downstream area (Sanjiang and Shidong). This result indicated a higher incision rate upstream, with the incision rate gradually decreasing downstream. The upper reaches of the Qingshuijiang River have experienced differential uplift under the influence of tectonism since the Late Pleistocene. This process was characterized by a large tectonic uplift in the west and a rapid incision rate, whereas the opposite pattern occurred in the east.
