Evolutionary economic geography (EEG) has highlighted the importance of technological relatedness in regional industrial development in developed countries. However, other key factors, such as labour and capital, should also be included in the analysis of regional industrial development in developing countries. The labour surplus, wages and scarcity of capital in developing countries change dramatically in the development process, which can impact the role of technological relatedness in regional industrial development. The purpose of this paper is to develop a new framework to understand the regional industrial development paths of developing countries by linking related theories with EEG studies. Drawing on the literature of Lewis dual economy, growth theory, catch-up theory of developing countries and evolutionary economics, this paper develops a new framework for understanding the changing regional industrial development paths in three developmental stages, namely, the Lewisian dual economy, the period after reaching the Lewis turning point and the innovation economy. In our framework, the regional industrial development paths are affected by both factor endowments and technological relatedness. This study has important implications for regional industrial development in developing countries.
Using the construction method of Human Development Index for reference, this paper reassesses the quality of urban livability environment from 5 aspects of economic condition, public services, residential environment, institutions and urban modernization based on the perspective of migrants’ well-being, and uses 2 periods of population migration data to analyze the interaction effect of human settlements in 279 prefecture-level city in China, and explore the dynamic impact mechanism between urban human settlements and population migration. Research has found that: 1) During the sample inspection period, the human settlements environment of Chinese cities has improved, to some extent, the gap in urban construction level has narrowed, and the space shows a “high in the east and low in the west” feature. The agglomeration effect of central cities is obvious, and cities in the eastern region have experienced a “club convergence” phenomenon, while inland areas in the central and western regions of China have fallen into the “low value trap” of the human livability environment. 2) The net migration pattern of the national population shows a “concave” spatial feature of “low in the middle and high in the surrounding areas”. The migration population is mainly distributed in mega and mega cities with a population size of over 5 million, but the main driving force for the growth of the migration population is small and medium-sized cities. 3) The improvement of urban human settlements environment has a positive effect on the increase of population migration. The difference in economic development and the level of social integration are the core factors that cause the spatial difference in the scale of population migration. Life comfort has gradually become an important source of driving population migration. At the provincial level, the driving factors reflect spatial heterogeneity. 4) The impact of population migration scale is not yet enough to become one of the leading factors in stabilizing changes in the urban livability environment. Therefore, the construction of urban human settlements environment with the key to enhancing well-being should be adapted to the changes in population characteristics in the new development stage to promote the high-quality development of new urbanization.
Studying the interrelationships and drivers of various ecosystem services in rapidly expanding urban areas is important for coordinating the sustainable development of regional society, economy, and ecology. Taking Wuhan City, which is in the process of rapid urbanisation, as an example, we analyse the spatial differentiation characteristics, trade-offs/synergies, and multiple factor interactions of the values of 11 ecosystem services in the study area based on multi-source data. The results showed that: 1) The urbanisation rate of Wuhan City increased by 25.43% from 2000 to 2020, and the land use structure changed significantly, among which the area of construction land increased the most, by 25.86%, and the area of lakes and arable land decreased significantly. 2) The total value of ecosystem services in Wuhan City shows a decreasing trend, decreasing by 670 million yuan, regulating services dominate, reaching more than 65.75% of the total, of which the total value of hydrological regulating services is the highest, mainly originating from lakes. 3) The relationship between 11 ecosystem services in Wuhan City from 2000 to 2020 was dominated by synergistic relationship, accounting for 80.00%, and the trade-off relationship mainly existed between food production and other services. 4) The results of the geodetector showed that socio-economic factors explained the spatial differentiation of ecosystem service values more than natural factors, and the per capita net income of rural households, food production, and population density had the greatest intensity of influence.
Talent is the core driving force behind the development of digital economy. By analyzing the key and core technologies in the fields of artificial intelligence, Internet of Things, cloud computing, and blockchain industries, and utilizing the LinkedIn Big Data Insight Platform, we obtained static location (2022) and dynamic migration (2021—2022) data on digital talents engaged in digital technology development and related management work in the Beijing-Tianjin-Hebei, Yangtze River Delta, and Guangdong-Hong Kong-Macao Greater Bay Area during the COVID-19 pandemic. A comparative analysis of the distribution and flow patterns of digital talent in the three major urban clusters in China reveals the following results: 1) Chinese digital talents are highly concentrated in major cities such as Beijing, Shanghai, Hong Kong, and Shenzhen, but there is still a significant gap in terms of talent levels compared to top global cities; 2) The coordination level of digital talents development in the Guangdong-Hong Kong-Macao Greater Bay Area and the Yangtze River Delta region is higher than that in the Beijing-Tianjin-Hebei region, and have led to the formation of prominent digital talent hubs in Hong Kong-Shenzhen and Shanghai-Suzhou; 3) Domestically, digital talent demonstrates a circular flow pattern, but on a global scale, it’s a non-zero-sum game, showing a trend of outward migration facilitated by Hong Kong acting as a pivot; 4) Based on the direction and activity level of digital talent flow, there are no net inflow active cities among the three major urban clusters, indicating severe risks of digital talent loss for all regions. Suzhou, Hangzhou, and Shenzhen are balanced type active cities, while Beijing, Shanghai, Nanjing, and Hong Kong are net outflow active cities, and Tianjin, Hefei, and Guangzhou are inactive cities. Finally, policy recommendations are proposed from global, regional, and local perspectives.
With the increase in demand for national tourism, China has fully entered the era of mass tourism. However, due to differences in conditions for the development of tourism in China, the development of the tourism economy is spatially unbalanced, which hinders the coordinated development of the regional tourism economy. The total tourism economy of Chinese excellent tourist cities accounts for a high proportion of the country’s total tourism economy, and a study with them as the target can more accurately reflect the situation of Chinese tourism economy, which is more meaningful for the enhancement of the tourism economy. Based on the panel data of tourism economy-related indicators of Chinese excellent tourism cities from 2008—2019, spatial autocorrelation, exploratory regression and Geographically Weighted Regression (GWR) were used to explore the spatial patterns and influencing factors of the tourism economy of Chinese excellent tourism cities. Adopting the GWR model considering “spatial non-stationarity” to explore the influencing factors of tourism economy can more scientifically measure the intrinsic influencing mechanism of regional tourism economic development differences. The results show that: 1) The tourism economy of Chinese excellent tourism cities has strong spatial correlation, and the number of cities with high-high (H-H) and low-low (L-L) agglomerations is high, showing a two-tiered pattern of “large dispersion and small agglomeration”. During 2008—2019, the overall agglomeration pattern of tourism economy in Chinese excellent tourism cities is relatively stable, but the agglomeration pattern in local areas has changed. 2) Based on exploratory regression analysis, the optimal combination of factors influencing domestic tourism income is: tourism resource endowment, industrial structure, transportation conditions, and ecological environment condition; the optimal combination of factors influencing tourism foreign exchange income is: tourism resource endowment, degree of opening to the outside world, degree of marketization, and ecological environment condition. 3) The results of GWR model analysis show that the degree of influence of each factor shows obvious spatial heterogeneity, indicating that the influence of each factor on the tourism economy of Chinese excellent tourism cities is not static, but changes with the change of sample units, and the research results can provide a scientific basis for each city to formulate corresponding measures for tourism economic development.
Taking the top 500 Chinese value brand enterprises as examples, this paper explores quantitatively the spatial and temporal pattern evolution, influencing factors of corporate brands from 3 levels: overall characteristics, spatial distribution, and regional spatial differentiation, using spatial unevenness, econometric modeling, with 19 urban agglomerations as research objects. The study shows that: 1) In terms of industry structure, five categories of brands, namely, food and beverage, real estate related, textile and garment, media, and communication and electronics, have a stable structure; in terms of quantity, food and beverage has the highest share, real estate related the second, and medicine the least; in terms of value, food and beverage has the highest share, finance the second, and medicine the least. 2) In terms of spatial and temporal pattern evolution, from 2004 to 2020, the number and value of enterprise brands in Beijing-Tianjin-Hebei, Guangdong-Hong Kong-Macao, and Yangtze River Delta far exceeded those in other city groups, while Shandong Peninsula and Chengdu-Chongqing performed better, Central and Southern Liaoning, Harbin-Changchun, and West Coast of the Strait were second, and Central Yunnan, Ningxia-along the Huanghe River, and Lanzhou-Xining performed poorly overall. The evolution of brand quantity and value confirms the enterprise distribution pattern of economic development in urban clusters, and the uneven spatial differentiation is relatively stable. 3) In terms of regional spatial differentiation, the brand value shows a decreasing trend from east to west. The intra-regional gap is the largest in the east, the inter-regional Gini coefficient is the smallest in the central-northeast and west-northeast, and the inter-regional gap is the largest in the east and west, and the spatial gap mainly comes from the hypervariable density. 4) In terms of influencing factors, GMM estimation shows a significant cyclic cumulative effect of brand value, with positive effects of market size, economic level, and marketization on brand development. This paper emphasizes the contribution of brands to urban agglomerations and regional economic growth, which brings a positive effect on the long-term development of Chinese brands and can provide reference for economic geospatial analysis and policy formulation.
This article takes six batches of 8171 Chinese traditional villages as the research object, and uses spatial analysis method to explore the spatial distribution characteristics and influencing factors of traditional villages based on geographic grid. The main conclusions were as follows. Firstly, the spatial distribution pattern of traditional villages is characterized by a significant inter provincial boundary phenomenon of concentrated traditional villages, with three major agglomeration areas. The border between Hebei, Shandong and Henan province, the border between Anhui, Zhejiang and Jiangxi province, and the border between Guizhou, Guangxi and Hunan province. Secondly, the spatial autocorrelation of traditional village spatial distribution is obvious, with “high and high” “low and high”, and “low and low” spatial clustering. The mountainous and hilly areas south of the Yangtze River are the dense areas of traditional village distribution in China, while the northeast and northwest border areas are the sparse areas of traditional village distribution. Thirdly, the impact of transportation and economic factors on the spatial distribution of traditional villages is not significant. The factors of terrain, temperature, ecology, and population have a positive impact on the spatial distribution of traditional villages, while precipitation factors have a small negative impact on the spatial distribution of traditional villages. The impact of population, town, and ecology factors on the spatial distribution of traditional villages has strong spatial heterogeneity. The research results have deepened two important understandings about the spatial distribution of traditional villages. One important understanding is that the phenomenon of inter provincial boundaries in the dense distribution of traditional villages is significant. These regions have similar geographical environmental characteristics, mainly consisting of mountainous and hilly landforms, with the environmental characteristics of “three lows and one excess” (low annual temperature, low road density, low economic development level, and high annual precipitation), which fully indicates that the natural geographical environment has a very important impact on the formation and protection of traditional villages. Another important understanding is that the factors that affect the spatial distribution of traditional villages have spatial heterogeneity. It can be seen that the factors with strong heterogeneity are mostly socio-economic factors. This conclusion provides useful insights for subsequent research on the spatial distribution and influencing factors of traditional villages. Compared with existing research literature, the contribution of this article lies in achieving the cross fusion of traditional village spatial location with multi-source data such as natural geographical factors and socio-economic factors, further verifying the effectiveness and scientificity of spatial regression models in analyzing the influencing factors of point vector elements, and enriching the technical methods for studying the spatial distribution and influencing factors of point vector elements.
The popularization of new-generation information technology has changed the pattern of rural financial development, and the financial system that used to cover only traditional financial services can no longer comprehensively reflect the inclusive development of rural finance in China. Based on the theoretical logic of rural financial inclusion in the perspective of virtual agglomeration, this paper further constructs a rural financial inclusion index system covering three dimensions of penetration, use utility and affordability, fits the index of the level of rural financial inclusion by using entropy weighting method, and analyzes the characteristics of spatial-temporal dynamic divergence and influencing factors of rural financial inclusion with the help of the Dagum Gini coefficient, kernel density estimation, and the GTWR model. The results show that: 1) The level of rural financial inclusion in all provinces of the country has been on the rise year by year, and the spatial distribution shows the gradient differentiation characteristic of “high on the east coast and low in the central and western regions”, and the “catching-up effect” in the central and western regions is significant; 2) The unevenness of rural financial inclusion shows a fluctuating downward trend and is characterized by polarization, with a particularly pronounced polarization trend in the eastern and central regions; the overall gap in rural financial inclusion shows a gradual fluctuating downward trend, with the inter-regional gap being the main source of the overall gap; 2) From the spatial characteristics analysis, during the sample period of 2014—2019, the Kernel density estimation of rural financial inclusion in each province showed a significant downward trend, and the width gradually expanded trend, which means that the unevenness of the level of rural financial inclusion across the country, although there is a downward trend, but there is a certain trend of polarization, and the trend of polarization in the eastern and central regions is particularly pronounced; in terms of Dagum Gini coefficient, inter-region differences of rural financial inclusion are significantly larger than the intra-region differences, and the inter-region differences of rural financial inclusion are the main source of the national differences in the level of rural financial inclusion. 3) From the analysis of influencing factors, the living standards of rural residents, the rural economic development environment and urban-rural coordination are important dimensions affecting the inclusive development of rural finance, and there is geospatial non-stationarity in their impact. The level of rural employment, the income level of rural residents, and financial literacy can effectively support the inclusive development of rural finance; there is spatial differentiation in the impact of the dimensions of rural economy and urban-rural coordinated development on financial inclusion, and the urban-rural income ratio is always a negative effect. In addition, the above influencing factors are more sensitive to changes in the North-South regional differences than in the East-West regions.The findings of this article can clarify the differences and spatial evolution characteristics of the development of rural financial inclusion in China, and help optimize financial resources, synergistically push forward the reform of rural financial markets and promote the level of financial inclusion.
Based on the data of the national censuses and 1% population sampling surveys from 1990 to 2020, this study investigates the characteristics and dynamic changes of the amount, source and destination selection of population outflow in the three northeastern provinces, namely Heilongjiang, Jilin, and Liaoning, from the perspective of structural and comparative analysis. The results show that population loss in the three northeastern provinces is a long-term phenomenon which could be dated back to the 1990s. However, the intensity of the population outflow in Northeast China is weaker than other regions with population loss such as central and western provinces according to the outflow amount and rate. The major cause of long-lasting population loss for Northeast China is not the huge amount of population outflow, but the combined effects of the low population inflow rate, the high outflow rate of registered residents, and the simultaneous outflow of urban and rural population. Compared with other provinces with population outflow such as Guizhou, Sichuan, Henan, and Anhui, the destinations of outflowed population from Northeast China are more diversified and decentralized. Economic factors are the common leading factors for the destination choice of the outflow population from Northeast China and other provinces. The outflow population from Northeast China prefer places that are geographically closer and have high-quality employment and public services, but no obvious evidence was found for their salient climate preference as previous studies claimed. The permanence and comprehensiveness of the lack of regional population attraction in Northeast China indicate that the regional governments not only should take measures to delay the outflow of population as much as possible, but also should actively explore new paths of high-quality development for better coping with population loss.
Non-agricultural employment is necessary for urban and rural residents to obtain income. Optimizing the layout of non-agricultural industries in urban and rural areas is important for promoting the development of thriving rural industries, keeping the villagers’ non-agricultural employment close to home, reducing rural population loss, and achieving coordinated urban and rural development. Studies have been conducted on non-agricultural employment, but they paid little attention to the spatial-matching relationship between non-agricultural employment jobs and the permanent population in urban and rural multi-level spaces within the country. They also failed to analyze the driving mechanism of non-agricultural industries. For this research, kernel density estimation analysis and Densi-Graph analysis were used on a case study of a typical county in Chongqing to quantitatively identify urban and rural multi-level space and construct the non-agricultural employment index based on factors including POI data and mobile phone data. This study quantitatively measures the non-agricultural employment index of urban and rural multi-level space to reflect the spatial-matching relationship between the non-agricultural employment jobs and the permanent population at the urban and rural multi-level space within the country, and verify the reliability of the non-agricultural employment index results in the field. This study’s results showed that: 1) The non-agricultural employment index can reliably characterize the spatial-matching relationship between the non-agricultural employment jobs and the permanent population. 2) The non-agricultural employment index has the differentiation characteristics of urban and rural multi-level, decreasing in the following order: market town, urban-rural fringe, urban core, and countryside. 3) Multi-level boundary effects exist between urban and rural areas according to the response of the non-agricultural employment index to influencing factors, among which the urban core area has the strongest response, and the countryside area has the weakest response. 4) There are positive drivers of non-agricultural employment in the adjacent areas from the layout of industrial parks and scenic areas, contributing to an endogenous double-cycle driving model of employment environment improved again and living environment improved again for the region. This study provides a scientific basis for guiding the spatial adaptation of non-agricultural industry development and permanent population in urban and rural multi-level space within the country, as well as realizing the coordinated development of urban and rural non-agricultural industry and population.
Clarifying the spatial variability and impact factors of the conservation level of red villages is crucial to the inheritance of red genes and the continuation of national spirit in the Hunan-Jiangxi border area. By constructing an analytical framework for the conservation of red villages and applying GIS spatial analysis methods, stepwise regression and geographically weighted regression models in turn, the spatial variation are explored in the level of conservation of red villages in Hunan-Jiangxi border area and the influencing factors. The study finds that: 1) The model for the study of red village conservation consists of an evaluation model and an impact factor model, with the evaluation model covering macro-regional conservation levels and micro-community conservation levels, and the regional conservation level based on spatial and temporal characteristics being the more appropriate model; the impact factor model includes two types of basic models based on physical geography and socio-economics. 2) The regional conservation level of red villages in research area is clearly spatially heterogeneous, and the spatial distribution shows a pattern of “one belt-three cores” continuous distribution structure, with Jinggangshan City having the highest preservation rate and preservation density, at 4.72% and 38.52 villages per 10 000 km2 respectively. 3) The natural geographic factors affecting the level of protection of red villages in research area are elevated landforms, water systems, central cities and counties, and transportation; there is significant spatial heterogeneity in the effects of socio-economic factors, with the degree of influence showing a ranking of cultural base > population size > economic development > industrial base.
Dynamic emotional experience in flow is the key to shaping place meaning. By using the mixed-methods approach and targeting tourists’ cycling around Qinghai Lake, this paper aims to reveal the characteristics of emotional dynamics of cycling tourists. Furthermore, by constructing and using a three-dimensional analytical framework of “location-locale-emotion”, which is based on John Agnew’s place theory and the Landscape Perception Theory, this paper also aims to identify the mechanism of how such emotional dynamics occur. Two types of data were obtained during three participatory field trips, together with cycling tourists’ around Qinghai Lake, which lasted for 18 days, i.e., between 4th July and 21st July. The first type of data was collected from 50 cycling tourists’ via questionnaire surveys using the PANAS scale (i.e., 9 positive and 9 negative emotion items), which recorded each cycling tourist’s 18 emotions in each of the ten nodes of the cycling itinerary, presenting a total of 9000 emotional records; while the second data was collected through in-depth interviews with 18 cycling tourists during these three participatory field trips. The quantitative data collected via questionnaire survey were used to calculate various emotional index, such as the emotional variability, emotional instability, and emotional diversity, to present the characteristics of emotional dynamics of cycling tourists. Meanwhile, the qualitative data collected via interview were coded by using Nvivo 12 to help identify the mechanism of how such emotional dynamics occur. The findings are as follows: 1) Regarding intensity, both the variability and instability of cycling tourists’ positive emotions are higher than that of cycling tourists’ negative emotions, and the numerical intervals are also higher, indicating that the fluctuation degree of positive emotional intensity is more obvious, and the differences among cycling tourists regarding the fluctuation degree is also greater. 2) In terms of diversity, the changes of cycling tourists’ negative emotional diversity, compared with positive emotional diversity, is most obvious during the journey; cycling tourists experience the most complicated negative emotions at the beginning of their journey, while they experience the most complicated positive emotions at the end of their journey. 3) The unique location features provide cycling tourists with differentiated experiences between places, while the locale features offer specific methods for place performances and place experience. Together, location and locale features constantly cause cycling tourists to have positive and negative emotions and make them change in terms of intensity and variety. With the changes of time and space, the cold road space is transformed by cycling tourists into a special place with rich and changeable human emotions.
The coordinated development of regional agricultural water and land resources is of great significance to ensure agricultural production. However, the study on coupling evaluation of agricultural water and land resources is still insufficient. This article applies the ecological footprint method to quantitatively assess the carrying capacity of local land and water resources, taking the Youyi Farm of Sanjiang Plain, a scientific and technological demonstration area of the Black Earth Granary, as an example. The results showed that: 1) The ecological footprint of per capita agricultural water resources was 5.26 hm2, and the ecological carrying capacity of per capita was 1.97 hm2. The ecological footprint of per capita water resources was far more than the ecological carrying capacity. The ecological deficit of water resources was 2.82. The utilization of water resources was in an unsafe state. Per capita ecological footprint of cultivated land resources was less than the ecological carrying capacity, ecological surplus of cultivated land resources, the average ecological pressure index was 0.69. The use of cultivated land resources was in a safe state. 2) The coupling coordination degree of the ecological footprint of agricultural land and water resources and the ecological carrying capacity of land and water resources of Youyi Farm is generally in the state of barely coordinated and primary coordination. 3) In summary, although Youyi Farm was rich in land resources, water resources were scarce, indicating a significant room for improvement in the level of water and land matching in the region. Analyzing whether the carrying capacity of agricultural water and land resources is within a reasonable and controllable range is of great significance for coordinating the relationship between agricultural water and land resources and realizing the sustainable development of agricultural water and land resources.
The study was conducted to understand the characteristics of ancient human activities by doing a detailed combing and analysis of the remains and relics of human activities such as ash pits, cultural layers, house sites, pottery kilns and pottery from the Shuigou site in the western Guanzhong Basin. At the same time, through the environmental magnetism and AMS14C chronology analyses of the Shuigou culture layer profiles, we recovered the climate change and the history of paleo-human activities since the Holocene in this area. The results show that the Shuigou paleo-human activity time is the climatically suitable period of 5530—4300 a B.P. in the middle Holocene, and its cultural development is in the transitional stage of the transition from cold and dry to warm and humid. With the technical support of GIS spatial analysis, we extracted the factors affecting the distribution of relics in the Shuigou site, such as elevation, slope, slope direction and distance from water sources, and constructed an index model to study the distribution law of the natural geographic environment suitability of the Shuigou site in Neolithic period. Ancient human activities were mainly concentrated in areas with superior natural environmental conditions, with an altitude of 900~960 m, a slope of less than 6°, a south-facing slope, and a distance from water sources of less than 200 m. The distribution of the relics of the Shuigou site in Neolithic period was investigated by constructing an index model. Explore the process and mechanism of response and adaptation of ancient human activities to environmental changes.
As an important ecological function reserve and ecologically fragile area in Southwest China, it is of great significance to achieve the coordinated development of society and ecology in Guizhou Province by analyzing the trade-off relationship of regional ecosystem services and rationally constructing an ecological security pattern. Therefore, this study evaluated the net primary productivity (NPP), soil conservation (SC) and water yield (WY) of this region from 2000 to 2020 by relevant models and methods, explored the spatial-temporal differentiation characteristics of the trade-off relationship between these three ecosystem services through root mean square error and spatial auto-correlation analysis. Then, a Bayesian belief network was built to analyze the driving forces of regional trade-off changes, and regional ecological security patterns was identified by setting different trade-off scenarios. It showed that these three ecosystem services increased during 2000 to 2020; but there was obvious trade-off relationship between them in local regions. The heterogeneity characteristics of ecosystem service trade-off relationship under different backgrounds was significant. Among them, the trade-off between WY and SC, WY and NPP was stronger than that between NPP and WY at different altitudes; the trade-off between SC and NPP, SC and WY increased with the raising of slope; the trade-off between SC and WY was more obvious in karst canyons and karst basins; and there was a stronger trade-off relationship between WY and NPP, WY and SC in ecological restoration area. The trade-off of these 3 ecosystem services was affected by multiple forces, and the dominant driver of the trade-off between different ecosystem services was different; afforestation area, actual evapotranspiration and total precipitation was the main forces causing the trade-off change between SC and NPP, SC and WY, NPP and WY, respectively. Wangmo County, Luodian County of the southern Guizhou and Jiangkou County, Yinjiang County of the northeastern part faced the greatest ecological risk. In the future, the ecological security level of these counties can be improved by adjusting the key state of the key forces.
Soil moisture plays a significant role in water infiltration, evapotranspiration, and solute transport in the unsaturated zone, there are remarkable differences in rainfall-infiltration characteristics within different rainfall intensities and different land use types, which is crucial for plant growth and the dynamics of ecosystem. In this article, taking the Hualong River, Ningbo City, Zhejiang Province, as an example, combining field in-situ fixed-point observation and model simulation analysis, a soil moisture simulation model HYDRUS-1D was established. And the model was calibrated and verified by using the typical rainfall events on September 29, 2016 and October 7, 2016. Moreover, a soil water infiltration simulation of different land use types under different rainfall intensities was carried out based on HYDRUS-1D model from 2016 to 2020, to reveal the law of rainfall-infiltration under different land use types in the eastern humid region of China. Results showing that: 1) According to the simulation of soil moisture change process, the HYDRUS-1D model has high accuracy in simulating the dynamic change characteristics of soil moisture, and the Nash efficiency coefficient of 90% simulation results is over 0.8; 2) Under the same rainfall intensity, the soil infiltration amounts of the four land use types were farmland, waxberry forest, sloping farmland, and bamboo forest in descending order. The infiltration rate was greatly affected by rainfall and rainfall intensity, and the soil infiltration amount increased significantly with the increase of rainfall intensity under different land uses. 3) Soil infiltration is also profoundly affected by rainfall duration and previous rainfall. When the rainfall intensity increases one level furtherly, the increase in infiltration of each land use type was less than 5%, and maintained a slight increase. And when extreme rainfall events occur, the rainfall intensity exceeds the infiltration intensity, resulting the infiltration excess runoff, causing the phenomenon of large rainfall but small infiltration. The research results can provide theoretical reference for agricultural production, three-water transformation law and ecological protection research in the humid region of eastern China.
Influenced by climate changes and anthropogenic activities, river discharge into Baiyangdian Lake has decreased dramatically, which has profoundly changed the spatial distribution pattern of water bodies in the Baiyangdian Lake Basin. The shift of water body pattern and its maintenance mechanism still remain uncertainty. This study simulated natural streamflow using VIC model, which was compared with actual observed streamflow to explain the streamflow reduction pattern, and distribution pattern of water bodies were also explored using GRACE data in the Baiyangdian Lake Basin, China. The results showed that: 1) The average annual inflow to Baiyangdian Lake was 23.79×108m3 for the natural scenario, while it was only 8.59 ×108m3 for the actual scenario and showed a decreasing trend; 2) The upstream river discharge may be meet the water consumption of Baiyangdian Lake for the natural scenario. Actually, the river discharge could meet the water consumption of the lake only in 1960—1979, and the lake had a long-term water deficit in 1980—1997, and was mainly recharged by an ecological water supply project in 1998—2016; 3) Terrestrial water storage in the Baiyangdian Lake Basin decreased significantly, and the decrease was greater in the southwestern basin than in the northeastern basin. Reduced water resources, an imbalance between water supply and consumption of Baiyangdian Lake, and over-reliance on artificial water allocation increase the water resource risk in the Baiyangdian Lake Basin. In particular, implementing the Xiong’an New Area Project has brought additional challenges to securing water resources in a changing climate, especially in water resource management and regulation.
This study examines the development and application of landslide Informatic-Tupu, utilizing the Koshi River basin as a case study. The landslide Informatic-Tupu is classified into 4 distinct types based on their content and functionality: formative factors Tupu, landslide inventory Tupu, landslide risk Tupu, and landslide risk management Tupu. Statistical methods are employed to analyze the spatial differentiation patterns of landslide distribution and evolution along both vertical and latitudinal axes within the study area. Vertically, landslides exhibit high point density within the elevation range of 200~1 000 m, with significant area proportions observed in the elevation ranges of 400~1 200 m and 2 800~3 600 m. Latitudinally, landslides are densely concentrated between 26°42′00″N and 26°48′00″N, with large area proportions found between 26°42′00″N and 26°48′00″N, as well as between 27°24′00″N and 27°30′00″N. Comparative analysis with landslide data from 1992 reveals an increase in landslide point density within the elevation ranges of 200~1 000 m and 2 800~3 600 m. Additionally, a larger area proportion is observed in the elevation range of 3 200~3 400 m. The study also identifies areas with high landslide point density between 26°48′00″N and 26°54′00″N, and between 27°48′00″N and 27°54′00″N, while regions with significant area proportions are located between 27°24′00″N and 27°42′00″N. This comprehensive analysis provides valuable insights into the spatial patterns and evolution of landslides in the Koshi River basin. The findings can inform risk assessment and management strategies, enhancing mitigation and preparedness efforts in landslide-prone areas. The categorization of landslide Informatic-Tupu into formative factors, inventory, risk assessment and risk management types presents a systematic approach to understanding and addressing landslide hazards, which can be applied to other regions facing similar challenges.
