New quality productive forces are advanced productivity that is freed from traditional economic growth mode and productivity development paths, features high-tech, high efficiency and high quality driven by technological innovation in the new era. From the geographical perspective, developing new quality productive forces is the ability to coordinate new human-earth relationships in the Anthropocene, where human activities dominate, promote the harmonious coexistence of humans and nature, transform green waters and mountains into gold and silver mines, drive high-quality development and layout, and comprehensively implement the construction of a beautiful China. Geography focuses on studying the emergence and development process, formation and evolution characteristics, spatial organization patterns, and regional differentiation laws of new quality productive forces driven by innovation, so as to promote the human-earth system to enter the ecological civilization stage of highly coupled and harmonious coexistence between humans and nature. Compared with traditional industries, the evolution of new quality productive forces has experienced a fluctuating process of evolution from low-quality productivity to medium-quality, medium-high-quality, and then to high-quality productivity, accompanying the emergence of continuous technological revolutions and industrial revolutions. They exhibit basic characteristics such as high coupling, deep integration, super correlation, rapid iteration, and spatial differentiation. The pivotal directions propelled by the drive of new quality productive forces for geographical research encompass the following: re-coordinating human-earth relationships to foster novel harmonious coexistence conducive to the realization of a beautiful China; restructuring industrial systems to align with the new quality productive forces, facilitating profound industrial transformation; reallocating geographical elements to establish a novel mechanism that harmonizes natural, humanistic, and data-driven components; reshaping spatial pattern to engender a fresh spatial paradigm wherein new quality productive forces and traditional industries are integrated deeply; rebuilding ecological environment to leverage them as green engines of productivity, thus enhancing the intrinsic value of ecological capital; revitalizing geographical science through the refinement and updating of theories and methods, thereby constructing a modern disciplinary landscape of geography.

Typhoons are among the most significant natural disasters affecting the eastern and southern coastal regions of China, inflicting substantial annual damage on both coastal and inland areas. Since the initiation of the reform and opening-up policy, the socioeconomic development of the coastal regions of China has been swift, leading to increased exposure to typhoons. In the context of global climate change, typhoons are expected to increase in frequency and intensity in China. Therefore, researching on the spatiotemporal pattern characteristics of typhoons impacting China is of critical importance for understanding the impact patterns and risk changes of typhoon disasters, as well as for formulating policies on disaster response, prevention, and mitigation. This study aims to provide valuable insights into the formulation of such policies. Based on these objectives, this study utilized a comprehensive dataset, including county-level socioeconomic and disaster statistics, historical typhoon wind and rainfall data, and high-precision topographic data. Using county-level administrative regions as spatial units, this study employed various methods, such as time-series statistical analysis, gravity model, geographical detector, spatial correlation analysis, and geographically weighted regression, to analyze the spatiotemporal distribution patterns and influencing factors of typhoon disaster conditions in China from 1978 to 2020. The findings of this study are as follows: (1) The number of deaths and missing persons, quantity of damaged housing, death, and missing rate per million people, and proportion of direct economic loss to GDP caused by typhoon disasters have all shown a declining trend, indicating significant achievements in disaster prevention and mitigation efforts. (2) The center of gravity of typhoon disaster-related losses has shifted southward, corresponding with the economic development of coastal regions, demonstrating a reduced disaster impact in coastal areas and an increased impact in inland areas. (3) Wind and rain induced by typhoons are the primary driving factors of disaster conditions, and topographical factors are also drivers of casualties and crop loss. (4) The two major regions, Zhejiang-Northern Fujian and Western Guangdong-Eastern Guangxi, exhibit significant characteristics of disaster condition agglomeration, closely related to typhoon activity patterns and levels of economic development. (5) There is a negative correlation between the gross local product and disaster conditions in some areas, reflecting the role of socioeconomic development in enhancing the capacity for disaster prevention and mitigation.

With the rapid advancement of science and technology, artificial intelligence (AI) has become a significant force driving scientific development and social progress. In the field of geographical sciences, the application of AI technology is deepening, bringing revolutionary changes to the collection, analysis, and application of big data and spatio-temporal information, and demonstrating innovative and application potential in multiple aspects. This paper systematically reviews the development and application of AI in geographical sciences, providing a detailed introduction to the development trajectories of various AI fields such as machine learning, computer vision, natural language processing, planning systems, and large AI models, as well as their applications in geography. It discusses the problems and challenges of AI applications in geography and provides an outlook on the future development of interdisciplinary research between AI and geographical sciences.

In the context of climate change, the escalating frequency of extreme weather phenomena has exacerbated the severity of compound floods in the southeastern coastal regions of China. Rising sea levels significantly contribute to the inundation of low-lying coastal urban areas. The quantitative assessment of compound flood risk offers scientific support for disaster prevention and reduction in coastal cities and for coastal management initiatives. Using Haikou City as a case study, the daily precipitation and maximum storm surge tide data from 66 typhoons that affected Haikou between 1960 and 2017 were utilized to construct compound flood combination scenarios. Based on the quantitative method of D-Flow FM (Delft3D-FLOW Flexible Mesh) numerical simulation, the potential risks of extreme rainfall and storm surge compound flood disasters under sea level rise scenarios were thoroughly investigated by integrating various scenarios. The findings revealed the following: 1) Storm surge was the primary factor contributing to compound flooding during typhoons, with the estuary of the Nandu River and the northern coast being the most affected. 2) In the scenario of maximum rainfall and storm surge combination, the inundation area of Haikou is about 148 km², which is approximately 15 times larger than the minimum rainfall and storm surge combination scenario. Moreover, in more than half of the inundated areas, the water depth exceeds 1 meter. 3) Under extreme rainfall and storm surge compound scenarios, the areas encompassing Haidian Island, Xinbu Island, and Jiangdong New Area were significantly affected by sea level rise. By 2100, the total flooding area is projected to reach about 203 km² under the RCP8.5 scenario. Sea level rise significantly amplifies urban flood risks, implying that coastal cities are poised to encounter heightened threats and manage future challenges. Through comprehensive comparisons of multiple rainfall and storm surge compound flooding scenarios under sea level rise, the temporal and spatial characteristics of the compound flooding risk were systematically evaluated. The results provide an important scientific basis for sustainable regional development, effective management, and prevention.

Against the backdrop of rapid global climate change, the frequency and severity of storm surges in coastal areas are increasing, particularly in tidal river segments that are affected by storm surges and upstream river flooding. Although existing storm surge models have introduced a variety of different boundary settings, the boundary conditions provided are limited and cannot meet the current generalization needs of complex hydraulic engineering projects in China. This study considered the Feiyun River Basin as the research subject and coupled the upstream hydrodynamic model IFMS with the oceanic storm surge model ADCIRC. By utilizing the strengths of both models, a flood evolution model for the estuarine tidal river segment was established, enabling the spatiotemporal simulation of tidal levels in the Feiyun River tidal segment. The model not only effectively considers the impact of storm surge propagation at the estuary on flood evolution in the tidal river segment, but also the effect of upstream river flooding on the area. The study first validated the model with Typhoon Meranti in 2016, where the simulation results showed a high degree of agreement with the observed data series and errors were within acceptable limits. Flood processes at the Ruian, Mayu, Bishan Liqiao, and Dongtou tidal stations during Typhoons Doksuri and Khanun were simulated. The results show that the peak flood errors at all four stations were below 0.30 m, with Nash coefficients >0.80, indicating the model's capability to accurately reflect tidal level fluctuations and effectively contribute to disaster prevention and mitigation efforts in estuarine tidal segments. Finally, the study analyzed the impact of the driving forces of the upstream and downstream boundaries on tidal level predictions at three stations (Ruian, Mayu, and Bishan Liqiao). It was concluded that, compared to Mayu and Bishan Liqiao stations, the influence of the upstream boundary on Ruian can essentially be ignored, suggesting that the error from the upstream boundary under the influence of Typhoon Khanun is negligible for predicting errors at Ruian. The degree of the impact of the downstream boundary fluctuations on the three stations, from largest to smallest, was Ruian, Bishan Liqiao, and Mayu. Compared to the changes in the upstream boundary, the downstream boundary had a greater overall impact on all three stations. Additionally, when the downstream boundary changed by the same magnitude, the variation in low tide levels showed a decreasing trend from downstream to upstream, whereas the variation in high tide levels, although following the same trend, did not show a significant difference between the three. In summary, compared to the upstream boundary, the downstream boundary had a greater impact on tidal-level predictions at the three stations. The result shows that the lower boundary has a greater impact on the tidal level forecasts at three stations compared to the upper boundary. The study not only provides a new method for tidal river flood simulation in coastal urbanized areas but also offers directions for improving model simulation accuracy through analysis.

Social and economic losses from typhoons are increasing owing to climate change. It is of practical significance to correctly understand new characteristics and trends in typhoon activity. Based on the best track dataset of tropical cyclones from the China Meteorological Administration, the temporal and spatial variation characteristics and evolution law of northward-moving typhoons from 1949 to 2022 were analyzed using the linear trend, Mann-Kendall test, and wavelet analysis method, and the impacts of the El Niño-Southern Oscillation (ENSO) on typhoon activities were also discussed. The results showed that: (1) 275 northward-moving typhoons occurred during the past 74 years, with an average of 3.7 per year. The interannual fluctuation in typhoon frequency was large, and the upward trend was not significant. The proportion of northward-moving typhoons to the total number of generated typhoons in the Northwest Pacific was between 2% and 30%, showing a significant upward trend. (2) Northward-moving typhoons were mainly generated from July to September, accounting for approximately 88.4% of the total typhoons. The highest number of typhoons entering the defined area was 114 in August. The life-cycle intensity of northward-moving typhoons is dominated by high-intensity grades, such as super typhoons and typhoons. Among them, super-typhoons accounted for 30.5% of the total number of northward-moving typhoons, and the intensity of typhoons and above grades exceeded 70% of the total amount. In recent years, the probability of high-intensity northward-moving typhoons has increased. (3) A total of 159 northward-moving typhoons landed in China over 74 years. Most of the turning-track typhoons made landfall in Taiwan, Fujian, and Zhejiang, whereas the landing locations of landed disappearing-track typhoons made landfall more northerly. Most unlanded turning-track typhoons turned eastward near 30°N and 125–130°E, showing a significant upward trend. The generating positions of the northward-moving typhoons were mainly concentrated in the ranges of 10—20°N and 130—150°E, with a density of 4.65/10,000 km². The central generation position of the landed northward-moving typhoons was 4.2° more westward than that of the unlanded typhoons. The latitude of the central generating position of the disappearing typhoons was 2.1° northward compared to that of the turning typhoons. (4) The Niño3.4 index had significant negative and positive correlations with the frequency and life-cycle intensity of northward-moving typhoons, respectively, and it also had an obvious effect on their generating positions. There were 4.5 northward-moving typhoons in the La Niña year, which was 1.67 times the El Niño year. However, the intensity of northward-moving typhoons generated during El Niño years was significantly higher than that generated during La Niña years, and the intensity of northward-moving typhoons increased with the Niño3.4 index. The central generating position of northward-moving typhoons during La Niña years was 5.8° northward and 12.4° westward compared to that during El Niño years, which was closer to China. This study provides a basis and reference for strengthening the risk management of typhoons and improving the efficiency of disaster prevention and reduction.

The dynamic risk assessment of typhoon disasters is an important decision-making basis for disaster response in the event of a major typhoon. Its goal is to dynamically predict the expected loss and disaster risk level caused by a typhoon so as to provide a basis for disaster risk early warning and emergency response. The traditional risk assessment model mainly fits the vulnerability curves of the hazard-affected bodies using historical disaster losses, and then establishes a disaster risk assessment model by coupling the risk of disaster factors, exposure, and vulnerability. However, the vulnerability curves generated by this method have problems of regional applicability, particularly in small-scale regions with small sample sizes available for fitting, leading to insufficient generalizability of the model. In addition, such models are complex and require phased hazard and vulnerability of the hazard-affected bodies research. Moreover, when employing the 3-element coupling process, it is difficult to consider other risk factors in the disaster system, such as hazard-formative environment and disaster prevention and mitigation capability. With the development of information technology, the availability of disaster risk factor data has been significantly improved, affording conditions for the fusion and application of disaster risk multi-source data. In recent years, many data-driven machine-learning models have been used to establish disaster risk assessment models. These models have the advantage that they can use large sample to improve the adaptability of the model, whereby the modeling process can consider more risk factors, the concepts of hazard and vulnerability are diluted, and the steps of model building are simplified. The integrated learning algorithm can not only improve the prediction accuracy, but more importantly, can be used to effectively evaluate the contribution value of the index to the final evaluation result. At present, China has established a six-level disaster reporting system at the national, provincial, municipal, county, township, and village levels, forming a long-term, high-precision database of disaster event cases since 2009, providing rich disaster loss information for the data fusion of risk elements. This study was based on 108 typhoon cases affecting five provinces in southeast China during 2009-2022. Nearly 4,000 county-level typhoon disaster loss samples were used to establish a dynamic typhoon risk assessment sample database that integrates 30 types of multi-source risk factor indicators. Six typhoon disaster risk assessment models were developed using the random forest algorithm to evaluate the affected population, emergency relocation population, crop-affected areas, collapsed and severely damaged houses, direct economic losses, and comprehensive risk level. Through the verification of actual disaster situations and model results, the overall accuracy of the disaster risk assessment results was found to be greater than 80%, indicating that the model has good generalizability and can be used for actual disaster assessment work. The experimental comparison shows that increasing the training sample size by 1-2 orders of magnitude can improve the accuracy of the model assessment by 3%-14%, indicating that the accumulation of disaster risk big data is of great significance in the study of disaster risk assessment. This study is expected to constitute a scientific reference for the quantitative analysis of the multiple impact factors of typhoon disaster damage and explore technical ideas for the application of disaster big data in risk management.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

Typhoon gales can lead to accidents such as the breakage and collapse of transmission line towers, affecting the operational safety of power systems. Therefore, the risk assessment of transmission line towers during typhoon disasters is important. Taking all transmission towers in Zhejiang Province as an example, a typhoon disaster vulnerability assessment model for transmission line towers based on "excess loss" for both continuous and discrete variables was proposed based on tower attributes, geographical information, and typhoon disaster data. Utilizing the reanalysis data of typhoon parameters and wind fields from the past 68 years, a typhoon gale hazard assessment model was established based on the extreme value theory, and the statistical parameters of wind speed intensity under typical scenarios were analyzed. Furthermore, based on the regional disaster system theory and through a coupling analysis between typhoon gales and tower vulnerability, a risk assessment model for typhoon transmission line towers was developed. The results indicate the following: (1) the hazard of typhoon gales decreases from southeast to northwest, with differentiated distributions due to the local terrain and other factors. As the return period increased, a nonlinear increasing trend was observed. Taking the maximum wind speeds with a return period of 20 years and 100 years as examples, the wind speed intensities across Zhejiang Province range from 23.5-50.9 m/s and 32.6-68.9 m/s, respectively. Therefore, different wind resistance strategies should be adopted based on specific prevention requirements. Notably, the typhoon parameter wind field model used in this study had certain errors compared to the actual measured wind speeds. Therefore, in practical applications, particularly in complex terrain areas, it is necessary to combine local observational data for model calibration and application. (2) The comprehensive vulnerability of towers under the influence of typhoons generally exhibits a distribution pattern that is high in the south and low in the north, which is closely related to the terrain. Regions with high vulnerability (>1) were mainly located in central and southern Zhejiang and the coastal areas. Moderate vulnerability (0.5-1) is distributed in the Jinqu Basin and the offshore areas from Taizhou to Ningbo. The northeastern plain of Zhejiang had a relatively low tower vulnerability (<0.5). (3) The risk of transmission line towers generally exhibits a pattern of being high in the south and low in the north, with higher risks along the coast and lower risks in inland areas. There are significant local differences. In southeastern Wenzhou, Taizhou, and southern Lishui, the risk level of the towers was the highest. The southern part of Ningbo, Zhoushan, western Quzhou, and eastern Jinhua had the second highest risk. Additionally, some areas in Shaoxing, Huzhou, and Hangzhou have towers with higher risks that need to be addressed, which is consistent with the actual investigation findings. These results provide the necessary technical support for disaster risk assessments. Risk management plans should be adopted based on regional differences.

Catastrophe insurance is an important financial tool to mitigate the risk of catastrophes. After the 2008 Wenchuan Earthquake, China accelerated its exploration of a catastrophe insurance system. As one of the most natural disaster-prone provinces in China, Guangdong experiences frequent rainstorms and typhoons. Severe natural disasters have not only led to significant losses to economic development and people's lives, but have placed considerable financial pressure on governments at all levels. To promote the transformation of government functions and use of catastrophe insurance as a modern financial tool to cope with major natural disasters, Guangdong has conducted pilot work since 2016 to explore and experiment with different aspects of catastrophe index insurance. This includes the design and application of insurance systems and products. The pilot work achieved remarkable results and formed the Guangdong catastrophe index insurance paradigm. However, few studies have examined the development and application of catastrophe index insurance programs in Guangdong Province. This paper describes the research and design process, data, and key methods of typhoon catastrophe index insurance in Guangdong, in accordance with the specific catastrophe index insurance practices. Furthermore, the application of the current catastrophe index insurance program from 2016 to 2023 is reviewed. Additionally, the advantages, characteristics, and shortcomings of the program are systematically analyzed, and potential directions for improvement in the future are discussed. Several notable conclusions were drawn from this study. First, the typhoon catastrophe index insurance, which is based on the circular catastrophe box and uses typhoon intensity levels as a stratification criterion for the payout structure, offers a straightforward methodology, easy recalculations, readily accessible data, and transparent results. Second, this form of insurance facilitates rapid claim settlements, incurs low operational costs, and effectively mitigates moral hazard. Third, the existing typhoon catastrophe index insurance program may encounter high basis risk and underestimate the severity of typhoon hazards, particularly in the context of climate change and the situation wherein a single typhoon impacts multiple municipalities. Finally, improvements to the current typhoon catastrophe index insurance program in Guangdong could be achieved by more deeply and comprehensively analyzing the spatial and temporal patterns of typhoon events, incorporating additional parameters with clear physical meanings, and refining the probability distributions of typhoon disaster events. The insights outlined in this paper may potentially enhance understanding among scholars and practitioners of typhoon catastrophe index insurance programs and provide guidance for extending catastrophe insurance in other typhoon-prone areas.

Rainfall and sea surface temperature grid data, as well as rainfall data from coastal stations in China, were used to obtain the spatiotemporal response characteristics of summer rainfall along the Chinese coast to ENSO and analyzed interdecadal changes in summer rainfall. The results show that: (1) Summer rainfall along the coast of China was significantly affected by ENSO and can be divided into three regions, with Lianyungang and Yunao as the boundaries. The Niño3.4 index was negatively correlated with summer rainfall along the Bohai and Yellow Sea coasts, positively correlated with that of the East China Sea coast, and not significantly correlated with that of the South China Sea coast. (2) On an interdecadal timescale, the relationship between summer rainfall along the coast of China and the Niño3.4 index was unstable. The negative correlation between summer rainfall along the Bohai and Yellow Sea coasts and the Niño3.4 index was significant before and after 1980 and 2010, respectively. The positive correlation along the East China Sea coast became insignificant after the 1980s, whereas the correlation along the South China Sea coast remained insignificant. (3) On the interdecadal timescale, the summer Niño3.4 index, winter Arctic Oscillation (AO) index in the previous year, and spring Antarctic Oscillation (AAO) index in current year were significantly negatively correlated with summer interdecadal rainfall along the Bohai and Yellow Sea coasts and positively correlated with summer interdecadal rainfall along the East China Sea coast. Summer interdecadal rainfall along the coast of the South China Sea was significantly negatively correlated with the spring Arctic Sea ice index in the current year. Regarding the Niño3.4 index, the high sea surface temperature in the Central and Eastern Pacific triggered a negative Pacific-Japan-type interconnection wave train in the 500 hPa geopotential height field, resulting in a decrease in interdecadal rainfall along the Bohai and Yellow Sea coast and an increase in interdecadal rainfall along the East China Sea coast. When the winter AO in the last year and spring AAO in the current year were in a positive phase, the abnormal anticyclone in the southern part of Baikal Lake at 850 hPa wind field guided the airflow in the mid to high latitudes southward, causing a weakening of the East Asian summer monsoon and a decrease in interdecadal summer rainfall along the Bohai and Yellow Sea. In addition, the strong, westward position of the subtropical high pressure in the northwest Pacific increases the upward movement, increasing interdecadal summer rainfall along the East China Sea coast. The interdecadal variation of spring Arctic Sea ice stimulates the opposite atmospheric circulation pattern that induced interdecadal variation of summer rainfall along the South China Sea coast in the 850 hPa wind and 500 hPa geopotential height fields.

Geographic environment has exerted profound effect on the origin and evolution of world civilizations. Chinese civilization budded and evolved on a vast and varied territory between Yellow and Yangtze rivers, and has been thus deeply affected by the local geographic conditions. But it has been hardly seen to explore the origin of Chinese civilization from the perspective of geography. On the basis of integrated scientific investigation in China's north-south transitional zone, geographic analysis of Neolithic culture distribution and interpretation of pre-Qin and Qin-Han ancient literature, the conclusions can be drawn as follows: (1) The early agriculture pattern of "Rice in the south and millet in the north" and the ancient astronomy formed before about 8000 years were the background for Chinese civilization. The geographic distribution of Neolithic Dadiwan, Yangshao, Majiayao and Longshan culture sites showed that the earliest civilization elements appeared in the upper reaches of West-Hanshui and Weihe rivers, with a spatial trend of spreading toward east. (2) The West Qinling Mts. region, located between the Tibetan Plateau and the Jialing River, especially its inner Chenghui and Xili basins, being characterized by superior natural conditions and resources, is closely related to the three major mysteries concerning the origin of Chinese civilization, i.e., the main areas of the ancient Di and Qiang ethnic groups, the location of ancient Kunlun Mts., and the site of Dayu water control. (3) The Qin ethnic group stepped onto the stage of history by assisting Dayu in water control, and in their history of multiple ups and downs, built the grand water control projects in ancient China, such as the Dujiangyan Irrigation Project, Zhengguo Canal, Lingqu Canal, etc., and pioneered the time of "Books with the same text" and "County system", forming the main line of the origin and early evolution of Chinese civilization. (4) The West Qinling areas are still basically a "blind zone" in archaeological and historical research. It is highly recommended to conduct systematic and in-depth archaeological and historical research in this region so as to realize the breakthrough in the exploration of the origin of Chinese civilization as soon as possible.

Typhoons are extreme weather phenomena that seriously affect the daily lives of residents and regular functioning of society. As one of the most typhoon-prone countries in the world, China is constantly affected by typhoons and their secondary disasters, which can cause significant casualties and economic losses. The extent of damage caused by typhoons is inversely proportional to the effectiveness of the emergency response. Therefore, accurate and comprehensive access to damage information is critical for rescue and recovery. Social media, which is characterized by low collection costs and rich content, is an important means of collecting disaster information. With the development of social media, it has become increasingly important to accurately and comprehensively identify social media texts related to typhoons. In this study, by combining typhoon attribute data and a multi-label classification method with Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) models, a typhoon damage identification method based on Weibo texts and deep learning is proposed to identify the damage caused by severe and super typhoons that made landfall in Guangdong Province from 2010 to 2019. First, texts related to typhoon damage were identified from the massive Weibo texts and further classified into five damage categories: transportation, public, electricity, forestry, and waterlogging. The typhoon damage characteristics were comparatively analyzed using spatial distribution, time curves, and quantity curves. The results showed that the accuracy of typhoon damage classification was high, with an F1 score of 0.907 for identifying typhoon damage-related texts and 0.814 for further classifying them into five damage categories. Typhoon attribute data and multi-label classification methods have improved the accuracy and comprehensiveness of typhoon damage identification. Compared to the use of Weibo texts only and the single-label classification method, typhoon attribute data provide information on the geographic context of the typhoon at the time of the texts' release, and the multi-label classification method allows the texts to belong to more than one damage category. This study shows that there are differences in the proportion of damage caused by different typhoons, which are related to the intensity and track of the typhoon, as well as the development level of the affected areas. In addition, before the typhoon makes landfall, precautions lead to transportation and public-related damage. After the typhoon makes landfall, the typhoon damage shows single and double-peak characteristics, and the different characteristics reflect the changing trends and features of typhoon damage. This study provides a scientific basis for typhoon damage identification and disaster relief in Guangdong Province.

The statistical definition of migration in China is attached with its unique household registration (hukou) system, the migrants so defined are also called the floating population. A new analytical paradigm is needed to deal with the complexity of sub-flows in the floating population. The paper classifies five types of the floating population in census context by referring the UN migration criteria, and constructs a new paradigm for analyzing the floating population in China. As a particular provincial-level region (hereafter province) is concerned, the inflow and outflow people belong to different hukou groups balanced by their own counter-flow, namely, inflow vs back-inflow (both have no hukou of the province), outflow vs back-outflow (both have hukou of the province). With the clue of inter-census migration cohort, a sub-flows model is constructed to identify the inter-provincial migration based on the retention rate. The annual retention rate of the inter-provincial migration cohort from 2010 to 2020 is 88.7%. Based on the provincial retention rates, an all-increment table of population change by province in China is made by modelling simulation. The paper surfaces new characteristics of population growth and inter-provincial migration: (1) Provincial population changes are divided into five types, among which the inflow-leading increase type is mainly found in municipalities and the eastern coastal areas, while the fertility-leading increase type and the fertility-overriding increase type are mainly in the western provinces and the agricultural provinces in the middle, and the outflow-overriding decrease type and the outflow-leading decrease type in Shanxi, Inner Mongolia, Gansu and the northeastern provinces. (2) The mechanical growth of population can be divided into four types: rapid increase, equilibrium, outflow-overriding decrease and dual decrease (both hukou and non-hukou migration are negative). A "W"-shaped mechanical growth rate profile from northwest to southeast is found with the equilibrium belt standing in its middle. (3) Population floating is divided into three types. The counter-flows are highly-correlated: inflow rate and outflow rate are negatively correlated while the inflow-back-inflow and outflow-back-outflow are significant positively correlated. The analytical paradigm and model of floating population in China can be further extended to the study of "citizenship seeking migration" including international migration, and even further to identity migration including migrations with status changing such as enrollment, employment and marriage.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1,000 m, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1,000 m resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 6.57% and 7.61% for peak terrain, 21.28% and 17.35% for valley terrain, and 22.85% and 23.37% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1,000 m, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

Using the Mann-Kendall method, the growth rate of China’s urbanization since the reform and opening up was divided into 2 stages, 1978—1994 and 1995—2020, and the pattern of China’s provincial urbanization in different stages was analyzed. Using panel data, the dynamic mechanism of this pattern was discussed from the intra-regional and inter-regional levels. The results show that: 1) The spatial and temporal dynamic differences of China’s provincial urbanization are significant. In 1978, China’s regional urbanization pattern showed a pattern of high in the north and low in the south, and high in the east and low in the west. In 1994, the pattern of urbanization presented the urbanization rate of the provinces in the north and southeast coasts is relatively high, and southwestern provinces are relatively low. The urbanization level in 2020 has formed a pattern of gradual decline from east to west. 2) The estimation results of spatial Durbin model show that labor demand as a pulling force has a stronger effect on the urbanization rate between regions than within regions; The effect of the income gap between urban and rural areas on urbanization rate is firstly suppressed and then promoted, and the intensity of the effect between regions is always stronger than that within regions. The regional economic development disparities, acting as an inter-regional push factor, have a significant positive effect on urbanization only in the initial phase; the income gap between urban areas, serving as an inter-regional pull factor, overall shows an effect that initially suppresses and then promotes urbanization rates, with the impact shifting from being stronger inter-regionally to being stronger intra-regionally. 3) The results of Geographically Weighted Regression model show that, in economically developed regions, labor demand and urban-rural income gap, as regional push and pull forces, have a positive driving effect on the urbanization of each province. The positive effect of regional economic development differences and inter-regional urban income gaps on urbanization has obvious fluctuations in space. But the change has become stronger over time, indicating that the development gaps between regions and between urban and rural areas are constantly promoting the urbanization development of various provinces as a push and pull force between regions.

Wetlands play an important role in flood regulation, water purification, and biodiversity maintenance, etc., which are closely related to human well-being and survival. Wetland Science is an important part of geographical science and is of great significance in supporting scientific development and serving the construction of national ecological civilization. The National Natural Science Foundation of China (NSFC) is the main channel to fund basic research of Wetland Science, and the funding status can reflect the research hotspots and development directions in Wetland Science. In this study, 519 projects related to Wetland Science funded by the discipline of Geographic Sciences (application code D01) in 1986—2023 were covered by titles or keywords including “wetland”“marsh”“peatland”“mangrove” or “mudflat”. The systematic analysis was conducted from the perspectives of application code, research area, research content and keywords. The results show that the funded projects in Wetland Science have experienced two “decade” of rapid and steady growth from 2002 to 2012 and from 2013 to 2023; these projects are mainly concentrated in landscape geography and integrated physical geography (D0105), remote sensing science (D0113) and biogeography and soil geography (D0103). In terms of research objects, inland marsh wetlands and coastal wetlands are the main focus; in terms of research contents, “remote sensing monitoring”“process”“climate change”“vegetation” and “function” appeared more frequently. The keyword network relationship shows that “remote sensing and spectrum”“remote sensing and vegetation” and “landscape and pattern” co-occur more frequently, which characterizes the geographical features of the current development of wetland science and the changing research methods. Currently, the Geographical Sciences discipline of NSFC is further optimizing the branch discipline layout and keywords, strengthening the cross-field and cross-disciplinary interactions and fusions, guiding focus on the fundamental theories and frontier hotspots of Wetland Science, and promoting the high-quality development of wetland science research in China.

The rapid growth of social media has introduced new concepts and technical approaches for disaster management. This paper reviews the characteristics of social media data and its application potential in disaster management research, providing a new research perspective for the field of disaster management. Taking the impact of Typhoon Doksuri in Fujian Province in 2023 as a case study, this research employs Latent Dirichlet Allocation (LDA) topic modeling to analyze the practical application effectiveness of social media data at different stages of disaster management from three perspectives: the spatiotemporal distribution of posts, trend analysis of different types of entities, and evolution of topic content. These findings indicate that the synchronous relationship between the popularity of related topics on Weibo and the impact of a disaster event confirms the effective application of social media data in disaster management. By monitoring the dynamics of information dissemination on social media, we can determine the occurrence status and impact scope of disasters in real time. During disasters, different user types have different foci. Individual users tend to focus more on the restoration of living facilities and the supply of relief materials, whereas organizational users concentrate on disseminating information about disasters and emergency response measures. The information provided by different types of users can provide a more comprehensive and diversified perspective on disaster perceptions for disaster management. Analysis of the evolution of topic content can reflect the evolution of emergency response dynamics and public attention needs in different cities at different stages of disaster management, thereby developing more practical emergency response strategies. Through the mining and analysis of social media data, this study recognizes the entire process of disaster occurrence from the perspective of social media data, thereby enriching the relevant theoretical and empirical research. Future research could be conducted from perspectives such as utilizing other multisource data, integrating machine learning and deep learning technologies to enhance the accuracy of topic information extraction, and exploring the application of social media data to post-disaster emergency rescue and infrastructure support.

Under the background of carbon peak and carbon neutrality, data centers with high energy-consumption characteristics are facing a huge challenge of energy saving and emission reduction, which is related to the achievement of the goal of green and high-quality development of new infrastructure. To address this challenge, the national level has formulated the “East Data and West Calculation” Project to leverage the advantages of resource endowment in the west and alleviate the pressure on resources and the environment in the east. The carbon emission reduction effect and spatial transfer law resulting from this strategy are scientific issues worth studying. To investigate these issues, this study constructs a carbon accounting framework based on the fine-grained data of data centers in each region of China. It simulates and predicts the amount of carbon emissions and the scale of spatial transfer of data centers in 2 scenarios: with or without the implementation of the “East Data and West Calculation” Project in 2020—2030. The study also analyzes the potential of energy saving and emission reduction associated with the strategy. The results of the study demonstrate that the “East Data and West Calculation” Project can achieve energy saving and emission reduction in data centers by optimizing the spatial distribution of computational resources. In the context of the strategy, the total carbon emissions of national data centers in 2030 are expected to reach 2.11×10⁸ t, which is a reduction of 22.74×10⁶ t compared to the scenario without the strategy. Specifically, the strategy effectively relieves the pressure on carbon emissions in the regions of the Beijing-Tianjin-Hebei Hub and the Yangtze River Delta Hub, resulting in a reduction of 55.45×10⁶ t of CO₂ in the east. Additionally, the project facilitates the transfer of 17.89×10⁶ t of CO₂ to the central region and 13.33×10⁶ t of CO₂ to the western region, thereby slowing down the rate of increase of carbon emissions in high-carbon regions. The conclusions of this study provide data support for understanding the scale and spatial transfer pattern of carbon emissions from data centers, a new type of infrastructure, in the context of the east data and west calculation strategy.