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Since the Ming Dynasty, the flood disaster has been one of the important natural disasters affecting our country. However, relatively few studies on the evolution law of flood disaster at large spatial–temporal scales have been reported. Based on the datasets of flood stations from 1470 to 2000, this paper quantitatively analyzes and discusses the spatial and temporal evolution pattrens of floods in China in the past 500 years by using regional gravity center model, wavelet analysis, Daniel index, and M–K test, and predicts the trend of flood disasters in the future. The results show that: (1) Since 1470, there has been an increasing trend of flood intensity index in China;(2) From 1470 to 2000, there were two high centers of flood disaster in 1560 and 1925, respectively, and one low center appeared in 1745; (3) Flood risk changes from easy occurrence in the eastern coastal area to uniform spatial distribution over the whole study region; (4) In the past 500 years, the graver center of flood disaster is located in Baokang County, Hubei Province, and the gravity center of flood disaster shows a trend of moving to northwest both at time scales of 50-year and 100 year; (5) The flood disaster in the studied area will still show an overall upward trend in the future.The research can provide decision support for the precise prevention and control of flood disasters in China.

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km2, including an increase of 222.63 km2 in natural wetlands and 87.27 km2 in artificial wetlands. In the conversion between wetland types, 218.73 km2 of artificial wetlands were converted into natural wetlands, and 75.18 km2 of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon’s diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management.

The Qinghai–Tibet plateau (QTP), as the “roof of the world” and the “Asian Water Tower”, provides important ecological resources for China and other Asian countries. The changing trend of ecological assets and their dominant influencing factors in different sub-regions and periods are not yet clear. In order to reveal the differences in driving mechanisms among sub-regions under the context of global changes, this study quantitatively analyzed the ecological assets and their spatial and temporal evolution patterns during 2000–2015 by using the value equivalent method. Then, the Geodetector was introduced to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. The results show the following. (1) From 2000 to 2010, the total value of ecological assets in Nakchu County was the highest, followed by Kangding County, while that in 2015 was the highest in Kangding County, followed by Nakchu County. (2) During 2000–2015, the average value of ecological assets of the Qinghai–Tibet plateau gradually decreased from east to west, while the average ecological asset value in the southern Qinghai–Tibet plateau was lower. (3) The QTP showed the highest value in 2005 with an increasing trend from 2000 to 2005, followed by a subsequent decrease from 2005 to 2015. (4) Between 2000 and 2015, the area of the stable zone (slight or no change) of ecological assets was the largest, followed by that of the decreasing zone. (5) During all the study period, the spatio-temporal evolution of ecological assets in different ecological sub-regions was mainly affected by natural factors, which were the main driving variables rather than human activities. These results could provide important support for decisions regarding the protection of ecosystems and resources in the Qinghai–Tibet plateau.

With the development of information and communication technologies, the current intelligent transportation systems (ITSs) will gradually become automated and connected, and can be treated as autonomous transportation systems (ATSs). Function, which unites cutting-edge technology with ATS services as a fundamental component of ATS operation, should be categorized into function domains to more clearly show how ATS operates. Existing ITS function domains are classified mostly based on the experience of experts or the needs of practitioners, using vague classification criteria. To ensure tractability, we aim to categorize ATS functions into function domains based on text analysis, minimizing the reliance on subjective experience. First, we introduce the Latent Dirichlet Allocation (LDA) topic model to extract text features of functions into distribution weights, reflecting the semantics of the text data. Second, based on the LDA model, we categorize ATS functions into twelve function domains by the k-means method. The comparison between the proposed function domains and the existing counterparts of other ITS framework demonstrates the effectiveness of the LDA-based classification method. This study provides a reference for text processing and function classification of ATS architecture. The proposed functions and function domains reveal the objectives in future transportation systems, which could guide urban planners or engineers to better design control strategies when facing new technologies.

Drought features prominently among natural disasters in Chinese history. Analysing the laws that govern the spatial and temporal evolution of drought could support decision-making for drought prevention and control. However, few studies have investigated the long-term evolution rules of drought events on different spatial and temporal scales. In this paper, the spatio-temporal change patterns of drought in China were analysed with geostatistical methods based on the 1470–2000a drought datasets in China, and then the possible future drought trend was predicted. Results showed that (1) the drought risk in the northern region was the highest during the past 500 years. And the drought intensity index showed an overall increasing trend with a detail pattern of weakening->strengthening-> weakening-> strengthening; (2) the drought condition in the north was severe than that in the south, but the drought trend in the south was significantly aggravated. (3) The drought gravity centres were mainly distributed in the north, but they showed a tendency to move southward. (4) From 1470 to 2000, the study area showed a significant drought enhancement, which was predicted to show an increasing trend of drought after 2000.

Under the stress of global climate change, soil wind erosion has become a major environmental issue in the Three-River Source Region (TRSR) of China. However, few large-scale studies have been conducted on soil wind erosion owing to the lack of investigational data or complex parameters. Moreover, the uncertainty and randomness in the weight determination process cannot be avoided using the traditional method. Thus, a cloud-analytic hierarchy process (cloud-AHP) model was proposed to construct a wind erosion intensity index model for the TRSR based on seven typical land surface parameters. The following results were obtained. (1) The cloud-AHP model can better eliminate the randomness and uncertainty in the weight determination process. (2) The proposed evaluation method of wind erosion intensity has better applicability in the TRSR with overall accuracy of 93%. (3) The overall wind erosion intensity in this region is moderate. The wind erosion intensity was the largest in the Yangtze River (0.55, moderate erosion) and smallest in the source region of the Lancang River (0.50, mild erosion). (4) Significant differences are observed in the influences of various vegetation types on wind erosion intensity. Bare land exhibits the highest wind erosion intensity, whereas a coniferous forest exhibits the smallest. Moreover, grassland is a key control zone of soil and water conservation because it has the largest spatial heterogeneity of internal erosion intensity. These results can provide data and technical support for preventing and controlling soil erosion and protecting the environment in the region.