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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.

What the dominant influencing factors are and how changes have occurred over different historical periods in Yellow River Basin are unclear. This study aims to retrieve the annual soil erosion modulus of the Yellow River Basin and utilizes the LMDI model and Geodetector model to identify the dominant driving factors of soil erosion evolution in different historical periods and regions at a pixel scale and landscape scale. Results showed that: (1) The serious (intensive, extreme intensive, and severe) erosion areas were mainly distributed in the Loess Plateau, while the slight and mild erosion areas were mainly distributed in the Hetao Plain, Ordos Plateau, and Guanzhong Plain; (2) During 1981-2019a, the gravity center of soil erosion moved from southwest to northeast, which indicated that the increment and increasing rate of soil erosion in the northeast of the Yellow River Basin were higher than that of the southwestern areas; (3) Increased soil erosion due to the R factor (ISER) was the most widely distributed, accounting for 79.21% of the area where soil erosion increased, while decreased soil erosion due to the C factor and R factor was the most widely distributed, accounting for 49.15% of the area where soil erosion decreased; (4) Before 2000a, precipitation, vegetation, and soil types were the dominant factors affecting the evolution pattern of soil erosion. Through the efforts of human intervention, such as returning farmland to forest or grassland, the explanatory power of land use change in the evolution pattern of soil erosion has increased.

The spatial interpolation of meteorological data have important applications in ecological environment monitoring, such as soil erosion, ecological vulnerability evaluation. However, there are significant differences in the interpolation accuracy of climatic elements under different topographic and geomorphic conditions. Based on data of 810 meteorological stations across the country, five typical interpolation methods, namely ordinary Kriging method, inverse distance weight method, spline function method, natural neighborhood method and trend surface method, were utilized in this paper to analyze and compare the interpolation accuracy of five climate factors, namely temperature, precipitation, accumulated temperature(>10°),wind speed and sunshine hours, under different topographic and geomorphological conditions. The results showed that: (1) Ordinary kriging method of temperature had better applicability in plain, hill and medium-large undulating mountain areas for temperature while the inverse distance weight method and spline function method had higher interpolation accuracy in the platform area and the small undulating mountain area, respectively. (2) The optimal interpolation of the precipitation in plain, platform and medium-large undulating mountain areas was ordinary kriging method while the inverse distance weight method and spline function method had better applicability in small undulating mountain area. (3) For accumulated temperature (>10 °C), the spline function method had higher interpolation accuracy in plain and platform areas, while ordinary kriging method had better applicability in hilly, small and medium-large undulating mountain area al (4) The optimal spatial interpolation of wind speed in plain and hilly areas was the inverse distance weight method. The natural neighborhood method and the spline function method had the best applicability in plateau, medium-large undulating mountainous areas and small undulating mountain areas, respectively. (5) For sunshine hours, the optimal spatial interpolations in plain and hilly areas were natural neighborhood method and spline function method, respectively, while the ordinary kriging method and the inverse distance weighting method had better applicability in platform, large undulating mountainous areas and small undulating mountainous areas, respectively .

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 Hengduan mountain area, located in the upper reaches of the Yangtze River of China, is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole. This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity (NPP) from 2000 to 2015, which were determined by the use of MOD17A3 NPP products. Additionally, the dominant driving factors of the spatial—temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000–2015. The results revealed that: (1) From 2000 to 2015, there was an increasing trend of vegetation NPP in the Hengduan mountain area. Throughout the whole study region, the vegetation NPP with a mean value of 611.37 gC·m−2·a−1 indicated a decreasing trend from southeast to northwest in terms of spatial distribution. (2) The gravity centers of vegetation NPP in 2000–2015 were mainly concentrated in Zhongdian County. During the study period, the gravity center of vegetation NPP moved northward, which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas. (3) The vegetation NPP showed a moderately positive correlation with temperature, accumulated temperature (>10°C), and sunshine, while there was an overall negative relationship between NPP and precipitation. (4) The dominant factors and interactive dominant factors changed in different sub-regions over different segments of the study period. The dominant factors of most sub-regions in Hengduan mountain were natural factors, and the climate change factors played an increasingly greater role over the 16 years of the study period.

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.

Due to the combined effects of global warming and human activities, the ecological environment of the Yellow River source area has undergone profound changes and desertification has become increasingly prominent. In this study, an optimal desertification monitoring index based on feature space was proposed for the Yellow River source area, and constructed using Landsat images. Then, the spatial and temporal variation of desertification in the Yellow River source area and its driving mechanism were studied using Geodetector. The main conclusions are as follows: (1) The newly proposed feature space-based desertification monitoring index has good applicability in the study area. The best inversion accuracy of the point-to-point Albedo-NDVI feature space model was 88.4%. (2) Desertification in the eastern and southern regions of the Yellow River source area has a tendency to increase, while the desertification situation in the central region is relatively stable. (3) From 1995 to 2015, there was a significant improvement in desertification in the study area, as evidenced by a decrease in desertification intensity. (4) As the intensity of human disturbance increases, the influence of natural factors on desertification gradually diminishes. The interaction of natural and anthropogenic factors has greater explanatory power for desertification than that of individual natural or anthropogenic factors. The research results can be used as a reference for decision-making on desertification control in the Three-River Source Region.

There were dramatic changes in the ecological vulnerability (EV) of the China-Pakistan Economic Corridor (CPEC) region due to the effects of climate change and human activity. Obtaining field observation and statistical data for the evaluation of the EV of the CPEC region is difficult due to its transnational status. This study proposes a novel remote sensing ecological vulnerability index (RSEVI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS), STRM3, DMSP-OLS, and NPP-VIIRS products. The RSEVI index was applied to the CPEC region to investigate the spatiotemporal changes in EV and the influencing factors during 2000–2019. The results showed that: (1) the large-scale application of the RSEVI index showed good applicability to the CPEC region, with a precision of 89.75%; (2) the average RSEVI value for the CPEC region was 0.83, thereby falling into a category of “intensive vulnerability”; (3) a stable trend in RSEVI was observed for the entire CPEC region during the study period, except for the Indus Basin where there was a significant change; (4) the increasing rate of the RSEVI in the northeastern parts exceeded that of other parts during 2000–2019; (5) precipitation, temperature, and vegetation coverage showed negative relationships with RSEVI, whereas RSEVI showed a positive relationship with slope. The RSEVI results indicated that ice and bare land were the most vulnerable, whereas cropland was the least vulnerable; (6) there were differences in the dominant factor and dominant interactive factor among different sub-regions. The results of this study could provide important decision support for the protection of the ecological environment and for economic development.

Gao, L.-L.; Jiang, Z.-G and Wei C.-X., 2019. Fatigue analysis of rubber powder and fiber concrete in coastal areas. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Re...