It is conducive to the sustainable development of human beings in karst regions to research the mechanism of karst rocky desertification(KRD) expansion. Whether the large-scale KRD in southwestern China is caused by climate change or human activities is still controversial. In this study, the evolution of the KRD in southwestern China over the past 2000 years was reconstructed through the high-precision δ~(13) C record of stalagmites from Shijiangjun(SJJ) Cave, Guizhou Province, China. Theδ~(13) C of the stalagmites from SJJ Cave exhibited heavy values from the Medieval Warm Period(MWP) to the Little Ice Age(LIA). Furthermore, the δ~(13) C records of other stalagmites and tufa from southwestern China also showed the same significant heavy trend. Because the stalagmite δ~(13) C could record the change of ecological environment, it indicated that the consistent change of the stalagmites δ~(13) C may record the process of KRD expansion in the karst regions of southwestern China. During the MWP, the stronger Asian summer monsoon and the northward movement of the rain belt led to a dry period in southwestern China and a wet period in northern China. In contrast, it was wet in southwestern China and dry in northern China during the LIA.In addition, after the Jing-Kang event(JK event, AD1127) occurred at the end of the Northern Song dynasty, the political and economic center of China migrated to southern China for the first time, which changed the population distribution pattern of larger population in the north and smaller population in the south. Therefore, the expansion of KRD in southwestern China was exacerbated in the MWP due to the change of climate in southwestern China, the migration of a large number of people, wars, the large-scale reclamation of arable land, and the cultivation of large areas of crops. 相似文献
Evaluating the benefits of sediment and runoff reduction in different vegetation types is essential for studying the mechanisms of soil and water conservation on the Loess Plateau.The experiment was conducted in shrub-grass plots with nine levels of mixed vegetation coverage from 0%to 70%,three slopes(10,15,and 20)and two rainfall intensities(1.0 and 2.5 mm/min).The results showed that the vegetation coverage and slope gradient significantly affect runoff and sediment yield.Shrub-grass vegetation coverage had a significant effect on the runoff start-time,runoff flow velocity,runoff rate,and soil erosion rate on hillslopes.Mixed vegetation coverage could effectively delay the runoff starttime and decrease the runoff flow velocity.However,the effects of the slope gradient on runoff and sediment yield are opposite to those of vegetation coverage.Shrub-grass vegetation coverage could effectively increase runoff and sediment yield reduction benefits,while their benefits were affected by the rainfall intensity.At the 1.0 mm/min rainfall intensity,the reduction in the sediment production rate was greater than that under the 2.5 mm/min intensity.However,when the shrub-grass vegetation coverage exceeded 42%,the runoff reduction benefit was more obvious at higher rainfall intensities.The cumulative sediment yield increased with increasing cumulative runoff,and the rate of increase in the cumulative runoff was greater than that of the cumulative sediment yield with increasing of shrub-grass vegetation coverage.Moreover,there was a power function relationship between cumulative sediment yield and cumulative runoff yield(P<0.05).Our paper is expected to provide a good reference on the ecological environment and vegetation construction on the Loess Plateau. 相似文献
AbstractThe rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect. 相似文献
Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities.