Abstract
The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. The statistical analyses demonstrated that the cumulative frequency distribution of the annual landslide number was empirically described by a power-law regression. Most landslides occurred from July to October. The relationship between landslide time interval and their cumulative frequency could be fitted using an exponential regression. The cumulative frequency of the landslide number could be approximated using the power-law function. Moreover, many landslides caused fatalities, and the number of fatalities was related to the number of landslides each month. Moreover, the cumulative frequency was significantly correlated with the number of fatalities and exhibited a power-law relationship. Furthermore, obvious differences were observed in the type and density of landslides between the Loess Plateau and the Qinba Mountains. Most landslides were close to stream channels and faults, and were concentrated in cropland at elevations from 600–900 m and on slope gradients from 30–40°. In addition, the landslide frequency increased as the annual rainfall levels increased over a large spatial scale, and the monthly distribution of landslides presented a significant association with the precipitation level. This study provides a powerful method for understanding the spatiotemporal distribution of landslides via a rare landslide catalog, which is important for engineering design and planning and risk management.
Highlights
Landslides are frequent geohazards that cause significant casualties and economic losses every year all over the world [1,2,3,4,5,6,7,8]
The results showed that the cumulative frequency decreased with the increase of the annual landslide number, and the relation could be described using a simple power-law regression
We found that most time intervals between landslide events were very short, and the relation between the cumulative frequency and landslide time interval could be approximated using an exponential regression
Summary
Landslides are frequent geohazards that cause significant casualties and economic losses every year all over the world [1,2,3,4,5,6,7,8]. Understanding the spatiotemporal distribution of landslide occurrence plays an essential role in assessing and modeling landslide hazards [13,14,15,16], estimating erosion and denudation rates [17,18], establishing effective landslide early warning systems [19,20], and providing clear knowledge of historical environmental changes [21,22]. Compared with other natural hazards, landslides are difficult to identify at a large scale due to the lack of consistent reports [28,29]. This gap must be filled by scientific and technical efforts [18,30]. It is important to accurately understand landslide risk by developing and analyzing a historical landslide database [31]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
