International Journal of Geosciences
Vol.4 No.3(2013), Article ID:31706,4 pages DOI:10.4236/ijg.2013.43053

An Early Warning System for Regional Rain-Induced Landslide Hazard

Shengshan Hou, Ang Li, Bin Han, Pinggen Zhou

China Institute of Geo-Environment Monitoring, Beijing, China

Email: houss@mail.cigem.gov.cn

Copyright © 2013 Shengshan Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received December 30, 2012; revised February 8, 2013; accepted May 7, 2013

Keywords: Landslide; Early-Warning; Monitoring; WebGIS; Yaan

ABSTRACT

Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is located in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the relationship between the factors and landslide. Using the bi-variate statistics method, we calculated the contribution to landslide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.

1. Introduction

Rain-induced landslides often occur in almost all alpine regions, and always resulted in serious human life and economic losses. To mitigate the loss of landslide, there is only two measures: one is stabiles the landslides and potential landslides, or move the people and infrastructures out of landslide-influence area, the other measure is strengthen the awareness of people live in danger regions, so that when rainfall-induced landslides do occur, people know how to save their lives very well, thus the potential landslide loss was reduced. So, to tell in what conditions the elements will be at risk is an efficient way to mitigate the landslide hazard [1].

As a natural phenomenon, the occurrence of landslide is controlled basically by geological conditions, morphological conditions, land cover conditions, etc. The landslide susceptibility analysis can give a clue of the dangerous degree site by site. A lot of methods of regional landslide susceptibility evaluations have been studied and developed [2,3]. The triggering factored was studied by the 66 landslide samples already occurred with detailed investigation, and then the threshold of landslide-trigger values was suggested.

So, an automatic landslide early warning system based on real-time rainfall monitor data was put forward in Yucheng District, Sichuan, China.

The study area is located in the west of the Sichuan Basin, right in the intersection of Sichuan Basin and the Tibetan Plateau. The coordinate is 102˚51' - 103˚12'E, 29˚40' - 30˚14'N. The area is about 1067 km2, 91% of which is mountainous region. Its population is about 330 thousand. The area is covered with the sedimentary rocks of Mesozoic and Cenozic ages, sandstone inter-bedded with mudstone, which is easy to slide. Yucheng District has a sub-tropical monsoonal climate, and it is titled “sky funnel” because of the high precipitation. The annual precipitation is about 1750 mm. Most of the rain falls during May to October, especially in July and August.

Landslide is one of the most serious hazard endangering the lives and properties in Yaan. Landslide and debris flow have caused 185 deaths, and the direct economic loss is over 2 million CNY.

2. Landslide Survey and Susceptibility Assessment

We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we made the regional landslide susceptibility assessment.

Based on the evolution law of the landslides, we select ten factors to study the relationship between the factors and landslide (Table 1).

Using the bi-variate statistics method [4,5], we calculated the contribution to landslide of every factor, also shown in Table 1. Then the contributions were summed up, and classified into four categories. So, we obtained the regional landslide susceptibility map. The accuracy of this method was shown in Figure 1. The landslide susceptibility map is reasonable and sound.

3. Rainfall-Induced Landslide Warning System

We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges in Yucheng District in 2005. From then on, the real-time rainfall is monitored. Using the landslide survey data, we studied the rainfall influencing of the regional landslides (Figures 2 and 3, Table 2). The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories [6].

Table 1. The 10 factors used in landslide susceptibility assessment.

Figure 1. Evaluation of the landslide susceptibility assessment.

Figure 2. The relationship between 3-day rainfall and landslide.

Figure 3. The relationship between 1-day rainfall and landslide.

Table 2. Rainfall trigging category to landslide.

We present a method to calculate the landslide dangerous degree using the susceptibility and triggering category, shown in Table 3. The forecasting dangerous level is also classified into four categories: most serious (FOUR); serious (THREE); medium (TWO) and pretty safe (ONE).

The GIS-based landslide warning software was developed and put into use. The early warning system has been carried on by the collaboration of Bureau of Land and Resources and Meteorological Bureau of local government during the rainy season since 2005 [7]. The Meteorological Bureau provides the weather forecasting information, the professional staff of Bureau of Land and Resources calculate the landslide dangerous degree, and then put forward to the people in potential hazard zone. These measures greatly strengthened the awareness of the coming hazard.

4. Conclusion

The landslide warning system research work has been carried on for about 10 years. We successfully set up the warning system, and the result is pretty good. The landslide warning system is built on the basis of the following work: 1) Detailed landslide mapping is carried on by professionals. The distribution, characteristic, controlling factor is studied. The landslide susceptibility map is produced qualitatively; 2) Triggering factors are studied and properly selected in the usage of early warning system; 3) Rainfall monitoring network is constructed. The coverage of each rain gauge is about 10 square kilometers; 4) GIS-based landslide warning software is developed. The implantation of the landslide warning system greatly

Table 3. Calculation table of landslide dangerous category.

strengthened the hazard mitigation work of local government.

5. Acknowledgements

This research was financially supported by the Ministry of Industry and Information Technology of People’s Republic of China (Grant No. 2010ZX03006-007), and China Geological Survey (Grant No. 1212011140016). The authors are grateful to an anonymous referee who greatly improved the manuscript.

REFERENCES

  1. C. Li, T. Ma, X. Zhu and W. Li, “The Power-Law Relationship between Landslide Occurrence and Rainfall Level,” Geomorphology, Vol. 130, No. 3-4, 2011, pp. 221- 229. doi:10.1016/j.geomorph.2011.03.018
  2. L. Cascini, S. Cuomo and M. D. Sala, “Spatial and Temporal Occurrence of Rainfall-Induced Shallow Landslides of Flow Type: A Case of Sarno-Quindici, Italy,” Geomorphology, Vol. 126, No. 1-2, 2011, pp. 148-158. doi:10.1016/j.geomorph.2010.10.038
  3. R. Fell, J. Corominas, C. Bonnard, et al., “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning,” Engineering Geology, Vol. 102, No. 3-4, 2008, pp. 85-98. doi:10.1016/j.enggeo.2008.03.022
  4. S. S. Hou, A. Li, P. G. Zhou and R. Wang, “Regional Landslide Susceptibility Assessment Using Bivariate Statistic Method—A Case Study of Yucheng District, Yaan City, Sichuan Province,” Hydrology and Engineering Geology, Vol. 33, No. 1, 2006, pp. 1-4.
  5. S. Lee, U. Chwae and K. Min, “Landslide Susceptibility Mapping by Correlation between Topography and Geological Structure: The Janghung Area, Korea,” Geomorphology, Vol. 46, No. 3-4, 2002, pp. 149-162. doi:10.1016/S0169-555X(02)00057-0
  6. A. Li, S. S. Hou and P. G. Zhou, “Rainfall Triggering Study of Landslide in Yucheng, Yaan, China,” The Chinese Journal of Geological Hazard and Control, Vol. 18, No. 1, 2007, pp. 15-17.
  7. P. G. Zhou, J. G. Mao, S. S. Hou, A. Li and S. D. Liu, “The Design and Approach of the Geohazard Warning System Based on WEBGIS,” Earth Science Frontiers, Vol. 14, No. 6, 2007, pp. 38-42.