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Triggering of landslides by increase of load applied to the slope
An increase in the weight of the landslide may be due to erosion processes or rainfall. For instance, Chigira and Yokoyama (2005) showed the influence of weight increase by water for landslides in non-welded ignimbrite (Kyushu, Japan). The weathering profile shows permeability variations, with layers of low permeability allowing for rain water to build up, increasing the weight of the weathered material and decreasing the suction within the material. The latter was the final trigger for this shallow landsliding case study. Basile et al. (2003) showed that an increase of water storage resulted in overloading of the slope, which produced the same values of soil tangential stress as those of peak strength and consequently brought the slope to failure.
Triggering of landslides by rise in groundwater level and pore water pressure
Ground water level and pore pressure changes result from the infiltration from surface (rainfall, snowmelt, leaking pipe…), or exfiltration from bedrock, preferential flow and convergent flow leading to water accumulation. This in turn leads to increased pore water pressure which reduces the soil strength and increases stress (Ray and Jacobs, 2007).
Caine (1980) first proposed, after a study of 73 worldwide natural slope failures, that rainfall intensities and durations associated with shallow landsliding and debris-flow activity suggested a limiting threshold for this type of slope instability, the limit having the general form: I = 14.82 D−0.39.
where I is the intensity of rainfall (mm/hr) to be overcome for triggering landslides, and D the duration of the rainfall (hrs; Fig. 1.1). Sidle and Ochiai (2006) developed these results and proposed two empirical relationships between the amount of rainfall and landsliding, depending on the antecedent water status of the soil. The first relationship is for soil that has endured more than 20 mm of rain on the two previous days before failure: I = 12.64 D−0.39.
while the other relationship is applicable for dry soils (less than 20 mm of rain on the antecedent two days): I = 19.99 D−0.58.
Triggering of landslides by frost weathering processes
Frost weathering processes are not associated with daily clustered rockfall crisis, which makes this triggering mechanism more difficult to recognize, in comparison to rainfall or earthquake triggering. Nevertheless, the role of frost weathering as a rockfall trigger has been demonstrated in several studies. In Norway, Sandersen et al. (1996) showed that the distribution of rockfalls through the year presented two maxima, in early spring and late autumn. These two periods not only correspond to the time of the highest rate of snowmelt for the former and of the highest precipitation for the latter, they also coincide with the piezometer DL 50C water height. Discrete movement events are triggered when the piezometer level rises from EL280.9 to EL281 m. From Macfarlane (2009). periods of most frequent variations of temperature around the freezing point (Fig. 1.3). Matsuoka and Sakai (1999) analysed rockfall activity in the Hosozawa Cirque, Japan, and concluded that intense rockfall activity does not reflect precipitation events but is primarily controlled by seasonal frost weathering. Frayssines and Hantz (2006) showed that the rockfalls in the French Alps were correlated with freeze-thaw cycles, suggesting that ice jacking could be the main physical process leading to failure by causing microcrack propagation.
Hales and Roering (2005, 2007) found that the scree deposits (corresponding to frequent, small magnitude – i.e. < 100 m3 – rock-fall events) in a 80 by 40 km transect in the Southern Alps of New Zealand were mostly influenced by altitude and not by lithology, seismicity or glaciation history (Fig. 1.4). Nearly 70% of the scree deposits were found to be confined in the 1200-1600 m elevation range, just below the altitude corresponding to the frost-cracking window (-3 to -8◦ C) where ice segregation processes are most efficient.
The New Zealand 1996-2004 landslide catalogue
GNS Science Ltd has compiled a landslide catalogue (http://www.geonet.org.nz) of 2100 events for the 1996-2004 period. Data in the catalogue were obtained from a variety of sources, such as media reports, aerial surveys and ground inspection. For each landslide, the available parameters include: date, location, type of material, type of movement, trigger mechanism, size, impact and references. Time accuracy varies from one day to one year, with a mean value of 1.77±0.95 day for the whole catalogue. Landslide location accuracy ranges from a few metres for GPS-located landslides to a few kilometres for events remotely located using news reports, e.g. the distance to the nearest village. Volumes were roughly estimated from aerial photographs or from visual ground surveys, without any quantifying tool. Information on the trigger of a given landslide is provided for more than 90% of the landslides. The trigger mechanism relates to any nearby triggering event (intense rainfall, >M4 earthquake, etc), on the basis of a one to two days correlation in time with the landslide occurrence.
By imposing known location and time accuracy within two days, we extract 1943 landslides from the catalogue (Fig. 2.1a). Landslide volumes are given for 12% of the 1943 landslides, the largest event being Vmax = 24 ∗ 106 m3. 98% of the 1943 landslide volumes are sorted in three volume classes (1 to 3) corresponding to 1) V < 103 m3, 2) 103m3 < V < 106 m3 and 3) V > 106 m3. The 3 classes containing 1775, 118 and 26 landslides respectively. The material type and the landslide type are unknown for 58% and 91% of events, respectively. 65% of the landslides are reported as rainfall-triggered on 142 different days, 30% were earthquake-triggered on 4 different days, 7% had no reported trigger on 105 different days and 2% were triggered by other mechanisms (flooding, anthropic works, etc.) on 19 different days. In the first instance, we use all the 1943 landslides from the catalogue, independently of their reported trigger, for an overview of landslide behaviour in New Zealand. Then, we check how the a priori classification for triggered landslides may influence the results.
The landslide catalogue can be split into two different periods, before and after July 2001, for which the sampling methods and accuracies were different. July 2001 corresponds to the first use of more sensitive data sampling techniques which resulted in an increase of the average daily rate in the July 2001-2004 period compared to the 1996-June 2001 period.
Earthquake and weather catalogues
Shallow (less than 50 km deep) earthquake data are from the 1996-2004 GeoNet earthquake catalogue compiled and maintained by GNS Science Ltd, New Zealand. The magnitude of completude of the catalogue, Mc = 3.0 ± 0.03, is estimated using the method of Ogata and Katsura (1993). We also use an earthquake catalogue as a counterpart to the landslide catalogue. To this end, we gathered the 1943 largest earthquakes – with M = 4.3 − 7.1 – from the complete catalogue, with a daily accuracy. Choices were made to prevent any biases due to scale, number of events or time accuracy effects when comparing the earthquake data to landslide data. Figure 2.1b gives the locations of the 1943 earthquakes.
The rainfall and temperature data for the 1996 to 2004 period are from the National Institute of Water and Atmospheric Research (NIWA), in New Zealand. They consist of i) New Zealand averaged rainfall depth per month ii) New Zealand averaged number of rain days per month iii) New Zealand averaged minimum and maximum temperatures per month. Since the data are New Zealand-averaged, only the global trend will be analysed. 1971 to 2000, respectively. The North Island is globally warmer than the more mountainous South Island and its mean annual rainfall presents smaller minima and maxima and less spatial variability than the South Island. The South Island presents two different rainfall regimes: the West Coast is extremely wet with mean rainfall from 4,000 to 10,000 mm/year whereas the East Coast is drier with mean rainfall ranging from less than 500 to 1,250 mm/year. These different characteristics emphasise the use of two separate sub-catalogues for the North and the South Island and the need to pay special attention to the South Island when studying the climate – landslide interactions.
Table of contents :
1 Landslide triggering
1.1 Triggering of landslides by increase of slope angle
1.2 Triggering of landslides by increase of load applied to the slope
1.3 Triggering of landslides by rise in groundwater level and pore water pressure
1.4 Triggering of landslides by frost weathering processes
1.5 Triggering of landslides by earthquake loading
2 Interaction among landslides, seismicity and climate in New Zealand
2.1 Abstract
2.2 Introduction
2.3 Data and methods
2.3.1 The New Zealand 1996-2004 landslide catalogue
2.3.2 Methods
2.3.3 Landslide catalogue completeness
2.3.4 Earthquake and weather catalogues
2.4 Correlation between landslide occurrences
2.4.1 Landslide daily patterns
2.4.2 Distributions of landslide waiting (inter-event) times
2.4.3 Distribution of landslide daily rate
2.5 Possible processes for landslide clustering in time
2.5.1 Earthquake-landslide interaction
2.5.2 Landslide-landslide interaction
2.5.3 Climate-landslide interaction
2.5.4 Discussion
2.5.5 Conclusion
3 Characterization and comparison of landslide dynamics in different tectonic and climatic settings
3.1 Abstract
3.2 Introduction
3.3 Databases
3.3.1 Landslide databases
3.3.2 Earthquake databases and tectonic settings
3.3.3 Weather databases and climatic settings
3.4 Correlation between landslide occurrences
3.4.1 Evidences for complex inter-relationship between landslide patterns, and rainfall and seismicity forcings
3.4.2 Landslide daily patterns
3.4.3 Distribution of landslide times and waiting times
3.4.4 Distribution of landslide daily rates
3.4.5 Distribution of landslide inter-event distances
3.5 Analysis of the possible processes for landslide triggering
3.5.1 Landslide – landslide interactions
3.5.2 Earthquake – landslide interactions
3.5.3 Climate – landslide interactions
3.6 Discussion and Conclusions
4 Unified picture for aftershocks and earthquake-triggered landslides
4.1 Introduction
4.2 Data and Methods
4.3 Characteristics of the 5 sequences
4.4 Results
4.4.1 Landslide and aftershock distance distributions
4.4.2 Ground motions and triggered landslide and aftershock space distributions
4.5 Discussion
Conclusions and perspectives
Appendices
A Frequency-volume distributions
B Local versus USGS earthquake catalogues
C Aftershock and landslide discrete distance distributions
D Construction of a landslide database
