Controls on the distribution of landslides triggered by the 2008 Wenchuan earthquake, Sichuan Province, China

Landsliding is the dominant mass wasting process in upland areas where the rate of river incision is higher than that of rock weathering of hillslopes. Although progressive erosional processes can provide sufficient conditions for slope failure, the majority of landslides are induced by earthquakes, rainstorms or a combination of these two. Landslides are also one of the most destructive geological processes, being the primary cause of damage and fatalities associated with severe storms and earthquakes in mountainous regions. On 12th May 2008 the magnitude 7.9 Wenchuan earthquake occurred in the Longmen Shan mountain range, on the northwest margin of the Sichuan Basin. Landsliding contributed greatly to the high death toll of over 70,000 and widespread infrastructural damage produced by the earthquake. The event offers an opportunity to both broaden the global database of seismically induced landslides and study the processes involved in earthquake-triggered landsliding, for a large continental thrust event with complex faulting mechanisms and diverse geophysical conditions. To achieve this, the following investigation builds upon recent advances in landslide remote sensing, to develop automated detection algorithms through which landslides can be accurately mapped using a range of satellite data. Using these techniques, a first order, regional landslide inventory map of slope failures triggered by the Wenchuan earthquake is produced, over an area of 12,000km2 along the main rupture zone. The production of this dataset demonstrates the application of automated classification techniques for the rapid generation of landslide data, for both geomorphological research and hazard management applications. The data is used to examine the interaction of fault rupture dynamics, topography and geology on landslide failure location, and identify key characteristics of the landslide distribution. Findings of the study demonstrate high levels of landslide occurrence along the entire mapped length of the rupture zone, and an exponential decay in landslide density with distance from the co-seismic surface ruptures. This is superimposed over a marked hanging wall effect, along with clear geological and topographic controls on landslide occurrence. Through generalised linear modelling, peak ground acceleration attenuation patterns, hillslope gradient, relief, local elevation and geology are identified as core controls on the location of landslides. The results of this research shed light on some increasingly recognised though poorly understood characteristics of seismically induced landslide distributions. The dataset produced contributes to the limited global database of earthquake-triggered landslide inventories, as well producing a widely applicable resource for further study of the Wenchuan earthquake and post-seismic landscape evolution

Probabilistic approach to provide scenarios of earthquake-induced slope failures (PARSIFAL) applied to the Alcoy Basin (South Spain)

The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake-Induced slope FAiLures) approach was applied in the basin of Alcoy (Alicante, South Spain), to provide a comprehensive scenario of earthquake-induced landslides. The basin of Alcoy is well known for several historical landslides, mainly represented by earth-slides, that involve urban settlement as well as infrastructures (i.e., roads, bridges). The PARSIFAL overcomes several limits existing in other approaches, allowing the concomitant analyses of: (i) first-time landslides (due to both rock-slope failures and shallow earth-slides) and reactivations of existing landslides; (ii) slope stability analyses of different failure mechanisms; (iii) comprehensive mapping of earthquake-induced landslide scenarios in terms of exceedance probability of critical threshold values of co-seismic displacements. Geotechnical data were used to constrain the slope stability analysis, while specific field surveys were carried out to measure jointing and strength conditions of rock masses and to inventory already existing landslides. GIS-based susceptibility analyses were performed to assess the proneness to shallow earth-slides as well as to verify kinematic compatibility to planar or wedge rock-slides and to topples. The experienced application of PARSIFAL to the Alcoy basin: (i) confirms the suitability of the approach at a municipality scale, (ii) outputs the main role of saturation in conditioning slope instabilities in this case study, (iii) demonstrates the reliability of the obtained results respect to the historical dat

Hillslope memory and spatial and temporal distributions of earthquake-induced landslides

Large earthquakes commonly trigger widespread and destructive landsliding. However, current approaches to modeling regional-scale landslide activity do not account for the temporal evolution of progressive failure in brittle hillslope materials. Progressive failure allows hillslopes to possess a memory of previous earthquakes, which has the potential to influence landslide activity in future earthquakes. The original contribution of this thesis is to address the influence of hillslope memory on spatial and temporal patterns of earthquake-triggered landslide activity, through a combination of landslide inventory analysis and numerical modeling. An understanding of spatial distributions of earthquake-triggered landslides is first established, through analysis of inventories of landslides triggered by five large (M_w > 6.7) earthquakes. The results show how current landscape conditions at the time of earthquakes influence hillslope failure probability. By identifying factors exhibiting a common influence on landslides triggered by all five earthquakes, general spatial models of landslide probability are developed, which are transferrable between different earthquakes and regions. Analysis of model performance for landslide distributions triggered by two sequential earthquakes is then used to establish where this spatial approach breaks down. Errors in the landslide distribution predicted for the second earthquake suggest that the legacy of damage to hillslope materials accrued from the first earthquake is an important control on landslide occurrence. Given the infrequent recurrence of large earthquakes and limited temporal coverage of landslide data, a new modelling approach is developed to understand how hillslope memory influences long-term patterns of earthquake-triggered landslide activity. The model integrates the site-scale evolution of hillslope progressive failure into modeling regional-scale earthquake-triggered landslide activity, in response to sequences of earthquakes. The model results suggest that the sensitivity of landscapes to landslide-triggering increases following large earthquakes, due to damage accumulated in hillslopes that do not reach the point of failure, and decays as these hillslopes fail in response to subsequent, lower-magnitude events. Prolonged elevated levels of rainfall-triggered landslide activity observed following large earthquakes appear to reflect this result. Using the model outputs, a methodology is proposed for predicting temporal variability in landslide activity using records of seismic data. The model results also suggest that, when hillslopes undergo progressive failure, relationships between seismic forcing and landslides are influenced by the magnitude-frequency distribution of earthquakes. As a result, current approaches that use these relationships to predict levels of long-term landslide hazard and erosion rates, but do not account for regional differences in earthquake distributions, may suffer from systematic under- or over-prediction. These significant implications for predicting the geomorphological and human impact of landslides highlight the need for detailed multi-temporal datasets recording the evolution of landslide activity following major earthquakes, in order to quantitatively investigate the influence of hillslope memory in real landscape settings

Earthquake‐induced landslide scenarios for seismic microzonation. Application to the Accumoli area (Rieti, Italy)

Scenarios of earthquake-induced landslides are necessary for seismic microzonation (SM) studies since they must be integrated with the mapping of instability areas. The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake‐Induced slope FAiLures) approach provides extensive analyses, over tens to thousands of square kilometers, and is designed as a fully comprehensive methodology to output expected scenarios which depend on seismic input and saturation conditions. This allows to attribute a rating, in terms of severity level, to the landslide-prone slope areas in view of future engineering studies and designs. PARSIFAL takes into account first-time rock- and earth-slides as well as re-activations of existing landslides performing slope stability analyses of different failure mechanisms. The results consist of mapping earthquake-induced landslide scenarios in terms of exceedance probability of critical threshold values of co-seismic displacements (P[D≥Dc|a(t),ay]). PARSIFAL was applied in the framework of level 3 SM studies over the municipality area of Accumoli (Rieti, Italy), strongly struck by the 2016 seismic sequence of Central Apennines. The use of the PARSIFAL was tested for the first time to screen the Susceptibility Zones (ZSFR) from the Attention Zones (ZAFR) in the category of the unstable areas, according to the guidelines by Italian Civil Protection. The results obtained were in a GIS-based mapping representing the possibility for a landslide to be induced by an earthquake (with a return period of 475 years) in three different saturation scenarios (i.e. dry, average, full). Only 41% of the landslide-prone areas in the Municipality of Accumoli are existing events, while the remaining 59% is characterized by first-time earth- or rock-slides. In dry conditions, unstable conditions or P[D≥Dc|a(t),ay]>0 were for 54% of existing landslides, 17% of first-time rock-slides and 1% of first-time earth- slides. In full saturation conditions, the findings are much more severe since unstable conditions or P[D≥Dc|a(t),ay]>0 were found for 58% of the existing landslides and for more than 80% of first-time rock- and earth-slides. Moreover, comparison of the total area of the ZAFR versus ZSFR, resulted in PARSIFAL screening reducing of 22% of the mapped ZAFR

Landsliding and sediment dynamics following the 2008 Wenchuan Earthquake in the Beichuan area of China

Extensive and widespread landsliding is a common feature in a post-earthquake mountainous environment. The intense seismic shaking of an earthquake leaves the ground destabilised and thus very susceptible to slope failure. In addition to co-seismic landsliding, many slopes retain the high potential to fail for a significant amount of time beyond seismic activity. Therefore there is a need to further develop our understanding of sediment dynamics of steep mountain environments once the shaking has stopped. The 2008 Wenchuan Earthquake in China resulted in widespread landsliding, generating large volumes of loose rock and soil. Examples from other recent large earthquakes warn of the potential secondary hazards associated with such loose material: up to 30m of river-bed aggradation was seen following the 1999 Chi-Chi Earthquake, Taiwan and it is thought that Sichuan may experience hazards of a similar magnitude. Preliminary reports and oblique photographs have displayed significant levels of sediment aggradation in certain areas and summer monsoonal rains continue to trigger further landslide failures. In addition to the associated hazards, this event has provided the opportunity to investigate sediment dynamics following a large earthquake (Mw = 7.9) in a unique area of heterogeneous lithology and wide ranging geophysical variables, which has been impacted upon by both seismic and post-seismic (rainfall) activity. This study uses a combination of desk-based and field-based research in order to examine the distribution and evolution of post-seismic landslide failures. Volume-area scaling laws are developed in order to allow erosion rates to be calculated and finally an innovative oblique photography technique is used to constrain the depth of sediment aggradation. The results demonstrate that as a source of material, the occurrence of landslides in this region is controlled by a combination of topographic, geologic and seismological factors. Resulting volume estimations and subsequent erosion rates indicate that the Wenchuan earthquake has potentially destroyed more material through erosion than it has built through surface uplift. To conclude the movement of sediment through a mountain catchment, levels of sediment aggradation show that a significant proportion of material from the hillslope is transported down into the valley bottom; this is seen to coincide with periods of intense rainfall. Overall, this research derives a unique assessment of sediment mobilisation in Sichuan in order to understand the controls on sediment remobilisation and secondary hazards. By constraining the extent of sediment sources and transfer, this research has the potential to aid the prediction of future post-earthquake hazards and landscape response in Sichuan, providing insight into the role of earthquakes in landscape evolution

Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps. <br><br> Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%