Volume expansion rates of seismic landslides and influencing factors: A case study of the 2008 Wenchuan earthquake

2019 ◽  
Vol 16 (8) ◽  
pp. 1731-1742 ◽  
Author(s):  
Si-yuan Ma ◽  
Chong Xu ◽  
Xi-wei Xu
2020 ◽  
Author(s):  
Wentao Yang ◽  
Wenwen Qi ◽  
Jian Fang

Abstract. Earthquake-triggered landslides can pose serious threats to mountain communities by remobilizing and providing loose materials for debris flows in post-seismic years. However, how long co-seismic landslides recover remains elusive due to limited observations. Using vegetation dynamics, we studied surface recovery of co-seismic landslides induced by the 2008 Wenchuan earthquake from May 2008 to July 2019 for over 20,000 km2. Landsat derived vegetation recovery on all co-seismic landslides has been assessed based on the Google Earth Engine, a cloud-based computing platform. We found most co-seismic landslides have been recovering after the earthquake but the spatial pattern is heterogeneous. The epicentre region with low elevations along the bottom of the Min River valley has the best landslide recovery, whereas many landslides on the high Longmen Mountain are poorly recovered ten years after the earthquake. These unrecovered hillslopes and gullies together with widespread loose debris indicate that surface processes on high mountains may still active and may provide source materials for debris flows, threatening communities at low elevations. To decipher possible mechanisms, we further analysed the relations between landslide recovery and twelve influencing factors, including slope, pre-seismic vegetation condition, landslide depth, landslide area, elevation, ground peak acceleration of the earthquake, aspect, slope curvatures, topographic positions, mean annual precipitation, ground cohesion strength and vegetation types. We found elevation, topographic position and pre-seismic vegetation condition are the most important factors that influence landslide recovery over all others. This work also demonstrates the efficiency of the Google Earth Engine for continuously monitoring landslide dynamics over large areas.


2020 ◽  
Author(s):  
Erin Harvey ◽  
Xuanmei Fan ◽  
Tristram Hales ◽  
Daniel Hobley ◽  
Jie Liu ◽  
...  

<p>Co-seismic landslides can mobilise up to 3 km<sup>3</sup> of loose sediment within minutes. However, the export rate of this sediment is largely unconstrained. For example, it is estimated that a decade after the 2008 Wenchuan earthquake at least 90% of the co-seismic sediment remains stored on the hillslope. Post-earthquake debris flows are the main conduit by which such hillslope debris reaches the fluvial network but the mechanics that govern the triggering and runout of such flows remain unclear and as such they appear to behave largely unpredictably.  Material grain size is a key control on both triggering and runout, since it affects both hydrological (e.g. water loss during flow; saturation state before triggering) and frictional properties of the system. However, our understanding of the role of grain size in the genesis and evolution of debris flows remains poorly explored, largely due to limitations in real field data. Existing estimates for landslide and debris flow deposit grain size distributions (GSDs) are currently limited by 1. inconsistency of applied methods; 2. the very poor sorting of these sediments; 3. inaccessibility, and 4. inherent intra-deposit variability in GSD. </p><p>Our research aims to better understand the role of grain size using an unprecedentedly detailed set of field-constrained GSDs across the post-seismic landslides and debris flows of the 2008 Wenchuan earthquake. Here we present data quantifying the grain size distribution across two debris flows using two different techniques. The two debris flows occurred in response to prolonged rainfall in August 2019 and mobilised co-seismic debris from the 2008 earthquake. In the field, we selected four to eight 1 m x 1 m x 0.5 m pits along the centre line of each debris flow at regular intervals and sieved the pit material into 8 cm, 4 cm, 2 cm and 1 cm fractions at 10 cm depth increments. Boulders >8 cm were measured and weighed individually. Smaller samples were then collected from the finer fraction (<1 cm) and sieved further in the laboratory. The coarse fraction was independently constrained from calibrated photogrammetry, and this was coupled to drone surveying to ensure the coarsest fraction (≥1 m) was correctly represented. This study presents a detailed estimate of post-earthquake debris flow GSDs with the overarching aim to better understand sediment transport and deposition from debris flows in the years following an earthquake.</p>


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