Rapid post-earthquake modelling of coseismic landslide magnitude and distribution for emergency response decision support

Abstract

Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. This study presents a new, rapid method for assessing the total distribution and relative magnitude of coseismic landsliding in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in GIS to assess which predisposing factors have influenced landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the relative magnitude (number density) of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high fidelity mapping of landslides following an earthquake is not necessary. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the model. This method is therefore suited to conditions in which imagery is affected by partial cloud cover, or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake, and may therefore inform emergency operations more effectively compared to current practice.

Presentation and Analysis of a Worldwide Database of Earthquake‐Induced Landslide Inventories

“…Even though landslide susceptibility assessment using different statistical analyses has become a common approach, the use of seismic indicators in these analyses to estimate EQIL hazard is still rare (Budimir et al, 2014;Carro et al, 2003;Gallen et al, 2016;Lee, 2014;Marzorati et al, 2002;Nowicki et al, 2014;Robinson et al, 2017). The generation of EQIL hazard maps for new or scenario events is complicated as each earthquake has specific characteristics, and existing EQIL inventories only reflect the characteristics of a single earthquake.…”

Rapid assessment of earthquake-induced landslides

“…Following the Gorkha earthquake, a new discussion began regarding inventory maps as an input layer of modeling studies. Robinson et al (2017) argued that the proposed models suffer from inadequate training data that are not representative of the site of prediction. They suggest using only a part of landslides mapped in first few hours or days immediately after an earthquake as the training set.…”

“…Creating an EQIL inventory is a time-consuming process (e.g., Wasowski et al, 2011) despite advances in mapping techniques. For example, the EQIL inventory for the 2008 Wenchuan earthquake required more than one year of image interpretation work (Xu et al, 2014b) and the one for the 2015 Gorkha, Nepal earthquake required about one month to create despite being part of one of the fastest global rapid hazard response campaigns ever undertaken (Kargel et al, 2016;Robinson et al, 2017). Thus, the time required to create an EQIL inventory is too long to be useful for search and rescue operations (Robinson et al, 2017).…”

“…For example, the EQIL inventory for the 2008 Wenchuan earthquake required more than one year of image interpretation work (Xu et al, 2014b) and the one for the 2015 Gorkha, Nepal earthquake required about one month to create despite being part of one of the fastest global rapid hazard response campaigns ever undertaken (Kargel et al, 2016;Robinson et al, 2017). Thus, the time required to create an EQIL inventory is too long to be useful for search and rescue operations (Robinson et al, 2017). Given these time constraints on producing an EQIL inventory, an alternative approach that predicts EQIL distributions in near real-time is needed to provide critical information regarding potential blockages of roads, streams and rivers, and other critical lifelines.…”

“…EQIL inventories are the primary data source to extend our knowledge of the relationship between earthquakes and the landslides they can trigger (e.g., Tanyaş et al, 2017). Using an EQIL inventory, we can assess the distribution of landslides and better evaluate the total earthquake impacts considering this secondary seismic hazard (e.g., Robinson et al, 2017).…”