EXCESS: The role of excess topography and peak ground acceleration on earthquake-preconditioning of landslides
Lead Research Organisation:
Plymouth University
Department Name: Sch of Geog Earth & Environ Sciences
Abstract
Landsliding is a collective term for physical processes that cause rock and soil to fail and move down slope. Landslides occur when steep slopes are destabilised by factors such as heavy rainfall, earthquakes, the removal of the base of the slope by natural processes (e.g., by rivers) or by the action of people causing material on the hillside to collapse.
Many thousands of landslides occur globally each year, killing thousands of people (e.g., from 2004 and 2016; 55,997 people died in 4,862 separate landslide events) and significantly damaging infrastructure, disrupting economies and hindering international development. Despite extensive research, the ability to forecast when and where a landslide will occur remains a fundamental scientific challenge. This is partly because scientists had thought that the rate of landsliding in a certain area is constant from year to year, and that landslides would occur in similar places in those landscapes. If this were the case, then it would be straightforward to understand where and when landslides would most likely occur, i.e., they would be 'predictable'.
Unfortunately, recent research shows that such assumptions are incorrect and in fact sudden extreme events such as storms and earthquakes will change the rates and patterns of landsliding. Being able to predict areas of elevated landslide risk thus remains an imperative frontier in hazard management. Earthquakes not only induce landslides because of ground deformation and shaking during the event, but also after an earthquake there are increased numbers of subsequent landslides over the next 1-10 years - this process has been termed "earthquake-preconditioning". This phenomenon poses an additional hazard and risk that is largely unrecognised and unquantified. Our recent ground-breaking research in Nepal suggests that there is a link between the strength of an earthquake and excess topography (areas in the landscape that are above a stable threshold slope) and subsequent landsliding. If this relationship is true in other parts of the world, we will have a highly innovative way of locating areas at higher risk.
This project will address this critical research frontier through the study of recent events and computer modelling. Firstly, we will create new landslide catalogues before, during and after recent large earthquakes for six different regions, using high-resolution (<5m) satellite imagery. These high-resolution data allow us to accurately determine the long-term average rate of landslide occurrence in each region and confidently identify the size and duration of periods of increased landsliding following an earthquake. The regions and earthquakes selected span a range of climates, tectonic settings, and earthquake sizes to enable us to investigate the influence, and determine the relative importance that different control factors (e.g., rainfall, slope, topography, earthquake size) have at a global level, ensuring that the research outputs have wide applicability. These datasets will then be used in landslide susceptibility models at regional level to form outputs that can be used in hazard and risk mitigation by national/regional governments and agencies.
Secondly, we will develop a new process-based computer model to investigate the mechanism of earthquake landscape damage and how this changes through time to cause observed patterns of landslides. Unlike empirical statistical models, process-based models explicitly simulate the drivers of landslide occurrence and can consider the impact of sudden and rapid environmental changes. The results of the model will be validated by the susceptibility maps, and the ability to model multiple earthquakes over 10s to 1000s of years will lead to new insights into the role of earthquake-induced and earthquake-preconditioned landslides in long-term landscape evolution, ultimately increasing the ability to accurately forecast the location of landslides across earthquake cycles.
Many thousands of landslides occur globally each year, killing thousands of people (e.g., from 2004 and 2016; 55,997 people died in 4,862 separate landslide events) and significantly damaging infrastructure, disrupting economies and hindering international development. Despite extensive research, the ability to forecast when and where a landslide will occur remains a fundamental scientific challenge. This is partly because scientists had thought that the rate of landsliding in a certain area is constant from year to year, and that landslides would occur in similar places in those landscapes. If this were the case, then it would be straightforward to understand where and when landslides would most likely occur, i.e., they would be 'predictable'.
Unfortunately, recent research shows that such assumptions are incorrect and in fact sudden extreme events such as storms and earthquakes will change the rates and patterns of landsliding. Being able to predict areas of elevated landslide risk thus remains an imperative frontier in hazard management. Earthquakes not only induce landslides because of ground deformation and shaking during the event, but also after an earthquake there are increased numbers of subsequent landslides over the next 1-10 years - this process has been termed "earthquake-preconditioning". This phenomenon poses an additional hazard and risk that is largely unrecognised and unquantified. Our recent ground-breaking research in Nepal suggests that there is a link between the strength of an earthquake and excess topography (areas in the landscape that are above a stable threshold slope) and subsequent landsliding. If this relationship is true in other parts of the world, we will have a highly innovative way of locating areas at higher risk.
This project will address this critical research frontier through the study of recent events and computer modelling. Firstly, we will create new landslide catalogues before, during and after recent large earthquakes for six different regions, using high-resolution (<5m) satellite imagery. These high-resolution data allow us to accurately determine the long-term average rate of landslide occurrence in each region and confidently identify the size and duration of periods of increased landsliding following an earthquake. The regions and earthquakes selected span a range of climates, tectonic settings, and earthquake sizes to enable us to investigate the influence, and determine the relative importance that different control factors (e.g., rainfall, slope, topography, earthquake size) have at a global level, ensuring that the research outputs have wide applicability. These datasets will then be used in landslide susceptibility models at regional level to form outputs that can be used in hazard and risk mitigation by national/regional governments and agencies.
Secondly, we will develop a new process-based computer model to investigate the mechanism of earthquake landscape damage and how this changes through time to cause observed patterns of landslides. Unlike empirical statistical models, process-based models explicitly simulate the drivers of landslide occurrence and can consider the impact of sudden and rapid environmental changes. The results of the model will be validated by the susceptibility maps, and the ability to model multiple earthquakes over 10s to 1000s of years will lead to new insights into the role of earthquake-induced and earthquake-preconditioned landslides in long-term landscape evolution, ultimately increasing the ability to accurately forecast the location of landslides across earthquake cycles.
