Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal

Coseismic landslides represent the first stage of a broader cascading sequence of geohazards associated with high-magnitude continental earthquakes, with the subsequent remobilisation of coseismic landslide debris posing a long-term post-seismic legacy in mountain regions. Here, we quantify the controls on the hazard posed by landslide remobilisation and debris runout, and compare the overlap between areas at risk of runout and the pattern of post-seismic landslides and debris flows that actually occurred. Focusing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we show that the extent of the area that could be affected by debris runout remained elevated above coseismic levels 4.5 years after the event. While 150 km2 (0.6% of the study area) was directly impacted by landslides in the earthquake, an additional 614 km2 (2.5%) was left at risk from debris runout, increasing to 777 km2 (3.2%) after the 2019 monsoon. We evaluate how this area evolved by comparing modelled predictions of runout from coseismic landslides to multi-temporal post-seismic landslide inventories, and find that 14% (85 km2) of the total modelled potential runout area experienced landslide activity within 4.5 years after the earthquake. This value increases to 32% when modelled runout probability is thresholded, equivalent to 10 km2 of realised runout from a remaining modelled area of 32 km2. Although the proportion of the modelled runout area from coseismic landslides that remains a hazard has decreased through time, the overall runout susceptibility for the study area remains high. This indicates that runout potential is changing both spatially and temporally as a result of changes to the landslide distribution after the earthquake. These findings are particularly important for understanding evolving patterns of cascading hazards following large earthquakes, which is crucial for guiding decision-making associated with post-seismic recovery and reconstruction

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

Soil-Water Conservation, Erosion, and Landslide

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Geovisualization

Geovisualization involves the depiction of spatial data in an attempt to facilitate the interpretation of observational and simulated datasets through which Earth's surface and solid Earth processes may be understood. Numerous techniques can be applied to imagery, digital elevation models, and other geographic information system data layers to explore for patterns and depict landscape characteristics. Given the rapid proliferation of remotely sensed data and high-resolution digital elevation models, the focus is on the visualization of satellite imagery and terrain morphology, where manual human interpretation plays a fundamental role in the study of geomorphic processes and the mapping of landforms. A treatment of some techniques is provided that can be used to enhance satellite imagery and the visualization of the topography to improve landform identification as part of geomorphological mapping. Visual interaction with spatial data is an important part of exploring and understanding geomorphological datasets, and a variety of methods exist ranging across simple overlay, panning and zooming, 2.5D, 3D, and temporal analyses. Specific visualization outputs are also covered that focus on static and interactive methods of dissemination. Geomorphological mapping legends and the cartographic principles for map design are discussed, followed by details of dynamic web-based mapping systems that allow for greater immersive use by end users and the effective dissemination of data

Landslides in the Nepal Himalaya: a quantitative assessment of spatiotemporal characteristics, susceptibility, and landscape preconditioning

Mountainous regions such as the Himalaya are severely affected by landslides. Strategies to manage landslide hazard often rely on statistical landslide susceptibility models that forecast the locations of future landslides. Susceptibility models are typically space and/or time independent. However, recent observations suggest that several processes (i.e., earthquake preconditioning, path dependency) are capable of imparting transient controls on landslide occurrence that invalidate the assumption of time-independence. Consequently, it is vital to improve understanding of processes that influence landsliding through space and time, and to assess how these affect typical landslide susceptibility approaches. Therefore, this thesis aims to quantify the spatiotemporal characteristics, distributions, and preconditioning of monsoon-triggered landslides in the Nepal Himalaya, and how these factors influence regression-based susceptibility modelling. This aim is achieved by developing a 30-year inventory of ~12,900 monsoon-triggered landslides, which is used to: 1) assess the overall characteristics and distributions of monsoon-triggered landsides; 2) systematically quantify spatiotemporal variations in landslide processes and distributions, and how this influences landslide susceptibility modelling; 3) determine empirical relationships between monsoon-strength and landsliding to determine how earthquake preconditioning and cloud-outburst storms transiently perturb landslide rates in Nepal, and 4) recommend a best-practice framework for modelling landslide susceptibility in regions impacted by spatiotemporally varying landslide processes. Spatiotemporal variations in landslide occurrence are found to relate to permafrost degradation, path dependency, earthquake-preconditioning, and the occurrences of storms. Such variation significantly compromises the applicability and accuracy of regression-based susceptibility models, with models developed from specific regions or time slices incapable of consistently predicting other landslide data. However, susceptibility models developed using 6–8 years of landslide data offered consistently reliable prediction. Overall, it is recommended that typical space-time independent regression-based susceptibility models are avoided in dynamic mountainous regions unless developed with 6-8 years of multi-temporal landslide data and/or specific knowledge of any spatiotemporally varying landslide processes

Modèle intégré basé sur l'apprentissage automatique pour l’estimation de la production des cultures au Mali

Au regard de la croissance actuelle de la population mondiale, qui atteindra selon l’Organisation des Nations Unies (ONU) environ 10 milliards de personnes d'ici 2050, il faudra une augmentation de 70% de la production alimentaire et de la superficie allouée aux cultures. Les pays de l’Afrique subsaharienne devront donc augmenter leur production alimentaire pour satisfaire la demande croissante en ce qui les concerne. Les pressions démographiques et le réchauffement climatique perturbent les moyens de subsistance et la sécurité alimentaire des populations. De plus, les systèmes agricoles des petits exploitants des pays africains font face à des problèmes de fertilité des sols. Cela est dû à de nombreux facteurs tels que : la taille des champs, l’hétérogénéité des pratiques de gestion, et la fragmentation des paysages qui en résulte et la présence généralisée d’arbres dans les champs. À cet égard, il est primordial de trouver une solution qui fournit des informations sur la productivité des cultures en milieu semi-aride. Ainsi, ce projet propose une approche basée sur la méthode d’échantillonnage aréolaire à partir des données satellitaires et a pour but de contribuer à l’amélioration de l’estimation de la production agricole en milieu semiaride. Nous avons utilisé Google Earth Engine (GEE) pour faire d’abord une classification par forêt aléatoire (RF) supervisée, basée sur des pixels, pour discriminer les principaux types de cultures. Ensuite, nous avons déduit certains paramètres biophysiques, qui nous ont permis d’analyser les changements dynamiques des indices de végétation, dans le temps et dans l’espace. Enfin, la méthode d’analyse de régression logistique a été appliquée à ces données géospatiales pour estimer le rendement et la production des types de cultures par parcelle. Cette solution permet l’élaboration des cartes de production et de rendement spatialement explicites et résolues dans le temps, afin de servir d’aide en matière de prise de décision stratégique. Cela contribuera à la mise en place des scenarios futurs qui permettrons de mieux gérer la production agricole et de prévenir les pénurie

Natural and Technological Hazards in Urban Areas

Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

Decreasing Trend of Geohazards Induced by the 2008 Wenchuan Earthquake Inferred from Time Series NDVI Data

The occurrence of aftershocks and geohazards (landslides, collapses, and debris flows) decreases with time following a major earthquake. The 12 May 2008 Wenchuan Earthquake in Sichuan, China, provides the opportunity to characterize the subsequent spatiotemporal evolution of geohazards. Following the 12 May 2008 Wenchuan Earthquake, the incidence of geohazards first increased sharply, representing a “post-earthquake effect”, before starting to decrease. We compared the spatial distribution of the area affected by vegetation damage (AVD) triggered by large and medium-scale geohazards (LMG). We studied the interval prior to the 12 May 2008 Wenchuan Earthquake (2001–2007), the co-seismic period (2008), and the post-earthquake interval (2009–2016) and characterized the trend of decreasing geohazards at a macro scale. In vegetated areas, geohazards often seriously damage the vegetation, resulting in pronounced contrasts with the surrounding surface in terms of color tone, texture, morphology, and Normalized Difference Vegetation Index (NDVI) which are evident in remote sensing images (RSI). In principle, it is possible to use the strong positive correlation between AVD and geohazards to determine indirectly the resulting vegetation and to monitor its spatiotemporal evolution. In this study we attempted to characterize the process of geohazard evolution in the region affected by the 12 May 2008 Wenchuan Earthquake during 2001–2016. Our approach was to analyze the characteristics of areas with reduced vegetation coverage caused by LMG. Our principal findings are as follows: (i) Before the Wenchuan Earthquake (during 2001–2007), there was no evidence for a linear increase in the number of LMG with time; thus, the geological environment was relatively stable and the geohazards were mainly induced by rainfall events. (ii) The 12 May 2008 Wenchuan Earthquake was the main cause of a surge in geohazards in 2008, with the characteristics of seismogenic faults and strong aftershocks determining the spatial distribution of geohazards. (iii) Following the 12 May 2008 Wenchuan Earthquake (during 2009–2016) the incidence of geohazards exhibited an oscillating pattern of attenuation, with a decreasing trend of higher-grade seismic intensity. The intensity of geohazards was related to rainfall and seismogenic faults, and also to the number, magnitude and depth of new earthquakes following the 12 May 2008 Wenchuan Earthquake. Our results provide a new perspective on the temporal pattern of attenuation of seismic geohazards, with implications for disaster prevention and mitigation and ecological restoration in the areas affected by the 12 May 2008 Wenchuan Earthquake