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

OPTIMIZING STOCHASTIC SUSCEPTIBILITY MODELLING FOR DEBRIS FLOW LANDSLIDES: MODEL EXPORTATION, STATISTICAL TECHNIQUES COMPARISON AND USE OF REMOTE SENSING DERIVED PREDICTORS. APPLICATIONS TO THE 2009 MESSINA EVENT.

Il presente lavoro di ricerca è stato sviluppato al fine di approfondire approcci metodologici nell'ambito della sucscettibilità da frana. In particolare, il tema centrale della ricerca è rappresentato dal tema specifico dell'esportazione spaziale di modelli di suscettibilità nell'area mediterranea. All'interno del topic specifico dell'esportazione di modelli predittivi spaziali sono state approfondite tematiche relative all'utilizzo di differenti algoritmi o di differenti sorgenti, derivate da DEM o da coperture satellitari.The present work has been developed in order to enhance current methodological approaches within the big picture of the landslide susceptibility. In particular, the central topic was the spatial exportation of landslide susceptibility models within the Mediterranean sector. Within the specific subject pertaining to the spatial exportation of predictive models, different algorithms as well as different data sources have been tested. Data sources experiments assessed the integration of DEM- and remotely sensed- derived parameters in order to improve the landslide prediction

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

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Tools for landscape-scale automated acoustic monitoring to characterize wildlife occurrence dynamics

In a world confronting climate change and rapidly shifting land uses, effective methods for monitoring natural resources are critical to support scientifically-informed management decisions. By taking audio recordings of the environment, scientists can acquire presence-absence data to characterize populations of sound-producing wildlife over time and across vast spatial scales. Remote acoustic monitoring presents new challenges, however: monitoring programs are often constrained in the total time they can record, automated detection algorithms typically produce a prohibitive number of detection mistakes, and there is no streamlined framework for moving from raw acoustic data to models of wildlife occurrence dynamics. In partnership with a proof-of-concept field study in the U.S Bureau of Land Management’s Riverside East Solar Energy Zone in southern California, this dissertation introduces a new R software package, AMMonitor, alongside a novel body of work: 1) temporally-adaptive acoustic sampling to maximize the detection probabilities of target species despite recording constraints, 2) values-driven statistical learning tools for template-based automated detection of target species, and 3) methods supporting the construction of dynamic species occurrence models from automated acoustic detection data. Unifying these methods with streamlined data management, the AMMonitor software package supports the tracking of species occurrence, colonization, and extinction patterns through time, introducing the potential to perform adaptive management at landscape scales

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term &ldquo;triggering&rdquo; precipitation, medium-term &ldquo;preparatory&rdquo; precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.</p

3rd Probabilistic Workshop Technical Systems, Natural Hazards

Modern engineering structures should ensure an economic design, construction and operation of structures in compliance with the required safety for persons and the environment. In order to achieve this aim, all contingencies and associated consequences that may possibly occur throughout the life cycle of the considered structure have to be taken into account. Today, the development is often based on decision theory, methods of structural reliability and the modeling of consequences. Failure consequences are one of the significant issues that determine optimal structural reliability. In particular, consequences associated with the failure of structures are of interest, as they may lead to significant indirect consequences, also called follow-up consequences. However, apart from determining safety levels based on failure consequences, it is also crucially important to have effective models for stress forces and maintenance planning ... (aus dem Vorwort

Impact of Climate Variability on the Frequency and Severity of Ecological Disturbances in Great Basin Bristlecone Pine Sky Island Ecosystems

Great Basin bristlecone pine (GBBP) (Pinus longaevaBailey) is one of the longest-lived organisms on Earth, and is one of the most highly fragmented high elevation conifer species. Throughout the Great Basin of the Intermountain West, GBBP are being impacted by changing disturbance regimes, invasive species, and climate change. To better understand the effects of climate variability and ecological disturbances in GBBP systems, three studies were designed and implemented. The first characterized the distribution of forest fuel in stands of GBBP and predicted how fuels may change under future climate scenarios. Using the Forest Inventory Analysis (FIA) plot variables of tree species, height, diameter at breast height (DBH), canopy base height (CBH), coarse (CWD) and fine (FWD) woody debris across elevational gradients, this study examined the effects of changes to fuel loading on predicted changes in fire behavior and severity. All classes of FWD decreased with elevation, and only 1000-hr fuels remained constant across elevational transects. This, combined with lower CBH and foliar moisture and increasing temperatures due to climate change, suggested increased fire potential at the GBBP treeline. The second study examined the role of volatile organic compounds (VOCs) and tree chemistry and their response to the environment. VOCs and within needle chemistry were collected and analyzed along elevational gradients near the northern and southern limits of GBBP. Random Forest analysis distinguished elevation using VOCs, with 83% accuracy, and identified the compounds most important for classification. Ordination revealed that temperature, heat load index, and relative humidity were each significantly correlated with VOCs. Within-needle chemistry provided less predictive value in classifying elevation (68% accuracy) and was correlated only with heat load index. These findings suggest that GBBP VOCs are highly sensitive to the environment. The final study explored the role of VOCs in host selection of mountain pine beetle (MPB). Mountain pine beetles oriented toward VOCs from host limber pine (Pinus flexilis James) and away from VOCs of non-host GBBP using a Y-tube olfactometer. When presented with VOCs of both trees, females overwhelmingly chose limber pine over GBBP. While there were only a few notable differences in VOCs collected from co-occurring GBBP and limber pine, 3-carene and D-limonene were produced in greater amounts by limber pine. There was no evidence that 3-carene is important for beetles when selecting trees, however, addition of D-limonene to GBBP VOCs disrupted the ability of beetles to distinguish between tree species. Climate change will impact how forests are managed and this research could provide insight into the mechanisms underlying the incredible longevity of this iconic tree species