4.7 Review

The future of landslides' past-a framework for assessing consecutive landsliding systems

期刊

LANDSLIDES
卷 17, 期 7, 页码 1519-1528

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-020-01405-7

关键词

Landslide; Path dependence; Dynamic susceptibility assessment; Local activation; Remote activation

向作者/读者索取更多资源

Landslides often happen where they have already occurred in the past. The potential of landslides to reduce or enhance conditions for further landsliding has long been recognized and has often been reported, but the mechanisms and spatial and temporal scales of these processes have previously received little specific attention. Despite a preponderance of qualitative and anecdotal evidence, analysis has been limited. As a result, there is little consensus on the meaning of terms such as landslide repetition, recurrence, and reactivation. This source of confusion is evident when such terms are also used to describe systems where landsliding is prevalent but unrelated to landslide history. Recent findings, partly based on a rare multi-temporal landslide inventory for an area in Italy, show that the impacts of earlier landslides affect a substantial fraction of landslides, that landslides following earlier landslides differ from those that do not, and that accounting for the effect of previous landslides can improve susceptibility assessments. These findings await confirmation in other landslide-prone landscapes but show that consecutive landsliding deserves more attention, which requires consistent terminology. No such terminology is presently available, and we therefore propose it in this manuscript. We use the term uncorrelated landsliding to describe situations where landslides are common, but where a correlation with environmental variables such as terrain steepness is not implied. We propose correlated landsliding to describe situations where landslides are common and correlations with environmental variables exist, and path-dependent landsliding to describe situations where causal relations exist between consecutive landslides, for instance, when landslides occur at the scarp of previous landslides. These are situations where past landslides impact future landslides. Within the path-dependent category, we distinguish three subcategories based on the spatial distance between earlier and later landslides: reactivation or continuation if essentially the same material recommences or continues to slide, local activation if an earlier slide causes changes in a local hillslope that cause a later slide, and remote activation if an earlier slide causes changes elsewhere in the landscape that cause a later landslide. We use this proposed set of terms to outline some prominent knowledge gaps and potential research questions.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Environmental Sciences

Numerical Analysis of the Effect of Subgrid Variability in a Physically Based Hydrological Model on Runoff, Soil Moisture, and Slope Stability

E. Leonarduzzi, R. M. Maxwell, B. B. Mirus, P. Molnar

Summary: This study investigated the impact of subgrid variability in coarse resolution hydrological modeling, showing that the variability in subgrid topography and soil thickness has a significant influence on hydrological processes, potentially leading to underestimation.

WATER RESOURCES RESEARCH (2021)

Article Environmental Sciences

Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

Francesco Fusco, Benjamin B. Mirus, Rex L. Baum, Domenico Calcaterra, Pantaleone De Vita

Summary: Incorporating the influence of soil layering and local variability into physics-based numerical models for distributed landslide susceptibility assessments remains a challenge, but a multi-scale approach has been proposed in this study to account for these factors. This approach allows for accurate simulation of slope failures without modifying the model structure, and has been successfully applied to assess landslide hazard in complex layered soil environments.
Article Geosciences, Multidisciplinary

Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future

Alessandro Cesare Mondini, Fausto Guzzetti, Kang-Tsung Chang, Oriol Monserrat, Tapas Ranjan Martha, Andrea Manconi

Summary: Landslides are geomorphological processes with serious threats to people, property, and the environment on all continents. Investigators have shown increasing interest in using Synthetic Aperture Radar (SAR) imagery for landslide detection and mapping, but challenges remain to be faced for effective utilization.

EARTH-SCIENCE REVIEWS (2021)

Article Geosciences, Multidisciplinary

Rapid-Response Unsaturated Zone Hydrology: Small-Scale Data, Small-Scale Theory, Big Problems

John R. Nimmo, Kim S. Perkins, Michelle R. Plampin, Michelle A. Walvoord, Brian A. Ebel, Benjamin B. Mirus

Summary: The unsaturated zone plays a crucial role in land and water resource management by controlling water flow and reducing vulnerability to contaminants. Rapid flow and transport in the unsaturated zone are becoming more common due to extreme hydroclimatic events, yet they are poorly understood. Scaling issues pose challenges in accurately representing these processes at larger spatial scales.

FRONTIERS IN EARTH SCIENCE (2021)

Article Engineering, Geological

Evaluation of techniques for mitigating snowmelt infiltration-induced landsliding in a highway embankment

Eric S. Hinds, Ning Lu, Benjamin B. Mirus, Jonathan W. Godt, Alexandra Wayllace

Summary: The study found that seasonal failures at the Straight Creek landslide were caused by rapid infiltration of snowmelt and differences in hydraulic conductivity between slope materials and the highway embankment. Remediation designs such as lightweight caissons and horizontal drains were implemented, but their effectiveness was limited by the low hydraulic conductivity of subsurface materials. Results suggest that an alternative drain design intercepting subsurface flow could improve stability during critical periods.

ENGINEERING GEOLOGY (2021)

Article Geography, Physical

A global landslide non-susceptibility map

Guoqiang Jia, Massimiliano Alvioli, Stefano Luigi Gariano, Ivan Marchesini, Fausto Guzzetti, Qiuhong Tang

Summary: This study proposes a non-susceptibility analysis method for selecting locations with negligible landslide occurrence likelihood, simplifying classification methods and applying it globally. The global map, which classifies 82.9% of landmasses with negligible landslide susceptibility, can be used for non-exposure analysis, land planning, and disaster response. Population and settlements are denser within non-susceptible areas, making the map potentially valuable for global-scale applications.

GEOMORPHOLOGY (2021)

Article Environmental Sciences

Data-driven flood hazard zonation of Italy

Ivan Marchesini, Paola Salvati, Mauro Rossi, Marco Donnini, Simone Sterlacchini, Fausto Guzzetti

Summary: The study introduces a data-driven and statistically-based procedure, Flood-SHE, for delineating potential areas of river floods. Results demonstrate that Flood-SHE accurately identifies potentially inundated areas, delineating larger areas compared to physically-based models, depending on the quality of flood information. This new data-driven approach shows promise for predicting flood risk and could be used where traditional hydrological models are not available.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)

Article Engineering, Geological

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

Massimiliano Alvioli, Michele Santangelo, Federica Fiorucci, Mauro Cardinali, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Fausto Guzzetti, Silvia Peruccacci

Summary: The study utilizes a data-driven approach to determine rockfall source points and simulate trajectories, extracting probabilistic maps of slope values based on expert mapping of potential source areas. Simulated trajectories are used to analyze rockfall susceptibility of railway segments, resulting in a network-ranked susceptibility map.

ENGINEERING GEOLOGY (2021)

Article Engineering, Geological

Landslide size matters: A new data-driven, spatial prototype

Luigi Lombardo, Hakan Tanyas, Raphael Huser, Fausto Guzzetti, Daniela Castro-Camilo

Summary: The article presents the development of landslide hazard assessment methods and introduces a statistically-based model capable of predicting aggregated landslide extent, which was tested on a global dataset. This new spatial predictive paradigm could be a breakthrough in landslide hazard studies and potentially become part of official landslide risk assessment protocols in the future.

ENGINEERING GEOLOGY (2021)

Article Multidisciplinary Sciences

Deep learning forecast of rainfall-induced shallow landslides

Alessandro C. Mondini, Fausto Guzzetti, Massimo Melillo

Summary: Rainfall-triggered landslides pose threats to people and the environment in all mountain ranges. Due to projected climate changes, the risk of landslides is expected to increase, emphasizing the need to anticipate their occurrence. This study proposes a deep-learning based strategy to link rainfall to landslide occurrence, which effectively predicts their location and timing, opening up the possibility of operational landslide forecasting based on rainfall measurements and meteorological forecasts.

NATURE COMMUNICATIONS (2023)

Article Geosciences, Multidisciplinary

Mapping Landslide Susceptibility Over Large Regions With Limited Data

J. B. Woodard, B. B. Mirus, M. M. Crawford, D. Or, B. A. Leshchinsky, K. E. Allstadt, N. J. Wood

Summary: Landslide susceptibility maps show the likelihood of landslide occurrence in different areas. Developing models for large or diverse terrains is challenging due to limited landslide data and variability in triggering conditions. This study introduces a statistical framework to evaluate the effects of different sampling strategies on model accuracy, and highlights the importance of using uniformly distributed data for training over spatially isolated data.

JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE (2023)

Article Geosciences, Multidisciplinary

The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy

Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, Maria Teresa Brunetti

Summary: Italy is frequently affected by landslides, which cause significant disruptions to the population, communication infrastructure, and economy. To mitigate landslide risks, accurate landslide catalogues are needed. ITALICA, the largest catalogue of rainfall-induced landslides in Italy, provides detailed and precise information on 6312 landslides that occurred between January 1996 and December 2021, making it crucial for decision-making and landslide risk management.

EARTH SYSTEM SCIENCE DATA (2023)

Review Engineering, Environmental

Revisiting landslide risk terms: IAEG commission C-37 working group on landslide risk nomenclature

Jordi Corominas, Fausto Guzzetti, Hengxing Lan, Renato Macciotta, Cristian Marunteranu, Scott McDougall, Alexander Strom

Summary: Significant effort has been made to develop methodologies for landslide hazard and risk assessment, but there is still debate on the usage of terms and their implementation. Harmonization of methodologies and terminology is necessary to facilitate communication within the landslide community and with stakeholders from other disciplines. In 2016, the IAEG established a working group to prepare a multilingual glossary for landslide hazard and risk terms, aiming for international harmonization.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2023)

Article Engineering, Electrical & Electronic

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Savinay Nagendra, Daniel Kifer, Benjamin Mirus, Te Pei, Kathryn Lawson, Srikanth Banagere Manjunatha, Weixin Li, Hien Nguyen, Tong Qiu, Sarah Tran, Chaopeng Shen

Summary: This paper investigates pixel-wise labeling of landslide areas in remotely-sensed images using deep learning, focusing on large-scale heterogeneous landslide data collection. The study introduces a mechanism for incremental training of semantic segmentation models, known as task-specific model updates (TSMU), which can be utilized for creating new landslide inventories or developing hazard maps following landslide events.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

Article Geosciences, Multidisciplinary

Invited perspectives: Landslide populations - can they be predicted?

Fausto Guzzetti

NATURAL HAZARDS AND EARTH SYSTEM SCIENCES (2021)

暂无数据