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Track of Hurricane Mitch through the western Caribbean and Central America, October 26 to November 1, 1998 (coordinates from National Hurricane Center). Large red circles show locations of low pressure eye of the hurricane at date and time indicated; times (Z) are Universal Time Coordinated. Yellow polygon is area of this study.
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How microgrids can provide risk mitigation to extreme weather events.
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... Hungr et al. [36] stressed the necessity of adopting a multidisciplinary approach to devise holistic solutions for mitigating landslide risks and crafting prevention mechanisms Li et al. [14], Bucknam et al. [37], and Glade et al. [38] further emphasized the multidisciplinary strategy by discussing the application of empirical models to refine rainfall thresholds that trigger landslides. ...
... Landslides occur in a diversity of landscapes around the world and rainfall is often the most prevalent trigger (Crosta and Frattini 2008;Tran et al. 2021). For example, the heavy rain caused by Hurricane Mitch in October and November 1998 induced thousands of landslides in Honduras, El Salvador, Guatemala, and Nicaragua (Bucknam et al. 2001). About 147 slope failures and 33 debris flows were recorded around Umyeon mountain in South Korea in July 27, 2011 due to heavy rainfall (Viet et al. 2016). ...
Landslides refer to a common type of natural disaster in the mountainous areas in Vietnam, potentially causing detrimental impacts on humans, property, and the environment. In light of the emerging damage pertaining to this type of natural disaster in recent years, the construction of an effective landslide early warning system appears to be more critically urgent than ever.
This study proposes a landslide early warning system based on a landslide susceptibility map and a rainfall threshold for Ha Long City (capital of Quang Ninh Prov-ince). Due to the difference in the collected data, the Spatial Multi-Criteria Evaluation (SMCE) method was used to create a landslide susceptibility map at a scale of 1:50,000 for Quang Ninh Province, while the empirical method was used to establish the landslide rainfall thresh-old for Ha Long City, using data from 2005 to 2016 on rainfall and landslides.
The results, which were verified with the inventory map (for the landslide susceptibility map) and with the landslide and rainfall data in the 2017–2021 period (for the rainfall threshold), showed the reliability of predicting the spatial and temporal occurrences of landslides.
Following that, the landslide susceptibility map and rainfall threshold can be added to a WebGIS-based land-slide early warning system along with information from automatic weather stations and rainfall forecasts. This will help authorities and local people in the study area get landslide warnings effectively.
... For the Guatemala-Hurricane Mitch data set (Bucknam et al., 2001), exceptionally long and narrow scars (likely representing mud-flow events) were eliminated from the data set. These mapped scars, which represent in-channel fluvial rather than gravitational mass-wasting slope events, were filtered out based on a ratio >20 between the area of a circle that bounds the mapped landslide polygon, and the area of the bounded polygon. ...
... These mapped scars, which represent in-channel fluvial rather than gravitational mass-wasting slope events, were filtered out based on a ratio >20 between the area of a circle that bounds the mapped landslide polygon, and the area of the bounded polygon. Volumes of the mapped scars in Guatemala (Bucknam et al., 2001) and Northridge (Harp & Jibson, 1996) were calculated using Equation 18 by Malamud et al. (2004) (as applied there) based on the relation suggested by Simonett (1967) (Equation 3 in Table 1). For Guatemala and Northridge data sets, a conservative upper volume boundary was used based on the volume-area relation suggested by ten Brink et al. (2006) (Figure 3 and Table 1). ...
... The probability analysis by Malamud et al. (2004) of the extensive mountain-landslide inventories of Northridge (southern California), which was triggered by the 1994 Northridge earthquake (Harp & Jibson, 1996), and the one of eastern Guatemala that was triggered by extreme rainfall during Hurricane Mitch in 1998 (Bucknam et al., 2001), display similar PDF distributions, despite their different triggering mechanisms. However, their GE distributions are distinct ( Figure 6): Northridge displays a hump-shaped GE curve with mid-large slides accounting for almost 20% of the total volume in the inventory and larger landslide magnitudes with decreasing PDF values that account for lower cumulative erosion volume. ...
Landslides are widely recognized as key components of landscape evolution in areas of steep topography. Here, we present a new framework for examining landslide inventories in the context of the volume‐based impact that different landslide sizes have on shaping the landscape, that is, their geomorphic effectiveness (GE). Focusing on an actively retreating coastal cliff in the Eastern Mediterranean and utilizing a LiDAR‐derived inventory of over 1,100 cliff landslides that occurred between 2014 and 2019, we show that segments of the cliff are characterized by two principal types of GE distributions: (a) A “humped” GE distribution where the accumulated erosion volume of the largest and rarest collapses in the inventory is similar or lower than that of more frequent, mid‐range collapses and (b) Nearly monotonically increasing GE distribution where the cumulative volume of larger collapses consistently surpasses that of smaller magnitude collapses. Regardless of the GE distribution type, we found that the cumulative geomorphic impact of the small and most probable collapses was negligible. Extending this new GE framework to 9 other previously published landslide inventories (coastal and mountainous), we demonstrate that precipitation and seepage‐induced landslide inventories are commonly characterized by monotonic‐type GE distributions, dominated by large landslides (>10⁻¹ of the volume of the largest landslide), and that hump‐shaped GE distributions, dominated by more frequent mid‐size landslides, commonly occur under “dry” triggers (e.g., earthquakes). We propose that the humped GE distribution could reflect the lack of deep mechanical weakening, which exerts a higher probability of the largest landslides in the inventories triggered by “wet” factor.
... A triggering event can generate single landslides or tens of thousands of landslides [7][8][9] and as such the affected areas span from single slopes to entire regions 5,10,11 . The most common landslide triggering events are meteo-climatic 1,[11][12][13][14][15] or seismic 10,[16][17][18][19] , but landslides can be also induced by volcanic eruptions or human activity 2 . ...
Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km² in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km². All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.
... Landslides, one of the most frequently occurring geological hazards in mountainous and hilly regions, cause billions of dollars (USD) of property and infrastructural damage and claim thousands of lives globally each year (Sidle and Ochiai 2006;Froude and Petley 2018). Heavy or extended precipitation is a principal triggering factor for many landslides (Bucknam et al. 2001;Cardinali et al. 2006;Tsai et al. 2010;Guzzetti et al. 2012), and as the frequency of extreme precipitation events increases in some areas as a consequence of anthropogenic climate change, the numbers of landslides will likely also increase (Soldati et al. 2004;Borgatti and Soldati 2010;Crozier 2010;Huggel et al. 2012;Ciabatta et al. 2016;Gariano and Guzzetti 2016;Ciervo et al. 2017;Sangelantoni et al. 2018;Haque et al. 2019). In 2019, one such extreme climate event, Cyclone Idai, impacted the southern African continent, triggering widespread flooding across Mozambique, Malawi, and eastern Zimbabwe. ...
Changes in climatic patterns, manifested as intensified cyclones and torrential rainfalls in a warming world will inevitably impact the frequency of landslides. One such climatic extreme, Cyclone Idai (March 2019), caused significant havoc across southeastern Africa, including Mozambique, Malawi, and eastern Zimbabwe, by triggering thousands of landslides and widespread concurrent flooding, both of which resulted in substantial loss of life. The study was conducted in the Chimanimani District of eastern Zimbabwe to understand the impact of Cyclone Idai on landslide initiation, quantify the volume of mobilized hillslope material during the event, and compare the event-triggered material release against the annual erosional yield across the study area using machine learning and remote sensing techniques. We evaluated satellite imagery of various resolutions, namely, PlanetScope (3m/px), RapidEye (5m/px), and Sentinel-2 (10m/px) and three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to identify landslides and compared the efficacy of the models. A total of nine predictor variables derived from the satellite imagery and a 30-m ASTER Global Digital Elevation Model were employed to identify landslides and differentiate them from concurrent hydrological flooding. The models classified the study area into three classes: (i) landslides, (ii) flooding, and (iii) unaffected area. The RF model using PlanetScope satellite data attained the highest prediction accuracy of 97.88%, whereas the accuracy of other machine learning model-satellite data combinations ranged between 94.58 and 97.27%. Subsequently, landslide size thresholds were applied on the initially mapped landslides to eliminate noise and uncertainty from the data before estimating the final Cyclone Idai event-triggered landslide volume. A probability density function, which corresponds to a logarithmic plot of non-cumulative landslide frequency against the mapped landslide area, was employed to calculate landslide size thresholds using divergence and rollover point cutoff values. Finally, a landslide area-to-volume power-law scaling relationship was exploited to derive landslide volumes in the study area that ranged between to m3 and to m for divergence and rollover point thresholds, respectively, across the different combinations of machine learning models and satellite sensors. The estimated landslide volume indicates hillslope material liberated by Cyclone Idai was 269 to 345 times greater than the estimated annual average background denudation of the study area computed from a topography-based local erosion model.
... They provide historical information on past landslide events, including their location, type, and triggers, such as heavy rain, rapid thaw, or an earthquake. Inventories also include statistics on the frequency of slope failures and provide relevant information to build models of landslide susceptibility or landslide risk [4][5][6][7][8][9][10]. ...
Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods—k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost—in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2/3 of ground truth samples of 671 slidden/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors.
... Rainfall associated with landfalling tropical cyclones (TCs) represents a key ingredient for flooding and landslides (e.g., Bucknam et al., 2001;Fuhrmann et al., 2008;Villarini et al., 2014a;Aryal et al., 2018), leading to major societal and economic repercussions (e.g., Rappaport, 2014;Czajkowski et al., 2017). These impacts are felt especially along the coast, where an increased concentration of population and wealth has led to increased vulnerabilities to these storms (e.g., Klotzbach et al., 2018). ...
This study focuses on the development of a probabilistic rainfall generator for tropical cyclones (TCs) affecting Louisiana. We consider 12 storms making landfall along the Louisiana coast during 2002–2017 and generate ensembles of high‐resolution (~5 km and 20 min) TC‐rainfall fields for each storm. We develop a data‐driven multiplicative model, relating observed rainfall to the rainfall obtained from a parametric TC rainfall model (Interagency Performance Evaluation Task Force Rainfall Analysis [IPET]) through the product of a deterministic and a stochastic component; the former accounts for rain‐dependent biases, while the latter for the stochastic nature of the rainfall processes. As a preliminary step, we describe the overall bias of the IPET model as a function of total TC rainfall within the state and maximum wind speed at landfall. We then estimate the rain‐dependent bias using a cubic spline. Finally, we characterize the random errors in terms of their probability distribution and spatial correlation. We show that the marginal distribution of the logarithm of the random errors can be described by a mixture of four Gaussian distributions, and its spatial correlation is estimated based on the nonparametric Kendall's τ. We then present a methodology to generate ensembles of random fields with the specified statistical properties. Here, the generation of probabilistic rainfall comes from the statistical modelling of the uncertainties between IPET rainfall and observations. While these results are valid for Louisiana and the IPET model, the methodology can be generalized to other parametric rainfall models and regions, and it represents a viable tool to improve our quantification of the risk associated with TC rainfall.
... The SM range is the first range hit by tropical depressions tracking from the Caribbean Sea. They trigger numerous landslides along the wettest slopes of the SM range (Ramos Scharrón et al., 2012;Bucknam et al., 2001). Because SM range soils are more frequently close to water saturation, they are more likely to be affected by landslides when earthquakes strike the range (Harp et al., 1981). ...
The rise of a mountain range affects moisture circulation in the atmosphere and water runoff across the land surface, modifying the distribution of precipitation and drainage patterns in its vicinity. Water routing in turn affects erosion on hillslopes and incision in river channels on surrounding mountain ranges. In central Guatemala, two parallel, closely spaced mountain ranges formed during two consecutive pulses of uplift, the first between 12 and 7 Ma (Sierra de Chuacús–Sierra de las Minas), and the second after 7 Ma (Altos de Cuchumatanes). We explore the climatic and tectonic processes through which the rise of the most recent range drove the slowing of river incision and hillslope erosion over the previously uplifted range. The 40Ar/39Ar dating of perched volcanic deposits documents the sequential rise and incision of these mountain ranges. Terrestrial cosmogenic 10Be in river sediments indicates that currently hillslopes in the older range erode more slowly than in the younger range (20–150 vs. 300 mMyr-1). These differences mimic the current distribution of precipitation, with the younger range intercepting the atmospheric moisture before it reaches the older range. River channel steepness and deformation of paleovalleys in the new range further indicate that the younger range has been rising faster than the older range up to today. We review how atmospheric moisture interception and river long-profile adjustment to the rise of the new range have contributed to the decline of erosion rates over the old range. We also explore the consequences of this decline and of aridification on the topographic evolution of the older range. The older range undergoes a slow topographic decay, dominated by backwearing, by the stacking of slowly migrating erosion waves along the mountain flanks, and by the formation of pediments around its base. The morphology of the old range is therefore transitioning from that of a front range to that of a dry interior range.
... For instance, within the novel statistical framework of the metastatistical extreme value distribution, Miniussi et al. (2020) showed that the distribution of TC rainfall is different from the non-TC rainfall in the Eastern United States, especially for multi-day events, and that these storms tend to result in larger rainfall values. The impact of the TC rainfall is remarkable not only along the coastline, but also hundreds of miles inland in terms of flooding (e.g., Villarini et al. 2014a;Khouakhi et al. 2017;Aryal et al. 2018) and landslides (e.g., Bucknam et al. 2001). Despite these negative effects, they can also bring water critical for groundwater recharge, water supply and drought mitigation (e.g., Abdalla and Al-Abri 2011;Kam et al. 2013;Zhang et al. 2017). ...
This study examines the climatology and structure of rainfall associated with tropical cyclones (TCs) based on the atmosphere-only Coupled Model Intercomparison Project Phase 6 (CMIP6) HighResMIP runs of the PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment (PRIMAVERA) Project during 1979–2014. We evaluate how the spatial resolution of climate models with a variety of dynamic cores and parameterization schemes affects the representation of TC rainfall. These HighResMIP atmosphere-only runs that prescribe historical sea surface temperatures and radiative forcings can well reproduce the observed spatial pattern of TC rainfall climatology, with high-resolution models generally performing better than the low-resolution ones. Overall, the HighResMIP atmosphere-only runs can also reproduce the observed percentage contribution of TC rainfall to total amounts, with an overall better performance by the high-resolution models. The models perform better over ocean than over land in simulating climatological total TC rainfall, TC rainfall proportion and TC rainfall per TC in terms of spatial correlation. All the models in the HighResMIP atmosphere-only runs underestimate the observed composite TC rainfall structure over both land and ocean, especially in their lower resolutions. The underestimation of rainfall composites by the HighResMIP atmosphere-only runs is also supported by the radial profile of TC rainfall. Overall, the increased spatial resolution generally leads to an improved model performance in reproducing the observed TC rainfall properties.
... The frequency-area distribution is the combined result of the distance to slope and the actual terrain constraints of landslide (Pelletier et al., 1997;Hovius et al., 2000;Guthrie and Evans, 2004). Some scholars have quantified the spatial distribution characteristics of the earthquake-induced landslides (Harp and Jibson, 1996;Cardinali et al., 2000;Bucknam et al., 2001;Stark and Hovius, 2001;Guzzetti et al., 2002;Brardinoni and Church, 2004;Guthrie and Evans, 2004;Malamud et al., 2004;Korup, 2005;Van Den Eeckhaut et al., 2007;Corominas and Moya, 2008;Guan, 2018). In some cases, the special probability distributions (e.g., the three-parameter inverse-gamma distribution) fit the landslide area distribution well (Malamud et al., 2004), yet no physical implications are found in the parameters. ...
Earthquake-induced landslide has various spatial characteristics that can be effectively described with the frequency–area curve. Nevertheless, the widely used power-law curve does not reflect well the spatial features of the distribution, and the power exponent does not show the association with the background factors. There is a lack of standards for building the relationship, and its implication on the spatial distribution of landslides has never been analyzed. In this study, we propose a new form of frequency distribution and explore the parameters in the typical watersheds along the highway from Dujiangyan to Wenchuan in the Wenchuan earthquake region. The obtained parameters are related to the landslide density and proportions of the large-scale landslides. Furthermore, a hot spot analysis of landslides in the watersheds is conducted to assess the relationship between the parameters and the spatial cluster patterns of landslides. The hot spots highlight the size and distance of landslide areas that cluster together, whereas the distribution parameters reflect the density and proportions of landslides. This research introduces a new method to analyze the distribution of landslides and their association with the spatial features, which can be applied to the landslide distribution in relation to other influential factors.