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A simple model of landscape evolution in Paonia-McClure Pass area. Although the present slopes in the area around the rivers and tributaries are not perfectly symmetrical as shown in the figure, the sketches present a concept of how the landscape in the area was modified over time from Quaternary to present. a Figure shows the beginning of the river incision with a large region of upland plateau. b As rivers undergo incision, the regions in close proximity to the rivers develop steep slopes and the slopes tend to stabilize by the shallow landslides. c The relief of the upland plateaus increases (h 3 >h 2 ) with two distinct slopes (S C2 >S C1 ). The steep slopes retreats by the shallow landslides, and the upland slopes reach to the instability threshold because of the high potential energy. d Because the incision processes are continuous, the high potential energy upland exceeds the instability threshold and under the effect of the gravity produces deep-seated large landslides. e These deep-seated landslides steepened the upland gentle slope by propagating upslope. The recent condition of the topography nearby Somerset is shown in (e)
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Mass movement can be activated by earthquakes, rapid snowmelt, or intense rainstorms in conjunction with gravity. Whereas mass movement plays a major role in the evolution of a hillslope by modifying slope morphology and transporting material from the slope to the valley, it is also a potential natural hazard. Determining the relationships of frequ...
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... on the field observations of landslides and the history of incision by rivers in the study area, we developed a simple conceptual model of the landscape evolution contributed by the incision and toe cutting of the North Fork Gunnison River and associated tributaries, shallow landslides and the deep-seated landslides (Fig. 13). Although the present slopes around the rivers and tributaries of the study area are not perfectly symmetrical, as shown in Fig. 13, the sketches present a concept of how the landscape was modified during the Quaternary to the present time. It is hypothesized that during Quaternary Period the topography of the area was very gentle with ...
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... a simple conceptual model of the landscape evolution contributed by the incision and toe cutting of the North Fork Gunnison River and associated tributaries, shallow landslides and the deep-seated landslides (Fig. 13). Although the present slopes around the rivers and tributaries of the study area are not perfectly symmetrical, as shown in Fig. 13, the sketches present a concept of how the landscape was modified during the Quaternary to the present time. It is hypothesized that during Quaternary Period the topography of the area was very gentle with large plateaus most probably developed by the incisional and aggradational nature of Ancient Colorado River (?) from Early Miocene ...
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... concept is supported by a 10 Ma basalt flow and the existence of ancient fluvial deposits capping many plateaus (e.g., Grand Mesa) around the study area. The first figure (Fig. 13a) shows the beginning of the river incision into a large region of upland plateau. The second figure (Fig. 13b) shows river incision in close proximity to steep slopes (S B2 >S B1 ) because of the base level being lowered. ...
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... first figure (Fig. 13a) shows the beginning of the river incision into a large region of upland plateau. The second figure (Fig. 13b) shows river incision in close proximity to steep slopes (S B2 >S B1 ) because of the base level being lowered. These slopes also tend to be stabilized by shallow landslides. ...
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... more unstable condition because of the increasing energy (energy=mgh 2 ) on steeper slopes. The shallow landslides are a process-response reaction to river inci- sion and frequent toe cutting. With continuous lowering of the base level, the area develops two distinct slopes (S C2 >S C1 ) and the relief of the upland plateaus increases (h 3 >h 2 ) (Fig. 13c). The steep slopes retreat as shallow landslides occur and the upland slopes reach an instability threshold because of the high energy (mgh 3 ). Because incision processes are continuous, the high levels of energy present in the uplands exceeds (mgh 1 <mgh 2 <mgh 3 ) the instability threshold. Under the effect of the gravity, ...
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... as shallow landslides occur and the upland slopes reach an instability threshold because of the high energy (mgh 3 ). Because incision processes are continuous, the high levels of energy present in the uplands exceeds (mgh 1 <mgh 2 <mgh 3 ) the instability threshold. Under the effect of the gravity, deep-seated large landslides are produced (Fig. 13d). These deep-seated landslides steepen the upland gentle slope by propagating upslope (Fig. 13e) and form three types of slopes (S E1 >S E2 <S E3 ). Figure 13e is similar to the topographic profile of the recent topography nearby Somerset and areas around the center of the study area. Although the large deep-seated landslides ...
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... the high energy (mgh 3 ). Because incision processes are continuous, the high levels of energy present in the uplands exceeds (mgh 1 <mgh 2 <mgh 3 ) the instability threshold. Under the effect of the gravity, deep-seated large landslides are produced (Fig. 13d). These deep-seated landslides steepen the upland gentle slope by propagating upslope (Fig. 13e) and form three types of slopes (S E1 >S E2 <S E3 ). Figure 13e is similar to the topographic profile of the recent topography nearby Somerset and areas around the center of the study area. Although the large deep-seated landslides (paleolandslides) were not introduced in the frequency-magnitude analysis, we presumed that the frequency ...
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... deep-seated landslides steepen the upland gentle slope by propagating upslope (Fig. 13e) and form three types of slopes (S E1 >S E2 <S E3 ). Figure 13e is similar to the topographic profile of the recent topography nearby Somerset and areas around the center of the study area. Although the large deep-seated landslides (paleolandslides) were not introduced in the frequency-magnitude analysis, we presumed that the frequency of the shallow landslides is larger than the frequency of deep-seated large landslides. ...
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A power‐law relation for the frequency‐area distribution (FAD) of medium and large landslides (e.g., tens to millions of square meters) has been observed by numerous authors. But the FAD of small landslides diverges from the power‐law distribution, with a rollover point below which frequencies decrease for smaller landslides. Some studies conclude...
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Citations
... Different experts and scholars have studied the evolution process of landslides by means of landslide spatial form, mutual superposition relationships, and terrace chronology in river valley areas [8][9][10][11][12]. Among them, Journault et al. [8] analyzed the geological history of the Thompson Valley, then monitored the deformation rate of the landslide using the InSAR measurements and found that most Thompson Valley landslides exhibit seasonal changes in displacement rate. ...
... Li et al. [9] analyzed the formation mechanism of the reservoir bank landslide with stepped sliding surfaces in the Three Gorges Reservoir area, established a representative geological model with multiple stepped sliding surfaces, and believed that the formation of the stepped landslide surface was closely related to the Jurassic strata with interlayers and weak hard layers. Regmi [10] studied the development characteristics of landslides, such as landslide area and landslide volume, through aerial photography and field investigation and established a conceptual model of river cutting, shallow landslides and deep large land-slides driving downslope facing highlands and plateaus. Chen et al. [11] analyzed the geological process of landslide development and river evolution in the Three Gorges Reservoir area of the Yangtze River. ...
The geological structure of the upper reaches of the Yellow River is complex, especially in the Sigouxia-Laganxia section. It has always been a high-incidence area of landslide disasters, which poses a threat to the safe operation of the upper reaches of the Yellow River. In this study, based on the high-precision remote sensing image data, the spatial distribution of each landslide was obtained by superimposing the remote sensing image and the 1:50,000 digital elevation model (DEM). Some typical landslides were selected for detailed field investigation and field verification. The results show that the remote sensing image characteristics of landslides in the upper reaches of the Yellow River are obvious. Through remote sensing interpretation and field investigation, a total of 508 landslides of various types were found, including 24 giant landslides. The spatial spreading patterns of landslides mainly include six types: dumb-bell shape, rectangle, saddle type, long arc shape, triangle, and side-by-side shape. The length and width of the landslide deposit are mainly concentrated at 550–1500 m and 600–1500 m, and the average elevation of the sliding body is mainly concentrated between 2000 and 2800 m. The average slope of the landslide is mainly distributed between 15–20°. Giant landslides are mainly distributed in the Jianzha basin area, surrounded by the Jishishan fault and the Lajishan fault in the West Qinling Mountains. The spatial distribution characteristics of giant landslides have obvious regional differences due to different factors such as lithologic differences and riverside erosion. The research results are of great significance for the early identification, prevention, and mitigation of landslide disasters in the upper reaches of the Yellow River.
... High lineament density areas, such as those with a 1.05 km/km 2 density pose a greater risk of landslide susceptibility. Additionally, regions closer to fault lines are more likely to experience landslides than those farther away (Regmi et al., 2014). In this study, lineament density and distance to fault layers were conducted using the ArcGIS v.10.7 line density, and 'Euclidean Distance' tools, respectively. ...
Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.
... Morfolojik kalıntıların izleri heyelan tipine ve yoğunluğuna bağlıdır. Heyelan tipi ve yoğunluğu topoğrafik, litolojik, klimatik ve tektonik özelliklere bağlı olarak lokal, bölgesel ve yamaçlara göre değişkenlik göstermektedir [16][17][18]. ...
Yukarı Ceyhan Havzası topoğrafik ve litolojik geçişlerin sık olduğu bir bölgedir. Çalışmanın amacı yerleşmenin, tektonik aktivitenin ve yüzey şekillenmesinin yoğun olduğu sınırları belirlenen alanda heyelanların dağılımı ve temel karakteristiklerini etkileyen faktörleri detaylı bir şekilde incelemektir. Bu amaçla yüksek çözünürlüklü uydu görüntülerinden heyelan envanteri tanımlanmıştır. Envanterin doğruluğu arazi çalışmaları ile sağlanmıştır. Çalışma alanında 441 farklı tip heyelanın havzanın %3,6'sını kapladığı tespit edilmiştir. Heyelanların çoğunun ana tip olarak kayma tipinde olduğu (%70), bunun yanı sıra düşme ve akmalarda belirlenmiştir. Heyelanların büyüklük dağılımının 0,0005 km2-10,1 km2 arasında oldukça geniş olduğu ortaya çıkmıştır. Buna çeşitli jeolojik ve jeomorfolojik süreçlerin etkili olduğu ve farklı mekanizmaların rol oynadığı belirlenmiştir. Bu doğrultuda literatürdeki heyelan ile ilişkilendirilen topoğrafik faktörler ve jeolojik faktörler uzman tarafından çalışma sahasına özgü olarak değerlendirilmiştir. Buna göre eğim, yükselti, rölyef ve topoğrafik nemlilik indeksi topoğrafik faktörlerin heyelan tipine göre farklı seviyelerde heyelanı kontrol etmektedir. Paleo heyelanların dağ-ova arasındaki keskin rölyef geçişinde gelişmektedir. Havzada heyelanlar kumtaşı-çamurtaşı-kireçtaşı birimlerinde yoğunlaşmaktadır. Faya yakın zonda küçük heyelanlar bulunmasına rağmen 06 Şubat 2023 depremleri bu sınırlarda küçük düşmelere neden olmuştur. Heyelanlar dağılım topoğrafik ve jeolojik faktörlerin özelliğine göre değişiklik göstermektedir. Sonuç olarak heyelanların jeolojik, topoğrafik ve çevresel karmaşık etkileşimi sonucunda meydana geldiğini söyleyebiliriz. Bu nedenle çalışmaya ait analizler bölge için heyelan duyarlılık, tehlike ve risk çalışmalarında dikkate alınması açısından önemlidir.
... Landslides also produce a significant hazard to lives and infrastructure, accounting for more than 8000 deaths a year globally (Petley, 2012;Perkins, 2012;Haque et al., 2019) and billions in property damage (Fleming & Taylor, 1980;Schuster & Fleming, 1986;Highland, 2006 (Lee, 1912;Mohr, 1922;Yeend, 1969;Bankey et al., 2004;Day & Bove, 2004;Cole et al., 2013;Cole, 2022), glaciation (Henderson, 1923;Retzer, 1954;Yeend, 1968;Rovinelli & Giardino, 1995) volcanism Cole et al., 2017), and topographic evolution (Baum & Odum, 1996;Baum, 1997;Baker et al., 2002;Baum & Odum, 2003;Betton et al., 2005;Darling et al., 2009;Aslan et al., 2010;Darling et al., 2012). Regmi et al. (2014) produced a landslide inventory and analysis of western Colorado, but this is the first in-depth landslide study of Grand Mesa. ...
... Multiple studies have formulated power-law relations (V ∝ A δ ) between the volume (V) and the runout deposit area (A δ ) of landslides (e.g., Klar et al., 2011;Larsen et al., 2010;Guzzetti et al., 2009;Regmi et al. 2014). This empirical relationship is nearly independent of slope geometry, scale, and processes. ...
Understanding the frequency, types, and causes of mass wasting has implications for human activities and landscape evolution. This dissertation explores mass-wasting phenomena on both Earth and Mars by compiling inventories in study areas in Grand Mesa, Colorado (Chapter 2) and Sacra Mensa, Mars (Chapter 4), and analyzing the results. Chapter 3 documents the mapping and analysis of a quadrangle on the Grand Mesa and the implications for the region’s paleohistory.
Chapter 2 investigated the recurrence interval of megaslides akin to the deadly 2014 West Salt Creek Landslide (WSCL) on Colorado's Grand Mesa. The recurrence interval required the determination of the age and number of landslides on the Grand Mesa. We identified 781 landslides with runout deposit areas larger than 1400 m2 using Lidar slopeshade maps, National Agriculture Imagery Program (NAIP) photographs, and field verification. The inventory included up to 33 parameters per slide. Correlation and regression tree analysis were employed for trend and causal analysis. The catalog includes 31 landslides comparable to or larger than the WSCL. Landslide age assessment used surface roughness analysis, six radiocarbon analyses, and geomorphic approaches. WSCL-scale occurrences (runout deposit volume >20 Mm3) happen on average once every 1000 years over the past 30 ka. For the entire inventory, one landslide occurs on average every 17 years during the late Holocene (5 ka). The background Late Quaternary (30 ka) recurrence interval is one event every 39 years. During the late Quaternary, the Mesa's flanks produced 52 hypermobile landslides (mobility index > 9) and 163 highly mobile landslides
(mobility index ≥ 6 < 8.99). Overall, 122 landslides had mobility indices that were higher than the WSCL. When adjusted by stratigraphic unit thickness, the combined Green River-Uinta Formations account for 34.8% of all Grand Mesa landslides and 90.5% of the high-volume landslides despite cropping out beneath only 28% of the study area. These two units' topographic elevation and lithology likely contribute to their dominant role in producing high-volume and high-mobility landslides on Grand Mesa's flanks.
Chapter 3 documents the process and publication of the 1:24,000 surficial and bedrock geological map of the Mesa Lakes, Colorado, quadrangle. The chapter focuses on how Lidar-based surficial mapping discovered more extensive slumping than previously documented. The mapping supports the conclusion that massive landslides frequently occur on the Mesa, with earthflow debris filling nearly every downslope valley. Also, the investigation precisely identified the last glacial (Pinedale) terminal moraine and at least two recessional moraines. This study's map of the glacial extent shows a marked reduction from earlier maps. In addition, the terminal moraine recorded in this work differs from the previously mapped Pinedale glacial extent, suggesting warmer mean annual temperatures during the last glacial maximum for this part of western Colorado than indicated by previous mapping studies.
Chapter 4 investigates the mass-wasting landforms of the enormous Sacra Mensa, a mesa perched between Kasei Valles outflow channels on Mars. On Sacra Mensa, four different types of mass-wasting landforms are inventoried: 1) 72 landslides, of which 55 were previously documented by Crosta et al. (2018a); 2) 17 lobate debris aprons (LDAs); 3) 142 talus cones; and 4) 24 slump blocks. This inventory revealed significant differences and intriguing parallels in the datasets' type, volume, location, frequency, and spatial density. The survey data also shows a substantial imbalance in mass wasting between the northern and southern flanks of the Mensa.
The southern flanks host 71% of the study area's mass-wasting volume, including 75% of landslides, 61% of LDAs, 97% of talus cones, and 70% of slump blocks. However, the southern flank contains 54% of the mass-wasting when normalized by the amount of sloping (>5°) topography. The amount of sloping terrain best explains the abundance of mass wasting in the south. The greater depth, increased steepness of southern flank slopes, and inferred greater stream power along the South Kasei Valles channel are also candidate explanations for the directional mass wasting asymmetry.
On the Sacra Mensa's caprock, there is a second mass-wasting asymmetry. In addition, over half of the landslides in the study area are located within one of the 56 major fossae (long, narrow troughs). Compared to other fossae, this "Landslide Fossa" is around 2.5 times deeper and is surrounded by degraded terrain. Finally, we discovered some geomorphological similarities between coalescing talus cones and LDAs, leading to the hypothesis that, in some instances, these two types of seemingly unrelated landforms may constitute the same phenomena at various stages of maturity.
... Consequently, as the distance from the tectonic feature increases the frequency and magnitude of landslides gradually decreases. According to Regmi et al. 2014 tectonic features decrease the strength of the rock and an area closer to faults is more prone to landslide than the area further away. ...
... To quantify this underestimation, area-frequency analysis is a valuable tool (as specified in the third mapping principle in Table 2). Several landslide datasets worldwide were tested with the Double Pareto [54] probability distribution function [2,[10][11][12][13][14][15][16][17]19]. ...
... Regarding the categorical variables, ASP classes corresponding to west (ASP.7), southwest (ASP.6), and south (ASP.5), and lithology classes representing 'Arenite, Siltstone' (15), ''Conglomerates, Arenite' (23), and 'Arenite, Mudstone' (13), are those ones which play the most important role in the classification of stable/unstable grid-cells. ...
... Regarding the categorical variables, ASP classes corresponding to west (ASP.7), southwest (ASP.6), and south (ASP.5), and lithology classes representing 'Arenite, Siltstone' (15), 'Conglomerates, Arenite' (23), and 'Arenite, Mudstone' (13), are those ones which play the most important role in the classification of stable/unstable grid-cells. ...
Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped (https://github.com/MicheleLicata/open_landslide_project). The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset's suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis.
... Fault lines (FAULTLINES) are geological variables in a landslides research that suggest tectonic breaks and reduce rock strength. In general, areas closer to the Faultline are more prone to landslides than areas further away [1,42]. The data count distribution of the feature FAULTLINES is depicted in Fig. 14. ...
Landslides have been a regular occurrence and an alarming threat to human life and property in the era of anthropogenic global warming. An early prediction of landslide susceptibility using a data-driven approach is a demand of time. In this study, we explored the eloquent features that best describe landslide susceptibility with state-of-the-art machine learning methods. In our study, we employed state-of-the-art machine learning algorithms including XgBoost, LR, KNN, SVM, and Adaboost for landslide susceptibility prediction. To find the best hyperparameters of each individual classifier for optimized performance, we have incorporated the Grid Search method, with 10 Fold Cross-validation. In this context, the optimized version of XgBoost outperformed all other classifiers with a Cross-validation Weighted F1 score of 94.62%. Followed by this empirical evidence, we explored the XgBoost classifier by incorporating TreeSHAP, a game-theory-based statistical algorithm used to explain Machine Learning models, to identify eloquent features such as SLOPE, ELEVATION, TWI that complement the performance of the XGBoost classifier mostly and features such as LANDUSE, NDVI, SPI which has less effect on models performance. According to the TreeSHAP explanation of features, we selected the nine most significant landslide causal factors out of 15. Evidently, an optimized version of XgBoost along with feature reduction by 40% has outperformed all other classifiers in terms of popular evaluation metrics with a Cross-validation Weighted F1 score of 95.01% on the training and AUC score of 97%.
... Soil type. Qtdd = Dihing and Dupi Tila Formation; Tbb = Boka Bil Formation; Tb = Bhuban Formation; Tt = Tipam Sandstone; ava = Valley Alluvium and Colluvium; QTdt = Dupi Tila Formation; QTg = Girujan Clay Fault lines indicate tectonic breaks, and it decreases the strength of the rock.Generally, an area closer to the Faultline is more prone to landslide than the area further away(Netra et al. 2014). This study used the Euclidean Distance tool in ArcGIS 10.7 to measure the distance to the fault lines and divided it into five classes(Fig. ...
The hilly districts of hill tract of Bangladesh are very prone to landslides in rainy season. in the study I have try to identify the courses and consequences as well remedy and suggestion to mitigate the losses of livelihoods, agriculture and transportation due to road block.
... Correlations of ε pdep with some other geometric parameters are illustrated with a correlation matrix in Fig. 8. Accordingly, ε pdep is poorly correlated with all other geometric parameters, suggesting that it can provide independent characterization of the landslide geometry. Landslide type had been suggested to be a major constraining factor of landslide ε by many researchers (e.g., Orris and Williams 1984;Parise and Jibson 2000;Wilcke et al. 2003;Yang and Lee 2006;Sezer 2010;Regmi et al. 2014;Gaidzik et al. 2017;Litoseliti et al. 2020;Tfwala et al. 2020). This is attributed to different types of landslides having different types of movement. ...
Length (L), width (W), and aspect ratio (ε, L/W) are elementary parameters widely adopted to characterize landslide geometry. Aspect ratio (ε) had been associated with the propagation of landslide along its movement path. However, current automatic measurement algorithms have not effectively considered the path of landslide movement, therefore only measure “path-independent” instead of “path-dependent” geometric parameters. This paper proposes a new approach for measuring path-dependent landslide L, W, and ε, in which path-dependent landslide L (Lpdep) is measured as the path distance from the landslide crown to its tip along the central line of landslide movement, and path-dependent landslide W (Wpdep) is measured as the weighted average lateral extent, across the central line of landslide movement. Specifically, Wpdep is calculated indirectly through dividing the landslide area by its length (A/Lpdep), and path-dependent landslide ε (εpdep) can be calculated by Lpdep2/A. The central line of landslide movement, i.e., landslide profile, is a three-dimensional (3D) polyline generated by the freely available software ALPA (Automatic Landslide Profile Analysis), and all these path-dependent geometric parameters can be measured either in 3D space or in two-dimensional (2D) space (in the horizontal plane). A case study is presented showing that path-dependent parameters can be effectively measured by the proposed profile-based approach, and could be significantly different from path-independent parameters especially for long-narrow and tortuous landslides. This paper also practically introduces new path-dependent definitions of landslide L, W, and ε, which inspires a worthwhile revisit of nomenclature. An overview shows that significant divergencies exist between current definitions of landslide L, W, and ε. In order to moderate the raised ambiguities and confusions, this paper accordingly suggests a “physically indicative” inclusive nomenclature somehow embracing all current representative definitions, and then appeals for constituting “type-specific” nomenclatures for each landslide type. However, some open questions still exist. We therefore suggest relevant academic organizations to work further on type-specific nomenclatures and effective measurement approaches for landslide L, W, and ε.
... The locations and characteristics of some of the landslides mapped from Google Earth and lidar shaded relief images were verified in the field and fall within the high to very high landslide susceptibility areas denoted by Bessette-Kirton et al. (2019). Landslide area was calculated in ArcGIS® and the volume of each landslide was modeled using an area (A) and volume (V) relationship (V = 0.0254 × A 1.45 ) proposed by Regmi et al. (2014) for shallow landslides of the North Fork Gunnison River catchment of western Colorado. We apply this equation because of the similarity in geomorphic and lithological environments of this study with those of the North Fork Gunnison River catchment. ...
... We apply this equation because of the similarity in geomorphic and lithological environments of this study with those of the North Fork Gunnison River catchment. In addition, the equation does not differ significantly from other area-volume relationships proposed for shallow landslides in various parts of the world (equations are listed in Regmi et al. (2014) and Tron et al. (2014)). However, a portion of the landslides mapped in this study are shallower than those documented in Regmi et al. (2014), suggesting the modeled volume and depths of some of the landslides in the Puerto Rico study may be slightly overestimated. ...
... In addition, the equation does not differ significantly from other area-volume relationships proposed for shallow landslides in various parts of the world (equations are listed in Regmi et al. (2014) and Tron et al. (2014)). However, a portion of the landslides mapped in this study are shallower than those documented in Regmi et al. (2014), suggesting the modeled volume and depths of some of the landslides in the Puerto Rico study may be slightly overestimated. The size (area, volume, and depth), frequency, and spatial density (Guzzetti et al., 2008) of observed landslides were then evaluated to explore if any relationship exists between spatial density and measured physiochemical characteristics of fluvial sediment. ...
Mass wasting plays an important role in carbon cycling and sequestration by exposing fresh bedrock and delivering hillslope sediments to lowlands and fluvial systems. Chemical weathering signatures of landslide‐derived fluvial sediments can be used to understand linkages between hillslope and fluvial processes and thus to characterize spatiotemporal dynamics of sediments. However, chemical signatures of fluvial sediments derived by landslides are yet to be fully understood. Here, we compare the bulk chemistry, mineralogy, and grain size of fluvial sediments collected pre‐ and post‐Hurricane Maria in the Rio Guayanés and Rio Guayabo watersheds in southeastern Puerto Rico to help fill this knowledge gap. Comparison of the mud fraction (<63 μm) of fluvial sediments collected before Hurricane Maria with similar mud fractions collected after the storm reveals that the post‐hurricane muds exhibit a wider range and higher average weathering index values, but coarser grain size modes. We infer that small landslides triggered by Hurricane Maria transported slope materials from shallow depths, including weathered topsoil and saprolite, as opposed to previous deep‐seated landslides, which likely sampled regolith and bedrock. Variances in weathering indices observed that pre‐ and post‐hurricanes do not necessarily reflect average climate signatures, but rather reflect subtle differences in a transport mechanism, which produce significant differences in weathering indices recorded by fluvial sediments. We propose that weathering indices provide a means to understand sediment dynamics in mountainous regions, particularly for sediment transported immediately after landslides triggered by extreme events, such as precipitation and earthquakes, and also provide important data sets required for mapping potential carbon sequestration across landscapes.
