Map showing the status of landslide inventory and susceptibility mapping in California

Taylor, Fred A.; Brabb, Earl E.

doi:10.3133/ofr86100

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Based on the type of mapping, the landslide inventory map can be classified as an archive or geomorphological inventory (Guzzetti et al 2000;Malamud et al 2004;Guzzetti et al 2012). An archive inventory shows the information on landslides that were taken from different literatures or other archive sources (e.g., Taylor and Brabb 1986;Guzzetti et al 2012;Dagdelenler et al 2020). In the present study area, there was no literature or other archive sources to be used in the analysis.…”

Section: Landslide Inventory Analysismentioning

confidence: 99%

Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

2021

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Landslide is the most frequently occurring geo-hazard in mountainous terrains of the world. It affects human life, infrastructures, landscapes, and human properties as well as their day-to-day activities. In the current study area which is found in the Gamo Zone of south Ethiopia, recurrent landslide hazards have occurred. To minimize this landslide hazard on human life and their properties, landslide susceptibility mapping is an important step for environmental planning. For this purpose, 1554 landslides and 9 landslide causative factors (both conditioning and triggering factors) were used. Each thematic layer has different classes and some of these classes influence landslide occurrence more than others. The most influencing factor classes which were identified by the frequency ratio model include slope classes between12 and 45°; convex and concave classes of the curvature; aspect classes of north, northeast, south, and southwest directions; and elevation classes in between 2118 and 2492 m. The distances factors, proximity to streams, and lineaments 0-100 m and 0-200 m respectively have a very high on landslide occurrences. Land use/land cover factor has different classes and they have different levels of direct and indirect influences on landslide occurrences. The landslide susceptibility map was classified as very low, low, moderate, high, and very high classes each accounting for 17.8%, 29.19%, 28.55%, 17.52%, and 6.91% of the area respectively. To evaluate the reliability of this model, the landslide susceptibility map was verified using a receiver operating characteristic (ROC) curve with a value of 82% and through field observation. Therefore, this can be used by local, zonal, regional, and federal governments for land use planning, disaster prevention, and mitigation as it offers first-hand information.

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Section: Landslide Inventory Analysismentioning

confidence: 99%

Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

2021

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“…Most mountain ranges are susceptible to landsliding due to their steep geomorphology, loose-soil development, geology, and high precipitation (e.g., Coe, 2016). Landslides disrupt aquatic habitats (May et al, 2009;Pollock, 1998), damage infrastructure such as roads, utilities, and dams (Ghirotti, 2012;Baum et al, 2008), and harm people (Wartman et al, 2016;Taylor and Brabb, 1986). Landslide hazards are expected to increase globally with growing climatic extremes (Coe, 2016;Haeberli et al, 2017;Crozier, 2010).…”

Section: Introductionmentioning

confidence: 99%

A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

Strauch

Istanbulluoglu

Riedel

2019

Nat. Hazards Earth Syst. Sci.

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Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

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“…Where the landslide processes intersect with human development, they cause property damage, disruption of infrastructure, injury, and loss of life (Taylor and Brabb, 1986;Baum et al, 2008a), contribute to sedimentation in reservoirs (Bathurst et al, 2005), and may even lead to dam failures (Ghirotti, 2012). Landslides provide punctuated sediment input to streams, affecting stream geomorphology (Benda and Dunne, 1997a, b) and ecosystem dynamics (Pollock, 1998;May et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

Strauch,

Istanbulluoglu,

Nudurupati

et al. 2018

HydroShare Resources

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Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

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Map showing the status of landslide inventory and susceptibility mapping in California

Abstract: and Associates made several corrections and supplied a few new references.

Cited by 6 publications

References 26 publications

Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

A hydroclimatological approach to predicting regional landslide probability using Landlab

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