We review rainfall thresholds for the initiation of landslides world wide and propose new empirical rainfall thresholds for the Central European Adriatic Danubian South-Eastern Space (CADSES) area, located in central and southern Europe. One-hundred-twenty-four empirical thresholds linking measurements of the event and the antecedent rainfall conditions to the occurrence of landslides are considered. We then describe a database of 853 rainfall events that resulted or did not result in landslides in the CADSES area. Rainfall and landslide information in the database was obtained from the literature; climate information was obtained from the global climate dataset compiled by the Climate Research Unit of the East Anglia University. We plot the intensity-duration values in logarithmic coordinates, and we establish that with increased rainfall duration the minimum intensity likely to trigger slope failures decreases linearly, in the range of durations from 20 minutes to ∼12 days. Based on this observation, we determine minimum intensity-duration (ID) and normalized-ID thresholds for the initiation of landslides in the CADSES area. Normalization is performed using two climatic indexes, the mean annual precipitation (MAP) and the rainy-day-normal (RDN). Threshold curves are inferred from the available data using a Bayesian statistical technique. Analysing the obtained thresholds we establish that lower average rainfall intensity is required to initiate landslides in an area with a mountain climate, than in an area characterized by a Mediterranean climate. We further suggest that for rainfall periods exceeding ∼12 days landslides are triggered by factors not considered by the ID model. The obtained thresholds can be used in operation landslide warning systems, where more accurate local or regional thresholds are not available.

Rainfall thresholds for the initiation of landslides in central and southern Europe

Published by the American Geophysical Union as part of the Water Resources Monograph Series, Volume 18. Landslides are a constant in shaping our landscape. Whether by large episodic, or smaller chronic, mass movements, our mountains, hills, valleys, rivers, and streams bear evidence of change from landslides. Combined with anthropogenic factors, especially the development and settlement of unstable terrain, landslides (as natural processes) have become natural disasters. This book charts our understanding of landslide processes, prediction methods, and related land use issues. How and where do landslides initiate? What are the human and economic consequences? What hazard assessment and prediction methods are available, and how well do they work? How does land use, from timber harvesting and road building to urban and industrial development, affect landslide distribution in time and space? And what is the effect of land use and climate change on landslides? [Book Synopsis]

Landslides: Processes, Prediction, and Land Use

Volcanoes of the World

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever-expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km2 catchment a near-annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an inﬁnite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process-based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ﬁve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide-producing storms. Landslide and soil production parameters were ﬁxed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ﬁrst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to deﬁne whether a rainstorm was a signiﬁcant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identiﬁed the 1982 storm as the only landslide-producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identiﬁed the three events, but also predicted that two other landslide-producing events should have occurred. This performance is signiﬁcantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non-producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signiﬁcant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.

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Testing a model for predicting the timing and location of shallow landslide initiation in soil-mantled landscapes

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.

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Use of satellite remote sensing data in the mapping of global landslide susceptibility

Old landslides in precarious conditions and new masses are moved or removed by particularly heavy rainfalls. Phenomena relative to rock slides and earth slides are examined to evaluate the relations between mass movements, rainfall and interstitial pressures.

Correlation between rainfall and landslides

Thermal stress in the Apuan marbles: Relationship between microstructure and petrophysical characteristics

This paper presents a geomorphological map of Mount Ararat/Agri Dagi in Eastern Anatolia (Turkey). Mount Ararat/Agri Dagi is a volcanic complex covered by a unique ice cap in the Near East. The massif is the result of multiple volcanic phases, and present day landforms are the result of subsequent and overlapping glacial, periglacial, and slope processes. The geomorphological mapping of Mount Ararat/Agri Dagi was firstly performed on the basis of desktop studies, by applying remote-sensing investigations using high-resolution satellite imagery (PLEIADES and SPOT images). A preliminary draft of the map was crosschecked and validated in the field as part of an interdisciplinary campaign carried out in the 2014 summer season. All the collected data suggest that the Mount Ararat/Agri Dagi glaciation played a crucial role in the evolution of the landscape and that even today glaciers are significant features in this area. Currently, ice bodies cover 7.28 km2 and include peculiar glacier types. Among th...

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Carlo Alberto Garzonio

Papers

Correlation between rainfall and landslides

Thermal stress in the Apuan marbles: Relationship between microstructure and petrophysical characteristics

Geomorphology of Mount Ararat/Ağri Daği (Ağri Daği Milli Parki, Eastern Anatolia, Turkey)

Relationships between the petrographical, physical and mechanical properties of some Italian sandstones

Dynamic response of the Baptistery of San Giovanni in Florence, Italy, based on ambient vibration test