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Landslide databases for climate change detection and attribution
Journal Pre-proof Landslide databases for climate change detection and attribution
J.L. Wood, S. Harrison, L. Reinhardt, F.E. Taylor PII:
S0169-555X(20)30033-7
DOI:
https://doi.org/10.1016/j.geomorph.2020.107061
Reference:
GEOMOR 107061
To appear in:
Geomorphology
Received date:
26 March 2019
Revised date:
24 January 2020
Accepted date:
27 January 2020
Please cite this article as: J.L. Wood, S. Harrison, L. Reinhardt, et al., Landslide databases for climate change detection and attribution, Geomorphology(2020), https://doi.org/ 10.1016/j.geomorph.2020.107061
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© 2020 Published by Elsevier.
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Landslide databases for climate change detection and attribution J.L. Wooda , S. Harrisona , L. Reinhardta , F.E. Taylorb College of Life and Environmental Sciences, University of Exeter, Penryn Campus, Treliever Road, Penryn, TR10 9FE b Department of Geography, Kings College London, Bush House (North East), 30 Aldwych, WC2B 4BG
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Abstract
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A systematic inventory of landslide events over regional spatial scales and through time is required for investigating changes in landslide frequency
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along-side changes in landslide triggers. This paper describes the methods used to compile a European-wide landslide inventory and some of the
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methodological and practical obstacles that inhibit better use and development of such inventories. We argue that these methods can be used more
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widely to provide a comprehensive picture of landslide populations and to further enrich our understanding of the impact of climate change and other
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drivers on landslide frequency and magnitude. Keywords: Landslides, Climate change, Detection and attribution 1. Introduction It has been suggested that climate change is likely to impact the characteristics of landslides (Keiler et al., 2010; Gariano and Guzzetti, 2016); although a climate change signal currently remains undetected in the observational landslide record (Huggel et al., 2012; Stoffel and Huggel, 2012). Preprint submitted to Geomorphology
January 29, 2020
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In order to detect and attribute changes in landslide occurrence with the climate, comprehensive, regional-scale inventories are required (Wood et al., 2015); at present, few substantially complete inventories exist (European Environment Agency, 2010; Kirschbaum et al., 2010; Guzzetti et al., 2012). In this paper we argue that while individual inventories are important and
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widely used for understanding rainfall thresholds, for landslide hazard, risk
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and susceptibility, they cannot individually cover sufficient spatial or temporal extents to discern the effect of subtle regional climate signals on landslide
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occurrence or how this may change in the future; thus there remains a significant gap in our understanding which needs to be addressed (Huggel et al.,
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2012; Stoffel and Huggel, 2012). In order to collate inventories in a unified
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database for this purpose, it is important that location, timing (date) and landslide type (classification) are documented as precisely as possible and
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that error is accounted for in the recording. Landslides are a significant geomorphological hazard in mountain regions;
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impacting life, infrastructure and resources. Due to these impacts, there have long been accounts of mass movement occurrences; with the term ’landslide’
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being first recorded in 1838 (Cruden, 2003) and the earliest landslide classification documented in 1862 by J.D. Dana (cited in Cruden, 2003). Dana’s classification of debris flows, earth spreads and rock slides was reproduced and discussed in Sharpe (1938, cited in Cruden, 2003), and has since been updated and enriched, more recently by (e.g.) Varnes (1978) and Cruden and Varnes (1996). Both the physical (in terms of the hazard) and academic significance of landslides has meant that inventories are maintained by experts and non-experts at a variety of spatial and temporal scales to
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document and describe different geomorphic processes. At a local scale, inventories are often process-based, for example recording the location and geomorphic setting of debris flows in a catchment (e.g. Malet et al., 2010); these local scale inventories are often collated by experts in the field as a way to better understand process. Regional scale inventories can be collated
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for myriad purposes: examples of these can include data from the insurance
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industry, collected by non-experts where mass movements are recorded only when these interact with infrastructure, life or property; geological survey in-
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ventories where data are collated and maintained by experts within national institutions, but which can be reported by non-experts (e.g. the British Geo-
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logical Survey in the UK or the Bureau de Recherches Gologiques et Minires
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in France). In this paper we show that despite these differences between inventories (both in terms of the spatial and temporal extent and the collation
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by experts and non-experts), more information can be obtained by collating and maintaining inventories in a unified format.
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Due to differences in expertise of the different inventories included in this paper, we use a broad term of landslide to describe any mass movement
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process which could feature in such an inventory (typically this includes rotational and translational landslides, debris and mud flows, rock falls and topples and complex mass movements). We maintain that every individual entry into the unified inventory needs to include a description of process, distinguished using the Varnes (1978) classifications (or variation thereof; see S1 Figure 1 for example). Previous work by the authors has presented a substantial landslide inventory (SLI) for the European Alps, focussing on inventory completeness
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(using power-law statistics), temporal consistency (Wood et al., 2015), and an analysis of the relationship between landslide occurrence and weather patterns (Wood et al., 2016). This paper deals primarily with the creation and maintenance of an SLI with the intention that these methods can be used and applied to other areas in order to form protocols for generating similar
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inventories for climate impacts research. We highlight ways in which to rec-
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oncile individual inventories so that they may be better used to understand the effects of climate change on landslide occurrence in the context of the
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most recent late 20th century and early 21st century warming. The European Alps are selected as the study site due to the well-documented history
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(∼100 years) of recording mass movements and process (e.g. Malet et al.,
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2010; Wood et al., 2015; BRGM, 2019) and the detailed record of temperature and precipitation across the region (e.g. Auer et al., 2007). Whilst we
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appreciate that there are many different geologies and topographies as well as climatologies which will affect and influence landslide type and occurrence, it
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is important to maintain long-term (covering at least a 30-year period) and substantially complete inventories of landslide events (see Wood et al., 2015,
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for discussion of substantial completeness). We address current gaps in our understanding of the relationship between climate change and landsliding, and the ways in which inventories might be better used to close these. 1.1. Temporal scales of climate change impacts Climate change and associated impacts occur at a range of timescales. Periods of increased landslide activity (landslide clusters) have been identified following significant climate changes; for example at the onset of the Holocene (Patzelt, 1987; Raetzo-Br¨ ulhart, 1997; Dapples et al., 2003; Soldati 4
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et al., 2004; Holm et al., 2004b; Prager et al., 2007). Whilst the PleistoceneHolocene transition represented a millennial-scale climatic shift and likely triggered landslides, increases in shallow landslide activity have also been documented at times of centennial and decadal scale climate change such as in response to the warming at the end of the Little Ice Age (e.g Holm
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et al., 2004a). More recently, since the mid-20th century there has been a
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rapid warming trend (Hansen et al., 2006) probably helping to drive changes in precipitation patterns and intensity (Beniston et al., 2007; Boer, 2009;
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Isotta et al., 2014; Christidis et al., 2015; Fischer and Knutti, 2015; Farinotti et al., 2016; Kellermann et al., 2016; Wood et al., 2016), significant glacial
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retreat (Haeberli et al., 1999; Zemp et al., 2006; Beniston et al., 2018) and
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permafrost degradation in mountain environments (Haeberli and Beniston, 1998; Haeberli et al., 2016); all which are likely to impact on the nature of
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landsliding (Keiler et al., 2010; Gariano and Guzzetti, 2016). In a recent systematic review of landslides under a changing climate,
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Gariano and Guzzetti (2016) highlight a need for longer term records to better understand the implications of recent (20th and 21st century) warming
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on landsliding. Achieving this requires long term (multi-decadal) datasets however, few such inventories exist; those which do cover narrow geographic areas, short or indiscriminate timescales (e.g. Kirschbaum et al., 2010; Van Den Eeckhaut et al., 2012; Van Den Eeckhaut and Herv´as, 2012) and are of insufficient temporal duration (e.g. <30 years) to sufficiently investigate climate change and landslide interactions. Issues relating to the duration of an inventory can be overcome by combining and unifying existing inventories. However, it is important to know the
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precision with which landslides in individual inventories have been dated as this will vary depending on the source inventory and will affect its applicability to other studies (see also Section 2 on modern and historic inventories). Inventories which are compiled by experts for determining rainfall trigger thresholds may require the precise time and day to be recorded (e.g. Segoni
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et al., 2015), whilst inventories collated by national institutions as a record
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of landslide location and occurrence, will be less precise. When creating a unified landslide inventory, it is fundamental to note the precision of dating;
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allowing portions of the inventory to be eliminated depending on the application (e.g. Wood et al., 2015). Despite these issues, collating and compiling
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inventories in a unified format provides greater potential to understand the
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effect of current changes in the climate on landslide occurrence (Wood et al., 2015; Gariano and Guzzetti, 2016).
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1.2. Spatial scales of climate change impacts
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At a global scale, climate change impacts over the 20th and 21st Centuries are distinguished by a marked rise in mean annual air temperature (Hansen
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et al., 2006), changes in ocean heat content (Levitus et al., 2000; Rhein et al., 2013) and annual sea ice loss (Rignot et al., 2008; Vaughan et al., 2013). At regional scales, climate change drives changes in phenomena such as drought (Hughes and Brown, 1992), fire (Dale et al., 2001; Swetnam, 1993; Seidl et al., 2017) and flood frequencies (Prudhomme et al., 2002; Milly et al., 2002; Hirabayashi et al., 2013), and in mountain regions, by changes in temperature (Beniston, 2004; Beniston and Stephenson, 2004; Beniston et al., 2018), permafrost distribution (Haeberli and Beniston, 1998; Haeberli et al., 2016), glacier mass balance (Haeberli et al., 1999; Zemp et al., 2006), 6
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changes in synoptic weather patterns and to the distribution, frequency and intensity of heavy precipitation events (Beniston et al., 2007; Boer, 2009; Isotta et al., 2014; Christidis et al., 2015; Fischer and Knutti, 2015; Farinotti et al., 2016; Kellermann et al., 2016; Wood et al., 2016). These (global and) regional scale impacts of climate change influence geomorphic processes
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including landsliding (Keiler et al., 2010; Knight and Harrison, 2013).
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As with the temporal scales at which landslide inventories are recorded, to understand the wider climate impacts it is important that wider geo-
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graphic areas are represented; encompassing differences in topography, geology and climate, from regional to global scales (Wood et al., 2015; Gar-
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iano and Guzzetti, 2016). These differences represent, and are indicative
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of longer-term processes such as mountain uplift, and so provide important context when looking and considering changes the effect of changing climate
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on landsliding and landside processes (see Section 2.1 for further discussion).
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2. Landslide inventories and approaches
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Landslide inventories are created for multiple purposes including: 1) the study of geomorphological features and landscape evolution, 2) definition of rainfall thresholds, 3) landslide susceptibility mapping, 4) understanding hazard and risk, 5) quantifying statistics (e.g. size-frequency distributions) and 6) climate change studies (Table 1). Metrics consistently recorded depend greatly on the purpose of the inventory; for example, susceptibility mapping (3) requires landslide location, morphological (e.g. hillslope gradient), lithological and hydrological data (e.g. Van Den Eeckhaut et al., 2010), while understanding the statistics of inventories (5) typically only requires 7
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landslide size and type to be recorded (e.g. Malamud et al., 2004a; Stark and Guzzetti, 2009a; Wood et al., 2015; Suwarno, 2018). Landslide inventories can be defined as either historic or modern types, each with their own advantages. Historic landslide inventories represent the sum of numerous landslide events over (up to) thousands of years (e.g. Mala-
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mud et al., 2004a; Korup and Clague, 2009; Guzzetti et al., 2012; Wood
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et al., 2015), enabling us to discern long-term trends and relate these to forcing mechanisms, such as glacial retreat and long-term fluctuations of both
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temperature and of precipitation following the Last Glacial Maximum (e.g. Soldati et al., 2004). The precise dating of landslides in this instance mat-
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ters less than the fact that they have been recorded. For historic inventories,
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metrics consistently documented are often limited to landslide location and size, with a bias towards large catastrophic events; and so many smaller- and
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medium-sized landslides are not included. Modern inventories are directly observed and recorded in the days and weeks following (triggered) landslide
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events; where multiple landslides resulting from a single trigger are systematically mapped. The higher resolution data enable a more comprehensive
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analysis of spatial patterns and frequency magnitude relationships than historic data.
Whilst a wealth of data exists in both modern and historic inventories, landslide location and timing remain the most important metrics for climate change impacts studies (Figure 1). These metrics are not always consistently recorded, and much depends of the inventory type. Despite the wealth of landslide inventory data which exists in individual inventories at local and regional levels, global coverage is limited to less than
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Landslide inventory
Date of occurrence
Landslide type
Size
Topographic metrics
Geology/ Lithology
e.g. latitude/ longitude
Including the level of precision
e.g based on Varnes (1978)
Area (m2) Volume (m3)
Elevation, aspect, slope gradient, slope curvature
Can be sampled based on location
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Location
Figure 1: Fields required in a comprehensive landslide inventory (adapted from Wood
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et al., 2015).
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1% of slopes (Guzzetti et al., 2012); with efforts by researchers to collate
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and compile exiting inventories being limited (e.g. Kirschbaum et al., 2010) and geographically biased (Gariano and Guzzetti, 2016). While individual
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inventories are important for understanding rainfall thresholds, for landslide hazard, risk and susceptibility they cannot cover sufficient spatial or tempo-
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ral extent to discern the effect of subtle regional climate signals on landslide occurrence or how this may change in the future (Huggel et al., 2012; Stoffel
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and Huggel, 2012). To achieve a comprehensive understanding of the spatial and temporal pattern of landsliding and assess the influence of climate, mul-
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tiple landslide inventories need to be systematically collated and maintained (Wood et al., 2015). Regional-scale landslide inventories are the minimum requirement to investigate links between the climate and landsliding; in part due to the resolution and reliability of statistically downscaled climate data (e.g. Dehn et al., 2000; Christensen et al., 2008; Kjellstr¨om et al., 2011; Christensen and Boberg, 2012; Gariano and Guzzetti, 2016).
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2.1. Drivers of landslides Landsliding is the dominant mechanism of erosion in high-relief regions (>1000m) such as the European Alps (Montgomery and Brandon, 2002; Korup et al., 2010). In such environments the pattern of tectonically driven uplift controls the general long-term pattern and frequency of landslides, but
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where slopes are at the critical angle for failure, the distribution of hillslope
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angles remains stable (Burbank et al., 1996; Binnie et al., 2007; Reinhardt et al., 2007; Larsen and Montgomery, 2012); thus areas of landslide prone
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terrain should correlate with areas of high slope angle, while areas of high
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frequency landsides (and thus rapid denudation) should correspond with areas of rapid uplift and vice versa (see Wittmann et al., 2007; Larsen and
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Montgomery, 2012). However, critical slope thresholds depend on local site conditions, bedding structure and jointing and fracturing within rock masses,
(Wood et al., 2015).
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and the occurrence of landsliding is also greatly influenced by climate change
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In a site specific context the important (local) controls on landslide type, size, frequency, and spatial distribution include: topography, in particular
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slope gradient and curvature, aspect and elevation (Van Asch and Sukmantalya, 1993; Guzzetti et al., 1996; Terlien, 1998; Dai and Lee, 2002; Santacana et al., 2003; Naoum and Tsanis, 2004; Ayalew et al., 2004; Ayalew and Yamagishi, 2005); geological controls, such as lithology, bedding structure and faulting (Dai and Lee, 2002; Santacana et al., 2003; Fourniadis et al., 2007; Grelle et al., 2011); and seismicity (Varnes, 1978; Keefer, 1984; Pearce and O’Loughlin, 1985; Kargel et al., 2016). Aspect affects soil moisture content, vegetation cover and orographic pre-
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cipitation for shallow-failures (e.g. Santacana et al., 2003; Naoum and Tsanis, 2004), while also playing an important role in freeze-thaw weathering (e.g. Mazzoccola and Hudson, 1996). Elevation is strongly coupled with temperature, which again has a bearing on freeze-thaw weathering (Mazzoccola and Hudson, 1996). Slope curvature influences the type, location and frequency
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of landslides (Guzzetti et al., 1996; Dai and Lee, 2002; Ayalew et al., 2004; Kargel et al., 2016), while slope gradient has been implicated in increased
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landslide frequency between the range of 15-30◦ (see Figure 2; Carrara et al.,
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1982; Carrara, 1983; Burbank et al., 1996; Santacana et al., 2003; Kargel et al., 2016), and so are both important factors which need to be constrained
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for landslides in order to understand longer-term landslide controls (e.g. cli-
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mate). Critical thresholds for these topographic metrics depend on local site conditions (i.e. geology, bedding and jointing; Dai and Lee, 2002; Santacana
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et al., 2003; Fourniadis et al., 2007); constraining these for landslides in the context of an SLI is therefore important and must be included where possible.
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Lithological and structural setting have both been shown to directly influence landslide type, frequency and size (Pearce and O’Loughlin, 1985;
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Guzzetti et al., 1996; Kargel et al., 2016). For areas with a varied geology, it is important to control for this in order to understand process, given that different landslides classes have also been shown to be preferentially distributed depending on lithological and geologic controls (e.g. Pearce and O’Loughlin, 1985; Guzzetti et al., 1996; Kargel et al., 2016). For landslides, sources which include these important topographic and geologic metrics are generally those resulting from academic research and are limited to catchment-scale studies (e.g. Soldati et al., 2004; Malet et al., 2010). In order to achieve a regional- to
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global-scale understanding of landslide occurrence, existing landslide inventories should be combined to create substantial landslide inventories across regions (e.g. for the European Alps, Wood et al., 2015; Kirschbaum et al., 2010, for a global inventory) however, to date this has been largely avoided due to the complex nature of the task.
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The European Alps (selected for this study) are geologically diverse and remain seismically active, currently experiencing rapid rates of uplift (of be-
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tween 1-2mm yr−1 ) and denudation (0.125mm yr−1 ; Fitzsimons and Veit,
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2001). This makes quantifying representative topographic and lithological slope stability thresholds across the region difficult (Dai et al., 2002; San-
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tacana et al., 2003; Fourniadis et al., 2007; Norton et al., 2011). This is
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highlighted in Figure 2 where we show that landslides in the SLI are most common in areas with hillslope angles between 10-30◦ ; this also emphasises
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as a whole.
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how relatively unusual are areas of steep slopes, when the Alps are considered
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3. Methods and results
Wood et al. (2015) suggest a number of metrics which should be consistently recorded in landslide inventories to quantify the relationship between climate and landslide frequency and magnitude over time: 1) location, 2) date of occurrence, 3) type (or landslide classification), 4) size, 5) topographic metrics (elevation, aspect, etc.) and 6) geological metrics (Figure 1). Having accurate landslide location data allows for any missing metrics (e.g. topography and geology) to be subsequently sampled into the inventory through the use of Geographic Information System (GIS) software however, 12
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Density
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Random sample (n=4,957, i=1,000) Landslides
Slope (degrees)
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Figure 2: Showing the slope gradient distribution for the European Alps (grey area) based on 1,000 random samples (i ) of n = 4,957 points. This is compared with slopes in the
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Alps on which landslides in the SLI were recorded.
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the accuracy of this depends on data within the original inventory (therefore the type of inventory; c.f. Table 2). Both modern and historic inventories often fail to record landslide type; this has long been considered an important metric as triggering conditions will vary between different classes of landslide (e.g. Sharpe, 1938; Varnes, 1978; Cruden and Varnes, 1996; Hungr et al., 2001; Cruden, 2003; Jakob, 2005; Hungr et al., 2014). Landslide type is also largely dependent on geological and topographic setting (Sharpe, 1938; Varnes, 1978; Keefer, 1984; Pearce and O’Loughlin, 1985; Van Asch and Sukmantalya, 1993; Guzzetti 13
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et al., 1996; Terlien, 1998; Dai and Lee, 2002; Santacana et al., 2003; Naoum and Tsanis, 2004; Ayalew et al., 2004; Ayalew and Yamagishi, 2005; Fourniadis et al., 2007; Grelle et al., 2011; Kargel et al., 2016), and so it is important to consistently record (or sample) these. Determining the effect of climate change on the occurrence of landslides
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in a given region requires the compilation of a substantial landslide inventory
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(SLI) to consistently record and include, where possible, important metrics (Figure 1). Landslide inventories are typically maintained in the form of
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a database, with columns marking the various metrics, and rows showing individual (or triggered, multiple) landslide events. The first step in creating
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an SLI is to set out these metrics (Figure 1) in a spreadsheet (Table 3);
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this allows easy manipulation within a variety of statistical and geographical software for displaying and analysing data, whilst also facilitating inventory
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compilation and maintenance. As previously addressed (Section 2), data collection methods and metrics vary substantially between studies depending
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on the purpose of the inventory (Tables 1 and 2). For statistical robustness, a substantially complete sample of a landslide population is required; this
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will vary depending on the spatial and temporal extent of individual studies. Established landslide frequency size distributions (e.g. Stark and Hovius, 2001; Malamud et al., 2004a,b) can be used to assess the completeness of any one inventory (e.g. Wood et al., 2015). To achieve statistical robustness, existing landslide inventories need to be amalgamated across national and municipal borders, and consistently maintained and updated. The methods described in the following sections were used to create an SLI for the European Alps (after Wood et al., 2015). The inventory is main-
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tained as a database (spreadsheet), and was compiled from a variety of different sources. The spatial extent covers the Swiss and French Alps (95% of landslides, n = 7524, have recorded location data; Wood et al., 2015), and landslides have been consistently recorded since the 1970s (with 65%, n = 5124, being dated to the day; Wood et al., 2015). Here we discuss the
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methods, and the success of these in maintaining an inventory for climate
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impacts research.
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3.1. Existing inventories
Existing landslide inventories are available from a variety of sources; in-
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cluding from academic literature, research institutes and insurance datasets. They can result from geomorphological mapping, aerial and satellite imagery,
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and can be either historical (i.e. documenting landslides in a region over time) or modern (e.g. for triggered landslide events; Malamud et al., 2004a).
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As previously stated, the metrics recorded in these largely depend on the
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aims of the individual study (Table 1), and when considering the effect of time-sensitive processes (such as rainfall thresholds or earthquake triggering
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of landslides) it is important to have an understanding of the precision of the recorded timing.
With aerial and satellite imagery it may be possible to date landslides to the day or month (where data are available) however, for geomorphological mapping the best resolution is possibly annual or decadal, depending on the age of the landslide. Where historical and modern inventories are concerned, the precision of dating largely depends on the initial inventory and purpose, with precision varying from the day to longer (less precise) timescales; with no definitive way in which to increase resolution. Conversely, where precise 15
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location is known, other metrics can be retrospectively sampled into the SLI. Individual inventories can also contain descriptive columns containing further details about individual landslides; including size, damage caused, fatalities, remediation works, and reactivations. There are issues with these subjective metrics in the translation and interpretation of these: for exam-
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ple, Ardizzone et al. (2002) highlight significant differences in the interpreta-
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tion and mapping of landslide deposits for landslide hazards maps, but they also show that statistical modelling greatly reduces these input errors. The
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exploration of these data can still provide useful information which is not consistently recorded within the inventory, but which would be classed as an
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important metric (Figure 1) to record.
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Existing landslide inventories are either freely available online or request access only access (the latter are typically academic and institutionally main-
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tained inventories). Both (freely available and request access inventories) can be appended to create an SLI in line with the required metrics (e.g. Figure 1),
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to further enrich the breadth of data recorded. In the following sections we present the methods used to combine a variety of existing inventory sources
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to produce an SLI for the European Alps. 3.1.1. Online open-access inventories Online datasets can (generally) be directly downloaded as a spreadsheet (commonly .xls, .csv and .txt files). Where data are available in spreadsheet format, these can be directly pasted into, and combined with other datasets in the SLI framework (Table 3). Within Europe, the Bureau de Recherches Gologiques et Minires (BRGM, 2019) provide data for France. In this open-access online inventory, data are provided via a Graphical User 16
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Interface (GUI) in the form of a map, and can be downloaded and extracted for individual landslides or entire municipalities as a .csv file. In Section 1.1 we discussed the need to note the precision of date information within individual inventories; for the BRGM this is done with a separate column detailing precision ranging from the day (n = 1,481) up to >100 years (n =
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34) (Table 2). These downloaded data can be directly copied into the SLI
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framework.
Where inventories are not available to directly download as a spreadsheet,
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data are often provided via a map-based GUI, where individual landslides are manually interrogated for the metrics required for an SLI (e.g. Figure 3).
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This method of data assimilation is labour-intensive, with important metrics
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(Figure 1) frequently missing from the source material; Figure 3 provides an example of such data gaps as the information pertaining to this landslide in-
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cludes only location (longitude, latitude and accuracy/lagegenauigkeit), classification (rockfall/steinschlag and rock and rock fall/berg- und felssturz ),
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and timing (alter der massenbewegung). The landslide inventory provided by the Geologische Bundesanstal (2019, henceforth GB) is via a map-based
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GUI for Austria. Individual landslides can be selected from the GUI (Figure 3a), data and links are then provided (Figure 3b); these data are then manually interrogated for important metrics (Figure 1). Obtaining detailed and accurate timing for these events is greatly dependant on the source material linked to the GUI; in the case of the GB, n = 24 are dated to the day, n = 10 to the month and n = 32 to the year, but it is not clear how accurate these dates are (Table 2). For datasets providing only a GUI, landslide location is determined by digitisation through satellite imagery; map-based GUIs are
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initially used to determine an approximate landslide location, with platforms such as Google Earth then being employed to determine exact landslide location. Data are then digitised and saved as a .kml file for use in GIS software, where coordinates are subsequently extracted into the SLI (Figure 3c).
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3.1.2. Request access inventories Data from academic and research institutes is not always freely available,
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but access can be requested via the institution or author. For the compi-
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lation of the European Alps SLI, inventories were obtained for Switzerland (from the Eidg. Forschungsanstalt f¨ ur Wald, Schnee und Landschaft (WSL);
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personal communication N. Hilker) and for the Barcelonette region of France (as part of the Safelands Project; personal communication J.P. Mallet); these
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were provided as spreadsheets directly from the authors. Columns containing important metrics (Figure 1) were selected from the inventories and copied
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directly into the SLI. In addition to this, the WSL and Safelands inventories both had descriptive columns containing further details about individual
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landslides; these included size, damage caused, fatalities, remediation works,
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and reactivations, but were not substantially complete. These columns were explored and relevant data (particularly that pertaining to landslide size) was extracted to the appropriate columns within the SLI. Inventory data were also obtained from the Service de Restauration des terrains en Montagne de lIsere (RTM); data here include dated rock landslides in France (see Table 2), and were provided as a .txt file from the author (personal communication A. Helmstetter). This inventory was added to the SLI, with event dates and volumes being extracted manually due to inconsistencies in the format of these throughout the RTM dataset. The Abele 18
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(a) Map-based GUI of the Geologische Bundesanstal
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-p
(2019).
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(b) An example of selected mass movement.
(c) Landslide nr.
Pr¨agraten located in Google
Earth. Figure 3: Images of the Geologische Bundesanstal (2019) database (a and b) and Google Earth (c) which was used for digitisation. Location, timing and classification are recorded
19
in the inventory (b) whilst topographic and geologic metrics can be subsequently sampled into the SLI based on location; in this case size is the only metric to be omitted.
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dataset is a historic dataset, covering large landslides in the European Alps (Figure 4); this was available as a scanned document (personal communication O. Korup), and was manually digitised to the SLI, with each landslide being recorded directly to the appropriate columns.
of
3.1.3. Archival sources Archival sources include academic journals, grey literature (e.g. confer-
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ence proceedings, technical and event reports, newspaper articles) and social
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media (Cuesta et al., 1999; Salvati et al., 2009; Damm and Klose, 2015; Taylor et al., 2015; Wood et al., 2015). Despite extensive scientific research on land-
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slides, globally and within data-rich regions, archival sources are not created with landslide inventories in mind, and extracting important and relevant
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data (e.g. Figure 1) from these is labour-intensive. This is further exacerbated by a lack of detailed location data for landslides in archival sources
na
(Table 2), which is often given at the regional level or from small-scale maps (Figure 5). For these reasons, the systematic interrogation of archival sources
ur
has not been widely employed to compile inventories, and so access to data
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remains limited.
Academic papers provide a wealth of freely available, detailed landslide data. These generally describe and discuss either individual landslides, reactivations, trigger mechanisms or geologic and topographic setting of individual or basin scale landsliding. Where individual or multiple landslides are presented, extracting this data and appending it within a landslide inventory framework (e.g. Table 3) is extremely time intensive. This method of data collection (using academic sources) was assessed for the European Alps SLI. A literature search was carried out using Google Scholar; a number of 20
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(a) Legend of the Abele (1974) dataset which describes the notations used
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throughout.
(b) An example of a typical page from the Abele (1974) scanned inventory.
21 Figure 4: The Abele (1974) database.
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papers were identified, and metrics manually recorded in the SLI. Despite many of the important metrics (Figure 1) being included in academic literature, landslide location is provided using regional-scale maps (Figure 5a) and through large-scale aerial photographs (Figure 5b). Google Earth was therefore used as a mapping tool for data collected from archival sources;
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maps and images from the source material were used alongside Google Earth
ro
in order to pinpoint landslide location (Figure 5c) and manually input into the European Alps SLI. This method is comparable to that used with the
-p
GB database (Section 3.1), where both required the interrogation of different sources, often containing data for one or multiple landslide events. Metrics
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from academic journals, such as date of occurrence and size, were input to
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the European Alps SLI in line with the important metrics (Figure 1 and Table 3). This method of data compilation proved extremely time intensive,
na
and provided <1% of the total inventory (Wood et al., 2015). Taylor et al. (2015) provide a similar methodology used to enrich the UK National Land-
ur
slide Database of the British Geological Survey using the Nexis UK digital newspaper archive. They highlight issues with the use of search terms and
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bias towards events that have affected people, but provide useful insights into archival sources of landslide inventory data and ways in which this process can be semi-automated to reduce search-time in the future. 3.2. External metrics 3.2.1. Coordinate conversion In order to maintain a unified database, it is important that coordinates are maintained in either a regional or global coordinate reference systems (CRS), and that the metadata is shared so the projection is known; this is so 22
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(a) Location of the Charmon´etier landslide (Couture et al., 1997,
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p.103).
(b) Aerial image of the Charmon´etier
(c) Charmon´etier land-
landslide (Couture et al., 1997,
slide in Google Earth
p.104).
(green polygon).
23
Figure 5: Example of archival source and how Google Earth can be used to pinpoint landslide location where this has been omitted. In Couture et al. (1997), metrics including date, volume, topography and geology were available within the text and Table 1.
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that data can be easily manipulated in a variety of software. In the case of the European Alps SLI, coordinates were maintained in latitude/longitude (WGS84) as the European Alps is located within several regional CRS. These can be converted in a number of ways however, with large numbers of landslides crossing numerous CRS, Python scripting in Linux can be (and was)
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used with the European Petroleum Survey Group (EPSG) codes appropriate
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to each countries CRS for the conversion to latitude/longitude (EPSG: 4326); issues include a lack of precision in the source material (whether location de-
-p
scribes the source or the deposit; Table 2) and distortions in area/shape
3.2.2. Geology and topography
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arising from conversion between source and WGS84 projections.
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Where landslide location is known, metrics such as elevation, aspect, slope angle and other relevant topographic (Figure 6a), geologic and lithological
na
metrics (Figure 6b) can be obtained from globally available datasets and directly sampled into an SLI using GIS software. In Quantum GIS (QGIS, an
ur
open-source GIS software), the Terrain Analysis plugin was used to calculate
Jo
aspect, slope and relief from 90 m NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), this was then sampled into the SLI using the Point-Sampling Tool in QGIS. Sampling these data into SLIs allows for interrogation and understanding of the topographic and geologic setting of different landslides to be established; which is important when investigating links between the controls on landslides and climate triggers at a regional scale. The size of the region being covered by the SLI, and the number of recorded landslides contained therein will have an effect on computational 24
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power required for analysis and the sampling of external data sources. For this reason, an assessment was made of the correlation between the lowerresolution 90 m SRTM DEM, which requires lower computational power over large spatial areas, compared with the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM for an area of
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the European Alps which is susceptible to landsliding. A random sample of n
ro
= 1,000 points was taken from both the SRTM and ASTER DEMs (Figures 7a and 7b). Linear regression was performed to assess the correlation be-
-p
tween the two sets of points for both elevation (Figure 7c) and slope gradient (Figure 7d).
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For elevation, there was a strong correlation between the two datasets,
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and other studies have shown that both SRTM and ASTER DEMs provide an analogous representation of Earths surface (e.g. Nikolakopoulos et al., 2006).
na
For slope gradient (Figure 7d), the relationship between SRTM and ASTER data is not significant (R2 ≈ 0.8), showing that the different resolutions
ur
will have an impact on derived slope gradients; although the slope of the relationship is close to 1 (slope = 0.96). It is important to consistently sample
Jo
slope gradient into SLIs so that biases (i.e. differences between the resolution of data) are systematic across the inventory. For landslide data points with limited accuracy, the increased precision of using a 30 m resolution DEM compared with 90 m is negligible. For geology and lithology, the OneGeology Portal (2019) provides an invaluable resource for geologic data (Figure 6b). Areas can be interrogated, and data downloaded directly from the OneGeology Portal (2019), or via hosting websites. These metrics can be directly sampled into the SLI through
25
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a variety of open-source GIS software. For the European Alps SLI, surface lithology data were obtained from the BRGM and Swiss Topo, via the OneGeology Portal (2019). BRGM lithology data were available at a resolution of 1:1 million, while the Swiss Topo data were available at 1:500 thousand (OneGeology Portal, 2019). Broad lithological groups (i.e. classes of sedi-
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mentary, metamorphic or igneous rocks, and for superficial sediments) were
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not available in the shapefiles, and so the .dbf file was additionally edited in R-Statistical Software (R Core Team, 2018), and broad lithological defi-
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nitions from the OneGeology Portal (2019) were assigned to each polygon;
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these data were then sampled into the SLI.
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4. Discussion
Creating a unified database across different municipality and country
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boundaries is problematic for a number of reasons. A limited number of existing landslide inventories are open-access and easily downloadable in a
ur
variety of different formats however, access to substantial existing invento-
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ries is often limited (Van Den Eeckhaut and Herv´as, 2012; Van Den Eeckhaut et al., 2012) and can be contractually confidential (Wood et al., 2015). Some datasets are only available through personal communication due to them being from academic and research institute sources, whilst others are no longer in print or not available online. Different methods used to compile existing inventories also depend on the intention of the inventory and can limit the applicability of these to other aspects of research; with archival sources there can be a bias towards human impacts, field mapping can often miss the largest landslides whilst remote sensing is biased towards the largest and 26
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(a) Showing the spatial extent of available 90 m SRTM data via the CGIAR -
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Consortium for Spatial Information (CGIAR-CSI, 2004). Available online: http:
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//srtm.csi.cgiar.org/SELECTION/inputCoord.asp.
(b) Screenshot of the OneGeology Portal database for global geology and lithology data. The resolution varies depending on location and data availability. Available online: http://portal.onegeology.org/OnegeologyGlobal/. Figure 6: Global sources of topographic (elevation; a) and geology (b) data.
27
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30m ASTER
45.8
of ro
45.4 45.2
45.6
45.8 45.6 45.4
6.6
45.2
Latitude
46.0
Latitude
46.0
46.2
46.2
90m SRTM
6.8
7.0
7.2
7.4
7.6
7.8
8.0
7.0
7.2
7.4
7.6
7.8
8.0
Longitude
-p
6.6
6.8
Longitude
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(b) Random sample of points (same point location as in 7a; n = 1000) of
1000) of SRTM data.
ASTER data.
2000
40
60
R2= 0.756 p < 0.001
20
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Slope = 1.003 Intercept = −1.964
1000
Slope gradient
ASTER (degrees)
ur
2000
Slope = 0.960 Intercept = 2.429
0
1000
ASTER (m)
na
R2= 0.999 p < 0.001
3000
4000
Elevation
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(a) Random sample of points (n =
3000
4000
0
10
SRTM (m)
20
30
40
50
SRTM (degrees)
(c) Correlation between elevation data
(d) Correlation between slope gradient
derived from SRTM (7a) and ASTER
derived from 90m SRTM (7a) and 30m
(7b) datasets for the randomly sam-
ASTER (7b) datasets for the randomly
pled points. Pearson correlation coef-
sampled points. Although the relation-
ficient (R2 =0.99) shows a statistically
ship is not significant (R2 =0.8), the
significant relationship.
28slope is ≈1.
Figure 7: Comparison between 90m SRTM and 30m ASTER datasets for use in landslide inventories. Data available online: https://earthdata.nasa.gov/.
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most recent landslides. Compiling new inventories (particularly SLIs) from archival sources is very labour-intensive, and can result in the addition of few landslides into new SLIs. Other issues relate to differences in language, with online datasets being only available in the host nations language; an issue which is particularly significant when attempting to unify landslide
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4.1. Landslide detection and attribution
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classifications (e.g. in accordance with Varnes, 1978; Wood et al., 2015).
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Detection and attribution is in its infancy for understanding the impact of climate change on extreme events such as rock slope failures and landslides
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in high mountain environments. It is used to distinguish between forced and
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unforced variability in a system; i.e. between systems whose behaviour is being driven by external changes in the climate compared with behaviour of
na
a system driven only by internal variability in the climate. 4.1.1. Temporal scales of climate change impacts
ur
As discussed (Sections 1.1 and 1.2), climate change impacts occur at a
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variety of different scales. Temporally, landslide clusters have been found to occur following significant changes in the climate (e.g. at the PleistoceneHolocene transition Patzelt, 1987; Raetzo-Br¨ ulhart, 1997; Dapples et al., 2003; Soldati et al., 2004; Holm et al., 2004b; Prager et al., 2007). To develop detection and attribution for landslides, we need (at least) high resolution records of landslide occurrence in the period preceding substantial greenhouse gas affected forcing. We would then need to compare this with a record of landslides during the period of recent greenhouse gas driven climate forcing. A lack of consistency in the temporal recording of data restricts attempts 29
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to attain a long-term perspective on the frequency of landslide events (Table 2), as it can be difficult to distinguish a real increase in frequency from an increase in recording frequency (e.g. Taylor et al., 2015; Wood et al., 2015). To overcome issues with temporal consistency, segmented models (which fit a series linear models to data) have been successfully applied to look for breaks
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in the recording of landslides in the SLI (Wood et al., 2015). These segmented
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models show that data in the SLI have been consistently recorded since the 1970s (S1 Figure 2); which lacks sufficient temporal resolution for detection
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and attribution. In the context of the most recent 20th and 21st Century warming, it is important to gain a perspective of landslide size and frequency
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both prior to, and during this period; requiring substantially complete and
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reliable landslide inventories covering (at least) the period 1940-present. 4.1.2. Spatial scale of climate change impacts
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When considering the scale of climate change impacts, it is difficult to attribute basin-scale changes in landsliding to specific changes in the climate
ur
due to the complexity of the system, and so wider-scale regions should be
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investigated (e.g. Gariano and Guzzetti, 2016). Frequency-size distributions (usually created for understanding the impacts of triggered events; e.g. Malamud et al., 2004a,b) have been successfully used to assess the completeness of inventories (e.g. Wood et al., 2015) over different regions and spatial scales (S1 Figure 3). These relationships can also be used to understand spatial and recording biases in existing inventories (S1 Figure 3). Database format, differences in language (e.g the translation of landslide definitions; Hungr et al., 2014) and classification have, in the past, obscured the possibility of attributing changes in the frequency and magni30
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tude of landsliding to climate change at a regional spatial scale. Data-gaps in existing inventories and within the literature could arise for a number of reasons including; conflict, land-use change, mitigation works, inconsistencies in recording, changes in funding and administration, and changes in peoples perception of risk. Although these serve to obscure relationships be-
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tween landslides and climate change, we hope that through the compilation
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of SLIs, issues with language difference and semantics, consistency in recording, database format and differences in language can be negated through the
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interrogation of additional information provided in individual databases and
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through international collaborative efforts to create homogenised datasets. 4.2. Substantial Landslide inventories and the European Alps SLI
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Despite these difficulties, an SLI was collated for the European Alps, comprising 7,919 landslides (Figure 1 and Table 3; Wood et al., 2015). Several
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databases used in the SLI omitted a large proportion of important metrics
ur
(Figure 1 and Table 2); such as magnitude, velocity and timing of failure, commonly recorded in modern databases (Sorriso-Valvo, 2002). These met-
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rics were included where possible, although recording of such data is inconsistent; e.g. the BRGM (2019) includes a category for landslide volume, yet only 7.5% (n = 289) landslides include volume data. The OneGeology Portal (2019) additionally provides an invaluable source for including geologic metrics, as these are rarely recorded in existing inventories (Table 2). When compiling a SLI, care must be taken when considering the inclusion of databases arising from triggered landslide, which may result in the overrepresentation of landslides at a particular time. Such inventories are perhaps not so useful when considering longer term changes in the climate, 31
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although will help to provide information about landslide populations. When analysing a SLI for the European Alps, Wood et al. (2015) found that when landslides recorded prior 1970 were excluded from analyses, results obtained were improved; the inclusion of triggered databases in SLIs are therefore important in gaining a broad perspective of the nature of landsliding in an area,
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but can be excluded from climate change analyses where appropriate.
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Recent advances in technology mean that landslide activity can be tracked through the use of Differential Interferometric Synthetic Aperture Radar (e.g.
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Schl¨ogel et al., 2015); although this is computationally intensive. Landslide recording can also be carried out through crowdsourcing data; such as the
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British Geological Surveys Report a Landslide page (British Geological Sur-
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vey, 2019), whereby people can report sightings via on online form or via email. OpenStreetMap (2019), through humanitarian projects such as Miss-
na
ingMaps (2019), have grown a mapping community which puts vulnerable communities on the map; OpenStreetMap (2019) have been leaders in setting
ur
strict fields for data recording, with on-the-ground ratification and validation from teams of experts. These advances may mean that landslide inventory
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data will expand significantly in the coming years, but will undoubtedly raise issues surrounding non-experts reporting information; issues which also exist within historical inventories, as these include a variety of reporting methods from non-experts. 5. Conclusions To date, few SLIs have been compiled from existing data sources across national boundaries and large regions. This is due to the inherent difficulties 32
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in compiling a diverse range of sources, and the time taken to achieve this. Some attempt has been made by researchers to create global catalogues, but these still fall short of capturing the wealth and variety of data which currently exists in individual inventories, globally. We hope that the methods and arguments presented here are discussed within the academic and wider
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community, and that international collaborations can build on this research
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to collate and maintain SLIs across a diverse range of climatic, topographic and geologic domains to gain a better perspective on the relationship between
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climate and landsliding.
33
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Table 1: Review of landslide inventories collected for different purposes and the methods employed to create landslides databases for the specified purpose (emboldened methods result from secondary data sources discussed in this paper). Metrics highlighted in bold text are those suggested by Wood et al. (2015) as important metrics for climate change detection and attribution studies. Papers
Metrics recorded
Coverage
Type
Geomorphological
Location; Tectonic setting; Topog-
Local,
Historic
(2002); Chigira and Yagi (2006); Galli
and field mapping;
raphy and relief; Rock strength; Ge-
regional
et al. (2008); Meunier et al. (2008);
Remote sensing;
ology; Landslide type; Size (vol-
known or approximated tempo-
Borgatti and Soldati (2010); Crozier
Modelling; Terrain
ume);
ral coverage
(2010b); Tarolli et al. (2012); Milledge
analysis
evolution; Associated landforms; Cou-
Cardinali
et
al.
na
(1999);
et al. (2014); Marc et al. (2015)
Landslide persistence;
Limitations Not
all
metrics
consistently
recorded between studies; Un-
Slope
pling (after Korup, 2003); Probable trigger
Archival sources
Location; Landslide type; Number
Local,
Modern,
Limited
temporal
coverage;
et al. (2000); Guzzetti et al. (2007,
(academic journals,
of triggered events; Date of occur-
regional
historic
Metrics
missing
between
2008); Brunetti et al. (2010); Berti
conference
rence and/or timing, precision not
archival sources (e.g. location,
et al. (2012); Peruccacci et al. (2012);
proceedings, event
always included but can be to the
date); Definition of thresholds
Nikolopoulos et al. (2014); Segoni
and technical
day, month or year; Hydrology; Ge-
are more appropriate for certain
et al. (2014); Gariano et al. (2015);
reports)
ology and/or lithology; Topography
landslide types (biased)
ur
2. Rainfall thresholds
34
Caine (1980); Terlien (1998); Glade
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1. Geomorphology
Methods
Brunsden
Marra et al. (2017); Peruccacci et al.
and/or morphology; Soil characteris-
(2017)
tics
3. Susceptibility
Parise and Jibson (2000); Dai and Lee
Geomorphological
Lithology
(2001); Dai et al. (2001); Van Westen
mapping; Aerial
graphic setting (inc.
et al. (2003); Ayalew et al. (2004);
photographs; Field
Land use; Proximity to triggers (e.g.
Lee
survey; GIS;
streams)
et
and al.
Talib
(2006a,b);
(2005); Komac
Guzzetti (2006);
Modelling (inc.
Van Den Eeckhaut et al. (2006);
probabilistic,
Hong et al. (2007); Lee (2007); Van
statistical, and
Den Eeckhaut et al. (2009); Herv´ as
logistic regression)
et al. (2010); Fuchs et al. (2013); Van Den Eeckhaut and Herv´ as (2012); Stanley
and
Kirschbaum
Reichenbach et al. (2018)
(2017);
and
geology;
Topo-
slope class);
Local,
Historic
Datasets are often substantially
regional,
incomplete (e.g. Guzzetti et al.,
global
2006b; Van Den Eeckhaut and Herv´ as, 2012); Metrics recorded vary between studies
f lP re -p ro o
Table 1 (cont.)
Papers
Metrics recorded
Digital archives;
Location;
Coverage
Type
type;
Size
Local,
Historic
Soldati (1999); Guzzetti (2000); Dai
Questionnaires
(event magnitude);
Velocity;
Ele-
regional
et al. (2002); Glade (2003); Glade
(national
ments at risk (e.g.
infrastructure);
et al. (2005); Picarelli et al. (2005);
databases); GIS
Frequency
Landslide
Limitations Used to determine spatial and temporal
probability
so
lack
temporal consistency and duration
Crozier and Glade (2006); Guzzetti (2006); Van Westen et al. (2006);
Uzielli et al. (2008); Kirschbaum et al.
na
4. Hazard and risk
Methods
Dikau et al. (1996); Flageollet (1999);
(2009); Van Den Eeckhaut and Herv´ as (2012)
(academic journals,
(2004b); Katz and Aharonov (2006);
national and
omitted; Geology is limited to
Komac (2006); Malamud and Turcotte
regional databases);
general discussion rather than
(2006); Galli et al. (2008); Lee et al.
Online sources
landslide-specific
ur
Archival sources
and Turcotte (2000); Malamud et al.
Size (area); Landslide type
Local,
Modern,
Location,
regional
historic
and topography are frequently
date of occurrence
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5. Statistics
35
Stark and Hovius (2001); Malamud
(2008); Brunetti et al. (2009); Stark and Guzzetti (2009b); Borgomeo et al. (2014); Guns and Vanacker (2014); Wood et al. (2015) 6. Climate change
Dehn (1999); Griffiths et al. (1999);
Archival sources;
Date, precision not always included
Local,
Buma et al. (2000); Buma and Dehn
Modelling (see
but can be to the day, month or year;
regional,
with few substantially complete
(2000); Collison et al. (2000); Corsini
Gariano and
Location; Size; Type; Topogra-
global
datasets exist (e.g. Stoffel and
et al. (2000); Soldati et al. (2004);
Guzzetti, 2016)
phy; Geology and Lithology
Prager et al. (2007); Borgatti and Soldati (2010); Crozier (2010a); Polemio and Petrucci (2010); Huggel et al. (2012); Stoffel and Huggel (2012); Saez et al. (2013); Wood et al. (2015); Gariano and Guzzetti (2016)
Historic
Geographically
biased
data
Huggel, 2012; Wood et al., 2015; Gariano and Guzzetti, 2016)
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Table 2: Important metrics (Figure 1) are not consistently recorded in existing landslide inventories and an understanding of their precision is frequently missed (including from within the source data). Examples of the required metrics are presented here showing the consistency of recording between different sources. Open access BRGM
Location
na
Precision ranging
GB
WSL
Request access Safeland
from the meter
Depending on
Coordinates provided,
Coordinates provided,
up to the
source material
but not precision
but not precision
Archival RTM
ur
years and unknown
Landslide type
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36
Date
Provided along with diagrams for reference
Academic papers & grey literature
Location Not provided
municipality
Day up to >100
Abele
provided but
Depending on
unclear CRS and
source material
precision Code Date: M =
Depending on
measurement, A =
source material
guess, O and X = unknown
Some dated to a precise date, but depends on the reliability of the archival source used
Some dated to a precise date, but
No dates
Depending on
others dated
provided
source material
approximately
Provided but varies Depending on source material
as described by non-experts - notes provide additional
Depending on source
All rock falls and
material
topples
Depending on source material, unclear
Described by experts
information
Size
Topographic metrics Geology/ Lithology
Provided in the notes,
Provided in the notes,
Provided, but
Depending on
but not precision,
but not precision,
Provided, but
Provided, but
Depending on
not precision
source material
depending on source
depending on source
not precision
not precision
source material
material
material
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Depending on source material Depending on source material
f lP re -p ro o na
Table 3: Example framework and structure for a SLI, with examples taken from the BRGM (2019) dataset. All column names are abbreviated, with spaces removed for ease of access with a variety of different software. Descriptions of each of the data
37
ur
are provided in the shaded row for illustration purposes only. ID
x
Lat
Lon
Year
Month
Day
depvol
mvmt
mvmt cd
ref
1
919900
2035099
27572
45024179
60411768
1978
3
21
15
slide
1
BRGM
2
997085
1878126
27572
43.7927
7.26738
1993
10
8
20
slide
1
BRGM
3
930237
2058937
27572
45.45103
6.559501
1995
2
25
8000
rockfall
2
BRGM
...
...
...
...
...
...
...
...
...
...
...
...
...
EPSG
Jo
y
depar
...
other
15 moutons ont perri
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Data availability Data for the BRGM are available online: http://infoterre.brgm.fr/ page/mouvements-terrainhttp://infoterre.brgm.fr/page/mouvements-terrain. Data for Austria are available online: https://tinyurl.com/Massenbewegungen-gb2019. Swiss data are request access only and can be obtained through the Eidg.
of
Forschungsanstalt f¨ ur Wald, Schnee und Landschaft. Other datasets are
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J.L. Wood, S. Harrison, L. Reinhardt, F.E. Taylor PII:
S0169-555X(20)30033-7
DOI:
https://doi.org/10.1016/j.geomorph.2020.107061
Reference:
GEOMOR 107061
To appear in:
Geomorphology
Received date:
26 March 2019
Revised date:
24 January 2020
Accepted date:
27 January 2020
Please cite this article as: J.L. Wood, S. Harrison, L. Reinhardt, et al., Landslide databases for climate change detection and attribution, Geomorphology(2020), https://doi.org/ 10.1016/j.geomorph.2020.107061
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Landslide databases for climate change detection and attribution J.L. Wooda , S. Harrisona , L. Reinhardta , F.E. Taylorb College of Life and Environmental Sciences, University of Exeter, Penryn Campus, Treliever Road, Penryn, TR10 9FE b Department of Geography, Kings College London, Bush House (North East), 30 Aldwych, WC2B 4BG
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Abstract
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A systematic inventory of landslide events over regional spatial scales and through time is required for investigating changes in landslide frequency
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along-side changes in landslide triggers. This paper describes the methods used to compile a European-wide landslide inventory and some of the
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methodological and practical obstacles that inhibit better use and development of such inventories. We argue that these methods can be used more
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widely to provide a comprehensive picture of landslide populations and to further enrich our understanding of the impact of climate change and other
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drivers on landslide frequency and magnitude. Keywords: Landslides, Climate change, Detection and attribution 1. Introduction It has been suggested that climate change is likely to impact the characteristics of landslides (Keiler et al., 2010; Gariano and Guzzetti, 2016); although a climate change signal currently remains undetected in the observational landslide record (Huggel et al., 2012; Stoffel and Huggel, 2012). Preprint submitted to Geomorphology
January 29, 2020
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In order to detect and attribute changes in landslide occurrence with the climate, comprehensive, regional-scale inventories are required (Wood et al., 2015); at present, few substantially complete inventories exist (European Environment Agency, 2010; Kirschbaum et al., 2010; Guzzetti et al., 2012). In this paper we argue that while individual inventories are important and
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widely used for understanding rainfall thresholds, for landslide hazard, risk
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and susceptibility, they cannot individually cover sufficient spatial or temporal extents to discern the effect of subtle regional climate signals on landslide
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occurrence or how this may change in the future; thus there remains a significant gap in our understanding which needs to be addressed (Huggel et al.,
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2012; Stoffel and Huggel, 2012). In order to collate inventories in a unified
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database for this purpose, it is important that location, timing (date) and landslide type (classification) are documented as precisely as possible and
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that error is accounted for in the recording. Landslides are a significant geomorphological hazard in mountain regions;
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impacting life, infrastructure and resources. Due to these impacts, there have long been accounts of mass movement occurrences; with the term ’landslide’
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being first recorded in 1838 (Cruden, 2003) and the earliest landslide classification documented in 1862 by J.D. Dana (cited in Cruden, 2003). Dana’s classification of debris flows, earth spreads and rock slides was reproduced and discussed in Sharpe (1938, cited in Cruden, 2003), and has since been updated and enriched, more recently by (e.g.) Varnes (1978) and Cruden and Varnes (1996). Both the physical (in terms of the hazard) and academic significance of landslides has meant that inventories are maintained by experts and non-experts at a variety of spatial and temporal scales to
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document and describe different geomorphic processes. At a local scale, inventories are often process-based, for example recording the location and geomorphic setting of debris flows in a catchment (e.g. Malet et al., 2010); these local scale inventories are often collated by experts in the field as a way to better understand process. Regional scale inventories can be collated
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for myriad purposes: examples of these can include data from the insurance
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industry, collected by non-experts where mass movements are recorded only when these interact with infrastructure, life or property; geological survey in-
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ventories where data are collated and maintained by experts within national institutions, but which can be reported by non-experts (e.g. the British Geo-
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logical Survey in the UK or the Bureau de Recherches Gologiques et Minires
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in France). In this paper we show that despite these differences between inventories (both in terms of the spatial and temporal extent and the collation
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by experts and non-experts), more information can be obtained by collating and maintaining inventories in a unified format.
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Due to differences in expertise of the different inventories included in this paper, we use a broad term of landslide to describe any mass movement
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process which could feature in such an inventory (typically this includes rotational and translational landslides, debris and mud flows, rock falls and topples and complex mass movements). We maintain that every individual entry into the unified inventory needs to include a description of process, distinguished using the Varnes (1978) classifications (or variation thereof; see S1 Figure 1 for example). Previous work by the authors has presented a substantial landslide inventory (SLI) for the European Alps, focussing on inventory completeness
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(using power-law statistics), temporal consistency (Wood et al., 2015), and an analysis of the relationship between landslide occurrence and weather patterns (Wood et al., 2016). This paper deals primarily with the creation and maintenance of an SLI with the intention that these methods can be used and applied to other areas in order to form protocols for generating similar
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inventories for climate impacts research. We highlight ways in which to rec-
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oncile individual inventories so that they may be better used to understand the effects of climate change on landslide occurrence in the context of the
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most recent late 20th century and early 21st century warming. The European Alps are selected as the study site due to the well-documented history
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(∼100 years) of recording mass movements and process (e.g. Malet et al.,
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2010; Wood et al., 2015; BRGM, 2019) and the detailed record of temperature and precipitation across the region (e.g. Auer et al., 2007). Whilst we
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appreciate that there are many different geologies and topographies as well as climatologies which will affect and influence landslide type and occurrence, it
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is important to maintain long-term (covering at least a 30-year period) and substantially complete inventories of landslide events (see Wood et al., 2015,
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for discussion of substantial completeness). We address current gaps in our understanding of the relationship between climate change and landsliding, and the ways in which inventories might be better used to close these. 1.1. Temporal scales of climate change impacts Climate change and associated impacts occur at a range of timescales. Periods of increased landslide activity (landslide clusters) have been identified following significant climate changes; for example at the onset of the Holocene (Patzelt, 1987; Raetzo-Br¨ ulhart, 1997; Dapples et al., 2003; Soldati 4
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et al., 2004; Holm et al., 2004b; Prager et al., 2007). Whilst the PleistoceneHolocene transition represented a millennial-scale climatic shift and likely triggered landslides, increases in shallow landslide activity have also been documented at times of centennial and decadal scale climate change such as in response to the warming at the end of the Little Ice Age (e.g Holm
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et al., 2004a). More recently, since the mid-20th century there has been a
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rapid warming trend (Hansen et al., 2006) probably helping to drive changes in precipitation patterns and intensity (Beniston et al., 2007; Boer, 2009;
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Isotta et al., 2014; Christidis et al., 2015; Fischer and Knutti, 2015; Farinotti et al., 2016; Kellermann et al., 2016; Wood et al., 2016), significant glacial
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retreat (Haeberli et al., 1999; Zemp et al., 2006; Beniston et al., 2018) and
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permafrost degradation in mountain environments (Haeberli and Beniston, 1998; Haeberli et al., 2016); all which are likely to impact on the nature of
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landsliding (Keiler et al., 2010; Gariano and Guzzetti, 2016). In a recent systematic review of landslides under a changing climate,
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Gariano and Guzzetti (2016) highlight a need for longer term records to better understand the implications of recent (20th and 21st century) warming
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on landsliding. Achieving this requires long term (multi-decadal) datasets however, few such inventories exist; those which do cover narrow geographic areas, short or indiscriminate timescales (e.g. Kirschbaum et al., 2010; Van Den Eeckhaut et al., 2012; Van Den Eeckhaut and Herv´as, 2012) and are of insufficient temporal duration (e.g. <30 years) to sufficiently investigate climate change and landslide interactions. Issues relating to the duration of an inventory can be overcome by combining and unifying existing inventories. However, it is important to know the
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precision with which landslides in individual inventories have been dated as this will vary depending on the source inventory and will affect its applicability to other studies (see also Section 2 on modern and historic inventories). Inventories which are compiled by experts for determining rainfall trigger thresholds may require the precise time and day to be recorded (e.g. Segoni
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et al., 2015), whilst inventories collated by national institutions as a record
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of landslide location and occurrence, will be less precise. When creating a unified landslide inventory, it is fundamental to note the precision of dating;
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allowing portions of the inventory to be eliminated depending on the application (e.g. Wood et al., 2015). Despite these issues, collating and compiling
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inventories in a unified format provides greater potential to understand the
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effect of current changes in the climate on landslide occurrence (Wood et al., 2015; Gariano and Guzzetti, 2016).
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1.2. Spatial scales of climate change impacts
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At a global scale, climate change impacts over the 20th and 21st Centuries are distinguished by a marked rise in mean annual air temperature (Hansen
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et al., 2006), changes in ocean heat content (Levitus et al., 2000; Rhein et al., 2013) and annual sea ice loss (Rignot et al., 2008; Vaughan et al., 2013). At regional scales, climate change drives changes in phenomena such as drought (Hughes and Brown, 1992), fire (Dale et al., 2001; Swetnam, 1993; Seidl et al., 2017) and flood frequencies (Prudhomme et al., 2002; Milly et al., 2002; Hirabayashi et al., 2013), and in mountain regions, by changes in temperature (Beniston, 2004; Beniston and Stephenson, 2004; Beniston et al., 2018), permafrost distribution (Haeberli and Beniston, 1998; Haeberli et al., 2016), glacier mass balance (Haeberli et al., 1999; Zemp et al., 2006), 6
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changes in synoptic weather patterns and to the distribution, frequency and intensity of heavy precipitation events (Beniston et al., 2007; Boer, 2009; Isotta et al., 2014; Christidis et al., 2015; Fischer and Knutti, 2015; Farinotti et al., 2016; Kellermann et al., 2016; Wood et al., 2016). These (global and) regional scale impacts of climate change influence geomorphic processes
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including landsliding (Keiler et al., 2010; Knight and Harrison, 2013).
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As with the temporal scales at which landslide inventories are recorded, to understand the wider climate impacts it is important that wider geo-
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graphic areas are represented; encompassing differences in topography, geology and climate, from regional to global scales (Wood et al., 2015; Gar-
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iano and Guzzetti, 2016). These differences represent, and are indicative
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of longer-term processes such as mountain uplift, and so provide important context when looking and considering changes the effect of changing climate
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on landsliding and landside processes (see Section 2.1 for further discussion).
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2. Landslide inventories and approaches
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Landslide inventories are created for multiple purposes including: 1) the study of geomorphological features and landscape evolution, 2) definition of rainfall thresholds, 3) landslide susceptibility mapping, 4) understanding hazard and risk, 5) quantifying statistics (e.g. size-frequency distributions) and 6) climate change studies (Table 1). Metrics consistently recorded depend greatly on the purpose of the inventory; for example, susceptibility mapping (3) requires landslide location, morphological (e.g. hillslope gradient), lithological and hydrological data (e.g. Van Den Eeckhaut et al., 2010), while understanding the statistics of inventories (5) typically only requires 7
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landslide size and type to be recorded (e.g. Malamud et al., 2004a; Stark and Guzzetti, 2009a; Wood et al., 2015; Suwarno, 2018). Landslide inventories can be defined as either historic or modern types, each with their own advantages. Historic landslide inventories represent the sum of numerous landslide events over (up to) thousands of years (e.g. Mala-
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mud et al., 2004a; Korup and Clague, 2009; Guzzetti et al., 2012; Wood
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et al., 2015), enabling us to discern long-term trends and relate these to forcing mechanisms, such as glacial retreat and long-term fluctuations of both
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temperature and of precipitation following the Last Glacial Maximum (e.g. Soldati et al., 2004). The precise dating of landslides in this instance mat-
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ters less than the fact that they have been recorded. For historic inventories,
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metrics consistently documented are often limited to landslide location and size, with a bias towards large catastrophic events; and so many smaller- and
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medium-sized landslides are not included. Modern inventories are directly observed and recorded in the days and weeks following (triggered) landslide
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events; where multiple landslides resulting from a single trigger are systematically mapped. The higher resolution data enable a more comprehensive
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analysis of spatial patterns and frequency magnitude relationships than historic data.
Whilst a wealth of data exists in both modern and historic inventories, landslide location and timing remain the most important metrics for climate change impacts studies (Figure 1). These metrics are not always consistently recorded, and much depends of the inventory type. Despite the wealth of landslide inventory data which exists in individual inventories at local and regional levels, global coverage is limited to less than
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Landslide inventory
Date of occurrence
Landslide type
Size
Topographic metrics
Geology/ Lithology
e.g. latitude/ longitude
Including the level of precision
e.g based on Varnes (1978)
Area (m2) Volume (m3)
Elevation, aspect, slope gradient, slope curvature
Can be sampled based on location
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Location
Figure 1: Fields required in a comprehensive landslide inventory (adapted from Wood
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et al., 2015).
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1% of slopes (Guzzetti et al., 2012); with efforts by researchers to collate
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and compile exiting inventories being limited (e.g. Kirschbaum et al., 2010) and geographically biased (Gariano and Guzzetti, 2016). While individual
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inventories are important for understanding rainfall thresholds, for landslide hazard, risk and susceptibility they cannot cover sufficient spatial or tempo-
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ral extent to discern the effect of subtle regional climate signals on landslide occurrence or how this may change in the future (Huggel et al., 2012; Stoffel
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and Huggel, 2012). To achieve a comprehensive understanding of the spatial and temporal pattern of landsliding and assess the influence of climate, mul-
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tiple landslide inventories need to be systematically collated and maintained (Wood et al., 2015). Regional-scale landslide inventories are the minimum requirement to investigate links between the climate and landsliding; in part due to the resolution and reliability of statistically downscaled climate data (e.g. Dehn et al., 2000; Christensen et al., 2008; Kjellstr¨om et al., 2011; Christensen and Boberg, 2012; Gariano and Guzzetti, 2016).
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2.1. Drivers of landslides Landsliding is the dominant mechanism of erosion in high-relief regions (>1000m) such as the European Alps (Montgomery and Brandon, 2002; Korup et al., 2010). In such environments the pattern of tectonically driven uplift controls the general long-term pattern and frequency of landslides, but
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where slopes are at the critical angle for failure, the distribution of hillslope
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angles remains stable (Burbank et al., 1996; Binnie et al., 2007; Reinhardt et al., 2007; Larsen and Montgomery, 2012); thus areas of landslide prone
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terrain should correlate with areas of high slope angle, while areas of high
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frequency landsides (and thus rapid denudation) should correspond with areas of rapid uplift and vice versa (see Wittmann et al., 2007; Larsen and
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Montgomery, 2012). However, critical slope thresholds depend on local site conditions, bedding structure and jointing and fracturing within rock masses,
(Wood et al., 2015).
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and the occurrence of landsliding is also greatly influenced by climate change
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In a site specific context the important (local) controls on landslide type, size, frequency, and spatial distribution include: topography, in particular
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slope gradient and curvature, aspect and elevation (Van Asch and Sukmantalya, 1993; Guzzetti et al., 1996; Terlien, 1998; Dai and Lee, 2002; Santacana et al., 2003; Naoum and Tsanis, 2004; Ayalew et al., 2004; Ayalew and Yamagishi, 2005); geological controls, such as lithology, bedding structure and faulting (Dai and Lee, 2002; Santacana et al., 2003; Fourniadis et al., 2007; Grelle et al., 2011); and seismicity (Varnes, 1978; Keefer, 1984; Pearce and O’Loughlin, 1985; Kargel et al., 2016). Aspect affects soil moisture content, vegetation cover and orographic pre-
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cipitation for shallow-failures (e.g. Santacana et al., 2003; Naoum and Tsanis, 2004), while also playing an important role in freeze-thaw weathering (e.g. Mazzoccola and Hudson, 1996). Elevation is strongly coupled with temperature, which again has a bearing on freeze-thaw weathering (Mazzoccola and Hudson, 1996). Slope curvature influences the type, location and frequency
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of landslides (Guzzetti et al., 1996; Dai and Lee, 2002; Ayalew et al., 2004; Kargel et al., 2016), while slope gradient has been implicated in increased
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landslide frequency between the range of 15-30◦ (see Figure 2; Carrara et al.,
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1982; Carrara, 1983; Burbank et al., 1996; Santacana et al., 2003; Kargel et al., 2016), and so are both important factors which need to be constrained
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for landslides in order to understand longer-term landslide controls (e.g. cli-
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mate). Critical thresholds for these topographic metrics depend on local site conditions (i.e. geology, bedding and jointing; Dai and Lee, 2002; Santacana
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et al., 2003; Fourniadis et al., 2007); constraining these for landslides in the context of an SLI is therefore important and must be included where possible.
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Lithological and structural setting have both been shown to directly influence landslide type, frequency and size (Pearce and O’Loughlin, 1985;
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Guzzetti et al., 1996; Kargel et al., 2016). For areas with a varied geology, it is important to control for this in order to understand process, given that different landslides classes have also been shown to be preferentially distributed depending on lithological and geologic controls (e.g. Pearce and O’Loughlin, 1985; Guzzetti et al., 1996; Kargel et al., 2016). For landslides, sources which include these important topographic and geologic metrics are generally those resulting from academic research and are limited to catchment-scale studies (e.g. Soldati et al., 2004; Malet et al., 2010). In order to achieve a regional- to
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global-scale understanding of landslide occurrence, existing landslide inventories should be combined to create substantial landslide inventories across regions (e.g. for the European Alps, Wood et al., 2015; Kirschbaum et al., 2010, for a global inventory) however, to date this has been largely avoided due to the complex nature of the task.
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The European Alps (selected for this study) are geologically diverse and remain seismically active, currently experiencing rapid rates of uplift (of be-
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tween 1-2mm yr−1 ) and denudation (0.125mm yr−1 ; Fitzsimons and Veit,
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2001). This makes quantifying representative topographic and lithological slope stability thresholds across the region difficult (Dai et al., 2002; San-
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tacana et al., 2003; Fourniadis et al., 2007; Norton et al., 2011). This is
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highlighted in Figure 2 where we show that landslides in the SLI are most common in areas with hillslope angles between 10-30◦ ; this also emphasises
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as a whole.
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how relatively unusual are areas of steep slopes, when the Alps are considered
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3. Methods and results
Wood et al. (2015) suggest a number of metrics which should be consistently recorded in landslide inventories to quantify the relationship between climate and landslide frequency and magnitude over time: 1) location, 2) date of occurrence, 3) type (or landslide classification), 4) size, 5) topographic metrics (elevation, aspect, etc.) and 6) geological metrics (Figure 1). Having accurate landslide location data allows for any missing metrics (e.g. topography and geology) to be subsequently sampled into the inventory through the use of Geographic Information System (GIS) software however, 12
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0.04
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40
60
80
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0.00
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0.02
Density
0.06
Random sample (n=4,957, i=1,000) Landslides
Slope (degrees)
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Figure 2: Showing the slope gradient distribution for the European Alps (grey area) based on 1,000 random samples (i ) of n = 4,957 points. This is compared with slopes in the
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Alps on which landslides in the SLI were recorded.
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the accuracy of this depends on data within the original inventory (therefore the type of inventory; c.f. Table 2). Both modern and historic inventories often fail to record landslide type; this has long been considered an important metric as triggering conditions will vary between different classes of landslide (e.g. Sharpe, 1938; Varnes, 1978; Cruden and Varnes, 1996; Hungr et al., 2001; Cruden, 2003; Jakob, 2005; Hungr et al., 2014). Landslide type is also largely dependent on geological and topographic setting (Sharpe, 1938; Varnes, 1978; Keefer, 1984; Pearce and O’Loughlin, 1985; Van Asch and Sukmantalya, 1993; Guzzetti 13
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et al., 1996; Terlien, 1998; Dai and Lee, 2002; Santacana et al., 2003; Naoum and Tsanis, 2004; Ayalew et al., 2004; Ayalew and Yamagishi, 2005; Fourniadis et al., 2007; Grelle et al., 2011; Kargel et al., 2016), and so it is important to consistently record (or sample) these. Determining the effect of climate change on the occurrence of landslides
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in a given region requires the compilation of a substantial landslide inventory
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(SLI) to consistently record and include, where possible, important metrics (Figure 1). Landslide inventories are typically maintained in the form of
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a database, with columns marking the various metrics, and rows showing individual (or triggered, multiple) landslide events. The first step in creating
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an SLI is to set out these metrics (Figure 1) in a spreadsheet (Table 3);
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this allows easy manipulation within a variety of statistical and geographical software for displaying and analysing data, whilst also facilitating inventory
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compilation and maintenance. As previously addressed (Section 2), data collection methods and metrics vary substantially between studies depending
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on the purpose of the inventory (Tables 1 and 2). For statistical robustness, a substantially complete sample of a landslide population is required; this
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will vary depending on the spatial and temporal extent of individual studies. Established landslide frequency size distributions (e.g. Stark and Hovius, 2001; Malamud et al., 2004a,b) can be used to assess the completeness of any one inventory (e.g. Wood et al., 2015). To achieve statistical robustness, existing landslide inventories need to be amalgamated across national and municipal borders, and consistently maintained and updated. The methods described in the following sections were used to create an SLI for the European Alps (after Wood et al., 2015). The inventory is main-
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tained as a database (spreadsheet), and was compiled from a variety of different sources. The spatial extent covers the Swiss and French Alps (95% of landslides, n = 7524, have recorded location data; Wood et al., 2015), and landslides have been consistently recorded since the 1970s (with 65%, n = 5124, being dated to the day; Wood et al., 2015). Here we discuss the
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methods, and the success of these in maintaining an inventory for climate
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impacts research.
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3.1. Existing inventories
Existing landslide inventories are available from a variety of sources; in-
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cluding from academic literature, research institutes and insurance datasets. They can result from geomorphological mapping, aerial and satellite imagery,
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and can be either historical (i.e. documenting landslides in a region over time) or modern (e.g. for triggered landslide events; Malamud et al., 2004a).
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As previously stated, the metrics recorded in these largely depend on the
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aims of the individual study (Table 1), and when considering the effect of time-sensitive processes (such as rainfall thresholds or earthquake triggering
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of landslides) it is important to have an understanding of the precision of the recorded timing.
With aerial and satellite imagery it may be possible to date landslides to the day or month (where data are available) however, for geomorphological mapping the best resolution is possibly annual or decadal, depending on the age of the landslide. Where historical and modern inventories are concerned, the precision of dating largely depends on the initial inventory and purpose, with precision varying from the day to longer (less precise) timescales; with no definitive way in which to increase resolution. Conversely, where precise 15
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location is known, other metrics can be retrospectively sampled into the SLI. Individual inventories can also contain descriptive columns containing further details about individual landslides; including size, damage caused, fatalities, remediation works, and reactivations. There are issues with these subjective metrics in the translation and interpretation of these: for exam-
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ple, Ardizzone et al. (2002) highlight significant differences in the interpreta-
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tion and mapping of landslide deposits for landslide hazards maps, but they also show that statistical modelling greatly reduces these input errors. The
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exploration of these data can still provide useful information which is not consistently recorded within the inventory, but which would be classed as an
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important metric (Figure 1) to record.
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Existing landslide inventories are either freely available online or request access only access (the latter are typically academic and institutionally main-
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tained inventories). Both (freely available and request access inventories) can be appended to create an SLI in line with the required metrics (e.g. Figure 1),
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to further enrich the breadth of data recorded. In the following sections we present the methods used to combine a variety of existing inventory sources
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to produce an SLI for the European Alps. 3.1.1. Online open-access inventories Online datasets can (generally) be directly downloaded as a spreadsheet (commonly .xls, .csv and .txt files). Where data are available in spreadsheet format, these can be directly pasted into, and combined with other datasets in the SLI framework (Table 3). Within Europe, the Bureau de Recherches Gologiques et Minires (BRGM, 2019) provide data for France. In this open-access online inventory, data are provided via a Graphical User 16
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Interface (GUI) in the form of a map, and can be downloaded and extracted for individual landslides or entire municipalities as a .csv file. In Section 1.1 we discussed the need to note the precision of date information within individual inventories; for the BRGM this is done with a separate column detailing precision ranging from the day (n = 1,481) up to >100 years (n =
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34) (Table 2). These downloaded data can be directly copied into the SLI
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framework.
Where inventories are not available to directly download as a spreadsheet,
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data are often provided via a map-based GUI, where individual landslides are manually interrogated for the metrics required for an SLI (e.g. Figure 3).
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This method of data assimilation is labour-intensive, with important metrics
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(Figure 1) frequently missing from the source material; Figure 3 provides an example of such data gaps as the information pertaining to this landslide in-
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cludes only location (longitude, latitude and accuracy/lagegenauigkeit), classification (rockfall/steinschlag and rock and rock fall/berg- und felssturz ),
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and timing (alter der massenbewegung). The landslide inventory provided by the Geologische Bundesanstal (2019, henceforth GB) is via a map-based
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GUI for Austria. Individual landslides can be selected from the GUI (Figure 3a), data and links are then provided (Figure 3b); these data are then manually interrogated for important metrics (Figure 1). Obtaining detailed and accurate timing for these events is greatly dependant on the source material linked to the GUI; in the case of the GB, n = 24 are dated to the day, n = 10 to the month and n = 32 to the year, but it is not clear how accurate these dates are (Table 2). For datasets providing only a GUI, landslide location is determined by digitisation through satellite imagery; map-based GUIs are
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initially used to determine an approximate landslide location, with platforms such as Google Earth then being employed to determine exact landslide location. Data are then digitised and saved as a .kml file for use in GIS software, where coordinates are subsequently extracted into the SLI (Figure 3c).
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3.1.2. Request access inventories Data from academic and research institutes is not always freely available,
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but access can be requested via the institution or author. For the compi-
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lation of the European Alps SLI, inventories were obtained for Switzerland (from the Eidg. Forschungsanstalt f¨ ur Wald, Schnee und Landschaft (WSL);
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personal communication N. Hilker) and for the Barcelonette region of France (as part of the Safelands Project; personal communication J.P. Mallet); these
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were provided as spreadsheets directly from the authors. Columns containing important metrics (Figure 1) were selected from the inventories and copied
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directly into the SLI. In addition to this, the WSL and Safelands inventories both had descriptive columns containing further details about individual
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landslides; these included size, damage caused, fatalities, remediation works,
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and reactivations, but were not substantially complete. These columns were explored and relevant data (particularly that pertaining to landslide size) was extracted to the appropriate columns within the SLI. Inventory data were also obtained from the Service de Restauration des terrains en Montagne de lIsere (RTM); data here include dated rock landslides in France (see Table 2), and were provided as a .txt file from the author (personal communication A. Helmstetter). This inventory was added to the SLI, with event dates and volumes being extracted manually due to inconsistencies in the format of these throughout the RTM dataset. The Abele 18
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(a) Map-based GUI of the Geologische Bundesanstal
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(2019).
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(b) An example of selected mass movement.
(c) Landslide nr.
Pr¨agraten located in Google
Earth. Figure 3: Images of the Geologische Bundesanstal (2019) database (a and b) and Google Earth (c) which was used for digitisation. Location, timing and classification are recorded
19
in the inventory (b) whilst topographic and geologic metrics can be subsequently sampled into the SLI based on location; in this case size is the only metric to be omitted.
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dataset is a historic dataset, covering large landslides in the European Alps (Figure 4); this was available as a scanned document (personal communication O. Korup), and was manually digitised to the SLI, with each landslide being recorded directly to the appropriate columns.
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3.1.3. Archival sources Archival sources include academic journals, grey literature (e.g. confer-
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ence proceedings, technical and event reports, newspaper articles) and social
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media (Cuesta et al., 1999; Salvati et al., 2009; Damm and Klose, 2015; Taylor et al., 2015; Wood et al., 2015). Despite extensive scientific research on land-
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slides, globally and within data-rich regions, archival sources are not created with landslide inventories in mind, and extracting important and relevant
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data (e.g. Figure 1) from these is labour-intensive. This is further exacerbated by a lack of detailed location data for landslides in archival sources
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(Table 2), which is often given at the regional level or from small-scale maps (Figure 5). For these reasons, the systematic interrogation of archival sources
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has not been widely employed to compile inventories, and so access to data
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remains limited.
Academic papers provide a wealth of freely available, detailed landslide data. These generally describe and discuss either individual landslides, reactivations, trigger mechanisms or geologic and topographic setting of individual or basin scale landsliding. Where individual or multiple landslides are presented, extracting this data and appending it within a landslide inventory framework (e.g. Table 3) is extremely time intensive. This method of data collection (using academic sources) was assessed for the European Alps SLI. A literature search was carried out using Google Scholar; a number of 20
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(a) Legend of the Abele (1974) dataset which describes the notations used
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throughout.
(b) An example of a typical page from the Abele (1974) scanned inventory.
21 Figure 4: The Abele (1974) database.
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papers were identified, and metrics manually recorded in the SLI. Despite many of the important metrics (Figure 1) being included in academic literature, landslide location is provided using regional-scale maps (Figure 5a) and through large-scale aerial photographs (Figure 5b). Google Earth was therefore used as a mapping tool for data collected from archival sources;
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maps and images from the source material were used alongside Google Earth
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in order to pinpoint landslide location (Figure 5c) and manually input into the European Alps SLI. This method is comparable to that used with the
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GB database (Section 3.1), where both required the interrogation of different sources, often containing data for one or multiple landslide events. Metrics
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from academic journals, such as date of occurrence and size, were input to
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the European Alps SLI in line with the important metrics (Figure 1 and Table 3). This method of data compilation proved extremely time intensive,
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and provided <1% of the total inventory (Wood et al., 2015). Taylor et al. (2015) provide a similar methodology used to enrich the UK National Land-
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slide Database of the British Geological Survey using the Nexis UK digital newspaper archive. They highlight issues with the use of search terms and
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bias towards events that have affected people, but provide useful insights into archival sources of landslide inventory data and ways in which this process can be semi-automated to reduce search-time in the future. 3.2. External metrics 3.2.1. Coordinate conversion In order to maintain a unified database, it is important that coordinates are maintained in either a regional or global coordinate reference systems (CRS), and that the metadata is shared so the projection is known; this is so 22
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(a) Location of the Charmon´etier landslide (Couture et al., 1997,
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p.103).
(b) Aerial image of the Charmon´etier
(c) Charmon´etier land-
landslide (Couture et al., 1997,
slide in Google Earth
p.104).
(green polygon).
23
Figure 5: Example of archival source and how Google Earth can be used to pinpoint landslide location where this has been omitted. In Couture et al. (1997), metrics including date, volume, topography and geology were available within the text and Table 1.
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that data can be easily manipulated in a variety of software. In the case of the European Alps SLI, coordinates were maintained in latitude/longitude (WGS84) as the European Alps is located within several regional CRS. These can be converted in a number of ways however, with large numbers of landslides crossing numerous CRS, Python scripting in Linux can be (and was)
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used with the European Petroleum Survey Group (EPSG) codes appropriate
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to each countries CRS for the conversion to latitude/longitude (EPSG: 4326); issues include a lack of precision in the source material (whether location de-
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scribes the source or the deposit; Table 2) and distortions in area/shape
3.2.2. Geology and topography
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arising from conversion between source and WGS84 projections.
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Where landslide location is known, metrics such as elevation, aspect, slope angle and other relevant topographic (Figure 6a), geologic and lithological
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metrics (Figure 6b) can be obtained from globally available datasets and directly sampled into an SLI using GIS software. In Quantum GIS (QGIS, an
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open-source GIS software), the Terrain Analysis plugin was used to calculate
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aspect, slope and relief from 90 m NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), this was then sampled into the SLI using the Point-Sampling Tool in QGIS. Sampling these data into SLIs allows for interrogation and understanding of the topographic and geologic setting of different landslides to be established; which is important when investigating links between the controls on landslides and climate triggers at a regional scale. The size of the region being covered by the SLI, and the number of recorded landslides contained therein will have an effect on computational 24
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power required for analysis and the sampling of external data sources. For this reason, an assessment was made of the correlation between the lowerresolution 90 m SRTM DEM, which requires lower computational power over large spatial areas, compared with the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM for an area of
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the European Alps which is susceptible to landsliding. A random sample of n
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= 1,000 points was taken from both the SRTM and ASTER DEMs (Figures 7a and 7b). Linear regression was performed to assess the correlation be-
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tween the two sets of points for both elevation (Figure 7c) and slope gradient (Figure 7d).
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For elevation, there was a strong correlation between the two datasets,
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and other studies have shown that both SRTM and ASTER DEMs provide an analogous representation of Earths surface (e.g. Nikolakopoulos et al., 2006).
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For slope gradient (Figure 7d), the relationship between SRTM and ASTER data is not significant (R2 ≈ 0.8), showing that the different resolutions
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will have an impact on derived slope gradients; although the slope of the relationship is close to 1 (slope = 0.96). It is important to consistently sample
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slope gradient into SLIs so that biases (i.e. differences between the resolution of data) are systematic across the inventory. For landslide data points with limited accuracy, the increased precision of using a 30 m resolution DEM compared with 90 m is negligible. For geology and lithology, the OneGeology Portal (2019) provides an invaluable resource for geologic data (Figure 6b). Areas can be interrogated, and data downloaded directly from the OneGeology Portal (2019), or via hosting websites. These metrics can be directly sampled into the SLI through
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a variety of open-source GIS software. For the European Alps SLI, surface lithology data were obtained from the BRGM and Swiss Topo, via the OneGeology Portal (2019). BRGM lithology data were available at a resolution of 1:1 million, while the Swiss Topo data were available at 1:500 thousand (OneGeology Portal, 2019). Broad lithological groups (i.e. classes of sedi-
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mentary, metamorphic or igneous rocks, and for superficial sediments) were
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not available in the shapefiles, and so the .dbf file was additionally edited in R-Statistical Software (R Core Team, 2018), and broad lithological defi-
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nitions from the OneGeology Portal (2019) were assigned to each polygon;
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these data were then sampled into the SLI.
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4. Discussion
Creating a unified database across different municipality and country
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boundaries is problematic for a number of reasons. A limited number of existing landslide inventories are open-access and easily downloadable in a
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variety of different formats however, access to substantial existing invento-
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ries is often limited (Van Den Eeckhaut and Herv´as, 2012; Van Den Eeckhaut et al., 2012) and can be contractually confidential (Wood et al., 2015). Some datasets are only available through personal communication due to them being from academic and research institute sources, whilst others are no longer in print or not available online. Different methods used to compile existing inventories also depend on the intention of the inventory and can limit the applicability of these to other aspects of research; with archival sources there can be a bias towards human impacts, field mapping can often miss the largest landslides whilst remote sensing is biased towards the largest and 26
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(a) Showing the spatial extent of available 90 m SRTM data via the CGIAR -
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Consortium for Spatial Information (CGIAR-CSI, 2004). Available online: http:
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//srtm.csi.cgiar.org/SELECTION/inputCoord.asp.
(b) Screenshot of the OneGeology Portal database for global geology and lithology data. The resolution varies depending on location and data availability. Available online: http://portal.onegeology.org/OnegeologyGlobal/. Figure 6: Global sources of topographic (elevation; a) and geology (b) data.
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30m ASTER
45.8
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45.4 45.2
45.6
45.8 45.6 45.4
6.6
45.2
Latitude
46.0
Latitude
46.0
46.2
46.2
90m SRTM
6.8
7.0
7.2
7.4
7.6
7.8
8.0
7.0
7.2
7.4
7.6
7.8
8.0
Longitude
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6.6
6.8
Longitude
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(b) Random sample of points (same point location as in 7a; n = 1000) of
1000) of SRTM data.
ASTER data.
2000
40
60
R2= 0.756 p < 0.001
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Slope = 1.003 Intercept = −1.964
1000
Slope gradient
ASTER (degrees)
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2000
Slope = 0.960 Intercept = 2.429
0
1000
ASTER (m)
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R2= 0.999 p < 0.001
3000
4000
Elevation
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(a) Random sample of points (n =
3000
4000
0
10
SRTM (m)
20
30
40
50
SRTM (degrees)
(c) Correlation between elevation data
(d) Correlation between slope gradient
derived from SRTM (7a) and ASTER
derived from 90m SRTM (7a) and 30m
(7b) datasets for the randomly sam-
ASTER (7b) datasets for the randomly
pled points. Pearson correlation coef-
sampled points. Although the relation-
ficient (R2 =0.99) shows a statistically
ship is not significant (R2 =0.8), the
significant relationship.
28slope is ≈1.
Figure 7: Comparison between 90m SRTM and 30m ASTER datasets for use in landslide inventories. Data available online: https://earthdata.nasa.gov/.
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most recent landslides. Compiling new inventories (particularly SLIs) from archival sources is very labour-intensive, and can result in the addition of few landslides into new SLIs. Other issues relate to differences in language, with online datasets being only available in the host nations language; an issue which is particularly significant when attempting to unify landslide
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4.1. Landslide detection and attribution
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classifications (e.g. in accordance with Varnes, 1978; Wood et al., 2015).
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Detection and attribution is in its infancy for understanding the impact of climate change on extreme events such as rock slope failures and landslides
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in high mountain environments. It is used to distinguish between forced and
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unforced variability in a system; i.e. between systems whose behaviour is being driven by external changes in the climate compared with behaviour of
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a system driven only by internal variability in the climate. 4.1.1. Temporal scales of climate change impacts
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As discussed (Sections 1.1 and 1.2), climate change impacts occur at a
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variety of different scales. Temporally, landslide clusters have been found to occur following significant changes in the climate (e.g. at the PleistoceneHolocene transition Patzelt, 1987; Raetzo-Br¨ ulhart, 1997; Dapples et al., 2003; Soldati et al., 2004; Holm et al., 2004b; Prager et al., 2007). To develop detection and attribution for landslides, we need (at least) high resolution records of landslide occurrence in the period preceding substantial greenhouse gas affected forcing. We would then need to compare this with a record of landslides during the period of recent greenhouse gas driven climate forcing. A lack of consistency in the temporal recording of data restricts attempts 29
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to attain a long-term perspective on the frequency of landslide events (Table 2), as it can be difficult to distinguish a real increase in frequency from an increase in recording frequency (e.g. Taylor et al., 2015; Wood et al., 2015). To overcome issues with temporal consistency, segmented models (which fit a series linear models to data) have been successfully applied to look for breaks
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in the recording of landslides in the SLI (Wood et al., 2015). These segmented
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models show that data in the SLI have been consistently recorded since the 1970s (S1 Figure 2); which lacks sufficient temporal resolution for detection
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and attribution. In the context of the most recent 20th and 21st Century warming, it is important to gain a perspective of landslide size and frequency
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both prior to, and during this period; requiring substantially complete and
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reliable landslide inventories covering (at least) the period 1940-present. 4.1.2. Spatial scale of climate change impacts
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When considering the scale of climate change impacts, it is difficult to attribute basin-scale changes in landsliding to specific changes in the climate
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due to the complexity of the system, and so wider-scale regions should be
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investigated (e.g. Gariano and Guzzetti, 2016). Frequency-size distributions (usually created for understanding the impacts of triggered events; e.g. Malamud et al., 2004a,b) have been successfully used to assess the completeness of inventories (e.g. Wood et al., 2015) over different regions and spatial scales (S1 Figure 3). These relationships can also be used to understand spatial and recording biases in existing inventories (S1 Figure 3). Database format, differences in language (e.g the translation of landslide definitions; Hungr et al., 2014) and classification have, in the past, obscured the possibility of attributing changes in the frequency and magni30
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tude of landsliding to climate change at a regional spatial scale. Data-gaps in existing inventories and within the literature could arise for a number of reasons including; conflict, land-use change, mitigation works, inconsistencies in recording, changes in funding and administration, and changes in peoples perception of risk. Although these serve to obscure relationships be-
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tween landslides and climate change, we hope that through the compilation
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of SLIs, issues with language difference and semantics, consistency in recording, database format and differences in language can be negated through the
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interrogation of additional information provided in individual databases and
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through international collaborative efforts to create homogenised datasets. 4.2. Substantial Landslide inventories and the European Alps SLI
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Despite these difficulties, an SLI was collated for the European Alps, comprising 7,919 landslides (Figure 1 and Table 3; Wood et al., 2015). Several
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databases used in the SLI omitted a large proportion of important metrics
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(Figure 1 and Table 2); such as magnitude, velocity and timing of failure, commonly recorded in modern databases (Sorriso-Valvo, 2002). These met-
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rics were included where possible, although recording of such data is inconsistent; e.g. the BRGM (2019) includes a category for landslide volume, yet only 7.5% (n = 289) landslides include volume data. The OneGeology Portal (2019) additionally provides an invaluable source for including geologic metrics, as these are rarely recorded in existing inventories (Table 2). When compiling a SLI, care must be taken when considering the inclusion of databases arising from triggered landslide, which may result in the overrepresentation of landslides at a particular time. Such inventories are perhaps not so useful when considering longer term changes in the climate, 31
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although will help to provide information about landslide populations. When analysing a SLI for the European Alps, Wood et al. (2015) found that when landslides recorded prior 1970 were excluded from analyses, results obtained were improved; the inclusion of triggered databases in SLIs are therefore important in gaining a broad perspective of the nature of landsliding in an area,
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but can be excluded from climate change analyses where appropriate.
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Recent advances in technology mean that landslide activity can be tracked through the use of Differential Interferometric Synthetic Aperture Radar (e.g.
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Schl¨ogel et al., 2015); although this is computationally intensive. Landslide recording can also be carried out through crowdsourcing data; such as the
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British Geological Surveys Report a Landslide page (British Geological Sur-
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vey, 2019), whereby people can report sightings via on online form or via email. OpenStreetMap (2019), through humanitarian projects such as Miss-
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ingMaps (2019), have grown a mapping community which puts vulnerable communities on the map; OpenStreetMap (2019) have been leaders in setting
ur
strict fields for data recording, with on-the-ground ratification and validation from teams of experts. These advances may mean that landslide inventory
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data will expand significantly in the coming years, but will undoubtedly raise issues surrounding non-experts reporting information; issues which also exist within historical inventories, as these include a variety of reporting methods from non-experts. 5. Conclusions To date, few SLIs have been compiled from existing data sources across national boundaries and large regions. This is due to the inherent difficulties 32
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in compiling a diverse range of sources, and the time taken to achieve this. Some attempt has been made by researchers to create global catalogues, but these still fall short of capturing the wealth and variety of data which currently exists in individual inventories, globally. We hope that the methods and arguments presented here are discussed within the academic and wider
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community, and that international collaborations can build on this research
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to collate and maintain SLIs across a diverse range of climatic, topographic and geologic domains to gain a better perspective on the relationship between
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ur
na
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re
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climate and landsliding.
33
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Table 1: Review of landslide inventories collected for different purposes and the methods employed to create landslides databases for the specified purpose (emboldened methods result from secondary data sources discussed in this paper). Metrics highlighted in bold text are those suggested by Wood et al. (2015) as important metrics for climate change detection and attribution studies. Papers
Metrics recorded
Coverage
Type
Geomorphological
Location; Tectonic setting; Topog-
Local,
Historic
(2002); Chigira and Yagi (2006); Galli
and field mapping;
raphy and relief; Rock strength; Ge-
regional
et al. (2008); Meunier et al. (2008);
Remote sensing;
ology; Landslide type; Size (vol-
known or approximated tempo-
Borgatti and Soldati (2010); Crozier
Modelling; Terrain
ume);
ral coverage
(2010b); Tarolli et al. (2012); Milledge
analysis
evolution; Associated landforms; Cou-
Cardinali
et
al.
na
(1999);
et al. (2014); Marc et al. (2015)
Landslide persistence;
Limitations Not
all
metrics
consistently
recorded between studies; Un-
Slope
pling (after Korup, 2003); Probable trigger
Archival sources
Location; Landslide type; Number
Local,
Modern,
Limited
temporal
coverage;
et al. (2000); Guzzetti et al. (2007,
(academic journals,
of triggered events; Date of occur-
regional
historic
Metrics
missing
between
2008); Brunetti et al. (2010); Berti
conference
rence and/or timing, precision not
archival sources (e.g. location,
et al. (2012); Peruccacci et al. (2012);
proceedings, event
always included but can be to the
date); Definition of thresholds
Nikolopoulos et al. (2014); Segoni
and technical
day, month or year; Hydrology; Ge-
are more appropriate for certain
et al. (2014); Gariano et al. (2015);
reports)
ology and/or lithology; Topography
landslide types (biased)
ur
2. Rainfall thresholds
34
Caine (1980); Terlien (1998); Glade
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1. Geomorphology
Methods
Brunsden
Marra et al. (2017); Peruccacci et al.
and/or morphology; Soil characteris-
(2017)
tics
3. Susceptibility
Parise and Jibson (2000); Dai and Lee
Geomorphological
Lithology
(2001); Dai et al. (2001); Van Westen
mapping; Aerial
graphic setting (inc.
et al. (2003); Ayalew et al. (2004);
photographs; Field
Land use; Proximity to triggers (e.g.
Lee
survey; GIS;
streams)
et
and al.
Talib
(2006a,b);
(2005); Komac
Guzzetti (2006);
Modelling (inc.
Van Den Eeckhaut et al. (2006);
probabilistic,
Hong et al. (2007); Lee (2007); Van
statistical, and
Den Eeckhaut et al. (2009); Herv´ as
logistic regression)
et al. (2010); Fuchs et al. (2013); Van Den Eeckhaut and Herv´ as (2012); Stanley
and
Kirschbaum
Reichenbach et al. (2018)
(2017);
and
geology;
Topo-
slope class);
Local,
Historic
Datasets are often substantially
regional,
incomplete (e.g. Guzzetti et al.,
global
2006b; Van Den Eeckhaut and Herv´ as, 2012); Metrics recorded vary between studies
f lP re -p ro o
Table 1 (cont.)
Papers
Metrics recorded
Digital archives;
Location;
Coverage
Type
type;
Size
Local,
Historic
Soldati (1999); Guzzetti (2000); Dai
Questionnaires
(event magnitude);
Velocity;
Ele-
regional
et al. (2002); Glade (2003); Glade
(national
ments at risk (e.g.
infrastructure);
et al. (2005); Picarelli et al. (2005);
databases); GIS
Frequency
Landslide
Limitations Used to determine spatial and temporal
probability
so
lack
temporal consistency and duration
Crozier and Glade (2006); Guzzetti (2006); Van Westen et al. (2006);
Uzielli et al. (2008); Kirschbaum et al.
na
4. Hazard and risk
Methods
Dikau et al. (1996); Flageollet (1999);
(2009); Van Den Eeckhaut and Herv´ as (2012)
(academic journals,
(2004b); Katz and Aharonov (2006);
national and
omitted; Geology is limited to
Komac (2006); Malamud and Turcotte
regional databases);
general discussion rather than
(2006); Galli et al. (2008); Lee et al.
Online sources
landslide-specific
ur
Archival sources
and Turcotte (2000); Malamud et al.
Size (area); Landslide type
Local,
Modern,
Location,
regional
historic
and topography are frequently
date of occurrence
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5. Statistics
35
Stark and Hovius (2001); Malamud
(2008); Brunetti et al. (2009); Stark and Guzzetti (2009b); Borgomeo et al. (2014); Guns and Vanacker (2014); Wood et al. (2015) 6. Climate change
Dehn (1999); Griffiths et al. (1999);
Archival sources;
Date, precision not always included
Local,
Buma et al. (2000); Buma and Dehn
Modelling (see
but can be to the day, month or year;
regional,
with few substantially complete
(2000); Collison et al. (2000); Corsini
Gariano and
Location; Size; Type; Topogra-
global
datasets exist (e.g. Stoffel and
et al. (2000); Soldati et al. (2004);
Guzzetti, 2016)
phy; Geology and Lithology
Prager et al. (2007); Borgatti and Soldati (2010); Crozier (2010a); Polemio and Petrucci (2010); Huggel et al. (2012); Stoffel and Huggel (2012); Saez et al. (2013); Wood et al. (2015); Gariano and Guzzetti (2016)
Historic
Geographically
biased
data
Huggel, 2012; Wood et al., 2015; Gariano and Guzzetti, 2016)
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Table 2: Important metrics (Figure 1) are not consistently recorded in existing landslide inventories and an understanding of their precision is frequently missed (including from within the source data). Examples of the required metrics are presented here showing the consistency of recording between different sources. Open access BRGM
Location
na
Precision ranging
GB
WSL
Request access Safeland
from the meter
Depending on
Coordinates provided,
Coordinates provided,
up to the
source material
but not precision
but not precision
Archival RTM
ur
years and unknown
Landslide type
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36
Date
Provided along with diagrams for reference
Academic papers & grey literature
Location Not provided
municipality
Day up to >100
Abele
provided but
Depending on
unclear CRS and
source material
precision Code Date: M =
Depending on
measurement, A =
source material
guess, O and X = unknown
Some dated to a precise date, but depends on the reliability of the archival source used
Some dated to a precise date, but
No dates
Depending on
others dated
provided
source material
approximately
Provided but varies Depending on source material
as described by non-experts - notes provide additional
Depending on source
All rock falls and
material
topples
Depending on source material, unclear
Described by experts
information
Size
Topographic metrics Geology/ Lithology
Provided in the notes,
Provided in the notes,
Provided, but
Depending on
but not precision,
but not precision,
Provided, but
Provided, but
Depending on
not precision
source material
depending on source
depending on source
not precision
not precision
source material
material
material
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Depending on source material Depending on source material
f lP re -p ro o na
Table 3: Example framework and structure for a SLI, with examples taken from the BRGM (2019) dataset. All column names are abbreviated, with spaces removed for ease of access with a variety of different software. Descriptions of each of the data
37
ur
are provided in the shaded row for illustration purposes only. ID
x
Lat
Lon
Year
Month
Day
depvol
mvmt
mvmt cd
ref
1
919900
2035099
27572
45024179
60411768
1978
3
21
15
slide
1
BRGM
2
997085
1878126
27572
43.7927
7.26738
1993
10
8
20
slide
1
BRGM
3
930237
2058937
27572
45.45103
6.559501
1995
2
25
8000
rockfall
2
BRGM
...
...
...
...
...
...
...
...
...
...
...
...
...
EPSG
Jo
y
depar
...
other
15 moutons ont perri
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