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Environmental Earth Sciences ISSN 1866-6280 Environ Earth SciDOI 10.1007/s12665-012-2181-2

Landslide temporal analysis andsusceptibility assessment as bases forlandslide mitigation, Machu Picchu, Peru

Jan Klimeš

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ORIGINAL ARTICLE

Landslide temporal analysis and susceptibility assessmentas bases for landslide mitigation, Machu Picchu, Peru

Jan Klimes

Received: 7 August 2012 / Accepted: 3 December 2012

� Springer-Verlag Berlin Heidelberg 2012

Abstract Multi-temporal landslide occurrence informa-

tion acquired through aerial photo interpretation and field

mapping was used to assess occurrence frequencies on the

slopes around the UNESCO cultural world heritage site of

Machu Picchu, Peru. This showed that the coarse time res-

olution of the historical landslide information may lead to

inaccurate interpretations regarding landslide occurrence

frequencies in some parts of the study area. In addition, the

assumption that the past landslide frequency can be used to

describe the future landslide occurrence was not proved in

the study area. Thereafter, unique conditional analyses were

undertaken to assess landslide susceptibility using a limited

number of preparatory factor maps. It showed that large

majority of the Inca City is located on least susceptible areas

within the region. The results of the susceptibility assess-

ment combined with landslide occurrence frequencies may

serve as a basis for the landslide hazard mitigation in the

studied area. For these purposes, pixel-based susceptibility

maps were generalized into expert-defined landslide man-

agement units. These units provide site managers with easily

understandable and applicable hence reliable information

about future landslide occurrences. An approach describing

usage of the resulting susceptibility maps for onsite miti-

gation purposes was described with respect to the needs of

Machu Picchu site managers.

Keywords Landslide inventory � Landslide frequency �Susceptibility map � Conditional analysis � Machu Picchu �Peru

Introduction

The UNESCO cultural world heritage site of Machu Picchu

is the most famous historic tourist attraction in Peru

(Fig. 1). It is considered an important national, cultural,

and political symbol. The site is also an important local

and national centre of economic activities. Therefore, it

receives great attention from local and central govern-

mental agencies which are managing not only the archeo-

logical site itself, but also the surrounding natural protected

area and all related tourist facilities.

Different types of landslides frequently occur on the

slopes surrounding the archeological site (Carreno and

Bonnard 1997; Sassa et al. 2000). These mainly cause

damage to access road and tourist paths avoiding the

archeological site itself. Despite the large number of sci-

entific works dealing with landslide phenomena at the Inca

City (e.g. Canuti et al. 2005; Casagli et al. 2005, Sassa

et al. 2005), so far no attempt has been made to evaluate

spatial and temporal landslide occurrence frequencies and

susceptibility zonation maps to support landslide hazard

mitigation of the valuable archeological site.

This work aims to assess the landslide susceptibility of

slopes surrounding Machu Picchu using historical landslide

inventory covering last 46 years. Attention is paid to

evaluate the applicability of its results for practical land-

slide management purposes.

The study area

The area under consideration is limited by the Urubamba

River, which meanders around the Machu Picchu (3,051 m

a.s.l.) and Huayna Picchu (2,700 m a.s.l.) Mts. These

mountains are connected by a narrow saddle (2,450 m

J. Klimes (&)

IRSM CAS, p.r.i., V Holesovickach 41,

Prague 8 18209, Czech Republic

e-mail: klimes@irsm.cas.cz

123

Environ Earth Sci

DOI 10.1007/s12665-012-2181-2

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a.s.l.), in which the main part of the Inca City is located

(Fig. 2). The bedrock comprises heterogeneously deformed

leucogranites and tonalites of the Machu Picchu pluton of

Permian age (Ponce et al. 1999). These rocks underwent

major deformation during the Laramide Orogenesis at the

Cretaceous/Palaeogene boundary. A NW–SE trending

thrust dipping to the NE and also the regional scale folds in

metasediments originated during this event (Fig. 3). Folds

with NE–SW trend were identified during the field research

(Vilımek et al. 2007). They are clearly shown on the rose

diagrams showing trends of faults measured along the

accessible paths and inside the Inca City. Intense folding of

the area caused alternation of the granites ranging from

cataclastic granites with slightly developed foliation sys-

tems to well-foliated ultramylonitic rocks (Vilımek et al.

2007). The later rocks outcrops in narrow stripe some

250 m bellow the Machu Picchu Mt. on its north slope. The

field research also showed very dense fracturing of the

granite rocks identified across the whole study area.

Quaternary deposits represent mostly coarse (sand to

boulder) sediments forming alluvial cones, talus and col-

luvial deposits filled with loam sand matrix of the

Fig. 1 Location of the study

area

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Fig. 2 Study site divided into

the landslide management units

named according local usage

Fig. 3 Overview geological

map prepared according to

Carlotto et al. (1999). Machu

Picchu citadel is indicated with

yellow letters IC—Inca City,

black lines are folds, blue linesare rivers

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weathered rocks with usually up to 0.2 m of top soil.

Slopes often expose fractured bed rock. More gentle slopes

are covered by deeply weathered elluvium with a thin layer

of top soil.

The climate in the vicinity of Machu Picchu is warm and

humid. The mean annual precipitation is 1,950 mm, whilst

the mean annual temperature varies between 12 and 15 �C

(Wright et al. 1997). Rainfall is most abundant from Jan-

uary to March with an average precipitation of

300 mm month-1 during this period. It is least abundant

from May to July with an average precipitation of only

40 mm month-1 during this period. The monthly air tem-

peratures range from a minimum of 6.8 �C to a maximum

of 23.4 �C (Wright et al. 2000). The mountain ranges

surrounding Machu Picchu are covered by forest, even on

high-altitude and steep slopes. The forest is subject to both

natural and human induced fires that result in a complex

pattern of vegetation cover. The forest fires are one of the

natural hazards possibly affecting the Inca City. Agricul-

tural activity is very limited within the study area and is

confined to the narrow valley floor. The seismic activity in

the vicinity of Machu Picchu is low. The nearest earth-

quake epicenter recorded during the last 30 years was

located 50 km away with magnitude of 4.9 mbGS

(6.3.1980, 19:17 local time, 13.44�S, 72.62�W, USGS

Earthquake Catalogue).

The first report of landslide activity near the archeo-

logical site describes four events that occurred between

December 1995 and January 1996 (Carreno et al. 1996).

Two rock slides affected the only access road to the site

that is located on the front slope. Carreno and Bonnard

(1997) considered that these landslides provided evidence

for the present-day activity of a much larger deep-seated

landslide. The estimated volume of this supposed landslide

was calculated to be 6 9 106 m3. Sassa et al. (2000)

defined several large rock slides on this slope and later

(Sassa et al. 2005) suggested semi circular shearing plane

for the deep-seated landslide which starts and divides the

archeological site in place of the Main Square (Fig. 2). The

authors think that possible future activity of such landslide

may be dangerous for the archeological site. In contrast,

other researchers have argued that there are no evidences of

dangerous slope movements that could possibly affect the

Inca City (McEvan and Wright, 2001). Debris flow activity

assessment on slopes surrounding the site was performed

by Casagli et al. (2005) focusing on the use of satellite data

for their mapping. All the identified features were rather

shallow landslides. A detailed geomorphological map of

the Front Slope shows different landslide types with dif-

ferent activity (Canuti et al. 2005). Only stabilized or

dormant slides were identified to border the archeological

site on the Front Slope area (Fig. 2). The authors also

suggest possible sagging which may affect the historical

site of Machu Picchu. Its areal extend roughly corresponds

with precursory landslide stage as described by Sassa et al.

(2005). Nevertheless, GPS and interferometric synthetic

aperture radar measurements of the area show no signifi-

cant down slope displacements during 1 year of observa-

tion (Canuti et al. 2005). In addition, no evidence of deeper

slope movements within the Inca City was detected by

dilatometric and extensometric measurements (Vilımek

et al. 2007).

Methods

Landslide inventory mapping

An historical landslide inventory map (Malamud et al.

2004) has been prepared from remote imagery and field

surveys. The interrogated remote imagery comprises aerial

photography from 1963, 1991, and 2000, GoogleEarth

images from 2004, and QuickBird images from 2002 and

2006. Field surveys were conducted in 2003, 2004, 2005,

2006, 2008, and 2009. Field mapping was confined to

accessible paths surrounding the archeological site and,

thus, the whole area has not been mapped in equal detail. In

addition, archive photographs from 1949 and 1960

(Museum of Southeastern Moravia, Zlın, Czech Republic)

of Machu Picchu were used to identify historical land-

slides. Homogeneity of the landslide historical inventory

map is affected by merging the results of field investiga-

tions and the interpretation of remotely sensed data. To

evaluate the effects of inhomogeneity of the data, two time

intervals of the historical landslide inventory maps are used

for landslide frequency assessment. The first map covers

the period from 1963 to 2000 and the second map covers

the period from 2001 to 2009. A single historical landslide

inventory map (1963–2009) has been used for suscepti-

bility analysis.

The following landslide types have been distinguished:

debris flows, rock slides, debris slides, and rock falls

(Cruden and Varnes 1996). Debris flows are usually char-

acterized by channelized flows that mainly comprise

angular blocks of fractured bedrock with a minor portion of

fine-grained sediments. They have high damaging potential

due to their high velocities. Rock slides mainly occur along

major fractures in bedrock exposed on the slope surface.

Slides mainly mobilize colluvial deposits and soil, and

have predominantly planar shear planes. Rock falls may be

represented by single boulder falls or events that move

large volumes of rock. In the majority of cases, falling

material is deposited on the upper parts of the talus slopes

or, in some cases, even the valley floor. Scarp areas were

identified during the photo interpretation and field work.

Landslide activity was defined based on the appearance of

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the mapped features. Freshly looking (seen usually in

bright light colors on the aerial photos), un-vegetated

landslides were defined as ‘‘active’’. Landslides partly

covered by vegetation or the features in more dark colors

were defined as temporarily inactive and such definitions

are largely subjective and do not respect the suggested

classification criteria (Cruden and Varnes 1996), thus the

landslide activity information was not used during the

landslide inventory analysis. Shear plane depth was not

used to further refine landslide types as no significant

variations were found during the field mapping and it is

very difficult to determine from remotely sensed imagery.

Very small features, less than 30 m, have not been included

in the landslide inventory map.

Geomorphological mapping

A geomorphological map was compiled based on the

interpretation of black and white aerial photography and

the results of previous field mapping (Vilımek et al. 2007).

The geomorphic classes were defined to characterize

homogenous units with similar slope stability characteris-

tics. Their definitions have been based on the landform

types and forming material.

Block fields are formed by individual rock blocks with a

diameter of C2 m placed chaotically over each other. In

the majority of the block fields, caves have been described

(Proyecto Ukhupacha 2003). Saddles are flat or slightly

dipping narrow in section, in the mountain ridges. They are

conditioned by the regional faults and their crossing. Flu-

vial sediments represent flat lying material transported by

rivers. Talus deposits have an inclined surface and occur at

the toe of a slope. They comprised boulders with varying

dimensions and degrees of angularity. Colluvial slopes

were divided according their dip, with a break value of 40�defined based on the field observations. Cliffs are bare rock

surfaces with a slope angle of greater than 50�. Gullies

formed in rock or colluvial material are not distinguished.

They were mapped in the field and verified through aerial

photo interpretation.

Slopes in the study area were further divided according

to the main process responsible for their formation. Three

genetic slope types were distinguished. Erosional slopes

are those affected by the recent erosion of streams. Struc-

tural slopes are mainly controlled by the tectonic and

structural setting (e.g. the presence of faults or well

developed fissure plains). Denudational slopes constitute

all other slopes within the study area. They represent slopes

previously eroded by the river network, without clear tec-

tonic or structural controls, which have been reshaped by

different types of geomorphic processes (e.g. sheet erosion,

slope movements).

Landslide susceptibility assessment

Statistical methods use information from known landslides

to evaluate the importance of preparatory factors for their

future occurrence at the same time (Carrara et al. 1995).

Regardless of the applied statistical method, all use infor-

mation about landslide density on selected preparatory

factor variables as weights for the modeling process

(Clerici et al. 2002, Suzen and Doyuran 2004, Van Den

Eeckhaut et al. 2006, Yilmaz et al. 2012). Thus, the spatial

accuracy and completeness of the used landslide invento-

ries directly affects modeling results.

Only a limited number of preparatory factors that affect

the spatial distribution of landslides are available and can be

mapped with equal detail for the whole study area. These

include slope dip and aspect information, geomorphological

units and a map of genetic slope types. The landslide sus-

ceptibility was assessed by conditional analysis (Clerici

et al. 2002), which calculates the probability of landslide

occurrence (P) on each unique conditional units, which

helps explain conditions of the landslide occurrence. In this

case, 25 m2 pixels were used as the mapping unit. Only

scarp areas extracted from the available landslide inventory

were used for the susceptibility analysis. When combining

the available input factor maps resulted in 1,444 unique

conditional units for which the landslide density was cal-

culated (Table 1). The resulting probability of landslide

occurrence was reclassified into three susceptibility classes:

stable, conditionally stable, and unstable. Intervals of pre-

dictive variables defining each susceptibility class were

determined subjectively so the unstable class is capable of

capturing maximum landslide scarps on minimum study

area. This susceptibility classification scheme was com-

pared with classes defined using the mean value of P as a

pivot point to which standard deviation is added or sub-

tracted (Suzen and Doyuran 2004). Then, the debris flows,

rock slides and rock falls were extracted from the historical

landslide inventory and separate susceptibility maps were

prepared for each of the landslide types.

Pixel-based susceptibility maps are very convenient for

data handling and analyses, but finding the correct location

Table 1 Calculation of probability of landslide occurrence according to Clerici et al. (2002)

P(L/UCU) = AL/AUCU

in which (P) is the probability of landslide (L) occurrence given a unique combination of factors (UCU) defined by the landslide density in

that specific UCU. AL stands for landslide area in the UCU, AUCU is area of respective UCU

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of the susceptibility zones defined by pixels is very difficult

in the rugged terrain of the study area. Therefore, so-called

landslide management units (Fig. 2) were defined based on

the expert knowledge of the local topography, lithology,

and structural settings, including identified and assumed

faults. These management units define zones on which

degree of susceptibility can be easily identified in the field

by the site managers and (ii) these units are thought to be

quasi-homogenous with respect to the environmental fac-

tors affecting landslide occurrence, type, and magnitude so

similar landslide mitigation techniques may be applied in

each of the zones. The management units were ranked

according to the percentage of respective unit covered by

the unstable susceptibility class identified during the sus-

ceptibility analyses. Rank number 3 (unstable) was

assigned to those landslide management units in which

more than 10 % of the area was associated with the

unstable susceptibility class. Rank number 2 (conditionally

stable) was assigned to that landslide management units in

which between 6 and 10 % of the area were associated with

the unstable susceptibility class. Rank number 1 (stable)

was assigned to those landslide management units in which

less than 6 % of the area was associated with the unstable

susceptibility class.

Landslide frequency assessment

Landslide persistency (Cardinali et al. 1999) and average

landslide occurrence frequencies in each mapping unit and

for each landslide type were calculated as ratio of number

of identified landslides and time span of the historical

landslide inventory (Guzzetti 2005; Corominas and Moya

2008). Two time periods, 1963–2000 and 2001–2009, were

evaluated separately because of the different methods of

inventory mapping.

Results

Geomorphological map

Gullies evolved on rock slopes along prominent structural

or lithological boundaries (e.g. fractures, different rock

type units) and have almost vertical sides built by base rock

and their width does not exceed 20 m. It contrasts with

gullies developing on colluvial slopes which have usually

wide and shallow forms and are often more than 5-m wide.

In the most cases, there is no permanent stream flowing

through the gullies although during the rainy season they

conduct large amounts of water. Several permanent fissure

springs are also found within them. The results of the

previous field investigation suggested that gullies are

closely related with the landslide occurrence since they

often act as source areas and transport channel for debris

flows. Nevertheless, ascertained landslide densities within

the gullies unit reach less than half of the value for the cliff

unit.

Two double ridges have been identified within the study

area. The first crosses the Inca City in NW–SE direction. It

probably developed due to the local structural setting

which was responsible for the development of an elongated

depression up to 4 m deep and subsequently filled with soil

and sub-soil material by the builders of the city (Mucho

Mamani et al. 2005). The second crosses the Sun Gate

saddle and continues along the top of the ridge to the

southwest. This runs parallel with a mapped fault. Due to

the very steep surrounding slopes and highly disintegrated

rocks inside the extension trench (up to 3 m wide), it is

assumed that creep movement may occur here. No mea-

surement data are available to confirm this assumption.

Accumulation forms are largely constrained to the val-

ley bottom and are represented by fluvial sediments (e.g.

flood plain sediments and river terraces), talus deposits,

and ancient rock slide accumulations. Talus deposits have

the highest landslide density of all the geomorphological

units. Slope sediments vary greatly in their thickness and

character even though the parent material is quasi-

homogenous. They vary from slightly weathered and

intensively fractured base rock outcropping at the surface

with only a thin soil layer formed primarily by organic rests

to well developed elluvium or colluvium formed by well

sorted loamy sandy matrix with small, auxiliary, or slightly

rounded and weathered granitic boulders (around 0.3 m).

Such colluvium was found on less steep slopes and flat

parts of the saddles and ridges. Their ascertained depth is

1.2 m. The most abundant slope sediment type contains

large, less weathered boulders (with diameter of around

0.7 m) covering the slope surface with a minor portion of

loamy sandy matrix.

Based on the geomorphological mapping, it is consid-

ered that the geomorphological map provides a useful

proxy for the main lithological conditions within the study

area. In general, the morphological features result from the

interaction of formative processes (e.g. erosion and

weathering, seismicity, and gravity) and the properties of

the bedrock. Specifically, some classes on the presented

map are directly defined as superficial deposits with dif-

ferent characteristics (e.g. talus, block fields, slopes with

colluvium) and these define landslide occurrence condi-

tions. In addition, the genetic slope types may also be used

as a lithological proxy. In particular, structural slopes

represent lithologically distinct areas that have been jux-

taposed by tectonic movements, as described in Vilımek

et al. (2007).

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Landslide inventory mapping

Within the study area, 59 landslides with an area greater

than 0.001 km2 have been mapped since 1963 (Table 2)

from which 29 landslides have been recorded since 2000.

From the total landslide number, 53 % were mapped as

active and 47 % as temporarily inactive landslides.

Debris flows and rock slides/debris flows are closely

related to concave surface topography. Both most com-

monly develop in gullies, due to high water saturation of

the material during rain events. Rock slides/debris flows

commence with sliding in a well-developed depletion zone.

They then transform into channelized flow due to the high

water content of the material. Rock slides/debris flows are

considerably larger than debris flows. The latter do not

usually have morphologically well-developed source areas.

Rock slides have often shear planes in the highly fractured

bedrock that outcrops near or at the surface. In contrast,

slides mobilize only weathered colluvium. Rock fall

occurrence is closely related to the steep Back Slope

(Fig. 2). This area is highly fractured and steepened by

deeply incised river erosion (Fig. 4).

More than 60 % of all the mapped landslides develop in

fractured rocks, whereas only 27 % (slides and debris

flows) incorporate a considerable amount of highly

weathered colluvium. In terms of average area, the largest

types of landslides are rock fall, whereas the smallest types

are slides. The average slope dip for each type of landslide

is quite similar, varying from 41� to 47�. The maximum

and minimum slope angles are very similar for debris

flows, rock slides, and rock slide/debris flow. Each of these

landslide types, start on very steep slopes and extend down

to the flat valley floor thereby proving their high mobility.

In contrast, slides show the smallest range of slope angle,

suggesting that their accumulation areas do not extend very

far and rest on the slopes. This is also evident for rock falls,

which have highest maximum (85�) and lowest minimum

slope dip (21�).

Evaluating landslide persistence by comparing different

time steps of the historical landslide inventory map reveals

that almost 59 % (34) of the mapped landslides occurred at

the same location or within 20 m of previously mapped

events. Therefore, most of these cases represent reactiva-

tion of dormant landslides. There are two sites, where the

historical landslide inventory map shows repeat landslide

activity. The first occurs on the cliff above the historic Inca

Table 2 Basic statistics of the historical landslide inventory 1963–2009 (X—value close to 0)

Number Total

area

(km2)

Landslide coverage

of the are under

study

Average

area

(km2)

Min.

area

(km2)

Max.

area

(km2)

Average

frequencies

1963–2009

Average

slope dip

(�)

Min.

slope

dip (�)

Max.

slope dip

(�)

(%)

Debris

flow

12 0.367 0.4 0.031 0.001 0.095 0.2 46 4 78

Rock slide 22 0.766 0.9 0.035 0.002 0.194 0.5 47 5 77

Slide 4 0.079 0.1 0.020 0.003 0.060 X 41 16 49

Rock fall 12 2.375 2.7 0.198 0.002 1.406 0.2 46 21 85

Rock

slide/

debris

flow

9 0.809 0.9 0.090 0.010 0.263 0.2 43 4 75

Total 59 4.40 5

Fig. 4 Geomorphological map (a) and map of genetic slope types

(b): 1—fluvial sediments, 2—talus deposits including ancient rock

slide accumulations, 3—colluvial slopes with dip less than 40�, 4—

colluvial slopes with dip more than 40�, 5—rock slopes (rock slope

bare or with less than 1 m of weathering mantle), 6—cliffs, 7—

gullies, 8—prominent ridge lines, 9—double ridges, 10—block fields,

11—prominent peaks, 12—saddle, 13—fissure springs, 14—Inca

Citadel, 15—Urubamba River; a—erosional slopes, b—denudational

slopes, c—structural slopes, d—valley floor

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Bridge on the Back Slope (Fig. 5a), where rock falls have

been detected in all years except 2000 and 2006. The

second occurs on the colluvial slope located on the north-

eastern flank of Huayna Picchu (Fig. 5b), which exhibited

activity has been recorded every year except 2008. This

area is also characterized by less dense vegetation cover.

Several other sites show reactivation after longer periods of

time. For example, fresh debris flow accumulations on the

Front Slope (Fig. 5c) are visible in the archive photograph

from 1949 with a new event in March 2006; thus, it reac-

tivated after 55 years. The site on the southeastern slope of

Machu Picchu Mt. (Fig. 5d) produced debris flows in 1963,

2000, and 2004. The latter damaged an important trail for

tourist access to the ruins of Machu Picchu. There are six

further sites where landslide activity has occurred twice

during the time span covered by the historical landslide

inventory map.

When evaluating landslide persistence as the percentage

of the landslide area identical with the preceding landslide

distribution (Guzzetti 2005), highly variable results were

obtained (Table 3). Years with similar number of land-

slides (1991 and 2004) give similar results for all the

mapped landslide types.

Landslide distribution on unique conditional units

The most susceptible UCU (with L [ 0.1) for debris flows

are associated with slopes that dip more than 60�, slopes

orientated north or northeast, cliffs or gullies, and denu-

dational slope types. When considering all UCU in which

there are debris flow scarp areas, 23 % of the total occurs

in gullies and 11 % on cliffs. However, 58 % of the totals

are associated with colluvial slopes equally distributed

among the two classes. Debris flows occur most frequently

(43 %) on slopes with dip 40�–50�, while 29 % occur on

slopes with dip greater than 60�. Of the UCU, 69 % are

defined by denudational slopes and the remainder by

structural slopes.

The most susceptible UCU for rock slide scarp areas are

associated with slopes that dip between 50� and 60�, slopes

orientated east, gullies, and denudational slope types. 21 %

of the total UCU with landslides occur in gullies whilst

72 % are associated with colluvial slopes. 99 % are con-

fined to the denudational slopes. No single slope orienta-

tion prevails and this preparatory factor is clearly highly

variable.

The most susceptible UCU for rock falls are markedly

different from those of the other landslide types as they

show the clear domination of just one class from each

factor map. 77 % of rock fall source areas are associated

with slopes that dip more than 60� and 58 % of the slopes

are orientated south, southeast or north, northwest; 75 %

are defined by cliffs and 79 % are defined by structural

slopes.

Landslide frequency

Inca Bridge landslide management unit provides an

example of how the method of landslide inventory map-

ping affects the ascertained landslide frequencies. High

increase of the landslide occurrence (Table 4) is caused by

field mapping. It allows identification of events, which are

otherwise masked on the aerial or satellite images by pre-

vious rock falls. The Sun Gate and Huayna Picchu land-

slide management units maintain similar recurrence

frequencies through whole time period evaluated.

The precise date of landslide origin or reactivation is

known in only two cases. The month of their occurrence is

known in six further cases. If we combine these data with

recorded debris flows in the wider vicinity of the study area

Fig. 5 The historical landslide

inventory maps (in the legend—

C city, R river, contour lines

have 50 m interval, capital

letters on a map are referred in

the text)

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(Vilımek et al. 2006), it is seen that all landslides are

constrained within the rainy season with peak occurrence

in the months of February and March. Only one landslide

was reported to occur in May.

Landslide susceptibility assessment

The prepared susceptibility model explained well the spa-

tial distribution of modeled landslides (Table 5). The

models were able to correctly depict from 69.5 % up to

86.1 % of the landslides. Incorrect model prediction,

defined by pixels of landslide dataset contained within the

stable susceptibility class, is only 4 % for the combined

landslide types model and 0 % for the debris flows and

rock fall models. The model performance did not improve

when the susceptibility classes were defined by standard

deviation of the P values.

The landslide susceptibility maps prepared through

unique conditional analyses are shown in Fig. 6. In all

cases, the majority of the Inca City belongs to the stable

class. The only area which exhibits landslide susceptibility

is on the south part of the agricultural sector and west rim

of the City. All together conditionally stable and unstable

susceptibility classes cover 12.7 % in the case of the all

landslides and 23 % for the debris flows susceptibility

maps of the Inca City.

It is evident that the spatial distribution of unstable

classes is quite different depending on whether the map

considers all landslide types together or specific landslide

types. For example, a comparison of the susceptibility maps

for all landslide types and debris flows shows that only 6

and 9 % of the unstable susceptibility class have common

spatial location. The largest spatial agreement was achieved

for stable classes, where 87 %, respectively, 76 % of the

class overlay. Common location of conditionally unstable

class share 32 % of pixels for the all landslide types and

45 % of the debris flows susceptibility maps.

Uncertainties related with the susceptibility assessment

performed in a large detail for each pixel, is used as an

argument to portray the results of susceptibility zoning on

larger mapping units, in this case called landslide man-

agement units (Fig. 2). Considerable details about suscep-

tibility information are lost with this transformation, thus

preventing possible overestimation of available suscepti-

bility information when used in management praxis. Gen-

eralized landslide susceptibility map is combined with

landslide inventory (Fig. 7) resulting into complex though

easy to understand information about future landslide

occurrence. It is quite easy to identify each susceptibility

class in the field as compared to the pixel-based suscepti-

bility maps.

Discussion

Landslide susceptibility

Landslide susceptibility assessment proved small propen-

sity of the Inca City to future landslide occurrence. It

reflects good site selection as well as considerable engi-

neering and construction works done by Incas, which lar-

gely affected the natural conditions of the site, mainly

slope angle and drainage. Landslide susceptibility maps

prepared separately for debris flows and rock falls show

similar distribution of the landslide data among the sus-

ceptibility classes (Table 4). Nevertheless, the spatial

Table 3 Landslide persistence

(X—no occurrence of specific

landslide type)

1991 (%) 2000 (%) 2002 (%) 2004 (%) 2006 (%) 2008 (%)

Debris flows 0 0 72 8 32 X

Rock slide 8 0 X 0 X X

Slide 0 X X 0 X X

Rock falls 65 X 2 23 X 79

Rock slide/debris flows X X X 73 0 X

All types 37 14 5 38 28 79

Table 4 Average landslide

frequency for each landslide

management unit (X—close to

zero)

Spatial units Average frequency

1963–2000

Average frequency

2001–2009

Average frequency

1963–2009

Sun gate 0.2 0.1 0.2

Front slope 0.1 0.9 0.2

Huayna Picchu 0.3 0.5 0.4

Back Slope X 0.7 0.2

Inca Bridge X 0.5 0.1

Study area 0.6 2.7 1.1

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distribution of pixels of each susceptibility class on both

maps is significantly different. Work by Sterlacchini et al.

(2011) noted low spatial agreement of susceptibility maps

prepared using different combinations of predictive factors,

while maintaining similar prediction rate of the models. It

stresses that correct identification and use of specific

landslide type and preparatory factors for susceptibility

analyses affects largely reliability of the resulting maps,

which otherwise seem to have similar predictive power.

Adding slope curvature derived from the DEM and map

of distances to the faults into the susceptibility model did

not improved its predictive performance. In agreement with

Glade and Crozier (2005), it shows that simply increasing

the number of used preparatory factor maps for suscepti-

bility mapping does not necessarily improve the model

performance. The presented landslide susceptibility zoning

is based on the existing reliable information about factors

affecting landslide occurrences, thus providing relevant

information about future spatial distribution of landslides

and such information has not been available until now for

the study area.

An investigation of the landslide distribution on unique

conditional units showed that similar preparatory factors

define those areas most susceptible to debris flows and rock

slides but that these differ significantly for rock falls. The

units most susceptible to debris flows and rock slides are

characterized by gullies and denudational slope types with

colluvial slope deposits. On the other hand, rock falls are

limited to cliffs and structural slopes. These differences can

also be seen when comparing the susceptibility maps for

each landslide type (Fig. 6). The debris flows and rock

slide maps are similar in terms of their susceptibility zone

distribution whereas the rock fall susceptibility map is

markedly distinct.

Landslide occurrence frequencies

The work shows how landslide occurrence frequencies may

be affected by the total time period covered by the historical

landslide inventory maps and their time resolution. Short

time periods with available landslide information or single

landslide events captured on the inventory map represent

only a ‘‘snap shot’’ of the landslide activity of the study

region and may therefore provide partly misleading infor-

mation with respect to future landslide occurrence. This is

also true when information about landslide occurrence is

available for long time periods (e.g. 50 years), but with only

low spatial and temporal resolution (e.g. 1963–2000 in this

article). The resulting frequencies may not correspond to

short periods when high spatial and temporal data are

available. The assumption that the past landslide frequency

can be used to describe the future landslide occurrence (e.g.

Remondo et al. 2008) may only be valid using comparable

landslide information over short time periods.

Using high-time resolution of the landslide inventory

helps characterize landslide activity throughout the docu-

mented time period. In some cases, the very frequent

occurrence of landslides on a small part of the slope

(Fig. 5a, b) was detected and can be described as ‘‘local-

ized instability’’. In the second case, landslides occur

periodically after several years or decades without sliding

activity (Fig. 5c, d). When only coarse time resolution for

the landslide inventory mapping is available, these two

landslide activity types may not be identified and important

implications for hazard assessment and management can

not be drawn. The effect of time resolution or time periods

with available landslide inventory data on the landslide

frequencies is documented in Table 4. Much higher aver-

age recurrence frequencies for the Front Slope and Back

Slope hazard units were calculated for past 9 years when

compared with the preceding 37 years. This can be

explained by the more detailed field mapping undertaken

during the more recent period. Some evidence also suggest

the possible effect of increasing positive cumulative dif-

ferences among monthly precipitation totals and long-term

monthly precipitation averages recorded at the Machu

Picchu meteorological station since 2002 (Klimes et al.

2007). However, more research about coupling precipita-

tion records and landslide occurrence is needed.

Sustainable landslide mitigation

The work presents two different maps which may be used

to identify areas susceptible to future landslide occurrence.

Table 5 Results of the landslide susceptibility assessment

All landslide types

% of study area in

susceptibility class

% of landslide

dataset area

Stable 68.4 4.0

Conditionally stable 23.2 21.7

Unstable 8.4 74.2

Debris flows

Stable 78.2 0

Conditionally stable 16.3 37.4

Unstable 5.5 62.6

Rock slides

Stable 70.6 0.5

Conditionally stable 20 13.4

Unstable 9.4 86.1

Rock falls

Stable 92.7 0

Conditionally stable 5.1 30.5

Unstable 2.2 69.5

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The most detailed information is provided by the pixel-

based susceptibility maps. Nevertheless, its practical use

has few drawbacks, which needs to be considered. It is very

difficult to identify correctly each pixel or their groups in

the rugged terrain of the study area. Calculated suscepti-

bilities have inherent uncertainties related mainly to the

accuracy of the input data (topographic and geomorpho-

logic maps) and completeness of the landslide inventory

limited by available historical information. When consid-

ering the limited number of known landslides within the

study area, the prediction of future landslides at the pixel

scale may be ambitious goal. The second option is land-

slide zoning performed for landslide management units

combined with historical landslide inventory. This map

offers susceptibility zones easily identified in the terrain

combined with reliable landslide inventory map which

shows high landslide occurrence persistency, thus it is good

indicator of possible future instabilities. Its very rough

spatial resolution could be seen as disadvantage or

advantage when seeking reliable and spatially consistent

susceptibility information.

It is suggested that the landslide management unit sus-

ceptibility map is used to constrain those areas where

possible future development is most likely to be affected by

landslides. It enables general management decisions iden-

tifying regions, where greater attention should focus on

dangerous slope processes. Pixel-based susceptibility map

could be used for further identification of areas of special

interest, where set of tools mitigating possible landslide

damages should be applied. These may include regular

checking of the identified sites for any signs of landslide

initiation, careful maintaining water drainage system

Fig. 6 The landslide

susceptibility maps based on the

unique conditional analyses

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preventing any leakages to the slope and site specific

research before initiation of any future developments of the

areas. The presented geomorphological map may be help-

ful since its classes are characterized with respect to

landslide probability occurrence (chapters 4.1 and 4.3). In

well-defined cases, warning signs informing tourists should

be placed to reduce their exposure to possible landslides.

This measure could be only applied during the rainy

season, since in general, the landslide hazard diminish

significantly during the period of low precipitations (May

to July).

Some of the above suggested measures are already being

performed by the site management (e.g. regular checking

of the susceptible areas, maintaining of the drainage sys-

tem), but it is done without more comprehensive knowl-

edge about the landslide occurrence frequencies (there is

no systematic landslide inventory kept, except the one

presented in the article) and further specification of regio-

nal landslide susceptibility within the area of interest. Thus,

from the point of view of long-term sustainable manage-

ment, inadequate decisions may be taken if ignoring the

available historical landslide information.

Conclusions

Historical landslide inventory mapping covering period

from 1963 to 2009 was prepared using multiple time infor-

mation. This describes in detail the spatial and temporal

landslide activity within the study area. It demonstrates the

uncertainties associated with drawing conclusions about

future landslide occurrence frequencies based on short time

period or coarse time resolution. In addition, the basic

assumption that the past landslide frequency can be used to

describe the future landslide occurrence was not proved in

the study area and in general its validity seems to be very

limited. This is very important to bear in mind when calcu-

lating landslide hazard.

Landslide susceptibility was assessed based on the

unique conditional units approach and presented on pixel

(25 m2) and slope (so-called ‘‘landslide management

units’’) scales identifying advantages and disadvantages of

the two landslide susceptibility zonation maps with respect

to their practical use. Approach combining the two scale

landslide susceptibility information for best practice in

landslide mitigation was described. It suggests that well

developed and already used landslide mitigation strategies

should be supplemented by new tools to ensure long-term

sustainable development of the studied area.

The landslide susceptibility assessment proved that the

majority of the Inca City is located on stable site with

susceptible areas located chiefly along its outskirts. The

historical landslide inventory identified no landslide

occurrence within its limits since 1963.

Acknowledgments The authors would like to acknowledge the

financial support provided by Charles University (Grant No.

MSM0021620831) and the IRSM CAS, v.v.i., (AV0Z30460519).

Special thanks go to Ministry of Culture and ANA Cusco for their

scientific and personal support.

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