Mapping of risk and susceptibility of shallow-landslide

GEOMOR-03883; No of Pages 15 Geomorphology xxx (2012) xxx–xxx

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Mapping of risk and susceptibility of shallow-landslide in the city of São Paulo, Brazil Fabrizio de Luiz Rosito Listo ⁎, Bianca Carvalho Vieira Geography Department, University of São Paulo, Brazil. Avenue Professor Lineu Prestes, 333. Cidade Universitária, São Paulo, Brazil

a r t i c l e

i n f o

Article history: Received 2 February 2010 Received in revised form 30 August 2010 Accepted 17 January 2012 Available online xxxx Keywords: Geomorphology Shallow landslide Risk analysis Susceptibility SHALSTAB model Urban area

a b s t r a c t In the city of São Paulo, where about 11 million people live, landslides and flooding occur frequently, especially during the summer. These landslides cause the destruction of houses and urban equipment, economic damage, and the loss of lives. The number of areas threatened by landslides has been increasing each year. The objective of this article is to analyze the probability of risk and susceptibility to shallow landslides in the Limoeiro River basin, which is located at the head of the Aricanduva River basin, one of the main hydrographic basins in the city of São Paulo. To map areas of risk, we created a cadastral survey form to evaluate landslide risk in the field. Risk was categorized into four levels based on natural and anthropogenic factors: R1 (low risk), R2 (average risk), R3 (high risk), and R4 (very high risk). To analyze susceptibility to shallow landslides, we used the SHALSTAB (Shallow Landsliding Stability) mathematical model and calculated the Distribution Frequency (DF) of the susceptibility classes for the entire basin. Finally, we performed a joint analysis of the average Risk Concentration (RC) and Risk Potential (RP). We mapped 14 risk sectors containing approximately 685 at-risk homes, more than half of which presented a high (R3) or very high (R4) probability of risk to the population. In the susceptibility map, 41% of the area was classified as stable and 20% as unconditionally unstable. Although the latter category accounted a smaller proportion of the total area, it contained a concentration (RC) of 41% of the mapped risk areas with a risk potential (RP) of 12%. We found that the locations of areas predicted to be unstable by the model coincided with the risk areas mapped in the field. This combination of methods can be applied to evaluate the risk of shallow landslides in densely populated areas and can assist public managers in defining areas that are unstable and inappropriate for occupation. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Since 2007, half of the world's population has lived in urban areas, and the number of people living in urban areas may double in the next thirty years (Véron, 2007). Growing populations increase the vulnerability of cities, making landslide-risk management more complex, especially in developing countries, where the number of people who inhabit risk areas has grown by seventy to eighty million per year (UN, 2005). The accelerated urbanization in developing countries has contributed to the construction of housing on steep hillsides, often using inadequate building standards, intensifying the occurrence of landslides and resulting in the expansion of risk areas. Additional factors have contributed to this expansion, including economic and social crises with long-term effects, historically inefficient policies for low-income housing, inefficient soil-use management, and a lack of appropriate legislation for the most susceptible areas and technical support for local populations (Ayala, 2002; Carvalho et al., 2007).

⁎ Corresponding author at: Avenue Senador Casimiro da Rocha, 1093. District Planalto Paulista. Zip code: 04047–002, São Paulo (SP) - Brazil. Tel.: +55 11 3091 8559; fax: +55 11 3091 3159. E-mail addresses: [email protected] (F.L.R. Listo), [email protected] (B. Carvalho Vieira).

Wijkman and Timberlake (1985), Sidle et al. (1985), Anderson and Decker (1992), Alexander (1993), Amaral (1997), Ayala (2002), and others have verified that in developing countries, the impact of landslides is a major cause of loss of human life in densely populated urban areas, whereas in developed countries, it is primarily associated with economic losses. This pattern can be explained by greater prevention initiatives in developed countries. In the principal cities of poorer countries, landslides assume catastrophic proportions due to widespread cuts in embankments, garbage deposits, deforestation, changes in drainage networks, and other anthropogenic pressures without preventive planning (Brunsden and Prior, 1984; Sidle et al., 1985; Crozier, 1986; Fernandes et al., 2004). In various languages, the concept of risk includes the possibility that a landslide (or another natural event) will cause significant social or economic damage to a particular population. Thus, risk areas are the places likely to be reached by natural or induced landslides. Susceptibility, in turn, is determined by the set of natural factors that may contribute to triggering these events. In other words, hillsides have a natural predisposition to landslides as a function of their geologic, topographic and climatic characteristics, among others. When sites with naturally high susceptibility are occupied in an inadequately managed manner (for example, by making cuts in the land and vegetation), there may be substantial risks to the population. Thus, landslide-associated accidents that cause significant social damage

0169-555X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2012.01.010

Please cite this article as: Listo, F.L.R., Carvalho Vieira, B., Mapping of risk and susceptibility of shallow-landslide in the city of São Paulo, Brazil, Geomorphology (2012), doi:10.1016/j.geomorph.2012.01.010

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F.L.R. Listo, B. Carvalho Vieira / Geomorphology xxx (2012) xxx–xxx

frequently occur in urban slums, forcing public managers to identify, analyze and manage risk areas to prevent accidents and plan for emergency situations (Arnould, 1976; Varnes, 1985; Augusto Filho, 1994; Wu et al., 1996; Cerri and Amaral, 1998; Fell et al., 1998; Guzzeti et al., 1999; Van Westen et al., 2003; Macedo et al., 2004; Remondo et al., 2008; Korup et al., 2010). Several studies concerned with preventing and reducing risks associated with landslides have been performed. Some of the most important are the ZERMOS program (Zones Exposed to Soil Movement Risks), a risk-mapping system in France that aims to supply details about potential soil movement and slope instability in particular areas (Antoine, 1977; Humbert, 1977); the methodology developed by the Geotechnical Control Office (CGC), which develops maps to evaluate of land instability in the Hong Kong region (an area of high susceptibility to landslides) (Brand, 1988); and the International Decade for Natural Disaster Reduction (IDNDR; 1990–1999), which was instituted by the United Nations General Assembly in response to the increasing number of accident victims and economic damage due to natural disasters throughout the world (WP/WLI, 1990; UN, 2005). Additional methods have been proposed by Varnes (1985); Einstein (1988, 1997); Terlien et al. (1995); IUGS (1997); Wilson and Jaiko (1997); Hartlén and Viberg (1988); Jibson et al. (1998); Campbell et al. (1999). In Brazil, the mapping of risk areas began around 1965 in the city of Rio de Janeiro, where slums had been located on elevated hillsides since the end of the nineteenth century. Since then, numerous landslide accidents have occurred. Various risk methodologies have subsequently been developed, implemented, and applied by research institutes and universities in other urban areas in Brazil. These projects include the use of risk maps in urban planning, the implementation of corrective and mitigating infrastructure in risk areas, the creation of emergencyresponse systems, the improvement of legislation dealing with landuse issues, and the public dissemination of information and training. However, despite the innumerable studies that have been performed, Brazil currently faces a complex challenge. The number of risk areas has grown quickly, and public agencies generally do not possess the technical resources to address this problem. One of the principal management tools, an accurate map of risk areas, is difficult and costly to produce because of the many activities (such as field inspections) necessary to create it. Here, we propose a methodological tool that can be applied to the management of the landslide-risk areas. Traditional risk-mapping approaches could utilize prior knowledge of topographic instability through mathematical models based on physical processes (e.g., the SHALSTAB model). Most of these models evaluate susceptibility through a combination of stability models based on hydrological and infinite-slope theories, independent of previous landslide events. Such models have been widely disseminated in the international literature, especially since the 1990s, when the development and improvement of Geographic Information Systems (GIS) promoted the emergence of new approaches to identifying and evaluating unstable areas, minimizing costs, and facilitating the management of risk areas (Dietrich et al., 1992; Montgomery and Dietrich, 1994; Carrara et al., 1995; Wu and Sidle, 1995; Guzzeti et al., 1999; Iverson, 2000; Morrisey et al., 2001; Pack et al., 2001; Dhakal and Sidle, 2003; Van Westen et al., 2003, 2006; Calcaterra et al., 2004; Van Westen, 2004). Existing methods must be strengthened because landslides are the natural events that cause the most casualties in urban areas in Brazil (Fig. 1). Landslides also cause major economic damage and block highways, especially during the summer, when the largest amounts of rainfall occur. Precariously situated urban areas are the result of a housing deficit of more than seven million homes throughout the country. A framework of social exclusion causes thousands of people to occupy inappropriate sites such as steep hillsides and floodplains of rivers, resulting in the presence of numerous slums. The intensive development of these slums began in Brazil during the 1960s, and they have expanded rapidly. The slums are characterized as illegal

Fig. 1. People killed by landslides in Brazil from 1988 to May 2010. There was a high number of casualties in the first five months of 2010, because of heavy rainfall volumes reaching several Brazilian states, especially Rio de Janeiro and São Paulo. Source: IPT (Technology Research Institute of the São Paulo State) database.

occupation of public or private land with inappropriately and densely arranged houses, encompassing areas that lack infrastructure and essential public services. The state of São Paulo, where this study was performed, has the largest housing deficit in Brazil. While the state has only about 4 million houses, the state capital (the city of São Paulo) has about 11 million inhabitants in an area of a little more than 1500 km². From the 1960s to the 1990s, the city of São Paulo experienced a territorial growth of 40%, resulting in the removal of 31% of the vegetative cover. Forests were replaced with inadequately planned roads that cut into the deep valleys and hillsides, and many slums were built (Meyer et al., 2004). In 2004, 562 landslide-risk areas were identified in the city of São Paulo, of which more than 50% were evaluated as having high or very high rates of risk for the population (SVMA and IPT, 2004). Consequently, deaths and damage triggered by precipitation occur on unstable hillsides every year during the rainy period. In January 2010, for example, about 600 mm of rainfall was recorded in 27 days, triggering landslides that killed 60 people and left more than 20,000 homeless. This was the greatest amount of rainfall measured during the corresponding period in São Paulo since January 1947, when about 480 mm was recorded (IAG, 2010). One of the most critical and largest basins in the city of São Paulo is that of the Aricanduva River, which is located in the eastern portion of the city and has an area of about 100 km². Significant flooding problems occur along the primary river course, which crosses an important avenue in that part of the city. The area is characterized by intense urbanization and the associated reduction in soil permeability. For years, catastrophic flooding events have been recorded during the summer due to rapid drainage of superficial rainwater to watercourses. The overflowing water volume reaches innumerable houses. Between December 2002 and March 2003, more than ten floods were recorded, caused by rainfall lasting between 30 and 120 minutes and amounts from 60 mm to 80 mm. These floods caused several economic and social damages (Canholi, 2005; IPT, 2005). The Aricanduva River basin has 22 tributaries, and numerous slums are built on steep hillsides or very close to drainage channels, especially in the upstream areas of the basin. Poorly executed embankments, road cuts, deforestation, and other anthropogenic pressures in this portion of the basin contribute to the production of sediments that are not retained in the alluvial plain because of the absence of a riparian zone. These sediments are transported downstream, aggravating the occurrence of silting and flooding. In other words, the drainage channels are blocked by debris and silting caused by erosion and landslides, resulting in the accumulation of garbage that obstructs the passage of water, further reducing outflow capacity.



We selected the Limoeiro River basin as a study area because of the frequent occurrence of landslides in the slums built on dissected topography there and the resulting socio-economic losses. The Limoeiro River is one of the headwater tributaries of the Aricanduva River basin and is located on the outskirts of the city of São Paulo. In January 2010, the Limoeiro River basin received heavy rainfall (103 mm in 24 hours), triggering shallow landslides that caused six deaths and considerable economic damage, destroying slums and forcing about 300 local residents to evacuate their homes. Moreover, the operation of public transportation was compromised, especially the circulation of trains and automobiles; water and electricity distribution were interrupted; and several parts of the basin were flooded. The general objective of this article is to analyze the probability of risk and susceptibility to shallow landslides in the Limoeiro River basin. Although the events described above were triggered by an extreme

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rainfall event in January 2010, the damaging soil-use practices and natural conditions in the basin may exacerbate landslides in this area. 2. Study area The Limoeiro River basin has an area of 9 km² (Fig. 2) and runs through a rocky plateau called the Paulistano Plateau, which is located more than 700 m above the Serra do Mar escarpment along the coast of the state of São Paulo. The Paulistano Plateau contains a diverse set of metamorphic and igneous rocks from the Precambrian and Eopaleozoic ages, with various forms of topography. In the study area, the most common types of mass movements are shallow landslides and creep, in addition to fluvial processes (undercutting of river banks, silting and flooding) and the less frequent occurrence of linear gully erosion (e.g., ravines). The present study

Fig. 2. Location of the study area. The circle on the detailed map (right) indicates where triggered landslides occurred in January 2010, also shown in Fig. 3.


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deals only with shallow translational landslides. Significant shallowlandslide events occurred in the basin in 1997 (IPT, 1997), 2005 (IPT, 2005), and January 2010. These events were triggered on steep hillsides made up of residual soils with a silt-clay to fine-sand texture, with an average thickness between two and three meters, derived from micaschists. Notably, discontinuities in the underlying rock (e.g., schistosity) function as geologic/geotechnical constraints on the occurrence of landslide events. News reports affirm that landslide-associated accidents occur frequently in the precariously situated urban areas of the basin, causing economic damage and deaths. This basin contains steep hillsides (with slopes greater than 17°), which favor rapid water infiltration. Slope gradient is an important morphometric parameter to consider in the dynamics of the gravitational process. The altitude of the basin varies from 780 m to 1000 m, and the primary axis of the basin is oriented in the general SSE-NNW direction. The average annual rainfall is between 1300 mm and 1400 mm, and temperatures exceed 30° during the summer. The location of the basin in the eastern part of the city of São Paulo favors the entrance of atmospheric fronts and sea breezes that combine with humidity to greatly intensify the occurrence of precipitation. Moreover, atmospheric instability is caused by ascending convective movements that produce cumulus and cumulus-nimbus clouds, which generate torrential rains that can initiate mass movements. The intensive human-occupation process that continues to occur in the basin by means of the construction of slums, road cuts, and embankments and by deforestation, has destroyed the vegetative cover that protected the soil surface, removing the upper soil horizons and promoting heavy sediment production. In 1973, 55% of the total basin area was occupied by vegetative cover (dense Atlantic Rain Forest), 12% by consolidated urban area (without basic infrastructure services) and 33% by fields and pastures. By 2007, these values had changed to 44% vegetative cover, 40% urban area and no fields and pastures [replaced in certain areas by exposed soil (16%)], indicating that vegetative cover decreased by 11% while urban areas increased by 28% between 1973 and 2007 in the absence of urban planning (Fig. 3). Surveys of the deep drainage channels in the basin by the DAEE (Department of Water and Electrical Energy of São Paulo state) (1999) have identified sediments with different grain sizes, from sands (fine and medium sands) to gravel and boulders, in addition to urban garbage, debris, and civil construction materials. These materials are derived from human-induced landslides and continue to accumulate in the drainage channels.

3. Materials and methods To analyze the probability of risk and susceptibility to shallow landslides, we used two distinct methodologies. In a direct field survey, we evaluated landslide risk based on natural and anthropogenic indicators. In an indirect approach, we verified susceptibility through the application of a deterministic mathematical model based on physical forces (Fig. 4). In the direct analysis, areas of risk were mapped using a cadastral survey form containing a checklist of the triggering factors for shallow landslides. This type of mapping has been proposed by the Working Party on World Landslide Inventory (WP/WLI), which is sponsored by the United Nations and composed of specialists from international scientific associations. During the International Decade for Natural Disaster Reduction, the WP/WLI proposed a cadastral survey form that can be found in multiple technical reports (WP/WLI, 1990, 1991, 1993, 1994). Brazilian researchers have adapted this form to the conditions found in the country for use in mapping areas of risk in many cities. In the indirect analysis, we employed the SHALSTAB deterministic mathematical model (SHAllow Landsliding STABility), which can define zones within the landscape that are susceptible to the occurrence of shallow translational landslides (Montgomery and Dietrich, 1994). This model has been applied successfully in various regions, including the western coast of the United States (e.g., Montgomery and Dietrich, 1994; Dietrich et al., 1995; Casadei et al., 2003), Argentina (e.g., Rafaelli et al., 2001), Italy (e.g., Santini et al., 2009; Cervi et al., 2010), and New Zealand (e.g., Claessens et al., 2005). The model has produced results of excellent precision when tested in comparison with other forecasting models. It has also been applied successfully in humid-tropical Brazilian landscapes by Ramos et al. (2002); Guimarães et al. (2003); Fernandes et al. (2004); Redivo et al. (2004); Vieira (2007) and Zaidan and Fernandes (2009). 3.1. Mapping and Analysis of the Probability of Risk The survey form used in the field inspections (Fig. 5) to identify and map the areas of risk was developed from a compilation of several studies performed in urban areas in Brazil (Alheiros and Augusto Filho, 1997; Cerri and Amaral, 1998; Macedo et al., 2004; MINISTÉRIO DAS CIDADES, Ministry of Cities of Brazil and IPT, Technology Research Institute of the São Paulo State, 2004; Ogura et al., 2004; IPT, 2005; Carvalho et al., 2007 and Mirandola, 2008).

Fig. 3. Aerial orthophotograph of the study area (2007), indicating the predominance of two land use types (Urban Area and Atlantic Rain Forest). The square on the map indicates the location of landslide occurrence in January 2010, where one of the scars is detailed in the figure.



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Fig. 4. Flow diagram outlining the materials and methods used in this study.

The form encompassed natural indicators (vegetation, surface cover and topography), anthropogenic indicators (number of houses, type of construction and distance from the house to the topographical feature), indicators of instability (landslide scars, cracks in houses and soil, and inclined trees, poles and walls). These indicators helped to determine the potential and magnitude of accidents that could pose a threat to the local population. Based on a systematic compilation of the field-survey results, we categorized the probability of risk as R1 (low risk), R2 (average risk), R3 (high risk), or R4 (very high risk). The criteria adopted to determine the probability of risk (Table 1) were based on the methodology developed by the MINISTÉRIO DAS CIDADES and IPT (2004), which mapped areas of risk in several Brazilian regions. The key criteria are listed below. a. Construction standards: different materials used to build houses (wood or masonry) have different resistance to landslides. Usually, masonry houses have greater resistance than those built with wood. b. Types of hillsides: natural hillsides are usually more stable, while those with cuts and embankments are more prone to instability. c. Distance between the houses and the hillside: the distance between the houses and the hillside defines the probability of risk at a given site because during a landslide event, a greater distance provides a greater cushioning area for the sediments and a lower

probability of houses being hit (Fig. 6). The houses closest to the top of the hillside generally have their back or front toward the crest of the hill, making the walls vulnerable to collapse (Fig. 6). d. Water release: houses without adequate sanitation that release sewage directly onto the hillside, houses with leaky, rudimentary plumbing, and houses built over water wells and pumps can rapidly saturate the soil, thus triggering landslides. During the survey, the origin and destination of the water were essential factors to verify because even though water is one of the main triggers of landslides in humid-tropical environments, it is not necessarily derived from intense rainfall in areas with precariously situated homes. e. Evidence of instability: these indicators signal the past occurrence of landslides and, consequently, the possibility of new events. Signals of instability, such as cracks in houses and soil, may precede a rupture. This category is of the most important indicators of increased risk. After completing the survey forms and developing the risk criteria, we used oblique low-altitude photographs obtained in a helicopter flight to define the sectors (landslide risk sectors) that would receive field visits (Fig. 7). We prepared 25 forms and delimited 14 areas of risk based on the interpretation of these photographs, including the slope of the terrain, the road network, the street pattern, and the


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Fig. 5. Model of cadastral survey form used in field for mapping of landslide-risk areas.

geographic coordinates obtained from GPS devices in the field. Due to their extent, some sectors required more than one form. We did not conduct any mapping in sectors containing only vegetative cover without housing at the top or bottom of the hillsides, or only other processes (e.g., flooding and erosion). We calculated the area of each sector in m² using a GIS application. 3.2. Mapping of susceptibility and joint analysis The Shallow Landsliding Stability model was developed by Dietrich et al. (1993) and Montgomery and Dietrich (1994) and later automated

in a routine called SHALSTAB by Dietrich and Montgomery (1998). This model calculates the susceptibility to shallow translational landslides at the scale of a hydrographic basin by combining a stability model based on the infinite-slope equation with a steady-state hydrological model considering constant subsurface flows, based on the work of Beven and Kirkby (1979) and O'Louglin (1986). In the combination of the stability and hydrological models, topographic parameters (hillside angle and contribution area) obtained from a digital terrain model and physical properties of the soil (cohesion, internal friction angle, soil thickness, and specific weight) are incorporated (Montgomery and Dietrich, 1994). This combination,


F.L.R. Listo, B. Carvalho Vieira / Geomorphology xxx (2012) xxx–xxx Table 1 Criteria to determine probability of risk. Source: MINISTÉRIO DAS CIDADES (Ministry of Cities of Brazil) and IPT (Technology Research Institute of the São Paulo State) (2004). Probability of risk

Description

1. The geological and geotechnical conditions (hillside angle, terrain type, among others) and the level of human intervention in sector are of low potential for landslide development. 2. No indicators of instability on the hillsides. 3. Maintaining the existing conditions not likely the landslide occurrence during regular rainy season. R2 (Average) 1. The geological and geotechnical conditions (hillside angle, terrain type, among others) and the level of human intervention in sector are of average potential for landslide development. 2. Presence of some indicators of instability on the hillsides, though incipient. Destabilization process in early stages of development. 3. Maintaining the existing conditions reduces the possibility of landslide occurrence during episodes of heavy rainfall and prolonged the rainy season. R3 (High) 1. The geological and geotechnical conditions (hillside angle, terrain type, among others) and the level of human intervention in sector are of high potential for landslide development. 2. Significant presence of indicators of instability (cracks, inclined trees, poles and walls, among others). Destabilization process in full development, but yet progress can be monitored. 3. Maintaining the existing conditions landslide occurrence is perfectly possible during episodes of intense and prolonged rainfall during the rainy season. R4 (Very 1. The geological and geotechnical conditions (hillside angle, High) terrain type, among others) and the level of human intervention in sector are of very high potential for landslide development. 2. The indicators of instability (cracks, inclined trees, poles and walls, landslides scars, among others) are significant and present in large numbers or magnitude. Destabilization process in advanced stage of development. It is the most critical condition, being very difficult to monitor the evolution of the landslides, given its high level of development. 3. Maintaining the existing conditions landslide occurrence is very likely during episodes of intense and prolonged rainfall during the rainy season.

and the hydrological ratio (Q/T), where the ratios are logarithmically transformed because of their low values (Montgomery and Dietrich, 1994). Further details of the theoretical-conceptual model framework can be obtained in Dietrich et al. (1993, 1995, 1998), Montgomery and Dietrich (1994) and Dietrich and Montgomery (1998).

R1 (Low)

which is solved by the SHALSTAB automated routine, is demonstrated in Eq. (1). This equation incorporates all the variables in the model and can also be written without considering the soil cohesion variable (C'), as shown in Eq. (2). Due to the difficulty of determining soil transmissivity (the product of the saturated hydraulic conductivity and soil thickness), Montgomery and Dietrich (1994) classified the values of Log (Q/T), as demonstrated in Eq. (3), allowing the area to be classified hierarchically in terms of susceptibility to landslides. The higher the absolute value of the ratio Q/T, where Q is equivalent to rainfall under critical conditions (m day -1) and T is the transmissivity of saturated ground (m² day -1), the greater the instability of the location (Montgomery and Dietrich, 1994). Thus, the susceptibility map generated by the model presents seven stability classes (Fig. 8 and Table 2) that follow the conditions defined by the equality between the ratio of the contribution area to the contour unit (a/b)

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Q¼ Q¼

T ρ senθ s ða=bÞ ρw

tanθ 1− tanϕ

T C′ ρ senθ þ s ða=bÞ ρw gzcos2 θtanϕ ρw

ð1Þ

1−

tanθ tanϕ

" # senθ C′ ρs tanθ þ LogðQ =T Þ ¼ 1− ða=bÞ ρw gzcos2 θtanϕ ρw tanϕ

ð2Þ

ð3Þ

Where Q is the rainfall required for rupture under critical conditions (m day -1); T is the saturated-soil transmissivity (m² day -1), θ is the hillside angle (°); a is the contribution area or upstream drainage area (m²); b is the unit-contour length (grid resolution given in m); C' is the effective soil cohesion (kPa); ρw is the water density (kg/m³); g is the acceleration due to gravity (m/s²); z is the ground thickness (m); Π is the internal-friction angle of the soil (°); and ρs is the specific weight of the saturated soil (kN/m³). The topographic parameters used by the model (hillside angle and contribution area) were obtained from a high-resolution digital terrain model (DTM) (4 m²) through the Topo-to-Raster module of the ArcGIS 9.3 software from a scanned topographic map at a scale of 1:10 000 with 5-m equidistant contours. Due to the absence of data for certain in situ soil physical properties, these properties were indirectly taken from the literature (Sousa Pinto et al., 1993) based on the lithology (schists) and soil texture (silt-clay to fine sand) of the area. These variables were cohesion (C'), 0 kPa; internal-friction angle (Π), 35º; soil thickness (z), 2 m; and specific weight (ρs), 1700 kN/m³. Sousa Pinto et al. (1993) obtained these values through laboratory tests in undisturbed residual-soil samples. They authors analyzed and correlated the parameters with reference to the granulometric constitution, compactability characteristics, permeability, deformability in an edometric test, shear strength in terms of effective tension, resistance in undrained solutions under natural and saturated conditions, and residual strength of the soil. The analysis of stable and unstable zones produced a distribution histogram of the number of cells for each susceptibility class, here referred to as Distribution Frequency (DF), which is equivalent to the ratio between the number of cells in each susceptibility class and the total number of cells in the basin. We conducted two correlative analyses using the risk map and the susceptibility map. In these analyses, we identified the cell number of each susceptibility class that coincided with the risk areas, generating two new indexes: Risk Concentration (RC) and Risk Potential (RP). The first index is the ratio of the number of cells in each susceptibility class (e.g., Unconditionally Unstable) affected by the risk areas to the

Fig. 6. Example of at-risk homes very close to top (A) and to base (B) of the hillside.


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Fig. 7. Landslide risk sectors from low-altitude photographs obtained in a helicopter flight and partial view of sector 1 (circle).

total number of cells in the basin, while the second index is the ratio of the number of cells in each susceptibility class affected by the areas of risk to the total number of cells in the same susceptibility class. 4. Results and Analyses 4.1. Map of landslide-risk areas In mapping the areas of risk, four of the fourteen mapped probability sectors were classified as R1 (130 houses), two were classified as R2 (130 houses), two were classified as R3 (35 houses), and six were classified as R4 (390 houses). The areas of these sectors varied from 7000 m² to 75,800 m², and approximately 685 houses were counted within the total area (Fig. 9 and Table 3). Houses were counted in the field and confirmed from field and oblique aerial photographs. In some cases, the close proximity of the houses or the subdivision of a single roof made it difficult to determine the exact number. However, it was important to consider the number of houses present in each risk sector because this issue was directly related to the magnitude of the damage caused by landslides. In other words, the greater the number of houses, the greater the number of inhabitants subject to risk, especially in R4 sectors, where the greatest concentration of houses was found. The sectors categorized as R4 (3, 5, 6, 7, 8, and 14) were sitting on unstable ground composed of residual and colluvial soils derived from metamorphic Precambrian rocks (schists) and on deposits of

more recent materials, resulting from the accumulation of materials transported by erosive processes and several types of anthropogenic debris (e.g., rubble and waste). Due to the fragility of the bedrock, some landslides were identified by scarring or, in some cases, by the mobilized material that reached the houses (Fig. 10). The topography of R4 sectors is dominated by concave hillsides with strong declivities and abrupt rectilinear hillsides. Bananas are cultivated on both types of slopes (Fig. 11), and the short, water-accumulating roots of these herbaceous plants contribute to the occurrence of landslides. The houses consist of a mixture of wooden and masonry homes with high occupation density and a lack of basic infrastructure services. The majority of wooden houses (Fig. 11) exhibit inadequate building techniques, from the foundation to the type of wood used in construction (fragile and of low rigidity). The construction of these houses has not maintained the original geometry of the hillsides, creating deep cuts and embankments without technical control and stability analyses of the topography. Moreover, the wood used in Table 2 Stability and saturation classes: A, B, C, D, E, F and G, according to the ratio a/b and tanθ on Fig. 8 and grouping the classes to simplify the interpretation of susceptibility map generated by the SHALSTAB model. Source: modified from Dietrich and Montgomery (1998). For parameter definition see text related to Eqs. (1), (2), (3). Classes (of SHALSTAB)

Stability classes

Condition

Significance

(A) Stable

Unconditionally stable and saturated Unconditionally stable and unsaturated Stable and unsaturated

a/b > (T/Q)senθ and tanθ≤tanϕ(1-ρw/ρs)

Stable Areas

(B) > − 2.2

(C) − 2.5 - - 2.2

(D) − 2.8 - - 2.5

Unstable and unsaturated

(E) − 3.1 - - 2.8

Unstable and saturated

(F) b − 3.1

Fig. 8. Graph showing the variation a/b (Y-axis) on tanθ (X-axis). The saturation limit is represented by the dashed line. The A, B, C, D, F and G quadrants represent the stability conditions, as shown in Table 2. For parameter definition see text related to Eqs. (1), (2), (3). Source: Montgomery and Dietrich (1994).

Unconditionally unstable and unsaturated Unconditionally (G) Unconditionally unstable and saturated unstable

a/bb(T/Q)senθ and tanθ≤tanϕ(1-ρw/ρs) tanθ T a=bb ρρws 1− tanϕ Q senθ a/b b (T/Q)senθ and tanϕ (1-ρw/ρs ) b tanθ btanϕ tanθ T a=b≥ ρρs 1− tanϕ Q senθ w a/b b (T/Q)senθ and tanϕ (1-ρw/ρs)b tanθ b tanϕ tanθ T a=b > ρρs 1− tanϕ Q senθ w a/b > (T/Q)senθ and tanϕ (1-ρw/ρs) b tanθ b tanϕ tanθ > tanϕ and a/b b (T/ Q)senθ

Averageinstability classes

Unstable areas

tanθ > tanϕ and a/b > (T/Q) senθ



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Fig. 9. Map of landslide-risk areas of the Limoeiro River Basin.

these houses shows considerable natural wear, probably due to lack of maintenance, increasing its instability. The basic sanitation systems exhibit plumbing leaks and water is directly released onto the hillside, leading to soil saturation (Fig. 11). The lack of control of surface water resulting from the release of sewage and waste, especially from wooden houses, is an important factor in the increased instability of these sectors, increasing the probability of shallow landslides on hillsides and embankments.

Table 3 List of the fourteen landslide risk sectors mapped, indicating the probability of risk, number of at-risk homes and area in square meters (m²). Sector Probability of risk Number of at-risk homes (approximately) Area (m²) 1 2 3 4 5 6 7 8 9 10 11 12 13 14

R3 R1 R4 R1 R4 R4 R4 R4 R2 R1 R3 R1 R2 R4

15 20 40 40 40 40 150 100 100 50 20 20 30 20

22 100 27 100 14 100 7000 75 800 33 600 9100 35 200 19 900 15 000 53 600 14 900 44 900 22 900

On the hillside, the houses are located in some instances at the top, at the base or both situations (Fig. 6 and Fig. 10). This placement enhances their risk compared to houses located at the middle or at the base of the hillside because the probability that the houses will be hit by the mobilization of upstream materials is generally greater than the probability that the houses located at the hillside crest will fall. In addition, the access roads, which are generally unpaved, modify the original geometry of the hillside and contribute to sediment mobilization. These sectors present various indicators of risk in the houses, such as cracks, inclined walls, poles and trees, subsidence (advanced stage of cracks in houses or soil), and compromised structures (Fig. 11). Although residents and Civil-Defense technicians report a large number of past landslides, it was not possible to establish a frequency category for landslides and other natural processes using concept of recurrence. Here, we assume that the adverse effects of anthropogenic activities in areas of risk will eventually function as a random variable of expected effect, decreasing land stability and increasing risk (i.e., the recurrence of landslides). In contrast, in the mapped sectors with a risk rate of R1 (2, 4, 10, and 12), the cut and natural hillsides, with heights between 20 and 30 meters, were protected by trees and low vegetation (although there were a few deforested areas) and by the installation of various geotechnical interventions, such as containment walls, cement grout and gabions, which decrease the probability of risk (Fig. 12). Most of the houses were built of masonry, presenting greater resistance to landslides, and the majority was built according to


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Fig. 10. Aerial partial view of sectors 8 (A), 5 (B) and 3 (C) with emphasis on shallow landslide scars. There are at-risk homes in sectors 8 and 5 and a risk of accidents in homes in sector 3, because of the proximity of housing to the base of the hillside. The scars are associated, mostly in sectors 8 and 5, with the presence of banana crop and water release directly on the hillside, which saturated the soil overtime, and the presence of waste and rubble in sector 5.

technical criteria based on the behavior of surface waters, the surrounding access roads and alleys, the careful protection of the hillsides, and the construction of proper foundations. Moreover, the houses possess infrastructure networks (water, electricity, and waste collection) and a functional basic sanitation system that does not release water or sewage directly onto the hillside. In these sectors, rainwater is distributed more evenly due to the paving of the access roads and the small amount of exposed soil and debris from civil construction.

The distance of the houses from the top and base of the hillside is greater than 2 m, reducing the possibility of mobilized material hitting them. Moreover, in the observed cuts and embankments, no evidence of instability was identified. The population did not report past landslides. Thus, destructive events with a high rate of risk are not expected to occur if the existing conditions are maintained. It is important to mention that these sectors exhibited a low but not non-existent risk rate. Despite the good conditions in these sectors, we must consider

Fig. 11. (A) Example of banana crop in sector 6; (B) At-risk wooden-built home in sector 3; (C) Exposed pipes with the presence of leaks in sector 14; (D) Water release directly on the hillside in sector 5; (E) Cracks in the at-risk home; and (F) Subsidence (both E and F are in sector 14).



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Fig. 12. Example of geotechnical interventions to contain hillsides in the sector 2: containment walls (A) and gabion (B).

that the soil characteristics (sandy and fragile) of the hillsides might reflect a high potential for mass movement. 4.2. Susceptibility map generated by the SHALSTAB and joint analysis About 20% of the basin was classified as Unconditionally Unstable and 41% as Stable. The percentage of land in each susceptibility class between the extreme categories (Log Q/T b − 3.1 until Log Q/T >

−2.2) varied between 7% and 11% (Fig. 13). The most stable classes were primarily located in the bottom of the valley, on low slopes and at low elevations, while the unstable classes were located on more fragmented topography, where the probability of rupture is greater, resulting in strong topographical control of the distribution of the susceptibility classes in the model. The basin areas that were classified as Unconditionally Unstable appeared associated with hillside angles greater than 17°, altitudes

Fig. 13. Susceptibility map simulated by the SHALSTAB model (C’ = 0 kPa; Ф = 35º; z = 2 m; ρs = 1700 kN/m³) and (A) Distribution Frequency (DF), (B) Risk Concentration (RC) and (C) Risk Potential (RP) on the susceptibility classes.


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greater than 800 m and an orientation from S to SW. Such areas are associated with the presence of more unstable rock material and less preserved vegetation, so there is less shading and soil-moisture maintenance. Classes with more instability corresponded to land located in the headwater areas of the basin, where there is greater water infiltration and greater probability of landslides. In contrast, the stable areas were mainly located along in low-angled portions of the hillsides. Near the main drainage channel, undercutting of the river bank, silting and flooding were present. We also observed a direct relationship between the contribution area highest values and unstable areas, probability due to increased pore pressure within soil reducing effective stress. With regard to curvature, one of the most important parameters for landslide occurrence, more susceptible hillsides predominantly had rectilinear forms with raised angles and thinner soil layers. We also verified in the field that these hillsides suffered cuts in their terrain, deforestation and overload due to embankments and waste, increasing their instability. Reneau and Dietrich (1987) and Dietrich and Montgomery (1998) have previously verified that concave and rectilinear forms are preferential areas for water concentration and more rapid elevation of pressure loads during a rainfall event; therefore, such slopes need a smaller volume of rainfall for rupture to occur. Convex forms, on the other hand, possess a preferential divergent flow, and water in the soil appears locally due to strong rains or to the heterogeneity of the rock, which forces the subsurface flow upward into the soil mantle. Thus, although they are divergent, these slopes require a large rainfall to increase their instability. Although the model ignores anthropogenic factors, thus considering exclusively natural factors in the prediction of landslides, about 40% of the areas of risk coincide with the Unconditionally Unstable class, and only 1.2% coincide with the Stable class (Fig. 13). The accuracy of the susceptibility map, demonstrating its potential in shallow-landslide forecasting in occupied areas, was confirmed by the RP index, which presented elevated values for the Unconditionally Unstable class (12%) and declined with increasing stability, reaching values below 1% for the Stable class (Fig. 13). Furthermore, no risk sector was completely included (100%) in the Stable class, even though this class presented the greatest DF, indicating high accuracy. 5. Discussion: prediction of susceptible areas associated with the probability of landslide risk We verified that the SHALSTAB model, which has been widely used to predict landslides in areas with low anthropogenic intervention, produces satisfactory results that are consistent with landslide-scar maps. In dense urban areas, however, its use is still uncommon. For example, Dietrich et al. (1998) applied the model in seven hydrographic basins in the Oregon chain near Coos Bay, California (U.S.A.), where they recorded approximately 844 landslides between 1979 and 1996 and found that 94% of landslide scars coincided with the Unconditionally Unstable class, showing the efficiency of the model in this type of approach. Even considering that these authors applied the model in sparsely occupied areas and correlated it with a landslide-scar map and not with a map of risk areas, as presented in this article, the more elevated values of the Risk Concentration Index (RC) in the Limoeiro river basin also coincided with the Unconditionally Unstable class, as occurred in the work by Dietrich et al. (1998). In Brazil, previous studies have followed the same trend, mostly applying the model in sparsely occupied areas. Guimarães et al. (2003), for example, have applied the model in Maciço da Tijuca in the Rio de Janeiro municipality, where hundreds of landslides occurred in 1996 due to intense rains. These authors found that 70% of shallow-landslide scars were found in unstable areas and 30% in stable areas. Vieira (2007) mapped 356 scars in Serra do Mar (SP), obtaining indices of scar concentration greater than 60% in the most unstable areas. When they analyzed the landslide potential, they

found better agreement between the predictions made by the SHALSTAB model and the landslide scar map, such that the highest potential indices were found in the Unconditionally Unstable class. Therefore, in these studies, higher values of the Concentration and Potential indices coincided with the Unconditionally Unstable class. One of the few studies to apply this model in densely occupied urban areas in Brazil is that of Zaidan and Fernandes (2009), in a hydrographic basin of the Juiz de Fora municipality (State of Minas Gerais). These authors also correlated the model using a scar map on which about 30 landslide scars were shown. From the SHALSTAB model, they found that 34% of the area was stable, 47% of the area exhibited average instability, and 19% of the area was unstable areas. Although unstable areas accounted for a smaller percentage of the total area, these areas had the greatest concentration of scars, indicating that the model made successful predictions in this area. We obtained similar results in the Limoeiro River basin, where there was a greater amount of stable areas defined by the SHALSTAB model. However, the highest concentration and the greatest risk potential coincided with the Unconditionally Unstable class. Thus, we verified that the studies described above (Dietrich et al., 1998; Guimarães et al., 2003; Vieira, 2007; Zaidan and Fernandes, 2009) produced satisfactory results in defining landslidesusceptible areas. Our results confirm the advantages of using the SHALSTAB model, including the application to different areas with different soil uses without elevated costs for the acquisition of input data (e.g., topographic parameters) and the simplicity of using the model in the GIS environment. In the specific case of the Limoeiro River basin, we verified that the risk sectors that coincided with the unstable areas defined by the SHALSTAB model were associated with steep hillsides where there is a greater probability of rupture. We recognize, however, that the isolated influence of the slope angle in topographic instability is difficult to evaluate because the relationship of the hillside to other factors becomes evident, such as soil thickness, bedrock foliation and schistosity. Therefore, we also verified that the unstable hillsides were those with rectilinear forms, directed toward the southern quadrant and situated in locations with greater contribution areas. Moreover, the lack of urban planning, the removal of vegetation, and the lack of supervision and control from the government have accelerated the inappropriate human occupation that has helped to increase land instability. In our mapping of areas of risk, we recognize that the methodology that we chose requires subjective judgments to classify probabilities of risk. Because these judgments are made by the researcher, this analysis is entirely related to his/her experience. However, this type of mapping has been previously applied by several authors in many urban areas, indicating the need to remove houses, install geotechnical interventions to alleviate hillslope instability and improve urban planning. Macedo et al. (2004), for example, mapped 96 slums in the southern and western zones of the city of São Paulo. They recorded 302 risk sectors, of which 16% presented a very high risk rate (R4), 27% presented a high risk rate (R3), 32% presented an average risk rate (R2) and 25% presented a low risk rate (R1), totaling 28 000 houses. Ogura et al. (2004) mapped seven districts in the city of Campos de Jordão (state of São Paulo) that were severely affected by shallow landslides in the summer of 2000, causing the death of 10 people and destroying hundreds of houses. The authors classified 100% of the sectors as having a very high risk (R4) due to their precarious occupation of hillsides with steep slope angles (greater than 30°). That area showed an elevated concentration of large-extent ruptures with high displacement energy and a destructive impact. The authors concluded that hillsides with slope angles greater than 30° were dangerously susceptible to the occurrence of destabilization processes. In Brazil, a Federal Law (Lehman Law) prohibits urban occupation of hillsides with angles greater than 17° due to the greater probability of landslides. This law is often ignored, as is the case in Campos de Jordão and in the Limoeiro River basin. The violation of this law results in considerable losses to society.



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Fig. 14. Detailing of the distribution of instability and stability cells (susceptibility classes) contained in three risk Sectors. For tonal scale see Fig. 13. In sector 12 (R1), 90% of the cells in stable classes, in sector 1 (R3), 95% of the cells in unstable classes and in sector 11 (R3), 56% of cells were placed in stable classes, 24% in the average-instability classes, and 20% of cells in the unstable classes, mostly in the Unconditionally Unstable class. This was the area where, in January of 2010, shallow landslides triggered by 103 mm of rainfall in 24 hours occurred.

Another important factor is the role of rainfall in triggering landslides. The occurrence of heavy precipitation events in the Limoeiro River basin has caused the destabilization of several hillsides, exacerbating the soil saturation due to water released from the houses. Saturated soil slides more quickly and with greater ease. In other countries, landslideassociated problems may be triggered by intense geologic or climatic events, such as strong earthquakes and hurricanes, which are not common in Brazil. For example, Ayala (2004) studied landslides in the Oaxaca and Guerrero provinces in Mexico, where a hurricane (Hurricane Pauline) caused rainfall of 411 mm in 24 hours in 1997, triggering landslides that killed 228 people and left 200 000 homeless and hundreds wounded. Moreover, in developed countries, the most intense damage frequently occurs to highways or other major infrastructure sites because there are few precariously situated urban areas (slums), as observed in Bajo Deba in the province of Guipúzcoa in northern Spain by Remondo et al. (2008), where several urban facilities were damaged by landslides. According to the results of our study, the R3 and R4 sectors generally presented the most precarious settlement standards, while the R1 and R2 sectors contained houses with more favorable conditions. However,

in a more complete analysis considering the probability of risk and the susceptibility class indicated by the model, sectors 12 (R1) and 1 (R3) exhibited the closest agreement. In sector 12, 90% of the cells in stable classes and in sector 1, 95% of the cells in unstable classes according to the model (Fig. 14). In sector 11 (R3), 56% of cells were placed in stable classes, 24% in the average-instability classes, and 20% of cells in the unstable classes, mostly in the Unconditionally Unstable class (Fig. 14). This was the area where, in January of 2010, shallow landslides triggered by 103 mm of rainfall in 24 hours occurred due to particular natural and anthropogenic characteristics and previous destabilization. We classified this sector as having a high probability of risk (R3) in the field. Thus, the natural susceptibility to landslides indicated by the model in the western part of this sector, the current anthropogenic pressures, and the extreme rainfall event triggered the landslides, which generated innumerable socio-economic losses. Our field classification, conducted prior to this event, and the predictive model were consistent with one another. This agreement showed the influence of susceptibility in the generation of risk areas and emphasized the role of human intervention in triggering landslides.


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6. Conclusions Several Brazilian cities have areas of landslide risk due to the predominance of weak building standards and lack of infrastructure. In regions with severe rainy periods, these areas of risk are frequently subjected to accidents that result in death and economic damage. In many Brazilian cities, the weak environmental land-use legislation tends to be applied more rigorously in districts that are more economically and financially important. In contrast, areas that are farther from the city center are neglected by the public administration, characterized by peripheral expansion, and marked by the presence of precariously situated dwellings. In the city of São Paulo, many residents live in areas of risk, including slums built on unstable hillsides in the city's peripheral areas. We conclude that the Limoeiro River basin, due to its location in a peripheral area of the city of São Paulo, has undergone severe anthropogenic pressures in naturally landslide-susceptible locations that were previously occupied by Atlantic-Rainforest vegetation, resulting in many areas of risk. In other words, the real-estate land valuation of lands that are more favorable for occupation in downtown São Paulo, associated with weak housing policies for the low-income population in the peripheral areas, have impelled people with low purchasing power to occupy the more geologically and geotechnically unstable land within the basin and to neglect the need for efficient control of soil use. These changes have occurred over a long period. During the summer, the heavy rainfall triggers shallow landslides, which are exacerbated by anthropogenic triggers such as cuts in steep slopes, lack of infrastructure (drainage and basic sanitation), leaking pipes, and concentrations of rain water. The methodological steps used here combine qualitative and quantitative techniques and direct and indirect analyses to elucidate the agents and triggers of landslides in urban areas and to model the destabilization processes. We believe that the combination of the two applied methodologies can establish the conditions and circumstances for the occurrence of landslides, that is, how and where they are likely to occur. Moreover, our procedure indicates the clear difference between the susceptibility map and the map of risk areas, assuming that the adoption of different terminology for susceptibility and risk will facilitate the adoption of these concepts in an international terminology. We also emphasize the possibility of combining these two methodologies to overcome the difficulty of mapping landslide scars in densely occupied urban areas (it is common for dwellings to be built on landslide-scar areas in slums). This pattern has occurred in the Limoeiro River basin, where intensive occupation has contributed to the rapid change of the landscape, concealing most evidence of prior landslides. Through the correlative Risk Concentration (RC) and Risk Potential (RP) analyses, we verified that the mathematical SHALSTAB model can supply important information for understanding the distribution of landslide-risk areas. The susceptibility map agreed closely with the risk areas mapped in the field. Thus, this model can be used by governmental agencies to define risk areas and to plan soil use. There are some unstable areas in the basin defined by the SHALSTAB model that have not yet been occupied. Likewise, we believe that the mapping of risk areas is a tool that can pinpoint the locations that most require emergency actions, socio-environmental projects, and removal of houses, and that adequate risk maps can assist public managers in negotiating priorities with local leaders. Moreover, because there are several areas that are subject to flooding risk in this basin, we indicate the need for future studies to analyze the susceptible areas and to map the areas of risk. Landslides are geomorphological processes with great importance in the dynamic development of topography in tropical and temperate environments. Despite the different social, economic, and climatic conditions and models of management and conservation found in these places, landslides can have catastrophic consequences anywhere in the world. Although the prediction of landslides is complex

due to their many natural and anthropogenic triggers, the understanding of land instability provided by SHALSTAB and the indication of the probability of risk are relevant to society because they may help to help save lives and protect urban structures. As the most unstable locations with the greatest probability of risk are identified, people will have more time to evacuate and avoid places that should not be occupied. In the Limoeiro River basin, our results may have a positive effect on future inhabitants if the government uses them in land-use planning.

Acknowledgements This research and this paper (figures in print version) were supported by FAPESP (São Paulo State Research Foundation). The authors thank their colleagues for continuing support and discussion: Kátia Canil, Luis Antonio Bittar Venturi, Emerson Galvani, Cristiane Incau Pinto Pimentel, Tulius Dias Nery, Nívia Marcello, researchers of IPT (Technology Research Institute of the São Paulo State) and Civil-Defense of São Mateus District (São Paulo city). This manuscript was significantly improved by the contributions made by two anonymous reviewers.

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