# Vulnerable settlements to debris flows in Arequipa, Peru: population characteristics, hazard knowledge, risk perception, and disaster risk management

Jean-Claude Thouret, M. Taillandier, E. Arapa, E. Wavelet

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Jean-Claude Thouret, M. Taillandier, E. Arapa, E. Wavelet. Vulnerable settlements to debris flows in Arequipa, Peru: population characteristics, hazard knowledge, risk perception, and disaster risk management. Natural Hazards, 2023, 10.1007/s11069-023-06167-8. hal-04261478

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<table><tr><td>Manuscript Number:</td><td>NHAZ-D-23-00511R2</td></tr><tr><td>Full Title:</td><td>VULNERABLE SETTLEMENTS TO DEBRIS FLOWS IN AREQUIPA, PERU: POPULATION CHARACTERISTICS, HAZARD KNOWLEDGE, RISK PERCEPTION, AND DISASTER RISK MANAGEMENT</td></tr><tr><td>Article Type:</td><td>Manuscript</td></tr><tr><td>Keywords:</td><td>Hazard; Flood; Risk perception; Disaster Risk Management; statistics; Arequipa.</td></tr><tr><td>Corresponding Author:</td><td>Jean-Claude Thouret, Ph.D.<br>Universite Clermont Auvergne - Campus des Cezeaux<br>Aubiere, FRANCE</td></tr><tr><td>Corresponding Author Secondary Information:</td><td></td></tr><tr><td>Corresponding Author's Institution:</td><td>Universite Clermont Auvergne - Campus des Cezeaux</td></tr><tr><td>Corresponding Author's Secondary Institution:</td><td></td></tr><tr><td>First Author:</td><td>Jean-Claude Thouret, Ph.D.</td></tr><tr><td>First Author Secondary Information:</td><td></td></tr><tr><td rowspan="4">Order of Authors:</td><td>Jean-Claude Thouret, Ph.D.</td></tr><tr><td>Marie Taillandier, MSc</td></tr><tr><td>Evelyn Rosbet Arapa, MSc</td></tr><tr><td>Emeline Wavlelet, MSc</td></tr><tr><td>Order of Authors Secondary Information:</td><td></td></tr><tr><td>Funding Information:</td><td></td></tr><tr><td>Abstract:</td><td>Approximately 75,000 people live in areas prone to volcanic hazards and floods in the large city of Arequipa, Peru. We have conducted three different surveys involving c. 280 respondents to appraise the socio-economic characteristics of urban dwellers living in informal settlements along two ravines, the extent to which they know hazards, perceive risk, and how they behave in case of disaster. This study also assesses how local communities consider, and civil authorities implement mitigation procedures in the city. The statistical analysis of the survey datasets included univariate, bivariate and multivariate techniques together with hierarchical agglomerative clustering. Low-income urban dwellers, with a minimum or without education, represent almost 32% of the vulnerable population living in four districts. Almost 45% of the respondents have a regular to minimum knowledge of hazards, and half of the population is worried or uncertain about volcano and debris flow threats. A large proportion of dwellers trust early warning messages, but almost half of them check them out before evacuating. Overall, between a third and half of people living in the four most exposed districts of Arequipa seem to be quite vulnerable in case of an imminent debris flow. Interviews and focus groups with risk managers aimed to understand why planning emergency operations and risk mitigation are not as efficient as the municipality and the communities would expect in Arequipa. Several issues hinder an adequate disaster risk management, as the underlying vulnerability factors of the exposed population are not accounted for. Under-investment in disaster risk management has led to diminish accountability among the risk managers and involvement of dwellers.</td></tr><tr><td>Response to Reviewers:</td><td>In response to the comment of reviewer 6: "The only issue I have with the manuscript is that there were too many descriptions of the hypothesis and too many different hypothesis presented in different parts of the manuscript. Please just stick to one</td></tr></table>
hypothesis and present that at the earliest part of the manuscript where relevant. », we revised two sections of the manuscript as follows:

Line 119 and the following lines: these are the four "working hypotheses", to which we come back while presenting the main results in the conclusion. We alluded to these working hypotheses in Lines 781-782.

Line 530 and Lines 532-545: we formulate and discuss the role of three parameters that would explain how respondents behave when they are facing flows and floods. These three parameters derive from data obtained through Factorial Correspondent analysis (FCA), and shown in biplots (Fig. 7). As we deal with FCA, the data shown in biplots represents mathematical hypotheses on links between pairs of parameters: 1) role of residence time in Arequipa on empowerment, i.e., enhancement of capabilities of people when they face hazardous flows, 2) role of interest in flows on enhancing people capabilities in similar situations, and 3) origin of knowledge and experience about flows.

Thus, FCA (one of the bivariate analyses) enables us to find out statistical links between pairs of parameters of interest. For example, we may link "time of residence in Arequipa" and "enhancement of capabilities of people", based on the assumption that people living longer on the flanks of the volcano and in this hazard-prone environment would get more experience and would learn how to overcome the impacts of flows.

Please see how we have described the FCA objective in Lines 271, 280 and 281.

Simultaneously, multivariate analytical techniques (MCA, HAC) help us identify groups of people according to multiple parameters, including social characteristics, hazard knowledge, risk perception. We expected that the multivariate analysis of multiple parameters would enable us to appraise the behaviour of people in case of crisis.

Bivariate and multivariate techniques are complementary analyses, and together with univariate analysis, they enabled us to deal with different issues related to the "working hypotheses" that we presented at the beginning of the article, section 1.1 (Lines 120-133).
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1 VULNERABLE SETTLEMENTS TO DEBRIS FLOWS IN AREQUIPA, PERU:

2 POPULATION CHARACTERISTICS, HAZARD KNOWLEDGE, RISK

3 PERCEPTION, AND DISASTER RISK MANAGEMENT

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5 Thouret J.-C.¹ª, Taillandier M.², Arapa E.³, Wavelet E.¹ᵇ

6 ¹ª Université Clermont- Auvergne, Laboratoire Magmas et Volcans, CNRS, OPGC, IRD, 6

7 avenue Blaise Pascal, Campus les Cézeaux, 63178 Aubière, France

8 1<sup>b</sup> UCA, now PhD student at Otago University, New Zealand

9 ² Laboratoire de Mathématiques, Université Clermont-Auvergne, Aubière, France

10 ³ Instituto Geofísico del Perú, Calle Badajoz 169 Mayorazgo IV Etapa, Ate Vitarte, Lima,
11 Perú

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13 Corresponding Author: jcl.thouret@hotmail.com

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Revised and re-submitted to *Natural Hazards*

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34 **ABSTRACT**

Approximately 75,000 people live in areas prone to volcanic hazards and floods in the large city of Arequipa, Peru. We have conducted three different surveys involving c. 280 respondents to appraise the socio-economic characteristics of urban dwellers living in informal settlements along two ravines, the extent to which they know hazards, perceive risk, and how they behave in case of disaster. This study also assesses how local communities consider, and civil authorities implement mitigation procedures in the city. The statistical analysis of the survey datasets included univariate, bivariate and multivariate techniques together with hierarchical agglomerative clustering.

Low-income urban dwellers, with a minimum or without education, represent almost 32% of the vulnerable population living in four districts. Almost 45% of the respondents have a regular to minimum knowledge of hazards, and half of the population is worried or uncertain about volcano and debris flow threats. A large proportion of dwellers trust early warning messages, but almost half of them check them out before evacuating. Overall, between a third and half of people living in the four most exposed districts of Arequipa seem to be quite vulnerable in case of an imminent debris flow.

Interviews and focus groups with risk managers aimed to understand why planning emergency operations and risk mitigation are not as efficient as the municipality and the communities would expect in Arequipa. Several issues hinder an adequate disaster risk management, as the underlying vulnerability factors of the exposed population are not accounted for. Under-investment in disaster risk management has led to diminish accountability among the risk managers and involvement of dwellers.

**Keywords:** hazard; flood; risk perception; disaster risk management; statistics; Arequipa.

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58 **1.INRODUCTION**

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65 Disaster impacts in Latin America are felt in the cities, affecting the poorest populations hardest: at least 111 million urban dwellers live in informal settlements that are highly vulnerable to disasters (Charveriat, 2000, UN, 2018). Large cities (above 500,000 inhabitants) are vulnerable to either cyclones, floods, droughts, earthquakes, landslides, and volcanic eruptions – or a combination of those. Recent earthquakes and/or tsunamis exacted a heavy toll and caused damage estimated to several billion US$ in Peru, Chile and Ecuador
66 (Alves, 2021; Statista, 2022). River floods, mudflows and flash floods claimed at least 317 lives, affected over 300,000 people, and caused heavy damage over the past ten years in South America (Statista, 2022). Peru ranked in the highest risk index due to the number of people exposed to seismic events per year (e.g., Tavera, 2020; Alves, 2021). Volcanic eruptions are less frequent than earthquakes, but the single VEI 6 eruption of Huaynaputina in 1600 CE wreacked havoc in Southern Peru and neighbouring countries, and had a global climate impact (Stoffel et al. 2015). Volcanic eruptions displaced 3,000 people and livestock around Ubinas, Peru in 2009 and 2014 (Statista, 2022). Currently, 14 active volcanoes are hazardous for the two million people living within a distance radius of 30 km around the active vents, while the most active Nevado Sabancaya and Ubinas near Arequipa are closely monitored by IGP and INGEMMET (Aguilar et al., 2021; Del Carpio, 2022).

77 Crucial to reduce the disaster risk of life and economic loss in cities are people's preparedness, good governance and resilient infrastructure. This work has the double purpose to assess the characteristics of vulnerable dwellers in one of the most exposed cities to natural hazards, in particular flows and floods, and to unravel some socio-economic and political factors that may hamper disaster risk management.
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## 1.1. Scope and objectives

82 The Index for Risk Management measures the risk of disaster and humanitarian crises in Latin American countries (INFORM, 2018). It ranks Peru third with one of the largest populations exposed to natural disasters with an average of 2.6 major disasters per year, only exceeded by Colombia and Brazil (Charvériat, 2000). In the past 20 years, around 73,300 natural disasters occurred in Peru according to the National Civil Defense Institute (INDECI). The most catastrophic natural disaster in the Peru's history was an earthquake combined with a subsequent landslide that caused ca. 67,000 fatalities in Ancash, northern Peru on 31 May 1970 (Casaverde, 1995). Two earthquakes near Arequipa in 2001 and Ica in 2007 caused 679 fatalities and affected ca. 655,000 people (Tavera, 2002, 2020; Tavera et al., 2008). Floods, debris flows, and landslides affected almost 4.50 million inhabitants in Peru over the last twenty years (Alves, 2021). Along the Peruvian Coast and the western flank of the Peruvian Andes, extreme floods have been associated with El Niño events (e.g., 1982-83, 1997, 2015). Extreme rainfall and floods in Peru have also been reported during La Niña episodes in 2009, 2012 and 2014 (Póveda et al., 2020).

97 This article presents results obtained from a research programme on hazards and impacts in the city of Arequipa funded by the French I-Site Cap 2020-2025, devoted to the effects of
natural disasters and socio-economic vulnerability of large, developing cities exposed to nearby volcanoes such as Arequipa. An earlier paper by Thouret et al. (2022a) described and modelled the impacts of potential tephra fallout and floods and debris flows on the vulnerable building stock of the city. Previous publications (Vargas et al., 2010; Martelli, 2011; Thouret et al., 2013, 2014) described the setting of the city as well as the principal hazards, among them flash floods, HCFs and DFs or lahars being the most frequent Recent studies also dealt with assessment and modelling of torrential flood hazards in Arequipa (e.g., Mazer et al., 2020). Charbonnier et al (2020) modelled the extent and runout of pyroclastic density currents (PDCs) from El Misti.

This article focuses on socio-demographic characteristics of urban dwellers living along valleys that convey frequent floods and flows, and how they understand hazards and perceive risk based on recurrent hazards, which occur every 2.7 years on average since 1915 (Thouret et al., 2022a). Figure 1 summarizes the objectives and methods used in this work:

1. To define the socio-demographic, economic characteristics and origin of district dwellers to assess the vulnerability of Arequipa's population living in exposed settlements.

2. To correlate the knowledge of hazards with levels of risk perception at the individual scale, with the aim to appraise their potential behaviour in case of an imminent flow event.

3. To identify how public institutions and neighbourhood boards manage disaster risk from floods and flows, with the aim to unravel potential sources of divergences between the district and city municipalities.

Four working hypotheses have guided the research project:

1. A majority of the low- to modest income population lives along the margins of the ravines where land is cheap or obtained through informal occupation, and housing poorly equipped and ill-protected. There, migrants from the Cordillera, who are not acquainted with El Misti volcano and the hazard-prone environment, have been mixed with people who originally lived in the city area.

2. Although urban dwellers have experienced the adverse effects of relatively frequent flows, a majority of them are not confident about how to behave during emergencies due to their limited knowledge of the occurrence, timing and consequences of the flows.
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3. Actions of neighbourhood boards against flow impacts may not be sustained due to limited resources and lack of civil protection works, hence the urban dwellers feel that they are left behind by the municipality.

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4. Potential conflicts have emerged between officials from overlapping institutions and administrations (districts, city hall and region of Arequipa, and state) in disaster risk management policy.

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## 1.2. Terminology

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Risk can be measured as the product of “probability × losses”, where the probability is a function of hazard and the losses depend on both exposure and external and internal vulnerability (Aspinall and Blong, 2015), comprising “the capacity of a community to cope with the consequences of a disaster” (UN, 2016; UNDRR, 2017). Risk assessment includes other components such as hazard knowledge, risk perception and community resilience (Gaillard, 2007, 2008; Gaillard and Dibben, 2008; Paton et al., 2008; Lavigne et al. 2008; Donovan et al., 2018; Thouret et al., 2022b).

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Risk perception is not restricted to its affective response (e.g., feelings about a volcano), but it has “the double dimension of perceived severity and probability of occurrence” (in: Thouret et al., 2022b). Here, we consider the double dimension of risk perception together with a number of associated variables from social sciences: “concern and hazard salience; hazard likelihood and severity; experience with the source of the risk; past behaviour and impact of memory on future behaviour; origin of experience and knowledge; flow attractiveness; empowerment and self-confidence, and; trust in authorities” (e.g., Davis et al., 2005).

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According to the UNDRR definition (2017), “Disaster risk management is the application of disaster risk reduction policies and strategies to prevent new disaster risk, reduce existing disaster risk and manage residual risk, contributing to the strengthening of resilience and reduction of disaster losses”. Disaster risk management can be broken down into three actions (UNDRR, 2017): 1) “prospective disaster risk management” to avoid new or increased risk; 2) “corrective disaster risk management” to remove or reduce risk, and; 3) “compensatory disaster risk management”, which involves affected communities at the local level. The compensatory disaster risk management includes preparedness, response and recovery activities, but also different financing instruments. By examining the disaster risk management policy in the city of Arequipa, we will consider to which extent existing plans of urban development (2016-2021) and disaster risk reduction (2019-2021) of the region of
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Arequipa set out the goals together with relevant actions from officials and communities to accomplish these objectives.

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Lahars (volcanic debris flows) and rain-triggered debris flows are potentially destructive water-saturated mass-flows in mountainous regions and in areas where steep slopes cut down into loose sediment. Lahars can be divided into two categories using sediment concentration, grain-size distribution and bulk density (Thouret et al., 2020, and references therein): 1. Debris flows (DFs) comprise a solid phase of at least 60 vol%, thoroughly mixed with water. 2. Hyperconcentrated flows (HCFs) are two-phase flows transporting between 20 and 60 vol% of sediment.

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## 1.3. Hazard and risk setting and population of Arequipa

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Arequipa, Peru's second largest city (1,137,087 inhabitants, INEI, 2018), is located in one of the fastest growing regions in the country, at the contact between the Andean highlands and the coast. Arequipa is situated at c. 2300 masl. in a tectonic basin between the west flank of the Western Cordillera and the Coastal batholith to the south (Thouret et al., 2022a). This basin was filled by ignimbrite deposits, partly covered by volcanoclastic deposits that make up the foothills of the Nevado Chachani (6080 masl.), El Misti (5822 masl.) and Pichu Pichu (5600 masl.) volcanoes. Chachani and Pichu Pichu are extinct volcanoes, but El Misti active stratocone currently exhibits seismic and fumarolic activity (Aguilar et al., 2021).

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Arequipa was founded in 1540 AD, but the early urbanized area remained close to the Río Chili, mainly on its eastern side, currently known as the historic Centre (Cercado), about 17 km from El Misti volcano. From 1960 onwards, the historic Centre did no longer restrain the demographic and construction growth. The rapid sprawl of the city towards the volcano was influenced by migrations due to the drought in the Cordillera and the economic growth of the Arequipa region (Gutiérrez et al., 1992). The urban area grew mainly to the north and east of the historic Centre, forming poorly designed suburbs and informal settlements, to cover about 68 km² in the 1970's (Thouret et al. 2014; Arela, 2021). In recent years, accelerated growth led to the expansion of peripheral areas towards the lower flanks of the Misti volcano and Chachani to the west, quadrupling its original area and reaching around 276 km² (Fig. 2; Arela, 2021). Over the 2007-2017 intercensal period, the annual economic rate accelerated to 6.4% in the province of Arequipa, with a population increase of 2.8% due in part to migration from the regions of Arequipa, Puno, and the Altiplano in general.
Between 50,000 to 75,000 inhabitants live < 1 km from the ravine network and Río Chili likely to be affected by flash floods or lahars, and many more if El Misti volcano, located 17 km NE of the city centre, produced tephra-fall and PDCs (Thouret et al., 2001; Charbonnier et al., 2020; Thouret et al., 2022a). Hydrometeorological events are the most recurrent hazards, inducing c.70% of all disasters that have occurred in the city (INDECI, 2020). Debris flows (DF) and hyperconcentrated flows (HCF) induced fatalities (at least 34 since 1915) and injuries, affected approximately 113,040 people and damaged 27,910 buildings, street network and infrastructure every 2.7 years on average in the city (Thouret et al., 2022a). One of the most destructive flows occurred on 8 February 2013 along the ravine Venezuela (Fig. 1). Triggered by heavy rainfall (124.5 mm in 3 hours), the hyperconcentrated flows caused 5 fatalities and impacted approximately 23,000 inhabitants (Ettinger et al., 2015).

The environment of Arequipa is propitious to flash floods (FF) and mass flows (HCF and DF), as the result of semi-arid climate (≤ 150 mm a year), with concentrated thunderstorms often delivering more rainfall than the average monthly amount (Thouret, 2018; Thouret et al., 2022a; see also Mazer et al., 2020). A series of factors favour torrential floods across Arequipa: (i) Short-lasting (3-4 h) and intense rainstorms (26-42 mm/hour) typical of a semiarid climate, (ii) rapid runoff (30-60 minutes) in small mountainous catchments (18-40 km²); (iii) the impervious network of roads and waterproofing of the built-up area that increase runoff, and; (iv) the steep, bare slopes of El Misti's foothills carved by ravine channels, which are filled by loose volcanic debris mixed with household and industrial waste (Thouret et al., 2022a).

A network of *quebradas* or * torrenteras* (local name of ravines, usually dry but conveying flows under Austral summer rainfall) drain the SW flank of Misti, cross the city in a NE-SW direction, and eventually converge at the permanent artery Río Chili (Fig. 2; Thouret et al., 2013, 2014). The main ravines are San Lázaro, *Venezuela-Miraflores*, Huarangal-Mariano Melgar and Paucarpata: both San Lázaro and Huarangal cross the studied area (Fig. 2; ESD Fig. 1). Ravine channel and terraces narrowed due to urban sprawl, causing overflows during the rainy season (January-March) when the maximum flow of the riverbed is exceeded from an average discharge c. 19.50 m³/s to a maximum of 124-425 m³/s, respectively (Thouret et al., 2022a), affecting adjacent houses and urban dwellers.

## 2. METHODS AND DATA ACQUISITION

### 2.1. Field surveys and data acquisition
We conducted three surveys including semi-directed interviews in September 2018 and 2019 to appraise the socio-economic characteristics of the population and to evaluate hazard knowledge and risk perception of the population in four districts adjacent to two ravines. Six researchers from the University of San Agustin of Arequipa (UNSA), the Instituto Geofísico del Perú, the University of Nariño (Pasto, Colombia) and the Université Clermont-Auvergne (France) collected a dataset from 220 households in 14 neighbourhoods of four districts along both margins of the two valleys: Alto Selva Alegre and Miraflores on the W and E margins of Qda San Lázaro to the North, and Mariano Melgar and Paucarpata on the W and E margins of Qda Huarangal-Mariano Melgar to the NE of the city (ESD Fig. 1). According to the INEI 2017 national census, the total population of the four districts is 350,773, i.e., c. 31% of Arequipa's inhabitants on a surface area of 151.26 km². A district has an average area of 37.8 km² and an average density of 2319 inhabitants/km², the Alto Selva Alegre district with 68 km² being the largest, and Paucarpata the most populated one with 135,923 inhabitants. These densely populated suburbs have been constructed north and east of the historic centre over the period 1930 and 1970 (INEI, 2018).

The surveys focused on the most exposed, ill-equipped neighbourhoods extending on low terraces near ravine channels, which are characterized by a combination of shanty homes, small and modest to regular houses, and a few 2 to 3-storey edifices on river banks (Thouret et al., 2013). Economic activity in the suburbs is dominated by informal jobs, handicraft, followed by employees or unskilled workers in small businesses, and civil servants (see section 3.1 below). After the 2018 national census at the district scale, we have collected data in 2019 and 2020 on population, economic activity, and emergency facilities from the district municipalities. A Trimble TC 1000 GIS mapper device, Google Earth maps and topographic maps have been used in QGIS to locate and collate structural observations on homes, offices, schools, health centres, churches, sport facilities, and markets.

Few limitations encumbered the field surveys on characteristics of people and their dwellings, which we collected in Spanish. We interviewed respondents available in the household, either male or female, through a random door-to-door approach with the help of local students. One limitation may be representativeness. Among 950,000 people around Misti, 50,000 to 75,000 live along the ravines and the Río Chili. Surveys involving 279 respondents from 220 households, which host at least 900 people, represent approximately 0.2% of people and 0.4% of homes in four districts. However, mapping the homes involved 14 neighbourhoods (c.
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9,800 people) most exposed to flows and floods, hence a little less than 3% of the population and 5% of homes in the four districts.

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## 2.2. Statistical analyses

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Statistical analyses explored a set of as many as 51 variables collected through three surveys (Fig. 1; ESD Text). The first group of 17 variables helped define the demographics and socio-economic status of the dwellers living in the most vulnerable suburbs along the ravines. The second group of 11 variables determined the hazard knowledge among these people. The third group of 23 variables helped assess their risk perception. Table 1 summarizes the statistical methods and techniques together with their purposes for coding, quantifying and validating three groups of variables. Statistical investigation conducted with the R software has involved four operations: univariate, bivariate and multivariate analyses together with clustering techniques (e.g., Chambers et al., 2018; Taillandier, 2019).

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1. Univariate analysis (UA, Table 1) is the first step for exploring and preparing a dataset for further analysis. UA summarizes descriptive statistics and provides graphical representations for their univariate distribution.

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2. Bivariate analysis (BA, Table 1) involves the analysis of two variables with the aim of testing simple association or relationship between two variables and attributes. The Chi-square test (with Monte Carlo simulations) aims to compare observed results with the expected results and to determine whether the variables are independent or dependent (ESD Table 1); in other words, to find out whether the difference between observed and expected data is random or due to a relationship between the variables under study. The Chi-square tests two hypotheses: either, the null hypothesis H₀ (i.e., the categorical variables are independent, without any relationship), or the alternative hypothesis H₁ (i.e., the categorical variables are dependent, with a relationship). The hypothesis H₀ can be rejected when the p-value is below the risk alpha threshold of 0.5%.

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In BA, Factorial Correspondence Analysis (FCA) was used to study links (e.g., cause to effect relationship) between the pairs of variables of interest, which resulted from the Chi² test, and further explore correlations and oppositions of categorical variables. One of the BA information derives from the Burt table (ESD Table 2, see below, 3b).

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3. Multiple Correspondence Analysis (MCA) is run to examine the links between several qualitative variables. MCA is a data analysis technique used to detect and represent
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underlying structures in a large categorical dataset (Abdi and Valentin, 2007). MCA

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represents data as points, in biplot graphs, and consists of the following steps (Table 1):

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a) Eigenvalues provide the variance while the sum of eigenvalues is the total inertia. We

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retained dimensions containing the most pertinent or relevant information from a dataset

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totalling 38 variables (5 from Survey A, 10 from Survey B and 23 from Survey C). Benzécri

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correction (Benzécri, 1979) applied to the number of dimensions (inertia) helps estimate how

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much information is included in each dimension. To carry out the MCA, we selected the

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smallest number of dimensions i.e., the first five dimensions which hold over 91% of the

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information available from the whole dataset (ESD Fig. 2).

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b) MCA biplots show the relationships between attributes of all variables, quantified by
contribution to the dimensions and coordinates of each attribute on the dimensions. ESD

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Table 2 shows the coordinates, square cosines and contributions, which help identify the most
contributory variables along the five principal MCA dimensions. The coordinates indicate the
position of attributes, the most distant from the barycentre of each biplot contributing most.

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4. Hierarchical Agglomerative Clustering (HAC; Table 1) constructs groups of people on the basis of the MCA results (MCA was run before HAC to transform qualitative variables into continuous variables). The HAC goal is to build up clusters of people who share similar characteristics, i.e., showing intra-class homogeneity. These clusters should be dissimilar from one another, i.e., showing inter-class heterogeneity. Each clustering is performed using a distance matrix that expresses the distance between each individual, two at a time. These individuals will be grouped according to the similarity criterion defined by the distance matrix and will thus form clusters.

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HAC consists in four steps: 1) A factor map helps identify clusters and their number on the

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basis of distance between attributes of the individuals forming a cluster; 2) Another Chi-

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square test allows to identify the most discriminant variables; 3) We detect the most frequent

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attributes of variables in each of the clusters; 4) A study of paragons, i.e., the five most

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representative people in each of the four clusters, helps identify the attributes of the most

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discriminant variables, and we compare them with the results of the three previous steps. For

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example, cluster 1 is characterized by individuals « who like to observe lahars », as verified

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by that the majority of the paragons of cluster 1 who answered that « they like to watch these
flows ».

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2.3. Survey on disaster risk management policy with civil authorities and communities
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Meetings, semi-structured interviews and focus groups were conducted with identified key actors, i.e., leaders of communities, officials of agencies in charge of disaster risk management, mayors of districts and the city governing body, to analyze how civil authorities and communities were organized and carried out relief, prevention and mitigation.

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Interviews were conducted using the “snowball sampling method” (Van Meter, 1990; Baltar and Brunet, 2012), which was based on a limited sample of population, i.e., a small subgroup of people from the city hall and district municipalities in charge of urban management in general. Seven semi-structured interviews included 5 to 10 persons each and used a guide of 27 questions related to training, knowledge and skills among local civil authorities, regarding proactive, corrective and compensatory disaster risk management policies: (ESD Table 5). Meetings of focus groups were held in each of the four districts, led by the Mayor of each of the district municipality, accompanied by the officials in charge of the urban environment and risk disaster management, as well as leaders of neighbourhoods, who represented their communities.

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Information collected from interviews and focus groups was analysed using the matrix analysis method (Bonilla and Rodríguez, 2000), whose purpose is to organize, structure and validate the qualitative dataset. With the objective of obtaining a valid perspective about the collected data, we grouped statements provided by key actors using a triangulation of qualitative data, which stem from semi-directed interviews, meetings, and focus groups. This method allows visualizing a problem from different angles, so that findings can be consistent and validated (Okuda & Gómez-Restrepo, 2005). The application of the matrix analysis method helps the researcher to evaluate the level of coherence and links between the hypotheses, proposed objectives and the answers to questions.

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# 3. RESULTS

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## 3.1. Demographic and socio-economic characteristics of the district dwellers

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Survey A included seven groups of questions to appraise the socio-demographic and economic characteristics of the population living on both sides of the ravines (See questions in EDS text): (1) Gender, age group, and marital status; (2) Household composition and age groups; (3) Education level; (4) Socio-professional categories; (5) Number of contributory persons and monthly income; (6) Geographical origins of respondents, parents and grandparents, and (7) Time living in the area. The results of the survey A were analysed using

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uni, bi- and multivariate analyses of the dataset from 279 respondents (Fig. 3A, B). Table 2 summarizes the survey results, allowing us to distinguish six socio-demographic and economic groups from the least vulnerable (groups A, B and C) to the most vulnerable individuals (groups D, E and F) (Table 2). This is based on four principal characteristics: 1) age group, 2) education level, 3) monthly wage above or below the national minimum wage (fixed to 930 PEN by a Presidential decree in April 2018, i.e., 230 € or 240 US$), and 4) origin with respect to the volcano and the city, which has a role on hazard knowledge.

As a result, vulnerable, low-income urban dwellers with a secondary education (Groups E and F) represent almost 20% of the cohort, to which we add 12% of urban dwellers (Groups D and F) without or a minimal education. In contrast, a majority of the respondents (Groups A and B, almost 55%) appear less vulnerable: young and mature adults, with at least a secondary education level, and a monthly income above the minimum wage. We provide more detail on each group below:

The largest group B (ca. 39%, Table 2) includes young and mature adults (19 to 30 and 31 to 60 years) who attended primary and secondary school, and a few of them high school. Their wage exceeds the national minimum, while these people came from outside of Arequipa. Group A (ca. 16%) is identical to Group B regarding the age group and monthly wage characteristics, but the level of education is slightly better, and the origin is different, as Arequipa is their birthplace.

Group C (20.50%) includes elderly and young people (over 60 years and 12 to 18 years). Their education level corresponds to either primary and secondary school or high school for some of them. Their monthly salary is above the national minimum, and these people came from various origins near and outside Arequipa.

Both groups D and E (c. 12%) present vulnerable young and mature adults and elderly people (10 to 30, 31 to 60, and > 60 years), with primary or secondary education. Group D, the smallest (c. 3%), without education, and a low-income (monthly salary *below* the national minimum), is more vulnerable.

The small Group F (c. 9%) includes the most vulnerable people, either elderly, adolescents or children (>60 years, 12 to 18 and 0 to 11 years). They have reached low education levels or remained without education, and their income is low, as the monthly salary stays *below* the minimum national wage. They came from distinct regions near and outside Arequipa.
We compare this low- to middle-income population sample (Fig. 3A-B, Table 2) with the socio-demographic data at the scale of the city according to the 2017 National Census (INEI, 2018; Fig. 4) to highlight a few social and economic characteristics that determine how vulnerable urban dwellers can be.

The population of the studied districts shows a similar young demographic structure, with a largest proportion of the population whose age is below 40 years old (c. 45% of the inhabitants), while an almost equal proportion of people over 40 years old (c. 28%) and under 18 years (c. 27%) (Fig. 4). Approximately 58% of the district households consist of 3 to 6 persons living under the same roof, but 41% of the households also include one or two children and 34% one or two grandparents.

The studied suburbs include more people with or without a minimal education than the average urban dwellers in Arequipa: ca. 75% of respondents attended primary or secondary school, while 20.50% earned a higher education diploma. The 2017 national census (Fig. 4) indicates that less than the majority of the city inhabitants (c. 43%) attended secondary school, followed by less than a third of them attending either elementary school (29.2%) or high school (28.1%).

A little less than 77% of the respondents declare that they earn over the minimum wage 930 PEN (229 €), but almost 34% are 'informal' workers (unregistered workers holding unskilled jobs), while almost 21% are housewives. However, an overwhelming 79.50% of the respondents own their house, as they inherit it from, or live with their family under the same roof.

Birthplace or migration origin plays a role on the knowledge of hazard and perception of the risk posed by a volcano and a ravine network, as three quarters (74%) of the respondents were not born in Arequipa, having a minimum knowledge about volcanoes. However, almost 72% of respondents have been living in Arequipa for a period of at least 16 years. About two thirds of dwellers immigrated in the city area from a distance > 100 km, mainly from the region of Puno (Altiplano without volcanoes), whereas 22% of the dwellers were born within a distance of 30 km of El Misti summit in the surrounding department of Arequipa (Fig. 3B).

## 3.2. Hazard knowledge

To estimate the knowledge of hazards among the population living along the two valleys, survey B contained 11 questions around the following five themes (EDS Text): (1)
name of neighbouring volcanoes and date of the most recent eruption of El Misti, (2) ability to read the Misti hazard-zone map and locate the neighbourhood, (3) three most frequent volcanic threats in the area, (4) lahar occurrence, timing and fatalities, and (5) eruption duration and lahar arrival time. Figure 5 presents the results of a univariate analysis of the survey B (Fig. 5), while Table 3 distinguishes four levels of knowledge among 118 dwellers, based on frequency analysis from the Survey B dataset.

We computed the scores of knowledge on the basis of indicators corresponding to the attributes of 11 variables. The resulting score ranges from the lowest 0 to the highest (expected) 24. The distribution of scores is almost symmetric and does not follow a normal law (the hypothesis H₀ has been rejected based on the Shapiro-Wilkinson test); thus, the equidistant (= equal width) discretization method may be more appropriate. We computed the range of classes as follows:

$$a=(max-min)/k,$$

where *k* indicates the number of expected classes, which yields: $a = (23-0)/4 = 5.75$.

Thus, the class 1 extends between the score thresholds 0 and 5.75, class 2 between 5.75 and 11.5, class 3 between 11.5 and 17.25, and class 4 between 17.25 and 23. We obtained a distribution of scores into four groups, with an interval of about six units.

Overall, the majority of respondents (c. 55%) demonstrate a good knowledge of hazards, including <6% having acquired a very good knowledge at school or from experiences with past flow events (Table 3). In contrast, we estimate that almost 45% of the respondents possess a regular to minimum knowledge of hazards. The adjective *regular* is assigned to hazard knowledge shared by ca. 35% of urban dwellers, because they know the occurrence of lahars and floods, but they cannot read a hazard-zone map and ignore the timing or the effects of past flow events.

A few key answers to survey B questions help us to better estimate the level of urban dwellers' knowledge, based on univariate analysis (Fig. 5):

An overwhelming proportion (c. 97.50%) of the respondents were able to name several volcanoes surrounding the city of Arequipa. However, as many as three quarters of the persons do not know or cannot recall the date of the most recent eruption of El Misti. Almost three thirds of the respondents (c. 74%) cannot read a hazard-zone map and are unable to
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locate their district, let alone their own neighbourhood. Reading maps, however, is not taught at elementary and secondary school in Arequipa.

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A reassuring result is that at least three quarters of the respondents (>76%) know, at least approximately, the existing natural hazards, while the majority can estimate the occurrence and arrival time of debris flows. Some hazard knowledge has spread across neighbourhoods near ravines thanks to TV programs, social network videos, and the dissemination of leaflets by research institutes, INDECI, and NGOs that depict hazards and hazard-prone areas across the city.

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3.3. Risk perception and associated variables

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The goal of Survey C (23 questions, ESD Text) was to evaluate how people perceive flood and volcanic risk from Misti, and compare the risk perception with hazard knowledge. Questions asked under seven themes and adapted from a few concepts of social science (e.g., Johnston et al., 1999; Paton, 2003; Paton et al., 2008; Davis et al., 2005; Haynes et al., 2008a, 2008b), aimed to define risk perception and associated variables, as shown in Table 4 and illustrated in Figure 6. These themes include: 1) concern and hazard salience; 2) experience, either direct, indirect or vicarious, and hazard likelihood and severity; 3) past behaviour and impact of memory on future behaviour; 4) origin of experience and knowledge; 5) flow attractiveness; 6) empowerment, i.e., enhancement of people capabilities and self-confidence; and; 7) trust in authorities. The answers to questions from the survey C were analysed using univariate analysis (Fig. 6A-C), a bivariate Chi² test and AFC (Fig. 7; ESD Table 1), MCA and frequency analysis on a dataset from 268 respondents (Table 4). We also identified the most contributory attributes using MCA biplots.

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To evaluate risk perception and associated variables among urban dwellers, we examined answers to six key questions from the themes mentioned above. The answers to C.Q1 (“Concern about volcano or debris flow”) and C.Q14 (“Flow attractiveness”) on the one hand, helped us to weigh people’s concerns about natural hazards or daily life issues. On the other hand, the answers to C.Q19 (“self-confidence in case of an eruption”), C.Q20 (“obedience to evacuation”), and C.Q23 (“Trust in messages or content needed to be checked”) allowed us to gauge people’s self-confidence and trust in authorities in order to appraise their potential behaviour in case of an imminent flow event. Several factors may play a role in how urban dwellers behave, but we suggest that the most relevant ones derive from: (1) how dwellers
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weight flood and volcano risk against personal or daily risk, and; (2) how confident or untrustful they are about early warning messages and evacuation orders.

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Despite the difficulties in interpreting complex answers, we distinguished four groups of population (termed Rp for risk perception) based on the analysis of the most frequent attributes using HAC replies to specific questions related to concern and hazard salience (theme no.1), past behaviour, and impact of memory on future behaviour (theme no.3).

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**Rp1.** A majority of people (c. 51.50%, A in Table 4) are worried and uncertain about the volcano and lahars and do not seem to be interested in flows. Respondents of Rp1 feel self-confident, trust and obey the authorities' messages.

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**Rp2.** Over a third of people (c. 35%, B in Table 4) are less worried about volcano events or debris flows than other issues. The group Rp2 recognizes the hazard likelihood or severity, and they are little interested in flows. These dwellers do not feel self-confident and did not entirely trust the authorities, as they check early warning messages.

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**Rp3.** About 12% of respondents (C in Table 4) are concerned by potential volcano events and debris flow threats. These people feel self-confident, but do not obey the warning messages, as they always check the early warning messages. However, many of them did not trust early warning messages or did not answer the question.

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**Rp4.** A very small group of respondents (c. 1.50%, D in Table 4) did not provide answers or they did not understand the questions. We hypothesize that these people may be new migrants in the city, therefore unconcerned and uninterested in flows, and they would not know what to do in case of flow event.

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Second, the univariate analysis of the Survey C dataset helped us to evaluate how the urban dwellers perceive risk and associated variables (Fig. 6):

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1. Hazard salience prevails among the communities living near the ravines: ca. 60% of the respondents report that they feel worried about the volcano and/or debris flows (Q1, Fig. 6A). Personal concern (Q2, Fig. 6A) seems to co-exist (Q2, Fig. 6A), but as many as 73% of respondents answered “no concern”, while c. 15% still declared that debris flow was a personal concern.

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2. More than three quarters (c. 76.50%) of respondents have witnessed a flow/flood event, and two thirds (c. 67%) remember a recent flow, but an equal number of people (73.50%) have not been injured or affected by floods and debris flows (Fig. 6A). Most people do not perceive

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well the likelihood and severity of lahars, but half of the respondents (51.50%) reasonably
believe that they could be injured by flows.

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3. Half of the respondents have behaved during flow events according to experiences, but more than one third of them (c. 39%) remained on the spot during a flow event and searched for advice. More worrisome, almost 18% of dwellers did not know what to do (Fig. 6A).

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4. Over half of the respondents (c. 57%) are able to describe flow hazards correctly. An overwhelming majority (c. 82%) state that their hazard knowledge stems from experiences with flows, school lessons and authorities' warnings, while a small proportion of respondents obtained advice from their family and/or neighbours (Fig. 6B).

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5. Almost two thirds (c. 63.50%) of the respondents feel that the population are empowered to surmount the hazardous flows/floods or their consequences. However, empowerment or self-confidence remains relatively limited, as less than one third (c. 31%) of them admit that they would be unable to overcome the phenomenon (Fig. 6B).

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6. Trust in official early warning messages and accurate information about imminent eruptions are two major issues for evacuation policy (Haynes et al., 2008b). Almost three quarters of the respondents (c. 74%) declare that they are ready to abide by the evacuation orders. An overwhelming majority of people (≥ 88%) trusts and abides by the early warning message, but almost 42% check them out before evacuating (Fig. 6C). A reasonable behaviour of respondents in case of imminent evacuation has been undermined due to either poor memories from previous evacuations or unwise decisions taken during past flow disasters. Despite early warning messages, respondents stated that they would keep searching for advice or remain inactive. Mistrust in government agencies is based on personal poor experiences during evacuations and lack of respect for the civil authorities. Thus, there is room for improving information about mitigation policy and risk communication, as non-negligible proportions of the dwellers do not trust anyone (8%), will not obey (9%), or do not answer the relevant questions (2 to 4%) (Fig. 6B, C).

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**3.4. Dwellers' risk perception and behaviour, and contributory variables**

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First, FCA biplots allowed us to discuss the role of three parameters that may explain how respondents behave when they are facing flows and floods (Fig. 7).

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Residence time in Arequipa: Respondents who have lived in Arequipa for a short period (6-15 years) believe that they are unable to overcome the effects of debris flows, in contrast to
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dwellers who have lived in the city for longer (>16 years) and answer that they would be able to overcome them (A.Q14 and C.Q18: Fig. 7A).

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*Interest in flows and role on empowerment:* Respondents who witnessed hazardous flows may be interested in watching them, in contrast to people who have not witnessed flows in the past (C.Q3 and C.Q14: Fig 7B). Respondents who witnessed debris flows coincide with those who believe to be able to overcome their consequences, in contrast to people who never witnessed lahars (C.Q3 and C.Q18: Fig. 7C). Those who know how to describe hazardous debris flows feel that they could overcome their consequences (C.Q11 and C.Q18: Fig. 7D).

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*Origin of knowledge and experience about debris flows:* People who cannot remember recent flows coincide with those whose knowledge has been acquired thanks to their family and neighbours (C.Q12 and C.Q8: Fig. 7E). Other respondents may have acquired a knowledge of flows from their own experience, school and authorities.

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Second, the correlation between all variables and the first two dimensions (Fig. 8) has enabled us to identify variables using the first MCA and select the 17 most contributory ones to be considered for the final MCA. In fact, these 17 variables that lie away from the barycentre of the biplot of Figure 8 are most correlated to the principal dimensions:

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(1) Eight variables of Survey B: B.Q3 district location, B.Q4 and B.Q5 zone colour meaning, B.Q6 hazard type definition, B.Q7, flow arrival time estimate, B.Q8 time interval after flow onset, B.Q9 lahar fatalities, and B.Q10 eruption duration;

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(2) Nine variables of risk perception: Survey C: C.Q9 past behaviour, C.Q10 memory on behaviour, C.Q8 memory of flow, C.Q3 flow witness, C.Q6 indirect experience, C.Q15 flow observation, C.Q16 flow attractiveness, C.Q19 empowerment, and C.Q5 flow capacity to hit people.

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The five illustrative variables from Survey A (Age group, education level, socio-professional category, time living in the area, and family origin) will be considered as hypotheses for the final MCA, because they are the least contributory variables (Fig. 8).

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**4. Four groups of city dwellers using Hierarchical Agglomerative Clustering**

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HAC has been carried out according to the results of the last MCA, i.e., on the 17 most contributory variables afore mentioned. This analysis was made on 92 respondents who answered all questions included in the three surveys A, B and C (Table 5, ESD Table 1).
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Table 6 shows the most frequent and the least frequent attributes found for each of the clusters.

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Results from HAC, including the Factor map based on the principle of distance between individuals (Fig. 9), enabled us to define four clusters among the population living along the two ravines (Table 6). A Chi² test on HAC variables allows us to identify and rank variables from the most to the least discriminant (ESD Table 3). The study of paragons (ESD Table 4), i.e., the most representative individuals with their attributes of each of the clusters, helps confirm the frequency analysis of the most frequent attributes illustrated in Table 6 and Table 7. The eight more discriminant variables are: B.Q4, B.Q5; C.Q3, C.Q8, C.Q9, C.Q10, C.Q14 and C.Q16 (ESD Table 3), which are shown with the relevant answers from paragons in Table 8. As a result, we distinguish four groups of urban dwellers based on the most discriminant variables (Table 6):

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**Group 1** (ca. 47% of the cohort)

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From Table 6, hazard knowledge of Group 1 (almost half of respondents) on lahars and floods is regular, but quite limited on volcanic phenomena. These people know about the occurrence and arrival time of lahars, but most of them do not have any idea about the location of their district and the meaning of the hazard-zone map.

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Their risk perception is based on experience from past flow events, and the majority of respondents like to watch them. All respondents have witnessed a debris flow at least once and memory of past flows had an impact on behaviour for many of the respondents. A majority of the respondents trusts civil authorities, but more than two thirds of Group 1 remained on the spot during the flow event instead of evacuating.

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From Table 7, we hypothesize that Group 1 are mostly mature, low- to modest income adults who attended at least secondary school, arrived from beyond the department of Arequipa, and have been living in the area for over 16 years.

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**Group 2** (ca. 31.50% of the cohort)

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From Table 6, volcanic hazard knowledge of Group 2 (almost one third of respondents) is minimal. These people cannot locate their neigbourhood and do not know the meaning of the hazard-zone map. They have a hard time to estimate the occurrence, arrival time and duration of the debris flows. and they do not like to watch them. However, many respondents acquired some experience with past debris flows and remember them.
<figure><img src="image_3.png" /></figure>

Their behaviour in case of a flow event is critical as the majority remained on the spot and/or provided no answer to the related question to trust in warning messages. Their risk perception is regular, as they are worried about the volcano and floods, but they trust official warnings more than all other groups.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

From Table 7, we hypothesize that Group 2 includes low- to middle-income, mature adults and elderly people, who are less educated, arrived from beyond the department of Arequipa, but have been living in the area for at least 30 years.

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

### Group 3 (ca. 18.50% of the cohort)

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

From Table 6, Group 3 hazard knowledge is regular on volcanic ash, pyroclastic flow and lahars or floods owing to education, and these people can estimate the arrival time and occurrence of debris flows. In contrast, they did not provide any answer to questions related to the hazard-zone map, coloured hazard zones, and the location of their neighbourhood.

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

They did not get any experience from past flows/floods and they do not like to watch them, although some flow aspects may be of interest to these people. Their risk perception is poor or regular, they try to check early warning messages, and they do not provide answer to the question “how did you behave that day?”.

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

From Table 7, we hypothesize that the small group 3 gathers relatively low-income, young people (several of them being students) and mature adults relatively well educated, while they have been living in the area for less than 6 or 15 years. They are less worried about volcano events or debris flows, but they show other concerns perhaps related to daily life issues.

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

### Group 4 (only 3.25% of the cohort)

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

From Table 6, hazard knowledge of the smallest Group 4 has fair knowledge about volcanic phenomena and lahar occurrence and arrival time. These people can locate their neighbourhood, and they know the meaning of the hazard-zone colours, and most recall the recent eruption of El Misti.

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

Their risk perception seems contradictory, as they perceive the risk of lahars and floods from education, internet and experience, but they declare that they try to check the early warning messages, they would self-evacuate, or would not know what to do in case of an evacuation. From Table 7, we hypothesize that the smallest group of respondents includes low-income, elderly and educated young people, having arrived from far away, and having settled in the area for 6 to 30 years. The small number of people in Group 4 prevents us from drawing solid

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>
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inferences from these correlations. They may correspond to persons who used to live in other districts of Arequipa further away and moved recently near the ravines for personal reasons.

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Overall, almost 22% of the urban dwellers (Groups 3 and 4) seem to be quite vulnerable in case of a flow event or an eruption, as a majority declares that they remain on the spot. Such a proportion of vulnerable dwellers may raise to 53% of the population if we include the less educated, low- to middle-income people of Group 2. Together, they form a large cohort that the civil authorities and risk managers should involve into the prevention and mitigation policy across the city of Arequipa.

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## 5. RISK MANAGEMENT POLICY IN AREQUIPA: AN OVERVIEW

The analysis of surveys has revealed that the majority of urban dwellers living along the hazard-prone valleys were wondering how to behave, and searched for advice in case of emergency and evacuation; hence, they remained in hazard-prone low river terraces.

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### 5.1. Meetings with key actors aimed at unravelling sources of potential difficulties

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During our investigation of factors that may influence people's behaviour in case of flow, we observed three sources of difficulties: 1) limited hazard knowledge and risk perception, coupled with minimal education; 2) catastrophic effects of flows (every five years on average; Thouret et al., 2022a), partly due to the lack of civil protection works, and; 3) delayed response of the institutions in charge of disaster risk mitigation in the city.

Meetings, interviews and focus groups, led by K. Mora (Universidad de Nariño, Colombia), aimed to examine the extent to which disaster risk management procedures have been implemented by a range of key actors in four districts adjacent to the ravines. The investigation pursued three objectives: 1) to identify how public institutions manage disaster risk related to floods and lahars; 2) to describe the actions promoted by neighbourhood boards to manage flood risk in the districts, and; 3) to unravel potential sources of conflict between the district municipalities, disaster risk management agencies, and the local communities.

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Disaster Risk Management in Peru is organized by Law No. 29664, voted in 2011, which created the National Disaster Risk Management System (Sistema Nacional de Gestión del Riesgo de Desastres SINAGERD). Two national institutions are in charge of disaster risk management: the National Center for Estimation, Prevention and Reduction of Disaster Risk (CENEPRED), responsible for disaster prevention and post-disaster reconstruction, and the National Civil Defense Institute (INDECI), responsible for emergency response activities.
The SINAGERD system generated a significant change with respect to technical, legal and administrative procedures in disaster risk management in Peru (Fig. 10). In this important legal procedure, proactive risk management is carried out to avoid future disasters and corrective risk management to reduce existing impacts. Such procedure should be applied at three levels of national, regional, and district governance, each with specific competences and responsibilities for prevention and reduction of disaster risk. Nevertheless, it is not possible to take suitable decisions to deal with disaster risk in the long term, because political changes in municipalities every five years require new training of officials and risk managers in legal procedures, in particular during emergencies.

## 5.2. Complex issues may hamper disaster risk management in Arequipa

The results of interviews, meetings and focus groups have highlighted a number of complex issues, as summarised below (Table 9):

1. Officials and local communities consider increased vulnerability to be rooted in the 'unlawful' occupation of land on El Misti's slopes and hazard-prone banks of the ravines, as low-income people or new migrants cannot afford homes in the urban territory. Since they often minimize the likelihood or severity of flows, low-income people often build homes in high-risk ravine channels and terraces or further up on the volcano slopes now within 9 km of the vent. Communities state that hazardous phenomena associated with future El Misti activity are distinct from, and less pivotal than daily concerns related to personal safety, family's health, lack of economic resources, and environmental issues. Although decision-makers and stakeholders respond to emergencies in the wake of flood and debris-flow disasters, urban dwellers claim that the factors of social and physical vulnerability are not adequately dealt with.

2. Civil servants and elected authorities acknowledge that hazards have impacts on their districts and, based on their own experience, have identified the most frequent hazards, such as heavy rainfall inducing catastrophic flows. However, decision makers face difficulties in incorporating the policies of disaster risk management within the structure, functions and competence of the public offices, as many lack knowledge in the fields of urban planning and environment management. The limited knowledge about institutional competences may be due to different electoral agendas and turnover of officials who need to be trained again every five years.
3. Overlapping responsibilities between institutions have hampered an adequate and comprehensive investment in risk management. Lack of co-ordination with ONGs like PREDES, which promoted tools for disaster risk management ten years ago, has not allowed a suitable investment in the disaster risk management. Overlapping jurisdictions is illustrated by the fact that ravines form natural boundaries between districts, so that prevention requirements such as cleaning river channels before the rainy season are not adequately coordinated between the district municipalities on both sides of the ravines.

4. A limited investment in disaster risk management policy and urban planning in general leads to diminish accountability among key actors. For example, district mayors are prime authorities in charge of disaster risk management policies at the district scale, which include disaster risk management in urban planning, management of environmental issues and public investments. Although existing policies have assigned functions to disaster risk managers, limited investment is preventing mitigation and emergency procedures from being implemented in district municipalities. Despite previous experiences acquired with about 40 earthquake and flow disasters in Arequipa over the past 105 years (INDECI, 2020; Thouret et al., 2022a), civil protection works are insufficient owing to minimal resources available for district offices and CENEPRED in charge of proactive mitigation procedures.

5. The city hall and INDECI elaborate on prevention, mitigation and disaster risk reduction plans, but those plans are partially implemented in the most vulnerable hazard-prone areas. This is due to a series of factors such as mistrust on behalf of the urban dwellers, so that there is no mutual links and collaboration between the threatened communities and the authorities in terms of response to emergency procedures. Decision makers and stakeholders organize and coordinate a Civil Protection committee in each district with the clear objective that emergency and relief operations need to be applied to help victims and support affected and displaced people during and after each destructive event (Fig. 10). However, the apparent lack of co-ordination between institutions prevents a fast response to cope with emergencies.

# 6. DISCUSSION

## 6.1. Limitations and uncertainties

Field surveys have included almost 280 respondents, representing at least 900 people living along the ravine channels. The final step of the statistical analyses, i.e., clustering using HAC, has led to a smaller number of respondents (92) who answered all questions from three surveys. This induces a low representativeness, although previous investigations carried out in
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the framework of our research Project, obtained comparable results from a more limited number of respondents (Heitz and Nagata-Shimabuku, 2017).

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Uncertainty is bound to our results in two ways: first, biases may arise from quick surveys collated during about 15 days, with some people missing in houses, being shy, or with prohibited access to the researchers, as well as safety issues in some ‘informal’ neighbourhoods. Second, uncertainty abounds in the challenging analysis of the answers provided by respondents about risk perception and associated variables. A few *people* did not understand some questions or could not formulate answers to questions from Survey C. Thus, evaluating risk perception and potential behaviour during flow events remains a challenge.

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## 6.2. Comparison with studies on risk representation and risk management practice

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A few studies focused on hazard knowledge and risk perception among the vulnerable population of Arequipa, compared to studies that dealt with hazards and risk from volcanic eruptions and torrential floods (e.g., Thouret et al., 2013, 2014, 2022a; Mazer et al., 2021). Sandoval and Sarmiento (2020) aptly pointed that poor urban development and planning has led to informal settlements, while contributions to improving living conditions are marginal in Arequipa. Recently, two studies have been conducted on risk representation as well as on vulnerability and resilience of the urban dwellers living in informal settlements.

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Heitz and Nagata-Shimabuku (2017) studied ‘risk representation’ in 16 exposed and ‘unexposed’ small neighbourhoods scattered throughout Arequipa. The survey methodology was based on self-administrated questionnaires, organized around three main themes: a general approach to how people estimate social issues, then questions on natural hazards, and lahar risk. Socio-economic characteristics for each individual, living conditions, salary, and profession were also specified. This study dealt with a smaller number of persons (209) and presented statistical results based on nine questions only. However, our results coincide with some of their findings. Respondents endow legitimacy in risk reduction to the national Civil Protection authority in charge of the risk management in the city of Arequipa, but questions were not asked about other key actors in charge of the disaster risk management. Two thirds of respondents consider that they are not sufficiently informed on the behaviour to adopt in case of a disaster, but factors explaining their behaviour were not investigated. The Authors’ findings point to a weak implication of the respondents in the adoption of individual initiatives to protect themselves and to important expectations towards authorities’ actions in decreasing their vulnerability.
More recently, Lièvre et al. (2022) studied the evolution of volcanic risk management practice in Arequipa from the 1990s to the present. They claimed that management practices in Arequipa have focused on the paradigm of vulnerability since the 1990s, instead of resilience. City dwellers seem to be aware that disaster prevention also depends on the resilience of society, a factor that the Authors think the “technology-based vulnerability paradigm minimize”. Lièvre et al. (2022) recognized that another theoretical framework appeared from 2015 around “geoheritage and resilience”. The Authors claim that this framework “tends towards the resilience paradigm by taking the territory and the capacity to adapt as a basic principle, and by emphasizing the involvement of the human factor”. Geosites, “landscape units that bring together what was previously generally separated”, are thought by the Authors to be “a more grounded approach around risk knowledge”. The geosite brings an essential “ecological and corporal anchoring” on the risk that is “contextualized instead of being only perceived” by surveys on risk perception whose results “do not seem to show a particular evolution in Arequipa”.

We do not share several points raised by Lievre et al. (2021), in addition to methodological biases involved in a review largely based on literature. Resilience had not been discarded in studies over at least the past 11 years, as shown by ONGs and publications (e.g., PREDES, 2012; Zeballlos-Velarde et al., 2019). We argue that hazard knowledge and risk perception have increased among city dwellers over the past 20 years thanks to the institutions, ONGs and the reorganization of SINAGERD since 2011. We also wonder how a ‘landscape unit’ provides a suitable solution to the difficulties met by local communities and institutions in disaster risk reduction.

Arequipa also faces major challenges related to water supply and consumption in conjunction with climate change (Salmoral et al., 2020). This situation in combination with poor governance and lack of planning has exposed the vulnerability of Arequipa water supply system to future shocks. Both their study and our results concur in the need for urgent actions to strengthen governance and enforcement, and agree on a collective vision for integrated land and water planning in the city.

## 7. CONCLUDING REMARKS

A number of issues can be gleaned from our study that we have compared with the “working hypotheses proposed in section 1.1”:
783

1. About 22% of the urban dwellers are quite vulnerable in case of a flow event or an eruption, a proportion that would raise to 53% in case of an imminent evacuation, as they report that they remained on spot or they did not know what to do during past flow events.

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2. Risk managers and local authorities state that the ‘unauthorized’ occupation of hazard-prone areas near the ravines is a pivotal challenge for disaster risk management. Such informal settlements have expanded over the past 25 years in Arequipa and Peruvian cities alike, in which urban informality and precariousness persist despite the national economic growth. The vulnerability roots of local communities remain to be dealt with, although urban development plans articulate risk governance and disaster resilience principles.

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3. Both respondents and disaster risk managers require knowledge and training (flood evacuation procedures similar to the existing earthquake simulations) should be extended for people living in flood-prone areas. The information needs to be targeted at the individuals' representation of the risk they incur and the level of legitimacy received from the risk managers, to compel the population to follow the authorities' recommendations and adopt a suitable behaviour in case of a catastrophic flow.

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4. Actions of neighbourhood boards against flow effects are not sustained due to the lack of civil protection works and relief operations during emergencies, while the legitimate communities' claims cannot be quelled only by NGOs or international relief on the long term.

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5. Potential conflicts have emerged between institutions despite the reorganization of SINAGERD in 2011. This is due to conflicting competences of the districts, municipality and region of Arequipa in urban planning, and overlapping jurisdictions of CENEPRED and INDECI in charge of the disaster risk management policy.

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Overall, risk management policy has proved to be undermined during recent destructive events, as recognized by all risk managers during meetings. Our study provides an example of current shortfalls in daily practice of risk reduction taken by city authorities and dwellers from a generally low-income society. The case study of Quito, Ecuador, similar with respect to torrential floods, may provide solutions rooted in shared governance between institutions in charge of risk management, involvement of local communities, and adaptive plans to a changing environment (e.g., Grieving et al., 2021). It may offer a framework for approaching adaptation and building resilience in vulnerable settlements, as it advocates for a distributed governance system to be shared between institutions and city dwellers.

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815 Acknowledgements

816 Fieldwork and laboratory analyses have been funded by the French government IDEX-ISITE
817 initiative 16-IDEX-0001 (CAP 20-25, IRC 4). The first authors thank the Institut de Recherche
818 pour le Développement (IRD) for constant logistical support in Peru. We thank our Peruvian
819 colleagues and students from INGEMMET, IGP and UNSA (in particular J. Mariño, M. Rivera,
820 F. Garcia, J. Belizario, K. Moroccoire), and INDECI as well (Mrs A. Arguedas, Mr J. Vasquez,
821 Mr H. Pareja), and from colleagues (K. Mora, A. Guerrero, D. Rodriguez) of the University of
822 Nariño, Pasto, Colombia as well as O. Santoni (FERDI-UCA) for their support over the years
823 about hazards and risk in the city of Arequipa.

824

825 Statements and Declarations

826 Funding: Fieldwork and laboratory analyses have been funded by the French government
827 IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25, International Research Centre 4,
828 Clermont). There is no grant number.

829 *Competing interests:* All authors declare they have no financial interests.

830 *Authors contribution:* All authors contributed to the study conception and design. The first draft
831 of the manuscript was written by JC Thouret and M. Taillandier. Artwork was performed by E.
832 Arapa and E. Wavelet, and all authors commented on previous versions of the manuscript. All
833 authors read and approved the final manuscript.

834

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Table captions

1012

**Table 1.** Statistical observations, methods, techniques, and their purposes.

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**Table 2.** Results of cross-tabulation analysis from Survey A on socio-demographics

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characteristics.

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**Table 3.** Results of cross-tabulation analysis from Survey B on hazard knowledge.

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**Table 4.** Results of cross-tabulation analysis from Survey C on risk perception. The most

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discriminant questions appear in bold.

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**Table 5.** Chi² test on all variables from Surveys A, B, and C.

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**Table 6.** Attributes with high and low frequencies used to distinguish clusters among

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respondents (see Table 7).

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**Table 7.** HAC showing four clusters based on the total frequency analysis of all survey datasets

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(see Table 6).
Table 8. Answers to questions representing the eight most discriminant variables that define the paragons (i.e., the five most representative individuals) for each of the four clusters of respondents.

Table 9. Risk management policy: principal results from interviews, meetings and focus groups including key actors on Disaster Risk Management in the city of Arequipa.

Figure captions

**Figure 1.** Flow chart: research steps, methods and parameters, objectives, and expected results.

**Figure 2.** Districts Alto Selva Alegre and Miraflores on the east and west margins of Qda. San Lazaro, Mariano Melgar and Paucarpata along the west and east margins of Qda. Mariano Melgar (called Huarangal upstream) and neighbourhoods under study north and NE of the city of Arequipa (insert Figure showing the location of studied districts with regard to the city and El Misti volcano). Population number and gender proportion for the districts and the city of Arequipa.

**Figure 3.** Diagrams showing the distribution of population living in the four studied districts per age groups, education level, and socio-professional categories, compared with the demographics in the city of Arequipa.

**Figure 4.** Results of univariate analysis of the Survey A dataset. A. Graphs A.Q1-Q9. B. Graphs A.11-15 and 17. Graphs A.Q10 and Q16 are located at the end of the Figure.

**Figure 5.** Results of univariate analysis of Hazard knowledge acquisition among the respondents from the Survey B dataset. Graphs B.Q1 to B.Q11.

**Figure 6.** Results of univariate analysis of the Level of risk perception and associated variables among the respondents from the Survey C dataset. A. Graphs C.Q1 to Q8. B. Graphs C.Q9 to Q16. C. Graphs C.Q17 to Q23.

**Figure 7.** AFC biplots showing attributes of five pairs of variables: A. B. C. D. E.

**Figure 8.** Biplot showing correlations between five illustrative variables from Survey A (in blue), active variables from Surveys B and C (in red), and principal dimensions 1 and 2.

**Figure 9.** Factor map from HAC showing four clusters.
1051 **Figure 10.** Flow chart of the Governance of Disaster Risk Management at the national scale
1052 in Peru and locally in the city of Arequipa.

1053 **Electronic Supplementary Data**

1054 **ESD Text.** List of questions of the surveys A, B, and C.

1055 **ESD Table 1.** $\text{Chi}^2$ test conducted separately on variables from each of the three surveys A, B, and C.

1057 **ESD Table 2.** Coordinates, squared cosine, and contributions. We only consider attributes that exceed or are equal to the average of contributions and squared cosine.

1059 **ESD Table 3.** $\text{Chi}^2$ test on HAC variables with p-value and degree of freedom.

1060 **ESD Table 4.** Paragons, i.e., the five most representative individuals for each of the four clusters of respondents. The first number identifies each of the individuals and the second number is the distance between individuals and the cluster centre (see Fig. 9).

1063 **ESD Table 5.** List of questions raised and topics covered in interviews, meetings and focus groups held with the local authorities and community boards about disaster risk management.

1065 **ESD Figure 1.** Neighbourhoods under study showing flood-prone areas along the ravine channels and buildings where three surveys A, B and C were collected. A. Quebrada San Lazaro. B. Quebrada Huarangal Mariano Melgar and its tributary, Qda. Dahlia.

1068 **ESD Figure 2.** Scree plot showing percentages of variance distributed in 14 dimensions and following Benzécri's correction.

1070 **ESD Figure 3.** Scores of hazard knowledge (based on questions/variables from survey B) obtained by using the 'equal width' discretization method.

1072
STATISTICAL APPROACH APPLIED TO THE SURVEY DATASET

Uni-, Bivariate analyses, Factorial and Multiple Correspondence Analyses, Hierarchical Agglomerative Clustering, Polytomic Logistic Regression

<table><thead><tr><th>RESEARCH STEPS, OBJECTIVES & METHODS</th><th>OBSERVATIONS<br>(51 variables, 14 neighbourhoods, c. 280 respondents)</th><th>EXPECTED RESULTS</th></tr></thead><tbody><tr><td>Field surveys<br>Data acquisition<br>Mapping</td><td><strong>Socio-demographic, economic characteristics (Survey A)</strong><br>1. Gender, age group<br>2. Household composition<br>3. Education level<br>4. Socio-professional categories<br>5. Number of resource person and monthly income<br>6. Place of birth or migration origin<br>7. Time living in the city</td><td>Characteristics of population sample living in suburbs along the hazard-prone ravines</td></tr><tr><td><strong>STEP 1</strong><br>Investigate the parameters of urban dwellers living along the hazar-prone ravines and exposed to floods and flows</td><td><strong>Hazard knowledge (Survey B)</strong><br>1. Name of neighbouring volcanoes and most recent Misti's eruption date<br>2. Ability to read Misti's hazard-zone map and locate neighbourhood<br>3. Three most frequent volcanic threats in the area<br>4. Lahar occurrence, timing and fatalities<br>5. Eruption duration and lahar arrival time since onset</td><td>Awareness<br>Sources of knowledge<br>Kinds of experience acquired with flow and flood events</td></tr><tr><td><strong>STEP 2</strong><br>Estimate the level of knowledge of volcano and flow related and the sources of knowledge among the urban dwellers living near the ravines</td><td><strong>Risk perception and associated variables (Survey C)</strong><br>1. Hazard salience<br>2. Sense of community and social capital<br>3. Hazard likelihood and severity<br>4. Past behaviour and memory<br>5. Self confidence, empowerment<br>6. Trust in authorities<br>7. Personal feelings</td><td>Preparedness and past behaviour during flow events</td></tr><tr><td><strong>STEP 3</strong><br>To evaluate how people perceive flood and volcanic risk from Misti, and compare the risk perception with hazard knowledge</td><td><strong>Disaster risk management policy in city districts</strong><br>1. Identification of key actors<br>2. Characteristics of authorities and communities' organization<br>3. Potential sources of conflict</td><td>Appraise how and why groups of urban dwellers living in hazard-prone areas behave in case of imminent evacuation</td></tr><tr><td><strong>STEP 4</strong><br>Correlation Hazard knowledge with Risk perception</td><td></td><td></td></tr><tr><td><strong>STEP 5</strong><br>Clustering, Hierarchical Ascending Classification, frequency analysis, study of parangons</td><td></td><td></td></tr><tr><td><strong>STEP 6</strong><br>Analysis of risk management policy. Interviews and focus groups with key actors</td><td></td><td>Understanding factors which hinder disaster risk management in the city</td></tr></tbody></table>
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<figure><img src="image_47.png" /><figcaption>Figure 4 A</figcaption></figure>

Click here to access/download;Figure;Figure 4A. Results
Survey A UA.pdf
Q14. Time living in the area

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Q15. Born in Arequipa

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Q17. Family origin: distance to El Misti summit

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Q10. Socio-Professional Category

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Q16. Family origin

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<figure><img src="image_53.png" /><figcaption>Q1. Can you name the nearest volcano to your home?</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>Q3. Can you locate your district on map?</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>Q5. If location is not identified, what does the colour zone mean?</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>Q7. Can you estimate the arrival time of lahar near your place, once the flow has been triggered?</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>Q9. Do you know of any victim due to lahar?</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>Q11. How long after a volcanic eruption can lahars occur?</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>Q2. Do you know the year of last eruption?</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>Q4. If location identified, what does the colour zone mean?</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>Q6. What are volcanic ash, pyroclastic flow, lahar?</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>Q8. If a lahar occurs, how much warning time would you get?</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>Q10. How long could a Misti volcanic eruption last?</figcaption></figure>

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<figure><img src="image_65.png" /><figcaption>Fig. 6A</figcaption></figure>
<figure><img src="image_66.png" /><figcaption>Fig. 6B</figcaption></figure>
<figure><img src="image_67.png" /><figcaption>Fig. 7A</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Fig. 7B</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>Fig. 7C</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>Fig. 7D</figcaption></figure>

<figure><img src="image_71.png" /><figcaption>Fig. 7E</figcaption></figure>
<figure><img src="image_72.png" /><figcaption>Figure 8</figcaption></figure>
<figure><img src="image_73.png" /><figcaption>Fig. 9</figcaption></figure>
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B

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<table><caption>Table 1</caption><thead><tr><th>Observations</th><th>Methods</th><th>Techniques</th><th>Purposes</th></tr></thead><tbody><tr><td>Set of variables from all surveys</td><td>Univariate Analysis UA</td><td>Frequency: barplot</td><td>To obtain the frequency of all categories of every variable</td></tr><tr><td>All couples of variables</td><td>Bivariate Analysis BA</td><td>Chi-square test<br>Contingence tables: Burt Table<br>Factorial Correspondence Analysis (FCA)<br>Biplot graphics</td><td>To identify independence between two variables<br>Allows to analyse the cross frequency between two variables<br>To show links/correlation between categories of two variables</td></tr><tr><td>- Illustrative variables entail 5 questions from Survey A, 10 from Survey B, and 23 from Survey C<br>- Active variables include all questions from four Surveys</td><td>Multiple Correspondence Analysis, MCA</td><td>Scree plot showing eigenvalues (with Benzécri's correction)<br>Projection of observations (groups) in biplots<br>Biplots<br>Scatter plots</td><td>To define the number of dimensions (5) retaining most information<br>To show correlation between variables/categories and dimensions 1 and 2<br>To show contributory categories that better describe each dimension and have similar behaviour</td></tr><tr><td>Explore the MCA outputs to elaborate on population categories and distinguish clusters</td><td>Hierarchical Agglomerative Clustering HAC</td><td>Dendrogram and Factor map<br>Chi-square test<br>Frequency analysis of all survey datasets (replies to questions) for each cluster<br>Study of parangons</td><td>To define the suitable number of clusters (4)<br>To identify links between 38 variables, selecting 21 discriminant variables having a small p-value &lt;0.05<br>To establish the socio-demographic, knowledge/perception and economic status for each cluster of individuals<br>To characterize the five more representative individuals in each of the clusters</td></tr></tbody></table>
SOCIO-DEMOGRAPHIC CHARACTERISTICS (Survey A, 239 respondents)

<table><thead><tr><th>Questions</th><th colspan="5">Answers</th></tr></thead><tbody><tr><td></td><td colspan="5"><strong>1.SOCIO-DEMOGRAPHIC CATEGORIES</strong></td></tr><tr><td>Q2. Age group</td><td>0 to 11 years/12 to 18 years</td><td>19 to 30 years</td><td>31 to 60 years</td><td>&gt; 60 years</td><td></td></tr><tr><td>Q3. Marital Status</td><td>Single</td><td>Single</td><td>Married/Free union</td><td>Single/ Divorced or widowed</td><td></td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 18 7.53%</strong></td><td><strong>33 13.81%</strong></td><td><strong>76 31.80%</strong></td><td><strong>16 6.69%</strong></td><td></td></tr><tr><td>Q4. Household: Total of people</td><td>≥ 7</td><td>3 to 6</td><td>3 to 6</td><td>≤ 2</td><td></td></tr><tr><td>Q6. 0-14y</td><td>1 to 11</td><td>1 to 5</td><td>0</td><td>0</td><td></td></tr><tr><td>Q7. 15-60y</td><td>≥ 1</td><td>1 to 5</td><td>0 to 6</td><td>≤ 2</td><td></td></tr><tr><td>Q8. &gt;60y</td><td>0 to 8</td><td>0 to 6</td><td>0 to 6</td><td>≤ 2</td><td></td></tr><tr><td>Q13. Number of people working</td><td>2 to 8</td><td>≤ 4</td><td>≤ 4</td><td>≤ 2/ No answer</td><td></td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 54 22.59%</strong></td><td><strong>56 23.43%</strong></td><td><strong>66 27.62%</strong></td><td><strong>24 10.04%</strong></td><td></td></tr><tr><td>Q9. Education level</td><td>High education</td><td>Seconday school</td><td>Primary school</td><td>Primary school/without education</td><td></td></tr><tr><td>Q10. Socio-professional category</td><td>Private and public employee/Informal/Commerce/ Housewife/ Student</td><td>Private and public employee/Informal/Commerce/Housewife/ Student/Unemployed</td><td>Informal/Commerce/Housewife/ Student</td><td>Informal/Commerce/Housewife/ Student</td><td></td></tr><tr><td>Q11. Minimum salary threshold (930 PEN, 2019)</td><td>Above</td><td>Above</td><td>Above</td><td>Below</td><td></td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 43 17.99%</strong></td><td><strong>108 45.19%</strong></td><td><strong>26 10.88%</strong></td><td><strong>16 6.69%</strong></td><td></td></tr><tr><td></td><td colspan="5"><strong>2.GEOGRAPHICAL ORIGIN AND ARRIVAL TIME IN THE AREA</strong></td></tr><tr><td>Q15. Geographical origin of the respondent (born in Arequipa)</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td></td></tr><tr><td>Q17.Family origin (Distance to El Misti summit)</td><td>&lt; 30 km</td><td>30 to 100 km</td><td>300 to 1 000 km</td><td>100 to 300 km</td><td></td></tr><tr><td>Q14. How long have you been living in the area?</td><td>≥ 16 years</td><td>≥ 6 years</td><td>≥ 6 years</td><td>≤ 6 years/ No answer</td><td></td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 38 18.00%</strong></td><td><strong>96 45.50%</strong></td><td><strong>58 27.49%</strong></td><td><strong>21 9.95%</strong></td><td></td></tr><tr><td></td><td colspan="5"><strong>GROUPS (Frequency analysis)</strong></td></tr><tr><td>A. Young and mature adults, independent and resource person, educated and born in Arequipa</td><td>B. Young and mature adults, independent and resource person, educated and born in Arequipa or elsewhere</td><td>C. Younger and older adults, independent, resource person and educated, born in the region?</td><td>D. Mature and older adults, independent resource person without education, born far away</td><td>E. Young and mature adults, without or low resources, and educated, born in</td><td>F. Younger and older adults, dependent on families, without or low resources, poorly or not educated, born far away</td></tr><tr><td><strong>Cohort 35 15.90%</strong></td><td><strong>93 38.91%</strong></td><td><strong>49 20.5%</strong></td><td><strong>7 2.93%</strong></td><td><strong>28 11.72%</strong></td><td><strong>21 8.79%</strong></td></tr></tbody></table>
HAZARD KNOWLEDGE (Survey B, 118 respondents)

<table><thead><tr><th>Question</th><th colspan="4">Answer</th></tr></thead><tbody><tr><td><strong>1. VOLCANO AND ERUPTION</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Q1. Can you name the nearest volcano to your home?</td><td>Mentions several (Should mention at least El Misti)</td><td>Mentions several</td><td>Mentions several</td><td>Unsure/No answer</td></tr><tr><td>Q2. Do you know what year this volcano last erupted?</td><td>A long time ago</td><td>Not sure, but has an idea</td><td>Does not know the date</td><td>Does not know the date/No answer</td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 20 16.95%</strong></td><td><strong>8 6.78%</strong></td><td><strong>83 70.34%</strong></td><td><strong>2 1.69%</strong></td></tr><tr><td><strong>2. HAZARD-ZONE MAP</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Q3. Can you locate your district?</td><td>Yes, can locate</td><td>Yes, can locate/Unsure</td><td>Yes, can locate/Unsure</td><td>Does not know</td></tr><tr><td>Q4. Do you know the hazard-zone colour meaning?</td><td>Yes, knows/ Unsure, but has an idea</td><td>Yes, knows/ Unsure, but has an idea</td><td>Unsure, but has an approximate idea/ Does not know</td><td>Does not know/ No answer</td></tr><tr><td>Q5. Can you show your village location and what does represent colour shown?</td><td>Knows</td><td>Unsure, but has an idea/No answer</td><td>Does not know</td><td>Does not know/ No answer</td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 4 3.39%</strong></td><td><strong>13 11.02%</strong></td><td><strong>7 5.93%</strong></td><td><strong>80 67.80%</strong></td></tr><tr><td><strong>3. LAHAR OCCURRENCE AND IMPACT</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Q7. Can you estimate the arrival time of a lahar to your village, once it has been triggered on the mountain?</td><td>Minutes, hours</td><td>Minutes, hours/Days</td><td>Minutes, hours/ Never/ Doesn't know</td><td>Never or doesn't know</td></tr><tr><td>Q8. Can you estimate how many times lahars can happen?</td><td>Minutes, hours</td><td>Minutes, hours/Days</td><td>Minutes, hours</td><td>Doesn't know</td></tr><tr><td>Q9. Do you know of any victim or/and injured people?</td><td>Yes</td><td>No</td><td>Another natural phenomenon</td><td>No/Another natural phenomenon/No answer</td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 6 5.08%</strong></td><td><strong>85 72.03%</strong></td><td><strong>7 5.93%</strong></td><td><strong>14 11.86%</strong></td></tr><tr><td><strong>4. ERUPTION AND LAHAR OCCURRENCE INTERVAL TIME</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Q10. How long could the next El Misti volcanic eruption last?</td><td>Minutes, hours</td><td>Minutes, hours/Days</td><td>Days</td><td>Does not know/No answer</td></tr><tr><td>Q11. How long after a volcanic eruption can lahars occur?</td><td>Minutes, hours</td><td>Minutes, hours/Days</td><td>Does not know</td><td>Does not know/No answer</td></tr><tr><td><strong>Sub-total</strong></td><td><strong>Cohort 15 12.71%</strong></td><td><strong>32 27.12%</strong></td><td><strong>16 13.56%</strong></td><td><strong>41 34.75%</strong></td></tr><tr><td><strong>LEVEL OF KNOWLEDGE</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>A. Very good knowledge</td><td>B. Good knowledge</td><td>C. Regular knowledge</td><td>D. Poor knowledge or un-educated</td><td></td></tr><tr><td><strong>Cohort 7 5.93%</strong></td><td><strong>58 49.15%</strong></td><td><strong>41 34.75%</strong></td><td><strong>12 10.17%</strong></td><td></td></tr></tbody></table>
RISK PERCEPTION AND ASSOCIATED VARIABLES (Survey C, 268 respondents)

<table><thead><tr><th>Question</th><th>Answer</th><th>Answer</th><th>Answer</th><th>Answer</th></tr></thead><tbody><tr><td>Q1. How do you feel about volcano or lahar?</td><td>Other or none/No answer</td><td>Other or none/No answer</td><td>Worried/Uncertain</td><td>Worried/Uncertain</td></tr><tr><td>Q2. Any concern related to you?</td><td>Volcano/Debris flow</td><td>Other or none/No answer</td><td>Volcano/Debris flow</td><td>Other or none/No answer</td></tr><tr><td>Sub-total</td><td><strong>Cohort 12 4.48%</strong></td><td><strong>58 21.64%</strong></td><td><strong>43 16.04%</strong></td><td><strong>155 57.84%</strong></td></tr><tr><td>Q3. Have you ever witnessed a debris flow?</td><td>Yes</td><td>Yes/Yes, but just a little</td><td>Yes/Yes, but just a little</td><td>No</td></tr><tr><td>Q4. Have you ever been affected by a debris flow?</td><td>Yes/A little</td><td>Never</td><td>Never</td><td>Never/No answer</td></tr><tr><td>Q5. Do you feel that a debris flow would hit you?</td><td>Yes/Perhaps</td><td>Yes/Perhaps</td><td>Never or does not know</td><td>Never or does not know/No answer</td></tr><tr><td>Sub-total</td><td><strong>Cohort 44 16.42%</strong></td><td><strong>115 42.91%</strong></td><td><strong>42 15.67%</strong></td><td><strong>35 13.06%</strong></td></tr><tr><td>Q6. Do you know of anyone among your family or friends who has been affected by a debris flow?</td><td>Yes</td><td>Yes</td><td>No/Uncertain</td><td>No/Uncertain</td></tr><tr><td>Q7. Traffic accident, family or friends?</td><td>Yes</td><td>No/Undecided or doesn't know</td><td>Yes</td><td>No/Undecided or does not know</td></tr><tr><td>Sub-total</td><td><strong>Cohort 48 17.91%</strong></td><td><strong>67 25%</strong></td><td><strong>49 18.28%</strong></td><td><strong>91 33.96%</strong></td></tr><tr><td>Q8. Can you identify or discuss a particular lahar that you remember?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No/Uncertain or doesn't know</td></tr><tr><td>Q9. How did you behave that day?</td><td>Evacuated or auto-evacuated/ Remained</td><td>Did not know what to do</td><td>Remained/Did not know what to do</td><td>No answer</td></tr><tr><td>Q10. Had this memory impacted the way you behaved that day?</td><td>Yes</td><td>Yes</td><td>No/Uncertain or doesn't know</td><td>No answer</td></tr><tr><td>Sub-total</td><td><strong>Cohort 109 40.67%</strong></td><td><strong>19 7.09%</strong></td><td><strong>37 13.91%</strong></td><td><strong>65 24.25%</strong></td></tr><tr><td>Q11. Can you describe a debris flow?</td><td>Yes</td><td>Yes</td><td>No</td><td>No/Uncertain or does not know</td></tr><tr><td>Q12. Where does your knowledge on debris flow come from?</td><td>Experience, school or authorities</td><td>Family or neighbour</td><td>Experience/ Family, neighbour</td><td>Family, neighbour/ No Answer</td></tr><tr><td>Q13. Do you believe that a lahar can return, and how long would it return?</td><td>Any rain season/ 1 to 3 years/ Several years or does not know</td><td>Any rain season/One to three years/ Several years or does not know</td><td>Any rain season/One to three years/ Several years or does not know</td><td>Several years or does not know/ No Answer</td></tr><tr><td>Sub-total</td><td><strong>Cohort 115 42.91%</strong></td><td><strong>18 6.72%</strong></td><td><strong>95 35.45%</strong></td><td><strong>7 2.75%</strong></td></tr><tr><td>Q14. Do you like to observe a debris flow?</td><td>Yes/a little</td><td>Yes/a little</td><td>Yes/a little</td><td>No</td></tr><tr><td>Q15. From where do you observe lahars?</td><td>At a long distance and elevation</td><td>At a long distance and elevation</td><td>At a short distance and elevation</td><td>Uncertain or doesn't know</td></tr><tr><td>Q16. What attracts he/she much in a debris flow?</td><td>Everything</td><td>Something</td><td>Everything/Something</td><td>Nothing</td></tr><tr><td>Sub-total</td><td><strong>Cohort 37 13.91%</strong></td><td><strong>45 16.79%</strong></td><td><strong>59 22.01%</strong></td><td><strong>103 38.43%</strong></td></tr><tr><td>Q17+Q18+Q19. Do you feel that such a phenomenon is so powerful than Mankind cannot do anything about it? Do you think of any alternative to overcome flow effects? Do you feel self-confident in case of an eruption?</td><td>Yes</td><td>No, too powerful</td><td>Yes</td><td>No, too powerful</td></tr><tr><td>Q20. Will you comply with the evacuation order in any type of situation?</td><td>Yes</td><td>Yes</td><td>Sometime or in extreme events</td><td>No/ Sometime or in extreme events</td></tr><tr><td>Q21+22. Whom do you receive warning from in case of lahar? Which type of device?</td><td>All media</td><td>All/one media</td><td>All/one media</td><td>None/One/All media</td></tr><tr><td>Q23. Do you believe messages and/or do you try to check the content?</td><td>More than one</td><td>One/More than one</td><td>One/More than one</td><td>One of them</td></tr><tr><td>Sub-total</td><td><strong>Cohort 30 11.19%</strong></td><td><strong>48 17.91%</strong></td><td><strong>30 11.19%</strong></td><td><strong>18 5.60%</strong></td></tr><tr><td>Q24. Will you comply with the evacuation order in any type of situation?</td><td>Yes</td><td>Yes</td><td>Sometime or in extreme events</td><td>No/ Sometime or in extreme events</td></tr><tr><td>Q25. Will you comply with the evacuation order in any type of situation?</td><td>All media</td><td>All/one media</td><td>All/one media</td><td>None/One/All media</td></tr><tr><td>Q26. Will you comply with the evacuation order in any type of situation?</td><td>More than one</td><td>One/More than one</td><td>One/More than one</td><td>One of them</td></tr><tr><td>Q27. Will you comply with the evacuation order in any type of situation?</td><td>Yes, trusts</td><td>Try to check</td><td>Yes, trusts/Try to check</td><td>No/Try to check</td></tr><tr><td>Sub-total</td><td><strong>Cohort 18 6.72%</strong></td><td><strong>84 31.34%</strong></td><td><strong>38 14.18%</strong></td><td><strong>19 7.09%</strong></td></tr><tr><td colspan="5">GROUPS (Survey C)</td></tr><tr><td>A.</td><td>B.</td><td>C.</td><td>D.</td><td></td></tr><tr><td><strong>Cohort 138 51.49%</strong></td><td><strong>94 35.07%</strong></td><td><strong>32 11.94%</strong></td><td><strong>4 1.49%</strong></td><td></td></tr></tbody></table>
<table><caption>Table 5 (cont'd) Chi-square Surveys A and B</caption><thead><tr><th rowspan="2"></th><th colspan="10">Survey A</th><th colspan="10">Survey B</th><th colspan="10">Survey C</th></tr><tr><th>2</th><th>9</th><th>10</th><th>14</th><th>17</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>17</th><th>18</th><th>19</th><th>20</th><th>21</th><th>22</th><th>23</th></tr></thead><tbody><tr><td rowspan="2">Survey A</td><td>2</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.48</td><td>0.54</td><td>0.27</td><td>0.33</td><td>0.25</td><td>0.44</td><td>0.81</td><td>0.64</td><td>0.80</td><td>0.14</td><td>0.26</td><td>0.69</td><td>0.39</td><td>0.02</td><td>0.61</td><td>0.71</td><td>0.34</td><td>0.60</td><td>0.04</td><td>0.74</td><td>0.27</td><td>0.02</td><td>0.14</td><td>0.45</td><td>0.80</td><td>0.53</td><td>0.82</td><td>0.25</td><td>0.00</td><td>0.51</td><td>0.04</td><td>0.91</td><td>0.46</td><td>0.12</td></tr><tr><td>9</td><td>0.00</td><td>0.00</td><td>0.15</td><td>0.26</td><td>0.03</td><td>0.91</td><td>0.22</td><td>0.07</td><td>0.14</td><td>0.24</td><td>0.31</td><td>0.13</td><td>0.64</td><td>0.39</td><td>0.74</td><td>0.27</td><td>0.25</td><td>0.27</td><td>0.06</td><td>0.15</td><td>0.08</td><td>0.54</td><td>0.64</td><td>0.82</td><td>0.62</td><td>0.75</td><td>0.39</td><td>0.53</td><td>0.16</td><td>0.16</td><td>0.30</td><td>0.74</td><td>0.40</td><td>0.08</td><td>0.55</td><td>0.39</td><td>0.44</td><td>0.04</td></tr><tr><td rowspan="2">Survey B</td><td>10</td><td>0.00</td><td>0.15</td><td>0.00</td><td>0.02</td><td>0.56</td><td>0.79</td><td>0.24</td><td>0.27</td><td>0.30</td><td>0.04</td><td>0.05</td><td>0.04</td><td>0.61</td><td>0.16</td><td>0.21</td><td>0.15</td><td>0.14</td><td>0.06</td><td>0.38</td><td>0.77</td><td>0.44</td><td>0.60</td><td>0.06</td><td>0.04</td><td>0.08</td><td>0.24</td><td>0.22</td><td>0.41</td><td>0.02</td><td>0.21</td><td>0.18</td><td>0.21</td><td>0.21</td><td>0.00</td><td>0.32</td><td>0.27</td><td>0.09</td><td>0.28</td></tr><tr><td>14</td><td>0.00</td><td>0.27</td><td>0.02</td><td>0.00</td><td>0.10</td><td>0.63</td><td>0.24</td><td>0.00</td><td>0.02</td><td>0.51</td><td>0.86</td><td>0.84</td><td>0.25</td><td>0.17</td><td>0.05</td><td>0.74</td><td>0.05</td><td>0.00</td><td>0.26</td><td>0.57</td><td>0.08</td><td>0.96</td><td>0.02</td><td>0.41</td><td>0.04</td><td>0.08</td><td>0.53</td><td>0.31</td><td>0.38</td><td>0.10</td><td>0.15</td><td>0.74</td><td>0.03</td><td>0.34</td><td>0.71</td><td>0.93</td><td>0.87</td><td>0.13</td></tr><tr><td rowspan="2">Survey A</td><td>17</td><td>0.48</td><td>0.02</td><td>0.56</td><td>0.10</td><td>0.00</td><td>0.29</td><td>0.22</td><td>0.47</td><td>0.86</td><td>0.03</td><td>0.13</td><td>0.16</td><td>0.69</td><td>0.26</td><td>0.88</td><td>0.19</td><td>0.47</td><td>0.42</td><td>0.64</td><td>0.69</td><td>0.06</td><td>0.31</td><td>0.58</td><td>0.20</td><td>0.86</td><td>0.18</td><td>0.37</td><td>0.12</td><td>0.69</td><td>0.30</td><td>0.78</td><td>0.83</td><td>0.08</td><td>0.75</td><td>0.27</td><td>0.09</td><td>0.48</td><td>0.54</td></tr><tr><td>2</td><td>0.55</td><td>0.92</td><td>0.79</td><td>0.63</td><td>0.28</td><td>0.00</td><td>0.38</td><td>0.25</td><td>0.21</td><td>0.19</td><td>0.93</td><td>1.00</td><td>0.02</td><td>0.33</td><td>0.31</td><td>0.03</td><td>0.34</td><td>0.09</td><td>0.25</td><td>0.67</td><td>0.01</td><td>0.78</td><td>0.18</td><td>0.28</td><td>0.21</td><td>0.81</td><td>0.50</td><td>0.90</td><td>0.47</td><td>0.31</td><td>0.53</td><td>0.82</td><td>0.13</td><td>0.71</td><td>0.31</td><td>0.39</td><td>0.31</td><td>0.10</td></tr><tr><td rowspan="2">Survey B</td><td>3</td><td>0.28</td><td>0.22</td><td>0.23</td><td>0.24</td><td>0.21</td><td>0.38</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.77</td><td>0.86</td><td>0.74</td><td>0.48</td><td>0.18</td><td>0.02</td><td>0.64</td><td>0.56</td><td>0.79</td><td>0.03</td><td>0.87</td><td>1.00</td><td>0.77</td><td>0.36</td><td>0.37</td><td>0.81</td><td>0.74</td><td>0.89</td><td>0.25</td><td>0.15</td><td>0.17</td><td>0.25</td><td>0.17</td><td>0.05</td><td>0.54</td><td>0.70</td><td>0.15</td><td>1.00</td></tr><tr><td>4</td><td>0.34</td><td>0.07</td><td>0.27</td><td>0.00</td><td>0.45</td><td>0.25</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.98</td><td>0.79</td><td>0.13</td><td>0.62</td><td>0.13</td><td>0.02</td><td>0.08</td><td>0.03</td><td>0.56</td><td>0.33</td><td>0.25</td><td>0.53</td><td>0.16</td><td>0.17</td><td>0.07</td><td>0.73</td><td>1.00</td><td>0.38</td><td>0.13</td><td>0.13</td><td>0.47</td><td>0.91</td><td>0.61</td><td>0.10</td><td>0.97</td><td>1.00</td><td>0.41</td><td>0.05</td></tr><tr><td rowspan="2">Survey A</td><td>5</td><td>0.26</td><td>0.14</td><td>0.29</td><td>0.02</td><td>0.86</td><td>0.20</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.04</td><td>0.90</td><td>0.88</td><td>0.09</td><td>0.17</td><td>0.00</td><td>0.01</td><td>0.05</td><td>0.05</td><td>0.77</td><td>0.14</td><td>0.12</td><td>0.52</td><td>0.12</td><td>0.05</td><td>0.01</td><td>0.83</td><td>1.00</td><td>0.41</td><td>0.49</td><td>0.19</td><td>0.17</td><td>0.58</td><td>0.51</td><td>0.05</td><td>0.42</td><td>1.00</td><td>0.47</td><td>0.53</td></tr><tr><td>6</td><td>0.43</td><td>0.23</td><td>0.04</td><td>0.53</td><td>0.03</td><td>0.18</td><td>0.01</td><td>0.00</td><td>0.04</td><td>0.00</td><td>0.17</td><td>0.17</td><td>0.76</td><td>0.05</td><td>0.17</td><td>0.53</td><td>0.08</td><td>0.44</td><td>0.63</td><td>0.07</td><td>0.24</td><td>0.46</td><td>0.45</td><td>0.31</td><td>0.81</td><td>0.46</td><td>0.80</td><td>0.48</td><td>0.84</td><td>0.74</td><td>0.22</td><td>0.48</td><td>0.45</td><td>0.41</td><td>0.69</td><td>0.04</td><td>0.31</td><td>0.68</td></tr><tr><td rowspan="2">Survey B</td><td>7</td><td>0.81</td><td>0.32</td><td>0.05</td><td>0.87</td><td>0.13</td><td>0.93</td><td>0.77</td><td>0.98</td><td>0.90</td><td>0.18</td><td>0.00</td><td>0.00</td><td>0.39</td><td>0.00</td><td>0.31</td><td>0.27</td><td>0.88</td><td>0.02</td><td>0.58</td><td>0.54</td><td>0.48</td><td>0.28</td><td>0.49</td><td>0.39</td><td>0.27</td><td>0.90</td><td>1.00</td><td>0.52</td><td>0.21</td><td>0.13</td><td>0.19</td><td>0.15</td><td>0.03</td><td>0.04</td><td>0.67</td><td>0.51</td><td>0.68</td><td>0.30</td></tr><tr><td>8</td><td>0.64</td><td>0.13</td><td>0.04</td><td>0.84</td><td>0.15</td><td>1.00</td><td>0.87</td><td>0.80</td><td>0.87</td><td>0.17</td><td>0.00</td><td>0.00</td><td>0.18</td><td>0.01</td><td>0.16</td><td>0.55</td><td>0.72</td><td>0.29</td><td>0.66</td><td>0.10</td><td>0.90</td><td>0.22</td><td>0.61</td><td>0.63</td><td>0.20</td><td>1.00</td><td>1.00</td><td>1.00</td><td>0.46</td><td>0.31</td><td>0.24</td><td>0.30</td><td>0.08</td><td>0.00</td><td>0.73</td><td>0.18</td><td>0.27</td><td>0.77</td></tr><tr><td rowspan="2">Survey A</td><td>9</td><td>0.79</td><td>0.63</td><td>0.60</td><td>0.25</td><td>0.68</td><td>0.02</td><td>0.75</td><td>0.12</td><td>0.09</td><td>0.77</td><td>0.38</td><td>0.18</td><td>0.00</td><td>0.27</td><td>0.34</td><td>0.19</td><td>0.15</td><td>0.81</td><td>0.59</td><td>0.05</td><td>0.00</td><td>0.24</td><td>0.24</td><td>0.11</td><td>0.00</td><td>0.52</td><td>1.00</td><td>0.45</td><td>0.23</td><td>0.83</td><td>0.40</td><td>0.66</td><td>0.83</td><td>0.28</td><td>0.57</td><td>0.21</td><td>1.00</td><td>0.88</td></tr><tr><td>10</td><td>0.14</td><td>0.38</td><td>0.17</td><td>0.17</td><td>0.29</td><td>0.33</td><td>0.48</td><td>0.61</td><td>0.17</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.25</td><td>0.00</td><td>0.00</td><td>0.25</td><td>0.41</td><td>0.46</td><td>0.85</td><td>0.11</td><td>0.93</td><td>0.42</td><td>0.80</td><td>0.33</td><td>0.56</td><td>0.68</td><td>0.24</td><td>0.75</td><td>0.39</td><td>0.10</td><td>0.15</td><td>0.72</td><td>0.67</td><td>0.13</td><td>0.45</td><td>0.05</td><td>0.89</td><td>1.00</td></tr><tr><td rowspan="2">Survey B</td><td>11</td><td>0.26</td><td>0.72</td><td>0.21</td><td>0.05</td><td>0.88</td><td>0.30</td><td>0.18</td><td>0.12</td><td>0.00</td><td>0.17</td><td>0.30</td><td>0.17</td><td>0.35</td><td>0.00</td><td>0.00</td><td>0.21</td><td>0.38</td><td>0.94</td><td>0.20</td><td>0.54</td><td>0.91</td><td>0.13</td><td>0.19</td><td>0.19</td><td>0.32</td><td>0.18</td><td>0.49</td><td>0.31</td><td>0.52</td><td>0.50</td><td>0.36</td><td>0.84</td><td>0.84</td><td>0.25</td><td>0.90</td><td>0.05</td><td>0.01</td><td>0.72</td></tr></tbody></table>
<table><caption>Table 5. Chi-square (Cont'd) Survey C</caption><thead><tr><th rowspan="2">Survey C</th><th colspan="10">Survey A</th><th colspan="10">Survey B</th><th colspan="10">Survey C</th></tr><tr><th>2</th><th>9</th><th>10</th><th>14</th><th>17</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>17</th><th>18</th><th>19</th><th>20</th><th>21</th><th>22</th><th>23</th></tr></thead><tbody><tr><td>0.69</td><td>0.26</td><td>0.16</td><td>0.74</td><td>0.20</td><td>0.04</td><td>0.01</td><td>0.02</td><td>0.01</td><td>0.54</td><td>0.26</td><td>0.54</td><td>0.20</td><td>0.25</td><td>0.20</td><td>0.00</td><td>0.60</td><td>0.80</td><td>0.22</td><td>0.01</td><td>0.03</td><td>0.06</td><td>0.36</td><td>0.31</td><td>0.03</td><td>0.51</td><td>0.47</td><td>0.21</td><td>0.80</td><td>0.75</td><td>0.67</td><td>0.86</td><td>0.28</td><td>0.16</td><td>0.80</td><td>0.74</td><td>0.20</td><td>0.90</td></tr><tr><td>0.40</td><td>0.24</td><td>0.13</td><td>0.05</td><td>0.47</td><td>0.35</td><td>0.63</td><td>0.08</td><td>0.05</td><td>0.09</td><td>0.87</td><td>0.73</td><td>0.15</td><td>0.39</td><td>0.38</td><td>0.60</td><td>0.00</td><td>0.11</td><td>0.87</td><td>0.07</td><td>0.00</td><td>0.31</td><td>0.14</td><td>0.07</td><td>0.28</td><td>1.00</td><td>1.00</td><td>0.37</td><td>0.73</td><td>0.66</td><td>0.58</td><td>0.64</td><td>0.89</td><td>0.59</td><td>0.20</td><td>0.51</td><td>1.00</td><td>0.84</td></tr><tr><td>0.02</td><td>0.26</td><td>0.07</td><td>0.00</td><td>0.41</td><td>0.08</td><td>0.55</td><td>0.03</td><td>0.05</td><td>0.46</td><td>0.02</td><td>0.28</td><td>0.82</td><td>0.45</td><td>0.95</td><td>0.79</td><td>0.10</td><td>0.00</td><td>0.56</td><td>0.05</td><td>0.00</td><td>0.23</td><td>0.02</td><td>0.00</td><td>0.02</td><td>0.03</td><td>0.15</td><td>0.05</td><td>0.01</td><td>0.14</td><td>0.08</td><td>0.24</td><td>0.00</td><td>0.37</td><td>0.23</td><td>0.12</td><td>0.37</td><td>0.48</td></tr><tr><td>0.62</td><td>0.06</td><td>0.40</td><td>0.25</td><td>0.64</td><td>0.26</td><td>0.78</td><td>0.57</td><td>0.77</td><td>0.63</td><td>0.57</td><td>0.67</td><td>0.60</td><td>0.86</td><td>0.21</td><td>0.22</td><td>0.87</td><td>0.54</td><td>0.00</td><td>0.45</td><td>0.03</td><td>0.29</td><td>0.29</td><td>0.05</td><td>0.39</td><td>0.11</td><td>0.74</td><td>0.62</td><td>0.55</td><td>0.74</td><td>0.31</td><td>0.69</td><td>0.38</td><td>0.83</td><td>0.93</td><td>0.56</td><td>0.04</td><td>0.20</td></tr><tr><td>0.71</td><td>0.15</td><td>0.78</td><td>0.58</td><td>0.68</td><td>0.68</td><td>0.03</td><td>0.33</td><td>0.14</td><td>0.06</td><td>0.55</td><td>0.10</td><td>0.05</td><td>0.12</td><td>0.55</td><td>0.01</td><td>0.08</td><td>0.05</td><td>0.44</td><td>0.00</td><td>0.00</td><td>0.16</td><td>0.01</td><td>0.03</td><td>0.01</td><td>1.00</td><td>0.51</td><td>0.27</td><td>0.08</td><td>0.42</td><td>1.00</td><td>0.09</td><td>0.75</td><td>0.24</td><td>0.51</td><td>0.25</td><td>0.07</td><td>0.69</td></tr><tr><td>0.36</td><td>0.07</td><td>0.43</td><td>0.07</td><td>0.05</td><td>0.01</td><td>0.88</td><td>0.25</td><td>0.12</td><td>0.24</td><td>0.47</td><td>0.91</td><td>0.00</td><td>0.92</td><td>0.92</td><td>0.03</td><td>0.00</td><td>0.00</td><td>0.03</td><td>0.00</td><td>0.00</td><td>0.04</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.19</td><td>0.08</td><td>0.04</td><td>0.14</td><td>0.46</td><td>0.34</td><td>0.62</td><td>0.10</td><td>0.34</td><td>0.46</td><td>0.48</td><td>1.00</td><td>0.38</td></tr><tr><td>0.59</td><td>0.52</td><td>0.61</td><td>0.95</td><td>0.30</td><td>0.79</td><td>1.00</td><td>0.54</td><td>0.52</td><td>0.45</td><td>0.29</td><td>0.22</td><td>0.25</td><td>0.43</td><td>0.13</td><td>0.07</td><td>0.32</td><td>0.22</td><td>0.30</td><td>0.17</td><td>0.04</td><td>0.00</td><td>0.06</td><td>0.26</td><td>0.01</td><td>0.64</td><td>0.73</td><td>0.08</td><td>0.76</td><td>1.00</td><td>0.86</td><td>0.87</td><td>0.85</td><td>0.10</td><td>0.62</td><td>0.86</td><td>1.00</td><td>0.67</td></tr><tr><td>0.04</td><td>0.63</td><td>0.06</td><td>0.02</td><td>0.58</td><td>0.18</td><td>0.77</td><td>0.15</td><td>0.11</td><td>0.47</td><td>0.50</td><td>0.62</td><td>0.25</td><td>0.80</td><td>0.19</td><td>0.35</td><td>0.13</td><td>0.02</td><td>0.30</td><td>0.01</td><td>0.00</td><td>0.05</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.00</td><td>0.04</td><td>0.43</td><td>0.07</td><td>0.16</td><td>0.37</td><td>0.07</td><td>0.02</td><td>0.37</td><td>1.00</td><td>0.40</td><td>0.37</td><td>0.34</td></tr><tr><td>0.73</td><td>0.82</td><td>0.04</td><td>0.42</td><td>0.19</td><td>0.29</td><td>0.34</td><td>0.17</td><td>0.05</td><td>0.31</td><td>0.39</td><td>0.63</td><td>0.11</td><td>0.32</td><td>0.19</td><td>0.31</td><td>0.07</td><td>0.00</td><td>0.05</td><td>0.03</td><td>0.00</td><td>0.25</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.22</td><td>0.07</td><td>0.02</td><td>0.30</td><td>0.32</td><td>0.43</td><td>0.96</td><td>0.01</td><td>0.75</td><td>0.94</td><td>0.17</td><td>0.45</td><td>0.48</td></tr><tr><td>0.26</td><td>0.63</td><td>0.08</td><td>0.04</td><td>0.85</td><td>0.21</td><td>0.37</td><td>0.07</td><td>0.01</td><td>0.81</td><td>0.27</td><td>0.21</td><td>0.00</td><td>0.56</td><td>0.32</td><td>0.03</td><td>0.29</td><td>0.02</td><td>0.40</td><td>0.01</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.33</td><td>0.17</td><td>0.59</td><td>0.41</td><td>0.46</td><td>0.45</td><td>1.00</td><td>0.20</td><td>0.46</td><td>0.07</td><td>0.51</td><td>0.60</td><td>0.11</td></tr><tr><td>0.02</td><td>0.76</td><td>0.24</td><td>0.07</td><td>0.18</td><td>0.81</td><td>0.83</td><td>0.72</td><td>0.82</td><td>0.48</td><td>0.90</td><td>1.00</td><td>0.51</td><td>0.68</td><td>0.17</td><td>0.50</td><td>1.00</td><td>0.03</td><td>0.12</td><td>1.00</td><td>0.19</td><td>0.64</td><td>0.00</td><td>0.21</td><td>0.32</td><td>0.00</td><td>0.03</td><td>0.62</td><td>0.28</td><td>0.24</td><td>0.49</td><td>0.03</td><td>0.00</td><td>0.57</td><td>1.00</td><td>0.12</td><td>0.78</td><td>0.80</td></tr><tr><td>0.13</td><td>0.40</td><td>0.23</td><td>0.51</td><td>0.39</td><td>0.48</td><td>0.74</td><td>1.00</td><td>1.00</td><td>0.81</td><td>1.00</td><td>1.00</td><td>1.00</td><td>0.24</td><td>0.49</td><td>0.48</td><td>1.00</td><td>0.15</td><td>0.74</td><td>0.51</td><td>0.09</td><td>0.74</td><td>0.04</td><td>0.08</td><td>0.17</td><td>0.03</td><td>0.00</td><td>0.22</td><td>0.30</td><td>0.92</td><td>0.84</td><td>0.06</td><td>0.23</td><td>0.12</td><td>0.74</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>0.45</td><td>0.52</td><td>0.39</td><td>0.32</td><td>0.11</td><td>0.90</td><td>0.87</td><td>0.38</td><td>0.41</td><td>0.48</td><td>0.51</td><td>1.00</td><td>0.45</td><td>0.74</td><td>0.32</td><td>0.21</td><td>0.37</td><td>0.06</td><td>0.63</td><td>0.26</td><td>0.04</td><td>0.08</td><td>0.43</td><td>0.02</td><td>0.59</td><td>0.63</td><td>0.23</td><td>0.00</td><td>0.13</td><td>0.03</td><td>0.32</td><td>0.58</td><td>0.92</td><td>0.91</td><td>0.08</td><td>0.93</td><td>0.01</td><td>0.06</td></tr><tr><td>0.79</td><td>0.16</td><td>0.02</td><td>0.37</td><td>0.70</td><td>0.45</td><td>0.26</td><td>0.13</td><td>0.46</td><td>0.84</td><td>0.21</td><td>0.46</td><td>0.24</td><td>0.38</td><td>0.51</td><td>0.81</td><td>0.72</td><td>0.01</td><td>0.55</td><td>0.08</td><td>0.15</td><td>0.75</td><td>0.09</td><td>0.31</td><td>0.40</td><td>0.26</td><td>0.29</td><td>0.13</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.90</td><td>0.63</td><td>0.90</td><td>0.69</td><td>0.07</td><td>0.07</td><td>0.11</td></tr><tr><td>0.53</td><td>0.15</td><td>0.22</td><td>0.11</td><td>0.31</td><td>0.31</td><td>0.15</td><td>0.12</td><td>0.18</td><td>0.75</td><td>0.13</td><td>0.31</td><td>0.83</td><td>0.10</td><td>0.51</td><td>0.76</td><td>0.66</td><td>0.15</td><td>0.74</td><td>0.42</td><td>0.47</td><td>1.00</td><td>0.16</td><td>0.32</td><td>0.47</td><td>0.23</td><td>0.92</td><td>0.03</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.64</td><td>0.33</td><td>0.67</td><td>0.13</td><td>0.37</td><td>0.14</td><td>0.16</td></tr><tr><td>0.82</td><td>0.30</td><td>0.19</td><td>0.13</td><td>0.80</td><td>0.52</td><td>0.16</td><td>0.46</td><td>0.16</td><td>0.22</td><td>0.20</td><td>0.24</td><td>0.40</td><td>0.15</td><td>0.35</td><td>0.67</td><td>0.57</td><td>0.08</td><td>0.30</td><td>0.99</td><td>0.34</td><td>0.86</td><td>0.36</td><td>0.44</td><td>0.46</td><td>0.47</td><td>0.85</td><td>0.33</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.54</td><td>0.31</td><td>0.54</td><td>0.28</td><td>0.05</td><td>0.19</td><td>0.37</td></tr><tr><td>0.26</td><td>0.74</td><td>0.22</td><td>0.76</td><td>0.84</td><td>0.82</td><td>0.26</td><td>0.91</td><td>0.60</td><td>0.48</td><td>0.15</td><td>0.31</td><td>0.67</td><td>0.71</td><td>0.82</td><td>0.86</td><td>0.65</td><td>0.23</td><td>0.70</td><td>0.08</td><td>0.62</td><td>0.86</td><td>0.06</td><td>0.96</td><td>1.00</td><td>0.03</td><td>0.06</td><td>0.59</td><td>0.90</td><td>0.64</td><td>0.56</td><td>0.00</td><td>0.17</td><td>0.01</td><td>0.06</td><td>0.58</td><td>0.09</td><td>0.57</td></tr><tr><td>0.00</td><td>0.41</td><td>0.21</td><td>0.03</td><td>0.08</td><td>0.14</td><td>0.16</td><td>0.60</td><td>0.52</td><td>0.45</td><td>0.03</td><td>0.07</td><td>0.82</td><td>0.66</td><td>0.84</td><td>0.28</td><td>0.89</td><td>0.00</td><td>0.39</td><td>0.76</td><td>0.09</td><td>0.86</td><td>0.02</td><td>0.01</td><td>0.19</td><td>0.00</td><td>0.23</td><td>0.92</td><td>0.64</td><td>0.34</td><td>0.30</td><td>0.16</td><td>0.00</td><td>0.22</td><td>0.66</td><td>0.18</td><td>0.52</td><td>0.48</td></tr><tr><td>0.50</td><td>0.07</td><td>0.00</td><td>0.37</td><td>0.76</td><td>0.71</td><td>0.04</td><td>0.10</td><td>0.06</td><td>0.41</td><td>0.04</td><td>0.00</td><td>0.28</td><td>0.14</td><td>0.25</td><td>0.17</td><td>0.59</td><td>0.38</td><td>0.82</td><td>0.24</td><td>0.33</td><td>0.09</td><td>0.36</td><td>0.76</td><td>0.44</td><td>0.59</td><td>0.13</td><td>0.91</td><td>0.90</td><td>0.66</td><td>0.54</td><td>0.01</td><td>0.22</td><td>0.00</td><td>0.87</td><td>0.83</td><td>0.37</td><td>1.00</td></tr><tr><td>0.04</td><td>0.55</td><td>0.31</td><td>0.69</td><td>0.27</td><td>0.31</td><td>0.55</td><td>0.97</td><td>0.43</td><td>0.70</td><td>0.67</td><td>0.73</td><td>0.58</td><td>0.45</td><td>0.91</td><td>0.79</td><td>0.19</td><td>0.23</td><td>0.93</td><td>0.51</td><td>0.48</td><td>0.62</td><td>1.00</td><td>0.94</td><td>0.06</td><td>1.00</td><td>0.75</td><td>0.09</td><td>0.69</td><td>0.14</td><td>0.28</td><td>0.06</td><td>0.66</td><td>0.87</td><td>0.00</td><td>0.63</td><td>0.03</td><td>0.91</td></tr><tr><td>0.91</td><td>0.42</td><td>0.27</td><td>0.93</td><td>0.11</td><td>0.39</td><td>0.71</td><td>1.00</td><td>1.00</td><td>0.04</td><td>0.53</td><td>0.17</td><td>0.22</td><td>0.06</td><td>0.05</td><td>0.73</td><td>0.50</td><td>0.12</td><td>0.56</td><td>0.26</td><td>0.49</td><td>0.85</td><td>0.39</td><td>0.16</td><td>0.51</td><td>0.12</td><td>1.00</td><td>0.94</td><td>0.07</td><td>0.37</td><td>0.05</td><td>0.59</td><td>0.18</td><td>0.83</td><td>0.64</td><td>0.00</td><td>0.22</td><td>0.29</td></tr><tr><td>0.47</td><td>0.44</td><td>0.10</td><td>0.87</td><td>0.48</td><td>0.30</td><td>0.15</td><td>0.41</td><td>0.47</td><td>0.31</td><td>0.66</td><td>0.27</td><td>1.00</td><td>0.89</td><td>0.01</td><td>0.19</td><td>1.00</td><td>0.38</td><td>0.04</td><td>0.06</td><td>1.00</td><td>1.00</td><td>0.36</td><td>0.46</td><td>0.60</td><td>0.79</td><td>1.00</td><td>0.01</td><td>0.07</td><td>0.13</td><td>0.21</td><td>0.09</td><td>0.52</td><td>0.36</td><td>0.04</td><td>0.22</td><td>0.00</td><td>1.00</td></tr><tr><td>0.12</td><td>0.04</td><td>0.27</td><td>0.12</td><td>0.55</td><td>0.10</td><td>1.00</td><td>0.06</td><td>0.54</td><td>0.66</td><td>0.31</td><td>0.77</td><td>0.87</td><td>1.00</td><td>0.72</td><td>0.90</td><td>0.83</td><td>0.48</td><td>0.21</td><td>0.67</td><td>0.37</td><td>0.66</td><td>0.34</td><td>0.47</td><td>0.11</td><td>0.78</td><td>1.00</td><td>0.06</td><td>0.11</td><td>0.16</td><td>0.38</td><td>0.56</td><td>0.50</td><td>1.00</td><td>0.92</td><td>0.29</td><td>1.00</td><td>0.00</td></tr></tbody></table>
<table><thead><tr><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th></tr></thead><tbody><tr><td><p>SC.Q16. What does attract you much in a debris flow?</p><p>SC.Q6. Do you know of anyone among your family or friends who has been affected by a debris flow? Yes</p><p>SC.Q3. Have you ever witnessed a debris flow? Yes</p><p>SC.Q9. How did you behave that day? Remained there and searched for advice</p><p>SC.Q14. Do you like to observe a debris flow? A little</p><p>SC.Q15. From where do you observe lahars? From a long distance and elevation<br>Something</p><p>SC.Q8. Can you identify or discuss a particular lahar that you remember? Yes</p><p>SB.Q4. Do you know the hazard-zone colour meaning? Unsure, but has an idea</p></td><td><p>SC.Q15. From where do you observe lahars? Uncertain or does not know</p><p>SC.Q16. What does attract you much in a debris flow? Nothing</p><p>SC.Q14. Do you like to observe a debris flow? No</p><p>SB.Q10. How long could the next El Misti volcanic eruption last? Does not know</p><p>SB.Q3. Can you locate his/her district? Does not know</p><p>SA. Q9. Education level: Primary school</p><p>SA. Q14. How long have you been living in the area? &gt; 30 yrs</p><p>SB.Q5. Can you show village location and what does represent colour shown? Does not know</p><p>SB.Q4. Do you know the hazard-zone colour meaning? Does not know</p><p>SB.Q8. Lahar occurrence estimate: Does not know</p></td><td><p>SC.Q10. Impact of memory on behaviour? No answer</p><p>SC.Q8. Can you identify or discuss a particular lahar that you remember? No</p><p>SC.Q9. How did you behave that day? No answer</p><p>SC.Q3. Have you ever witnessed a debris flow? No</p><p>SC.Q6. Do you know of anyone among your family or friends who has been affected by a debris flow? No</p><p>SA.Q10. Socio-Professional category: Student</p><p>SA. Q14. How long have you been living in the area? &lt;6 yrs/6 to 15 yrs</p><p>SA. Q9. Education level: Secondary school</p><p>SB.Q5. Can you show village location and what does represent colour shown? No answer</p><p>SC.Q5. Do you feel that a debris flow would hit you? Perhaps</p><p>SB.Q6. What are volcanic ash, pyroclastic flow, lahar? Knows</p></td><td><p>SB.Q11. How long after a volcanic eruption can lahars occur? Minutes, hours</p><p>SB.Q10. How long could the next Misti volcanic eruption last? Minutes, hours</p><p>SC.Q10. Impact of memory on behaviour: No</p><p>SB.Q6. What are volcanic ash, pyroclastic flow, lahar? Knows</p><p>SB.Q4. Do you know the hazard -zone colour meaning? Yes, knows</p><p>SB.Q3. Can you locate his/her district? Yes, can locate</p></td></tr><tr><td><p>SC.Q10. Impact of memory on behaviour. No answer</p><p>SC.Q6. Do you know of anyone among your family or friends who has been affected by a debris flow? No</p><p>SC.Q8. Can you identify or discuss a particular lahar that you remember? No</p><p>SC.Q9. How did you behave that day? No answer</p><p>SC.Q15. From where do you observe lahars? Uncertain or does not know</p><p>SC.Q16. What does attract you much in a debris flow? Nothing</p><p>SC.Q14. Do you like to observe a debris flow? No</p></td><td><p>SB.Q5. Can you show village location and what does represent colour shown? Unsure, but has an idea</p><p>SC.Q15. From where do you observe lahars? From a long distance and elevation</p><p>SB.Q10. How long could the next El Misti volcanic eruption last? Minutes, hours</p><p>SC.Q14. Do you like to observe a debris flow? Yes</p><p>SC.Q15. From where do you observe lahars? From a short distance and elevation</p><p>SC.Q16. What does attract you much in a debris flow? Something</p><p>SA. Q9. Education level: Secondary school</p></td><td><p>SA. Q9. Education level: Primary school</p><p>SA. Q14. Do you like to observe a debris flow? A little</p><p>SA. Q14. How long have you been living in the area? &gt; 30 yrs</p><p>SC.Q5. Do you feel that a debris flow would hit you? Yes</p><p>SC.Q9. How did you behave that day? Remained there but searched for advice</p><p>SC.Q6. Do you know of anyone among your family or friends who has been affected by a debris flow? Yes</p><p>SC.Q10. Impact of memory on behaviour: Yes</p><p>SC.Q3. Have you ever witnessed a debris flow? Yes</p></td><td><p>SB.Q3. Can you locate his/her district? Does not know</p><p>SB.Q5. Can you show your village location and what does represent colour shown? Does not know</p><p>SB.Q4. Do you know the hazard -zone colour meaning? Does not know</p></td></tr></tbody></table>
<table><thead><tr><th>CLUSTERS</th><th>1.</th><th>2.</th><th>3.</th><th>4.</th></tr></thead><tbody><tr><td colspan="5">SOCIO-DEMOGRAPHIC CHARACTERISTICS OF THE POPULATION (FROM SURVEY A)</td></tr><tr><td>ISSUES<br>QUESTIONS AND ANSWERS</td><td colspan="4">1. SOCIO-DEMOGRAPHIC CATEGORIES</td></tr><tr><td>A.Q2. Age group</td><td>31 to 59<br>≥ 60<br>19 to 30</td><td>51.16%<br>20.93%<br>23.26%</td><td>31 to 59<br>≥ 60<br>19 to 30</td><td>48.28%<br>41.38%<br>10.34%</td><td>19 to 30<br>≥ 60</td><td>29.41%<br>35.29%<br>17.65%</td><td>≥ 60<br>19 to 30</td><td>33.33%<br>66.67%</td></tr><tr><td>A.Q9. Education level</td><td>High education<br>Secondary school</td><td>13.95%<br>76.74%</td><td>Primary school<br>Secondary school</td><td>37.93%<br>37.93%</td><td>Secondary school</td><td>94.12%</td><td>Secondary school</td><td>100%</td></tr><tr><td>A.Q10. Socio-professional category</td><td>Housewife, informal workforce,<br>public employee, private<br>employee, commerce and student</td><td></td><td>Housewife, informal workforce,<br>public employee, private<br>employee, commerce and student</td><td></td><td>Housewife, student, informal<br>workforce and private<br>employee</td><td></td><td>Private employee, informal<br>workforce and commerce</td><td></td></tr><tr><td colspan="9">2. ARRIVAL TIME IN THE AREA AND GEOGRAPHICAL ORIGIN</td></tr><tr><td>A.Q14. How long have you been<br>living in the area?</td><td>> 16 years</td><td>93.02%</td><td>> 30 years</td><td>82.76%</td><td>> 16 years<br>< 16 years</td><td>52.94%<br>47.06%</td><td>6 to 30 years</td><td>100%</td></tr><tr><td>A.Q17. Family origin (Distance to<br>El Misti summit)</td><td>> 100 km<br>< 30 km</td><td>62.79%<br>18.60%</td><td>> 100 km<br>< 30 km</td><td>68.97%<br>10.34%</td><td>> 100 km<br>< 100 km</td><td>58.82%<br>41.17%</td><td>> 300 km<br>< 30 km</td><td>66.67%<br>33.33%</td></tr></tbody></table>

Table 7 c'td
<table><thead><tr><th>CLUSTERS</th><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><td colspan="5">HAZARD KNOWLEDGE (FROM SURVEY B)</td></tr><tr><td colspan="5">ISSUES<br>QUESTIONS AND ANSWERS</td></tr><tr><td>B.Q2. Do you know what year this volcano last erupted?</td><td>A long time ago<br>Does not know</td><td>30.23%<br>62.79%</td><td>Does not know<br>A long time ago</td><td>86.21%<br>10.34%<br>Doesn't know<br>Unsure</td><td>82.35%<br>17.65%</td><td>A long time ago<br>66.67%</td></tr><tr><td colspan="5">2. HAZARD-ZONE MAP</td></tr><tr><td>B.Q3. Can you locate your district?</td><td>Unsure/ Does not know</td><td>86.05%</td><td>Does not know</td><td>96.55%</td><td>Does not know</td><td>70.59%</td><td>Yes</td><td>100%</td></tr><tr><td>B.Q4. Do you know the hazard-zone colour meaning?</td><td>Does not know</td><td>72.09%</td><td>Does not know</td><td>96.55%</td><td>Does not know</td><td>64.71%</td><td>Yes, knows</td><td>100%</td></tr><tr><td>B.Q5. Can you show your village location and what does represent colour shown?</td><td>Does not know</td><td>74.42%</td><td>Does not know</td><td>96.55%</td><td>Does not know/No answer</td><td>82.35%</td><td>Knows</td><td>100%</td></tr><tr><td>B.Q6. What are volcanic ash, pyroclastic flow, and lahar?</td><td>Unsure, but has an idea</td><td>74.42%</td><td>Unsure, but has an idea</td><td>68.97%</td><td>Unsure, but has an idea<br>Knows</td><td>52.94%<br>17.65%</td><td>Knows/ Unsure, but has an idea</td><td>100%</td></tr><tr><td colspan="5">3. LAHAR OCCURRENCE AND IMPACT</td></tr><tr><td>B.Q7. Can you estimate the arrival time of a lahar to your village, once it has been triggered on the mountain?</td><td>Minutes, hours</td><td>83.72%</td><td>Minutes, hours</td><td>72.41%</td><td>Minutes, hours</td><td>88.24%</td><td>Minutes, hours</td><td>100%</td></tr><tr><td>B.Q8. Lahar occurrence estimate</td><td>Minutes, hours</td><td>86.05%</td><td>Minutes, hours</td><td>75.86%</td><td>Minutes, hours</td><td>94.12%</td><td>Minutes, hours</td><td>100%</td></tr><tr><td>B.Q9. Do you know of any victims or/and injured people?</td><td>No</td><td>95.35%</td><td>No</td><td>89.66%</td><td>No</td><td>76.47%</td><td>No</td><td>66.67%</td></tr><tr><td colspan="5">4. ERUPTION AND LAHAR OCCURRENCE INTERVAL TIME</td></tr><tr><td>B.Q10. How long could the next El Misti volcanic eruption last?</td><td>Minutes, hours<br>Days</td><td>37.21%<br>37.21%</td><td>Does not know</td><td>65.52%</td><td>Days<br>Minutes, hours</td><td>41.18%<br>35.29%</td><td>Minutes, hours</td><td>100%</td></tr><tr><td>B.Q11. How long after a volcanic eruption can lahars occur?</td><td>Does not know<br>Days</td><td>46.51%<br>37.21%</td><td>Does not know</td><td>72.41%</td><td>Does not know<br>Days</td><td>41.18%<br>41.18%</td><td>Minutes, hours</td><td>100%</td></tr></tbody></table>
<table><thead><tr><th>GROUPS</th><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><td colspan="5">RISK PERCEPTION AND ASSOCIATE VARIABLES (from Survey C)</td></tr><tr><td>C.Q1. How do you feel about the volcano or a debris flow?</td><td>Worried<br>Uncertain</td><td>62.79%<br>18.60%</td><td>Worried<br>Other concern or none</td><td>68.97%<br>27.59%</td><td>Worried<br>Other concern or none</td><td>58.82%<br>35.29%</td><td>Worried<br>Other concern or none</td><td>66.67%<br>33.33%</td></tr><tr><td>C.Q2. Any concern related to you?</td><td>Other or none<br>Debris flow/lahar</td><td>81.40%<br>9.30%</td><td>Other or none<br>Debris flow/lahar</td><td>75.86%<br>13.79%</td><td>Other or none<br>Debris flow/lahar</td><td>47.06%<br>17.65%</td><td>Other or none</td><td>100%</td></tr><tr><td>C.Q3. Have you ever witnessed a debris flow?</td><td>Yes</td><td>100%</td><td>Yes</td><td>93.10%</td><td>No</td><td>64.71%</td><td>Yes</td><td>100%</td></tr><tr><td>C.Q4. Have you ever been affected by a debris flow?</td><td>Never<br>Few occasions</td><td>72.09%<br>6.98%</td><td>Never<br>Yes</td><td>72.41%<br>24.14%</td><td>Never</td><td>88.24%</td><td>Never</td><td>100%</td></tr><tr><td>C.Q5. Do you feel that a debris flow would hit you?</td><td>Yes<br>Perhaps</td><td>69.77%<br>11.63%</td><td>Yes<br>Perhaps</td><td>72.41%<br>13.79%</td><td>Perhaps<br>Does not know</td><td>41.18%<br>35.29%</td><td>Does not know</td><td>66.67%</td></tr><tr><td>C.Q6. Do you know of anyone among your family or friends who has been affected by a debris flow?</td><td>Yes</td><td>93.02%</td><td>Yes</td><td>68.97%</td><td>No</td><td>88.24%</td><td>No</td><td>66.67%</td></tr><tr><td>C.Q7. Traffic accident, family or friends?</td><td>Yes<br>No</td><td>44.19%<br>53.49%</td><td>Yes<br>No</td><td>41.38%<br>58.62%</td><td>No</td><td>82.35%</td><td>No</td><td>100%</td></tr><tr><td>C.Q11. Can you describe a debris flow?</td><td>No<br>Yes</td><td>76.74%<br>23.26%</td><td>No</td><td>79.31%</td><td>No</td><td>88.24%</td><td>No<br>Yes</td><td>66.67%<br>33.33%</td></tr><tr><td>C.Q12. Origin of knowledge</td><td>Experience, school, authorities</td><td>100%</td><td>Experience, school, authorities</td><td>93.10%</td><td>Experience, school, authorities</td><td>88.24%</td><td>Experience, school, authorities</td><td>100%</td></tr><tr><td>C.Q8. Can you identify or discuss a particular lahar that you remember?</td><td>Yes</td><td>97.67%</td><td>Yes</td><td>79.31%</td><td>No</td><td>94.12%</td><td>Yes</td><td>100%</td></tr><tr><td>C.Q9. How did you behave that day?</td><td>Remained</td><td>69.77%</td><td>Remained<br>No answer</td><td>51.72%<br>31.03%</td><td>No answer</td><td>100%</td><td>Did not know what to do / Evacuated or auto-evacuated</td><td>100%</td></tr><tr><td>C.Q10. Impact of memory on behaviour</td><td>Yes</td><td>74.42%</td><td>Yes</td><td>65.52%</td><td>No answer</td><td>100%</td><td>Yes</td><td>66.67%</td></tr><tr><td>C.Q14. Do you like to observe a debris flow?</td><td>Yes<br>A little</td><td>41.86%<br>55.81%</td><td>No<br>A little</td><td>75.86%<br>20.69%</td><td>No/ A little</td><td>70.58%</td><td>No/ A little</td><td>100%</td></tr></tbody></table>
<table><tbody><tr><td rowspan="2">C.Q15. From where do you observe lahars?</td><td>From a long distance</td><td>60.47%</td><td rowspan="2">Does not know</td><td rowspan="2">82.76%</td><td>Does not know</td><td>47.06%</td><td>Does not know</td><td>66.67%</td></tr><tr><td>From a short distance</td><td>34.88%</td><td>From a long distance</td><td>29.41%</td><td></td><td></td></tr><tr><td rowspan="2">C.Q16. What does attract you much in a debris flow?</td><td>Something</td><td>81.04%</td><td>Nothing</td><td>79.31%</td><td>Nothing</td><td>47.06%</td><td>Nothing/Everything</td><td>100%</td></tr><tr><td>Yes</td><td>88.37%</td><td>Yes</td><td>89.66%</td><td>Yes</td><td>41.18%</td><td>Yes</td><td>100%</td></tr><tr><td rowspan="2">C.Q17+18+19. Do you feel that such a phenomenon is so powerful than mankind cannot do anything about it?</td><td>One solution</td><td>90.70%</td><td>One solution</td><td>79.31%</td><td>One solution</td><td>76.47%</td><td>One/More than one solution</td><td>100%</td></tr><tr><td>May be</td><td>55.81%</td><td>More than solution</td><td>13.79%</td><td>Does not know</td><td>17.65%</td><td></td><td></td></tr><tr><td rowspan="2">Alternatives to overcome flow effects?</td><td>No</td><td>32.56%</td><td>Yes</td><td>37.93%</td><td>May be</td><td>58.82%</td><td>May be</td><td>100%</td></tr><tr><td></td><td></td><td>May be</td><td>37.93%</td><td>No</td><td>35.29%</td><td></td><td></td></tr><tr><td rowspan="2">Do you feel self-confident in case of an eruption?</td><td>Yes</td><td>79.07%</td><td>Yes</td><td>65.52%</td><td>Yes</td><td>100%</td><td>Yes</td><td>66.67%</td></tr><tr><td>One of them</td><td>93.02%</td><td>Sometimes or during extreme events</td><td>31.03%</td><td>One of them</td><td>88.24%</td><td>One of them</td><td>33.33%</td></tr><tr><td rowspan="2">C.Q20. Will you comply with the evacuation order in any type of situation?</td><td>One of them (TV or smartphone)</td><td>72.09%</td><td>One of them</td><td>89.66%</td><td>None</td><td>11.76%</td><td>One of them</td><td>100%</td></tr><tr><td>More than one</td><td>27.91%</td><td>One of them (TV or smartphone)</td><td>89.66%</td><td>One of them (TV or smartphone)</td><td>88.24%</td><td>One of them (TV or smartphone)</td><td>100%</td></tr><tr><td rowspan="2">C.Q21+22. Whom do you receive warning messages from in case of lahar?</td><td>Yes, I trust</td><td>46.51%</td><td>Yes, I trust</td><td>55.17%</td><td>Yes, I trust</td><td>52.94%</td><td>Yes, I trust</td><td>33.33%</td></tr><tr><td>Yes, I trust but I try to check</td><td>53.49%</td><td>Yes, I trust but I try to check</td><td>44.83%</td><td>Yes, I trust but I try to check</td><td>47.06%</td><td>Yes, I trust but I try to check</td><td>66.67%</td></tr></tbody></table>

<table><caption>Table 7</caption><thead><tr><th>GROUPS</th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th></tr></thead><tbody><tr><td>SUB-TOTAL</td><td>46.74% (43)</td><td>31.52% (29)</td><td>18.48% (17)</td><td>3.26% (3)</td></tr><tr><td>TOTAL</td><td>100% (92)</td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th>Clusters</th><th>Individuals</th><th>B.Q5</th><th>C.Q10</th><th>B.Q4</th><th>C.Q8</th><th>C.Q3</th><th>C.Q9</th><th>C.Q16</th><th>C.Q14</th></tr></thead><tbody><tr><td rowspan="5">1</td><td>No.123</td><td>Does not know</td><td>Uncertain or does not know</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained there but searched for advice</td><td>Everything</td><td>Yes</td></tr><tr><td>231</td><td>Does not know</td><td>Uncertain or does not know</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Something</td><td>Yes</td></tr><tr><td>104</td><td>Does not know</td><td>Yes</td><td>Unsure, but has an idea</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Something</td><td>Yes</td></tr><tr><td>247</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained there but searched for advice</td><td>Something</td><td>A little</td></tr><tr><td>261</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained there but searched for advice</td><td>Something</td><td>A little</td></tr><tr><td rowspan="5">2</td><td>No.220</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Nothing</td><td>No</td></tr><tr><td>236</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained on the spot but searched for advice</td><td>Nothing</td><td>No</td></tr><tr><td>239</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained on the spot but searched for advice</td><td>Nothing</td><td>No</td></tr><tr><td>266</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Nothing</td><td>No</td></tr><tr><td>148</td><td>Does not know</td><td>Yes</td><td>Does not know</td><td>Yes</td><td>Yes</td><td>Remained there but searched for advice</td><td>Nothing</td><td>No</td></tr><tr><td rowspan="5">3</td><td>No.125</td><td>Does not know</td><td>No answer</td><td>Does not know</td><td>No</td><td>No</td><td>No answer</td><td>Something</td><td>Yes</td></tr><tr><td>101</td><td>Unsure, but have an idea</td><td>No answer</td><td>Does not know</td><td>No</td><td>Yes</td><td>No answer</td><td>Something</td><td>A little</td></tr><tr><td>119</td><td>Does not know</td><td>No answer</td><td>Does not know</td><td>No</td><td>No</td><td>No answer</td><td>Nothing</td><td>No</td></tr><tr><td>112</td><td>Does not know</td><td>No answer</td><td>Does not know</td><td>No</td><td>Yes</td><td>No answer</td><td>Something</td><td>Yes</td></tr><tr><td>225</td><td>Does not know</td><td>No answer</td><td>Does not know</td><td>No</td><td>No</td><td>No answer</td><td>Nothing</td><td>No</td></tr><tr><td rowspan="4">4</td><td>No.102</td><td>Knows</td><td>Yes</td><td>Yes, knows</td><td>Yes</td><td>Yes</td><td>Evacuated or auto-evacuated</td><td>Nothing</td><td>No</td></tr><tr><td>105</td><td>Knows</td><td>Yes</td><td>Yes, knows</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Everything</td><td>A little</td></tr><tr><td>116</td><td>Knows</td><td>No</td><td>Yes, knows</td><td>Yes</td><td>Yes</td><td>Did not know what to do</td><td>Nothing</td><td>No</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

### Survey B

B.Q4. Do you know the hazard-zone colour meaning?

B.Q5. Can you locate your neighbourhood and what does represent colour shown in the hazard-zone map?

### Survey C

C.Q3. Have you ever witnessed a debris flow/lahar?

C.Q8. Can you identify or discuss a particular debris flow that you remember?

C.Q9. How did you behave that day?

C.Q10. Did your memory have an impact on behaviour?

C.Q14. Do you like to observe a debris flow?

C.Q16. What does attract you much in a debris flow?
<figure><img src="image_76.png" /><figcaption>Table 9. Results from interviews, meetings and focus groups with key actors and officials in charge of disaster risk management.</figcaption></figure>

<table><thead><tr><th>Disaster Risk Management DRM</th><th>Topic</th><th>KEY ACTORS</th><th>ANSWERS, OBSERVATIONS AND PROPOSED REGULATIONS</th><th>Domain inferred from answers and field of application</th></tr></thead><tbody><tr><td rowspan="3">Training, knowledge, and skills</td><td rowspan="3">Perception and knowledge</td><td>Heads of neighbourhoods</td><td>Leaders recognize a number of issues:<br/>- Volcanic hazard is one of the characteristics of the city of Arequipa<br/>- Earthquakes are a constant Hazard, based on their own experience<br/>- Heavy rainfall is a constant hazard on an annual basis.<br/>They state that a large number of the urban dwellers do not know which areas are most prone to volcanic hazards</td><td>Cultural beliefs on risk<br/>Hazard knowledge</td></tr><tr><td>Civil servants</td><td>Officials admit that earthquakes and heavy rainfall show some periodicity acknowledge that pyroclastic flows from El Misti volcano represent danger<br/>State that many 'unlawful' edifices are built in the vicinity of active ravines and many edifices near the Qda. San Lazaro exhibit physical vulnerability.</td><td>Recurrent flow/flood hazards adopted as 'inevitable' in the city<br/>Unlawful or non-authorized land occupation in some areas of the districts</td></tr><tr><td>District Mayors</td><td>Mayors know which hazards are commonly acting in their district and national regulations related to disaster risk management<br/>Mayors admit their limited knowledge of the guidelines for the incorporation of disaster risk management into the organisation and functions of their districts.</td><td>Knowledge about disaster risk management</td></tr><tr><td>Proactive risk management</td><td>Planning and mitigation</td><td>Heads of neighbourhoods</td><td>Leaders suggest that contention walls be built in pilot reaches along ravines as well as street network be re-organized so that evacuation would be easier in case of an emergency<br/>They acknowledge to be in charge of the communication between the city hall and the urban dwellers<br/>They wish they had more training in themes related to hazards and risk posed to their neighbourhoods<br/>They admit that dwellers do not fully trust the actions of the State (Region and Peru's governance) in terms of risk mitigation</td><td>Mitigation actions on disaster risk on behalf of the community<br/>Self-management suggested to be applied to natural disasters<br/>Mistrust towards national governance</td></tr></tbody></table>
<table><tr><td rowspan="4">Corrective management</td><td rowspan="4">Disaster risk reduction</td><td>Civil servants in public offices in charge of DRM</td><td>The officials suggest a number of required actions:<br/>1) further studies on hazards that affect the district<br/>2) activities together with neighbours, families and school children on knowledge of hazards (earthquakes and heavy rainfall in the first place) and risk prevention<br/>3) Planning to prevent and mitigate disaster risk<br/>4) Planning of actions to be carried out during rainy season<br/>5) Report edifice construction in hazard-prone and non-authorized areas close to quebradas<br/>6) To identify vulnerability of housing and street network in the vicinity of quebradas</td><td>City growth<br/>Vulnerable house building in hazard-prone areas<br/>Prevention plans to reduce vulnerability</td></tr><tr><td>District Mayors</td><td>Meetings including leaders of neighbourhoods that aim to let them know regulations of the disaster risk management..<br/>Information is given to dwellers about areas not to be constructed due to high risk, but these hazard-prone areas are unlawfully occupied.<br/>Relocation of population has been studied and proposed, but this operation is not within their competence and very difficult due to the lack of economic resources.</td><td>Policies to reduce disaster risk<br/>‘Informal’ and non-authorized constructions</td></tr><tr><td>Heads of neighbourhoods</td><td>Dwellers communicate via smartphones on hazards related to the volcano<br/>Plans exist about construcción of temporary barricades along selected reaches to prevent or reduce flooding by intense rainfall.<br/>Students and pupils who receive information about hazards in high schools should share this information with their families.<br/>Mistrust is widespread with respect to plans and projects of reduction of risk on behalf of the State.</td><td>Mistrust towards the State<br/>Community auto-management</td></tr><tr><td>Civil servants in public offices</td><td>To identify and delineate safe areas in case of emergency.<br/>Functions of the state create insatisfaction with respect to the corrective actions on persistent risk in the district (e.g., contention walls and check dams).<br/>No action can be undertaken against unlawful occupation and construction in hazard-prone areas, due to the lack of laws regulating this issue.</td><td>Areas &amp; shelters designed for evacuation<br/>Habitat vulnerability</td></tr><tr><td>District Mayors</td><td>They carry out operations before the rainy season, including cleaning of the ravine channel, repair of the streets near the ravine and cleaning of sewers.<br/>Consider that existing hazard-zone maps should be updated.<br/>There is no budget to adapt or retrofit the infrastructure near the ravines due to the fact that the majority of constructions are non-authorized and national regulations to solve the issue are lacking.</td><td>Mitigation of risk<br/>Budget<br/>State policy for disaster risk management</td></tr></table>
<table><tr><td rowspan="3">Compensatory management</td><td rowspan="3">Institutional preparation and ability to cope with potential conflicts with the community</td><td>Heads of neighbourhoods</td><td>The community is being organized following a common sense to cope with natural disasters.<br/>Dwellers require that ravine channels full of debris and garbage be cleaned.<br/>Houses with high physical vulnerability are constructed on un-authorized land, as the new dwellers are low-income and with limited resources.<br/>Due to mistrust, collaborative work on emergency policy between the community and civil authorities is often lacking.</td><td>Common sense in front of hazard and risk<br/>Lack of social capital and community awareness<br/>Unlawfully occupied land</td></tr><tr><td>Civil servants in offices in charge of DRM</td><td>Existing planning of operations of emergencies in case of earthquakes and intense rainfall is not entirely undertaken.<br/>Co-ordinate with the Committee of Civil Protection of each District the required actions to help victims and support affected people from natural disasters.<br/>There is a lack of network between distinct public offices of the municipality and neighbourhood boards to provide an integrated response to emergency procedure.<br/>Promote training and workshops to disseminate prevention of disaster risk to the exposed people and city dwellers in general.<br/>Risk is increasing due to socio-economic vulnerability of the settlements in unlawfully occupied areas.</td><td>Integrated regulations towards disaster risk reduction<br/>Actions to cope with population affected by natural disasters<br/>Training and workshops</td></tr><tr><td>District Mayors</td><td>Budget allotted to the district municipalities often is insufficient to operate emergency relief and to promote retrofitting or reconstruction.<br/>They should elaborate on and distribute the budget for disaster risk management according to their priorities and the amount allocated by the Government.<br/>They are in command of the District Civil Protection committee, organising the necessary actions for relieving the population affected by natural disasters.</td><td>Participative budgets<br/>Difficulties in elaborating on and tapping budgets towards disaster risk management</td></tr></table>
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