The 5th ICEED, --UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Lamek N. et al P1-14

Spatial Distribution of Landslides Vulnerability for the Risk Management in District of Rwanda

Lamek Nahayo1, 2, 3, 4, 5*, Lanhai Li1, 2, 3 and Christophe Mupenzi5

*Corresponding Author. E-mail: [email protected] 1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, Xinjiang, 830011, China 2Ili Station for Watershed Ecosystem Research, Urumqi, 830011, Xinjiang, China 3Xinjiang Regional Center of Resources and Environmental Science Instrument, CAS, Urumqi, 830011, Xinjiang China 4University of Chinese Academy of Sciences, Beijing 100049, China 5University of Lay Adventists of Kigali (UNILAK), P. O. Box 6392, Kigali-Rwanda

Abstract: Understanding people’s hazard vulnerability magnitude enhances its risk awareness and preparedness as well. The objective of this study was to spatially differentiate landslides vulnerability toward relevant management of risk within , western Rwanda. The authors employed ten vulnerability triggering factors divided into social factors: population density, literacy rate, possession rate of mud houses and communication tools like mobile phone, radio or television, employment and rate of using unimproved drinking water. Whereas the ecological factors were rainfall, elevation, land use/cover and slope. The normalized weighting method in ArcGIS was employed to estimate and map landslides vulnerability and risk in the study area. The results indicated that the sectors largely inhabited with high elevation, slope, rainfall and limited communication tools along with low literacy rate record high landslides vulnerability and risk. This landslides vulnerability and risk mapping can help the vulnerable populations to understand the extent of their risk exposure. While policy makers can find out the way of formulating appropriate vulnerability lessening and risk reduction measures.

Key words: Landslides; Ngororero district; Risk; Rwanda; GIS; Vulnerability.

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1. Introduction turn, saves its life, protects long term development activities and strengthens the The vulnerability to natural hazards is resilience over time. In Rwanda, the increasingly becoming exacerbated by the temperature recorded increasing trend within rapid human population growth and its daily the last fifty years ago, and markedly led to life choices (Frodella et al. 2018; Narsimlu et incremental risk namely flood, mudslides, al. 2013).Although poverty is consistently landslides and drought (Muhire and Ahmed considered as the key community vulnerability 2015; Haggag et al. 2016).These hazards driver, previous scholars (Asad 2015; caused immense losses, among which there are Khunwishit et al. 2018; Kienberger 2012; more than one million people affected, 4,573 Awal 2015) suggested to consider other lost livestock, sixty thousand hectares of society’s socio-economic, political, cultural, cropland and fifty thousand houses damaged. environmental and physical factors including While the areas severely affected are poor and not limited to the age, education, information largely inhabited (Nahayo et al. 2017; sharing systems, environmental and natural Nsengiyumva et al. 2018; MIDIMAR 2017). resources management, etc. These together, Moreover, the incidence of this gradual strengthen or weaken the community’s ability vulnerability in Rwanda, is exacerbated by its to resist, cope and recover from the hazard, in rapidly growing population and predominantly case of occurrence. The vulnerability varies young. It grew from 2.525 million in 1955 up from individual, family to community level to 7.235 and 11.917 million in 1990 and 2016, over time, and as previously reported (Askman respectively (Muhoza et al. 2016; Petroze et al. et al. 2018; Jackson et al. 2017; Nirupama 2015). This consequently leads to increasing 2012) vulnerable groups are not only at risk vulnerability by the fact that people likely because they are exposed to a hazard, but as a inhabit risk prone areas through better result of marginality of the above mentioned livelihoods searching. patterns within the society. Accordingly, it is becoming hard, specifically for the poor and Moreover, high elevation of the north and densely populated areas to cope with the rising western parts receiving frequent high rainfall risks due to lack of appropriate mitigation and facilitate easy runoff then exposes the regions adaptation capabilities (Aitsi-Selmi et al. to severe mudslides, landslide and floods 2016; Gero et al. 2011; Amri et al. 2017). losses. Whereas the south and eastern parts are under limited rainfall causing crop failure, This expresses the role of early warning and famine and droughts (Nsengiyumva et al. financial capabilities to enhance the vulnerable 2018; Haggag et al. 2016). The Ngororero risk awareness and preparedness, which in district, one of seven districts of the western 2

The 5th ICEED, KIGALI-RWANDA-UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Lamek N. et al P1-14 province of Rwanda is reported (Dawson and vulnerability and suggest relevant measures Martin 2015; Nahayo et al. 2017) to be for the resilience strengthening and risk shocked by both flood and landslides, mainly reduction in Ngororero district of the western due to its location with high elevation and Rwanda. rainfall and the reason that, the district is 2. Methods and Materials among the densely populated districts of the western Rwanda. However, on the best of the 2.1 Study area authors’ knowledge, there is no current vulnerability assessment carried within the Ngororero district is 54 percent rural with a district to reveal the factors with high priority total surface of about 678 Km2 and a total to be considered for the vulnerability lessening population of 282,249. The Ngororero district and risk reduction. Only the field reports (Fig.1.a) is composed by thirteen sectors: (Gakwerere et al. 2013; Bizimana and Sönmez Bwira, Gatumba, Hindiro, Kabaya, Kageyo, 2015) were prepared after disaster occurred. Kavumu, Matyazo, Muhanda, Muhororo, This expresses the need of carrying out a Ndaro, Ngororero, Nyange and Sovu. The systematic vulnerability analysis to strengthen district (Fig.1.b) is bordered by the Nyabihu the vulnerable risk awareness, preparedness district in north, to the south, and resilience as well. Hence, the objective of district to the east and Rutsiro this study is to spatially distribute landslides district to the west (Gakwerere et al. 2013).

Fig.1. Landslides inventory map in Ngororero district (a), its location and neighboring districts in Rwanda (b).

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2.2 Datasets 2.2.2 Ecological factors

2.2.1Landslides inventory map The Digital Elevation Model (DEM) related data and Landsat 8 images downloaded from Previous scholars (de Loyola Hummell et al. the United States Geological Survey (USGS 2016; Liangfeng et al. 2002) have suggested 2018) were used to estimate the aspects of that for any disaster risk and vulnerability elevation and slope (Fig.2 (a,b)), and land use assessment, there should be the factual and land cover (Fig.2 (d)) classes. These collection of past events to help in analyzing Landsat 8 images were radiometrically the likely future trends. Therefore, landslides corrected, the cloud shadows were masked and inventory map (Fig.1 (a)) was produced for the gap-filling algorithm was used to get a this study by using previous historical cloud-free image. After the images were landslides records provided by the Rwandan classified using the supervised maximum Ministry of Disaster Management and classification method. Then the land use/cover Refugees (MIDIMAR 2017). The above was classified into six classes: forestland, landslides inventory map (Fig.1 (a)) identified grassland, cropland, built-up land, wetland and 22 landslides, and used the affected people water bodies based on the East African (killed, injured and homeless), cropland and classification of the Regional Center for houses damaged and lost livestock, and the Mapping and Resources Development occurrence frequency between 2010 and 2017 (RCMRD 2017). within Ngororero district.

Fig.2. Study area’s slope (a), elevation (b), rainfall (c) and land use and land cover (d). 4

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Moreover, this study employed the rainfall due density, possession rate of mud houses and to the fact that, the change on rainfall communication tools (mobile phone, radio or frequency and intensity is among the television) employment and literacy rate, and landslides driving forces especially in highly percentage of people consuming unimproved elevated areas (Frodella et al. 2018; Moayedi drinking water. These datasets were provided et al. 2011). This is similar with the study area by the National Institute of Statistics of with high altitude and slope (Fig.2 (a, b)) as Rwanda (NISR 2017). These social datasets well as its land cover type with high were used due to reason that, as previously proportion of cropland (Fig.2 (d)), the runoff reported (Birhanu et al. 2016; Khazai et al. drivers in case of rainfall. Thus, authors 2014; Ratemo and Bamutaze 2017), they interpolated the mean monthly rainfall data likely characterize the community’s socio- ranging between 1990 and 2017 from the economic function, and can strengthen or available meteorological stations. The rainfall weaken the community’s ability to resist a data were provided by the Rwanda hazard. For example, lack of communication Meteorology Agency (RMA 2018). channels (access to mobile phones, radio and television) limits access to information. 2.2.3 Social datasets Similarly, literacy rate indicates the extent to which the community can read/transmit a Moreover, as illustrated in Fig.3, the authors message to others in case of warning. employed the social datasets: population

Fig.3. Spatial social factors distribution in Ngororero district namely the (a) population density, (b) communication assets representing the possession rate of mobile phone, television and radio, (c) employment rate as the percentage of adult people employed, (d) literacy rate as the percentage of people who easily can read and write, (e) mud houses, the percentage of population owning mud houses and (f) the percentage of people consuming unimproved drinking water. 5

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2.3 Vulnerability analysis by using the normalized weighted vulnerability as follows: This study estimated landslides vulnerability 10 caused by the considered factors as shown in %퐶푖 퐶푖 푇퐿푉 = × (1) section 2.2. Nevertheless, authors recognized %퐶푖 푝 푖=1 the fact that, each vulnerability factor Where TLV is the total landslides considered likely contributes individually to vulnerability, Ci is the category attribute; %Ci landslides vulnerability. Thus, these factors is the category attribute percentage and P is were considered separately into: (1) social the total population of the Ngororero district. vulnerability resulting on the population The above equation 1 was used to generate density, communication assets (possession rate social, ecological and total landslides of mobile phone, television or radio), vulnerability. The authors adopted the above employment rate as the percentage of adult GIS-based normalized weighting method, as people employed, literacy rate indicating the suggested by recent scholars (Smith et al. percentage of residents in Ngororero district 2016; Wang et al. 2018; Hassaan et al. 2017; who easily can read and write, mud houses as Armenakis et al. 2017) due to its application the percentage of population owning mud and good results in terms of spatial distribution houses and the percentage of people with of disaster risk vulnerability within an area of unimproved drinking water. While the (2) interest. The obtained landslides vulnerability ecological vulnerability was determined by was divided into five classes: very low, low, using the study area’s elevation, slope, land moderate, high and very high vulnerability use and land cover and rainfall. In order to with values ranging between 1 and 5 (Table 1) standardize these vulnerability causal factors, by using the natural break method. the percentage of each factor was calculated

Table 1. Vulnerability evaluation scale Scale Definition 1 The factors contribute equally to landslides vulnerability (equal relative importance) 2 The factors contribute more slightly to landslides vulnerability compared to other factors (low relative importance) 3 The factors contribute moderately to vulnerability compared to other factors (moderate relative importance) 4 The factors contribute highly to landslides vulnerability compared to other factors (high relative importance) 5 The factors contribute very highly to landslides vulnerability compared to other factors (very high relative importance) 2.4 Risk estimation operation between the hazard and vulnerability. This spatial intersection was The spatial risk index for each spatial unit has performed with use of the raster multiplication been estimated by using the spatial layer operation. The index value for each grid raster 6

The 5th ICEED, KIGALI-RWANDA-UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Lamek N. et al P1-14 cell was estimated and the ranked map based highly populated sectors. While the sectors on the index value has been generated. Thus, recording high ecological landslides the landslides risk map was produced as the vulnerability (Fig.4 (b)) were those with high intersection of the landslides inventory map elevation, slope, land use and land cover and and vulnerability spatial layers, as shown in rainfall. However, sectors with low possession the following equation 2. of communication assets (telephone, radio and television), high percentage of mud houses and 퐿푅 = LI ∩ TLV (2) low literacy rate revealed, at high extent, both Where LR is the landslides risk, LH is the social and ecological landslides vulnerability landslides inventory and TLV is the total (Fig.4 (a, b)). This lack of appropriate landslides vulnerability. communication tools, which could help in risk information sharing, expresses the likely 3. Results increasing vulnerability, unless appropriate 3.1 Social and ecological vulnerability risk awareness enhancing mechanisms among The results, as illustrated in Fig.4 (a), showed the community are regarded. high social landslides vulnerability within the

Fig.4. Spatial social (a) and ecological (b) landslides vulnerability indicated variation on the landslides vulnerability. The sectors with high vulnerability caused by the socio-economic (population density, literacy rate, communication system, employment rate, mud houses) are not the same as sectors vulnerable to landslides as a result of elevation, slope, LULC and elevation.

3.2 Total landslides vulnerability 7

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Fig.5.Total spatial landslides vulnerability in Ngororero district shows large extent of landslides vulnerability in the areas of the district with high elevation, slope and rainfall.

3.3 Landslides Risk

Fig.6.Spatial estimated distribution of landslides risk in Ngororero district, shows large extent of landslides risk within sectors with high slope, elevation, rainfall and land use/cover compared to their counterparts with low record. The sectors highly vulnerable to landslides population density, elevation and rainfall. To risk in Ngororero district register high minimize the risk exposure, this population

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The 5th ICEED, KIGALI-RWANDA-UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Lamek N. et al P1-14 needs to be approached to enhance its risk shown in Table 2, the total landslides awareness, and indicate to them how to wisely vulnerability is not similar within all thirteen use and manage the available land in order to (13) sectors of Ngororero district; each sector reduce the runoff likelihood. This requires, for experiences its level of vulnerability. The example, promoting the use of bench terraces Kabaya Kageyo and Muhororo sectors are and rainfall harvest to minimize the rainfall- highly vulnerable while the Sovu sector is the triggered runoff in high elevated sectors in least vulnerable to landslides with 4.8 percent. order to reduce the resulting mudslides and This vulnerability variation expresses the call landslides causing severe losses among the for approaching each sector based on its residents of Ngororero district. Moreover, as vulnerability extent and the causal factors.

Table 1 Estimated landslides vulnerability and risk per sector (in %)

Sector Vulnerability Risk Kabaya 11.4 11 Kageyo 10 8 Muhororo 9 11 Bwira 8 8 Hindiro 8 9 Matyazo 8 6.7 Muhanda 7.4 9.3 Ndaro 7.3 8 Ngororero 7.2 6 Kavumu 6.9 9 Gatumba 6 7 Nyange 6 3 Sovu 4.8 4 Total 100 100 The above Table 1 shows high landslides vulnerability within Kabaya (11.4%), Kageyo (10%) and Muhororo (9%) while the Sovu sector (4.8%) has the lowest landslides vulnerability record. The landslides risk is high in Muhororo and Kabaya sectors with 11 percent then Nyange sector records low landslides risk at 3 percent.

4. Discussion (Eiser et al. 2012; Birkmann et al. 2013). This therefore, expresses that considering each The disaster losses do not only result on the area’s wide range of factors could help in magnitude and duration of the event, but also better detecting its vulnerability extent. In from the community’s inability for the self- addition, lack of information sharing tools protection and recovery form the hazard. such as radio, television, newspapers, and Vulnerability has been used in several fields local meetings limit the access to the risk including not limited to disaster risk, climate information among the vulnerable, and change, sustainable development and many minimize the awareness and preparedness. more, However, its spatial variation makes it This as consequence makes the community difficult to be well determined due to the unaware of the way to use and manage the reason that, one factor may contribute to available resources and the kind of behaviour vulnerability in one area but not to the other to adopt to reduce its exposure. 9

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Based on the above, it is worthy note that, the the education, mainstream disaster risk population in Ngororero district is exposed to reduction in science, development and landslides risk due to lack of risk awareness environmental policies. While the mainly exacerbated by low level of possession identification of underlying drivers through the of information sharing tools (Fig.3 (b)). In this integrated and multidimensional approach area, residents possessing communication would help to figure out key drivers, how tools (radio, television and mobile phones) are vulnerability is generated, how it increases and not higher than 32 percent within Ngororero how it builds up in this area. district. This limited risk information sharing 4. Conclusion is noticed amongst the vulnerability triggering factors, where the sectors with high landslides This study applied the Geographic Information vulnerability in Ngororero districts (Fig.4 (a)) Systems (GIS) techniques and spatially are among those with limited communication estimated the landslides vulnerability resulting tools. For this, the community-based disaster on the employed ecological and socio- risk reduction is suggested (Yin et al. 2011; economic triggering parameters within thirteen Nahayo et al. 2017; Gero et al. 2011) to sectors of Ngororero district of Rwanda. The enhance the capacity of vulnerable groups to results indicate high vulnerability within analyse and understand the vulnerability Kabaya, Kageyo and Muhororo sectors highly causal factors and ways of lessening the populated with high elevation, rainfall and vulnerability. slope, low communication tools and literacy rate. Also, it is noted that, more than 50 This can be applied in Ngororero district percent of the land is under cropland, which (Fig.4 and 5) to strengthen vulnerable reveals that the land is not well covered and community landslides risk awareness and expresses easy runoff facilitated by the study preparedness, and help policy makers to build area’s high altitude, slope and rainfall, which a people-centred and equitable development in turn generate mudslides and landslides. In and a resilient society. Moreover, it is good to addition, the vulnerability to landslides risk is note that, some sectors record high rainfall likely to increase in Ngororero district as long compared to others (Fig.2 (c)) with the as the information sharing systems are not resulting vulnerability as illustrated in Fig.4 empowered, while those operating are not (b). Hence, under climate change generating equally reaching the whole community for rainfall patterns and its resulting risk, the early risk awareness, preparedness and exposure to severe losses will likely be resilience. The analysis suggests to (1) registered in Ngororero district. Therefore, it is approach the local community to strengthen its good to ensure that the community is timely risk awareness and preparedness through provided with climate related alert, prioritize education, local trainings and meetings, (2) 10

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