Contribution of GIS to two-dimensional numerical modeling of mudflow hazards originating from the Mikeno volcano in the Virunga volcanic province, Democratic Republic of Congo

Contribution of GIS to two-dimensional numerical modeling of mudflow hazards originating from the Mikeno volcano in the Virunga volcanic province, Democratic Republic of Congo | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contribution of GIS to two-dimensional numerical modeling of mudflow hazards originating from the Mikeno volcano in the Virunga volcanic province, Democratic Republic of Congo MUMBERE Yves Mutima, CIZA Delphin ASSANI, MUNGUIKO Olivier Munyamahoro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8566722/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mikeno is a volcano in the Virunga volcanic province located in eastern Democratic Republic of Congo (DRC). This volcano is dormant, but is prone to mudflows that originate at its summit and flow down its slopes, including inhabited areas. These mudflows have caused disasters in the inhabited areas around Mikeno. In order to simulate possible future mudflows, this study uses laharz.py modeling with SRTM as the main input data. A simulation of three volumes of mobilizable material (10 4 , 10 5 , and 10 6 m 3 ) reveals three possible mudflow scenarios. Sludge can originate from eight points and flow through drains. From these drains, the mudflows can spread and even reach inhabited areas. The results are also presented in terms of the length and width of the mud deposits, which are low, moderate, and high for the three volumes, respectively. The three scenarios are combined to produce a final map of the risks of mud escape. There are three major areas with different levels of vulnerability: the highly vulnerable area, with an average length and width of 2.4 km and 28.8 m respectively; the moderately vulnerable area, which is circumscribed around the first area, extends 80.6 m beyond the first area in terms of width and has an average length of 6.4 km; the third area, with low vulnerability, has an average length of 8.7 km and is circumscribed beyond the first two areas over 77.6 m. Mikeno mud GIS SRTM Virunga modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Within the western branch of the East African Rift System, more specifically north of Lake Kivu, lies a volcanic province known as Virunga (Smets, 2013). It comprises eight volcanoes (two active and six dormant) and several dozen associated cones. These volcanoes have emitted lava flows in several episodes dating from 9 to 3 million years ago, placing the Virunga province in the Pliocene-Pleistocene period of the Cenozoic era. The lava is ultra-alkaline, potassic, and highly undersaturated. Most of this lava was discharged along fracture systems transverse to the main axis of the rift system (Kampunzu et al., 1998 ). The volcanic activity of the Virunga has also been explosively dynamic, as evidenced by dozens of volcanic cones distributed throughout the volcanic province (Denaeyer, 1954 ; Denaeyer, 1963 ; Poppe et al., 2016 ). These cones are made of pyroclastic rocks. Several towns have been built in the vicinity of these volcanoes. These volcanoes (both active and dormant) pose numerous disaster risks due to the various hazards they present, but also because of their high exposure to lava flows, mudflows, and pyroclastic flows towards inhabited areas. Mountainous areas are often prone to hazards such as mudslides (Laigle et al., 2003 ). These are among the world's most deadly and damaging disasters (CEOS, 2003). With rapid population growth in the vicinity of volcanoes, and tourist activities in the case of the Virunga volcanoes, populations and infrastructure are under severe pressure due to their location in risk-prone areas. The Mikeno volcano, which is the subject of this study, is one of the volcanoes in the Virunga province. It is dormant: its last eruption was 200,000 years ago (Guibert et al., 1975). This volcano is heavily dissected by erosion (Guibert et al., 1975) and its highest point is 4,440 m above sea level. In the inhabited areas around this volcano, mudslides have caused disasters in the recent past, but there is no scientific documentation other than reports from humanitarian services. These disasters occurred in 1951, 1952, 1971, 2010, and 2021 (OCHA, 2010 ). The disasters resulted from mudflows caused by volcanic material destabilized and liquefied by glaciers and rainwater in inhabited areas, fields, and infrastructure. The human and material toll was recorded in villages engulfed by the monstrous mudflows (OCHA, 2010 ). This entity at risk consists of villages straddling the territories of Nyiragongo and Rutshuru in North Kivu province, DRC with an estimated population of approximately 50,000 (IOM, 2022). In the future, managing similar disasters will therefore be a major concern. It is necessary to focus on predicting flow paths. In light of this, this study proposes to reduce the risks of these flows by providing answers to the central question: What are the possible scenarios for a potential mudflow in this area? (roads, distances traveled, volume). The objective is to contribute to the simulation of possible future mudflows in this area with a view to reducing them. This could lead to better management and a faster response in the event of a disaster. The results are: a map simulating these future mudslides, charting the points of origin of the mudslides; areas that are highly susceptible, moderately susceptible, and less susceptible. This study provides policy makers and civil protection services with tools for better management of these types of risks/crises in the future. 2. Geological context The Mikeno volcano (29.421°, -1.461°, 4,440 m: coordinates of the highest point) is one of eight volcanoes in the Virunga volcanic province. This province is geologically located in the western branch of the East African Rift. It extends over more than 50 km in an E-W direction north of Lake Kivu, transverse to the local orientation of the rift (Smets, 2013). This province (Fig. 1 ) comprises eight major volcanoes ranging in height from 2,474 m to 4,507 m. They are subdivided into three groups, namely: the eastern group comprising the Muhavura (4,127 m), Gahinga (3,474 m), and Sabinyo (3,647 m) volcanoes; the central group, which includes the Visoke (3,911 m), Karisimbi (4,506 m), and Mikeno (4,437 m) volcanoes; and finally, the western group, comprising the Nyiragongo (3,470 m) and Nyamulagira (3,056 m) volcanoes. (Hamaguchi and Zana, 1990 ) The Mikeno volcano rises to 4,440 m. It has very steep, eroded slopes that descend to 2,000 m and 3,000 m to the west and east, respectively. Its major volcanic activity is suggested to have occurred between 2.5 and 0.8 Ma based on K/Ar geochronology on 10 rock samples collected by (Guibert et al., 1975) on this edifice. Terminal activity is suggested to have occurred between 0.5 and 0.2 Ma. The fields of the Mikeno and neighboring Visoke volcanoes include tephrites, basanites, phonotephrites, and foidites (Barette et al., 2017 ). The dormant summit of the Mikeno volcano is permanently covered with ice, which melts to form glaciers responsible for the erosion observed at Mikeno (Guibert et al., 1975). 3. Methodology Numerous studies have attempted to model potential mudflow routes and areas prone to flooding using GIS and remote sensing (Ahmed et al., 2011, Mathieu, 2010 ; Kumar et al. 2000 ; Patton 1988 , Macka 2001 ; Ozdemir and Bird 2009 ; Band 1986 ; Gurnell and Montgomery 1999 ). This study adds to this list by using GIS to propose scenarios for mudflows from the summit of Mikeno volcano to its flanks and downstream areas. It combines spatial and non-spatial data from multiple sources (Table 1 ). Table 1 Spatial and non-spatial data used in this study Data type Description Quality Date of of production Source Use in the study Historical Information on previous mudslides in the study area, photographs of the events of 2010 and 2021 Non-spatial 2010, 2022 Reports from NGOs and public services involved in risk and disaster management Status of past mudslides, frequency, and routes Shapefiles Administrative boundaries of the Nyiragongo territory and surrounding areas, border between the DRC and Rwanda, localities in the study area, public infrastructure spatial 2016–2021 https://extract.bbbike.org/ Geographical delimitation of areas likely to be affected in the event of a mudslide Topographic raster SRTM (Shuttle Radar Topography Mission) of the area around the Mikeno volcano spatial 2000 www.usgs.gov Main input data in the model for generating scenarios First, the source of the transportable materials, such as mud, had to be determined. This source is the highest point of Mikeno Mountain. From there, the materials are mobilized through drains, which must first be mapped. Depending on the volume of materials, these drains can overflow and spread the mud to surrounding areas. The drainage areas are valleys/thallwegs or even watercourses. Drainage networks can be identified using traditional methods such as field observations and topographic maps, or using advanced methods involving remote sensing and GIS (Macka 2001 ; Maidment 2002 ). Numerous studies have highlighted the difficulty of mapping all drains from field observations due to their extent over rugged terrain covering large areas. In this regard, topographic raster data (DEM) can be used to extract drainage networks capable of transporting flows (Ozdemir and Bird 2009 ). The extraction of drainage networks from DEMs is based on gravity: water flows from high to low elevations along the steepest slope. For the purposes of this study, evapotranspiration and infiltration were neglected in the modeling. Only the presence of interceptions was considered: these consist mainly of relief features. To generate mudflow scenarios, SRTM 30 m data from 2000 year was used. QGIS 3.16 software was used to perform the initial pre-processing of the DEM file, in particular to fill in the file. The r.fillnulls tool was used for this purpose. Filling pits is one of the most complex steps in the drainage extraction process. The model used is laharz.py (Iverson et al. 1998 , Schilling 1998 ). This is a semi-empirical model for predicting lahars, but it was used in this study because of the similarities between the hazards studied. The filled DEM file was then used as the main input data for the model. The model takes into account the topography from the volcano (where the flow is produced) to the flow route areas, including the volcano and drains in the study area. The input data also includes the height of the cone (for conical volcanoes), its basal length to the normal, and the volume of potentially mobilizable materials. This volume is difficult to determine. For this study, we entered volume ranges to identify potential areas susceptible to mudflow deposits. The laharz.py model has two main creation functions: (1) the creation of initiation points, and (2) the creation of flows. Through these two main functions, other files are generated. The first function will generate the drainage network, the cone energy line, and the points where the mud flow begins after crossing the cone energy line. These drains are shown in Fig. 2 . For the rest of the process, the following information relating to the study area must be prepared (Table 2 ). Table 2 Input data for the laharz.py model Input files in the model Model Result SRTM LaharZ Probabilistic map of mudflows H/L ratio of the volcanic edifice Shapefiles of drains and thalweg Shapefiles of the initiation points of the flow Volume of available materials The final step in the model is the generation of mudflow escape areas through the previously created drains. Ranges of mobilizable material volumes were proposed to present possible scenarios. For this study, there are three volumes (in m 3 ): 10 4 , 10 5 , and 10 6 . All the elements (data) extracted from the model were superimposed in a GIS on other spatial data from the study area to refine the preparation of the maps. 4. Results The hazard covered by this study is the “mudflow” originating from the dormant Mikeno volcano. It is located in an area of high geological instability (steep slopes and unstable volcanic terrain) and climatic instability. Meteorological data indicate high rainfall with abundant precipitation, an annual average of 108.75 mm and a total annual rainfall of 1305 mm. (Fig. 3 ). This, combined with snowmelt, is the basis for the high erosivity of the volcanic edifice and the production of unstable materials that descend from the heights to lower altitudes (Guibert et al., 1975). Beyond that, villages are built around the volcanic edifice, despite armed conflicts that cause the displacement of some populations (Kabumba, 2020 ). Unstable, fluidized volcanic materials are likely to be mobilized and then carried away by a mudflow from upstream. The flow can increase its total volume by carrying along materials it encounters along its path. For Mikeno, the combination of steep slopes, intense rainfall, and snowmelt promotes the occurrence of hazards such as landslides and mudflows. This, combined with the densely populated settlements built on the slopes of the volcano, is the cause of the high risks. The main villages built on the slopes of this volcano are Kibiriga, Kibumba, Malyaso, Rugari, Kisigari, Moboga, and Kanombe. For three ranges of volumes of materials that could be mobilized, three possible sludge flow scenarios are constructed. These volumes are 10 4 , 10 5 , and 10 6 m³, respectively, corresponding to scenarios (Fig. 4 a, Fig. 4 b, and Fig. 4 c). For scenario 1, with a volume of 10 4 m³ of mobilizable material, the results are presented in terms of the length and width of the sludge deposit, which are small, but the area covered by the sludge is at high risk. These results also indicate that the materials likely to be mobilized will not spread too far. For the second and third scenarios, the volume of mobilizable materials is significant. These scenarios are accompanied by a large extension of the flow in both length and width. The deposits are likely to reach inhabited areas as well as public infrastructure along the Mikeno River. (a) For a volume of 10 4 m³, (b) For a volume of 10 5 m³, (c) For a volume of 10 6 m³ The three scenarios can be combined to propose a classification of risk areas from the volcanic edifice to downstream of the drains. First, the EC cone is a 3D space generated in accordance with the H/L ratio of 0.45. The area covered by this cone is a proximal risk zone or an area with a higher risk of mudflow due to the steep slopes near the summit. By making this proximal risk zone transparent, it is possible to see deep thalwegs in this area, caused by previous erosion and mudflows (Fig. 5 ). This proximal risk zone corresponds to the volcanic edifice. The mudflow begins beyond this cone through the drains. These points are listed in Table 2 . From these points, the mudflow begins to advance towards the slopes of the Mikeno volcano. From there, three areas are categorized according to the degree of risk. The final map of mudflow risks is obtained by combining the three suggested scenarios. Based on the topography around the volcanic edifice, it provides a spatial simulation of potential mudflows that could start at Mikeno and spread to the flanks. It is part of a prevention and land-use planning approach to assess risk levels. These results are not combined with vulnerability data (building fragility, etc.) to enable a quantitative risk assessment. The simulation map shows three major areas with different levels of mudflow risk: (1) High-risk area: shown in dark red. This area is less thick and less long. The average length and width are 2.4 km and 28.8 m, respectively. This area affects several inhabited areas south of the Mikeno volcano, particularly in the villages of Kibumba and Kibiriga. It is mainly located on the southern and southwestern slopes of the volcano. (2) Moderate risk area: symbolized by the color orange. It directly surrounds the high-risk area, extending 80.6 m beyond the first zone. The moderate risk area has an average length of 6.4 km and reaches several homes and public infrastructure. (3) The low-risk area, symbolized by the color yellow. It surrounds the two previous areas and is 8.7 km long on average. It is therefore the largest of the risk areas. The last two areas (orange and yellow) represent moderate and low risks, depending on the morphology of the drainage valleys. The Table 3 summarizes the length and width parameters for these areas. Table 3 Length and width parameters for areas potentially covered by sludge, based on initiation points (IP) Zone length parameter (km) Zone width parameter (m) High Moderatly Less High Moderatly Less IP1 2,1 6,5 9,1 33 62 64 IP2 2,3 7,05 9,6 26 123 147 IP3 2,2 6,57 7,9 29 121 60 IP4 2,48 5,93 7,22 30,7 89,3 89,3 IP5 2,43 6,06 10,6 29,7 59,6 57,5 IP6 1,52 4,3 6,36 27,8 61,54 61,5 IP7 2,83 6,21 8,82 29,43 61,37 58,6 IP8 3,64 8,65 9,86 24,9 67,4 83,5 5. Interpretations and discussions In terms of impact on inhabited areas, the villages of Kibumba, Malyaso, and Rugari (white circles) are located directly in the potential paths of the flows. Moboga, Buhogoma, and Kisigari are also on these paths, but at a greater distance from critical areas, implying a moderate to low risk. They may be indirectly affected by road closures due to mud deposits. Road and public infrastructure cross several risk areas, indicating a high vulnerability of communication networks. The localities affected in this simulation are the same as those affected by the mudflows of 2010 and 2021, as reported by non-governmental risk management organizations (OCHA, 2010 ). The lahar.py model does not relate the intensity of rainfall in the source edifice to the area that can be flooded during the mudflow. It can be seen that valleys are clearly visible on the volcanic edifice, corresponding to the drains extracted using laharz.py (Fig. 2 ). It is likely that these are the same routes prepared by previous flows. It is well known that during events such as mudslides, unexpected increases in water levels in drains and high flow rates can result in the transport of large quantities of debris, rocks, and even non-rock materials such as uprooted trees and other materials (Douvinet et al., 2013 ). It is therefore highly likely that this will be the case for any future mudslides in the Mikeno area. It is suggested that the flows could also carry other non-rock materials along their path. This was the case for the event in May 2010 (OCHA, 2010 ). In this previous event, the flow split into three directions ranging from 50 to 150 m in width. This width range is not included in the ranges modeled by this study. GIS- and remote sensing-based approaches and methods for modeling mudflows quickly provide very satisfactory results (Kumar et al., 2000 ; Geertsema et al., 2006 , Begueria et al., 2009 , Iverson et al. 1998 , Schilling 1998 , Ahmed et al., 2011). Several models are used, all of which have DTM as their input data. These results could be combined in future studies with vulnerability data (building fragility, etc.) to enable a quantitative risk assessment. Declarations Funding Declaration Funding for this research came exclusively from the authors’s own resources. Author Contribution MUMBERE Yves Mutima designed the research project, established the methodology, and prepared the manuscript, tables, and figures. CIZA Delphin ASSANI and MUNGUIKO Olivier Munyamahoro proofread the manuscript. References Ahmed M, Biswajeet P (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. 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(a) Virunga Volcanic Province (Smets, 2013), (b) East African Rift System (Chorowicz, 2005)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/de0a2753763ba97f8453a0df.png"},{"id":102404060,"identity":"e36c7d99-cc0c-471d-adb7-ef0e72720174","added_by":"auto","created_at":"2026-02-11 10:58:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1223498,"visible":true,"origin":"","legend":"\u003cp\u003eMap of drains in the study area\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/b9919821e41486b840b2b490.png"},{"id":102320257,"identity":"e2ea6e72-aad2-4eda-9968-3b0cef0665c0","added_by":"auto","created_at":"2026-02-10 13:32:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28842,"visible":true,"origin":"","legend":"\u003cp\u003eOmbrothermic diagram of the study area (2021 data, Source: https://aquastat.fao.org/climate-information-tool/)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/24437d8b62e510cf7ebe8c52.png"},{"id":102320259,"identity":"269c9ddd-f4bd-4211-ab98-24e6a68bd1f4","added_by":"auto","created_at":"2026-02-10 13:32:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1480476,"visible":true,"origin":"","legend":"\u003cp\u003eMudflow scenarios around Mikeno Volcano\u003c/p\u003e\n\u003cp\u003e(a) For a volume of 10\u003csup\u003e4\u003c/sup\u003e m³, (b) For a volume of 10\u003csup\u003e5\u003c/sup\u003e m³, (c) For a volume of 10\u003csup\u003e6\u003c/sup\u003e m³\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/61ff25eb20c2522e6994359c.png"},{"id":102320261,"identity":"1dca3f5a-9fbe-4b91-b5c2-a2307f58f360","added_by":"auto","created_at":"2026-02-10 13:32:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1457805,"visible":true,"origin":"","legend":"\u003cp\u003eProbabilistic mudflow escape map around Mikeno volcano\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/f9e582a00ec4f7b3c4478545.png"},{"id":106401257,"identity":"f96a13d2-6f8f-497e-b07c-e4591a0b68c4","added_by":"auto","created_at":"2026-04-08 08:45:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6879496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8566722/v1/af973693-9b24-4a8e-a73f-397b20f28433.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contribution of GIS to two-dimensional numerical modeling of mudflow hazards originating from the Mikeno volcano in the Virunga volcanic province, Democratic Republic of Congo","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWithin the western branch of the East African Rift System, more specifically north of Lake Kivu, lies a volcanic province known as Virunga (Smets, 2013). It comprises eight volcanoes (two active and six dormant) and several dozen associated cones. These volcanoes have emitted lava flows in several episodes dating from 9 to 3\u0026nbsp;million years ago, placing the Virunga province in the Pliocene-Pleistocene period of the Cenozoic era. The lava is ultra-alkaline, potassic, and highly undersaturated. Most of this lava was discharged along fracture systems transverse to the main axis of the rift system (Kampunzu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The volcanic activity of the Virunga has also been explosively dynamic, as evidenced by dozens of volcanic cones distributed throughout the volcanic province (Denaeyer, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1954\u003c/span\u003e; Denaeyer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Poppe et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These cones are made of pyroclastic rocks. Several towns have been built in the vicinity of these volcanoes. These volcanoes (both active and dormant) pose numerous disaster risks due to the various hazards they present, but also because of their high exposure to lava flows, mudflows, and pyroclastic flows towards inhabited areas. Mountainous areas are often prone to hazards such as mudslides (Laigle et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These are among the world's most deadly and damaging disasters (CEOS, 2003). With rapid population growth in the vicinity of volcanoes, and tourist activities in the case of the Virunga volcanoes, populations and infrastructure are under severe pressure due to their location in risk-prone areas. The Mikeno volcano, which is the subject of this study, is one of the volcanoes in the Virunga province. It is dormant: its last eruption was 200,000 years ago (Guibert et al., 1975). This volcano is heavily dissected by erosion (Guibert et al., 1975) and its highest point is 4,440 m above sea level. In the inhabited areas around this volcano, mudslides have caused disasters in the recent past, but there is no scientific documentation other than reports from humanitarian services. These disasters occurred in 1951, 1952, 1971, 2010, and 2021 (OCHA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The disasters resulted from mudflows caused by volcanic material destabilized and liquefied by glaciers and rainwater in inhabited areas, fields, and infrastructure. The human and material toll was recorded in villages engulfed by the monstrous mudflows (OCHA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This entity at risk consists of villages straddling the territories of Nyiragongo and Rutshuru in North Kivu province, DRC with an estimated population of approximately 50,000 (IOM, 2022). In the future, managing similar disasters will therefore be a major concern. It is necessary to focus on predicting flow paths. In light of this, this study proposes to reduce the risks of these flows by providing answers to the central question: What are the possible scenarios for a potential mudflow in this area? (roads, distances traveled, volume). The objective is to contribute to the simulation of possible future mudflows in this area with a view to reducing them. This could lead to better management and a faster response in the event of a disaster. The results are: a map simulating these future mudslides, charting the points of origin of the mudslides; areas that are highly susceptible, moderately susceptible, and less susceptible. This study provides policy makers and civil protection services with tools for better management of these types of risks/crises in the future.\u003c/p\u003e"},{"header":"2. Geological context","content":"\u003cp\u003eThe Mikeno volcano (29.421\u0026deg;, -1.461\u0026deg;, 4,440 m: coordinates of the highest point) is one of eight volcanoes in the Virunga volcanic province. This province is geologically located in the western branch of the East African Rift. It extends over more than 50 km in an E-W direction north of Lake Kivu, transverse to the local orientation of the rift (Smets, 2013). This province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) comprises eight major volcanoes ranging in height from 2,474 m to 4,507 m. They are subdivided into three groups, namely: the eastern group comprising the Muhavura (4,127 m), Gahinga (3,474 m), and Sabinyo (3,647 m) volcanoes; the central group, which includes the Visoke (3,911 m), Karisimbi (4,506 m), and Mikeno (4,437 m) volcanoes; and finally, the western group, comprising the Nyiragongo (3,470 m) and Nyamulagira (3,056 m) volcanoes. (Hamaguchi and Zana, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Mikeno volcano rises to 4,440 m. It has very steep, eroded slopes that descend to 2,000 m and 3,000 m to the west and east, respectively. Its major volcanic activity is suggested to have occurred between 2.5 and 0.8 Ma based on K/Ar geochronology on 10 rock samples collected by (Guibert et al., 1975) on this edifice. Terminal activity is suggested to have occurred between 0.5 and 0.2 Ma. The fields of the Mikeno and neighboring Visoke volcanoes include tephrites, basanites, phonotephrites, and foidites (Barette et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The dormant summit of the Mikeno volcano is permanently covered with ice, which melts to form glaciers responsible for the erosion observed at Mikeno (Guibert et al., 1975).\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eNumerous studies have attempted to model potential mudflow routes and areas prone to flooding using GIS and remote sensing (Ahmed et al., 2011, Mathieu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Patton \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, Macka \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ozdemir and Bird \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Band \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Gurnell and Montgomery \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study adds to this list by using GIS to propose scenarios for mudflows from the summit of Mikeno volcano to its flanks and downstream areas. It combines spatial and non-spatial data from multiple sources (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial and non-spatial data used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate of of production\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUse in the study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInformation on previous mudslides in the study area, photographs of the events of 2010 and 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-spatial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReports from NGOs and public services involved in risk and disaster management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatus of past mudslides, frequency, and routes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShapefiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative boundaries of the Nyiragongo territory and surrounding areas, border between the DRC and Rwanda, localities in the study area, public infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003espatial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://extract.bbbike.org/\u003c/span\u003e\u003cspan address=\"https://extract.bbbike.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGeographical delimitation of areas likely to be affected\u003c/p\u003e \u003cp\u003ein the event of a mudslide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographic raster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRTM (Shuttle Radar Topography Mission) of the area around the Mikeno volcano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003espatial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://extract.bbbike.org/\" target=\"_blank\"\u003ewww.usgs.gov\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMain input data in the model for generating scenarios\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFirst, the source of the transportable materials, such as mud, had to be determined. This source is the highest point of Mikeno Mountain. From there, the materials are mobilized through drains, which must first be mapped. Depending on the volume of materials, these drains can overflow and spread the mud to surrounding areas. The drainage areas are valleys/thallwegs or even watercourses. Drainage networks can be identified using traditional methods such as field observations and topographic maps, or using advanced methods involving remote sensing and GIS (Macka \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Maidment \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Numerous studies have highlighted the difficulty of mapping all drains from field observations due to their extent over rugged terrain covering large areas. In this regard, topographic raster data (DEM) can be used to extract drainage networks capable of transporting flows (Ozdemir and Bird \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The extraction of drainage networks from DEMs is based on gravity: water flows from high to low elevations along the steepest slope. For the purposes of this study, evapotranspiration and infiltration were neglected in the modeling. Only the presence of interceptions was considered: these consist mainly of relief features.\u003c/p\u003e \u003cp\u003eTo generate mudflow scenarios, SRTM 30 m data from 2000 year was used. QGIS 3.16 software was used to perform the initial pre-processing of the DEM file, in particular to fill in the file. The r.fillnulls tool was used for this purpose. Filling pits is one of the most complex steps in the drainage extraction process. The model used is laharz.py (Iverson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Schilling \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This is a semi-empirical model for predicting lahars, but it was used in this study because of the similarities between the hazards studied. The filled DEM file was then used as the main input data for the model. The model takes into account the topography from the volcano (where the flow is produced) to the flow route areas, including the volcano and drains in the study area. The input data also includes the height of the cone (for conical volcanoes), its basal length to the normal, and the volume of potentially mobilizable materials. This volume is difficult to determine. For this study, we entered volume ranges to identify potential areas susceptible to mudflow deposits. The laharz.py model has two main creation functions: (1) the creation of initiation points, and (2) the creation of flows. Through these two main functions, other files are generated.\u003c/p\u003e \u003cp\u003eThe first function will generate the drainage network, the cone energy line, and the points where the mud flow begins after crossing the cone energy line. These drains are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the rest of the process, the following information relating to the study area must be prepared (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput data for the laharz.py model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInput files in the model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLaharZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eProbabilistic map of mudflows\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH/L ratio of the volcanic edifice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShapefiles of drains and thalweg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShapefiles of the initiation points of the flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of available materials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe final step in the model is the generation of mudflow escape areas through the previously created drains. Ranges of mobilizable material volumes were proposed to present possible scenarios. For this study, there are three volumes (in m\u003csup\u003e3\u003c/sup\u003e): 10\u003csup\u003e4\u003c/sup\u003e, 10\u003csup\u003e5\u003c/sup\u003e, and 10\u003csup\u003e6\u003c/sup\u003e. All the elements (data) extracted from the model were superimposed in a GIS on other spatial data from the study area to refine the preparation of the maps.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe hazard covered by this study is the \u0026ldquo;mudflow\u0026rdquo; originating from the dormant Mikeno volcano. It is located in an area of high geological instability (steep slopes and unstable volcanic terrain) and climatic instability. Meteorological data indicate high rainfall with abundant precipitation, an annual average of 108.75 mm and a total annual rainfall of 1305 mm. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This, combined with snowmelt, is the basis for the high erosivity of the volcanic edifice and the production of unstable materials that descend from the heights to lower altitudes (Guibert et al., 1975).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond that, villages are built around the volcanic edifice, despite armed conflicts that cause the displacement of some populations (Kabumba, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unstable, fluidized volcanic materials are likely to be mobilized and then carried away by a mudflow from upstream. The flow can increase its total volume by carrying along materials it encounters along its path. For Mikeno, the combination of steep slopes, intense rainfall, and snowmelt promotes the occurrence of hazards such as landslides and mudflows. This, combined with the densely populated settlements built on the slopes of the volcano, is the cause of the high risks. The main villages built on the slopes of this volcano are Kibiriga, Kibumba, Malyaso, Rugari, Kisigari, Moboga, and Kanombe. For three ranges of volumes of materials that could be mobilized, three possible sludge flow scenarios are constructed. These volumes are 10\u003csup\u003e4\u003c/sup\u003e, 10\u003csup\u003e5\u003c/sup\u003e, and 10\u003csup\u003e6\u003c/sup\u003e m\u0026sup3;, respectively, corresponding to scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). For scenario 1, with a volume of 10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3; of mobilizable material, the results are presented in terms of the length and width of the sludge deposit, which are small, but the area covered by the sludge is at high risk. These results also indicate that the materials likely to be mobilized will not spread too far. For the second and third scenarios, the volume of mobilizable materials is significant. These scenarios are accompanied by a large extension of the flow in both length and width. The deposits are likely to reach inhabited areas as well as public infrastructure along the Mikeno River.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) For a volume of 10\u003csup\u003e4\u003c/sup\u003e m\u0026sup3;, (b) For a volume of 10\u003csup\u003e5\u003c/sup\u003e m\u0026sup3;, (c) For a volume of 10\u003csup\u003e6\u003c/sup\u003e m\u0026sup3;\u003c/p\u003e \u003cp\u003eThe three scenarios can be combined to propose a classification of risk areas from the volcanic edifice to downstream of the drains. First, the EC cone is a 3D space generated in accordance with the H/L ratio of 0.45. The area covered by this cone is a proximal risk zone or an area with a higher risk of mudflow due to the steep slopes near the summit. By making this proximal risk zone transparent, it is possible to see deep thalwegs in this area, caused by previous erosion and mudflows (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This proximal risk zone corresponds to the volcanic edifice. The mudflow begins beyond this cone through the drains. These points are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. From these points, the mudflow begins to advance towards the slopes of the Mikeno volcano. From there, three areas are categorized according to the degree of risk.\u003c/p\u003e \u003cp\u003eThe final map of mudflow risks is obtained by combining the three suggested scenarios. Based on the topography around the volcanic edifice, it provides a spatial simulation of potential mudflows that could start at Mikeno and spread to the flanks. It is part of a prevention and land-use planning approach to assess risk levels. These results are not combined with vulnerability data (building fragility, etc.) to enable a quantitative risk assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe simulation map shows three major areas with different levels of mudflow risk: (1) High-risk area: shown in dark red. This area is less thick and less long. The average length and width are 2.4 km and 28.8 m, respectively. This area affects several inhabited areas south of the Mikeno volcano, particularly in the villages of Kibumba and Kibiriga. It is mainly located on the southern and southwestern slopes of the volcano. (2) Moderate risk area: symbolized by the color orange. It directly surrounds the high-risk area, extending 80.6 m beyond the first zone. The moderate risk area has an average length of 6.4 km and reaches several homes and public infrastructure. (3) The low-risk area, symbolized by the color yellow. It surrounds the two previous areas and is 8.7 km long on average. It is therefore the largest of the risk areas. The last two areas (orange and yellow) represent moderate and low risks, depending on the morphology of the drainage valleys.\u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the length and width parameters for these areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLength and width parameters for areas potentially covered by sludge, based on initiation points (IP)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eZone length parameter (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eZone width parameter (m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModeratly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModeratly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Interpretations and discussions","content":"\u003cp\u003eIn terms of impact on inhabited areas, the villages of Kibumba, Malyaso, and Rugari (white circles) are located directly in the potential paths of the flows. Moboga, Buhogoma, and Kisigari are also on these paths, but at a greater distance from critical areas, implying a moderate to low risk. They may be indirectly affected by road closures due to mud deposits. Road and public infrastructure cross several risk areas, indicating a high vulnerability of communication networks. The localities affected in this simulation are the same as those affected by the mudflows of 2010 and 2021, as reported by non-governmental risk management organizations (OCHA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The lahar.py model does not relate the intensity of rainfall in the source edifice to the area that can be flooded during the mudflow. It can be seen that valleys are clearly visible on the volcanic edifice, corresponding to the drains extracted using laharz.py (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It is likely that these are the same routes prepared by previous flows.\u003c/p\u003e \u003cp\u003eIt is well known that during events such as mudslides, unexpected increases in water levels in drains and high flow rates can result in the transport of large quantities of debris, rocks, and even non-rock materials such as uprooted trees and other materials (Douvinet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is therefore highly likely that this will be the case for any future mudslides in the Mikeno area. It is suggested that the flows could also carry other non-rock materials along their path. This was the case for the event in May 2010 (OCHA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this previous event, the flow split into three directions ranging from 50 to 150 m in width. This width range is not included in the ranges modeled by this study.\u003c/p\u003e \u003cp\u003eGIS- and remote sensing-based approaches and methods for modeling mudflows quickly provide very satisfactory results (Kumar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Geertsema et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Begueria et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Iverson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Schilling \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Ahmed et al., 2011). Several models are used, all of which have DTM as their input data. These results could be combined in future studies with vulnerability data (building fragility, etc.) to enable a quantitative risk assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research came exclusively from the authors\u0026rsquo;s own resources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMUMBERE Yves Mutima designed the research project, established the methodology, and prepared the manuscript, tables, and figures. CIZA Delphin ASSANI and MUNGUIKO Olivier Munyamahoro proofread the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed M, Biswajeet P (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. 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Nat Hazards Earth Syst Sci\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChorowicz J (2005) The East african rift system. \u003cem\u003eJournal of African Earth sciences\u003c/em\u003e, 43(2005), 379\u0026thinsp;\u0026ndash;\u0026thinsp;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenaeyer ME (1954) Les anciens volcans sous lacustres de la bordure du Nord du lac Kivu. Bull Soc Belge Geol Paleont Hydro 63:280\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenaeyer ME (1963) Les hyaloclastites de la rive Nord du lac Kivu (Congo). Bull Volcanol 25:201\u0026ndash;2015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouvinet J, Delahaye D, Langlois P (2013) Measuring surface flow concentrations using a cellular automaton metric: a new way of detecting the potential impacts of flash floods in sedimentary context. Geomorphology, Relief, Environment, Processes 1: 27\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeertsema M, Clague J, Schwab W, Evans S (2006) An overview of recent large catastrophic landslides in northern British Columbiaa, Canada. Eng Geol 83:120\u0026ndash;143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuibert, Philippe DELALOYE, Michel HUNZIKER Johannes. Contribution \u0026agrave; l\u0026rsquo;\u0026eacute;tude g\u0026eacute;ologique du volcan Mikeno, Cha\u0026icirc;ne des Virunga (R\u0026eacute;publique du Za\u0026iuml;re). In: Compte rendu des s\u0026eacute;ances de la Soci\u0026eacute;t\u0026eacute; de physique et d\u0026rsquo;histoire naturelle de Gen\u0026egrave;ve, 1975, vol. N.S., vol. 10, n\u0026deg; 1, pp. 57\u0026ndash;66\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurnell AM, Montgomery AR (1999) Hydrological applications of GIS. 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Integr Flood Manage Tools Ser No. 16\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mikeno, mud, GIS, SRTM, Virunga, modeling","lastPublishedDoi":"10.21203/rs.3.rs-8566722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8566722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMikeno is a volcano in the Virunga volcanic province located in eastern Democratic Republic of Congo (DRC). This volcano is dormant, but is prone to mudflows that originate at its summit and flow down its slopes, including inhabited areas. These mudflows have caused disasters in the inhabited areas around Mikeno. In order to simulate possible future mudflows, this study uses laharz.py modeling with SRTM as the main input data. A simulation of three volumes of mobilizable material (10\u003csup\u003e4\u003c/sup\u003e, 10\u003csup\u003e5\u003c/sup\u003e, and 10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e) reveals three possible mudflow scenarios. Sludge can originate from eight points and flow through drains. From these drains, the mudflows can spread and even reach inhabited areas. The results are also presented in terms of the length and width of the mud deposits, which are low, moderate, and high for the three volumes, respectively. The three scenarios are combined to produce a final map of the risks of mud escape. There are three major areas with different levels of vulnerability: the highly vulnerable area, with an average length and width of 2.4 km and 28.8 m respectively; the moderately vulnerable area, which is circumscribed around the first area, extends 80.6 m beyond the first area in terms of width and has an average length of 6.4 km; the third area, with low vulnerability, has an average length of 8.7 km and is circumscribed beyond the first two areas over 77.6 m.\u003c/p\u003e","manuscriptTitle":"Contribution of GIS to two-dimensional numerical modeling of mudflow hazards originating from the Mikeno volcano in the Virunga volcanic province, Democratic Republic of Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 13:32:17","doi":"10.21203/rs.3.rs-8566722/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4eab3853-16f1-40e2-a524-7817ccfee6a0","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T08:43:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 13:32:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8566722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8566722","identity":"rs-8566722","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}