During the past century, Nyamulagira (or Nyamuragira), the westernmost volcano of the Virunga Volcanic Province (VVP), erupted at intervals of 1–4 years (Burt et al., 1994; Smets et al., 2010a, 2015). It is considered as one of the most active African volcanoes, with at least 39 documented eruptions since 1882 (Smets et al., 2010a). Most of historical eruptions of Nyamulagira occurred along its flanks, sometimes more than 10 km far from the central edifice. Nyamulagira eruptions commonly last few days to few weeks, but some voluminous and less frequent historical events lasted several months or years (Pouclet, 1976; Smets et al., 2015). Nyamulagira's activity is characterised essentially by lava fountaining activity along an eruptive fissure, which produces lava flows and progressively build, together with ejected tephra, a spatter-and-scoria cone along the fissure (Pouclet, 1976; Smets et al., 2015).

On 2 January 2010, an eruption of Nyamulagira started in the central caldera and along its SSE flank, emitting ∼ 45.5×106 m3 of lava (Smets et al., 2014a). The related lava flows were of low viscosity, causing loss of vegetation in the Virunga National Park. The dense plume of gases that escaped from the eruptive vents affected the surrounding population (Cuoco et al., 2013). The estimated surface area covered by the 2010 lava flow is 15.17 ± 2.53×106 m2 (Smets et al., 2014a).

Due to the presence of densely populated zones and a National Park in the vicinity of Nyamulagira, lava flow invasion probability associated with its frequent fissural eruptions must be assessed. In addition to detailed field surveys and literature reviews performed to better characterise Nyamulagira's eruptive activity and, hence, better assess eruption hazards, simulations are practical tools to create hazard maps and detect the most probably affected areas during an eruption. For lava flows, a wide diversity of both probabilistic and deterministic simulation models exist in the volcanological literature (e.g. Crisci et al., 1986, 1997; Ishihara et al., 1989; Wadge et al., 1994; Kuauhicaua et al., 1995; Felpeto et al., 2001; Favalli et al., 2005; Damiani et al., 2006) and offer different degrees of accuracy, depending on the mathematical methods used to make calculations and the input parameters considered. Unfortunately, most of these models only exist in the scientific literature and their source codes are not freely available. Furthermore, some require complex calculations and significant CPU usage, which is not always available.

Felpeto et al. (2007) have built a volcanic hazard assessment software package (VORIS 2.0.1) within ArcGIS 9.1 (^©ESRI), which includes a probabilistic lava-flow model based on a previously developed model (Felpeto et al., 2001). Unlike existing lava-flow models, VORIS is free (downloadable from http://www.gvb-csic.es), easy to use and does not entail high computing requirements. However, VORIS may be less accurate than other more sophisticated deterministic lava-flow simulation models, which thus creates a dilemma as to whether a high precision or a quicker, easier-to-use – but less precise – tool is preferable.

The Goma Volcano Observatory (GVO) is in charge of conducting volcano monitoring and assessing volcanic hazards in the Democratic Republic of the Congo (DRC). Thus, as part of its work, it needs to adopt adequate methodologies and tools – tried and tested in other areas – that can be used by its scientists and technicians to comply with the most essential of its tasks and according to its scientific and technological resources. To check whether VORIS could be of use to GVO for lava flow hazard assessment in the DRC volcanoes, and for quickly estimating the potential impact of new lava flows in the event of a new eruption, we tested this package's lava-flow simulation model by replicating the Nyamulagira 2010 eruption, most of whose parameters and pre-eruption topography are known.

The VVP is part of the western branch of the East African Rift and is situated between Lake Edward, in the north, and Lake Kivu, in the south (Fig. 1) (Verhoogen, 1939; Smets, 2010b). It is composed by eight large volcanic edifices: Muhabura (4127 m), Gahinga (2474 m), Sabinyo (3647 m), Visoke (3711 m), Karisimbi (4507 m), Mikeno (4437 m), Nyamulagira (3058 m), and Nyiragongo (3470 m). Volcanic activity in the VVP started during the Upper Miocene (Pouclet, 1976). However, only Nyiragongo and Nyamulagira showed regular activity over the recent historical time (e.g. Hamaguchi, 1983).

Nyamulagira (1.41∘ S, 29.20∘ E) is located in the Virunga National Park, which was declared as a World Heritage Site in 1979 and has an endangered one since 1994 (http://whc.unesco.org/fr/list/63). Nyamulagira lavas are mostly basic, SiO2-undersaturated and K-rich (Kampunzu et al., 1982; Aoki and Yoshida, 1983; Aoki et al., 1985), which form low viscosity flows able to travel for tens of kilometres. In recent decades, the eruptive activity took the form of periodic fissure-fed effusive eruptions, sometimes accompanied with summital activity (e.g. Hamaguchi and Zana, 1983; Wadge and Burt, 2011; Smets et al., 2014a, 2015). Since April 2014, activity at Nyamulagira is restricted to episodic lava fountaining and the presence of a semi-permanent lava lake in a pit crater located in the NE sector of the central caldera (Smets et al., 2014b).

We used VORIS 2.0.1 (Felpeto, 2009; http://www.gvb-csic.es) to simulate the lava flows emitted by Nyamulagira during the 2010 eruption. VORIS (Volcanic Risk Information Systems) was developed in an ArcGis 9.1 (^©ESRI) Geographical Information System (GIS) framework and allows eruptive scenarios and probabilistic hazards maps to be created rapidly for the commonest volcanic hazards (lava flows, fallout and pyroclastic density currents).

The lava flow model included in VORIS is a probabilistic (Monte Carlo algorithm) model based on the assumption that both topography and flow thickness play major roles in determining the path followed by a lava flow (Felpeto et al., 2007 and references therein). The model computes several possible paths for the flow on the basis of two simple rules: (i) the flow will only propagate from one cell to one of its eight neighbours if the difference in corrected topographic height between them is positive, and (ii) the probability that the flow will move from one cell to one of its neighbours is proportional to the difference in height. The calculation of the probability of a point being invaded by lava is performed by computing several random paths using a Monte Carlo algorithm (see Felpeto, 2009 for more details).

The input data for this simulation include a digital elevation model (DEM), from which we can chose either a single vent or a vent area including several vents, the maximum flow length (i.e. the total length of the path followed by the lava flow, not just the maximum longitudinal distance), and a height correction (i.e. the average thickness of the flow). All these input parameters remain essentially constant during the simulation and characterise the resulting model. The maximum flow length and the height correction or average thickness are integers defined in metres. In addition, the “iterations number” must be considered. This number is proportional to the number of calculations made and will determine the CPU time required and the accuracy of the results obtained. However, if a greater number of iterations suggests a more accurate results, it also increases the risk to overload the microprocessor. This is why in our study we paid special attention to this input parameter, in order to determine the most appropriate number of iterations for guaranteeing reliable results without using too much computing time.

VORIS allows to select one single vent (a single pixel) or a vent area (several pixels, e.g. a linear fissure). The Nyamulagira 2010 eruption occurred along an ENE–WSW-trending fracture in the S part of the caldera and a 600 m long fissure that opened on the SSE flank of the main edifice, which generated the lava flows we have simulated in this study. We simulated the vent area both as a single vent and as a fissure, obtaining very similar results due to the radial orientation of the fissure and the strong topographic control on lava emplacement. For simplification we only show results from a single vent, which we located at the middle of the eruptive fissure.

An accurate DEM is a fundamental requirement for modelling volcanic processes and associated risks, including pyroclastic, lava, and mud flows (Sheridan et al., 2004). In the present case study, the DEM plays a major role in determining the pathways of lava flows (Felpeto et al., 2007), as it is the numerical base used to compute the path the lava will follow, based on the principle used by VORIS. To test the model, we used three different DEMs, the best available ones to the GOMA Volcano Observatory scientists at the time of writing: SRTM1, SRTM3, and ASTER GDEM (Mukerejee et al., 2013). These three DEMs were created before the 2010 eruption, but only the ASTER DEM contains the topography of the neighbouring Nyamulagira 2006 eruption. The three DEMs also differ either by their resolution or by the way they were produced. SRTM (NASA's Shuttle Radar Topography Mission) DEMs were produced by radar interferometry, using radar images acquired during a space mission, with NASA's Space Shuttle. SRTM1 in the studied area has a spatial resolution of 31 m, while SRTM3 has 93 m-wide pixels. In recent years, SRTM DEMs were the main source of height data for the VVP. The ASTER GDEM (ASTER Global Digital Elevation Model) is obtained by stereophotogrammetry, using nadir and backward images acquired by the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical sensor, onboard the TERRA satellite (NASA/METI/J-Spacesystems). ASTER GDEM data are published with a spatial resolution of 30 m.

In order to evaluate the sensitivity of models to changes in input parameters, we first tested separately the influence of the flow length, the number of iterations and the lava-flow thicknesses using the SRTM1 DEM. We then conducted additional simulations using different DEMs with the most appropriate length, thickness and number of iterations determined by the first tests. The comparison of results obtained with the three different DEMs is important for testing the suitability of each model in this type of lava-flow simulations and for testing their aptness for use with VORIS. In equatorial zones, such as in the VVP, the quality of the ASTER GDEM is considerably affected by cloud cover. As a consequence, artifacts appear in the lava fields (Arefi and Reinartz, 2011). Compared to differential GPS ground control points acquired in the southern Nyiragongo lava field (Albino et al., 2015), the SRTM DEM has a better vertical accuracy than the ASTER GDEM. Hence, it is expected to have more reliable results using the SRTM DEMs.

We conducted several simulations of lava-flow emplacement using VORIS 2.0.1 (Felpeto et al., 2007), in order to replicate the lava flow emitted by the 2010 Nyamulagira eruption and test the aptness of this model for conducting lava-flow hazard assessment at the Nyamulagira volcano. Model calibration was performed by changing the values of the model parameters until simulations best matched the 2010 lava flow. Next, this calibrated simulation was performed on three different DEMs to detect their influence on results.

This analysis shows that some input parameters can drastically change results. Too few iterations produce very poor results with a high degree of inaccuracy, while too many lead to considerable overestimates and a consequent increase in computing time. In our study, a number of iterations between 5000 and 10 000 gave the best results and the greatest degree of coincidence (see Tables 1–4 and Figs. 3, 5, 7, and 9) between the simulations and the real lava flow, with a total computing time of less than 20 min for each run. Simulations were less sensitive to changes in height correction (i.e. average thickness). However, we observed that the best results were obtained with a value of 3 m, which coincides with the average thickness of the actual lava flow. Concerning the length parameter, our results indicate that an overly small length value underestimates the probability of being covered by the lava flow, while an overly large value tends to overestimate the maximum longitudinal or run-out distance, as well as the lateral extent of the lava flow. This highlights the strong influence of irregular topography on the final coverage estimates. Results also show that the model calibration must be adapted to the DEM used, as both spatial resolution and DEM quality strongly affect the results.

In summary, models generated using VORIS 2.0.1 succeeded to replicate the emplacement of the Nyamulagira 2010 lava flow with a reasonable degree of accuracy. Considering that this is a quick, easy-to-use software and one of the few that are freely available on the Internet, it is perfectly adapted for lava flow hazard assessment performed at Goma Volcano Observatory. Further work will be dedicated to establish a method for determining the best input parameters (lava flow length, thickness and number of iterations) to be used when no a priori lava flow surface is known. This will be done by testing the modelling with other known lava flows from Nyamulagira. Additional work will also be required to adapt simulations to the neighbouring Nyiragongo volcano, for which the flank eruption style and lava flow propagation are different.