Lava flow simulations help to better understand volcanic hazards and may
assist emergency preparedness at active volcanoes. We demonstrate that at
Fogo Volcano, Cabo Verde, such simulations can explain the 2014–2015 lava
flow crisis and therefore provide a valuable base to better prepare for the
next inevitable eruption. We conducted topographic mapping in the field and a
satellite-based remote sensing analysis. We produced the first topographic
model of the 2014–2015 lava flow from combined terrestrial laser scanner
(TLS) and photogrammetric data. This high-resolution topographic information
facilitates lava flow volume estimates of
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Effusive volcanic eruptions are associated with lava flows that may cause damage and long-lasting impact on infrastructure and economy. The comune San Sebastiano al Vesuvio in Italy was destroyed by Mount Vesuvius' lava flows in 1944 for the third time in less than 100 years, yet was rebuilt (Kilburn, 2015). In January 2002, lava flows advancing from Nyiragongo Volcano overran the city of Goma in the Democratic Republic of Congo, which was later rebuilt on top of this lava flow (Chirico et al., 2009). Destructive effusive volcanic eruptions also occur frequently at places such as the island of Hawai'i(Kauahikaua and Tilling, 2014; Poland et al., 2016), or at Mount Etna, Sicily, Italy (Favalli et al., 2009b). Yet, a common observation in many of these classic examples is that, for various reasons, residents rebuild their houses and return to live in hazardous areas. Studies of effusive eruptions and the mechanisms of lava flow emplacement over time, as well as lava flow hazard assessment and the proposal of risk mitigation strategies, therefore, have developed into fundamental branches of volcano sciences. Recent crises, such as the 2014–2015 Pahoa lava flow crisis at Kīlauea Volcano, Hawai'i (Poland et al., 2016), and the highly destructive 2014–2015 eruption of Fogo Volcano, Cabo Verde (González et al., 2015; Cappello et al., 2016; Bagnardi et al., 2016), have again shown that up-to-date lava flow hazard information is needed in inhabited volcanic environments and that this information has to be effectively communicated to the officials in charge of public safety.
A variety of algorithms have been developed with the common aim of understanding the dynamics of lava flow emplacement, forecasting lava flow paths, and constructing lava flow hazard maps (e.g. Favalli et al., 2005; Harris and Rowland, 2015; Del Negro et al., 2008). These algorithms have been applied to numerous volcanoes, including but not limited to Nyiragongo Volcano, Mount Cameroon, and Mount Etna (Favalli et al., 2009a, 2011b; Tarquini and Favalli, 2011). Modelling techniques follow either the probabilistic or the deterministic approach. The MAGFLOW simulation code (Del Negro et al., 2008) is a deterministic approach that relies on pre-existing knowledge or at least simplified assumptions about the physical and rheological characteristics of flowing lava (Cappello et al., 2015; Tarquini and Favalli, 2015). The FLOWGO model by Harris and Rowland (2015), also a deterministic model, allows for the simulation of changing physical properties, e.g. changes in velocity or thermorheology of flowing basaltic lava following a predefined channel downslope (Harris et al., 2015). Here we use the DOWNFLOW probabilistic code (Favalli et al., 2005) to create lava flow hazard maps. Based on the law of gravitation, DOWNFLOW follows the simple assumption that lava flows downhill from an eruption site. One main advantage of this code over deterministic models is that only basic physics applies, therefore no pre-existing knowledge or assumptions on physical properties of the lava flows are needed. The most important input for the DOWNFLOW simulation is an accurate and up-to-date digital elevation model (DEM) and the location of the eruptive vent.
High-resolution topographic information does not only serve as an essential prerequisite for lava flow simulations, it is also one of the first requirements for any effusive eruption as it allows for lava flow thickness and volume estimates. Modern remote sensing techniques, such as photogrammetry, airborne and terrestrial light detection and ranging (lidar), terrestrial laser scanning, and synthetic aperture radar interferometry (InSAR) are among the most commonly used sources of terrain information for detailed analyses of the Earth's surface. Major differences between these methods relate to the achievable spatial resolution and coverage, as well as information quality and accuracy. The decision of which method to use highly depends on the specific application and the user's needs. Modern high-resolution satellite systems, such as Pléiades (optical) and TanDEM-X (radar), need to be tasked to acquire topographic data in response to a volcanic crisis. Updates of pre-existing topographic information can also be achieved using ground-based technologies, such as terrestrial laser scanning and camera- or drone-based photogrammetry. These methods are often more flexible than satellite observations with respect to the acquisition time and date. For instance, we produced a posteruptive DEM from ground-based data in January 2015, while the next (and only other) available posteruptive DEM data were acquired more than 5 months after the end of the 2014–2015 eruption (on 20 June 2015) by the Pléiades satellite (Bagnardi et al., 2016). TanDEM-X bistatic data are not available for Fogo after the 2014–2015 eruption. Ground-based techniques are especially effective for effusive volcanic eruptions, where only the directly affected areas need to be updated. The potential of very long-range terrestrial laser scanner (TLS) instruments to survey the dynamics of active lava flow fields and to map the topographic changes associated with the emplacement of new flows was shown at Mount Etna, Italy (James et al., 2009). We produced the first posteruptive topographic map of the 2014–2015 Fogo lava flow using TLS and ground-based photogrammetric data in order to update a pre-eruptive photogrammetric DEM of Fogo Island, both featuring a 5 m spatial resolution. We estimated lava flow characteristics, such as lava flow thickness and volume. We also generated and compared pre- and posteruptive lava flow hazard maps.
Fogo Island is one of the youngest volcanic islands of the Cabo Verde
archipelago in the Atlantic Ocean and is built up from the remnants of one single giant
volcano, known as the Monte Amarelo Volcano (Day et al., 1999). The eastern coastline
reflects a catastrophic flank collapse event in the island's geologic history
(Day et al., 1999; Ramalho et al., 2015), which is thought to date back
Map of Fogo Island
Photographs taken during (
Since the time of Portuguese discovery and settlement in
On 2 April 1995 a fissure eruption started at Pico Pequeno (Amelung and Day,
2002), a small cone WSW of Pico do Fogo (Fig. 1). All residents were
evacuated, but houses as well as
Almost 20 years later, on 23 November 2014, a new eruption started at Pico
Pequeno with the opening of a fissure located
After the 1995 Fogo eruption, lava flow simulations were tested based on a 15 m Digital Terrain Model (DTM) (Quental et al., 2003). The Cellular Automata (CA) technique was applied to simulate the time- and space-dependent flow emplacement. Results were in agreement with the actual lava flow coverage, but only the first 2 days of the 1995 eruption were reproduced successfully. A general, yet provisional, volcanic hazard map for the scenario of a renewed phreatomagmatic explosive eruption comparable to the major 1680 eruption was provided by Jenkins et al. (2014) on the base of investigations by Day and Faria (2009, unpublished data). This map suggested that the entire area of the Chã as well as the eastern flank of the volcano were areas of high hazard resulting from lava flows, 2–10 m ash fall, possible pyroclastic surges, and rock avalanches. Furthermore, Cappello et al. (2016) used HOTSAT satellite data and the physically based MAGFLOW model to simulate the lava flow emplacement during the ongoing 2014–2015 eruption. In the study at hand, we constructed probabilistic lava flow hazard maps for the Chã das Caldeiras and the eastern flank of Fogo Volcano. We were particularly interested in whether the 2014–2015 eruption has significantly changed the lava flow hazard in the affected areas, as this has important implications for temporal and spatial changes of lava flow hazards in general.
One of the first requirements for an effusive crisis is an up-to-date model of the new topography. In response to the 2014–2015 Fogo eruption, a Hazard and Risk Team (HART) of the German Research Centre for Geosciences (GFZ) processed high-resolution satellite radar data and went to Fogo Island in order to acquire high-resolution topographic data between 11 and 21 January 2015. The topographic data are needed to estimate lava flow characteristics, such as erupted volumes, and serves as the most crucial input data for our lava flow simulations and hazard assessment. The frequent synthetic aperture radar (SAR) satellite data acquisitions allow us to map lava flow emplacement over time, information that helps us to better understand the lava flow model performance.
We used SAR data acquired by the German satellite TerraSAR-X (TSX) to monitor
the emplacement of the 2014–2015 lava flow. The TSX satellite operates at a
wavelength of 3.1 cm (X-band) of the electromagnetic spectrum. The data were
acquired in the satellite's SpotLight mode (
Interferometric coherence is a measure of the correlation between the phase components of two SAR images of the same track (i.e. the same viewing geometry) (Hanssen, 2001). Coherence values range from 0 (low coherence, decorrelation) to 1 (high coherence, strong correlation between SAR acquisitions). As a consequence of the time delay between two acquisitions (11 days for TSX), temporal decorrelation occurs in repeat-pass InSAR as the scatterers within a resolution cell move, change their dielectric properties, or are replaced by a new set of scatterers (e.g. upon lava flow emplacement or ash deposition) (e.g. Zebker et al., 1996). As the Chã is sparsely vegetated, decorrelation in the study area is mostly associated with steep slopes and surface changes.
We created an updated DEM for Fogo Volcano, which is composed of three different data sets: firstly, a commercial pre-eruptive DEM acquired by GRAFCAN (Sect. 3.2.1); secondly, our TLS DEM that is composed of eight combined point clouds that cover in total 87.7 % of the 2014–2015 lava flow (Sect. 3.2.2); and finally, four separate, very small DEMs produced by applying the structure from motion (SfM) method to optical camera data (Sect. 3.2.3) in order to fill the remaining gaps. We merged the data sets by minimizing the vertical distance between our TLS and SfM point clouds and the commercial pre-eruptive reference DEM using the Minuit minimization tool (Sect. 3.2.4).
We use a commercial pre-eruptive DEM of Fogo Island featuring a 5 m pixel spacing that was generated from contours on the base of photogrammetric data. The DEM was georeferenced using a limited number of points acquired during a mapping campaign in 2003–2004. According to GRAFCAN, the company who generated the DEM, the horizontal and vertical accuracies are 40 and 50 cm respectively. However, the data were delivered in integer values. We therefore expect the accuracy to be smaller. From the delivered DEM grid we generated contour lines with 1 m spacing (in elevation) and reinterpolated the surface.
We used a TLS instrument of the type Riegl VZ-6000, with the capability to
produce submetre resolution topographic data of target objects at a maximum
distance of 6000 m from the scanner position. The instrument combines the 3-D
laser scanning and laser ranging techniques. Data are acquired by the
controlled deflection of a laser beam into different directions by means of
an oscillating mirror, and the 360
Coverage of the TLS data sets (in grid format) and
corresponding scanner locations on Monte Saia, Monte Beco, and Monte Amarelo
GPS coordinates of scanner positions and accuracies of point cloud combinations.
During the field campaign in January 2015, we acquired eight TLS point clouds, which were used for the generation of the updated topographic map (Table 1). We chose three main scanner locations, namely Monte Beco, Monte Saia, and Monte Amarelo (Fig. 3a). The terrestrial laser scanning technique is commonly associated with the occurrence of shadow areas due to the acquisition geometry. We minimized the shadow effects by scanning from slightly different positions with different fields of view at Monte Beco and Monte Amarelo so that our TLS data points cover 87.7 % of the area overrun by the 2014–2015 lava flow and most of the Chã. We combined the individual scans for these two main scan locations separately by means of a minimum of three common tie points (reflectors) that we installed in the field. Rough orientation of the point clouds was done using handheld GPS positions of the reflectors. At five of the scanner positions (one on Monte Saia and four on Monte Beco), we acquired differential GPS data (Table 1); all other scanner positions were collected by the scanners internal, less precise GPS instrument. Differential GPS data (where acquired) were collected for at least 1 h (up to 3 h 30 min) and at one site (SAIA1) data were acquired on 2 different days. Differential GPS processing was done in two main steps. At first, the average positions of a network of seven permanently installed GPS stations were estimated with respect to the ITRF2008 global reference frame (Altamimi et al., 2011). These stations were installed and maintained by C4G (Co-laboratory for Geosciences, Portugal) and INMG (Instituto Nacional de Meteorologia e Geofísica, Cabo Verde) and continuously acquired GPS data during the 2014–2015 Fogo eruption (Fernandes et al., 2015). The computation of the reference positions was done using the precise point positioning strategy (PPP) implemented in the GIPSY-OASIS software package (Zumberge et al., 1997). More details on the methodology used here are provided by Neves et al. (2014). Secondly, the coordinates of the TLS stations were estimated with respect to the permanent network using the TBC (Trimble Business Center) software. This software allows for the adjustment of the position of the TLS sites by estimating the baselines between all simultaneously observed points. Scanner locations and the coverage of the acquired point clouds are shown in Fig. 3a. Details on the scanner positions, and tie point registration accuracies are listed in Table 1.
Photogrammetric data were collected from positions along the upper Bordeira ridge using dSLR 15.1-megapixel Canon EOS Rebel cameras with CMOS sensor. We used the SfM method as introduced by Verhoeven (2011) to generate four small DEMs from a total of 77 camera images. A first, rough georeferentiation was performed using manually selected ground control points (GCPs) before following the error minimization procedure described below. This way we were able to fill 92 % of the remaining TLS data gaps (red areas in Fig. 3b).
In order to merge the separate point clouds a common reference frame
was needed. We therefore
minimized the root mean square error
(RMSE) between the TLS and SfM point clouds and a reference DEM using the
Minuit2 5.18/00 package, developed at CERN (James and Winkler, 2004 and
references therein). Minuit is a tool to find the minimum value of
multi-parameter functions and can be freely downloaded
(
Acquisition and processing of SfM data.
In order to assess the lava flow hazard at Fogo Volcano, Cabo Verde, we used
the probabilistic lava flow simulation code DOWNFLOW (Favalli et al., 2005).
A lava flow simulation follows a number of
DOWNFLOW calibration. The maximum fitness of
Lava flow length constraint. Green triangles show the vent elevation and lava flow lengths of lava flows that stopped on land (including the year of the eruption). Corresponding vent locations are given in Fig. 6. Red points indicate lava flows that reached the ocean.
Probability distribution of future vent opening for the Fogo scenario. White points mark the locations (and eruption years) of historic vents producing lava flows that stopped on land. Black dots mark locations of historic vents producing lava flows that reached the ocean. The black circle around Pico Pequeno indicates the PDF domain for the Pico Pequeno scenario.
Lava flow outline over time from TerraSAR-X coherence maps (cf. Appendices C and D).
The coordinates 24.35341
For calibration we analysed the whole parameter space in order to maximize
the fit,
Using a single DOWNFLOW simulation we reconstructed the 2014–2015 lava flow which originated from a known vent (Sect. 3.3). The simulation of a future scenario requires a fundamental lava flow hazard assessment; this includes the simulation of lava flows for all possible future vents. In order to create meaningful hazard maps, these simulations need to be weighted by two factors: (a) the lava flow length constraint, in order to stop the flows before they reach the end of the data set (Sect. 3.4.1) and (b) the probability function of future vent opening, because in reality a vent opening at some locations is more likely than at others (Sect. 3.4.2).
We apply a lava flow length constraint that is based on information about
historic eruptive vent locations and corresponding lava flow lengths. For
other volcanoes it was shown that a negative correlation exists between vent
elevation and lava flow lengths, e.g. at Mount Etna (Favalli et al., 2009b),
Nyiragongo Volcano (Favalli et al., 2009a), and Mount Cameroon (Favalli et
al., 2011b). According to these examples, vents located at higher elevations
tend to produce shorter lava flows. For Fogo Volcano, we use a geologic map
as a base of information (Torres et al., 1997 in Texier-Teixeira et al.,
2014). We also take into account an updated map published recently by
Carracedo et al. (2015). We georeferenced the maps, determined the historic
vent locations, and plotted the vent elevations against the lengths of the
corresponding lava flows (Fig. 5). This plot distinguishes lava flows that
reached the sea from lava flows that
stopped onshore. While the first only
helped to find the minimum lava flow
length, the latter were used as the
base for the length constraint. A Fogo-specific limitation is that we only
have a record of five historic lava flows that did not reach the sea, and all
of the corresponding vents are located within a narrow elevation range of
less than 300 m (between 1760 and 2020 m a.s.l.). Therefore, we cannot
infer a negative correlation between vent elevation and corresponding lava
flow lengths. Instead, we consider a constant distribution of lava flow
lengths in between a minimum (3000 m) and a maximum (9000 m) lava flow
lengths. This means that we assume that a point that lies within 3000 m
downflow from a given vent will always be invaded (probability
We base the probability of vent opening on the record of historic vents assuming that future vents are more likely to open in areas where historic vents cluster. The vent locations were selected on the basis of our posteruptive DEM and the geologic map of Fogo Volcano (Torres et al., 1997 in Texier-Teixeira et al., 2014). They were used to estimate the probability density function (PDF) of vent opening by applying a Gaussian smoothing kernel with a bandwidth of 800 m (Bowman and Azzalini, 2003; Favalli et al., 2011b). Bartolini et al. (2013) suggested a method for finding the optimal bandwidth of a Gaussian smoothing kernel. According to their approach, the optimal bandwidth for our Fogo case study would be 3600 m. This would result in one maximum in our distribution, centred on the Pico do Fogo stratocone. However, realistically it is unlikely Pico do Fogo represents the maximum probability of vent opening. Therefore, we decided to find the best bandwidth by trials in order to avoid over- or undersmoothing.
We describe two different scenarios and create the corresponding hazard maps,
namely the “Pico Pequeno scenario” and the “Fogo scenario”. The first
scenario considers a vent opening at Pico Pequeno. We consider a future vent
opening at this location to be a likely future scenario, as the two most
recent eruptions occurred from Pico Pequeno. For this scenario the entire PDF
domain is a circle of 1150 m in diameter centred between the 1995 and
2014–2015 vents (circle in Fig. 6). We consider a PDF that is constant
inside this circle and 0 outside. The second scenario applies to Fogo Volcano
as a whole. For the Fogo scenario, 42 vents within the Chã were
considered (Fig. 6) as we do not have any record of historic eruptions
outside the Chã. In the Fogo scenario (Fig. 6), the area at and
around Pico Pequeno (as indicated by the circle) has an overall probability
of vent opening of
We create lava flow hazard maps for Fogo Volcano that show the probability
In practice this means we perform 82 000 DOWNFLOW simulations, each with
Comparison of the DOWNFLOW reconstruction of the
2014–2015 lava flow
Furthermore, lava flow hazard maps are created for two time steps based on the two available DEMs; the pre-existing, pre-eruptive DEM is used to reconstruct the 2014–2015 lava flow and assess the pre-eruptive lava flow hazard within the Chã and on the eastern flank of the volcano. We then repeat the same simulations using the updated DEM. We assess to what extent the most recent eruption has changed the probability of lava flow inundation in the affected area and the whole eastern part of the island.
Figure 7 shows the areal coverage of the 2014–2015 lava flow over time as
mapped using TerraSAR-X coherence images (provided in Appendix D). This map
shows that the NW lava lobe had already travelled almost 4 km within the
first 2 days of the eruption, but had not quite reached the village of
Portela yet. The S lava lobe was also already emplaced. It did not advance
much after emplacement, except for some minor widening. Between
25 November 2014 and 1 December 2014, primarily the NW lava lobe widened,
destroying the first houses of Portela. We also observe minor propagation and
widening at the W lava lobe. This trend of widening and engulfing more houses
of Portela continued throughout 6 December 2014. Until that time, lava flowed
in a well-defined channel north of Monte Saia (cf. Fig. 8a, point #1 and
Fig. 8b, profile C–C
Pre-
Pre-
We achieve a RMSE of 1.08 m for the global geolocation of our final
posteruptive point cloud (cf. Table 1). This value was calculated using the
Minuit minimization procedure between the final, combined point cloud and the
pre-existing DEM. The error is smaller when comparing posteruptive and
pre-eruptive grids at a 5 m pixel resolution (RMSE
The vertical difference between the pre- and posteruptive DEMs is a measure
for lava flow characteristics, such as the lava flow thickness (Fig. 8b and
c) and volume. The 2014–2015 lava flow has an average thickness of 8.9 m.
According to Fig. 8b and c, the 2014–2015 lava flow has a maximum thickness
of
We calculate a total erupted lava volume of
43.2
The DOWNFLOW simulation output is a grid with the same cell size as the input
DEM, where each pixel value gives the number of
The DOWNFLOW reconstruction of the 2014–2015 flow is given in Fig. 8a. The simulated lava flow covers 75 % of the actual 2014–2015 lava flow area. We observe that single flows, i.e. flows that are not affected by multiple phases of emplacement (e.g. #1, #5, and #4), are reproduced well by the DOWNFLOW simulation. However, lava flows that are emplaced in later effusive pulses are not as well represented (points #6 and #7). Pixels located within topographic ponds are hit by a simulation more often. In the area that was affected by the 1995 lava flow (in between the NW and W lava lobes of the 2014–2015 lava flow) the lava flow coverage is overestimated by the DOWNFLOW simulation.
Our hazard maps show the probability of lava flow invasion, i.e. the likelihood that a future lava flow will inundate a specific pixel before the vent location is known. The pre-eruptive hazard maps are calculated on the basis of the pre-eruptive DEM that was acquired after the end of the 1995 eruption, but before the onset of the 2014–2015 eruption (on 23 November 2014).
Figure 9a shows the pre-eruptive hazard map for the Pico Pequeno scenario. We find that the locations of the villages (#1, #2, and #3 in Fig. 9a) are zones of very high lava flow hazard (more than 75 %). By comparing the hazard map to the outline of the actual lava flow, we find that any vent opening within the given circle around Pico Pequeno would have resulted in approximately the same lava flow coverage as the 2014–2015 eruption. In fact, before the eruption, a small portion of the area that is now covered by lava flows even had 100 % probability of being invaded by a lava flow. On the other hand, areas that had 0 % probability of invasion are now also covered with lava (Fig. 9a, cf. points 6 and 7 in Fig. 8a). At the same time, some parts of the area that is surrounded by the NW and W lava lobes, which is the area that is covered by the 1995 lava flow (Carracedo et al., 2015), were almost certain to be invaded according to this map, but were not affected by the 2014–2015 lava flow (Fig. 9a).
The pre-eruptive lava flow hazard map for the Fogo scenario is shown in Fig. 10a. Generally areas of high lava flow hazard cluster along the Bordeira wall, but low probability of lava flow invasion can be observed at the Pico do Fogo stratocone. Some of the larger cones within the Chã appear to serve as a barrier and produce a lava flow hazard shadow area behind them. This is especially true for Monte Beco, but also for the southern vent of the 1951 eruption and at the 1852 vent (Fig. 10a, for vent locations cf. Fig. 6). In this map, part of the area covered by the 1995 flow shows a probability of invasion of more than 7 % (yellow areas outside the borders of the 2014–2015 lava flow). As for the volcano's eastern flank, comparably high lava flow hazard exists, especially along the edges of the landslide amphitheatre. Considering the fact that this map is not specific for vents around Pico Pequeno, it is striking that all three flow fronts of the 2014–2015 lava flow cover areas of increased or very high hazard (10–28.4 %). The two villages of Portella and Bangaeira as well as the small settlement of Ilhéu de Losna were located in these high hazard zones of the NW and W lava lobes.
We refer to this period as “posteruptive”. The posteruptive hazard maps reveal the probability of lava flow invasion for the next eruption of Fogo Volcano.
The posteruptive hazard map for the Pico Pequeno scenario shows that the
lava flow hazard is generally higher in the northern part of the Chã
(Fig. 9b), with a maximum probability of lava flow invasion of 88.42 %.
Especially closer to the vent (Pico Pequeno), channelling is not as strongly
pronounced as before the 2014–2015 eruption (cf. Fig. 9a). The probability
of lava flow invasion has not noticeably decreased for the locations of the
villages of Portela (point #2 in Fig. 9b) and Bangaeira (point #1 in
Fig. 9b), in fact, for the latter the lava flow hazard has even slightly
increased. Only for the small settlement of Ilhéu de Losna (point #3
in Fig. 9b), the probability of lava flow invasion has significantly
decreased by 46 %. “Islands” of no lava flow hazard (probability of
invasion
The posteruptive lava flow hazard map for the Fogo scenario is shown in Fig. 10b and also available as supplementary material (Richter et al., 2016). According to this map, the probability of lava flow invasion during the next eruption of Fogo Volcano lies between 0 and 29.3 %, but 0 % probability only occurs on top of cones or at some places along the Bordeira wall. The locations of the former towns of Portela and Bangaeira are still among the very high hazard zones and on the eastern flank of the volcano, other villages (e.g. Fonseco and Tinteira) are at risk.
The letter “H” in Figs. 9 and 10 indicates the location of a new
building that was constructed on top of the 2014–2015 lava flow (personal
communication Mustafa Kerim Eren, November 2015), at 24.37360
To answer this question we provide the area of vent opening that, according to the DOWNFLOW simulation, could potentially result in a lava flow able to invade Portela and Bangaeira (Fig. 11). Such “catchment maps” are already widely used in other volcanic environments, such as at Mauna Loa, Hawai'i (e.g. Kauahikaua et al., 1995), Mount Etna, Italy (Favalli et al., 2009b), and Mount Cameroon, Cameroon (Favalli et al., 2011b). In Fig. 11, areas are ranked according to the minimum length that lava flows need to travel before reaching the village. In other words, it shows the area of future vent opening that potentially threaten Portela and Bangaeira (indicated by polygon) in colour. Any vent outside the coloured area lies downslope from the villages or belongs to a different catchment, and will therefore produce lava flows that will not harm the area covered by the polygon.
For example, if a future vent opens in the red/orange area, even short lava flows will reach the villages. On the other hand, if a future vent opens in the blue area, the lava flows need to travel a long distance before reaching the villages of Portela and Bangaeira. The length that a lava flow will have to reach before engulfing the villages is given in kilometres in Fig. 11. Technically, such a map can be provided for any pixel or area of interest on the basis of our Fogo DOWNFLOW simulation database.
The coloured area shows all locations of future vents that will likely produce lava flows reaching and therefore affecting the villages of Portela and Bangaeira. Vents are herein ranked according to the minimum length that lava flows need to travel to reach the villages (black dotted polygon).
The 2014–2015 eruption caused substantial damage on the island of Fogo. Approximately 90 % of the houses in the villages of Portela and Bangaeira and the small agricultural settlement of Ilhéu de Losna as well as 24.6 % of the farmlands within the Chã were destroyed by lava flows. This left approximately 1000 people homeless and seriously endangered their source of livelihood (United Nations, 2015). Because the volcano provides some of the most fertile soils on the island and facilitates geotourism, people are already returning to live in the Chã.
We are providing comprehensive lava flow hazard maps that are valid for the next eruption of Fogo Volcano. However, we did not investigate either vulnerability or preparedness. For hazard mitigation to be successful, it is of utmost importance to effectively communicate scientific results as well as the uncertainties related to lava flow hazards to the local emergency management authorities and decision makers (Kauahikaua and Tilling, 2014; Poland et al., 2016).
SAR coherence is known to be a valuable tool for defining lava flow boundaries (Zebker et al., 1996) and continues to be used for lava flow mapping purposes (e.g. Dietterich et al., 2012; Copernicus Emergency Management Service, 2014). A common disadvantage of this approach is that decorrelation may occur due to many factors besides lava flow emplacement, factors such as vent opening, vent growing, and ash deposition. We generate TSX coherence maps wherein we cannot delineate these eruptive processes apart in close proximity to the active vent at Pico Pequeno, causing our lava flow boundaries to be unprecise there (Appendix D). In other volcanic areas, vegetation and snow coverage will further limit the applicability of coherence maps to track flow emplacement (Dietterich et al., 2012), both of which are minimal or nonexistent at Fogo Volcano. Both the TSX coherence analysis and the vertical DEM difference give the same final 2014–2015 lava flow extent (cf. Fig. 7 and Appendix B), therefore, we are confident that our TSX data interpretation is sound.
Depending on the instrument, TLS is capable of acquiring topographic data
over distances of up to
Here we acquired a set of eight TLS scans to cover
The acquired TLS point cloud has an unprecedented resolution and quality, sufficient for the presented application, but the most critical remaining limitation of our TLS data set is the shadowing. In addition to minor shadows resulting from the viewing geometry, we have to mention one major shadow area at the north side of the 2014–2015 vent that had to be interpolated between the pre-existing and the posteruptive DEMs. Due to the ongoing strombolian explosions during field work, the scanner could not be set up on the slopes of the Pico do Fogo cone, looking towards the west.
While the TLS data acquisition is time consuming and logistically challenging
compared to the ground-based SfM technique, its precision is higher and the
processing time is faster (Westoby et al., 2012). The general quality of our
TLS data is better than that of our photogrammetric data, as camera images
were acquired from locations along the upper ridge of the Bordeira wall at a
rather large distance of
The data and techniques used were designed to generate topographic information on the new lava flows; a survey to cover the whole island was not anticipated. For the study at hand we produced a DEM featuring a resolution of 5 m to meet the resolution of a pre-existing data set. On a more regional scale, where the point cloud is dense (e.g. at the active vent or at the buried villages of Portela and Bangaeira), our data can also be used at a much higher spatial resolution (centimetre scale).
From the vertical difference between pre- and posteruptive DEMs, we estimate
a total erupted volume of 43.7
The DEM difference method used here gives a total erupted bulk volume.
Assuming a 25 % vesicularity, as often used in literature (e.g. Wolfe et
al., 1987; Poland, 2014; Bagnardi et al., 2016), the dense-rock equivalent
(DRE) value is
Using the DOWNFLOW algorithm to simulate the final 2014–2015 lava flow
coverage we achieve a maximum fit of
DOWNFLOW is known to work well on steep terrain (Favalli et al., 2009a,
2011b; Tarquini and Favalli, 2011). In this study the DOWNFLOW simulation has
proved to perform well on rather flat areas like the Chã das Caldeiras.
This implies that lava flow paths are largely controlled by the topography
even, and maybe especially, in relatively flat terrain. In order to discuss
the DOWNFLOW performance, we compare the simulation (Fig. 8a) to the real
lava flow coverage (Figs. 7, 8b). The very early phase of the 2014–2015
eruption, when lava travelled in a well-defined channel (Fig. 7; Fig. 8b,
profile C–C
In summary, differences between the simulation and the real lava flow
coverage occur due to two main facts: first, the DOWNFLOW simulation runs
until the lava flows hit the end of the DEM, while the actual lava flows stop
when effusive activity ceases. Second, the DOWNFLOW simulation (of
Our volcano-wide hazard maps (Fig. 10a and b) allow speculations about infilling mechanisms of giant landslide amphitheatres of volcanic origin. We find that high lava flow hazard areas are located mainly along the wall of the landslide scarp. Flows are then likely to follow pathways down the flanks, along the edges of the scarp. Depending on the probability distribution of vent opening, we would expect generally similar main lava flow hazard patterns for other ocean islands with infilling landslide amphitheatres and comparable topographic structure, such as Piton de la Fournaise (La Réunion, France) or Teide Volcano (Tenerife, Spain).
However, regarding the lava flow hazard estimation, we are left with
uncertainties. The DOWNFLOW hazard map generation depends on the topography,
Future studies are needed to address the question why very similar subsurface pathways were reused and adjacent dikes developed during the 1995 and 2014–2015 eruptions (Amelung and Day, 2002; González et al., 2015). Therefore, we do not state that a future vent will open within the circle that is shown in Figs. 6 and 9b, at approximately the same location as during the two most recent eruptions. Rather we provide a hazard map for this scenario because at this point of time and knowledge, we cannot ignore the possibility that a future vent will be located there. In the same way that the pre-eruptive hazard maps would have been useful in forecasting the 2014–2015 lava flow paths, the posteruptive hazard maps are valid for the next eruption of Fogo Volcano. Now that we know the exact location of the 2014–2015 vent, we can state that the pre-eruptive hazard map for the Pico Pequeno scenario (Fig. 9a) predicts the lava flow path of this eruption with a high level of confidence. This is especially true for the early phase of the eruption. Therefore, this map can be treated as a forecast of the 2014–2015 lava flow path. However, the above discussed limitations related to the modified topography during an eruption (Sect. 5.3) also result in uncertainties in our lava flow hazard maps, e.g. areas with 0 % probability of invasion are now covered with lava while other areas that were 100 % certain to be invaded are not covered by the 2014–2015 lava flow (Fig. 9a, Sect. 4.4.1). However, our results suggest that if the next eruption occurs at Pico Pequeno, the initial lava flow path will most likely be the one provided by the posteruptive Pico Pequeno hazard map (Fig. 9b).
When comparing the pre- (Figs. 9a and 10a) and the posteruptive hazard maps (Figs. 9b and 10b), the 2014–2015 eruption changed the local lava flow hazard significantly at areas that are now covered by the 2014–2015 lava flow (general decrease) and along its edges (general increase). The risk for the village of Portela has not changed significantly in terms of the maximum probability of lava flow invasion (89 %, both before and after the 2014–2015 eruption). However, the distribution of hazard has changed. Within the town, the highest probability now exists for the parts of Portela that had low probabilities before the eruption and were also not covered by the lava flows (especially obvious in Fig. 10a and b). The hypothesis that lavas are unlikely to inundate areas that were previously overflowed is a common assumption at Fogo (personal communication with residents). The new building (indicated by letter “H” in Figs. 9 and 10) is located where the 2014–2015 lava flow is about twice as thick as the average 2014–2015 flow thickness. Nevertheless, in this area the lava flow hazard remains high (Figs. 9 and 10, Sect. 4.4.2).
Our results (Figs. 9 and 10) also show that the village of Bangaeira is just as prone to be invaded by future lava flows as before the 2014–2015 eruption. Therefore, the assumption that previously overflowed areas are now safe and will not be overrun during the next eruption cannot be confirmed for this region either. The only chances for the villages to remain untroubled would be either to have an eruption from a vent outside the catchment (Fig. 11), or a vent inside the catchment but producing shorter lava flows as compared to the previously observed cases, or that early effusive pulses update the local topography in a way that our hazard maps are no longer valid.
Of the inhabited areas within the Chã, solely Ilhéu de Losna shows a significant decrease (46 %) in lava flow hazard. That said, probability values out of range of the statistics are always possible, meaning that the next lava flow might also affect areas with very low probability.
The 2014–2015 Fogo eruption ended on 8 February 2015 after 78 days of
activity; meanwhile three prominent villages were destroyed by lava flows.
Satellite radar observations allowed time-dependent mapping of the lava flow
evolution during the 2014–2015 eruptive crisis of Fogo Volcano. Furthermore,
we used combined TLS and photogrammetric data to quantify the thickness of
the lava flows. We observed a maximum lava flow thickness of
Based on thousands of lava flow simulations, we produced lava flow hazard
maps for Fogo Volcano. Provided that these are communicated to decision
makers and major investments in education are made, our results can be
considered when planning new infrastructure and the
resettlement of the villages. The maps were produced using the DOWNFLOW
stochastic model (Favalli et al., 2005) on the basis of our high-resolution
topographic map. Results of our lava flow hazard analysis imply that the two
main villages within the Chã, Portela, and Bangaeira, remain at high
risk. Even the area west of Portela, where the topographic relief was
partially infilled with a lava flow of up to
We provide the posteruptive combined TLS and photogrammetric DEM in geotiff-format in the Supplement of this publication. We also deliver our hazard maps, the thickness map of the 2014–2015 lava flow, the catchment map, and shaded reliefs of the pre-eruptive, posteruptive, and TLS DEMs in kml format in the Supplement. Furthermore, the post-2015 lava flow hazard for Fogo Volcano, Cabo Verde is provided in map format by Richter et al. (2016).
During our field work between 11 January 2015 and 21 January 2015, Pico
Pequeno displayed three different phases of activity. (1) Mild degassing from
the vent, sometimes in combination with lava flows from the mount SW of Pico
Pequeno (cf. Fig. C1), accompanied by minor ash puffs without much explosive
activity and no audible sounds. This activity was observed several early
mornings. (2) Distinct ash puffs every 0.5 to 5 min of up to 700 m
height. These explosions emit bombs which mostly fall back into the vent. A
sloshing sound can be heard from the lava within the conduit and explosions
are sometimes so loud that an echo from the Bordeira can be heard. This
activity was mostly observed in the afternoon. (3) Intense explosive
activity, almost continuous high lava fountains (up to 300 m) occurred well above the
crater rim. Explosions eject bombs up to 700 m, impacting half way up Pico
and two-thirds down Pico Pequeno. Ash explosions rose up to 1000 m, were
then drifting above Pico and raining out mostly on its flanks. Intense,
almost constant noise, with many echoes from the Bordeira was audible. This
activity was observed on 4 different days around sunset (18:30 to
19:00 LT, UTC
Minuit RMSEs (1) in the blue regions from top to bottom (SfM DEMs vs. posteruptive DEM) at 0.75 m, 1.45 m, 1.79 m, 0.69 m and (2) in a buffer area outside the lava flow (post- vs. pre-eruptive topography): 0.81 m (overall).
A small lava flow was still active during our field work on 12 January 2015.
In order to map possible changes in the flow field, including active lava
flows, the set-up on Monte Saia was kept the same over the duration of our
field campaign, i.e. six reflectors and the scanner tripod were permanently
installed (see Fig. C1). From this position (see scanner position in Fig. C1
and SAIA1 in Table C) we acquired three multitemporal, very
high-resolution 360
Only the first of these scans was used in combination with the Beco and
Amarelo scans to produce the updated DEM (due to slightly worse weather
conditions on the other days). Therefore, the active lava flow is not
completely included in our posteruptive DEM, which is only updated until 16
January 2015 (the acquisition date of scans MBC 4 and MBC 5, cf. Table 1).
Even though the TLS points acquired on 17 January 2015 and 21 January 2015
were too sparse to create a DEM (cf. No. of points in Table C), they were dense
enough for estimating the flow volume to roughly amount to
0.15
Thickness maps of the active lava flow calculated from the vertical difference between the TLS data acquired on 21 January 2015 minus (1) the TLS data acquired on 12 January 2015 and (2) the TLS data acquired on 17 January 2015 (all in grid format). Outlines of the lava flows are taken from TerraSAR-X coherence (Sects. 3.1, 4.1, 5.1, Fig. 7, and Appendix D). Inset shows a photograph taken in the early morning of 12 January 2015 from a position on Monte Saia, looking south-east.
GPS coordinates of scanner position and point cloud accuracies of the multitemporal TLS data acquired from Monte Saia.
TerraSAR-X coherence maps (georeferenced, i.e. north is up). Panels 1, 3, 5, 7, 8, 9, and 10 are TSX scenes acquired along orbital path 57 (ascending), panels 2, 4, and 6 are ascending acquisitions acquired along orbital path 148, and panel 11 is a coherence map between two SAR images acquired along the descending track 155.
Nicole Richter, Elske de Zeeuw-van Dalfsen, and Judith Levy carried out the field work. Sónia Silva Victória provided assistance in the field and contributed photographs. Nicole Richter processed the TerraSAR-X data. Massimiliano Favalli developed the DOWNFLOW model and code. Massimiliano Favalli and Nicole Richter performed the TLS and SfM data processing and model simulations. Rui Manuel da Silva Fernandes processed the GPS data. Thomas R. Walter supervised the study. Thomas R. Walter and Alessandro Fornaciai helped with the photogrammetric data processing. Nemesio M. Pérez provided the pre-eruptive DEM. Nicole Richter prepared the manuscript with contributions from all coauthors.
This work was supported by the Helmholtz Alliance Remote Sensing and Earth System Dynamics (HGF EDA). Field work was funded by the GFZ Hazard and Risk Team (HART) program. Access to the Riegl instrument was kindly provided by Niels Hovius. Operating instructions were given by Kristen Cook. The TerraSAR-X data were provided by the German Aerospace Center (DLR) through the proposal ID 1505. The C4G monitoring campaign was made possible through an emergency financial support provided by FCT (Fundação para a Ciência e Tecnologia), Portugal. FCT also funded the GPS data processing in the framework of the FIRE (PTDC/GEO-GEO/1123/2014) project. We are grateful to Eleonora Rivalta, Jacqueline Salzer and Adam Mehlhorn, whose valuable suggestions helped to improve the manuscript. We especially thank Michael Poland, Matthieu Kervyn, and Sónia Calvari for detailed and constructive reviews that greatly improved the manuscript. We also thank Paulo Fernandes Teixeira and Lourenco Francisco Fernandes for their invaluable assistance during fieldwork on Fogo Island.The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: O. Katz Reviewed by: M. P. Poland, M. Kervyn, and S. Calvari