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A new map of the lava flow field of Nyamulagira (DR Congo) from satellite imagery


Abstract and Figures

Nyamulagira (3058 m a.s.l.), a volcano of the Virunga volcanic province in the western branch of the East African Rift, is Africa’s most active volcano with one eruption every 2–4 years. It represents a hazard for the Virunga National Park and its vicinity. Despite such a frequent activity, Nyamulagira remains poorly studied. The only existing volcanological map was produced in the sixties by Thonnard et al. (1965). The occurrence of 19 eruptions since its publication makes it obsolete. In the present study we mapped the Nyamulagira lava flows from 1938 up to the last eruption to date in 2010 using optical (Landsat, ASTER) and radar (ENVISAT-ASAR, ERS, JERS) imagery. The results are integrated into a Geographical Information System (GIS) and coupled with additional data sources. GIS use makes the new database a flexible – and easy-to-update – tool for scientific purposes as well as for risk, environmental and humanitarian management. Here a new lava flow map was produced. Volumes of the successive lava flows and affected areas of the Virunga National Park were estimated.
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A new map of the lava flow field of Nyamulagira (D.R. Congo) from satellite imagery
Smets Benoît
, Wauthier Christelle
, d’Oreye Nicolas
Royal Museum for Central Africa, Geology Department, Leuvensesteenweg 13, B-3080 Tervuren, Belgium
University of Liège, Department ArGEnCo, Sart Tilman B52, B-4000 Liège, Belgium
National Museum of Natural History, Geophysics/Astrophysics Department, rue Josy Welter 19, L-7256 Walferdange, Luxembourg
article info
Article history:
Available online 10 August 2010
Lava flow
Remote sensing
Nyamulagira (3058 m a.s.l.), a volcano of the Virunga volcanic province in the western branch of the East
African Rift, is Africa’s most active volcano with one eruption every 2–4 years. It represents a hazard for
the Virunga National Park and its vicinity. Despite such a frequent activity, Nyamulagira remains poorly
studied. The only existing volcanological map was produced in the sixties by Thonnard et al. (1965). The
occurrence of 19 eruptions since its publication makes it obsolete. In the present study we mapped the
Nyamulagira lava flows from 1938 up to the last eruption to date in 2010 using optical (Landsat, ASTER)
and radar (ENVISAT-ASAR, ERS, JERS) imagery. The results are integrated into a Geographical Information
System (GIS) and coupled with additional data sources. GIS use makes the new database a flexible – and
easy-to-update – tool for scientific purposes as well as for risk, environmental and humanitarian manage-
ment. Here a new lava flow map was produced. Volumes of the successive lava flows and affected areas of
the Virunga National Park were estimated.
Ó2010 Elsevier Ltd. All rights reserved.
1. Introduction
Nyamulagira (or Nyamuragira in Rwandese) is a 3058 m high
shield volcano with a 2 2.3 km caldera located south of the
Virunga National Park (SE part of N-Kivu, D.R. of Congo) (Fig. 1).
It is one of the two active volcanoes in the Virunga, a volcanic
province in the western branch of the East African Rift (EAR). This
volcano is Africa’s most active volcano with, on average, one
eruption every 2–4 years. Nyamulagira’s lava field covers over
1100 km
and contains more than 100 flank cones. Lavas are basic,
-undersaturated and K-rich (Kampunzu et al., 1982; Aoki and
Yoshida, 1983; Aoki et al., 1985). Such composition results in low
viscosity lavas able to flow for tens of kilometres. Comprehensive
descriptions of the Nyamulagira geology and eruptive history are
given by Pouclet (1976) and Hamaguchi (1983).
The only existing volcanological map of Nyamulagira was estab-
lished by Thonnard et al. (1965). This map was constructed by
interpreting three independent sets of aerial photographs. The
mosaics were assembled with the help of nine triangulated points
and theodolite field measurements (Thonnard, 1961; Thonnard
and Denaeyer, 1965). Comparison of the digitized and geocoded
map with recent field GPS measurements revealed local distortions
in the map, which induce errors of location reaching sometimes
more than 2 km in places. Contour lines on the 1965 map only give
a general idea of the relief as they lack absolute or relative eleva-
tion values. Nyamulagira also erupted 19 times since the produc-
tion of the 1965 map, which makes the latter totally outdated.
Partially updated maps are found as figure in publications (e.g.
Ueki, 1983; Zana, 1984; Louant, 1987; Zana and Hody, 1989) and
recently, Colclough (2005) and Kitagawa et al. (2007) used radar
remote sensing techniques to map the 1996, 1998 and 2001 lava
flows. In fine, no comprehensive and up-to-date compilation of his-
torical lava flows at Nyamulagira has been published yet.
An updated map with recent lava flows is essential as a tool for
scientific purposes, as well as for risk, environmental, urban and
humanitarian management. Nyamulagira’s eruptions can pose a
non-negligible hazard for local populations and their environment.
Coupled with deforestation, political and social unrest as well as
important demographic and urban growth, the potential risk/im-
pact from Nyamulagira’s volcanic hazards increases accordingly.
Villages, roads and the protected forest of the Virunga National
Park (VNP) are under the threat of volcanic eruptions. Given the
VNP is a UNESCO World Heritage since 1979 with ‘‘endangered sta-
tus” since 1994 (UNESCO, 1992–2009), impacts of the Nyamulagi-
ra’s volcanic activity upon forest and its unique ecosystem should
be assessed in future.
In order to meet these needs, lava flows from eruptions in 1938–
2010 were mapped with both optic and radar satellite imagery and
compiled into a GIS database together with data from other sources
1464-343X/$ - see front matter Ó2010 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +32 (0) 2 769 54 48; fax: +32 (0) 2 769 54 32.
E-mail address: (B. Smets).
Journal of African Earth Sciences 58 (2010) 778–786
Contents lists available at ScienceDirect
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Fig. 1. Location of Nyamulagira and of the Virunga volcanic province.
B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786 779
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(maps, catalogues of geodata, publications, unpublished notes and
field data) using the ESRI ArcGIS
Software. The present paper de-
scribes the methodology used to generate the new lava flow map,
highlights scientific information that can be extracted from it and
discusses the contribution of the map to volcanic risk management.
2. Remote sensing data and methods
2.1. Remote sensing mapping of lava flows
Since the emergence of moderate-high spatial resolution space
imagery (15–30 m pixel size), satellite data have been used to map
volcanic terrains (e.g. Francis and Baker, 1978; Francis and Wells,
1988; Carn, 1999; Lu et al., 2004). Remote sensing is a powerful
tool for studying large structures, often impossible to appreciate
on the ground especially when they are remote and/or situated
in areas difficult to access, such as some volcanoes in developing
countries (Kervyn et al., 2007; Ernst et al., 2008). If multispectral
data like Landsat, ASTER or SPOT imagery enable accurate lava flow
mapping, cloud cover often limits their use, mainly in tropical
zones (Carn, 1999; Lu et al., 2004). In addition, frequent eruptions
generate lava flow overlap for which the flows boundaries are not
always clearly visible on optical images.
Radar sensors allow data acquisition at any time of day or night
and through cloud cover (Hanssen, 2001; Kervyn, 2001). Further-
more, Synthetic Aperture Radar (SAR) data can be processed to ob-
tain different and complementary products (amplitude, phase and
coherence) usable for lava flow mapping (Lu et al., 2004). SAR
amplitude images give information on the backscattering proper-
ties of the ground, which vary spatially with the nature and struc-
ture of the targeted terrain. Coherence images, which correspond
to the degree of similarity of the backscattering properties between
the two images used to form an interferogram (Massonet and Feigl,
1998), depict the changes of ground surface characteristics with
time (Hanssen, 2001). SAR interferometry (InSAR) also allows the
production of Digital Elevation Models (DEMs). Lava flow area
and volume can be measured by subtracting pre- and post-erup-
tion DEMs (e.g. Rowland et al., 1999; Lu et al., 2004). However
these InSAR tools may yield poor results when applied over vege-
tated areas because of the loss of coherence (or decorrelation) that
vegetation induces. In order to compensate for their respective
limitations, lava flows at Nyamulagira were mapped using a com-
bination of optical and radar-derived datasets.
2.2. Optical remote sensing data
Archive data from Landsat satellites, covering a time span of
three decades, has become progressively available at no charge
during 2008 ( or http://glovis.usgs.
gov). A time-series of images can be easily created. For Nyamulagi-
ra, a set of 8 Landsat 4–5 TM, 12 Landsat 7 ETM+ and 3 ASTER
images was used. We used Landsat spectral bands 5, 4 and 3 to en-
sure the best contrast between lava and vegetation, and between
the different overlapping lava flows, as suggested by Lu et al.
(2004). Band 5 is sensitive to soil and vegetation moisture whereas
bands 4 and 3, usually used to calculate a vegetation index, are
respectively sensitive to biomass and chlorophyll absorption. To
benefit from the highest spatial resolution of ASTER (i.e. 15 m),
we combined the spectral bands 3, 2 and 1. Details of these images
are summarized in Tables 1 and 2.
2.3. Radar remote sensing data
In the framework of volcano monitoring projects using InSAR
(Kervyn et al., 2005; d’Oreye et al., 2008; van Overbeke et al.,
in press) hundreds of SAR images, including ENVISAT ASAR,
ERS, JERS images (see Table 3 for data specifications and charac-
teristics), acquired over Nyiragongo and Nyamulagira in the last
15 years were processed. At the time of writing, over 2500 geo-
coded interferograms and associated coherence maps were com-
puted providing us with a huge amount of data for lava flow
mapping. This was done by tracking the decorrelation on syn-
eruptive coherence images, or by detecting the improvement of
coherence on post-eruptive coherence images. Nyamulagira’s lava
flows proved to be too thin (typically <5 m thick) to enable accu-
rate mapping by comparison of pre- and post-eruptive InSAR-de-
rived DEMs.
2.4. Mapping lava flows at Nyamulagira
With a rich optical dataset of 22 images over a 21 years period,
impact of cloud cover and lava flow superposition were minimized
Table 1
Specifications of satellites, sensors and optical products considered in this study. All these satellites are in a 705 km altitude sun-synchronous orbit with a 16-days repeat cycle.
Launch year Landsat 4 and 5 Landsat 7 Terra
1982 and 1984 1999 1999
Sensor used TM (Thematic Mapper) ETM+ (Enhance Thematic Mapper) ASTER (Advanced Spaceborne Thermal
Emission and Reflection radiometer)
Spectral bands 1 (B) = 0.45–0.52
m 1 (B) = 0.45–0.52
m V1 (G) = 0.52–0.60
2 (G) = 0.52–0.60
m 2 (G) = 0.53–0.61
m V2 (R) = 0.63–0.69
3 (R) = 0.63–0.69
m 3 (R) = 0.63–0.69
m V3 (NIR) = 0.76–0.86
4 (NIR) = 0.76–0.90
m 4 (NIR) = 0.78–0.90
m S4 (SWIR) = 1.6–1.7
5 (SWIR) = 1.55–1.75
m 5 (SWIR) = 1.55–1.75
m S5 (SWIR) = 2.145–2.185
6 (TIR) = 10.4–12.5
m 6 (TIR) = 10.4–12.5
m S6 (SWIR) = 2.185–2.225
7 (SWIR) = 2.08–2.35
m 7 (SWIR) = 2.09–2.35
m S7 (SWIR) = 2.235–2.285
8 (Pan) = 0.52–0.90
m S8 (SWIR) = 2.295–2.365
S9 (SWIR) = 2.260–2.430
T10 (TIR) = 8.125–8.475
T11 (TIR) = 8.475–8.825
T12 (TIR) = 8.925–9.275
T13 (TIR) = 10.250–10.950
T14 (TIR) = 10.950–11.650
Spatial resolution 30 m 30 m 15 m (visible and NIR)
120 m (band 6) 60 m (band 6) 30 m (SWIR)
15 m (Panchromatic) 90 (TIR)
Scene size 185 172 km 185 172 km 60 60 km
Two V3 bands exist for DEM production: V3 N (nadir view) and V3B (backward view).
780 B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786
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and most of the lava flows were mapped. Radar products were
needed to complement mapping of the 1996, 1998, 2000, 2001
and 2004 flows. A manual mapping method was preferred to auto-
mated methods (e.g. supervised and unsupervised classifications)
which yielded confusion among the frequently overlapping lava
flows and required important post-processing. The volcanological
map (scale 1/50,000) by Thonnard et al. (1965), geocoded and
orthorectified with ASTER images, was also used to help localize
and identify the 1938–1940, 1948, 1951, 1954, 1956, 1957 and
1958 lava flows.
The general location of each event was found in papers describ-
ing eruptive activity of the last century (Hamaguchi, 1983; Burt
et al., 1994; Smithsonian Institution, 1996–2009) and served to
identify the source of each flow. Frequent eruptions along the
NNW–SSE fracture network crossing the caldera make the location
of some recent eruptive vents and corresponding lava flows diffi-
cult. SAR coherence images were used to locate them (Fig. 2). Once
located, these lava flows were mapped using optical images. Next,
SAR data (both coherence maps and amplitude images) were used
to complete the lava flow mapping, especially for the 1996, 1998,
2000, 2001 and 2004 lava flows, which were only partially mapped
with optical images. Fig. 3 shows the lava flows from 1938 to
The accuracy of lava flow mapping is directly proportional to
the spatial resolution of the images. Mapping errors depend mainly
on vegetation regrowth over fresh lava and ability of the operator
to visually discriminate the ground coverage. According to Pouclet
(1976), vegetation growth makes lava flow boundaries on images
Table 2
List of optical images used for lava flow
mapping. The path and row of the images are
respectively 173 and 61.
Satellite and
Acquisition date
Landsat 5 TM July 19, 1986
Landsat 5 TM August 7, 1987
Landsat 4 TM August 4, 1989
Landsat 4 TM August 20, 1989
Landsat 4 TM September 21,
Landsat 4 TM May 19, 1990
Landsat 5 TM October 13, 1994
Landsat 5 TM January 17, 1995
Landsat 7 ETM+ September 17,
Landsat 7 ETM+ October 3, 1999
Landsat 7 ETM+ December 6, 1999
Landsat 7 ETM+ October 3, 2000
Landsat 7 ETM+ December 8, 2000
Landsat 7 ETM+ February 26, 2001
Landsat 7 ETM+ August 5, 2001
Landsat 7 ETM+ December 11,
Landsat 7 ETM+ January 28, 2002
Landsat 7 ETM+ December 30,
Landsat 7 ETM+ January 15, 2003
Landsat 7 ETM+ January 31, 2003
Terra ASTER January 23, 2006
Terra ASTER June 19, 2007
Table 3
Specifications of satellites and SAR sensors considered in this study. All these satellites are in a sun-synchronous orbit. As the dataset contains lots of images and SAR-derived
products, only the time spanned by data from each satellite is given.
Country EU EU Japan
Launch year 1991 and 1992 2002 1992
Altitude 782–785 km 800 km 568 km
Repeat cycle 35 days 35 days 44 days
Sensor used SAR (Synthetic Aperture Radar) ASAR (Advanced Synthetic Aperture Radar) SAR (Synthetic Aperture Radar)
Spectral bands C-band C-band L-band
Frequency = 5.3 MHz Frequency = 5.3 MHz Frequency = 1.275 MHz
Wavelength = 5.66 cm Wavelength = 5.66 cm Wavelength = 23.5 cm
Polarization = VV Polarization = VV, HH, VV/HH, HV/HH, VH/VV Polarization = HH
Spatial resolution 30 m 30–150 m 18 m
Scene size 100 100 km 100 100 km 75 75 km
Time spanned by data 21/02/1996 to 16/12/2004 25/12/2002 to now 19/10/1996 to 28/02/1997
Fig. 2. Example of lava flow mapping assisted by InSAR products: the May 8–28, 2004 eruption. Comparing pre- (A) and syn-eruptive (B) coherence images from ENVISAT
interferograms allows the localization and identification of new lava flows (black flow on panel C). By modifying the ground coverage, the new lava flow changes the
backscattering conditions and induces decorrelation (black zones on A and B) clearly visible in contrast to the still highly coherent former flows (white areas on A and B).
Interferometric products are produced from ENVISAT images in ascending orbit, swath 2. The coherence maps on A and B span December 5th 2002–January 14th 2004 and
December 5th 2002–May 3rd 2006 respectively.
B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786 781
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indistinct after few decades. Thus, optical images used were cho-
sen as close as possible to eruption dates in order to minimize this
effect. Errors from both vegetation recovery (highly variable in
time and space) and manual mapping do not exceed one pixel
width on optical data, which corresponds to the accuracy of the
There are two additional sources of error: (1) local misinterpre-
tation in near-vent area related to tephra deposits, and (2) quality
of SAR data (noise, effects of relief on coherence, etc.). These errors
are not expected to strongly influence the final result. As errors due
to tephra deposits cannot be assessed without field measurements
and lava flows were only mapped locally with SAR data, these two
Fig. 3. Map of most recent (1938-present) lava flows from Nyamulagira (darker flows are the most recent ones). The Nyiragongo lava field is also identified with its two most
recent flows from 1977 and 2002 eruptions. The new volcanological map based on this lava flow mapping contains additional information such as topography, petrology,
fractures and faults, adventive cones, VNP boundaries, roads, and urban areas.
782 B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786
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error sources were neglected for surface and volume calculations,
even if they must be taken into account for the interpretation of
the results.
2.5. GIS integration
The Geographic Information System (GIS) has become an essen-
tial tool in any cartographic project. It enables comparison and
overlap of any kind of data with geographic coordinates (i.e. lay-
ers), rapid production or update of thematic maps and quantitative
characterization of geographic features (e.g. length, area). GIS is the
most suitable technique for processing geographic data in areas
with constant changes such as active volcanic zones.
In addition to the lava flow layers, our GIS database contains
information related to the petrography of lava flows, the geological
and geomorphological structures (cones, faults, fissures and linea-
ments), the topography (DEMs and resulting contour lines),
hydrography (rivers and lakes), land use (e.g. urban areas, Virunga
National Park) and geography (e.g. roads, localities, political
3. The new map of Nyamulagira
The new map of Nyamulagira combines data from the two remote
sensing techniques and from the literature. Table 4 summarizes the
different geographic data (layers) integrated in the new map. The
lava flows are represented by a specific colour for each eruption.
Lighter and darker colours correspond respectively to older and
younger events. Lava petrology with corresponding location on the
map is from Thonnard et al. (1965), Aoki and Yoshida (1983) and
Aoki et al. (1985). Urban areas and lakes were mapped from 2001
to 2003 Landsat ETM+ and 2006–2010 ASTER imagery. The name
and location of localities were obtained from RDC-Humanitaire
NGO’s Map & GIS Data Center (
spip.php?rubrique30). The road and hydrographical networks result
from Landsat image interpretation and from the BEGo project
(2005). Contour lines were derived from the Shuttle Radar Topogra-
phy Mission (SRTM) DEM (
The map is frequently updated when new data are available
(e.g. very high spatial resolution images) or when a new eruption
occurs (e.g. 2010 eruption). The map is only available at the Royal
Museum for Central Africa and will be available for purchase soon,
along with an explanatory booklet in English or French (see http://
for future information).
4. Contribution to scientific research and risk management
The new map and the GIS database could have great potential
for scientific research. They could also prove valuable as basis for
hazard mapping and risk assessment (De La Crùz-Reyna et al.,
2000; Newhall, 2000). As an example, we use them to estimate
the volume of the different lava flows and to assess the impact of
historical eruptions on the area.
4.1. Lava flow volume estimation
Estimation of erupted volumes through time at a volcano pro-
vides volcanologists with information on the volcano dynamics
(e.g. effusion rate) and can be used for statistical analysis or sto-
chastic modelling of the eruption history (Burt et al., 1994).
As already explained, unlike the surfaces, the flow heights could
not be determined by DEM comparison due to their limited thick-
ness. Previous volume estimations at Nyamulagira typically used a
mean lava flow thickness of 3 m (e.g. Pouclet, 1976; Pottier, 1978;
Brousse et al., 1979, 1981, 1983; Caron et al., 1982; Ueki, 1983;
Burt et al., 1994; Colclough, 2005; Kitagawa et al., 2007). According
to Burt et al. (1994), this mean value is probably affected by an
uncertainty of only a few tens of percent. In the absence of new
field measurements, still prevented by local insecurity and fre-
quent civil unrest, we use the same mean thickness to estimate
lava flow volumes.
As previously discussed, the uncertainty in lava flow surface
mapping corresponds to the image accuracy that is ±1 pixel for
the location of lava flow limits. Thus, the error on the estimated
areas (E
) can be calculated and is given by:
where Pis the lava flow perimeter and pis the pixel size. The real
lava flow area (S) is then expressed as:
where S
is the estimated area. We postulated that the mean thick-
ness (H) corresponds to 3 m for each lava flow. Consequently, the
volume (V) corresponds to:
where E
, the error on estimated volume, corresponds to:
is directly proportional to E
and only an indicative error estimate
due to the uncertainty on Hthat is not taken into account. Hence it
serves mainly for comparing our results with other estimates using
the same mean lava flow thickness.
In the last 70 years, single-event erupted volumes are found to
have an average volume of 72 10
. The largest volume,
201.55 10
, was emitted during the 29 month-long eruption
in 1938–1940 and the smallest one was erupted in the 1956 erup-
tion that lasted only 2 days and was limited to the summit caldera
(1 10
Table 5 summarizes the date, location and estimated lava flow
area and volume. Fig. 4 compares the estimated volumes from
the present study with previous estimates from Burt et al.
(1994), Colclough (2005) and Kitagawa et al. (2007). Results from
the latter two studies, also based on remote sensing techniques,
do not differ by more than 10% from ours (see Fig. 4). Results from
Table 4
List of information visible on the new volcanological map of
Nyamulagira. Cones, fractures and lava fields come from remote
sensing and field data. The petrology comes from Thonnard et al.
(1965) and Aoki et al. (1985). Geographical layers come from
remote sensing and BEGo (2005).
Geographical information
Name Type
Main towns and localities Point
Road network Polyline
Urban areas Area
National boundaries Area
Boundaries of the Virunga National Park Area
Hydrographic network Polyline
Lakes Area
Contour lines (every 200 m) Polyline
Colored shaded relief Image
Volcanic fractures of the Nyamulagira and
Nyiragongo volcanic fields
Adventive cones of Nyamulagira and
Nyamulagira’s lava flows of 1938–2006
Nyiragongo’s lava flows of 1977 and 2002
Petrography of lavas from Nyamulagira Point
B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786 783
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Burt et al. (1994) show larger discrepancies (i.e. 10–30%) with our
estimated volumes. This can probably be explained by the fact that
Burt et al. (1994) used data from previous works based on the 1965
map and did not have the advantage of modern techniques such as
differential GPS or high spatial resolution spaceborne remote
4.2. Impact of lava flows on VNP and human infrastructures
The GIS database enables the cross-correlation of mapped lava
flows with other geographic data. For instance, we observe that
the total Nyamulagira lava field (mapped using Landsat images)
covers at least 1117 km
, more than 85% of which are within the
VNP (see Fig. 1). The multiple lava flows from the 25 mapped erup-
tions that occurred between 1938 and 2006 occupy 40% of the to-
tal lava field, which attests to the rapid re-coverage of the area by
new lavas. Table 6 summarizes the surfaces and proportion of fea-
tures of natural or human interest (roads, park or urban areas) that
are covered or within the lava field of Nyamulagira and Nyiragon-
go. These observations of past events illustrate the lava flow hazard
and its recurrence in association with volcanic activity in the VNP.
The fact that Nyamulagira caused damages to crops, livestock and
Table 5
Summary of the eruptive history of Nyamulagira, from 1938 to present. The dates come from Burt et al. (1994), Smithsonian Institution (1996–2009), Bluth and Carn (2008) and
field observations for the last eruption.
Start date End date General location Cone name Lava flow
area (10
±Error Lava flow
volume (10
28/01/1938 25/06/1940 or after Summit, SE & SW flanks Tshambene 67.18 4.76 201.55 28.54
1/03/1948 15/07/1948 or after SW flank Gituro and Muhoboli 18.95 1.29 56.86 7.75
16/11/1951 16/01/1952 NNW fissure zone Shabubembe and Ndakaza 5.81 0.63 17.42 3.80
21/02/1954 28/05/1954 SSE fissure zone Mihaga 22
17/11/1956 18/11/1956 Summit caldera 0.33 0.06 1.00 0.37
28/12/1957 29/12/1957 Summit caldera & SSE fissure zone 1.82 0.37 5.47 2.24
7/08/1958 21/11/1958 N flank Kitsimbanyi 47
23/04/1967 3/05/1967 N flank Gakararanga 32
24/03/1971 28/04/1971 or after WNW flank Rugarama 30
23/12/1976 April 1977 SSW flank Murara and Harakandi 21
30/01/1980 23/02/1980 N flank Gasenyi 22.31 1.73 66.94 10.37
25/12/1981 14/01/1982 SE flank Rugarambiro 41.89 2.74 125.67 16.47
23/02/1984 14/03/1984 NW flank Kivandimwe 22.48 1.46 67.45 8.76
16/07/1986 20/08/1986 South flank 18.41 1.37 55.22 8.20
30/12/1987 4/01/1988 N flank Gafuranindi 2.05 0.35 6.14 2.09
23/04/1989 After 4/08/1989 Summit, SE & E flanks 31.74 1.99 95.22 11.94
20/09/1991 8/02/1993 NE flank Mikombe and other 43.68 1.82 131.04 10.90
4/07/1994 24/07/1994 West flank Kimera 14.32 0.92 42.97 5.49
1/12/1996 16/12/1996 or after N flank 16.58 2.83 49.75 8.49
17/10/1998 29/10/1998 Summit & NW flank 23.06 3.49 69.18 10.48
27/01/2000 11/02/2000 or after SE flank Ngerageze 15.77 2.13 47.30 6.40
6/02/2001 5/04/2001 or after N & SSE flanks 46.71 3.78 140.14 11.33
25/07/2002 Between 9/08 and 27/09/2002 Summit caldera, N & S flanks 18.89 1.45 56.67 8.69
8/05/2004 28/05/2004 or after Summit & NNW flank 22.83 1.97 68.48 11.84
27/11/2006 5/12/2006 or after South flank Ushindi 14.73 1.13 44.19 3.39
2/01/2010 27/01/2010 SE flank 17.65 5.98 52.96 17.94
Volumes from Burt et al. (1994), not calculated in this study.
Fig. 4. Histogram comparing the lava flow volume estimates from Burt et al. (1994) in black, Colclough (2005) in light grey, Kitagawa et al. (2007) in dark grey and from this
study in white. The volume estimates calculated in this study are in close agreement with those from former studies by Colclough (2005) and Kitagawa et al. (2007). Relative
differences between this study and these previous estimates for the 1996, 1998 and 2001 flow volume are 9.5%, 2.6% and 5.3%, respectively. Results from Burt et al. show
slightly lower agreement (see text).
784 B. Smets et al. /Journal of African Earth Sciences 58 (2010) 778–786
Author's personal copy
infrastructure at least nine times since 1938 (Smithsonian Institu-
tion, 1996–2009) highlights that this volcano represents a direct
hazard that must be taken into account for a safe and sustainable
development of the area. Concerning Nyiragongo, the present re-
sults are also consistent with the hazard assessments by Favalli
et al. (2009) and Chirico et al. (2009), which suggest that Nyiragon-
go eruptions can lead recurrently to fatalities and substantial infra-
structure damage, particularly in the densely populated city of
Goma and its environs (see Table 6).
5. Conclusions
The combined use of optical and radar remote sensing tech-
niques allowed us to successfully map the lava flows of the last
26 eruptions of the Nyamulagira. Integrated in a GIS database with
complementary datasets, a new thoroughly updated volcanological
map is now available and easily updatable and adaptable to the
needs of different users. The lava flow mapping was urgently
needed for scientific studies as well as for enhanced risk, environ-
mental and humanitarian management. As examples, accurate sur-
face estimation of lava-covered areas allowed us to calculate lava
volumes for successive eruptive phases and provide us with essen-
tial information for hazard assessment. It shows in particular that
Nyamulagira poses a lava flow hazard in the region, especially for
the protected forests of the Virunga National Park. Although most
of the lavas flow toward the northern uninhabited regions, the
Nyamulagira’s lavas also potentially threaten several tens of thou-
sand people in the southern region of Sake. Indirect hazards should
also be considered in addition to the lava flow hazard. Contamina-
tion by gas and ash as well as loss of crops and livestock can ma-
jorly affect the larger population living up to several hundreds of
kilometres away from the lava fields.
These new remote sensing results must, however, be validated
in the field, which is currently prevented by social and political un-
rest of the region.
5.1. Note added in proof
As mentioned in the present work Nyamulagira is the most ac-
tive volcano in Africa. It actually erupted in January 2010 during
the reviewing procedure of the current paper, leading us to add a
preliminary mapping of its last lava flows in Fig. 3 and update
Tables 5 and 6. The preliminary mapping of the new lava flows
was performed using the TIR bands of an ASTER image taken on
29th January 2010. This mapping is less accurate than for previous
eruptions due to the lower spatial resolution (i.e. 90 m) of the TIR
bands used and the possible effect of cloud cover on some pixel
The current study was carried out in the frame of the projects
‘‘SAMAAV: Study and Monitoring of Active African Volcanoes” (funded
by the Belgian Federal Science Policy) and ‘‘GORISK: A multi-ap-
proach tool for the volcanic risk management of the Goma region
(North Kivu)” (funded by the Belgian Federal Science Policy (SR/
00/113) and the National Research Fund of Luxembourg (FNR/STE-
REOII/06/01)). SAR data are provided in the frame of the European
Space Agency (ESA) Category-1 Project No. 3224 and ESA-JAXA
(Japanese Space Agency) Project No. 3690. Precise orbits are pro-
vided by the Delft Institute of Earth Observation and Space Systems
(DEOS) and ESA. Interferograms are computed with the DORIS
(TUDelft) and the ROI-PAC (JPL/Caltech) open source softwares.
SRTM digital elevation models are provided by the US Geological
Survey. We thank J. Lavreau, F. Kervyn and M. Kervyn for helpful
comments. We also thank S.A. Carn and G.G.J. Ernst for their review
of the manuscript.
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Virunga National Park Roads (km) Urban areas (km
) Goma Sake
Nyamulagira lava field 954.2 km
(12% of the park) 72.9 4.2 2 km
(93% of the town)
1938–2010 Lava flows from Nyamulagira 437.2 km
(5.7% of the park) 8.4 0.5 0.5 km
(22% of the town)
Nyiragongo lava field 165.5 km
(2% of the park) 239.2 39 37 km
(100% of the city)
1977 Lava flows from Nyiragongo 7.4 km
(0.1% of the park) 17.4 1.6
2002 Lava flows from Nyiragongo 3.8 km
(0.05% of the park) 11 3.7 3.7 km
(10% of the city)
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... It has been shown that flank eruptions have followed a counter-clockwise rotation on the lower slopes of the Nyamuragira shield , particularly for the 1958, 1967, 1971, and 1976 events. This was also the case in 1984, 1986, 1989, 1991(Smets et al. 2010. Eruption events are located in the reverse direction of the stress action, thus the stress must have rotated in a clockwise direction. ...
... The 1958 lava field was thus remapped by Pouclet (1976). The post-1958 activity has been successively described by Pouclet (1975b), Burt et al. (1994), Smets et al. (2010 and, and Coppola et al. (2016). ...
... This tectonic system, having an axial direction of N 155°, can be considered as a mega tension gash . In the NNW flank of the Nyamuragira caldera, there is a spectacular fan-shaped succession of fractures where thirteen eruptive events have taken place since the beginning of the 1900s Smets et al. 2010). ...
Nyiragongo and Nyamuragira are two active volcanoes of the Western branch of the East African Rift in the Virunga area. They were built at the Kivu rift axis ca. 12,000 years ago and set above two tectonic steps separated by the Kameronze Fault. Both volcanoes have displayed a succession of intra-crater and flank eruptions that have been observed and documented since the end of the nineteenth century. Here, we have collated and reviewed these publications and reports. Nyiragongo is famous for its semi-permanently active lava lake, which at the time of writing (2020) was the largest in the world. During the construction of the main stratovolcano, which ended a few centuries ago with a caldera collapse, the lava composition changed from melilitite to leucite and then to melilite-bearing nephelinite. The historically active lava lake is believed to be directly fed from an upper intra-volcano reservoir, a shallow reservoir situated a few kilometres below the volcano in the granite basement, and a deeper intra-crustal magma chamber. Historic activity has been documented since 1894 and can be divided into eight stages, on the basis of sudden changes between lava filling and draining, with cycles of rising lava lake activity and overflows, followed by sinking and complete or partial drainage. Twice in recent history (in 1977 and 2002), major flank eruptions were accompanied by complete drainage of the lava lake and the upper plumbing system. The lava that filled the crater since 1948, and then again after the 1977 and 2002 drainage events have been calculated at a cumulative volume of around 324 × 106 m3. In comparison, the 1977 and 2002 flank eruptions involved 47 × 106 m3 of lava. The average annual output rate associated with crater filling is thus estimated at between 4 and 13 × 106 m3. Lava lake behaviour changes from equilibrium, with alternation between gas pistoning and spattering regimes through disequilibrium with intermittent activity, to complete disappearance of the lava lake. These changes can be related to the conditions of the descent of dense degassed magma from the upper conduit into the shallow reservoir. However, since 1959, the chemical composition of the leucite and melilite-bearing nephelinite lavas has not significantly changed, which implies a magma supply from the same magma batch. Nyamuragira was characterised by a shield building phase of activity until a caldera collapse, ca. 300 to 500 years ago. The post-caldera phase has involved effusive activity in the caldera and at numerous flank fissures. The plumbing system consists of an upper reservoir roughly at the basement-volcano interface and averaging a volume of 400 × 106 m3, a shallow upper-crust stratified reservoir, and a middle-crust mafic magma chamber. Volcanic activity has involved a succession of filling and emptying events at the upper reservoir. Lava volumes of historic eruptions reveal annual output rates averaging 14 × 106 m3 between 1901 and 1976, and 40 × 106 m3 between 1976 and 2012. A drastic increase in activity occurred in December 1976. This event coincided with the January 1977 flank eruption of Nyiragongo and resulted from a main tectonic event in the rift basement that improved the efficiency of magma ascent at both volcanoes. The historic lava composition can be related to six cycles of magma accumulation in, and withdrawal of, the upper reservoir from the shallow stratified reservoir. Similar magma storage and transport systems are known in many effusive systems: the Nyiragongo lava lake shares behavioural characteristics similar to those observed at Kilauea, Erebus, and Erta’Ale. At Nyiragongo and Nyamuragira, magma supply and persistent activity with sudden changes of the magma output rates in relation to tectonic events are also comparable with those of Kilauea, Piton de la Fournaise of Réunion island, and Mount Etna.
... The period 2004 -2018 showed an increase in the bare area of 90% in the southern part of VNP ( Figure 2). The bare area increase is mainly due to Nyamuragira's major eruption (2010-2011) that turned its vegetation zones into lava plains classified as bare areas (Smets et al., 2010). This eruption of Nyamuragira eruption (2010-2011) transformed 8.36 Km 2 of the forest, 11.6 Km 2 of grassland, and almost 1 Km 2 of shrubland into lava plains between 1990 and 2018 ( Table 1). ...
... The volcanoes located to the east of the national park showed that highest vegetation decline between 1990 and 2018. It can be noted that the vegetation decline, southern part ( Figure 4C) is mainly due to the volcanic activity that takes place almost every year (Smets et al., 2010) and have severely affected the dense forest where the heads of trees were partly burned by acid rainfalls and volcanic plume (Vaselli et al., 2010). The coral trails seen near the volcanoes are recently dried lava flows ( Figure 4C). ...
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The change on Virunga National Park (VNP) landscape due to climate change, anthropogenic activities and natural disasters calls for appropriate monitoring and management policies. This study aimed to assess and compare the factors conditioning landscape change during volcanic eruption at VNP located in the East Africa from 1990 to 2018. Land use and land cover Landsat 5 images of 1990 and 2004, and Landsat 8 images of 2018 were analyzed by the supervised maximum likelihood classification techniques. The calculation of Normalized Difference Vegetation Index (NDVI) and its relationship with precipitation and temperature referred to the same images. Remote Sensing (RS) and Geographic Information System (GIS) facilitated data analysis. The results highlighted human activities as key drivers to landscape changes recorded in the central and northern parts. The southern part recorded high extent of changes which transformed 8.36 Km2 of the park into new lava flow linked to 11.6 Km2 of grassland and almost 1 Km2 of shrub land changed into lava plains. The two volcanoes are associated with volcanic plume and gas emission, which mainly cause NDVI perturbation. This study can serve as a baseline in the conservation and management of protected areas and reduction of volcanic effects on national parks.
... Nyiragongo and Nyamulagira, located in the Virunga National Park (Fig. 1), are among the most active African volcanoes [11] and more than one million people living in the eastern Democratic Republic of Congo (DRC) are potentially exposed to their hazardous effects. The volcanic plumes from both volcanoes are dispersed over several hundred kilometres in a fairly constant direction, mainly to the west-south-west (266 ± 12 deg) [12]. ...
... Nyiragongo (with two historical eruptions, 1977 [13] and 2002 [14]) has the largest persistent lava lake worldwide that produces a SO 2 - rich plume. Its neighbour Nyamulagira (more than 40 eruptions, last in 2012) has an impressive activity average of one eruption every 2-4 years [11]. Most importantly, unlike explosive volcanic eruptions that send particulates and gases into the stratosphere, Nyamulagira's effusive eruption emits gases and aerosols into the lower troposphere, hence raising concerns for human health [15,16]. ...
Full-text available
Background: Nyamulagira and Nyiragongo are active volcanoes situated close to Goma (North Kivu, Democratic Republic of Congo). These volcanoes are among the most prolific sources of volcanic SO2 pollution on earth. Objective: We investigated the possible spatiotemporal relationships between volcanic degassing represented by eruptive emissions of SO2 that occurred between 2000 and 2010, and the incidence of acute respiratory symptoms (ARS) in populations living in areas up to more than 100 km from the volcanoes. Methodology: The total flux of SO2 emitted during eruptions since 2000 and the average spatial distribution of the volcanic plume (2004-2008) were based on publicly available remote sensing data. The monthly numbers of adults and children reporting acute respiratory symptoms were extracted from health data collected routinely by selected local health centres and hospitals between 2000 and 2010. The monthly numbers of persons with ARS recorded during or after eruptions were compared with those recorded before eruptions, using negative binomial regression models allowing the calculation of incidence rate ratios (IRR) and their 95% confidence intervals. We first compared years with and without eruptions and then considered shorter time-windows (months). Results: In the investigated area, ARS were the second most frequent cause of medical visits (12.2%, n = 3.2 million cases), after malaria (32.3%, n = 8.4 million cases). SO2 emissions gradually increased 30 to 50 times in 2010 compared to 2002. Taking 1999 as a reference, the IRR for ARS increased three-fold between 2000 [0.9 (0.8, 1.1)] and 2009 [2.8 (2.2, 3.7)]. Although the incidence of ARS appeared to increase after some eruptions, especially in areas close (< 26 km) to the volcanoes, we did not find a consistent temporal association between the yearly incidence of ARS and volcanic eruptions when considering the entire observation period. When we analysed shorter time-windows (6 months in the year preceding an eruption), we observed increased ARS incidences in eruptive months, except in 2010. IRRs were increased for centres situated close to volcanoes (< 26 km) in 2001 and 2002. Conclusion: ARS incident cases increased over the years in populations living around the Nyamulagira and Nyiragongo volcanoes, but we found no consistent evidence for an association between the yearly incidence of ARS and volcanic eruptions or the intensity of SO2 emissions, possibly because of interference with man-made events, including massive population displacements caused by insecurity in the area. Nevertheless, some evidence was found for increased incidence of ARS following eruptions, especially in areas close to volcanoes. Assessing personal, ground level exposure to SO2 and particulates with adequate controlling for confounding, such as viral and other infections, could clarify the contribution, if any, of volcanic emissions of SO2 to the high burden of respiratory diseases in this region.
... During a volcanic crisis, fieldwork is typically limited by accessibility and safety, so remote sensing has become a common method for characterising volcanic activity (Wright et al., 2002;Watson et al., 2004;Joyce et al., 2009;Pyle et al., 2013), including mapping fresh deposits (Kervyn et al., 2007;Smets et al., 2010;Ganci et al., 2018;Pallister et al., 2019). Flow outlines can be mapped using a range of satellites and airborne imagery, but estimating height changes is more challenging. ...
Full-text available
The estimation of the volume of volcanic flows during an ongoing eruption is challenging but this information is crucial for improving risk assessment and for forecasting future events. Although previous studies have shown the ability of TanDEM-X satellite data to derive the thickness and the volume of lava flow fields during effusive eruptions, the method has not been explored yet for pyroclastic flows. Using bi-static interferometry, we produce TanDEM-X DEM on Fuego volcano (Guatemala) to measure the significant topographic changes caused by the 3rd June 2018 eruption, which destroyed the town of San Miguel Los Lotes. We estimate the volume of the Pyroclastic Density Currents (PDCs) to be 15.1 ± 4.2 × 106 m3. The deposits are likely to be the source of lahars during future rainy seasons. We identify the main channel of deposition (positive elevation changes) and the source region of pyroclastic material, areas of significant substrate erosion, and vegetation destruction (negative elevation changes). Our results show that the June 3rd 2018 pyroclastic flow was predominantly composed of material which had gravitationally collapsed from a location close to the vent. The eroded material increased the volume of the flow (bulking) and likely caused the run-out distance of the 2018 PDC to be larger than previous eruptions (1999–2017). This study highlights the potential of remote sensing techniques for actively monitoring topography changes in inaccessible locations and to rapidly derive deposit volumes.
... Kampunzu et al. 1998;Barette et al. 2017). Nyiragongo and Nyamuragira volcanoes, west of VVP (Fig. 1), are the two most recent volcanoes and are mostly covered with few-year to fewcentury lava flows (Smets et al. 2010Poppe et al. 2016). The other volcanoes, in central and eastern VVP, are covered with lavas flows of 10 to 250 ka ages (Kampunzu et al. 1998;Pouclet et al. 2016), with, in some places, layers of pyroclastic deposits (Jost 1987). ...
Understanding when landslides occur and how they evolve is fundamental to grasp the dynamics of the landscapes and anticipate the dangers they can offer up. However, knowledge on the timing of the landslides remains overlooked in large parts of the world. This is particularly the case in low-capacity regions, where infrastructures are weak or absent and data scarcity is the norm. The tropics stand out as such regions, despite being affected by high and increasing landslide impacts. There, persistent cloud cover, rapid natural vegetation regeneration, cultivation practices and high weathering rates further challenge the harvest of timing information. Based on a synthesis of our recent work, we present new findings on the characterisation of the timing of the landslides in the North Tanganyika-Kivu Rift region, a tropical environment with very low capacity and high population density. Our aim is also to highlight the methodological approaches and research strategies that we adopt to investigate such slope processes in a large region lacking baseline studies. From an inventory of more than 9000 landslides with various timing accuracy (from daily to thousands of years), we identify causes and triggers of the slope instabilities in a context of important human-induced landscape changes. This is achieved through a holistic approach that combines field work, satellite remote sensing, historical photograph processing and geomorphic marker understanding. The role of the needs of the local stakeholders in the setting up of the research strategy is also highlighted, and research perspectives are discussed.
... Interferometric Synthetic Aperture Radar (InSAR) is a technique that combines repeat SAR satellite acquisitions to image ground displacement over specific regions of the Earth [1]. Over the past 25 years, InSAR has been used to examine ground displacement in active volcanic regions [2,3], allowing for an improved understanding of the extent of the storage and transport of magma in magma plumbing systems [4][5][6], as well as volcanic edifice growth and stability [7][8][9][10][11]. ...
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Interferometric Synthetic Aperture Radar (InSAR) studies of ground displacement are often plagued by tropospheric artifacts, which are phase delays resulting from spatiotemporal variations in the refractivity of air within the troposphere. In this study, we focus on COSMO-SkyMed (X-band) InSAR products obtained over two different types of volcanoes in Nicaragua: the Telica stratovolcano and the Masaya caldera. We examine the applicability of an empirical linear correction method and three Global Weather Models (GWMs) with different spatial and temporal resolutions for removing the tropospheric phase component. We linearly invert the tropospheric-corrected interferograms using the Small BAseline Subset (SBAS) time-series technique to produce time-series of ground displacement. Statistical assessments were performed on the corrected interferograms to examine the significance of the applied corrections on the individual interferograms and time-series results. We find that the applicability of the correction methods is highly case-dependent and that in general, the temporal resolution of GWMs influences their ability to capture turbulent tropospheric phase delays. At the two target volcanoes, our study shows that none of the GWMs are able to accurately capture the tropospheric phase delays. Our study provides a guide for researchers using InSAR data in tropical regions who wish to use tropospheric model corrections to carefully assess the applicability of the different types of tropospheric correction methods.
In analyzing the Earth’s surface pattern, geochronology, natural resources, natural hazards and landscape growth Anthropogeomorphological mapping has played an important role. It includes dividing the field into metaphysical space entities that use criteria such as morphology (form), genetics (process), shape and function, chronology, correlations between the biophysical environment (land cover, soils, ecology), and spatial and topological connections between surface characteristics (landforms). This chapter explores the importance of geospatial technologies in Anthropogeomorphology and its use of different ways for the study of geomorphology and human involvement. Instead of seeking to cover wider areas of GIS-based scientific research, this chapter focuses on geomorphological problems and knowledge criteria dictating application creation and refinement. It also illustrates the relevance of the shortcomings of perception with regard to depiction, size, measurement and remote sensing.
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The ever‐increasing amount of Synthetic Aperture Radar (SAR) data motivates the development of automatic processing chains to fully exploit the opportunities offered by these large databases. The Synthetic Aperture Radar Interferometry (InSAR) Mass processing Toolbox for Multidimensional time series is an optimized tool to automatically download SAR data, select the interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is available. New challenges relate to data management and processing load. We address them through methodological improvements dedicated to optimizing the InSAR pair selection. The proposed algorithm narrows the classical selection based on the shortest temporal and spatial baselines thanks to a coherence proxy and balances the use of each image as Primary and Secondary images thanks to graph theory methods. We apply the processing to three volcanic areas characterized with different climate, vegetation, and deformation characteristics: the Virunga Volcanic Province (DR Congo), the Reunion Island (France), and the Domuyo and Laguna del Maule area (Chile‐Argentina border). Compared to pair selection based solely on baseline criteria, this new tool produces similar velocity maps while reducing the total number of computed differential InSAR interferograms by up to 75%, which drastically reduces the computation time. The optimization also allows to reduce the influence of DEM errors and atmospheric phase screen, which increase the signal‐to‐noise ratio of the inverted displacement time series.
In the Virunga area, Oligocene volcanic products differ from those of Pliocene age. The early lavas, rich in Na, yielded basic and later slightly oversaturated rocks by fractional crystallization. The undersaturated character of the late magmas, very rich in K, is relatively constant. These features and this type of evolution have also been observed in the High Ruzizi area of S Kivu.-A.-M.F.
Three simple models of the behaviour of a series of basaltic eruptions have been tested against the eruptive history of Nyamuragira. The data set contains the repose periods and the volumes of lava emitted in 22 eruptions since 1901. Model 1 is fully stochastic and eruptions of any volume with random repose intervals are possible. Models 2 and 3 are constrained by deterministic limits on the maximum capacity of the magma reservoir and on the lowest drainage level of the reservoir respectively. The method of testing these models involves (1) seeking change points in the time series to determine regimes of uniform magma supply rate, and (2) applying linear regression to these regimes, which for models 2 and 3 are the determinsstic limits to those models. Two change points in the time series for Nyamuragira, in 1958 and 1980, were determined using a Kolmogorov-Smirnov technique. The latter change involved an increase in the magma supply rate by a factor of 2.5, from 0.55 to 1.37 m3s-1. Model 2 provides the best fit to the behavior of Nyamuragira with the ratio of variation explained by the model to total variation. R2, being greater than 0.9 for all three regimes. This fit can be interpreted to mean that there is a determinstic limit to the elastic strength of the magma reservoir 4–8 km below the summit of the volcano.
The assessment of risks posed by natural hazards such as floods, droughts, earthquakes, tsunamis or cyclones, may not reflect the full range or magnitude of events possible. As human populations grow, especially in hazard-prone areas, methods for accurately assessing natural hazard risks are becoming increasingly important. Jonathan Nott describes the many methods used to reconstruct such hazards from natural long-term records. He demonstrates how long-term records are essential in gaining a realistic understanding of the variability of natural hazards, and how short-term historical records can often misrepresent likely risks.