Tailings flows result from the breach of tailings dams. Large-scale
tailings flows can travel over substantial distances with high velocities
and cause significant loss of life, environmental damage, and economic costs.
Runout modelling and inundation mapping are critical components of risk
assessment for tailings dams. In an attempt to develop consistency in
reporting tailings data, we established a new tailings-flow runout
classification system. Our data analysis applies to the zone corresponding
to the extent of the main solid tailings deposit, which is characterized by
visible or field-confirmed sedimentation, above typical surface water levels
if extending into downstream water bodies. We introduced a new database of
33 tailings dam breaches by independently estimating the planimetric
inundation area for each event using remote sensing data. This paper
examines the applicability of a semi-physical area–volume relationship using
the new database. Our results indicate that the equation A=cV2/3, which
has been used previously to characterize the mobility of other types of mass
movements, is a statistically justifiable choice for the relationship
between total released volume and planimetric inundation area. Our analysis
suggests that, for a given volume, tailings flows are, on average, less
mobile than lahars but more mobile than non-volcanic debris flows, rock
avalanches, and waste dump failures.
Introduction
Tailings dams are a critical piece of mining infrastructure
(Blight, 2009). These dams retain mine tailings, a waste
product of the mineral processing operations that includes finely ground
rock and process water. Some of these wastes may be classified as hazardous
material (Vick, 1990). When a tailings dam breach occurs, a
destructive flow of mine tailings can develop (e.g. Macías et al., 2015).
These flows may travel over substantial distances and impact large areas
(Rico et al., 2008a). The ability to understand and predict the motion of flowing
tailings is a crucial step in protecting people, infrastructure, and the
environment from these events.
More than 350 tailings dam breaches have been recorded worldwide since the
early twentieth century (Chambers
and Bowker, 2019; International Commission on Large Dams (ICOLD, UNEP), 2001; Rico
et al., 2008a; Santamarina et al., 2019; WISE, 2020) (Fig. 1). The records
indicate that approximately one-third of these events led to loss of life
and/or the release of more than 100 000 m3 of tailings and/or water
(Chambers and Bowker, 2019). For example, the fluorite
tailings dam at Stava, Italy, failed in 1985 and released a total volume of
185 000 m3 of muddy debris. As a result, the Stava and Tesero villages
were destroyed and 243 people lost their lives (Chandler
and Tosatti, 1995; Luino and De Graff, 2012; Pirulli et al., 2017; WISE,
2020). The 2014 Mount Polley tailings dam failure in British Columbia,
Canada, resulted in the release of about 25 million cubic metres
of water and tailings into Polley Lake, Hazeltine Creek, and Quesnel Lake.
The tailings inundation area was estimated to be approximately 2.4 million square meters (Cuervo et al., 2017; Miller and Zapf-Gilje, 2016). The 2015 Fundão tailings dam failure in Brazil
resulted in the release of about 35 million cubic metres of tailings materials. This
event killed 19 people and caused long-lasting environmental damage to
several water channels in the basin of the Doce River (Carmo
et al., 2017; Hatje et al., 2017; WISE, 2020). More recently, another
disastrous tailings dam breach occurred at the Feijão mine near
Brumadinho, Brazil, on 25 January 2019. Almost 12 million cubic metres of tailings
left the impoundment and the resulting tailings flow travelled for almost 9 km and inundated an area of approximately 3.0 million square meters before reaching the
river Paraopeba (WISE, 2020); 259 people were reported killed,
and 11 were reported missing as a result of this failure (WISE,
2020). All of these events also resulted in substantial financial losses for
the mine operators and investors.
Decadal frequency and cumulative frequency of tailings dam
breaches worldwide (n= 355) between 1930 and 2019. Sources:
Chambers and Bowker (2019), ICOLD, UNEP (2001), and WISE (2020).
Tailings dam breach runout analysis studies the behaviour of tailings flows.
The term “tailings flow” refers to various forms of tailings outflow
movement resulting from the breach of a tailings dam. This may include a
partial or a total release of the stored tailings and associated water
(Blight, 2009; Rico et al., 2008a, b; Villavicencio et al., 2014). Tailings flows
exhibit different characteristics depending on various factors, including
sediment concentration, the presence of surface water, embankment
configuration, failure mechanism, liquefaction potential, and downstream
topography (Martin et al., 2019; Small et al., 2017).
Tailings flows can take various forms, ranging from a massive debris flood
consisting of water and sediment to a flowslide
(Hungr et al., 2014). These flows can travel long
distances at extremely rapid velocities (> 5 m s-1)
(Blight, 1997; Blight et al., 1981; Harder and Stewart, 1996; Jeyapalan et al., 1983a, b; Kossoff et al., 2014; Macías et al., 2015; Rico et al.,
2008a). Runout modelling and inundation mapping of tailings dam breaches are
essential steps for estimating the potential consequences of a tailings dam
failure, determining appropriately stringent design criteria and developing
emergency response and preparedness plans (Canadian Dam Association
(CDA), 2014; Knight Piésold Ltd., 2014; Martin et al., 2015, 2019; McDougall,
2017). In recent years, there has been an increase in the study of the
consequences of tailings dam breaches following several major disasters
worldwide (Roche et al., 2017;
Santamarina et al., 2019; Schoenberger, 2016). However, much uncertainty
still exists in this field. The number of available empirical–statistical
runout models is limited (Sect. 2). Most of the commonly used numerical
models were developed primarily for either clear water flood analysis
(Brunner, 2016; Danish
Hydraulic Institute (DHI), 2007; Martin et al., 2019) or the analysis of
flow-like landslides (McDougall,
2017; McDougall and Hungr, 2004; Pastor et al., 2002; Pirulli et al., 2017)
and do not necessarily account for the compositional variety of tailings and
its potential influence on the downstream flow behaviour
(Dibike
et al., 2018; Macías et al., 2015; Pirulli et al., 2017). Due to these
limitations, hazard maps delimiting potential inundation areas based on
current techniques may not reliably characterize the extent and intensity
(e.g. flow depth and velocity) of possible tailings dam breach scenarios.
The purpose of this paper is to (i) introduce a runout zone classification
method in an attempt to develop consistency in reporting runout distances
and inundation areas of tailings flows, (ii) introduce a new database of 33
tailings dam breaches where released volume was reported and the planimetric
inundation areas were quantitatively measured for all of the events, (iii) examine the applicability of a semi-physical area–volume relationship for
tailings-flow cases to help characterize the mobility and potential impacts
of these types of failures, and (iv) investigate the effects of additional
attributes of the tailings and downstream topography, such as tailings mine
types and confinement of travel path, which could potentially be used to
refine these empirical–statistical relationships. The present work builds on
previous work described in Ghahramani et
al. (2019).
Previous empirical studiesEmpirical runout studies for tailings dam breaches
Empirical runout analysis of tailings dam breaches is a relatively new
research topic. Rico et al. (2008a) proposed a set of empirical correlations
that relates tailings-flow characteristics (e.g. released volume and runout
distance) to the geometric characteristics of tailings dams (e.g. dam height
and total impoundment volume). A database of 28 tailings dam breaches (from
1965 to 2000) containing information on released volume and runout distance
was used in their study (Rico et
al., 2008a).
Rico et al. (2008a) found positive correlations between (i) the total volume
of the tailings in the impoundment at the time of failure and the released tailings
volume and (ii) the released tailings volume and the tailings
runout distance. The released tailings volumes in their work were extracted
from existing databases and publications with no information on the
distinction between the volume of released solid tailings, interstitial
(pore) water, and surface (free) water. A parameter referred to as the “dam
factor” (the product of the dam height and released tailings volume,
H×VF) was used to improve the correlations in their study. This
parameter was originally developed by Hagen and the Committee on the Safety
of Existing Dams for the peak discharge estimation of water dam breaks
(Committee on the Safety of Existing Dams, 1983;
Costa, 1985; Hagen, 1982). The related equations by Rico et al. (2008a) are
provided in Table 1.
The Tailings Dam Breach Working Group (WG) of the Canadian Dam Association
(CDA) Mining Dam Committee compiled a tailings dam breach database that
includes the 28 cases presented by Rico et al. (2008a),
plus 51 additional cases (Small et al., 2017). Their study
discussed the limited information provided in the Rico et al. (2008a)
database and listed additional factors that could influence the behaviour of
tailings flows. The WG proposed a four-element classification matrix based
on two main factors: (i) the presence of free standing water in close
proximity to the breach and (ii) tailings liquefaction potential. The
empirical relationships of Rico et al. (2008a) were re-examined based on
the proposed classification (Small et al., 2017).
Larrauri and Lall (2018) updated the database presented in Rico et al. (2008a) and reexamined their empirical correlations. They introduced a new
predictor, called Hf, which is defined as H× (VF/VT)×VF, where VT is the total volume of the
tailings impoundment and VF is the total released volume. Using the
updated database, they concluded that the relationship between Hf and
runout distance has a stronger correlation (R2= 0.53, Table 1) than
the relationship between dam factor and runout distance (R2= 0.44)
(Larrauri and Lall, 2018). However, arguably
both correlations are fairly weak, and the physical basis of the Hf factor
was not discussed in their study. Rico et al. (2008a) and Larrauri and Lall (2018) both noted that uncertainties in their databases suggest that the
results need to be treated with caution.
Empirical relationships proposed by others for the runout analysis
of tailings dam breaches.
Input parameterOutput parameterEquationR2ReferencesImpoundment volume (VT)Total released volumeVF= 0.354 VT1.010.86Rico et al. (2008a)Total released volume (VF)Maximum runout distanceDmax= 14.45 VF0.760.56Rico et al. (2008a)Dam height (H)Maximum runout distanceDmax= 0.05 H1.410.16Rico et al. (2008a)Dam factor (HVF)Maximum runout distanceDmax= 1.61 (HVF)0.660.57Rico et al. (2008a)Impoundment volume (VT)Total released volumeVF= 0.332 VT0.950.89*Larrauri and Lall, 2018)Hf(H(VF/VT)VF)Maximum runout distanceDmax= 3.04 Hf0.5450.53*Larrauri and Lall (2018)
* These values were calculated using a different database from Rico et
et al. (2008a). In all the relationships, VT and VF are in
millions of cubic metres, H is in metres, and Dmax is in kilometres.
Empirical runout relationships – area and volume
Several authors have investigated the relationship between inundation or
deposit area (A) and flow volume (V) for different types of flow-type
landslides (e.g. Berti
and Simoni, 2007; Davies, 1982; Delaney and Evans, 2014; Golder Associates
Ltd., 1995; Griswold and Iverson, 2008; Hungr, 1981; Hungr and Evans, 1993;
Iverson et al., 1998; Li, 1983; Simoni et al., 2011) (Table 2). Li (1983)
presented an empirical relationship between rock avalanche deposit area and
volume for 76 major European rock avalanches. The deposit area and volume
were logarithmically transformed to apply a linear least-squares regression
analysis (Li, 1983). Hungr and Evans (1993) applied a similar
methodology to a different dataset of rock avalanches. However, they made an
assumption that the deposits at various scales retain a similar geometry,
which resulted in the following scaling relation for the area–volume
relationship:
A=cV2/3,
where A is the inundation area, V is the total flow volume, and c is a
constant related to flow mobility (Hungr and Evans, 1993) (i.e.
for a given event volume, a higher mobility flow results in a higher
planimetric inundation area). Golder Associates Ltd. (1995) derived an
area–volume relationship for mine waste dump failures using a database of 22
cases. Iverson et al. (1998) presented similar area–volume relationships as
in Hungr and Evans (1993) for lahars (Table 2). Statistical analysis of a
dataset containing 27 lahars was used to calibrate and test the validity of
those equations (Iverson et al.,
1998). Berti and Simoni (2007) and Griswold and Iverson (2008) extended the
same methodology to non-volcanic debris flows. Griswold and Iverson (2008)
also substantially expanded the database of rock avalanches and found a
slightly different correlation than Hungr and Evans (1993) (Table 2).
Selected empirical relationships between volume and inundation area
proposed by others for various landslide types.
Database typeEquationnR2c CoefficientReferencesin Eq. (1)Rock avalanchesA= 76 V0.57760.78–Li (1983)aRock avalanchesA= 12 V2/340–12Hungr and Evans (1993)bLaharsA= 200 V2/3270.90200Iverson et al. (1998)bDebris flowsA= 20 V2/3440.9120Griswold and Iverson (2008)bDebris flowsA= 18 V2/311518Simoni et al. (2011)bRock avalanchesA= 20 V2/31420.7920Griswold and Iverson (2008)b
a The original equation from Li (1983) is presented in
power-law format to facilitate comparison.
bA and V are planimetric area and flow volume, respectively (A is in
square metres and V is in cubic metres).
MethodologyDataset compilation
Tailings dam breaches have been recorded since the beginning of the
twentieth century (Chambers and Bowker, 2019; ICOLD, UNEP,
2001). Several compilations and summaries of the characteristics of
significant tailings dam breaches can be found in the literature
(Chambers and Bowker, 2019; ICOLD, UNEP, 2001;
Small et al., 2017; WISE, 2020). These summaries contain key information
about the events, such as dates, causes and triggers of failure, dam heights
and construction methods, and the volumes of released and impounded
tailings. However, most of the records lack consistency in the reported data
related to runout, including information related to factors that may better
characterize tailings flows, due to the lack of a systematic methodology in
reporting. In the present study, we first compiled available information for
71 tailings dam breaches and then assessed the runout characteristics of
each case individually. Data sources included existing literature on
individual tailings dam breach events, existing databases, and remote
sensing data obtained from satellite images or aerial photos.
We classified the inundation areas into two zones (Fig. 2). Zone 1 is the
primary impact zone, defined as the extent of the main solid tailings
deposit, which is characterized by remotely visible or field-confirmed
sedimentation, above typical bankfull elevations if extending into
downstream river channels. Zone 2 is the secondary impact zone, defined as
the area downstream of Zone 1 that is further impacted by the tailings flow
in some form. Secondary impacts may include flood or displacement wave
impacts (i.e. fluid impacts above typical downstream water levels) and
sediment plume impacts (i.e. below typical downstream water levels).
An idealized representation of a tailings dam breach showing the
two runout limit classifications. Zone 1 represents the primary impact zone,
defined as the extent of the main solid tailings deposit, which is
characterized by remotely visible or field-confirmed sedimentation, above
typical water levels if extending into downstream streams. Zone 2 is the
secondary impact zone, defined as the area downstream of Zone 1 that is
still impacted by the tailings flow in some form and includes the distal
limit of the flow.
Figure 3 shows a flowchart that summarizes our data compilation methodology,
including the screening of data sources, the impact zone classification, the
delineation of Zone 1, and the estimation of uncertainty due to image
resolution. The extent of Zone 2 is typically more challenging to estimate
than the extent of Zone 1, due to the variability of downstream flow mixing
conditions, the relatively transient nature of secondary impacts, and the
inherent limitations (e.g. image resolution) of the remote detection methods
used. The focus of this study was therefore on Zone 1.
Schematic representation of the methodology applied to obtain data
for tailings-flows inundation area.
Applying our methodology to the preliminary database comprising 71 tailings
dam breaches resulted in 33 cases for which we were able to obtain
satisfactory imagery and independently estimate runout distance and
planimetric inundation area (Table 3). Figures 4 and 5 illustrate two
examples of delineating the extent of Zones 1 and 2 for the tailings dam
breaches at the Feijão mine near Brumadinho, Brazil, 2019, and the
Cieneguita mine in Mexico, 2018, respectively. The primary impact zone for
Feijão (red dashed polygon in Fig. 4) was established through a detailed
comparison of pre-event and post-event PlanetScope (3 m) imagery. After
entering the Paraopeba River, the Feijão tailings flow exhibited no
visible sedimentation above the bankfull level (blue dashed line in Fig. 4)
and the channel width stayed the same. However, we observed changes in water
colour for over 100 km downriver, which we interpret to represent the
secondary impact zone (Zone 2). A similar methodology was applied for the
Cieneguita mine tailings dam breach on 4 June 2018 in Mexico, for which the
runout distance was reported to be between 26 and 29 km
(Chambers and Bowker, 2019; WISE, 2020). Based on our
methodology, the transition between Zone 1 and Zone 2 occurs where the
extent of the tailings deposits significantly decreased. Normalized
difference vegetation index (NDVI) change detection analysis (Fig. 5 inset
a) was used to help identify the tailings deposits. The estimated Zone 1
runout distance was approximately 15 km.
Aerial view of the tailings dam breach at the Feijão mine near
Brumadinho, Brazil, 25 January 2019. Zone 1 is shown in the red dashed
polygon. The portion of Zone 2 that is visible in this image is shown in the
blue dashed polygon. Image courtesy of Planet Labs, Inc. (29 January 2019).
Aerial views of the tailings dam breach at the Cieneguita mine in
Mexico, 4 June 2018. The NDVI differencing change detection technique was
used to help delineate Zones 1 and 2 inundation areas (a). Zones 1 and 2
are shown in the red and blue dashed polygons, respectively (b). The inset
image (c) shows the transition between Zones 1 and 2 (red dot). Image
courtesy of Planet Labs, Inc. (12 June 2018).
Compared with the hundreds of tailings dam breach cases listed in previous
databases (Chambers
and Bowker, 2019; ICOLD, UNEP, 2001; Rico et al., 2008a; Small et al., 2017), the
relatively limited number of cases (33) in our new database reflects the
limited availability of suitable imagery, especially for older cases that
predate satellite imagery. We used a simple approach to quantitatively
estimate the uncertainty due to limitations in image resolution in our area
measurements based on the pixel sizes of the images. The maximum percentage
uncertainty due to image resolution was considered to be equal to the ratio
of the total area of the pixels intersected by the perimeter of Zone 1 to
the inundation area multiplied by 100. Our database contains
information on the percentage uncertainty of each case (Table 3).
Additional key attributes are included in our database (Table 3). We
classified our cases using the four-element classification matrix in Small
et al. (2017), described above. We also used the following two categories
proposed by Golder Associates Ltd. (1995) to classify the confinement of the
travel path: (i) confined, in which the flow path is constrained by
relatively steep side slopes of a gully or valley, and (ii) unconfined, in
which the flow path is on an open slope or relatively flat surface and the
topography permits spreading of the tailings flow from an early stage.
Similarly, to classify the tailings mine type, we used the following two
categories introduced by Small et al. (2017): (i) hard-rock mine tailings,
which includes lead–zinc, copper, gold–silver, molybdenum, nickel from
sulfide deposits, and uranium, and (ii) soft-rock mine tailings, which
includes coal, potash, fluorite, gypsum, and aluminum
(Bussière, 2007; Small et al., 2017;
Vick, 1990).
The dam height and total released volume data were collected from existing
databases and publications. We also included information on the volume of
free water released, if available. However, for the empirical analysis, only
the total reported released tailings volume was considered. We note that
there is limited information available on how the reported released volumes
within the existing databases were obtained (including the distinction
between the volume of released solid tailings, interstitial water, and
surface water).
Database of 33 tailings dam breaches (tailings flows) containing
independently estimated measurements of Zone 1 runout distance and
planimetric inundation area.
a The procedures used to classify the cases based on path confinement
and tailings type and WG classification matrix can be found in Sect. 3.1.
b Information on released volumes was collected from other databases
(tailings released volume is the released volume of solids and interstitial
water; free water released volume is the released volume of surface water).
Statistical analysisVolume dependency of Zone 1 inundation area
In this study, the scaling relationship adopted in previous studies
(Davies,
1982; Golder Associates Ltd., 1995; Griswold and Iverson, 2008; Hungr and
Evans, 1993; Iverson et al., 1998; Li, 1983) was applied to the new
tailings-flow database. The analysis relates the estimated Zone 1 inundation
area (dependent variable) to the reported total released volume (independent
variable) in Table 3. A simplifying assumption was made that the released
volume approximately matches the volume deposited downstream in Zone 1 (i.e.
the potential contribution of entrainment and erosion to the total volume of
the deposited material was not considered).
We used our tailings dam breach database (n= 33) to fit a regression
model and examine the applicability of Eq. (1) for tailings flows. We
transformed the data into a log-log scale and applied the standard
least-squares linear regression method. A linear regression model was fit to
the data using a specified two-thirds slope and was compared to the standard
least-squares linear regression. The uncertainty in the tailings release
volume estimates is not considered for this analysis.
Effect of other factors on Zone 1 inundation area
Exploratory analyses were completed to investigate the effects of
qualitative factors, such as the tailings mine type and travel path
topographic confinement, on the area–volume relationship. This analysis was
achieved by creating box plots of the regression residuals and colour-coding
the data points in the area–volume plot to visually assess whether there were
trends that could potentially be incorporated into the regression analysis
to reduce the uncertainty.
Results
Figure 6 shows the log-linear regression line for Zone 1 inundation area as
a function of total released volume with the 95 % confidence interval of
the best-fit regression line. Please note that the 95 % confidence
intervals account for the uncertainty of the regression line and not the
individual observations. The regression with a specified two-thirds slope (i.e.
based on Eq. 1) plots within the 95 % confidence interval of the
best-fit regression, supporting the hypothesis that this scaling
relationship is valid for the tailings breach data. Table 4 compares the
output from the regression analysis for the best-fit and the specified two-thirds
slope regression models. The following regression equation was obtained in
power-law form for the specified two-thirds slope regression model:
A=80VR2/3,
where VR (m3) is the total released volume and A (m2) is the
planimetric inundation area.
Log-log scatter plot of planimetric Zone 1 inundation area versus
total released volume for 33 tailings-flow cases (Table 3). The specified
two-thirds slope regression line (in red) is fitted to the data. The best-fit
regression line (in black) and the 95 % confidence intervals (dashed
lines) of the best-fit regression are plotted for comparison.
Statistical results of the regression analysis for the best-fit and
specified two-thirds slope models. n/a stands for not applicable.
ParameterBest-fit regressionSpecified two-thirds slopeSlope (α)0.730.67Intercept of line at log V=0 (Log(β))1.521.90β3380Number of data, n3333Standard error of model, σ0.560.55Standard error of volume coefficient0.11n/aStandard error of intercept0.650.10Coefficient of determination, r20.580.57
The power-law form of the equation: A=(β)Vα. The linear form
of the equation in log-log scale: Log(A) =α Log(V) + Log(β). For α=2/3, β=c coefficient in Eq. (1).
The residuals (i.e. observed inundation area minus predicted inundation
area) of the regression line with a specified two-thirds slope were analyzed to
investigate whether the variation could be explained through qualitative
descriptions of the tailings type or confinement of the tailings runout
path. This analysis was completed by plotting the distribution of the
regression residuals as a box plot, where the lowest bar is the minimum of
the residual distribution, the lower box represents the first quartile to
the median residual, the upper box is from the median to the third quartile,
and the upper bar is the maximum of the residual distribution. If the
distributions show stratification (e.g. one distribution has all four
quartiles that are lower than the quartiles for a second predictor), it is
an indication that there is a consistent difference in behaviour based on
the descriptive predictors.
Figure 7a shows that, for a given volume, the inundation area for unconfined
flow paths tends to be smaller than that for confined flow paths. Similarly,
Fig. 7b shows that, for a given volume, the inundation area for hard-rock
mine tailings tends to be smaller than that for soft-rock mine tailings.
While these differences in the mean or median values can also be observed in
the respective box plots, the regression residuals are not strongly
stratified overall. These qualitative factors were used as indicator
variables to fit new regression models, but the associations were found to
be too weak for application.
Colour-coded data points with respect to path confinement (a) and
tailings type (b). The solid black line is the specified two-thirds regression
line. The insets show the box plots of the area–volume residuals for the
bivariate regression line with a specified two-thirds slope.
Discussion
The results listed in Table 4 indicate that Eq. (1) is a
statistically justifiable expression for the relationship between total
released volume and planimetric Zone 1 inundation area, with coefficients of
determination of 0.65 and 0.64 for the best-fit and the two-thirds slope
regressions, respectively. Furthermore, the specified two-thirds slope line falls
within the 95 % confidence interval curves for the best-fit regression,
suggesting that the scaling relationship adopted by previous studies to
characterize the geometry of other types of mass movements is also valid for
tailings flows. An analysis of the residuals from the regression grouped by
tailings type and flow path confinement indicates that these factors have an
effect on the mobility (i.e. the extent of planimetric inundation area for a
given volume) of tailings flows; soft-rock mine tailings tend to have
greater mobility than hard-rock mine tailings, and confined flow paths tend
to enhance mobility relative to unconfined paths; however, the data are not
stratified enough to incorporate these factors into the regression analysis
yet.
Figure 8 shows Zone 1 inundation area as a function of total released volume
with the specified two-thirds regression line and its 95 % prediction intervals,
which account for the uncertainty of the individual data points. The
difference between the lower and upper 95 % prediction intervals reflects
the variability of tailings flows and the considerable uncertainties in the
prediction of inundation area using this approach. Nonetheless, the
prediction range that is achievable with this method is useful for
first-order (screening level) risk assessment purposes, ideally within a
probabilistic framework that acknowledges the level of uncertainty. This
method is also useful for cross-checking numerical dam breach modelling
results (i.e. to confirm that the simulated inundation area falls within a
reasonable range relative to the cases included in this database). Note
that, while the method is able to provide independent estimates of
inundation area, it must be combined with other empirical and/or numerical
methods that estimate cross-sectional area and runout distance in order to
determine an appropriate spatial distribution of the estimated area, similar
to the approaches that have been used for other hazard types, such as
Iverson et al. (1998) and Mitchell et al. (2020). Further
study is currently underway to estimate the cross-sectional area for
tailings flows and incorporate both volume–planimetric and cross-sectional
area relationships in a GIS-based empirical model (Innis et
al., 2020). Regardless of the approach used, significant professional
judgement must be applied in interpreting the empirical results.
Log-log scatter plot of planimetric Zone 1 inundation area versus
total released volume for the 33 tailings-flow cases. The specified two-thirds
slope regression line (in red) is fitted to the data, and the 95 %
prediction intervals (dashed lines) of this regression line are also
plotted.
Figure 9 shows the area–volume scatter plot of tailings flows alongside
previously published data for lahars
(Iverson et al., 1998), debris
flows, rock avalanches (Griswold and
Iverson, 2008), and mine waste dumps (Golder Associates Ltd.,
1995). The tailings data points clearly show a positive linear pattern
along with the other data, although the scatter is relatively high,
especially at higher volumes. The area–volume data for tailings flows show
considerable overlap with other databases, corresponding with the upper
volume range for debris flows and the lower volume ranges for lahars and
rock avalanches (Fig. 9). One of the possible impacts of the assumption that
the released volume approximately matches the volume deposited downstream in
Zone 1 (Sect. 3.2.1) is the deposited volume may be underestimated due to
the entrainment of material along the flow path. This simplification may
lead to overestimating the y intercept of the regressions.
The differences between the c coefficient of Eq. (1) indicate the relative
mobility of the various mass movement processes, on average
(Berti and Simoni, 2007;
Griswold and Iverson, 2008; Jakob, 2005). A comparison of c coefficients for
different types of mass movements is shown in Table 2. The coefficient of
c= 80 obtained for the tailings-flow data indicates that, on average,
tailings flows are less mobile than lahars but more mobile than mine waste
dumps, debris flows, and rock avalanches for a given volume. There is a
significant amount of scatter in all of the datasets shown in Fig. 9, which
highlights the importance of considering the potential variability in these
events for forward analysis (i.e. using probabilistic methods).
Comparison of the runout inundation area as a function of flow
volume for tailings flows (red symbols; n=33)), waste dump failures
(yellow symbols; n= 22), lahars (green symbols; n= 27), non-volcanic
debris flows (pink symbols; n= 44), and rock avalanches (blue symbols; n= 142). The black two-thirds slope line is drawn as a guide for visual comparison
only.
Five tailings dam breaches exhibit higher inundation areas than lahars for
their given volumes (Fig. 9), and among those cases, the tailings dam
breaches at the Ajka bauxite mine in Hungary in 2010 and the Mishor Rotem
phosphate mine in Israel in 2017 (ID numbers 20 and 28 in Table 3) were
examined in greater detail to demonstrate how site-specific information can
be used to infer conditions that enhance mobility.
At the Ajka mine, a release of approximately 1.6 million cubic metres of high-pH
bauxite tailings, about 30 % of which was solid residue, occurred through
the northwest corner of the embankment
(Bánvölgyi, 2018; Mecsi, 2013). The release
produced a Zone 1 runout distance of approximately 18 km, despite the
near-horizontal topography of the flow path (∼0.2∘), and covered approximately 6 million square meters. The Ajka bauxite tailings had very
weak geotechnical properties, with medium- to high-plasticity, thixotropic
(shear thinning) clays with very loose structure and slow consolidation
rates, thus reducing pore fluid drainage and increasing the potential for
liquefied flows (Mecsi, 2013). In addition to the volume
of interstitial water, the bauxite tailings were overlain by a large
supernatant pond that deepened towards the northwest corner of the
impoundment; the average and maximum depths of the pond were 4.45 and 8 m,
respectively, which greatly exceeded the maximum permitted pond depth of 1.5
m (Bánvölgyi, 2018). We therefore attribute this
secondary source of water, along with the observed thixotropic behaviour of
bauxite tailings, to the augmented mobility of the Ajka tailings flow.
The Mishor Rotem mine failure is estimated to have released approximately
0.1 million cubic metres of highly acidic phosphogypsum tailings (Bowker,
2017). The ensuing tailings flow travelled for 28 km through a dry creek
channel with an average travel path angle of about 1.6∘ and
inundated a Zone 1 area of approximately 1.8 km2. As of yet, very
limited information is available for this tailings flow, but a few authors
have commented on the dominant contribution of high water content to the
composition of phosphogypsum tailings (80 %–97 %) compared to that of
typical metal tailings (40 %–60 %) (Bowker, 2017; Tao et al.,
2010; Wang et al., 2014). We, therefore, propose three factors that
contributed to the extreme runout behaviour (i.e. long runout distance and
large inundation area for the given total released volume) of the Mishor
Rotem tailings flow: (i) high water content (interstitial and supernatant);
(ii) a narrow, dry channel situated within a stable desert environment with
no physical obstacles to flow; and (iii) a potential increase in the
transported volume due to entrainment along the narrow channel.
Unlike natural hazards, tailings dams are human-made structures with
impoundment volumes that increase over the course of mine operation. In most
cases, when a dam breach occurs, only a portion of the impounded material is
released. The amount of this portion depends on a variety of factors, such
as the presence of a water pond, the tailings rheological properties, breach
geometry, the age of the impounded material, and the triggering factors
(Rico et
al., 2008a).
The maximum volume that can be released in an extreme scenario equals the
total impoundment volume. Compared with some types of landslides, the source
volume of a tailings dam breach is relatively well-constrained. The
uncertainty associated with this input parameter can, therefore, be
accounted for explicitly when using Eq. (1) to make runout predictions.
However, we note that relatively high confidence in the released volume
estimate does not necessarily translate into high confidence in the
inundation area estimate. Information on tailings type and topographic
factors such as confined or unconfined travel path can potentially be used to
better constrain the uncertainty in predicting the inundation area as more
data points are added.
Further investigation should focus on increasing the size of high-quality
tailings-flow databases, which should lead to more robust statistical
analyses. Some effort should also focus on quantifying the potential
contribution of entrainment to the total volume of the deposited material.
Conclusions
Our empirical investigation of historical tailings dam breaches provides new
insights into tailings-flow processes and characteristics and introduces new
relationships that can potentially be used for first-order inundation
mapping. In this study, we established a data compilation methodology and
introduced a runout zone classification system to improve consistency and
reduce uncertainties associated with previously reported data. Using this
methodology, we compiled a database of 33 tailings dam breach case studies
and estimated the planimetric Zone 1 inundation areas for all of the events.
The degree of mobility of the events in the database was investigated using
a well-established semi-physical area–volume relationship, and the result
was compared with similar relationships established for other mass flow
processes. Our analysis suggests that the relationship A=cV2/3 is a
statistically valid relationship between total released volume (VR) and
planimetric inundation area (A). The c coefficient of 80 from the analysis of
our database suggests that, on average, tailings flows are less mobile than
lahars (c=200) but more mobile than mine waste dumps, debris flows (c=17–20), and rock avalanches (c=12–20). This paper is part of an ongoing
project. We are currently building the database and investigating the
effects of other attributes of the tailings and downstream topography, which
could potentially be used to refine the area–volume empirical–statistical
relationship.
Data availability
All datasets presented in the current study are included in this published article.
Author contributions
NG and SM conceived the research idea and NG developed the methodology. NG and NMR
performed investigation. AM and NG performed the
statistical analysis. NMR, NG, and AM
verified the compiled database. SM, SGE, and
WAT supervised the project. SM, SGE, and
WAT provided financial support for the project leading to this
publication. NG prepared the original draft with contributions
from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge the support of the Department of
Natural Resources of Newfoundland for providing the information on the
inundation area of the 2012 Gullbridge tailings dam breach. We would like to
thank Sophia Zubrycky for her assistance with graphic design and Sahar Ghadirianniari for her assistance with data collection. The authors also
wish to acknowledge valuable discussions with Vanessa Cuervo during the
early stages of this work.
Financial support
This research has been supported by The University of British Columbia, Department of Earth, Ocean and Atmospheric Sciences (grant no. 6456) and the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant no. 533226-18). This work is part of the CanBreach Project, which
is supported by funding through an NSERC Collaborative Research Development
Grant and from the following industrial partners: Imperial Oil
Resources Inc., Suncor Energy Inc., BGC Engineering Inc., Golder Associates
Ltd., and Klohn Crippen Berger.
Review statement
This paper was edited by Filippo Catani and reviewed by Renato Macciotta and one anonymous referee.
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