Surveying
technical
Statistical modelling for
sinkhole risk management
by Michael Williams and Heath Rasco, SPADAC
Hurricanes are the natural phenomenon most commonly associated with property damage and destruction
in the US state of Florida. The prevalence of sinkhole formation is not as widely recognised but also has a
significant impact on the landscape of the region.
O
ne of the smallest counties in
the state, Pasco County, is well
known for its sinkhole problems,
earning the nickname “sinkhole alley”.
In fact, sinkhole claims in Pasco County
account for two-thirds of statewide
sinkhole claims.
Pasco is the 38th fastest growing
county in the US, and as population
growth continues to increase,
further demands will be placed on
sinkhole-prone terrains. Significant
environmental stress on these
areas will lead to increased rates of
sinkhole collapse, sinkhole flooding,
and considerable deterioration in
groundwater quality without responsible
risk management [1]. It is important
that we gain a better understanding of
areas with higher potential for sinkhole
occurrences so that more intensive
investigation and preventive measures
can be undertaken to minimise
either the chance of occurrence or
the putative chances of catastrophic
damage [2].
POTENTIOMETRIC
SURFACE
Background geology
According to the Florida State
Geological Survey, sinkholes are
defined as “a landform created by
subsidence of soil, sediment, or rock
as underlying strata are dissolved
by groundwater.” More specifically,
sinkholes may form from collapse of
overburden into subterranean voids
created by the dissolution of limestone
or dolostone from acidic groundwater
flow and accumulation (Fig. 1). Further,
sinkholes can occur during the phase in
which the strata are being dissolved.
Sinkholes are most common in areas
where groundwater levels fluctuate and
flow through caverns and void spaces
in underlying carbonate rocks and
salt beds; rocks that are susceptible
to dissolution over time. What affects
the size of the sinkhole depends
on the thickness of the overburden
rocks and soil and how well they stay
intact during the formation of the
subterranean void. Ultimately, if there
are not enough strata supporting very
PERCOLATING
WATER
WATER TABLE
SAND
CLAY
LIMESTONE
Fig. 1: Schematic diagram of geological and hydrological conditions leading to sinkhole
development [3].
26 thick layers of overlying rocks and soil,
catastrophic sinkholes will occur in
dramatic fashion.
The overburden rocks and sediments
that cover buried cavities in the
aquifer systems are delicately balanced
by groundwater fluid pressure.
Groundwater pumping for urban needs
and irrigation can accelerate water
table drops and subsequent sinkhole
development. Drought conditions,
common in the winter months in
Florida, are yet another catalyst.
Virtually the entire state of Florida
is susceptible to sinkhole formation.
This is largely due to the presence
of three carbonate rock formations
– very susceptible to dissolution – in
the underlying strata throughout
the county. These formations
vary in carbonate purity, and thus
have differing rates of dissolution.
Additionally, thickness in the sediments
and rocks above these formations vary
across the county.
The state geological survey recognises
and defines four distinct sinkhole areas
in Florida, of which three occur in Pasco
County (Fig. 2). Much of Pasco County
lies within Areas I and III. Sinkhole
development in Area I is defined as
being shallow and gradually developing.
The sinkholes in this area are generally
not catastrophic as there is very little
sediment and rock overburden. The
most common type of sinkhole in Pasco
County, however, is the Cover Collapse,
which occurs in Area III. These
sinkholes develop in the carbonate
rocks where the water table is below
the aquifer and dissolution is greatest
from vigorous water circulation. Also
within Area III, dense and impermeable
clayey soils add significant overlying
pressure and contribute to the typical
development of large and potentially
catastrophic collapses. Research has
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SURVEYING
shown that Area III sinkholes seem
to increase after the water table is
lowered from drought or pumping
[4]. This is unfortunate, as large
land development projects requiring
extensive constructional pumping are
commonplace in Pasco County as a
result of urban sprawl from the city of
Tampa. In addition to this development
stress, much of the west coast of
Florida is experiencing its worst drought
in two decades.
Traditional methods of geospatial
analysis leverage GIS tools to create
density, or hotspot, maps that highlight
increased sinkhole risk within areas
where these events have already
occurred, giving decision-makers little
insight into the location of future
sinkhole occurrences. Planners and
officials need advanced predictive
analysis capabilities to truly minimise
the catastrophic risk associated with
sinkhole occurrence.
Signature Analyst from SPADAC
is a geospatial predictive analysis
modelling software that is used to
create a signature of virtually any type
of geographically-based occurrence
or event, including sinkholes. By
comparing the previous location
of sinkholes to a wide variety of
data layers, also known as factors,
that characterise the surrounding
environment, an analyst can measure
and empirically define the geosignature
for sinkholes, which can then be
projected into a defined area of interest
(AOI) to generate predictive patterns
useful for understanding where new
sinkholes are more likely to emerge.
The software’s inductive methodology
requires a baseline of spatial factor
data that characterise the environment
within an area of interest. These factors
might consist of physical terrain,
infrastructure, geophysical data,
demographics, cultural terrain data,
etc. (Fig. 3).
While much of the geospatial data
is freely available through a variety
of commercial and government
sources, some data layers needed to
effectively represent an environment
must be produced by subject matter
or technical experts, who can provide
context and analytical judgments to
the geospatial assessment. Other
examples include the analysis of the
spatial representation of an ethnic
tribe or complexity of a terrain that
influences the pattern of the events
being modelled.
For each geospatial signature, the
software generates a gradient of
shaded areas from low to high (also
known as hotspots) around the data
points of events used in the model
while also displaying additional hotspots
for the same events in other areas
(Fig. 4). It is these other areas that
possess the same geospatial conditions
as the events dataset used to train the
model. Thus, the modelling process of
the software helps narrow the search
space and identify new areas which are
technical
further evaluated and analysed for the
presence or emergence of the modelled
object or event of interest. This gives
both experts and non-experts the
ability to process an assessment and
understand where modelled events
might be or might emerge.
In addition to the geospatial
assessment, the product provides
several analytical tools to aid modellers
in understanding the phenomenon
being modelled. The tools include
model evaluation metrics, cluster
analysis of events' locations and their
geospatial similarities in factor space,
and a series of factor metrics, which
allow for interpretation of the top
factors contributing to a phenomenon's
geospatial signature.
Implementation and application:
Pasco County
Signature Analyst was applied at
two different scales within Pasco
County to demonstrate that it is
scalable, updateable, and flexible. The
small-scale case study was applied
county-wide to identify generalised
areas of higher risk. A small-scale
assessment has a coarser resolution
but can empower planners and
developers to avoid or minimise
the risks of sinkhole hazards. The
large-scale case study concentrates
on the subdivision level to identify
local areas of higher sinkhole risk.
A large-scale assessment has more
detailed resolution and can empower
homeowners, developers, and insurers
to mitigate their risks.
Small-scale application: defining an
area of interest and events
Fig. 2: Map depicting the categorised and defined areas of sinkhole development in the
state of Florida [5].
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A small-scale (county-wide) sinkhole
risk assessment was conducted using
the Florida Geological Survey (FGS)
Sinkhole database for model inputs.
The database was started in the early
1980s, and the available record covers
a twenty-five year period. According
to the FGS database, Pasco County
had 249 reported sinkhole events from
1969–2006. These events represent
only those sinkholes officially reported
by observers. The reported sinkholes
tend to cluster in populated areas
where they are readily seen and
commonly affect roads and dwellings
[5]. The FGS discontinued updating the
sinkhole database in September 2006.
While the database lacks more recent
sinkhole events and has an inherent
bias to developed areas, it is the only
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comprehensive public record of sinkhole
events in the state. A total of 38 model
input points (sinkholes) that met the
following criteria were extracted from
the FGS database:
•
Events between 1969 and 1989
•
Summer occurrences during
drought and heavy rainfall cycles
(May-September)
•
Sinkholes with a width equal to or
greater than 1,5 m
•
Sinkholes with heavy rain reported
as the trigger mechanism
The conditions outlined above
were used to create homogenous
candidate model inputs by capturing
naturally occurring sinkholes. Broken
water mains or other catalysing
anthropogenic influences are the
reported cause of many sinkholes and
were, therefore, removed for modelling
purposes.
Small-scale application: geospatial
data layers/factors
The baseline of geospatial factor
data layers that characterise the
environment within Pasco County
consist of anthropogenic and
environmental influences as well
as the geologic and hydrogeologic
framework. These geospatial data
layers used to represent the modelling
environment, or factors, were
acquired from numerous sources
including county, state, and federal
agencies. Anthropogenic influences
are man-made and can affect natural
hydrological or hydrogeological
characteristics of the aquifer systems.
These features include but are not
limited to fire stations, schools,
roads, underground gasoline storage
tanks, well fields, landfills, and
storm water ponds. Environmental
influences include but are not limited
to wildlife habitats, conservation
areas, and public lands. Geologic and
hydrogeologic framework factors are
peer-reviewed, professionally published
data which include but are not limited
to soil characteristics and hydraulic
conductivity, soil drainage, aquifer head
difference, water-table elevation, and
aquifer thickness and extent (Fig. 5).
Small-scale application: sinkhole risk
assessment
The depicted Pasco County assessment
represents the top 25% probability
score of the entire geospatial sinkhole
risk assessment, equating to a 75%
area reduction (from 868 mi² to
28 Fig. 3: Feature space preference methodology [6].
217 mi²) of the entire county (Fig. 6).
Area reduction allows for more effective
focus of resources on areas with the
highest associated sinkhole risk based
on anthropogenic, environmental,
geologic and hydrogeologic factors.
When compared to sinkhole events
that took place between 1990-2006,
the accuracy of the model in predicting
sinkhole events is 90,9%. This
confirms that those events used to
train the model are significantly similar
geospatially to the characteristics
of successive sinkhole events. The
resulting assessment can be used by
insurers to improve risk determinations
and by planners/developers to
successfully accommodate growth
and development while minimising
environmental impact.
Large-scale application: defining an
area of interest and events
The subdivision (larger-scale)
application of the model demonstrates
the flexibility of the model in leveraging
higher accuracy data to provide a
higher degree of detail. Often the
small-scale application of the model
helps to identify areas where it is
necessary to “drill down” on a specific
subdivision. In this case study we will
use Subdivision 1 and “drill down” to
“Village A” (Fig. 7).
Sinkhole event locations were selected
from an overlay analysis that included
closed topographic depressions
derived from 10 m digital elevation
models, detailed hydrology modelling
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technical
Fig. 6: Pasco county sinkhole risk assessment.
Fig. 4: Represents suitability for the presence
or emergence of the event modelled [6].
appearance has proven useful in
correlating surface representations of
subsurface geological dynamics. The
vegetation is shown in red due to the
high reflectance of infrared light in the
imagery. Lineaments can accelerate
the introduction of acidic precipitation
to the underlying limestone, resulting
in an increased likelihood of sinkhole
formations [7].
Large-scale application: sinkhole risk
assessment
Fig. 5: FGS geologic and hydrogeologic data
layers [5].
analysis (high sink flows), and visual
confirmation from the most recent
1-metre imagery. A total of 16 model
inputs (sinkholes) were developed from
the overlay and the Florida Geological
Survey database for use in the model.
The depicted subdivision-level
assessment represents the top
10% probability score of the entire
geospatial sinkhole risk assessment,
equating to a 90% area reduction
(from 37,2mi² to 3,72 mi²) (Fig. 9). As
previously mentioned, area reduction
can improve resource allocation by
concentrating attention on areas with
the highest associated sinkhole risk
based on geologic, topographic, and
hydrogeologic factors.
Taking a closer look at “Village A”
within Subdivision 1 (see Fig. 7) reveals
a more accurate way to characterise
local risk based on empirically-based
statistical modelling. By magnifying this
example (see Fig. 10), we are able to
show a four-level classification scale
of low / moderate / moderate-high /
high to indicate sinkhole-associated
risk. Low sinkhole risk properties
located in hotspots; moderate sinkhole
risk properties are located in yellow
hotspots; moderate-high risk is
indicated by the orange hotspots;
and high sinkhole risk properties are
located in red hotspots. The resulting
high detail assessment can be used by
homeowners, insurers, planners, and
developers requiring a high degree of
detail to mitigate their risks and insure
their investments.
Conclusion
Applying an empirically-based
statistical modelling methodology
to sinkhole risk management can
Large-scale application: geospatial
data layers/factors
As the area of interest is only 40%
developed, the baseline of geospatial
factor data layers that characterise the
environment within “Subdivision 1”
consists of geologic, topographic, and
hydrogeologic layers. Given that the
“Subdivision 1” area of interest is more
manageable, we were able to create
and include lineament and fracture
factors. Lineaments can be identified
by linear patterns of growth depicted
by the yellow polylines (Fig. 8). The
methodology employed of extracting
lineaments from colour-infrared
imagery based on their characteristic
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Fig. 7: Large-scale area of interest (AOI) "Subdivision 1".
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technical
Fig. 8: Lineament extraction for use in the model using ENVI software.
have significant benefits to local
government, planners and developers
in successfully accommodating growth
and development while minimising
environmental impact. The model
can also improve risk determinations
for insurers so they can offer more
affordable property insurance while
mitigating losses and reducing property
insurance provider premium error
rates. It is critical to understand and
identify higher risk areas for sinkhole
occurrences so more intensive
investigation and preventive procedures
can be undertaken to minimise either
the occurrence chances or the putative
catastrophic damage chances [6].
Fig. 10: "Village A" sinkhole risk assessment.
This study demonstrates the
References
applicability, flexibility, and accuracy
[1] P Kemmerly: “Sinkhole Hazards
and Risk Assessment in a Planning
Context.” Journal of the American
Planning Association, Vol. 58(2) , pp.
227, 1993.
needed for natural hazard risk
assessments. The model not only offers
a visual representation of the associated
sinkhole risks; it also discerns subtle
among disparate data.
[2] I Lerche and W Glaesser:
Environmental Risk Assessment:
Quantitative Measures, Anthropogenic
Influences, Human Impact. Springer,
2006
Acknowledgement
[3] United States Geological Survey,
www.usgs.gov
yet powerful insights and the causal
factors that contribute to these events,
revealing correlations and patterns
This article is based on a presentation at
the ESRI Business GIS Summit held in
Chicago, Illinois, April 2008
[4]
W C Sinclair: Sinkhole development
resulting from groundwater
withdrawal in the Tampa area,
Florida: U.S. Geological Survey Water
Resources Investigations pp. 81-50,
19, 1982.
[5] Florida Department of Environmental
Protection – Florida Geological Survey,
903 W. Tennessee St., Tallahassee, FL
32312, USA.
[6] SPADAC. Signature Analyst User
Manual 3.0.0. McLean, VA: SPADAC,
March 2008
[7] HP Rasco: Multiple Data Set
Integration and GIS Techniques
Used to Investigate Linear Structural
Controls in the Southern Powder
River Basin, Wyoming, West Virginia
University MS Thesis, pp. 97.
[8] Pasco County GIS Department, 7530
Little Road, New Port Richey, FL
34654, USA
[9] Southwest Florida Water Management
District, 2379 Broad Street,
Brooksville, FL 34604, USA
[10] United States Department of
Agriculture – Natural Resources
Conservation Service.
Contact Mike Smith, SPADAC,
Fig. 9: Subdivision-level sinkhole risk assessment.
30 [email protected] 
PositionIT - March 2009