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UTILIZING GEOGRAPHIC INFORMATION SYSTEMS TO IDENTIFY POTENTIAL
LAHAR PATHWAYS IN PROXIMITY TO CASCADE STRATOVOLCANOES
MOUNT SAINT HELENS, COWLITZ & SKAMANIA COUNTIES, WASHINGTON
AS CASE STUDY
A THESIS PRESENTED TO
THE DEPARTMENT OF GEOLOGY AND GEOGRAPHY
IN CANDIDACY FOR THE DEGREE OF
MASTER OF SCIENCE
By
SAMANTHA R.Z. BANKER
NORTHWEST MISSOURI STATE UNIVERSITY
MARYVILLE, MISSOURI
JULY, 2008
IDENTIFYING POTENTIAL LAHAR PATHWAYS
Utilizing Geographic Information Systems to
Identify Potential Lahar Pathways in Proximity to Cascade Stratovolcanoes
Samantha R.Z. Banker
Northwest Missouri State University
THESIS APPROVED
Thesis Advisor, Dr. Yi-Hwa Wu Date
Dr. Ming-Chih Hung Date
Dr. James C. Hickey Date
Dean of Graduate School, Dr. Gregory Haddock Date
iii
Utilizing Geographic Information Systems to
Identify Potential Lahar Pathways in Proximity to Cascade Stratovolcanoes
Abstract
This project focused on the creation of a simple lahar pathway identification
model for Mount Saint Helens that can be readily reproduced using publicly available
datasets. The model parameters included: slope derived from a depressionless digital
elevation model (DEM), land cover, and a hydrological network. Previous lahar studies
utilized complex mathematical equations or process modeling schema such as the
LAHARZ model. While slope had been used in previous modeling efforts, most lahar
models examined lahar inundation zones from the perspective of flow volume by
calculating cross-sectional and planimetric area.
Two modeling methods, simple overlay and weighted overlay, and two
classification schema of slope factors, steep slope and inverse slope, were investigated to
determine potential lahar pathways and compared against United States Geological
Survey (USGS) volcanic hazard zones in the vicinity of Mount Saint Helens. The inverse
slope overlay models were more successful at determining potential lahar pathways in
low-lying valley and the steep slope overlay models would be more useful in identifying
locations where a lahar could begin. The inverse slope weighted overlay model with the
highest overall accuracy of 57.3 % performed better overall when compared against the
steep slope overlay models with the highest overall accuracy of 17.9 %. Utilizing USGS
volcanic hazard zone map as ground truth in accuracy assessment might cause the low
iv
accuracy value because zone 2 was primarily focused on pyroclastic surge hazards
instead of lahar hazards which in could have potentially led to model results being
inaccurately evaluated. The results were displayed in a map with recreational and
transportation infrastructure to show the relationship between the recreational and
transportation infrastructure and potential lahar pathways.
The model results were not perfect, but they show that simplistic lahar pathway
identification models can utilize publicly available data, and that there is potential for the
development and refinement of simplistic modeling in volcanic hazard applications.
With further research, simplistic lahar models might be used for preliminary lahar hazard
mapping in local communities where budgetary constraints limit GIS users to only
publicly accessible data sources. In addition, simplistic lahar models could provide
useful information for further data collection efforts enabling the development of more
precise lahar models.
v
TABLE OF CONTENTS
ABSTRACT iii
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
ACKNOWLEDGEMENTS x
LIST OF ABBREVIATIONS xi
GLOSSARY OF TERMINOLOGY xii
CHAPTER 1: INTRODUCTION 1
1.1 Research Background 3
1.2 Research Objectives 4
1.3 Study Area 5
CHAPTER 2: LITERATURE REVIEW 7
2.1 Mount Saint Helens 7
2.2 Lahar Hazards 10
2.3 GIS Models for Lahars 12
CHAPTER 3: CONCEPTUAL FRAMEWORK AND METHODOLOGY 18
3.1 Research Issues and Problems 18
3.2 Description of Data 21
3.3 General Methodology 24
3.4 Data Processing and Reclassification 26
3.5 Model Structure 31
CHAPTER 4: ANALYSIS AND RESULTS 34
4.1 Parameter Analysis 34
4.2 Simple Overlay Model 40
4.3 Weighted Overlay Model 41
4.4 Accuracy Assessment of Simple and Weighted Model Runs 44
4.5 Inverse Slope Model 50
4.6 Accuracy Assessment of Inverse Slope Overlay Model Runs 55
4.7 Discussion 58
vi
CHAPTER 5: CONCLUSION AND FUTURE RESEARCH 61
5.1 Summary 61
5.2 Limitations 63
5.3 Further Improvement and Future Research 65
REFERENCES 68
vii
LIST OF FIGURES
Figure 1: Study Area 6
Figure 2: The Eruptive History of the Cascade Range 7
Figure 3: The Destruction of the 1980 Eruption at Mount Saint Helens 9
Figure 4: Lahar Flowing from Mount St. Helens 13
Figure 5: Raster Hydrological Network 19
Figure 6: Vector Hydrological Network 20
Figure 7: Research Framework 25
Figure 8: Simple Overlay Model 32
Figure 9: Weighted Overlay Model 33
Figure 10: Digital Elevation Model Parameter Analysis 35
Figure 11: Depressionless DEM 35
Figure 12: Reclassified Slope 36
Figure 13: Land Cover Parameter Analysis 37
Figure 14: Reclassified Land Cover 37
Figure 15: Hydrological Network Parameter Analysis 38
Figure 16: Hydrological Network Buffer 39
Figure 17: Reclassified Hydrological Network Buffer 39
Figure 18: Simple Overlay Model Parameters 40
Figure 19: Simple Overlay Model Results 40
Figure 20: Weighted Overlay Model Parameters 42
Figure 21: Weighted Overlay Model Result One 42
Figure 22: Weighted Overlay Model Result Two 43
Figure 23: Weighted Overlay Model Result Three 43
viii
Figure 24: USGS Volcanic Hazard Zone Dataset 45
Figure 25: The visual comparison between steep slope overlay 45
models and USGS map
Figure 26: Simple Overlay Results using Inverse Slope 51
Figure 27: Inverse Weighted Overlay Model Result One 51
Figure 28: Inverse Weighted Overlay Model Result Two 52
Figure 29: Inverse Weighted Overlay Model Result Three 53
Figure 30: Inverse Weighted Overlay Model Result Four 53
Figure 31: Inverse Weighted Overlay Model Result Five 54
Figure 32: The visual comparison between inverse slope overlay 54
models and USGS map
Figure 33: Map of Potential Lahar Pathways and Local Infrastructure 60
Figure 34: The visual comparison between steep slope overlay 66
models and USGS map (Zones 1 & 3)
Figure 35: The visual comparison between inverse slope overlay 66
models and USGS map (Zones 1 & 3)
ix
LIST OF TABLES
Table 1: Expected travel times for lahars triggered by a large eruption of 13
Mount Saint Helens
Table 2: Overall Classification Schema 29
Table 3: Slope Reclassification 29
Table 4: Land Cover Reclassification 30
Table 5: Hydrological Buffer Reclassification 30
Table 6: Weighting Combinations of Model Parameters 42
Table 7: Error Matrix of Simple Overlay Model 49
Table 8: Error Matrix of Weighted Overlay Model Run #1 49
Table 9: Error Matrix of Weighted Overlay Model Run #2 49
Table 10: Error Matrix of Weighted Overlay Model Run #3 49
Table 11: Summarized Accuracy Assessment of the Overlay Models 49
Table 12: Slope Reclassification Inverted 52
Table 13: Weighting Combinations of Model Parameters (Slope 52
Classification Inverted)
Table 14: Error Matrix of Simple Overlay Model 57
Table 15: Error Matrix of Inverse Weighted Overlay Model Run #1 57
Table 16: Error Matrix of Inverse Weighted Overlay Model Run #2 57
Table 17: Error Matrix of Inverse Weighted Overlay Model Run #3 57
Table 18: Error Matrix of Inverse Weighted Overlay Model Run #4 57
Table 19: Error Matrix of Inverse Weighted Overlay Model Run #5 58
Table 20: Summarized Accuracy Assessment of Inverse Slope 58
Overlay Models
x
ACKNOWLEDGMENTS
The author wishes to thank Dr. Yi-Hwa Wu, Dr. Ming Chih-Hung, and Dr. James
Hickey for their encouragement and their support in research efforts. Additional thanks
are extended to a very loving and supportive family and circle of friends who have
fostered an environment based in faith, perseverance, and a passion for the acquisition of
knowledge.
The author would also like to acknowledge the United States Geological Survey
for making available a wealth of information and resources that supported this study.
Volcanic hazard zone data was an important element in performing the accuracy
assessment, while photographs, figures, factsheets, and publications supported the study
by providing a thorough background on Mount Saint Helens, its local environment, and
its eruptive history.
xi
GLOSSARY OF TERMINOLOGY
Definitions for Lahar and Stratovolcano acquired from the United States Geological
Survey Cascades Volcano Observatory website. Definitions for Geology, Subduction,
and Volcanology acquired from the American Geological Institute Glossary of Geology.
Term Definition
Geology
The study of the planet Earth the materials of
which it is made, the processes that act on these
materials, the products formed, and the history
of the planet and its life forms since its origin.
Lahar
Are mixtures of water, rock, sand, and mud that
rush down valleys leading away from a
volcano; they have the strength to rip huge
boulders, trees, and houses from the ground and
carry them down valley.
Stratovolcano
Typically steep-sided, symmetrical cones of
large dimension built of alternating layers of
lava flows, volcanic ash, cinders, blocks, and
bombs and may rise as much as 8,000 feet
above their bases; typically erupt with explosive
force, because the magma is too stiff to allow
easy escape of volcanic gases.
Subduction The process of one lithospheric plate
descending beneath another.
Volcanology The branch of geology that deals with
volcanism, its causes and phenomena.
1
CHAPTER 1
INTRODUCTION
Remote Sensing has seen considerable use within the field of volcanology, a
subfield of geology that focuses on the study of volcanoes and volcanic activity (Agnes
1999). Remotely-sensed data is most commonly used to detect changes in land surface
deformation and to study the thermal properties of volcanoes and fault systems (Tralli et
al 2005; Pergola et al 2004). More recently, Geographic Information Systems (GIS) has
become more frequently used in hazard mapping, hazard modeling, and for the analysis
of the relationships between features (e.g. geologic, volcanic, natural resource, habitats,
and etc.) around volcanoes (Pareschi et al 2000). As such, GIS has demonstrated its
usefulness within the field of volcanology (Schilling 1998), especially when combined
with remotely-sensed data sources for lahar modeling (Hubbard et al 2007; Huggel et al
2005).
Lahars are debris flows that occur when melting snow and glacial ice, water from
intense rainfall, or sudden failure of a natural dam occurs atop an erupting or seismically
active volcano. The term lahar is an Indonesian term used to describe the mud and debris
flows that flow down the slopes of a volcano (Brantley & Power 1985). The catastrophic
eruption of Mount Saint Helens on May 18, 1980 triggered a lahar flow by the sudden
melting snow and glacial ice from hot volcanic rocks and subsequent pyroclastic flows.
Even several years after the initial volcanic eruption, lahars still posed great danger to
downstream areas due to the heavy precipitation, as seen at Mount Pinatubo in the
Philippines. Thousands of lahars have occurred since its enormous eruption on June 15,
2
1991, and nearly all were triggered by intense rainfall. Lahars can occur with or without
a volcanic eruption. Earthquakes and rainfall can trigger landslides that can quickly
transform into a lahar due to overflowing water and eroded volcanic materials.
An abundant water source can lead to the development of mudflows that are
capable of containing large objects (e.g. trees, boulders, debris, and etc.) mixed with
loose volcanic materials and in some cases lahars can be very high in temperature due to
the energy put forth by the volcano during eruption. Lahars typically utilize hydrologic
networks as corridors for movement, they are dependent on slope for speed, and they
have historically traveled long distances, impacting many communities in valleys and
other low-lying areas with little warning. The use of GIS in analyzing lahar hazards
allows users to take into account multiple environmental factors when determining lahar
paths and areas.
This study is focused on evaluating the capabilities of GIS and remotely-sensed
data in lahar pathway identification modeling. Lahars have occurred all around the
world, especially in locations like the Cascade Range and the Andes Mountains where
tall stratovolcanoes coincide with subduction zones. They might occur during a volcanic
eruption (e.g. 1980 eruption of Mount Saint Helens, 1985 eruption of Nevado del Ruiz),
after an eruption (e.g. after 1991 at Mount Pinatubo), or without eruption (Casita Volcano
in 1999). Their occurrence is typically unexpected, leaving considerable damage and/or
loss of life in their wake. It is important to be proactive in preparation and prediction.
The stratovolcanoes within the Cascade Range, more specifically Mount Saint Helens, is
chosen for study because of the availability of GIS and remotely-sensed data sources and
the well-documented eruptive past of the volcano. The development of a lahar pathway
3
identification model may be applied to other stratovolcanoes in the region if the model
results prove to be meaningful and accurate.
1.1 Research Background
Most of the volcanoes within the Cascade Range are not within a close distance to
major population centers, but certain volcanic hazards still pose a major threat to these
population centers as urban expansion continues to transform the landscape. Studies
conducted on Mount Rainier’s eruptive past have found the existence of multiple major
lahars that continually blanketed the land over the course of several thousands years.
Tacoma and its growing suburbs are built atop these ancient lahars. The concerns for the
possibility of future lahars have increased the study of lahars and the efforts to prepare
and protect the population that could be impacted (Sisson 1995). Following the 1980
eruption at Mount Saint Helens there were several major lahars that impacted the
communities residing in the major river valleys immediately within the vicinity of the
volcano. One lahar destroyed over two hundred homes, twenty-seven bridges, and
reached a depth as high as thirty-nine feet in some locations downstream (Brantley &
Myers 2000).
The Cascade Range is composed of thirty-seven volcanoes at varying stages in
their lifecycle (USGS 2004). Eight of the Cascade volcanoes are considered major
composite volcanoes which are also called stratovolcanoes (USGS 2001).
Stratovolcanoes are typically steep-sided, with symmetrical cones that are built up as a
result of layers of lava flows, ash, lahars, and other volcanic debris (Tilling 1985).
Another important characteristic of stratovolcanoes are their height. It is common for
4
stratovolcanoes to rise 8,000 feet or more above the surrounding landscape. It is also
important to note that the Cascade volcanoes developed as a result of the subduction of
the Juan de Fuca plate beneath the North American plate. The combination of being a
product of subduction and being a stratovolcano could make volcanoes like Mount Saint
Helens erupt explosively because the magma traveling through the volcano is too viscous
to allow the simple release of volcanic gases (Kious & Tilling 1996). Lahars almost
always occur on or near stratovolcanoes because these volcanoes tend to erupt
explosively and their tall, steep cones are either snow covered, topped with a crater lake,
constructed of weakly consolidated rock debris that is easily eroded, or internally
weakened by hot hydrothermal fluids.
1.2 Research Objectives
The primary objective is to create a simple, yet viable lahar pathway identification
model with free publicly accessible data using commercial GIS software for emergency
officials and the local community. This study also attempts to determine what GIS
analysis functions and modeling methodology could be utilized in analyzing, and
identifying potential lahar pathways. Lahars can cause tremendous damage and loss of
life, often coming with little warning. A simplistic descriptive lahar pathway
identification model would be beneficial for easy replication in future research efforts at
different sites, to guide field work in remote areas or as a source of lahar hazard warning
information for policy-makers and the public in emergency planning. It also has the
potential to provide preliminary reports to focus data collection efforts for complex lahar
prediction models.
5
Hazard mapping is essential to the study of volcanoes and it can increase public
awareness of what could happen in the event of a volcanic eruption and associated
disasters. This research provides descriptive GIS modeling frameworks that are used to
identify potential lahar paths around Mount Saint Helens which could then be applied to
other volcanic systems during the early stages of investigation. Identifying these areas
could help national park officials, scientists, and the local communities in their
preparation for future lahars. The potential for increased use of Geographic Information
Systems in hazard mapping and modeling, especially as it relates to volcanic hazards
could also increase as more people realize and understand its valuable utility.
1.3 Study Area
Mount Saint Helens is a stratovolcano that straddles the border of Cowlitz County
and Skamania County in Washington State. The volcano is located in the Gifford
Pinchot National Forest which lies within Washington’s Cascade Mountain Range (see
figure 1). Modern day Mount Saint Helens stands at 8,363 feet, but its pre-1980 eruption
elevation was 9,677 feet. The volcano can be found in southwestern Washington 50
miles to the northeast of Portland, Oregon and it falls along the Pacific Ring of Fire, a
zone of geologic unrest which is frequented by strong earthquakes and volcanic activity
(Tilling et al. 1990). Mount Saint Helens is considered the youngest of the Cascade
volcanoes and by far the most active in the contiguous United States. The topography of
the region around Mount Saint Helens is mountainous, and is made up of ridges and
valleys. Complex networks of rivers and streams follow the valley floors. There are also
several major lakes that lie adjacent to the volcano. In the early 1980’s, President Ronald
6
Reagan set aside thousands of acres of land around and including Mount Saint Helens as
a national volcanic monument under the jurisdiction of the United States Forest Service.
This distinction was the first of its kind in the United States and it has made the
monument a popular recreational destination for hiking, camping, water sports, and for
those that are curious to see what scars the 1980 eruption left on the landscape.
Figure 1: Study Area
7
CHAPTER 2
LITERATURE REVIEW
2.1 Mount Saint Helens
Mount Saint Helens is located in the western part of the Cascade Range,
approximately 50 miles South of Mount Rainier, the tallest of the Cascade volcanoes. It
is geologically young when compared to the other major Cascade volcanoes but it is
considered the most active of the Cascade volcanoes (see figure 2). Some Native-
Americans of the Pacific Northwest called Mount Saint Helens “Louwala-Clough” or
“Smoking Mountain”. Prior to 1980, most people saw Mount Saint Helens as a beautiful
and tranquil mountain recreational area abundant with wildlife and accessible year-round
for recreational activities (Clynne, et al. 2005).
Figure 2: The Eruptions History of Cascade Range (Source: USGS)
8
Prior to the major eruption, the first signs of geologic unrest at Mount Saint
Helens were a sequence of small earthquakes that began on March 16, 1980. By May 17,
1980, the volcano had been shaken by over 10,000 earthquakes and the north flank of the
volcano had grown outward. The next day, May 18, 1980, Mount Saint Helens exploded
with cataclysmic force. The eruption was one of the greatest natural disasters that has
occurred in United States history, resulting in a loss of 0.67 cubic miles of rock from the
volcano’s edifice, most of which had resulted from nearly 4,000 years of lava dome
building eruptions. Within seconds following a moderate earthquake, the mountain’s
summit elevation plunged from 9,677 feet to 8,363 feet, leaving a north-facing,
horseshoe-shaped crater over 2 kilometers or roughly 2,084 feet deep (Brantley & Myers
2000). Throughout the day, prevailing winds carried 520 million tons of ash eastward
across the United States. Figure 3 illustrates the distribution of the devastation of the
1980 Mount Saint Helens eruption.
The destructive lahar developed throughout the day from water and sediment
flowing out of the huge debris landslide deposit. The lahar eventually flowed into the
Cowlitz River. The lahars peak stage at Castle Rock, about 50 miles downstream from
the volcano, wasn’t reached until midnight, more than fifteen hours after the landslide
had commenced. The timing is significant in that the effects of lahars, while extremely
destructive, are not instantaneous. Communities downstream have time to initiate
mitigation efforts especially if there are existing plans in place for such an event
(Brantley & Myers 2000).
Following the 1980 eruption, Mount Saint Helens has remained active. A large
lava dome began extruding in the center of the volcano’s crater episodically. In October
9
1980, a new lava dome that reached a height of 876 feet above the crater floor resulted
from 17 eruptive periods. Minor explosive activity and lahars accompanied several of
these episodes that spanned five years. In addition, hundreds of minor explosions which
were primarily bursts of steam and gas occurred, carrying ash up to several miles above
the volcano. The larger explosions littered the floor of the crater with rocks and generated
small lahars on occasion (Brantley & Myers 2000).
From the fall of 1986 to the fall of 2004, Mount St. Helens transitioned and
maintained a period of relative inactivity. Occasionally, short-lived seismic swarms
interrupted this quiet, along with increases in underlying seismicity reflecting the
replenishment of magma deep beneath the volcano. Minor steam explosions also
Figure 3: The Destruction of 1980 Eruption in Mount Saint Helens (Source: USGS)
10
occurred as late as 1991 (Brantley & Myers 2000). A new glacier developed in the crater,
which encircled itself around and partially buried the lava dome.
Mount St. Helens reawakened in September 2004 with a multitude of minor,
shallow earthquakes within and underneath the 1980-1986 lava dome. The accelerated
size and incidence of the earthquakes and a noticeable deformation of the glacier inside
the crater signified that magma was moving toward the surface, prompting scientists to
issue a variety of volcanic hazard notifications. On October 1, 2004, an explosion
launched steam and ash several thousand feet and sent rock fragments flying one-half
mile across the western half of the glacier and across the 1980-1986 lava dome. Three
more explosions of steam and ash occurred through October 5, 2004. From October 5th
through March 2005, earthquake rates and sizes increased and decreased. The new lava
dome grew rapidly, reaching a height of nearly 1,400 feet above the level of the 1980
crater floor. The ridge and the new lava dome collectively covered an area comparable to
about 60 city blocks (Major et al. 2005).
2.2 Lahar Hazards
Lahars by their very nature are born out of environmental instability in a volcanic
environment. Lahars can occur when glacial ice and snow are quickly thawed by the
intense heat from volcanic activity, but they can also occur when large amounts of rain
mix with unconsolidated rock, ash, and soil. Rapid melt in conjunction with steep
volcanic slopes and unconsolidated soil and rock create the environment for the
movement of lahars down into lower-lying areas. Slope plays an important role in lahar
movement. Lahar movement can vary in speed depending on the degree of slope.
11
Higher degrees of slope can increase the speed of transport and the degradation of land
cover, resulting in the growth of a lahars volume to include boulders, vegetation, trees,
and structures (Wolfe & Pierson 1995). In addition, as lahars move into river valleys,
additional sediment, water, and other objects can be deposited from or added to lahar
volume depending on the rate of movement of the lahar.
Mount Saint Helens continues to be an active and dangerous volcano. Lahars
pose a greater threat to life and property in the communities of the Cowlitz and lower
Toutle River drainages than any other volcanic phenomenon. Previous lahars, including
those from the May 18, 1980 eruption, traveled 30 to 60 miles, frequently reaching the
Columbia River via the Kalama, Lewis, or Toutle Rivers. Non-eruption events such as
intense storm runoff over and through unconsolidated sediment, landslides, or failure of
the Castle Lake impoundment can generate lahars. A large debris avalanche and a major
lateral blast like those that occurred during the May 1980 eruption is not likely now that a
deep, open crater has formed (Wolfe & Pierson 1995).
The amount of available water provides the driving force of a potential lahar.
Rapid melting of snow and ice within the crater or a sudden failure of Castle Lake are the
most likely mechanisms to cause a lahar. A number of hydroelectric power reservoirs in
close proximity to the volcano in the Lewis River valley could also play an important role
in potential lahars. The Swift Reservoir and downstream lakes are capable of trapping a
lahar and impeding its advance. The natural dam at Castle Lake could produce a lahar on
its own if the blockage were to fail. Based on the behavior of lahars from the May 1980
eruption, estimated travel times have been developed for lahars traveling down the North
Fork Toutle River valley, and the South Fork Toutle River, Pine Creek, Muddy River,
12
and Kalama River valleys (Wolfe & Pierson 1995). Table 1 depicts these general
estimates for lahar transit times and the associated distance from Mount Saint Helens via
the surrounding river valleys. Figure 4 shows a lahar flowing from the crater into the
North Fork Toutle River valley.
2.3 GIS Models for Lahar
The issue of lahar hazard mapping and modeling has been examined from several
viewpoints using GIS. Most studies are focused on the use of the LAHARZ model or
models involving complex mathematical equations, while another study favored a more
GIS-focused simplistic model (Schilling 1998; Fagents & Baloga 2005; Iverson et al.
1998; Renschler 2005). One study involved the compilation of several scientists’
equations, a digital elevation model, and input lahar volumes into GIS-driven software
called LAHARZ (Schilling 1998). A second study utilized a digital elevation model to
calculate lahar transit times, via lahar flow models and mathematical equations (Fagents
& Baloga 2005). A third study took a more scientific approach to lahar hazard mapping,
with its primary focus residing in the input of complex equations into GIS (Iverson et al.
1998). Renschler (2005) discussed the construction of a GIS model for volcanoes that
uses a specified scaling theory. These studies show that GIS is a valuable tool in
mapping lahars and each of the studies offer insight into methodologies that have been
proven successful. The unifying theme is that they all utilize aspects of GIS for lahar
modeling.
13
Table 1: Expected travel times for lahars triggered by a large eruption of Mount Saint
Helens. [NFT = North Fork Toutle River; SFT, P, M, K = South Fork Toutle River, Pine
Creek, Muddy River, and Kalama River.] (Source: Wolfe & Pierson 1995)
Distance from
Mount Saint
Helens in km
(mi)
Lahar Estimated
Travel Time in
hr: min
-------- NFT SFT, P, M, K
10 (6.2) 0:37 0:11
20 (12.4) 1:08 0:30
30 (18.6) 1:37 0:54
40 (24.9) 2:16 1:21
50 (31.1) 2:53 1:49
60 (37.3) 3:27 2:20
70 (43.5) 3:48 2:53
80 (49.7) 4:43 3:31
90 (55.9) 6:36 4:18
100 (62.1) 8:50 5:12
Figure 4: Lahar Flowing from Mt. St. Helens (Source: USGS, Tom Casadevall, 1982)
14
The use of the LAHARZ model has been especially valuable to scientists working
with the United States Geological Survey. LAHARZ takes into account the cross-
sectional area and the planimetric area in relation to user-defined stream and hazard zone
boundaries, and then calculates and outputs lahar inundation zones around a volcano in a
concise and replicable manner (Schilling 1998). The determined hazard zones can then
be incorporated with other hazard or thematic information to produce volcanic hazard
maps. Schilling (1998) identified the processes necessary to successfully produce output
lahar inundation zones, but one downside is that the user must specify lahar volumes and
this can only be precisely determined by someone with considerable knowledge and
experience.
Knowing the rate at which lahars flow across the landscape is essential for
emergency planners in communities that have been impacted historically. Fagents and
Baloga (2005) determined that the incorporation of a digital elevation model proved to be
problematic for the calculation of lahar transit times. It was determined that resolution
issues affected the ability to acquire accurate topographic representation across the
landscape. To counter these problems, they utilized complex calculus-based equations in
an effort to derive accurate flow depth and advance rates for lahar flows. Results showed
that flow depths and transit times were dependent on changes in slope on a local scale
over the course of the flow path.
Understanding the paths of past lahars enable the potential identification of the
paths of future lahar inundation zones, but historic data for large lahars and general data
for small lahars is limited. Thus, Iverson et al. (1998) incorporated predictive equations
based upon the criteria of cross-sectional and planimetric area in relation to lahar volume.
15
These predictive equations were programmed into GIS and were integrated with
topography derived from a DEM to output lahar inundation zones. The resulting output
hazard maps showed that lahar inundation zones decrease with distance from the volcano
and with elevation above the valley floor. This study utilized the LAHARZ program for
its calculations. In addition, problems associated with the accuracy and resolution of
DEM’s was addressed along with the lack of focus on land features in the region (e.g.
water bodies and etc.).
Previously discussed models have involved the incorporation of GIS and process
models. The challenge for model developers is how to maintain the rigor and flexibility
of a complex model while making the model accessible and appropriate for potential
users. None of the studies implemented its model directly inside commercial GIS
software. All models are loosely-coupled with GIS. GIS is not the primary agent in the
existing hazard models but rather a database management system and visualization tool.
The potential users have to understand the process models (including the complicated
mathematical equations) and the linkages between models and GIS.
Renschler (2005) proposed using scaling theory to incorporate process model and
GIS in volcanic hazards modeling. The research framework utilized GIS as the primary
agent in overall model construction and analysis because GIS allows effective model
applications based on practical data accessibility and environmental situations. The
spatial and temporal variability of volcanic processes could be scale up to be represented
in models for full-scale volcanic hazard scientific research or scale down due to the data
availability and user’s interest for small-scale local hazard assessment. This approach of
scaling theory provide a framework to construct practical procedures for applying GIS-
16
based volcano models that allow effective model application based on realistic data
availability and environmental settings. Unfortunately, the detail modeling process
structure is still unavailable.
Selecting a suitable model to aid in environmental management decisions requires
consideration of whether the necessary data needed to run the model is easily accessible
for potential users. Models that utilize only readily accessible data are more likely to be
employed than models that require the user to spend time and money on further data
compilation. The process-based prediction models with high quality data are more likely
to produce more precise results but the data and the models must be monitored and
executed by research universities or government agencies. Some users (e.g., local
government, preliminary field survey, etc.) might choose a more simplistic model over
the more intensive model because the simplistic model requires less time and has the
potential to be cost effective.
The construction of a lahar model requires many different elements. As seen
throughout the literature there are a variety of different methods that can be utilized to
arrive at reliable results. Grayson et al. (1993) concluded that while complex process-
based models are typically useful in research, models used for management decisions
such as volcanic hazard mitigation by first-responders and decision-makers should be
simple and modest, with only a few data requirements and clearly stated assumptions.
Renschler (2005) also argued that although all the relevant components of a multi-scale
environmental assessment approach can be assembled by a combination of the most
advanced technology, most detailed data used by a state-of-the-art environmental model
and GIS, it does not automatically guarantee that accurate and useful simulation results
17
will be produced. The models often ended up misused due to realistic data availability not
matching model requirements or the users did not fully comprehend the model
assumptions and limitations.
This study originated as a result of interest in understanding how GIS could be
utilized to determine potential lahar pathways and potentially impacted areas in the
vicinity of Mount Saint Helens. Instead of incorporating complex process modeling
procedures, this study focused on a general interest in lahar hazard mapping and utilizing
ready-to-use publicly accessible data in commercial GIS software.
18
CHAPTER 3
METHODOLOGY
3.1 Research Issues and Problems
Some of the obstacles faced in the research segment of this project were the
availability of supporting literature for explicit modeling structure of simplistic lahar
modeling in commercial GIS. Several previous investigations had employed various
GIS-based approaches with respect to lahar modeling and mapping efforts. All of these
sources utilized some type of mathematical equation or scientific reasoning in their
process modeling efforts in an attempt to determine accurate lahar volumetric and down
slope information across the landscape. Aspects of GIS was utilized by all of the sources,
but it was never utilized as primary modeling agent as what this research project is based
upon. While the LAHARZ model was written using ARCINFO Macro Language
(AML), and operates within a segment of ARCINFO, the GRID program, the processes
and technical background required to acquire and run this particular model were
determined to be too difficult for this study (Schilling, 1998). The level of complexity
and the cost of acquiring the software (e.g. ARCINFO) and hardware necessary to run
LAHARZ made it an unviable option because the goal of the study was to determine
whether or not a simplistic modeling methodology could be developed utilizing publicly
accessible data to identify potential lahar pathways.
In the overlay modeling process, many model test-runs were performed to
determine which overlay weighting schemes derived the best coverage of potential lahar
pathways in comparison to USGS volcano hazard zones, within which lahar hazards
occur. In addition to modeling, analysis was paramount to the GIS project phase,
19
especially with respect to the comparison of vector and raster-based hydrological sources
in determining potential lahar paths. When attempts were made to derive hydrological
data from the digital elevation model, the hydrological network was not connected as a
hydrological network should be (see figure 5). Thus, vector hydrological data sources
were utilized instead to aid in the delineation of potential lahar pathways (see figure 6).
Figure 5: Raster Hydrological Network
20
With respect to data issues, a land cover classification was originally performed
using Landsat 7 Enhanced Thematic Mapper + satellite imagery which was acquired in
September 2004. The land cover classification was implemented in Leica Geosystems
ERDAS IMAGINE 9.1 software. Both supervised and unsupervised classifications were
performed to determine which would be best suited for the model. The results were not
used due to a lack of readily available ancillary data (e.g. color aerial imagery) to aid in
making reliable ground truth for accuracy assessment. There were also issues with
shadowing on the north face of ridges because of the sun angle at the time of image
capture, and there was some obvious image garbling that most likely resulted from
mosaicing the image prior to public dissemination. Land cover data from the National
Land Cover Dataset 2001, was chosen as a suitable and more reliable replacement.
Figure 6: Vector Hydrological Network
21
Some model outputs also encountered issues, especially when hydrological data
was incorporated in the weighted overlay modeling scheme. The issues were primarily
noticed in the output values. Even though all values were reclassified on a classification
scale of 1 to 6 prior to running the model, all weighted model results that incorporated the
hydrological network into the modeling scheme at a high percentage of weighting
resulted in output values of 2 to 6. This issue was also found in the output of the
reclassification of the hydrological network buffer raster. Efforts were made to
determine why this issue occurred within the processing of the hydrological network. To
date, no particular cause or reason has been determined.
3.2 Description of Data
Data sources included elevation data from a 10 meter (1/3 arc second) digital
elevation model (DEM), land cover for the region, hydrological data, cartographic
boundary files, roads, and infrastructure. . Digital elevation models and their associated
slope parameters were the most frequently discussed model parameter in lahar modeling
in the previous studies. The DEM provided the necessary means to attain reasonably
accurate slope and elevation information for the volcano and the surrounding landscape.
The 10 meter DEM is part of the National Elevation Dataset and was acquired via the
United States Geological Survey Seamless Data Distribution website. The ultimate goal
in acquiring a finer resolution DEM was to minimize accuracy issues. In a previous
course project pertaining to lahar suitability modeling, a 30 meter DEM was utilized and
the model results were not of comparable quality to the model results that have resulted
22
following the use of the 10 meter DEM. Prior to the derivation of the slope parameter,
the DEM was processed to fill sinks so that the DEM would be depressionless.
It is important to know what lower-lying land cover features are most likely to be
impacted by lahars. Land cover data was acquired from the National Land Cover Dataset
2001 via the United States Geological Survey Seamless Data Distribution website. The
NLCD dataset has a spatial resolution of 30 meters. Land cover as a model parameter
was not addressed in the previous research, but it could be a potential model parameter
because previous lahar flows followed certain paths and there were certain land cover
types that typify the areas in and around those certain paths, especially low-lying river
valley environments where the predominate land cover types are water, wetland, bare
rock, and ash/mud.
Cartographic boundaries, more specifically, state and county administrative
boundaries were acquired from the United States Census Bureau and were utilized in the
delineation of the study area. Roads are a vital means of getting into and out of the area.
Knowing which roads and bridges could be affected by a lahar flow could maximize the
likelihood of evacuation and staying out of harm’s way. Road and bridge data was
acquired from the Washington Department of Transportation and was used in a final
output map to illustrate the degree of impact that could be sustained to transportation
infrastructure in comparison to potential lahar pathways. The inclusion of roads and
bridge infrastructure was important in the output map because of the impacts that
previous lahars have had on the infrastructure. Lahars are capable of destroying
transportation infrastructure, including carrying bridges downstream. This occurred
following major lahars around Mount Saint Helens on May 18th and 19
th, 1980.
23
Hydrological data was obtained from the Washington Department of Natural
Resources and it was utilized as a model parameter in the models as a baseline path
through which lahars typically flow. Recreational infrastructure such as trails, visitor
centers, campgrounds, and other national park facilities were acquired from the United
States Department of Agriculture Forest Service. Infrastructure, as well as residential
areas, that exist in low-lying areas could be impacted in the event of a lahar, so knowing
the physical location of buildings and other recreational infrastructure in the region is
vital for the safety of national park officials and visitors and areas down gradient of the
forestlands.
Hydrological data establishes potential lahar paths across the landscape, since it
had been discussed that lahars typically follow hydrological corridors in previous
research. This study utilized it as a base for potential lahar paths. However, there was no
specific discussion of vector hydrological data and associated buffers being used in the
modeling process. Prior to the incorporation of a vector hydrological data set, a raster
hydrological dataset had been used to be derived from the DEM. After creating a
depressionless DEM and then processing it with hydrological tools such as Flow
Accumulation, it was determined that the DEM might not be suitable for providing a
hydrological network because the network that was output was not well-defined (see
figure 5). Hubbard et al. (2007) discovered this error when attempting to derive
hydrological networks from Shuttle Radar Topography Mission (SRTM) and Advanced
Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM’s. In as
similar course of action, they chose to utilize vector stream networks that were digitized
from 1: 50,000 topographic maps. In most cases, hydrological networks are derived from
24
a DEM, but in our attempts to derive the hydrological networks from the DEM we found
that the networks did not connect like they were supposed to. This would have resulted
in an irregular hydrological network that did not account for the actual river and stream
paths which are an important model parameter (see figure 5).
The Volcanic Hazards Zone Dataset was obtained from the United States
Geological Survey Cascade Volcano Observatory website and it was utilized in the
accuracy assessment stage to gauge the users, producers, and overall accuracy of the
output model results. The dataset has a 51 meter spatial resolution and it is divided into
three hazard zones. Each zone is susceptible to multiple volcanic hazards, with the most
severe hazards being experienced in zones one and two since these zones are in closer
proximity to the volcano. Zone 3 is prone to lingering flowage hazards such as lahars.
3.3 General Methodology
The following research methodology outlined the essential steps to derive the
desired output descriptive model results. Data sources and analysis procedures varied as
the project was undertaken to arrive at the desired results. The essential model
parameters included slope, land cover, and hydrological data. Slope represented the
mountainous nature of the study area. Steeper slopes created the environment necessary
for the development and initial movement of lahars because of the greater force of gravity
on the materials (e.g. unconsolidated ash, rock, and soil), while flatter slopes allowed for
continued movement of lahars. Land cover showed what lied within a potential pathway
and aided in understanding the potential damage of infrastructure, and the environment.
Certain land cover types were more prevalent in the lower-lying areas that had been
25
impacted by lahars near Mount Saint Helens previously, including: water, wetlands, some
deciduous forest, mud, and bare rock/ground. Hydrological data was essential to the
model because lahars within the study area had historically flowed through the river
valleys surrounding the volcano. Utilizing the hydrological data in the model aids in
providing a base line from which potential lahar paths could be delineated. Figure 7
illustrated the steps taken throughout the lahar pathway model process.
Figure 7: Research Framework
26
3.4 Data Processing and Reclassification
The data acquisition phase involved acquiring the previously mentioned data
sources (e.g. digital elevation model, land cover, cartographic files, hydrology data,
volcanic hazard zones, and aerial/satellite imagery) from organizations such as the United
States Geological Survey, the National Forest Service, the Washington Department of
Natural Resources, the United States Census Bureau, among other sources. The DEM
provided essential information such as slope parameters that were included in the lahar
pathway model. Land cover representing features in and around Mount Saint Helens,
including the river valleys were investigated and analyzed (e.g. ash, bare rock, old lahar
flows, water bodies, and forest cover.). Land cover was examined in coordination with
Landsat 7 ETM+ satellite imagery (taken on September 28, 2004) to determine previous
lahar paths. This aided in determining which land cover types occurred in potential
future lahar pathways. Cartographic boundary files were primarily used for spatial
delineation of the focus area in the analysis and output maps. Hydrological data was
buffered at distances of 0.5, 1, 2, 4, 6, and 8 kilometers from the centerline of the major
river valleys to determine areas that were more likely to be overcome in the event of
lahars. The drainage divides were not considered in this study since the model is static in
nature without considering the processing force from the origin. The buffer distances
were chosen to include all of the study area in the analysis and to allow all areas to be
included in the classification schema. Roads and infrastructure data were used in
resulting maps to determine which features would be overcome or destroyed if they fell
within the potential lahar paths. The volcanic hazards zone dataset was first converted to
a coverage from an ArcExport Interchange File (.e00). The coverage was then converted
27
to a shapefile. The shapefile was converted to Raster and was then reclassified into (1, 0)
and (1, NODATA) for use in the accuracy assessment. A USGS volcanic hazard map
was utilized to gauge their predicted lahar flow zones against output model lahar
pathways following the completion of the analysis.
The modeling phase began with the determination of functions as they related to
the differing model parameters. The land cover model parameter only required
reclassification of values prior to be input into the model. The digital elevation model
(DEM) first needed being made depressionless before the slope could be derived. The
process of making a DEM depressionless involved the determination of an appropriate
fill factor and then the use of the Fill function in the Spatial Analyst Toolbox within the
Hydrology toolset. Then, the Sink function was used to determine whether or not it was
successfully depressionless. Once DEM achieves depressionless status, the slope was
derived using the Slope function in the Spatial Analyst Toolbox (measured in degrees)
within the Surface toolset. The output slope values were reclassified using the reclassify
function, prior to the slope model parameter’s input into the model.
The hydrological network model parameter was first buffered using the multiple
ring buffer tool at distances of 0.5, 1, 2, 4, 6, and 8 kilometers. The output buffers were
converted from a feature class to a raster using the Features to Raster function in the
Conversion Toolbox within the To Raster toolset (cell size 10 meters) before it could be
reclassified and then input into the model.
Reclassifying model parameters was an important step in the process between
data acquisition and the commencement of modeling. It allowed the user to prioritize the
importance of parameters where the essential parameter was given greater emphasis and
28
the negligible parameter was not as involved in the overall modeling process. For all
factors, the greater likelihood of a potential lahar path was given higher value in the
model (see table 2).
Slope was the most important modeling parameter. In this model, areas with the
steepest slope in degrees were given the highest suitability classification because areas of
steep slope and elevation provide the gravity force for the lahar flow. The areas with the
gentle slope in degrees were given the lowest possibility because the lahar would likely
stop at the flat area (see table 3). Slope values in degrees were by default assigned to the
Natural Breaks classification scheme in ArcGIS 9.2.
Land cover was a little more difficult to reclassify. Satellite imagery was used for
comparative purposes to determine which land cover classes occurred relative to older
lahar paths. It was determined that land cover classes containing water and barren land
(includes volcanic materials such as rock, ash, and older lahar flows) were most likely to
occur in a lahar path because these areas were evidence of previous lahars in the study
area. Shrub/scrub and grasslands were determined to be more suitable. Woody wetlands,
emergent herbaceous wetlands, and deciduous forest were determined to be moderately
suitable. Mixed forest and pasture were determined to be less suitable in the analysis
because they did not occur near river valleys. Pasture was not identified in the land cover
of the region, but is a parameter in the land cover dataset and had to be classified.
Evergreen forest and cultivated crops were determined to be less suitable. Evergreen
forest occurred most often along higher ridges, while cultivated crops were negligible in
the immediate vicinity. Developed land (open space, low-intensity, medium intensity,
29
Table 2: Overall Classification Schema
Classification Value Suitability
6 Most Suitable
5 More Suitable
4 Moderately Suitable
3 Somewhat Suitable
2 Less Suitable
1 Not Suitable
Table 3: Slope Reclassification
Slope in Degrees Reclassification Value
69.940705 - 81.757172 6
54.9306 - 69.940705 5
38.643038 - 54.9306 4
21.397385 - 38.643038 3
6.706643 - 21.397385 2
0 - 6.706643 1
and high intensity) was very sporadic and typically not found in low-lying areas. Its
occasional occurrence was an exception, not the rule, thus its not suitable classification
with respect to being impacted by lahars (see table 4).
The hydrological network’s output buffer zones were reclassified based upon the
distance away from the centerline of the river valley. Distances from the centerline up to
0.5 kilometers were determined to be most suitable. Distances between 0.5 and 1
kilometer were more suitable. Between 1 and 2 kilometers were deemed moderately
suitable. Distances between 3 and 4 kilometers were somewhat suitable. Between 5 and
6 kilometers were deemed less suitable. Distances between 7 and 8 kilometers were
determined to be not suitable (see table 5). Higher values were included in the study to
insure that the entire study area would be included in the model. The theory behind this
30
Table 4: Land Cover Reclassification
Land Cover Types Reclassification Value
Open Water; Barren Land 6
Ice/Snow; Shrub/Scrub;
Grassland/Herbaceous 5
Deciduous Forest; Woody Wetland;
Emergent Herbaceous Wetlands 4
Mixed Forest; Pasture/Hay 3
Evergreen Forest; Cultivated Crops 2
Developed (Open Space; Low, Medium,
and High Intensity) 1
Table 5: Hydrological Buffer Reclassification
Distance from Centerline
(in Kilometers) Reclassification Value
0 - 0.5 6
0.5 - 1.0 5
1.0 - 2.0 4
3.0 - 4.0 3
5.0 - 6.0 2
7.0 - 8.0 1
schema, especially in a mountainous environment was that the farther away from the
center line you go, the more the terrain will change and the more the potential lahar paths
width decreases. This region is primarily a mountainous ridge and valley system, so the
likelihood of a lahar moving over a mountainous ridge is unlikely.
Following the reclassification of model parameters, the model parameters were
input into both a simple overlay model and a weighted overlay model. The final phase
involved the examination of model results and an accuracy assessment to validate model
results. This phase also compared output potential lahar paths against existing
infrastructure in maps. It was important to determine which of the varying types of
31
infrastructure (e.g. recreational sites, bridges, roads, and trails) would be impacted to
varying degrees in the event of future lahars.
3.5 Model Structure
The simple overlay model and the weighted overlay model utilized the same
processes up to the point of reclassification. For the simple overlay model, the model
parameters were added together using the Plus function in the Spatial Analyst Toolbox
within the Math toolset. Following reclassification, the weighted overlay model first
required each model parameter being weighted based upon its relative impact in
determining potential lahar paths. The prevalence of DEM’s in previous lahar modeling
efforts made the derived slope the most viable candidate for a high percentage of weight
in the model. Since land cover and hydrological networks were not specified as model
parameters in the previous studies, several model iterations with varying weights were
performed to determine how land cover and hydrological networks impact modeling
potential lahar paths. All weighting combinations totaled 100 percent among the values.
Figure 8 and 9 illustrate the two different modeling schemes, the first for the
simple overlay model and the second for the weighted overlay model. The modeling
schemes displayed the processes that were used in ArcGIS Model Builder.
32
Figure 8: Simple Overlay Model
33
Figure 9: Weighted Overlay Model
34
CHAPTER 4
ANALYSIS AND RESULTS
4.1 Parameter Analysis
Each parameter required differing degrees of processing and analysis prior to
being input into the models. The first model parameter to be derived was slope. To
derive the slope the DEM have to be made depressionless. The original DEM did not
have a considerable amount of sinks, but all sinks had to be resolved nonetheless. A z-
factor of 40 was determined as the necessary value to fill all sinks in the DEM to make it
depressionless. This course of action was only accepted after many failed and time-
consuming attempts at running the Sink and Fill functions in the Spatial Analyst Toolbox
within the Hydrology toolset. The Flow Direction function was created to make sure that
the DEM was in fact depressionless. Success was confirmed when the output flow
direction DEM displayed with no sink. The slope was then derived from the DEM with
an output measure in degrees and reclassified using the Reclassify function in the Spatial
Analyst Toolbox within the Reclass toolset. It was reclassified on a scale of 1 to 6 with
the areas of lowest slope being considered least suitable because of the potential to be
overcome by lahars and the areas of highest slope in degrees being considered most
suitable which accounts for potential lahar pathways on the slopes of the volcano (see
table 3). Figures 10-12 illustrate the DEM parameter analysis and the various stages of
the DEM processing.
35
Figure 10: Digital Elevation Model Parameter Analysis
Figure 11: Depressionless DEM
36
Land cover was a secondary model parameter. The land cover dataset only
required having the land cover values reclassified prior to it being input into the models.
Land cover types were reclassified on a scale of 1 to 6 with 6 representing land cover
types that were most suitable in terms of potential to be overcome by lahars, while land
cover types that were not suitable were assigned a value of 1. The distance from the lahar
source to the land cover was not included in this prototype. Figures 13 and 14 show the
land cover parameter analysis and the results of the analysis function performed on the
land cover.
The hydrological network was another secondary model parameter in the analysis.
After several attempts to derive a hydrological network from the digital elevation model,
it was determined that a vector dataset would be acceptable for use in this analysis. The
hydrological network shapefile was then buffered using the Multiple Ring Buffer tool
Figure 12: Reclassified Slope
37
within the Analysis Toolbox in the Proximity toolset. The network was buffered to
distances of 0.5, 1, 2, 4, 6, and 8 kilometers from the river centerline. Upon completion
of the initial buffer, the dataset were converted from feature to raster using the Features to
Raster function within the Conversion Toolbox in the To Raster toolset (cell size 10
meters).
Figure 13: Land Cover Parameter Analysis
Figure 14: Reclassified Land Cover
38
The output buffer raster was then reclassified on a scale of 1 to 6 with 6 representing the
shorter distance values and 1 representing the longer distance values since a closer
distance to the river centerline are more likely to be overcome by lahars. Issues were
discovered following the reclassification of the hydrological network. The legend values
do not follow the reclassification schema of 1 to 6 as previously stated. All reclassified
hydrological network figures and model outputs that incorporated the hydrological data
with a high percentage of weighting exhibit a range of 2 to 6 in the legend. Figures 15-
17 illustrate the hydrological network parameter analysis and the various stages of the
hydrological network processing.
Figure 15: Hydrological Network Parameter Analysis
39
Figure 16: Hydrological Network Buffer
Figure 17: Reclassified Hydrological Network Buffer
40
4.2 Simple Overlay Model
The simple overlay model utilized arithmetic operations to overlay the reclassified
slope, hydrological network, and land cover to arrive at the final model results. Each of
the three model parameters had the same value or weight going into the model (see figure
18). The output model results showed that despite the fact that the parameters had the
same value going into the model; the simple overlay results were equally reliable when
compared to the weighted overlay model results (see figure 19).
Figure 18: Simple Overlay Model Parameters
Figure 19: Simple Overlay Model Results
41
4.3 Weighted Overlay Model
The weighted overlay model utilized varying weights to overlay the reclassified
slope, hydrological network, and land cover to arrive at the final model results. Each of
the three model parameters was assigned a different weight going into the model. The
total of the model parameter weights sum equaled 100 percent (see figure 20). It is
important to note that each of the cell values within the reclassified slope, land cover, and
hydrological network analysis results are multiplied by the assigned weight and the
output results are then calculated and displayed in the output model results.
Table 6 showed the differing weighting combinations that were are tested prior to
choosing the most appropriate weighting combination for the weighted overlay model.
The weighting combinations listed in table 6 were performed using the reclassification
schema in table 3 which is focused more on determining potential lahar pathways on the
flanks of the volcano. In the first weighted model run using the weighting combinations
in row 1 of table 6, slope was assigned a weight of 50 percent and the hydrological
network was assigned a weight of 50 percent, while land cover was assigned a weight of
zero percent (see figure 21).
In the second model run using the weighting combinations in row 2 of table 6,
slope was assigned a weight of 50 percent and land cover was assigned a weight of 50
percent, while the hydrological network was assigned a weight of zero percent (see figure
22).
In the third model run using the weighting combinations in row 3 of table 6, slope
was assigned a weight of 30 percent and the hydrological network was assigned a weight
of 70 percent, while land cover was assigned a weight of zero percent (see figure 23).
42
Table 6: Weighting Combinations of Model Parameters
Run Slope Land Cover Hydrological Network
1 50 0 50
2 50 50 0
3 30 0 70
Figure 20: Weighted Overlay Model Parameters
Figure 21: Weighted Overlay Model Result One
43
Figure 22: Weighted Overlay Model Result Two
Figure 23: Weighted Overlay Model Result Three
44
4.4 Accuracy Assessment of Simple and Weighted Model Runs
The model results were compared to the USGS volcanic hazard zone dataset (see
figure 24) to assess the quality. An error matrix was computed based on the cell to cell
comparison. The following accuracy-parameters were calculated from the error matrix
for additional comparison of the quality of the modeling results. Overall accuracy was
the sum of all correctly classified cells (lahar and non-lahar) divided by the sum of cells
in the entire raster grid. Producer accuracy was the sum of all correctly classified cells
that belong to the class divided by the sum of pixels in the USGS lahar map that belong
to the class. User accuracy was the sum of all correctly classified cells that belong to the
class divided by the sum of cells in the model outputs that belong to the class.
The USGS volcanic hazard zone dataset was first converted to raster in the same
cell size as the model results. The next step involved reclassifying the USGS lahar zone,
the simple overlay model results, and the weighted overlay modeling results using the
Reclassify function within the Spatial Analyst. Zones 1, 2, and 3 of the USGS volcanic
hazard zone dataset were assigned a value of 1 to represent the potential lahar zone in the
reclassification. This choice of classification does not take into account that with greater
distance from the volcano the potency of the lahar will be diminished, it just accounts for
the potential for a lahar to occur. With respect to the model results, values of 5 and 6
were assigned a value of 1 in the reclassification process, while the remaining values
were assigned a value of 0 or NoData to represent non-lahar areas. Figure 25 shows the
visual comparison of lahar and non- lahar zones between the steep slope overlay models
and the USGS map. The dark area shows the potential lahar area and the light area show
the non-lahar area.
45
Figure 24: USGS Volcanic Hazard Zone Dataset
(Source: USGS, Steve P. Schilling, 1996)
Figure 25: The visual comparison between steep slope overlay models and USGS map
Simple Overlay Model
Weighted Run 1
Weighted Run 2
Weighted Run 3
USGS Volcanic
Hazard Zones
46
All raster’s have been processed into two layers, one for calculating the
producer’s accuracy and the second for calculating the user’s accuracy. The first output
depicting lahars are assigned a value of 1 and all other areas reassigned as 0 (1, 0). The
second output depicting lahars are once again assigned a value of 1 and all other areas are
reassigned as NoData (1, NoData). The calculations of the producer’s and user’s
accuracy did not yield any exclusive non-lahar values. NoData values were included in
the reclassification of model parameters and of model outputs to try to account for the
non-lahar values, but the extent of non-lahar areas could not be determined and were not
output following the model runs. The non-lahar areas would be areas that fall within the
study area that do not occur within the outermost USGS lahar zone boundary.
The simple and weighted overlay model results were assessed three times, once
for the producer’s accuracy, once for the user’s accuracy, and once for the overall
accuracy. For this project, the producer’s accuracy was the percentage of the USGS lahar
zone that was actually identified by the model results. The user’s accuracy was the
percentage of the model results that actually fell within the USGS lahar zone.
Determining the producer’s accuracy required adding the USGS lahar zone (1, NoData)
to the simple and weighted overlay model results (1, 0) using the Raster Calculator in
Spatial Analyst. The output results were 2, 1, NoData. Cells in the USGS lahar zone that
were actually identified by the model results were assigned a value of 2. Cells in the
USGS lahar zone that were not identified by the model results are assigned a value of 1.
All areas that do not fall within the USGS lahar zone were assigned as NoData. The
producer’s accuracy was calculated by dividing the number of cells with a value of 2 by
the total number of cells with a value or 1 or 2.
47
The user’s accuracy required adding the USGS lahar zone (1, 0) to the simple and
weighted overlay model results (1, NoData). The output results were 2, 1, NoData. Cells
in the model results that were actually within the USGS lahar zone were assigned a value
of 2. Cells in the simple and weighted overlay model results that did not fall into the
USGS lahar zone were assigned a value of 1. The NoData value represented areas that
did not fall within the simple and weighted overlay model results. The user’s accuracy
was calculated by dividing the number of cells with a value of 2 by the total number of
cells with a value of 1 or 2.
The error matrix of the simple overlay model was shown in table 7. Tables 8-10
showed the error matrices for the weighted overlay model runs. Table 11 provided a
summarized accuracy assessment of the overlay model results.
The producer’s accuracy for the simple overlay model results was 11.2 %. The
producer’s accuracy for the weighted overlay model results ranged from 12.3 % to 23.2
%. From a producer’s accuracy perspective, the best model run was run number 3 (see
table 11), with a producer’s accuracy of 23.2 %, and in which slope was weighted at 30
% and the hydrological network was weighted at 70 %. Simply stated, 11.2 % and 23.2
% of the cells within the USGS lahar zone were correctly identified by the simple and
weighted overlay model results, respectively.
The user’s accuracy for the simple overlay model results was 85.0 %. The user’s
accuracy for the weighted overlay model results ranged from 77.9 % to 82.4 %. From the
user’s accuracy perspective, the best model run was model number 2 (see table 11), with
a user’s accuracy of 82.4 %, and in which slope was weighted at 50 %, land cover was
weighted at 50 %, and the hydrological network was weighted at 0 %. Simply stated,
48
85.0 % and 82.4 % of the cells in the simple and weighted overlay model actually fell
within the USGS lahar zone. The overall accuracy of the simple and weighted overlay
models was very low. The low overall accuracy could potentially be attributed to the use
of higher degrees of slope in the lower-lying areas. Lahar paths are not likely to occur on
steeper banks along ridges and river valleys. In addition, the volcanic hazard zone
dataset that was used for comparison in the accuracy assessment does account for all
volcanic hazards and not just specifically lahar hazards, even though lahar hazards have
occurred in all three of the zones within the dataset (see figure 24). In addition, the
volcanic hazard zone dataset also has a coarser spatial resolution at 51 meters. None of
the runs achieved an overall accuracy higher than 17.9 %. The results provide evidence
that the use of higher degrees of slope is not plausible when trying to determine potential
lahar pathways beyond the flanks of a volcano (see table 11).
Based on the accuracy assessment results, the models utilizing steeper slope
values for highest possibility of potential lahar paths yielded insufficient results with
respect to determining the actual potential lahar pathways around Mount Saint Helens.
The premise of applying steeper slope values to the study resulted from a need to
determine where potential lahar pathways occur at higher elevations on and around the
volcano. The results of accuracy assessment, more specifically the extremely low
producers and overall accuracies, proved that the use of higher degrees of slope in
conjunction with a few different weighting combinations is not suitable for determining
potential lahar pathways. Therefore, a second set of models using inverse slope
classification schema were tested in an attempt to derive better modeling results.
49
Table 7: Error Matrix of Simple Overlay Model
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 2764 21844 24608
Simple
Overlay
Model Non-Lahar 485 ----- 485
Column Total 3249 21844 25093
Table 8: Error Matrix of Weighted Overlay Model Run #1
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 3931 20734 24665
Weighted
Overlay
Run #1 Non-Lahar 1112 ----- 1112
Column Total 5043 20734 25777
Table 9: Error Matrix of Weighted Overlay Model Run #2
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 3041 21624 24665
Weighted
Overlay
Run # 2 Non-Lahar 651 ----- 651
Column Total 3692 21624 25316
Table 10: Error Matrix of Weighted Overlay Model Run #3
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 4639 20026 24665
Weighted
Overlay
Run # 3 Non-Lahar 1198 ----- 1198
Column Total 5837 20026 25863
Table 11: Summarized Accuracy Assessment of the Overlay Models
Model Run
Producer's
Accuracy
User's
Accuracy
Overall
Accuracy
Simple Overlay 11.2% 85.0% 11.0%
Weighted #1 15.9% 77.9% 15.3%
Weighted #2 12.3% 82.4% 12.0%
Weighted #3 23.2% 79.5% 17.9%
50
4.5 Inverse Slope Model
In the inverse slope model, simple (see figure 26) and weighted overlay models
were run, and the slope classification scale was reversed, with higher degrees of slope
being least suitable for potential lahars and lower degrees of slope being most suitable for
potential lahar impact. The use of this inverted scale in the model accounts for potential
lahar pathways in lower-lying areas once the lahar had moved off of the volcano’s flanks
(see table 12).
A variety of weighting combinations were sampled in an attempt to find the best
weighting combination for obtaining potential lahar pathways. Five model runs were
performed, with slope primarily being assigned the highest weighting value (see table
13). Slope was typically assigned the highest percentage because previous modeling
efforts discussed in the literature sources utilized it as a primary model parameter.
In the first weighted model run using the weighting combinations in row 1 of
table 13, slope was assigned a weight of 50 percent, while both land cover and the
hydrological network were assigned equal weights of 25 percent (see figure 27). In the
second weighted model run using the weighting combinations in row 2 of table 13, slope
was assigned a weight of 50 percent, land cover a weight of zero percent, and the
hydrological network a weight of 50 percent (see figure 28). In the third weighted model
run using the weighting combinations in row 3 of table 13, slope was assigned a weight
of 50 percent, land cover a weight of 50 percent, and the hydrological network a weight
of zero percent (see figure 29). In the fourth weighted model run using the weighting
combinations in row 4 of table 13, slope was assigned a weight of 70 percent, land cover
a weight of zero percent, and the hydrological network a weight of 30 percent (see figure
51
30). In the fifth weighted model run using the weighting combinations in row 5 of table
13, slope was assigned a weight of 70 percent, land cover a weight of 30 percent, and the
hydrological network a weight of zero percent (see figure 31). Figure 32 shows the
visual comparison of lahar and non-lahar zones between the inverse slope overlay models
and the USGS map.
Figure 26: Simple Overlay Results using Inverse Slope
Figure 27: Inverse Weighted Overlay Model Result One
52
Table 12: Slope Reclassification Inverted
Slope in Degrees Reclassification Value
0 - 6.706643 6
6.706643 - 21.397385 5
21.397385 - 38.643038 4
38.643038 - 54.9306 3
54.9306 - 69.940705 2
69.940705 - 81.757172 1
Table 13: Weighting Combinations of Model Parameters
(Slope Classification Inverted)
Run Slope Land Cover Hydrological Network
1 50 25 25
2 50 0 50
3 50 50 0
4 70 0 30
5 70 30 0
Figure 28: Inverse Weighted Overlay Model Result Two
53
Figure 29: Inverse Weighted Overlay Model Result Three
Figure 30: Inverse Weighted Overlay Model Result Four
54
Figure 31: Inverse Weighted Overlay Model Result Five
Figure 32: The visual comparison between inverse slope overlay models and USGS map
Weighted Run 1
Weighted Run 2
Weighted Run 5
Weighted Run 4
USGS Volcanic
Hazard Zones
Simple Overlay
Weighted Run 3
55
4.6 Accuracy Assessment of Inverse Slope Overlay Model Runs
The inversed slope model results were compared to the USGS volcanic hazard
zone dataset (figure 24) to assess the quality with the same accuracy assessment
procedures. The error matrixes were computed for the simple overlay model run and
each of the five weighted overlay model runs, and the producer’s accuracy, the user’s
accuracy, and the overall accuracy were calculated for each model run (tables 14 - 19).
The producer’s accuracy for the inverse slope simple overlay model results was
41.1 %. The producer’s accuracy for the inverse slope weighted overlay model results
ranged from 38.6 % to 73.0 %. From a producer’s accuracy perspective, the best model
run was run number 3 (see tables 13 and 20), with a producer’s accuracy of 73.0 %, and
in which slope was weighted at 50 %, land cover was weighted at 50 %, and the
hydrological network was weighted at 0 %. Simply stated, 41.1 % and 73.0 % of the
cells within the USGS lahar zone were correctly identified by inverse slope simple and
weighted overlay model results.
The user’s accuracy for the simple overlay model results was 81.2 %. The user’s
accuracy for the inverse slope weighted overlay model results ranged from 72.8 % to 79.4
%. From the user’s accuracy perspective, the best model run was the simple overlay
model run (see table 20), with a user’s accuracy of 81.2 %. Simply stated, 81.2% and
79.4 % of the cells in the inverse slope simple and weighted overlay models actually fell
within the USGS lahar zone. Overall accuracy for the inverse weighted overlay model
runs was predominately average with respect to overall percentage. The highest overall
accuracy achieved was 57.3 %. Table 20 provides a summarized accuracy assessment of
the inverse slope simple and weighted overlay model results. The inverse slope overlay
56
models overall accuracy was markedly better than the previous models undertaken in this
study, but the values were not as high as they were expected to be. The low overall
accuracies are believed to have resulted because of the volcanic hazard zone dataset that
was utilized in comparison with the model results in the accuracy assessment. The
dataset’s spatial resolution was a coarse 51 meters, and is comprised of three hazard
zones each of which contains a variety of volcanic hazards at differing levels of severity.
The use of this dataset was chosen because lahars have occurred and are accounted for in
each of the three zones.
The third model run (see figure 28) was determined to be the best weighting
combination among the weighting combinations using the inverted slope because it had
the highest producer’s and overall accuracy following the accuracy assessment and it best
displayed potential lahar paths when compared to all of the other model results. Slope
was assigned a weight of 50 percent.
The use of land cover and vector hydrological networks as modeling parameters
were not discussed in other lahar modeling efforts, so a subjective determination of their
values in determining potential lahar paths had to be weighed during the model runs.
After researching land cover types in the region and determining which land cover types
are more likely to be overcome by lahars, given the geography of the region, the land
cover model parameter was determined as the second influencing factor in this model and
assigned a weight of 50 percent. The final model parameter, the hydrological network,
was not assigned a weight because previous model runs proved that while it does aid in
delineating potential lahar pathways, it was not as crucial when compared to the other
model parameters.
57
Table 14: Error Matrix of Simple Overlay Model
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 10126 14482 24608
Simple
Overlay
Model Non-Lahar 2352 ----- 2352
Column Total 12478 14482 26960
Table 15: Error Matrix of Inverse Weighted Overlay Model Run #1
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 15057 9608 24665
Weighted
Overlay
Run #1 Non-Lahar 4891 ----- 4891
Column Total 19948 9608 29556
Table 16: Error Matrix of Inverse Weighted Overlay Model Run #2
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 9518 15147 24665
Weighted
Overlay
Run # 2 Non-Lahar 2773 ----- 2773
Column Total 12291 15147 27438
Table 17: Error Matrix of Inverse Weighted Overlay Model Run #3
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 18009 6656 24665
Weighted
Overlay
Run # 3 Non-Lahar 6745 ----- 6745
Column Total 24754 6656 31410
Table 18: Error Matrix of Inverse Weighted Overlay Model Run #4
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 10171 14494 24665
Weighted
Overlay
Run # 3 Non-Lahar 2632 ----- 2632
Column Total 12803 14494 27297
58
Table 19: Error Matrix of Inverse Weighted Overlay Model Run #5
USGS Lahar Zone
Lahar Non-Lahar Row Total
Lahar 16305 8360 24665
Weighted
Overlay
Run # 3 Non-Lahar 5916 ----- 5916
Column Total 22221 8360 30581
Table 20: Summarized Accuracy Assessment of Inverse Slope Overlay Models
Weighted Model
Run
Producer's
Accuracy
User's
Accuracy
Overall
Accuracy
Simple Overlay 41.1% 81.2% 37.6%
Weighted #1 61.0% 75.5% 50.9%
Weighted #2 38.6% 77.4% 34.7%
Weighted #3 73.0% 72.8% 57.3%
Weighted #4 41.2% 79.4% 37.3%
Weighted #5 66.1% 73.4% 53.3%
4.7 Discussion
The results of the accuracy assessment confirmed that the output model results
from the inverse slope weighted overlay model were of average quality for the
determination of potential lahar pathways. The results indicated the inverse weighted
overlay model with slope and land cover both being assigned a weight of 50 percent as
being the most suitable model in outlining potential lahar pathways. In performing the
multiple iterations for the weighted overlay modeling process, taking into account both
higher and lower degrees of slope, there was not a considerable difference among the
output model results when the hydrological network and land cover weights were
adjusted against the slope. Overall, the results showed that the inverse slope weighted
overlay model was definitely more successful at identifying potential lahar pathways in
59
the low-lying areas than the steep slope overlay model results. This is particularly
evident when you examine the difference between overall accuracies. The best steep
slope overlay model results achieved an overall accuracy of 17.9 %, while the best
inverse slope weighted overlay model achieved an overall accuracy of 57.3 %.
Unfortunately, none of the non-lahar areas in USGS hazard zone were identified in any of
the models. It is uncertain as to what bearing the lack of this information could have in
determining potential lahar paths, but it could be beneficial in determining just how
accurate lahar models are relative to USGS hazard zones, therein it could have an effect
on the producer’s, user’s, and overall accuracies. In addition, the USGS hazard zones
dataset could have had an impact on the overall results since it was used for comparison
to gauge the accuracy of the model results in the accuracy assessment. The spatial
resolution of the dataset is 51 meters which is lower in resolution than the model results
at 30 meters, and while lahar hazards are included in all three of the zones in the dataset,
the specific areas that are prone to lahar were not delineated within the zones.
One of the primary goals in this research was to determine whether or not a
simple and easily-reproducible model could be built to derive decent potential lahar
pathways and this scenario proved that it is in fact possible. If given a chance to
incorporate of newer and higher quality datasets, the overall results might be vastly
improved, but given what was publicly available at the time this model was built, the
current modeling results showed potential for further research.
The results phase of the project was also focused on determining what
infrastructure could be potentially impacted in relation to the output potential lahar paths.
This included inputting roads, bridges, and recreational infrastructure, USGS hazard zone
60
outline, along with output model results focused on the most suitable potential lahar paths
into output maps as both a means of determining which features could be impacted and as
a means of gauging the overall effect on the natural environment within the National
Volcanic Monument and the National Forest (see Figure 33).
Figure 33: Map of Potential Lahar Pathways and Local Infrastructure
61
CHAPTER 5
CONCLUSION AND FUTURE RESEARCH
5.1 Summary
The comparison of simple overlay modeling techniques and weighted overlay
modeling techniques was a central theme in this study. Determining which modeling
methodology was most suitable for locating potential lahar pathways, in addition to
deciding how the model parameters exercised influence within the different modeling
methods was also important. In the end, the inverse slope weighted overlay modeling
technique when combined with the modeling parameters yielded the best overall results
in determining potential lahar pathways. This was further proved during the accuracy
assessment when the producer’s, users, and overall accuracies were calculated and
compared against the remaining simple and weighted overlay model runs.
Following the completion of all simple and weighted overlay model runs and the
accuracy assessment, it was determined that using lower degrees of slope in the analysis
yielded better results than using higher degrees of slope. While higher degrees of slope
were valuable in determining where lahars may commence, it was not suitable in
determining potential lahar pathways once the lahar has moved down the flanks of the
volcano. The only steep slopes that occur near the lower-lying environment of
hydrological corridors are the slopes of ridges and mountains. The environment around
Mount Saint Helens is composed primarily of ridges and river valleys. The output model
results using a higher degree of slope as most suitable would result in potential lahar
paths that line the sides of valleys and mountains, not in the actual river valleys where
they have occurred historically. Models utilizing higher degrees of slope as a parameter
62
would be best suited to small scale projects focusing on the volcano and where lahars
could develop. This concept alone could explain the very low accuracy percentages that
resulted for the models that utilized higher degrees of slope.
Lower degrees of slope which are typically seen in low-lying river valleys proved
to be more suitable in this particular study. This was especially true because lower-lying
areas with lower degrees of slope are more likely to be overcome by potential lahars and
lahars typically follow the lower-lying hydrological corridors until the sediment load can
no longer be suspended. This scenario has occurred historically at Mount Saint Helens
where lahar flows have surged up to 50 miles downstream via river channels. Lahars
typically develop as a result of high slope and they leave a lasting impact on everything
in their path. Areas of higher slope are not as well pronounced on Mount Saint Helens as
a result of the explosive nature of the eruptions that occur at the volcano and the volcanic
materials that result from the eruption (e.g. debris avalanche, pyroclastic flows, lateral
blast, and etc.) altering the landscape. The continued movement of lahars is dynamic
because the exact ratio of the fluid to sediment load varies from one occurrence to
another, but in this study the choice of whether to use higher degrees of slope or lower
degrees of slope were challenged and the lower degrees of slope was determined to be
better in the modeling methods used.
This study was more spatially-based than scientifically-based and the research
objectives were to utilize a variety of geospatial layers (e.g. slope, land cover, and
hydrological network) within simplistic model structures to determine the best scenarios
for identifying potential lahar paths relative to USGS volcanic hazard zones. The
dynamic nature of lahars is what brought about interest in wanting to attempt to model
63
them in GIS. Modeling lahars is similar in many respects to modeling hydrological
networks, but lahars are different with respect to their sediment load and that can
influence the rate of movement and the overall impact on the environment.
5.2 Limitations
First and foremost, this research was limited by insufficient experience with
respect to modeling volcanic hazards. A general understanding of the topic was
beneficial enough for specific focus with regard to the simplistic model, but a more
scientifically-based understanding of lahar flow dynamics and mathematical equations
associated with those dynamics could have enabled more detailed modeling and results.
There were several instances discussed in the Literature Review section where science
and mathematics were taken into account in lahar modeling and proved to be beneficial
overall, but for the sake of creating a simple, reproducible model, a more simplistic
approach was studied and applied. Most of the literature review sources were only
slightly helpful in understanding the dynamics of lahar modeling, but only a couple of the
sources directly aided in simplistic modeling efforts which were the primary focus in this
study. Having a more mathematical or scientifically-based model would be more
accurate and definitely a better predictive tool, but most people are not adept in the more
difficult mathematics and science. Therefore, simplistic models could be more readily
utilized by community planners and emergency officials to provide an illustration of a
problem, but not necessarily to make decisions.
As a result of a lack of modeling experience, human-induced error could
potentially be a factor and could have impacted the overall model results. In addition,
64
publicly accessible data was utilized within this model; consequently data accuracy and
precision were two issues that could potentially have impacted overall model results as
well and should be considered in future modeling efforts. The digital elevation model
and land cover had differing levels of spatial resolution at 10 meters and 30 meters,
respectively. Thus the results of the models were produced at a spatial resolution of 30
meters. With newer and higher resolution land cover data, the model could be vastly
improved.
The USGS volcanic hazard zone dataset proved to be an inadequate ground truth
data source for this research, especially after the accuracy assessment was completed and
the overall accuracy values were calculated and compared. The dataset is believed to be
inadequate because the resolution is very coarse relative to the data sources used and
because the dataset represents multiple volcanic flowage hazards and not just lahars.
While it was not used in the model, its use may have impacted the overall resulting
accuracy values which in turn could have created uncertainty in the determination of
which models performed better in this study. It is possible that the lack of specificity of
the lahar hazards in the volcanic hazard zone dataset could have resulted in a lack of
identification of the non-lahar areas. In addition, the inclusion of zone 2 in the accuracy
assessment is believed to be the primary cause of lower overall accuracy values. Zone 2
was primarily focused on pyroclastic surge hazards and not on lahar hazards as originally
believed. If zone 2 is removed from the assessment, the overall accuracy values could be
higher. Figures 34 and 35 provide a visual comparison of the steep slope and inverse
slope overlay models against the USGS volcanic hazard zone map with only zones 1 and
3 displayed. Examination of the USGS Zone 1/3 hazard layer and the model results
65
shows that there appears to be a better defined relationship, but there are still some
noticeable differences. Zone 1 does include other volcanic hazards (e.g. lateral blast, lava
flows, and debris avalanche) in addition to lahar flows and there is a chance that the
inclusion of these additional hazards is enough to impact the quality of the accuracy
assessment. Zone 3 was not examined by itself because its location within the study area
would have been a small strip at the left margin of the study area and would not have
made for a meaningful comparison.
5.3 Further Improvement and Future Research
This research demonstrated the use of GIS and remotely-sensed data as it relates
to determining potential lahar paths around Mount Saint Helens in the Counties of
Cowlitz and Skamania in the State of Washington. The results of the analysis output
potential lahar paths and illustrated what roads and infrastructure are likely to be
impacted. Satellite imagery was used as a means of studying what land cover types
occurred within older lahar pathways. In addition, the results had been compared with a
USGS volcanic hazard zonation dataset of the Mount Saint Helens area in an accuracy
assessment to determine whether or not the results were similar. It was determined that
the output potential lahar paths that resulted from the analysis do follow some of the
paths that previous lahars had taken historically, but the overall accuracy of the outputs
are only as good as the dataset used to gauge the overall accuracy in the accuracy
assessment. The purpose of this model was to determine if potential paths could be
determined using a simple GIS-based suitability model and publicly accessible data, and
that proved to be correct. When compared to current USGS lahar hazard zonation maps,
66
Figure 34: The visual comparison between steep slope overlay models and USGS map
(Zones 1 & 3)
Figure 35: The visual comparison between inverse slope overlay models and USGS map
(Zones 1 & 3)
Weighted Run 1 Weighted Run 3
USGS Hazard
Zones 1 & 3
Weighted Run 2
Weighted Run 5
Weighted Run 4 Simple Overlay
Weighted Run 2
Weighted Run 1
Weighted Run 3
USGS Hazard
Zones 1 & 3
Simple Overlay
67
the output model results were comparable in the sense that they did fall within the areas
that the USGS had predicted lahars could occur, but the percentage of accuracy for the
best weighted overlay model results were average, not exact.
This research could be used as a base in future volcanic hazard mapping projects.
Scripts and macros could be included to automate some of the processes, and additional
analysis functions could be used to improve output model results. In the future, it would
be best to split the model into two component parts. The first part would incorporate
steep slope values in a study of the volcano and its flanks because higher degrees of slope
occur most frequently on a volcano. The second part would incorporate lower slope
values for the low-lying valleys because lower slope values primarily occur in a lower-
lying environment as opposed to on a volcano.
68
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