utilizing geographic information systems to identify ... · as case study a thesis presented to ......

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

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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

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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

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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)

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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 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 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.

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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,

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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

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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

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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)

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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)

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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,

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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.

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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)

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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-

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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 3931 20734 24665

Weighted

Overlay

Run #1 Non-Lahar 1112 ----- 1112

Column Total 5043 20734 25777


USGS Lahar Zone


Lahar 3041 21624 24665

Weighted

Overlay

Run # 2 Non-Lahar 651 ----- 651

Column Total 3692 21624 25316


USGS Lahar Zone


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 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 15057 9608 24665

Weighted

Overlay

Run #1 Non-Lahar 4891 ----- 4891

Column Total 19948 9608 29556


USGS Lahar Zone


Lahar 9518 15147 24665

Weighted

Overlay

Run # 2 Non-Lahar 2773 ----- 2773

Column Total 12291 15147 27438


USGS Lahar Zone


Lahar 18009 6656 24665

Weighted

Overlay

Run # 3 Non-Lahar 6745 ----- 6745

Column Total 24754 6656 31410


USGS Lahar Zone


Lahar 10171 14494 24665

Weighted

Overlay

Run # 3 Non-Lahar 2632 ----- 2632

Column Total 12803 14494 27297

58


USGS Lahar Zone


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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