U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2007–5276

Version 1.1

Mobility Statistics and Automated Hazard Mapping for 

Debris Flows and Rock Avalanches

Mobility Statistics and Automated Hazard 

Mapping for Debris Flows and Rock 

Avalanches

By Julia P. Griswold and Richard M. Iverson

Scientific Investigations Report 2007–5276

Version 1.1

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

Revised 2014

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Suggested citation:

Griswold, J.P., and Iverson, R.M., 2008, Mobility statistics and automated hazard mapping for debris flows and rock 

avalanches (ver. 1.1, April 2014): U.S. Geological Survey Scientific Investigations Report 2007-5276, 59 p.

Manuscript approved for publication, December 12, 2007

Text edited by Tracey L. Suzuki

Layout by Luis E. Menoyo
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Description of Mass-Flow Phenomena.............................................................................................1

Hazard-Zone Prediction Methods...............................................................................................................2

Historic and Geologic Evidence .........................................................................................................2

Physically Based Models ....................................................................................................................3

Empirical Models .................................................................................................................................3

Statistical Models Motivated by Physical Scaling..........................................................................3

Objectives........................................................................................................................................................5

The Database..................................................................................................................................................6

Statistical Analysis of the Data ...................................................................................................................7

Test for Differences in Slope ..............................................................................................................7

Test for Difference in Intercepts ........................................................................................................9

Error and Uncertainty in Calibrated Prediction Equations...........................................................18

Discussion and Interpretation of Predictive Equations................................................................18

Debris-Flow Hazard Delineation by using DEMs and GIS ....................................................................21

Data Input ............................................................................................................................................21

Application Area for Debris-Flow Hazard Mapping—Scottsburg, Oregon ..............................22

History of the Scottsburg Area................................................................................................22

Topography and Geology..........................................................................................................22

Topographic Dataset—Acquisition and Description...........................................................23

Background for Flow Volume Assessment for Scottsburg, Oregon..................................24

LAHARZ Application..................................................................................................................25

Hazard Map Summary........................................................................................................................25

Conclusions...................................................................................................................................................27

Acknowledgments.......................................................................................................................................29

References Cited..........................................................................................................................................29

Appendix A—Data Table ............................................................................................................................33

Appendix B—Data Bibliography...............................................................................................................49

Figures

1. Schematic inundation limits of a debris flow moving down-valley from a 

source area....................................................................................................................................4

2. Diagram showing the maximum inundated cross-sectional area, and total inundated 

planimetric area, of a lahar runout path downstream from a source area 

on a volcano ..................................................................................................................................5

3. Scatter plot of all data compiled for debris flows, rock avalanches, and lahars ..............8

4. Debris-flow data and three regression models for cross-sectional area ........................10

5. Debris-flow data and three regression models for planimetric area ................................10
iv

Figures—Continued

6. Rock-avalanche data and three regression models for cross-sectional area ................10

7. Rock-avalanche data and three regression models for planimetric area........................10

8. Rock-avalanche and lahar data used to compute statistics as a combined dataset.....17

9. Debris-flow and lahar data used to compute statistics as a combined dataset .............17

10. Debris-flow and rock-avalanche data used to compute statistics as a 

combined dataset.......................................................................................................................18

11. Lahar data and best-fit regression line with 99-percent confidence interval for 

regression and 99-percent confidence interval for prediction after Iverson and 

others............................................................................................................................................19

12. Debris-flow data and best-fit regression line with 99-percent confidence interval 

for regression and 99-percent confidence interval for prediction.....................................19

13. Rock-avalanche data and best-fit regression line with 99-percent confidence 

interval for regression and 99-percent confidence interval for prediction ......................20

14. Aerial photograph of the Scottsburg, Oregon, area .............................................................21

15. Shaded-relief map with highways and logging roads for the study area west of the 

town of Scottsburg in the central Coast Range of Oregon, at about 40 river 

kilometers from the coast..........................................................................................................23

16. Shaded relief map constructed from a DEM, overlaid with orange where slopes 

exceed 30 degrees .....................................................................................................................24

17. Shaded relief map of upper half of the test basin.................................................................25

18. Examples of nested, inundation-hazard zones computed for four flow volumes............26

19. Debris-flow inundation-hazard map computed for four flow volumes ranging 

from 103 to 104.5 m3 .......................................................................................................................27

20. Oblique perspectives of the test basin with debris flow-hazard zone overlays, 

view to the southeast.................................................................................................................28

Tables

1. Parameters and analysis-of-variance statistics for alternative linear models of 

log-transformed debris-flow data for inundated cross-sectional area, as a 

function of flow volume .............................................................................................................11

2. Parameters and analysis-of-variance statistics for alternative linear models of 

log-transformed debris-flow data for inundated-planimetric area, as a 

function of flow volume .............................................................................................................11

3. Parameters and analysis-of-variance statistics for alternative linear models of 

log-transformed rock-avalanche data for inundated cross-sectional area, as 

a function of flow volume ..........................................................................................................11

4. Parameters and analysis-of-variance statistics for alternative linear models of 

log-transformed rock-avalanche data for inundated-planimetric area, as a 

function of flow volume .............................................................................................................12

5. Summary table of the best-fit regression equations with calibrated slopes and 

intercepts .....................................................................................................................................12
v

Tables—Continued

6. Summary table of the best-fit regression equations with specified 2/3 slopes and 

calibrated intercepts..................................................................................................................12

7. Summary of results of F-tests comparing the specified-slope models to the 

best-fit regression model with adjustable slope ...................................................................13

8. Dummy variable terms and equations for null model and alternate models....................14

9. Parameters for computing the F-statistic to compare the specified 2/3-slope 

models with combined and individual datasets ....................................................................14

10. Parameters for computing the F-statistic to compare the best-fit regression 

models with combined and individual datasets ....................................................................14

11. F-test comparison between the combined dataset and individual datasets for 

rock avalanches and lahars......................................................................................................15

12. F-test comparison between the combined dataset and the individual datasets for 

debris flows and lahars .............................................................................................................15

13. F-test comparison between the combined dataset and the individual datasets for 

rock avalanches and debris flows...........................................................................................16

14. F-test comparison between combined rock avalanche and lahar datasets and the 

listed individual datasets for a best-fit regression slope.....................................................16

15. F-test comparison between combined debris flow and lahar datasets and the 

listed individual datasets for a best-fit regression slope.....................................................16

16. F-test comparison between combined rock avalanche and debris flow datasets 

and the listed individual datasets for a best-fit regression slope ......................................16
Abstract

Power-law equations that are physically motivated and 

statistically tested and calibrated provide a basis for forecasting areas likely to be inundated by debris flows, rock 

avalanches, and lahars with diverse volumes. The equations 

A=α1V2/3 and B=α2V2/3 are based on the postulate that the 

maximum valley cross-sectional area (A) and total valley planimetric area (B) likely to be inundated by a flow depend only 

on its volume (V) and the topography of the flow path. Testing 

of these equations involves determining whether or not they 

fit data for documented flows satisfactorily, and calibration 

entails determining best-fit values of the coefficients α1 and 

α2 for debris flows, rock avalanches, and lahars. This report 

describes statistical testing and calibration of the equations by 

using field data compiled from many sources, and it describes 

application of the equations to delineation of debris-flow 

hazard zones.

Statistical results show that for each type of flow (debris 

flows, rock avalanches, and lahars), the dependence of A and 

B on V is described well by power laws with exponents equal 

to 2/3. This value of the exponent produces fits that are effectively indistinguishable from the best fits obtained by using 

adjustable power-law exponents. Statistically calibrated values 

of the coefficients α1 and α2 provide scale-invariant indices of 

the relative mobilities of rock avalanches (α1 = 0.2, α2 = 20), 

nonvolcanic debris flows (α1 = 0.1, α2 = 20), and lahars 

(α1 = 0.05, α2 = 200). These values show, for example, that 

a lahar of specified volume can be expected to inundate a 

planimetric area ten times larger than that inundated by a rock 

avalanche or nonvolcanic debris flow of the same volume.

The utility of the calibrated debris-flow inundation equations A=0.1V2/3 and B=20V2/3 is demonstrated by using them 

within the GIS program LAHARZ to delineate nested hazard 

zones for future debris flows in an area bordering the Umpqua 

River in the south-central Oregon Coast Range. This application requires use of high-resolution topographic data derived 

form LIDAR surveys, knowledge of local geology to specify a 

suitable range of prospective debris-flow volumes, and development and use of a new algorithm for identification of prospective debris-flow source areas in finely dissected terrain.

Introduction

Debris flows and rock avalanches are two types of 

rapid mass movement that can pose great hazards to areas 

downslope and downstream. This report presents a methodology to assess areas likely to be affected by downslope and 

downstream runout of debris flows and rock avalanches by 

deriving and employing inundation-area statistics, analogous 

to an approach used by Iverson and others (1998) for lahars. 

Establishing inundation-area relationships for differing types 

of flows aids in understanding flow mobility and enables 

delineation of hazard zones that may be inundated by future 

lahars, nonvolcanic debris flows, and rock avalanches.

The lahar-inundation equations developed by Iverson and 

others (1998) are currently used to compute hazard zones in 

areas adjacent to volcanoes around the Pacific Rim. Schilling 

(1998) implemented the set of lahar-inundation equations in 

LAHARZ, a Geographic Information System (GIS)-based 

computer program that facilitates rapid calculation and 

delineation of the hazard zones for a range of probable flow 

volumes and any flow-path topography represented by a 

digital elevation model (DEM). This study expands the use of 

LAHARZ with modifications for hazard mapping of nonvolcanic debris flows and rock avalanches.

Description of Mass-Flow Phenomena

Rapid mass flows on Earth’s surface include the following related phenomena: dry, granular-rock avalanches, 

water-saturated debris flows, and hyperconcentrated stream 

flows (for example, Pierson and Costa, 1987; Iverson and Vallance, 2001). Flows are described by many terms in common 

practice and in the scientific literature, but our nomenclature 

is restricted to rock avalanche, debris flow (nonvolcanic), and 

lahar (volcanic debris flow). In this section, we discuss the 

general nature of these flows.

Rock avalanches commonly initiate as rock-slab failures 

or rockfalls and transform into unsaturated, granular flows. 

Rockfalls result from failure along a bedrock discontinuity 

(fracture, bedding, foliation), or from failure of a pocket of 

rubble perched in a bedrock face. Their motion is influenced 

Mobility Statistics and Automated Hazard Mapping for 

Debris Flows and Rock Avalanches

By Julia P. Griswold and Richard M. Iverson
2 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

by gravity, intergranular vibrational energy, and Coulomb friction (Iverson, 2003). Rock avalanches tumble from mountainsides and commonly travel to distal areas where they terminate 

on flatter slopes. Deposits may include massive hummocks 

composed of coarse debris and partially saturated sediment 

(Crandell, 1989). Although small rock avalanches occur 

commonly, they are seldom well documented, and volumes 

of well-documented rock avalanches generally exceed 106 m3

(cubic meters). At Mount Shasta in California, the deposit of 

one prehistoric rock avalanche is estimated to have a volume 

~45 km3 (>1010 m3; Crandell, 1989). The large volume of some 

rock avalanches can result in burial and filling of entire valleys, potentially damming rivers and creating breeching hazards for some time after the initial event. Additional descriptions and reports on rock avalanches are provided by Voight 

(1979), Eisbacher and Clague (1984), Ui and others (1986), 

Siebert (1984), Ui (1983), and Siebert and others (1987).

Debris flows differ from rock avalanches primarily 

because of their high water content and resulting fluidity; they 

typically initiate in steep drainages (generally sloping 

> 30o) where sufficient unconsolidated sediment and water are 

available. The water-saturated sediment liquefies during initial 

slope failure or subsequent entrainment and stays fluid due to 

the persistence of high pore-fluid pressure, which is facilitated 

by the presence of fine matrix sediment (Iverson and others, 

1997). Steep slopes, the presence of shallow soils, colluvium, 

alluvium, or poorly consolidated bedrock, and the presence 

of abundant surface water (for example, ponded water, snow, 

ice, runoff, outbreak flood) or shallow ground water (perched 

or return flow) provide conditions that favor development of 

debris flows. Local topographic features commonly play a role 

in focusing water flow on slopes where failure occurs. These 

topographic features include gullies, swales, hollows, or convergent slopes and also can include constructed features such 

as road-fill prisms. Focusing of shallow ground water also is 

influenced by stratigraphy that may aid saturation and development of high pore-water pressures (for example, Reid and 

Iverson, 1992). Termination of debris-flow motion downvalley on lesser slopes of stream channels, alluvial fans, or other 

gently sloping areas commonly results in a coarse depositional 

snout and bounding levees that enclose a liquefied interior of 

finer material (Iverson, 1997). Volumes of nonvolcanic debris 

flows rarely exceed 106 m3, and although debris flows are 

commonly smaller than rock avalanches, they occur frequently 

and are responsible for similar numbers of deaths as reported 

in Schuster’s (1996) list of “The 25 most catastrophic landslides of the 20th century”. Additional descriptions and reports 

about debris flows are provided by Jakob and Hungr (2005), 

Iverson (1997), Iverson and others (1997), Major (1996), 

Fannin and Rollerson (1993), Takahashi (1991), and Johnson 

(1984).

Lahars are debris flows that originate on the flanks of 

volcanoes where abundant, loose sediment is available, and 

they may be triggered by volcanic eruptions, earthquakes, 

glacier or lake break-out floods, or torrential rains (Myers 

and Brantley, 1995). Hydrothermal alteration and (or) a high 

degree of fragmentation of volcanic rubble makes for a readily 

erodible and commonly clay-rich source material (Vallance 

and Scott, 1997). The failed volcanic material can incorporate 

large volumes of water, ice, and snow to reach full saturation. 

Lahars generally inundate the lower flanks of volcanoes, as 

well as downstream reaches of streams that originate on those 

flanks. Volumes of well-documented lahars generally exceed 

those of nonvolcanic debris flows and are typically >105 m3. 

Observed runout distances of lahars indicate they are typically 

more mobile than rock avalanches. For example, large lahars 

originating on Mount Rainier have traveled more than 

120 km to Puget Sound, filling the White River Valley to 

depths greater than 100 m (Vallance and Scott, 1997). Additional descriptions and reports about lahars are provided by 

Crandell and Mullineaux (1967, 1975), Janda and others 

(1981), Major (1984), and Pierson (1985).

Hazard-Zone Prediction Methods

Numerous methods have been proposed to predict areas 

likely to be inundated by future granular mass flows. These 

methods include (1) use of historic and geologic evidence of 

past flows to estimate inundation limits of future flows, (2) use 

of physically based models that invoke conservation of mass, 

momentum, and energy and rheological properties to calculate 

prospective inundation limits, (3) use of statistically calibrated 

empirical equations derived from analysis of inundation data, 

and (4) use of statistically calibrated inundation equations that 

are motivated by physical-scaling arguments. Method 4 is 

employed in this report, as described below. The context for 

use of this method, however, is clarified by providing a brief 

synopsis of the first three methods.

Historic and Geologic Evidence

Traditionally, mass-flow hazard maps have been derived 

from inspection of historic and geologic evidence and use of 

this evidence to posit future inundation patterns (for example, 

Scott and others, 1998). Although this method is well established, it has two inherent limitations. First, documented past 

events do not necessarily provide an adequate sample of the 

population of all events (both past and future) that might occur 

in a particular area. Therefore, the extent of inundation during 

future events can exceed limits forecast on the basis of past 

events. This problem is most serious where a dearth of documentation of past events results from limited historical records 

and geological fieldwork. A second problem with this forecasting method is reliance on geological inference and consequent 

lack of reproducibility. Different geologists may examine the 

same historical records and field evidence, but draw different 

conclusions regarding the potential for future inundation. Lack 

of reproducibility is best overcome by formalizing predictions 

through use of mathematical models, which may have a physical basis, a statistical basis, or both.
Hazard-Zone Prediction Methods 3

Physically Based Models

Physically based mathematical models for predicting 

inundation by mass flows have varying degrees of sophistication, but all such models are built on a foundation of physicalconservation laws. The most elementary models invoke only 

one-dimensional momentum conservation for a translating 

point mass (that is, Newton’s second law of motion). The first 

model of this type was presented by Heim (1932), and it led 

to the famous equation H/L = tan φ, where H is the vertical 

distance descended by the mass, L is the predicted horizontal 

distance traversed by the mass, and φ is the Coulomb angle of 

sliding friction, which typically ranges from about 30 to 

40 degrees in experimental tests. This model famously underpredicts the extent of runout (L), particularly if mass flows 

are saturated with water (for example, debris flows, Iverson, 

1997), or if their volumes exceed about 106 m3 (Heim, 1932; 

Hsu, 1975; Scheidegger, 1973).

One approach to remedying the failing of the Heim 

(1932) model involves use of resistance formulae other than 

that for Coulomb friction. For example, viscosity coefficients 

or fixed yield-strength coefficients have been suggested as 

alternatives to tan φ (for example, Voight and others, 1983; 

Johnson, 1984; Dade and Huppert, 1998). A significant problem with this approach is that the relevance of such resistance 

coefficients is not supported by experimental data (Iverson, 

2003).

Basal fluid pressure can be invoked as a means of 

modifying Coulomb friction (for example, Shreve, 1968; 

Sassa, 1988). Pore-fluid pressure effects are clearly evident 

in experimental data, but it is difficult to estimate the degree 

to which high basal fluid pressures will develop and persist in 

any particular mass flow. Some authors have assigned a basal 

pore-pressure distribution that fits experimental observations 

(for example, Iverson, 1997), whereas others have calculated pore-pressure distributions based on the porous-media 

consolidation theory (for example, Savage and Iverson, 2003), 

but good constraints for applying these approaches to field 

phenomena are still lacking.

The most elaborate physically based models take into 

account multidimensional mass and momentum conservation, 

as well as pore-pressure evolution, and they thereby reduce the 

need for calibration of flow resistance (for example, Iverson 

and Denlinger, 2001; Denlinger and Iverson, 2001). However, 

such physically-based models demand considerable input data, 

as well as computationally intensive solution techniques, and 

they remain an active area of research (Denlinger and Iverson, 

2004; Iverson and others, 2004; Iverson, 2005). Application 

of such models to practical hazard assessment is in its earliest 

stages.

Empirical Models 

Empirical equations, that are statistically calibrated, provide an alternative to physically based mathematical models. 

For example, analysis of data on the distal limits of inundation 

by rock avalanches has led several authors to propose that 

H/L, as used in the Heim (1932) equation, depends systematically on avalanche mass or volume (for example, Heim, 1932; 

Scheidegger, 1973; Hsu, 1975). Calibration of the relationship 

between H/L and V then provides a basis for prediction. However, this method takes no account of the effect of runout-path 

topography on the distal or lateral limits of inundation, an 

effect that can be apparent to casual observers.

Some authors used empirically calibrated limits for 

debris-flow stoppage to predict debris-flow progress through 

successive channel cross-sections. Benda and Cundy (1990) 

used an empirical model based on channel-junction angles 

(≥70°) and channel gradients (<20°) to predict runout termination for small debris flows in finely dissected, forested 

landscapes. Fannin and Rollerson (1993) used channel confinement (width to depth ratios) to track debris-flow progress 

down valley through analysis of the channel at successive 

sections. Fannin and Wise (2001) used slope geometry and net 

changes in volume for successive sections of a channel to infer 

whether or not a debris flow was likely to entrain or deposit 

sediment. Changes in flow volume were then assessed for each 

section until the cumulative volume was zero.

Other authors have focused on debris-flow flow volume 

as a basis for predicting runout. Adopting the methodology 

developed by Iverson and others for lahars (1998), Crosta 

and others (2003) found that planimetric areas inundated by 

diverse debris flows were nearly proportional to flow volume 

raised to the 2/3 power. Rickenmann (1999) used the product 

of debris-flow volume and height of source area to predict 

runout distance. Cannon (1989) reported that the lengths of 

debris-flow runout paths are proportional to the rate at which 

flow volume is lost due to progressive deposition.

For rock avalanches, Li Tianchi (1983) related volume 

to inundated planimetric area, (log(Area) = 1.8807 + 0.5667 

logV), and with statistical methods, produced a pair of prediction curves for runout-path length and width for a given 

vertical relief and flow volume. Kilburn and Sorenson (1998) 

concluded that the runout distance L is described by L=αV1/2 

where α = 3-40 is a calibrated coefficient. For both volcanic rock avalanches and lahars, Vallance and Scott (1997) 

observed a dimensionally homogenous power-law relationship between flow volume and planimetric area of inundation, 

analogous to the result reported by Crosta and others (2003) 

for debris flows.

Statistical Models Motivated by Physical 

Scaling

Several authors have used scaling arguments to bolster 

the proposition that planimetric areas inundated by rock 

avalanches should be proportional to V2/3 (for example, Davies, 

1982; Hungr, 1990; Dade and Huppert, 1998; Kilburn and 

Sorensen, 1998; Legros, 2002). All of these authors presented 

data that broadly supported this contention, resulting in a compelling body of evidence.
4 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Iverson and others (1998) extended these ideas by postulating that both the total, planimetric area (B) and the maximum valley cross-sectional area (A) inundated by a passing 

lahar should be proportional to flow volume raised to the 

2/3 power. Statistical analysis of a dataset for 36 lahars at 9 

volcanoes was used to test and confirm the validity of these 

2/3 power laws. Optimal values of the proportionality coefficients were determined by using regressions of log-transformed data, yielding the following predictive equations: 

A = 0.05 V 2/3 for cross-sectional area, and (1) 

B = 200 V 2/3 for planimetric area. (2)

These equations have quantitative confidence limits, 

which were determined by statistics associated with the regression analysis. The coefficients 0.05 and 200 in equations 1 

and 2 were specified by using only one significant digit, as the 

confidence limits imply that greater precision is inappropriate. Another important aspect of equations 1 and 2 is that they 

describe the maximum extent of inundation by an average 

flow. Such inundation limits do not necessarily correspond 

with the limits of deposits left by the flow because some 

reaches of the flow path may not be subject to deposition.

Together, equations 1 and 2 suffice for delineating 

inundation limits on maps, provided that V is known, and the 

Figure 1. Schematic inundation limits of a debris flow moving downvalley from a source area. The maximum inundated valley crosssectional area, A, is shown for four transects (yellow). These highlighted 

cross sections represent inundation limits during peak flow passage, 

not necessarily the limits of post-flow deposition. The total inundated 

planimetric area, B, is shown by the dashed red line. Photograph is from 

the July 20, 2003, debris flow at Minamata in Kyushu, Japan, (Sidle and 

Chigira, 2004).
Objectives 5

topography downslope or downstream from the lahar source 

area is known (fig. 1). Relative to other empirical methods, 

an advantage of the Iverson and others (1998) method is that 

it makes full use of three-dimensional topographic constraints 

for forecasting inundation.

Because flow-volume V is the independent variable in the 

method of Iverson and others (1998) and because the volumes 

of future lahars are indeterminate, forecasts of inundation 

limits generally postulate a range of prospective V-values. 

Inundation limits A and B are then calculated for the range 

of postulated V’s. This procedure results in a nested set of 

inundation hazard zones, which depict the combined effect of 

uncertainties about the volumes and behaviors of future flows. 

Selection of appropriate V-values depends on geological 

knowledge, and ideally, on recurrence probabilities determined 

for flows with various V’s (Iverson and others, 1998). However, data suitable for calculating such probabilities commonly 

are unavailable, and geological inferences about hydrologic 

contributing areas, thicknesses of soil mantles, and the magnitudes of events in similar settings provide the main basis for 

selecting prospective flow volumes.

Objectives

The overall objective of this study is to extend the 

hazard-zone delineation methodology of Iverson and others 

(1998) to nonvolcanic debris flows and rock avalanches. To 

attain this objective, several steps are taken and are enumerated here.

(1) Assemble a database consisting of flow volumes (V) 

paired with maximum inundated valley cross-sectional areas 

(A) and (or) total inundated planimetric areas (B) for a large 

number of nonvolcanic debris flows and rock avalanches 

(fig. 2). This database parallels that assembled for lahars by 

Iverson and others (1998) and partly reproduces the rockavalanche databases assembled by Li Tianchi (1983), Legros 

(2002), and others.

(2) Use the database to test whether power-law equations 

with specified 2/3-exponents satisfactorily predict inundatedplanimetric and cross-sectional areas as functions of flow 

volume for nonvolcanic debris flows and rock avalanches. 

This test involves determining the goodness of fit of the 2/3 

power-law equations, as well as statistical comparison of these 

Figure 2. Diagram showing the maximum inundated cross-sectional area, A, and total inundated 

planimetric area, B, of a lahar runout path downstream from a source area on a volcano. The downstream 

edge of the source area is delineated by using an H/L cone in this instance. Figure modified from Iverson 

and others (1998).

Trimline

A
6 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

equations to alternative statistical models. If the 2/3 power-law 

relationships are satisfactory, then for each type of flow it is 

appropriate to pursue a subsequent step— testing the hypothesis that the inundation-area equations,

A=α1V2/3, and (3)

B=α2V2/3, (4)

with calibrated α-coefficients, provide distinguishable models 

for different types of flows. In this context, differing values of 

the calibrated α-coefficients imply differing degrees of flow 

mobility.

(3) Implement the calibrated predictive equations by 

embedding them in the LAHARZ computer program and 

modifying the criteria for identification of source areas to 

extend the use of the software to nonvolcanic flows. Iverson 

and others (1998) identified prospective lahar source areas 

within any valley that drains the upper slopes of a volcano. 

Upper slopes were defined by using an “H/L cone” in which 

L is the horizontal distance from the volcano summit to any 

point downslope, and H is the elevation difference between 

those two points (fig. 2). Generally, H/L-values between 

0.2 and 0.3 are well suited for identifying lahar source areas 

on large composite volcanoes (for example, Mount Rainier in 

the Cascade Range of the United States), and values up to 0.7 

are used for smaller or multipeaked volcanoes (for example, 

Santa Ana volcano in El Salvador). However, it is more difficult to identify prospective source areas in dissected terrain 

that is not characterized by a dominant topographic feature, 

such as a volcano. Therefore, a new method for identification 

of source areas of nonvolcanic debris flows is described in the 

LAHARZ Application section.

(4) Use GIS and a modified version of LAHARZ to 

create a debris-flow hazard map for a range of hypothetical 

flow volumes in a drainage basin in a geographic area where 

precise digital-topographic data are available.

The Database

Diverse data sources and events in diverse geographic 

locations (with diverse climate, bedrock, topography, and vegetation) were used to assemble a dataset suitable for extending 

the methodology of Iverson and others (1998) to nonvolcanic 

debris flows and rock avalanches. All data and data sources 

are tabulated in appendixes A and B.

In assembling the dataset, descriptions of documented 

runout paths of debris flows and rock avalanches of prehistoric 

and historic events were found in scientific literature, unpublished reports and maps, and personal communications or field 

notes. As described in the original sources, evidence used to 

infer maximum inundated cross-sectional area, A, includes 

high-flow marks indicated by strandlines, levees, embedded 

gravels, or the height of stripped bark on the trunks of adjacent 

trees, and the height of log jams. Therefore, area A depends 

on the maximum stage (for example, elevation) of inundation 

during passage of flow through a cross section—and not on 

the area blanketed by deposits. Evidence used to infer total, 

inundated planimetric area, B, includes the extent of coarse 

deposits that form the levees and snout of a granular flow, or 

the lateral limits of any evidence of high-flow lines. This total, 

planimetric area includes all parts of the flow path, whether or 

not they are blanketed by deposits, but does not include areas 

affected by subsequent flooding or hyperconcentrated stream 

flow. Pairing of data on inundated cross-sectional area A and 

(or) planimetric area B with flow-volume data is necessary 

because, from a statistical standpoint, flow volume, V, is the 

independent variable, and A and B are the dependent variables. 

Ideally, values of all three variables are known for any particular event.

Determination of the inundation variables A, B, and V

commonly is complicated by lack of accurate knowledge of 

topography before and immediately after an event. The most 

useful data come from surveys made shortly after an event that 

record high-flow marks and from surveys that include detailed 

topographic maps of the land surface before and after an 

event. However, because the motivations behind the various 

papers and reports on debris flows and avalanches differ, the 

type and quality of data vary significantly. In many cases areas 

A and B implicitly include the effects of any channel scour that 

occurred during or following flow events because areas A and 

B are based entirely on post-event evidence.

Many reports did not document the values of A, B, and 

V explicitly, thus the quality and quantity of diverse information dictated whether or not the report could be used to 

reconstruct the values of A, B, and V. Reports fell into three 

categories. Reports that included detailed maps of deposit 

extent and channel cross-section surveys that identified the 

pre-flow surface were most useful. The second type of report 

offered sketches with descriptive details on total runout 

distance; maximum- and average- deposit dimensions at road, 

railway, or trail crossings; and maximum and average flow 

widths in well-constrained channels. These dimensions were 

used to calculate one or more of the inundation variables in a 

piece-wise fashion. In these cases, calculations made by using 

average, maximum, or minimum dimensions were used to 

estimate the difference in calculated outcomes. As long as the 

results agreed to one significant digit, calculations from these 

descriptive reports were included in the database. The third 

report category either provided data that could not be used to 

reconstruct the inundation variables objectively and reproducibly, or it provided data suitable for reconstructing only one 

of the three inundation variables (V, A, B). Reports from this 

third category were excluded from the database.

If reports included maps of the deposit without a quantitative assessment of inundation variables, a simple method for 

determining area B was used, which entailed overlaying a fine 

grid on the map, counting the boxes within the mapped extent, 
Statistical Analysis of the Data 7

and using the map scale to calculate the total area. The same 

procedure was used to determine cross-sectional area, A, for 

any surveyed channel profiles that included topography from 

before and after an event.

Despite the availability of some high-precision field measurements obtained through detailed surveying or calculations 

done in GIS, the database in appendix A lists only one significant digit for each volume and area entry. Values of volume 

and area generally are accurate to only one or two significant 

digits due to the following factors (1) the small scale at which 

surveys were done and the maps were produced, (2) the degree 

of erosion of deposits and loss of reconstructable evidence 

between the time of the event and the time of the study; and 

(3) the uncertainty of inferences from descriptive reports used 

to reconstruct the inundation variables where they were not 

specifically reported. It is important to point out that precision 

greater than one significant digit would have little effect on 

results reported in this study as the data are analyzed in the 

context logarithmic plots and power-law equations. Therefore, 

the order of magnitude of the data is paramount.

Some atypical and ambiguous events were intentionally 

excluded from the dataset. For example, events in which multiple debris flows coalesced, such that inundation variables for 

a single event were obscured, were excluded. Also excluded 

were events that followed in rapid succession such that flowpath features were overprinted or undifferentiable, or where 

pre- and post- event topography was not discernible. Cases in 

which the flow would be characterized as a hyperconcentrated 

water flood during a portion of the runout also were excluded 

from the dataset.

Data for 64 debris flows (44 V-B pairs and 50 V-A area 

pairs) and 143 rock avalanches (142 V-B pairs and 12 V-A 

pairs) are included in the database (appendix A). Rock-avalanche volumes range between 105

 and 1011 m3 and correspond 

respectively to the Felsberg event (Heim, 1921) and the Flims 

event (Jackli, 1957), both in The Alps. Debris-flow volumes 

range between 101 and 107 m3, which correspond to USGS 

debris-flow flume experiments (Iverson and others, 1992) and 

the 900 years B.P. (before present) Upper Lillooet River debris 

flow in British Columbia (Jordan, 1994). For lahars, we used 

data compiled by Iverson and others (1998), which include 

events ranging from about 105 to 1010 m3 in volume.

All data assembled for lahars, debris flows, and rock 

avalanches are plotted in figure 3. A log-log graph is used 

to depict the data because on such a graph, power laws of 

the form A = αV2/3 plot as straight lines with slopes of 2/3. 

Although considerable data scatter is evident in figure 3, the 

linear-data trends evident in the figure help motivate investigation of such 2/3-power laws as suitable models.

Statistical Analysis of the Data

The second objective of this report is to use the assembled datasets to develop and test predictive inundation equations. We focused on power-law equations because preliminary analyses by Griswold (2004) showed that power laws fit 

the data better than did alternative simple regression models 

(that is, linear, exponential, or quadratic).

First, we used results from an analysis of variance 

(ANOVA) to determine whether equations 3 and 4, with specified 2/3 exponents and statistically calibrated α-coefficients, 

provide appropriate models for both debris flows and rock 

avalanches; and we compared these models to best-fit powerlaw regression models with adjustable exponents. Second, 

we examined the statistical difference between the calibrated 

α-coefficients by using a technique involving a dummy 

variable and multiple linear-regression analysis to determine 

whether one set of predictive equations would be adequate 

for multiple types of flows. Third, we described the statistical 

uncertainty inherent in the predictive equations. Finally, we 

considered geological interpretation and physical implications 

of the calibrated inundation equations for the various types of 

flows. 

Prior to statistical testing, the data and power-law equations are transformed logarithmically. Use of log-transformed 

data acknowledges that data scatter increases roughly in 

proportion to the data magnitude, as shown in figure 3. Log 

transformation also enables use of standard least-squares 

regression methods when fitting power-law equations to the 

data. Following log-transformation, the power-law equations 

to be tested and calibrated become 

log A = logα +1 β1 logV , and (5)

log B = logα +2 β2 logV , (6)

where the logα terms represent y-intercepts, and the 

β-coefficients (slopes) are hypothesized to be 2/3.

Test for Differences in Slope 

In this section we examine whether the proposed equations with a specified slopes (β=2/3) and calibrated intercepts (log(α)) provide viable alternatives to linear regression 

equations with best-fit slopes and intercepts. A preliminary 

question is whether or not there is a significant relationship between the dependent and independent variables, and 

answers to both questions rely on the outcome of an analysis 

of variance (ANOVA) and the associated F-statistic.

For each paired data set (V,A or V,B for each type of 

flow) the best-fit linear regression models (referred to hereafter as “Model 1”) have two adjustable parameters (slope and 

y-intercept) to be calibrated by using a standard procedure 

of minimizing the residual sum of squares. The specified 

2/3-slope models (“Model 2”), as well as specified zero-slope 

models (“Model 3”), each have one adjustable parameter (the 

y-intercept) to be calibrated by minimizing the sum of squares. 

The two questions to be addressed are (1) “Do the models with 
8 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Figure 3. Scatter plot of all data compiled for debris flows, rock avalanches, and lahars. Flow volume, V, is the independent variable 

and maximum inundated cross-sectional area, A, and total, planimetric area, B, are the dependent variables. All data and data sources 

are tabulated in appendixes A and B.

debris flows

rock avalanches

lahars

debris flows

rock avalanches

lahars

107

106

105

104

103

102

101

100

1010

109

108

107

106

105

104

103

102

101

slope = 2/3

102 103 104 105

Cross-sectional area, 

A (m2

)

Planimetric area, 

B (m2

)

106

Flow volume, V (m3)

107 108 109 1010 1011
Statistical Analysis of the Data 9

nonzero slopes fit the data significantly better than a specified zero-slope model (a horizontal line expressing no linear 

relationship between the variables)?” and (2)” If the models 

with nonzero slopes fit the data best, is there a significant 

difference between the fit attained by using the best-fit linear 

regression model and the specified 2/3-slope model?” If there 

is no statistically significant difference, then the 2/3-slope 

model provides a suitable representation of the data.

The datasets and regression lines representing the three 

alternative models for each dataset are plotted in figures 4–7. 

The corresponding ANOVA results are listed in tables 1–4 

under column headings DF-A1 for debris flow V,A dataset 

Model 1; DF-B2 for debris flow V,B dataset Model 2; and so 

forth. For debris flows and rock avalanches with 2 datasets 

each (V,A pairs and V,B pairs) and 3 statistical models of each 

dataset, there are 12 models to consider. Tables 5 and 6 summarize the candidate predictive equations for Models 1 and 

2 (the best-fit regression and specified 2/3 slope models) for 

rock avalanches and debris flows and also list the analogous 

predictive equations for lahars (reproduced from Iverson and 

others, 1998).

Values of the coefficient of determination (r2) listed in 

tables 1–4 indicate how well the sloped models surpass the 

zero-slope model in describing the data. Following Weisberg 

(1985), the coefficient of determination, r2, is computed as

, (7)

where SS is the residual sum of squares that summarizes 

deviations of data values from a model trend line. The residual 

sum of squares for the sloped models is necessarily smaller 

than those of the zero-slope models, provided that some linear 

relationship exists between V and A and between V and B. Our 

datasets, all have r2-values greater than 0.77 (tables 1–4), and 

we infered that sloped models are better than the zero-slope 

models that use only the mean value to represent the data.

The F-test is used to compare the specified-slope models 

(Models 2 and 3 in tables 1–4) against the best-fit regression 

model (Model 1) for each dataset. More specifically, the F-test 

evaluates the credibility of a null hypothesis (H0) and alternate 

hypothesis (H1) that state 

H0, The specified-slope model (Model 2, with β1=2/3; or 

Model 3, with β1=0) fits the data as well as the best-fit regression model (Model 1, with β1

 adjustable);

H1, The specified-slope model (Model 2 or Model 3) does 

not fit the data as well as the best-fit regression model 

(Model 1).

The F statistic is computed as (Weisberg, 1985) 

(8) 

where SS is the residual sum of squares and DF is the residual 

degrees of freedom. For each model, DF = n-N, where n is 

the number of data pairs and N is the number of calibrated 

parameters in the model. (N=2 for the best-fit regression models and N=1 for the specified-slope models.) The computed 

F statistic-value is compared against tabulated values of the 

F-distribution (for example, Haan, 1977), and a probability of 

the F-statistic falling within a specified range of the F-distribution is determined. For example, a probability (p-value) 

of 0.01 indicates that, if the null hypothesis were true, there 

would be at least a 1 percent chance of obtaining a calculated 

F-value smaller than the tabulated F-value. Our key questions 

are whether or not calculated F-statistics support rejection of 

Model 3 (specified zero slope), but do not support rejection of 

Model 2 (specified 2/3 slope).

For both debris flows and rock avalanches, the calculated 

F-statistics for the specified 2/3-slope models (Model 2) are 

smaller than tabulated F-values for p=0.01, and confidence 

that the associated null hypothesis can be rejected consequently is small (table 5). The computed F-statistics for the 

specified zero-slope models (Model 3) are much greater than 

tabulated F-values for p=0.01, and therefore, the confidence 

with which this null hypothesis can be rejected is great 

(table 7). Thus, there is little statistical evidence that the bestfit regression models fit the data significantly better than the 

specified 2/3-slope models, but there is strong evidence that 

the best-fit regressions provide a better fit to the data than does 

a horizontal line. Therefore, for purposes of forecasting hazard 

zones, we adopted the 2/3-slope models (table 6) as acceptable 

fits to the data.

Test for Difference in Intercepts

Having shown that power-law equations with specified 

2/3 slopes and calibrated intercepts provide suitable models 

of the data, we examined the assumption that the inundation areas for the three types of landslides (debris flows, rock 

avalanches, and lahars) yield power-law equations that are 

statistically distinct. This can be done by showing that the 

y-intercepts of the log-transformed power-law equations are 

statistically different.

Testing the intercepts for significant differences can be 

accomplished by combining any two datasets (for example, 

rock avalanche and debris flow V, A data), calibrating a regression equation for the combined group, and testing whether the 

resulting regression equation fits the data as well as either of 

the two regression equations for the individual datasets. If the 

individual and combined regressions are shown to have statistically similar intercepts, then one equation adequately represents the data for both flow types. Alternatively, the intercept 

for the single dataset may be significantly different from that 

for the combined datasets, in which case separate equations for 

each flow type are warranted. Comparing pairs of datasets (for 
10 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Figure 4. Debris-flow data and three regression models for 

cross-sectional area (denoted by DF-A1, A2, A3 in table 1). Model 

1 is the best-fit regression. Model 2 is the specified 2/3-slope 

model. Model 3 is the specified zero-slope model expressing that 

cross-sectional area has no dependence on flow volume.

Figure 5. Debris-flow data and three regression models for 

planimetric area (denoted by DF-B1, B2, B3 in table 2). Model 

1 is the best-fit regression. Model 2 is the specified 2/3-slope 

model. Model 3 is the specified zero-slope model expressing that 

planimetric area has no dependence on flow volume.

Figure 6. Rock-avalanche data and three regression models 

for cross-sectional area (denoted by RA-A1, A2, A3). Model 1 is 

the best-fit regression. Model 2 is the specified 2/3-slope model. 

Model 3 is the specified zero-slope model expressing that crosssectional area has no dependence on flow volume.

Figure 7. Rock-avalanche data and three regression models for 

planimetric area (denoted by RA: B1, B2, B3). Model 1 is the 

best-fit regression. Model 2 is the specified 2/3-slope model. 

Model 3 is the specified zero-slope model expressing that 

planimetric area has no dependence on flow volume.

10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

FLOW VOLUME, V, IN CUBIC METERS

CROSS-SECTIONAL AREA, 

A, IN SQUARE METERS

10 -3

10 -2

10 -1

10 0

10 1

10 2

10 3

10 4

10 5

debris-flow data

specified 2/3 slope

best-fit regression

specified zero slope

10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

PLANIMETRIC AREA, 

B, IN SQUARE METERS

10 0

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

FLOW VOLUME, V, IN CUBIC METERS

debris-flow data

specified 2/3 slope

best-fit regression

specified zero slope

FLOW VOLUME, V, IN CUBIC METERS 

10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12

CROSS-SECTIONAL AREA, 

A, IN SQUARE METERS

10 0

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

debris-flow data

specified 2/3 slope

best-fit regression

specified zero slope

FLOW VOLUME, V, IN CUBIC METERS 

10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12

PLANIMETRIC AREA, 

B, IN SQUARE METERS

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

rock-avalanche data

specified 2/3 slope

best-fit regression

specified zero slope
Statistical Analysis of the Data 11

Table 1. Parameters and analysis-of-variance statistics for alternative linear models of log-transformed debris-flow data for 

inundated cross-sectional area, A, as a function of flow volume, V.

Parameter

Models for prediction of cross-sectional area of inundation, A

Best-fit regression,

(Model DF-A1)

Specified 2/3 slope,

(Model DF-A2)

Specified zero slope,

(Model DF-A3)

Calibrated slope of the line 0.59 0.67 0

Calibrated intercept of line at log V =0

α-coefficient

-0.66

log -1(-0.66) = 0.22

 -0.97

log -1(-0.97) = 0.11

 1.6

log -1(1.6) = 44

Number of data pairs, n 50 50 50

Residual degrees of freedom, DF 48 49 49

Residual sum of squares, SS 9.0 9.6 41

Residual mean square, MS 0.19 0.20 0.84

Standard error of model, σ 0.43 0.44 0.91

Coefficient of determination, r2 0.78 0.77 0.0

F-statistic, (comparison to model A1) NA 3.2 170

Table 2. Parameters and analysis-of-variance statistics for alternative linear models of log-transformed debris-flow data for 

inundated-planimetric area, B, as a function of flow volume, V.

Parameter

Models for prediction of planimetric area of inundation, B

Best-fit regression,

(Model DF-B1)

Specified 2/3 slope,

(Model DF-B2)

Specified zero slope,

(Model DF-B3)

Calibrated slope of the line 0.73 0.67 0

Calibrated intercept of line at log V =0

α-coefficient

 1.0

log-1(1.0) = 10

 1.3

log-1(1.3) = 19

 4.0

log-1(4.0) = 9,000

Number of data pairs, n 44 44 44

Residual degrees of freedom, DF 42 43 43

Residual sum of squares, SS 4.2 4.5 50

Residual mean square, MS 0.099 0.10 1.2

Standard error of model, σ 0.31 0.32 1.1

Coefficient of determination, r2 0.92 0.91 0.0

F-statistic, (comparison to model B1) NA 3.7 470

Table 3. Parameters and analysis-of-variance statistics for alternative linear models of log-transformed rock-avalanche data for 

inundated cross-sectional area, A, as a function of flow volume, V.

Parameter

Models for prediction of cross-sectional area of inundation, A

Best-fit regression,

(Model RA-A1)

Specified 2/3 slope,

(Model RA-A2)

Specified zero slope,

(Model RA-A3)

Calibrated slope of the line 0.71 0.67 0

Calibrated intercept of line at log V =0

α-coefficient

 -1.0

log -1(-1.0) = 0.10

 -0.64

log -1(-0.64) = 0.23

 4.7

log -1(4.7) = 52,000

Number of data pairs, n 13 13 13

Residual degrees of freedom, DF 11 12 12

Residual sum of squares, SS 1.9 2.3 12

Residual mean square, MS 0.17 0.19 0.98

Standard error of model, σ 0.41 0.44 0.99

Coefficient of determination, r2 0.84 0.80 0.0

F-statistic, (comparison to model A1) NA 2.7 58
12 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Table 4. Parameters and analysis-of-variance statistics for alternative linear models of log-transformed rock-avalanche data for 

inundated-planimetric area, B, as a function of flow volume, V.

Parameter

Models for prediction of planimetric area of inundation, B

Best-fit regression,

(Model RA-B1)

Specified 2/3 slope,

(Model RA-B2)

Specified zero slope,

(Model RA-B3)

Calibrated slope of the line 0.75 0.67 0

Calibrated intercept of line at log V =0

α-coefficient

0.73

log-1(0.73) = 5.3

1.4

log-1(1.4) = 24

6.8

log-1(6.8) = 6,000,000

Number of data pairs, n 142 142 142

Residual degrees of freedom, DF 140 141 141

Residual sum of squares, SS 27 28 130

Residual mean square, MS 0.19 0.20 0.92

Standard error of model, σ 0.44 0.45 0.96

Coefficient of determination, r2 0.79 0.79 0.0

F-statistic, (comparison to model B1) NA 6.2 540

Table 6. Summary table of the best-fit regression equations with specified 2/3 slopes and calibrated intercepts (Model 2). Only one 

significant digit is reported for the α-coefficients.

Debris flows, models DF-A2 and DF-B2 A=0.1 V 2/3 B=20 V 2/3

Rock avalanches, models RA-A2 and RA-B2 A=0.2 V 2/3 B=20 V 2/3

Lahars, (Iverson and others, 1998) models LA-A2 and LA-B2 A=0.05 V 2/3 B=200 V 2/3

Table 5. Summary table of the best-fit regression equations with calibrated slopes and intercepts (Model 1).

Debris flows, models DF-A1 and DF-B1 A=0.22V 0.59 B=10V 0.73

Rock avalanches, models RA-A1 and RA-B1 A=0.10V 0.71 B=5.3V 0.75

Lahars, (Iverson and others, 1998) models LA-A1 and LA-B1 A=0.062V 0.65 B=110.V 0.69
Statistical Analysis of the Data 13

lahars, debris flows, and rock avalanches), for both V, A and V, 

B data, to each of the individual datasets results in 12 tests.

The test procedure is formalized by adding a term to 

the simple linear-regression model (equation 9) to produce a 

multiple-regression model (equation 10):

1 1 log l A V = + ogα β log , and (9)

1 1 log log log A V = + α β + βZ Z . (10)

The additional term in equation 10 includes a dummy 

regression variable, Z, commonly used with categorical data—

also referred to as an indicator variable or binary variable (for 

example, Wesolowsky, 1976). Following Wesolowsky (1976), 

values for Z are assigned as 0 or 1 to differentiate between two 

datasets used in any test. The Z-value assignment is 0 for the 

combined datasets (for example, rock avalanches and lahars 

planimetric areas) and 1 for the individual datasets. The form 

of equations 9 and 10 then implies that the individual dataset has y-intercept, logα1

+βZ; and the combined dataset has 

intercept logα1. The null hypothesis states that the intercepts 

for both the combined and individual datasets are the same, for 

example, that βZ is zero. Rejecting the null hypothesis means 

that the difference in intercepts for the combined and individual datasets is significantly different from zero.

As in the preceding section, we used the F-test to evaluate 

the confidence with which a null hypothesis can be accepted 

or rejected. In this case, however, we considered whether or 

not null models (in which Z=0 in equation 10) for combined 

datasets are statistically distinct from models for individual 

data sets (denoted by Z=1 in equation 10). For the models 

summarized by equations 9 and 10, the model equations and 

residual degrees of freedom for this series of F-tests are summarized in table 8. For the case in which β1

=2/3, the residual 

degrees of freedom for the null models (combined dataset) are 

n-1, whereas the residual degrees of freedom for the alternate 

models (individual datasets) are n-2. (The null model with 2/3 

slope has one free coefficient to calibrate, logα1, whereas the 

alternate model representing an individual dataset with 2/3 

slope has two coefficients to calibrate, β0 and βZ). If the slope 

β1 is treated as a free parameter (calibrated by regression) in 

each case, then the residual degrees of freedom are reduced by 

1. Thus, the best-fit regression model for an individual dataset 

has n-3 residual degrees of freedom in this test. Tables 9 and 

10 list the parameters used to compute the F statistics.

The results of the F-test comparing combined and 

individual models for cases with a specified slope of 2/3 

are summarized in tables 11–13, and results for equivalent 

tests for cases with best-fit slopes calibrated by regression 

are summarized in tables 14–16. The tabulated values show 

that at the p=0.01 level, the implications of the F-test results 

are the same, regardless of whether or not the models with 

specified 2/3 slopes or freely calibrated slopes are considered. 

Therefore, because we are interested primarily in the 2/3-slope 

models, our discussion below focuses exclusively on the F-test 

results for those models.

The F-tests comparing the intercepts for the combined 

datasets (for example, rock avalanches and lahars) to those 

for the individual datasets (either rock avalanches or lahars) 

yield mixed results (fig. 8–10 and tables 11–13). In half of the 

cases, the null hypothesis can be rejected at the p=0.01 level, 

and thus, separate y-intercepts are warranted for individual 

datasets. In the other cases, the F-test results imply that little 

statistical difference exists between dataset y-intercepts. 

Detailed examination of the statistical results for specific 

F-tests, however, reveals some subtleties that complicate this 

inference.

The F-tests of greatest interest compare inundation equations for lahars and rock avalanches, which have flow-volume 

magnitudes that largely overlap (fig. 8, table 11). The F-tests 

generally confirm the visual impression that the data sets are 

distinct, and there is little doubt that planimetric-area inundation equations for lahars and rock avalanches have distinct 

y-intercepts and warrant different inundation equations. On 

the other hand, there is some ambiguity in the F-test results for 

cross-sectional area inundation. The tests show that the lahar 

data are distinguishable from the combined data, but the rock 

avalanche data are not. The paucity of cross-section inundation 

data for rock avalanches is at least partly responsible for this 

outcome, as it results in a small value for the residual degrees 

of freedom, which weakens the statistical test. The second 

comparison is for lahars and debris flows, which have flowvolume magnitudes that overlap between 105

 and 107 m3

(fig. 9, table 12). On this basis alone, the datasets might be 

Table 7. Summary of results of F-tests comparing the specified-slope models to the best-fit regression model with adjustable slope. 

Degrees of freedom

(numerator, denominator)

F-value,

p= 0.01 

Specified 2/3-slope regression model 

compared to null model

Specified zero-slope regression model 

compared to null model

F-statistic p-value F-statistic p-value

Debris flows

V,A 1, 48 7.2 3.2 0.080 170 2.2 x 10 -17

V, B 1, 42 7.3 3.7 0.061 470 2.0 x 10 -24

Rock avalanches

V, A 1, 11 9.6 2.7 0.13 58 1.0 x 10 -5

V, B 1, 140 6.8 6.2 0.014 540 6.8 x 10 -50
14 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Table 10. Parameters for computing the F-statistic to compare the best-fit regression models with combined and individual 

datasets. 

Combined datasets (null model)

Rock avalanche Lahar Debris flow Residual sum of squares, SSnull Number of samples, n1+n2

X X V, A 12.81

V, B 31.38

63

186

X X V, A 7.410

V, B 46.09

31

169

X X V, A 11.18

V, B 10.60

68

71

Individual datasets (multiple-regression model)

Rock avalanche Lahar Debris flow Residual sum of squares, SSregr Number of samples, n

X V, A 2.274

V, B 26.77

13

142

X V, A 1.967

V, B 1.867

18

27

X V, A 8.968

V, B 4.145

50

44

V, A denotes the paired dataset for volume-cross sectional area.

V, B denotes the paired dataset for volume-planimetric area.

Table 8. Dummy variable terms and equations for null model (combined datasets) and alternate models (individual datasets). 

Model Equation Residual Degrees of Freedom 

Null with 2/3 slope A logV 3 log log 2 = α1 + n-1

Alternate with 2/3 slope A = α + + logV βZ Z 3 log log 2 1 n-2

Null with best-fit slope log A = logα +1 β1 logV n-2

Alternate with best-fit slope log A = logα + 1 + β1 logV β Z Z n-3

Table 9. Parameters for computing the F-statistic to compare the specified 2/3-slope models with combined and individual datasets.

Combined datasets (null model)

Rock avalanche Lahar Debris flow Residual sum of squares, SSnull Number of samples, n1+n2

X X V, A 13.01

V, B 32.83

63

186

X X V, A 8.008

V, B 46.13

31

169

X X V, A 13.34

V, B 21.55

68

71

Individual Datasets (multiple-regression model)

Rock avalanche Lahar Debris flow Residual sum of squares, SSregr Number of samples, n

X V, A 2.314

V, B 27.95

13

142

X V, A 2.008

V, B 2.064

18

27

X V, A 9.557

V, B 4.505

50

44

V, A denotes the paired dataset for volume-cross sectional area.

V, B denotes the paired dataset for volume-planimetric area.
Statistical Analysis of the Data 15

considered separate because the physical processes they represent typically occur at different scales. (Nonvolcanic debris 

flows generally do not achieve volumes greater than 

106 m3 because the terrain in which they initiate, transport, and 

deposit does not provide adequate relief and erodible material 

to produce large flows. Lahars entrain and deposit material 

across large distances and through great relief where ample 

unconsolidated material is available, and they are, therefore, 

able to achieve greater volumes.) The equations for inundated 

planimetric area have distinct y-intercepts based on F-test 

results, and therefore, separate equations for predicting inundated planimetric areas are warranted. On the other hand, the 

intercepts for the cross-sectional area-inundation equations are 

statistically indistinguishable. Therefore, although lahars and 

debris flows can have widely differing flow volumes (ranging 

from 10 to 1010 m3), some aspects of their behavior probably 

are similar, as discussed below.

The third comparison is between debris flows and rock 

avalanches (fig. 10, table 13). For this case, the y-intercepts 

for the planimetric inundation equations are known to be similar (see the discussion of α-values in the previous section). 

Thus, the F-tests indicate that there is ambiguity in differentiating the planimetric-area inundation equations. Moreover, 

the F-tests show that, for prediction of cross-sectional area, 

the difference between the debris-flow equations and rockavalanche equations is not statistically significant. Therefore, 

areas inundated by modest-sized debris flows and by great 

rock avalanches exhibit statistically indistinguishable dependencies on flow volume.

Table 11. F-test comparison between the combined dataset and individual datasets for rock avalanches and lahars.

Combined datasets (rock avalanches and lahars)

V, A

Note: log(y-intercept) for combined datasets is 0.09

Individual dataset for comparison

Proposed 

log (y-intercept),

α

F p Outcome

Rock avalanches

Lahars

0.2

0.05

1.42

3.41

0.28

0.01

accept null

reject null

V, B

Note: log(y-intercept) for combined datasets is 30

Individual dataset for comparison

Proposed 

log (y-intercept),

α

F p Outcome

Rock avalanches

Lahars

20

200

3.25

3.73

0.000002

0.0002

reject null

reject null

Table 12. F-test comparison between the combined dataset and the individual datasets for debris flows and lahars.

Combined datasets (debris flows and lahars)

V, A

Note: log(y-intercept) for combined datasets is 0.08

Individual dataset for 

comparison

Proposed 

log (y-intercept), α F p Outcome

Debris Flows

Lahars

0.1

0.05

1.00

1.77

0.48

0.10

accept null

accept null

V, B

Note: log(y-intercept) for combined datasets is 40

Individual dataset for

comparison

Proposed 

log (y-intercept) , α F p Outcome

Debris Flows

Lahars

20

200

5.68

5.25

0.0000003

0.00002

reject null

reject null
16 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Table 13. F-test comparison between the combined dataset and the individual datasets for rock avalanches and debris flows.

Combined Datasets (debris flows and rock avalanches)

V, A

Note: log(y-intercept) for combined datasets is 0.1

Individual dataset for 

comparison

Proposed 

log (y-intercept), α F p Outcome

Rock avalanches

Debris flows

0.2

0.1

1.00

1.24

0.54

0.28

accept null

accept null

V, B

Note: log(y-intercept) for combined datasets is 20

Individual Dataset for 

Comparison

Proposed 

log (y-intercept) , α F p Outcome

Rock avalanches

Debris flows

20

20

0.54

1.85

0.99

0.01

accept null

reject null

Table 14. F-test comparison between combined rock avalanche and lahar datasets and the listed individual datasets for a best-fit 

regression slope (non-2/3). Statistical results are the same as the case where the regression slope is forced to 2/3.

V, A F p Outcome

Rock avalanches

Lahars

1.19

2.96

0.40

0.02

accept null

reject null

V, B F p Outcome

Rock avalanches

Lahars

3.58

3.98

0.0000003

0.0001

reject null

reject null

Table 15. F-test comparison between combined debris flow and lahar datasets and the listed individual datasets for a best-fit 

regression slope (non-2/3). Statistical results are the same as the case where the regression slope is forced to 2/3.

V, A F p Outcome

Debris flows

Lahars

0.61

1.38

0.88

0.25

accept null

accept null

V, B F p Outcome

Debris flows

Lahars

2.28

2.50

0.008

0.009

reject null

reject null

Table 16. F-test comparison between combined rock avalanche and debris flow datasets and the listed individual datasets for a 

best-fit regression slope (non-2/3). Statistical results are the same as the case where the regression slope is forced to 2/3.

V, A F p Outcome

Rock avalanche

Debris flows

0.91

1.44

0.62

0.17

accept null

accept null

V, B F p Outcome

Rock avalanche

Debris flows

0.53

1.88

0.99

0.01

accept null

reject null
Statistical Analysis of the Data 17

Figure 8. Rock-avalanche and lahar data used to compute statistics as a combined dataset (see table 11). On the cross-sectional 

area plot, the data for rock avalanches and lahars are not obviously separated. On the planimetric-area plot, the data trends are visibly 

separate.

Figure 9. Debris-flow and lahar data used to compute statistics as a combined dataset (see table 12). On the cross-sectional area plot, 

the data for rock avalanches and lahars generally occupy different potions of the flow volume range, but the data trends are not visibly 

separate. On the planimetric-area plot, the data trends appear somewhat separate.

FLOW VOLUME, V (m3)

101 102 103 104 105 106 107 108 109 1010 1011

PLANIMETRIC AREA, 

B (m

2)

101

102

103

104

105

106

107

108

109

1010

1011

101 102 103 104 105 106 107 108 109 1010 10

CROSS11 SECTIONAL AREA, 

A (m

2)

10-2

10-1

100

101

102

103

104

105

106

107

108

rock avalanches

rock avalanches

lahars

lahars

FLOW VOLUME, V (m3)

FLOW VOLUME, V (m3)

101 102 103 104 105 106 107 108 109 1010 1011

PLANIMETRIC AREA, 

B (m

2)

101

102

103

104

105

106

107

108

109

1010

1011

101 102 103 104 105 106 107 108 109 1010 10

CROSS11 SECTIONAL AREA, 

A (m

2)

10-2

10-1

100

101

102

103

104

105

106

107

108

debris flows

debris flows

lahars lahars

FLOW VOLUME, V (m3)
18 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

Error and Uncertainty in Calibrated Prediction 

Equations

The standard errors and predictive uncertainties of the 

2/3-slope models with best-fit calibrated α-coefficients 

(table 6) have important ramifications for how the equations 

are interpreted and used. The standard errors, σ, of these 

models (tables 1–4) characterize the variability of behavior 

exhibited by past flows, and the 99-percent confidence interval 

curves for prediction (figs. 11–13) characterize the uncertainties of predicting areas inundated by future flows if volume is 

known with certainty (cf. Helsel and Hirsh, 1992). The factors 

of error (10σ ) for predicting inundation areas (A, B) for rock 

avalanches (10σ = 2.7, 2.8) are somewhat larger than the factors of error for debris flows (10σ = 2.7, 2.1) and lahars 

(10σ = 2.2, 1.9).

As an example of the effect of uncertainty, we considered 

prediction of planimetric areas inundated by debris flows. 

Taken alone, the inundation-area equation predicts that a 

debris flow of 105 m3 would inundate a planimetric area of 

43,000 m2. The 99-percent confidence interval for prediction 

of the inundation area (outer set of curves, fig. 12) for the 

same flow volume, however, indicates a wide range of possible inundation areas, between 6,000 and 300,000 m2

. At the 

95-percent confidence level (not shown in figures) this range 

shrinks to between 10,000 and 200,000 m2, but the uncertainty 

remains considerable. The confidence intervals for prediction are great owing to the data scatter associated with diverse 

natural events, but if a lower degree of confidence can be tolerated, then the range of prediction uncertainty is reduced. We 

chose to stipulate a high degree of confidence (99-percent), 

resulting in portrayal of wide confidence limits.

Discussion and Interpretation of Predictive 

Equations

Statistical results show that the power-law equations summarized in table 6 adequately relate inundation areas to flow 

volumes, and that power-law equations with a specified slope 

of 2/3 are indistinguishable from best-fit power-law regression 

equations. Therefore, equations of the form Area = α Volume 2/3 are adopted for purposes of hazard-zone delineation. 

Only one significant digit is used to specify the α-coefficients 

in the predictive equations listed in table 6; greater precision is 

unwarranted given the uncertainties inherent in the predictive 

models.

Constant power-law exponents (constant slopes on loglog plots) in our predictive equations imply a fractal scaling, 

and such geometric fractals are said to be scale invariant (Peitgen and others, 1992). Thus, for a wide range of flow volumes, 

inundated areas will appear similar when portrayed on maps 

of any scale. Clearly, however, there is a practical limit below 

which the power-law equations do not apply. For example, as 

volume falls below the smallest observed flow volume 

(101 m3) and approaches zero, we should not expect the equations to apply. This is an important consideration because 

Figure 10. Debris-flow and rock-avalanche data used to compute statistics as a combined dataset (see table 13). On the crosssectional area plot, the data for rock avalanches and debris flows occupy different potions of the flow-volume range, but the data trends 

are not visibly separate. On the planimetric-area plot, the data trends appear somewhat separate.

FLOW VOLUME, V (m3)

101 102 103 104 105 106 107 108 109 1010 1011

PLANIMETRIC AREA, 

B (m

2)

101

102

103

104

105

106

107

108

109

1010

1011

101 102 103 104 105 106 107 108 109 1010 10

CROSS11 SECTIONAL AREA, 

A (m

2)

10-2

10-1

100

101

102

103

104

105

106

107

108

debris flows

debris flows

rock avalanches rock avalanches

FLOW VOLUME, V (m3)
Statistical Analysis of the Data 19

Figure 11. Lahar data and best-fit regression line (solid) with 99-percent confidence interval for regression (inner pair of dashed 

curves) and 99-percent confidence interval for prediction (outer pair of dashed curves; computed following Helsel and Hirsch, 1992) 

after Iverson and others (1998). Note that Iverson and others used 95-percent confidence intervals.

Figure 12. Debris-flow data and best-fit regression line (solid) with 99-percent confidence interval for regression (inner pair of dashed 

curves) and 99-percent confidence interval for prediction (outer pair of dashed curves; computed following Helsel and Hirsch, 1992).

FLOW VOLUME, V, IN CUBIC METERS 

10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11

FLOW VOLUME, V, IN CUBIC METERS 

10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11

PLANIMETRIC AREA, 

B, IN SQUARE METERS

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

10 11

CROSS-SECTIONAL AREA, 

A, IN SQUARE METERS

10 -1

10 0

10 1

10 2

10 3

10 4

10 5

10 6

10 7

lahar data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction

lahar data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction

10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

10 0

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 -3

10 -2

10 -1

10 0

10 1

10 2

10 3

10 4

10 5

FLOW VOLUME, V, IN CUBIC METERS 

10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

FLOW VOLUME, V, IN CUBIC METERS 

PLANIMETRIC AREA, 

B, IN SQUARE METERS

CROSS-SECTIONAL AREA, 

A, IN SQUARE METERS

debris-flow data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction

debris-flow data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction
20 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

log-transformed versions of the equations would imply a 

non-zero y-intercept where volume is equal to zero, and this is 

physically impossible. This observation doesn’t imply that the 

equations do not hold true, but that they operate across a wide 

but finite range of scales.

According to the coefficients of the calibrated 2/3-powerlaw models (table 6), lahars typically inundate a planimetric 

area roughly ten times greater than do debris flows or rock 

avalanches of the same volume. Rock avalanches typically 

inundate valley cross-sectional areas four times greater than 

does a lahar of equal volume, and two times greater than a 

debris flow of equal volume. The relatively large cross-sections inundated by rock avalanches imply the existence of a 

relatively resistive, bulky cross-sectional surge front, whereas 

debris flows flow more fluidly, and lahars flow more fluidly 

still. The explanation for these differences probably lies in 

the typical compositions and typical topographies traversed 

by each type of flow (for example, Vallance and Scott, 1997). 

However, physical or geological interpretations of differing 

α coefficients in our power-law equations must be tempered in 

view of statistical results that show that differing coefficients 

may not be truly distinct. Statistical comparison of the calibrated equations for inundated cross-sectional areas 

(A = 0.05V2/3, A = 0.1V2/3 and A = 0.2V2/3 for lahars, debris 

flows, and rock avalanches, respectively) shows that the 

α-coefficient values (0.05, 0.1, and 0.2) are not distinct at the 

99-percent confidence level. This lack of distinction implies 

that there is considerable overlap in the physical behavior of 

these three types of flows, at least insofar as behavior determines cross-sectional inundation. For inundation-prediction 

purposes, we gained some resolving power by retaining 

distinct equations for the three types of flows, and this is the 

course we adopted in practice.

With respect to equations for prediction of planimetric 

inundation areas, however, there is a clear statistical distinction between the lahar equation B = 200 V2/3 and the equation 

that applies to both debris flows and rock avalanches, B = 

20 V2/3. Separate inundation equations should be applied for 

separate types of flows.

The development and comparison of the inundation-area 

equations listed in table 6 is the lack of clear statistical distinction between the equations that apply for debris flows and the 

equations that apply for rock avalanches. When considering 

this statistical similarity, however, it must be borne in mind 

that the rock-avalanche data included in the dataset apply to 

events with volumes that are, on average, several orders of 

magnitude larger than the volumes of debris flows included 

in the data set. In terms of inundation patterns, large rock 

avalanches tend to appear like small, nonvolcanic debris flows 

viewed at a proportionally smaller scale. The reasons for this 

geometric similarity may be severalfold. Nonvolcanic debris 

flows (with volumes generally less than 106 m3) commonly 

Figure 13. Rock-avalanche data and best-fit regression line (solid) with 99-percent confidence interval for regression (inner pair of 

dashed curves) and 99-percent confidence interval for prediction (outer pair of dashed curves; computed following Helsel and Hirsch, 

1992).

10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

10 11

10 0

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

FLOW VOLUME, V, IN CUBIC METERS 

10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12

FLOW VOLUME, V, IN CUBIC METERS 

PLANIMETRIC AREA, 

B, IN SQUARE METERS

CROSS-SECTIONAL AREA, 

A, IN SQUARE METERS

rock-avalanche data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction

rock-avalanche data

best fit regression

99-percent confidence

interval for regression

99-percent confidence

interval for prediction
Debris-Flow Hazard Delineation by using DEMs and GIS 21

entrain large boulders, logs, and other coarse debris that 

concentrate at the flow front and produce a high-friction zone 

there (Iverson, 1997). In many cases, individual clasts at the 

flow front have dimensions comparable to the flow thickness 

or the flow width. Nonvolcanic debris flows are commonly 

channeled into narrow stream courses in finely dissected 

terrain, and a large degree of form drag thereby retards flow 

runout. In this sense, the fronts of nonvolcanic debris flows 

may behave much like the fronts of large rock avalanches 

that surmount small-terrain obstacles and respond only to the 

largest features of topography. Much evidence exists to suggest that large rock avalanches are, to some degree, fluidized 

at their bases, facilitating distal runout (for example, Legros, 

2002). Although there is still controversy concerning the exact 

mechanism by which this fluidization can occur in the absence 

of saturation with water, there is no doubt that large rock avalanches can to some extent behave like much smaller, liquefied debris flows. Thus, the similarity in power-law inundation 

equations for nonvolcanic debris flows and much larger rock 

avalanches is perhaps unsurprising.

Debris-Flow Hazard Delineation by 

using DEMs and GIS

As an example of application of our inundation-area 

equations to hazard-zone delineation, we considered an area 

in the south-central Oregon Coast Range where significant 

hazard from debris flows exists (fig. 14). Creating a hazard 

zone map requires (1) obtaining topographic data of adequate 

grid cell size, accuracy, and precision to represent the size 

of the anticipated events and of sufficient extent to cover the 

area of interest, (2) identifying potential debris-flow source 

areas, (3) selecting appropriate potential flow volumes, (4) 

using inundation-area equations to calculate the predicted Aand B-values corresponding to the selected V-values, and (5) 

computing and displaying the delineated planimetric areas as 

nested hazard zones that depict uncertainty and relative levels 

of hazard posed by the suite of potential flows. Automation 

of the computations and display process is important, especially for high-resolution data covering large areas, and it also 

ensures reproducibility.

As a platform for automation, we use GIS-based software, LAHARZ, written in the ArcInfo Macro Language 

(AML) (Schilling, 1998). This software has been used to 

construct dozens of volcano hazard assessment maps around 

the world (for example, Schilling and others, 2001; Vallance 

and others, 2001; Scott and others, 2001; Gardner and others, 

2004) and also used and compared to other methods of runout 

prediction (for example, Sheridan and others, 1999; Haapala 

and others, 2005; Sorenson, 2003). Below we summarize the 

data requirements and programming modifications necessary 

to adapt LAHARZ for application to nonvolcanic debris flows.

Data Input 

Data input for LAHARZ includes a range of postulated 

flow volumes, digital topographic data, a suite of flow-path 

data derived from the topography, and identification of source 

areas where debris flows may originate. Specification of 

appropriate flow volumes generally requires an estimate of 

available surficial material on slopes or within channels, as 

well as a historical perspective of the range of volumes that a 

given geographic area has produced. For example, the Coast 

Range in Oregon would not likely produce a flow with a 

volume of 1 km3 because the topographic relief in prospective 

source areas is typically of the order of 100 m, and therefore, 

a maximum credible flow volume might be 0.001 km3. The 

range of postulated flow volumes should span 2 or 3 orders of 

magnitude to encompass a range of probability of occurrence. 

Volumes ranging from smaller (more frequent) to larger (less 

frequent) depict areas of decreasing inundation hazard because 

areas that are likely to be inundated more frequently have 

greater hazard associated with them.

Selecting the increment between successive flow volumes used to forecast hazards can be done so as to mirror the 

intrinsic statistical error of the models. For debris flows the 

standard error, σ, of the calibrated power-law models (tables 

6–9) is < 0.5 (ranging from 0.32 to 0.45), such that a conservaFigure 14. Aerial photograph (Google Earth) of the Scottsburg, 

Oregon, area. The dark ribbon is the Umpqua River. Mapped 

recent debris-flow paths are in red, perennial streams are in blue, 

and the yellow line denotes the extent of the Oregon Department 

of Forestry’s research area (Robinson and others, 1999).
22 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

tive factor of error (10σ) is half an order of magnitude (100.5). 

Deliberate use of such a flow-volume increment is a technique 

that effectively enables the display of error bars in the form of 

nested hazard zones (Iverson and others, 1998). Depicting successive hazard zones in a nested fashion implies that any given 

flow volume produces a unique, predicted inundation area ± 

the area of the closest larger and smaller nested-hazard zones.

The demand for resolution and precision of topographic 

data depends on the volumes of the flow events that are postulated. For example, the digital elevation model (DEM) used in 

the lahar hazard assessment at Mount Rainier, Washington, has 

62.5-m grid cells for a map spanning much of western Washington (Iverson and others, 1998). This resolution is adequate 

for large-volume lahars (> 107 m3) that inundate broad areas, 

as potential inundation areas are much greater than the size of 

the individual grid cells. Small-volume debris flows (<105 m3) 

may have flow widths less than 10 m and, therefore, require 

cell-size resolution and accuracy of roughly a few meters.

The main LAHARZ algorithm computes hazard zones 

downslope or downstream from source areas, but these areas 

must be identified by some independent means. Source-area 

identification in highly dissected topography is particularly 

challenging because many prospective source areas may exist. 

Our modified version of LAHARZ addresses the source-area 

problem by using a combination of three criteria appropriate 

for identifying debris-flow initiation sites in the Oregon Coast 

Range, our target for model application: 

(1) A specified minimum contributing-upslope source area 

must exist, analogous to contributing areas commonly 

used to evaluate surface-water runoff in hydrologic 

models. In our implementation, the default minimum 

contributing source area for debris flows is 103 m2. This 

value derives from evidence of initial failure scars and the 

onset of debris-flow inundation in site-specific reports for 

the Coast Range of southwestern Oregon (for example, 

Harvey and Squier, unpub. data, 1998).

(2) A specified minimum slope angle must exist within the 

source area or along the computer-generated thalweg fed 

by the source area. This criterion is based on the observation that debris flows generally initiate on slopes 

θ>30 degrees (Iverson and others, 1997), and we used 

30 degrees as the default minimum slope. 

(3) Slopes must be uniformly steep in the vicinity of a prospective initiation site. Here, uniformly steep is defined 

as 95-percent of grid cells, exceeding 30 degrees, in a 

100-m2 area centered on a prospective initiation cell. This 

criterion excludes small patches of steep ground that are 

unlikely to produce slope failures of significant size. 

Criteria 1 and 2 serve to identify many potential initiation 

cells in a DEM, and criterion 3 limits the number of cells that 

define the starting points for debris-flow inundation. Criterion 3 also serves to limit the potential for DEM errors to 

lead to false identifications of sufficiently steep ground. The 

specifications listed here are default settings for our modified 

LAHARZ program, but they may be adjusted after a sensitivity analysis is performed for any particular study area.

Used in combination, the initiation criteria 1–3 are 

somewhat similar to the criteria used to forecast sites of 

shallow-landslide initiation in spatially distributed models, 

such as SHALSTAB (Montgomery and Dietrich, 1994). Like 

the criteria in SHALSTAB, our criteria are intended to identify 

all prospective source areas in a drainage basin simultaneously. This simultaneous delineation does not imply that all 

source areas will generate debris flows. Rather, it implies 

that many source areas have equal probabilities of spawning 

debris flows, thereby leading to simultaneous delineation of 

many possible inundation paths downstream. In contrast to the 

criteria used in SHALSTAB, our simple flow-initiation criteria 

are not intended to emulate physical processes, but instead are 

entirely empirical — although they do exploit the common 

observation that slope angle and upslope contributing area are 

important morphometric parameters in determining flowinitiation sites. 

Application Area for Debris-Flow Hazard 

Mapping—Scottsburg, Oregon

History of the Scottsburg Area

In the south-central Oregon Coast Range (fig. 14), the 

town of Scottsburg and the Rock Creek-Hubbard Creek 

watershed (also known as Stump Acres) in southern Douglas 

County, Oregon, became infamous when two debris flows 

caused five fatalities between November 17 and 19, 1996. 

The deaths were the result of debris flows inundating a home, 

overtaking a pedestrian, and pushing a car off the road (The 

Oregonian, 1996; Mapes and Tims, 1996; Harvey and Squier, 

unpub. data, 1998). Highway 38 was blocked at multiple locations by debris flows that inundated the road, and many more 

debris flows occurred in the vicinity that winter season (fig. 

14).

Topography and Geology

The map area shown in fig. 15 (~5 km2) is characterized 

by narrow, dissected valleys and sharp ridges, and the elevations within this area range from ~3 to 390 meters msl (above 

mean sea level). Several unnamed perennial streams flow 

along lower portions of tributary valleys feeding the Umpqua 

River. The average and maximum slopes for the map area are 

35 degrees and 77 degrees, respectively, if flat areas (river and 

roads) are excluded from consideration. These hillslopes are 

exceedingly steep, even by Coast Range standards.

The entire map area is underlain by the Tyee Formation, 

a late Eocene, eastward dipping, rhythmically bedded sandstone and siltstone (Baldwin, 1961). The weakly consolidated 

bedrock is easily weathered and eroded, and it yields a succession of highly dissected ridges and small valleys. Where 

the sandstone dominates, ridgelines are sharp; and where the 

siltstone dominates, ridges are moderately rounded. Based 
Debris-Flow Hazard Delineation by using DEMs and GIS 23

on the Oregon Department of Forestry (ODF) Storm Impacts 

Study (Robinson and others, 1999) and the State of Oregon’s 

Emergency Management Plan (OEMP, 2000), the highest 

debris flow/torrent hazard is within steeply sloped areas in 

the Tyee Formation (or similar sedimentary rocks) in Western 

Douglas, Coos, and Western Lane Counties. Most hillslopes 

that are steeper than 35 degrees can produce rapidly moving landslides, regardless of the underlying geologic unit. In 

designating high-risk areas, the ODF uses lower slope criteria 

in the Tyee Formation than for other geologic types (Robinson 

and others, 1999).

Topographic Dataset—Acquisition and 

Description

LIDAR (LIght Detection And Ranging) data acquired 

by ODF on November 9, 1997 for the area around Scottsburg 

were collected as part of a pilot study of damage assessment 

of the impacts of the torrential storms and debris flows of 

1996. LIDAR technology uses an airborne laser transmitter 

and receiver coupled with GPS to map elevation profiles as 

the beam reflects off of surfaces such as tree canopy or bare 

earth. The elevation data are filtered to recover only bare-earth 

surface return times for the production of a 1-meter DEM 

with high resolution and accurate X, Y, and Z locations (for 

example, Schickler and Thorpe, 2001).

The topographic data were processed by ODF to minimize the effects of the dense tree canopy and derive elevations 

Figure 15. Shaded-relief map with highways and logging roads for the study area west of the town of Scottsburg in the central Coast 

Range of Oregon, at about 40 river kilometers from the coast.

123°51'0"W

123°51'0"W

123°52'0"W

123°52'0"W

43°39'0"N

43°39'0"N

Umpqua River

Highway 38

Mill Stream

0 500

Meters
24 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

of the bare earth surface. The study area excludes regions of 

less dense data coverage around the periphery and therefore 

avoids coarse interpolation and false slopes. Within the DEM, 

however, some areas have relatively few elevation data due to 

dense tree coverage and fewer laser beams reaching the bare 

earth surface. The accuracy of the data in these areas is still 

high, but the elevation postings or resolution is reduced. The 

data points were used to create a TIN (triangulated irregular 

network), interpolated to a grid, and drainage enforced. Drainage enforcement is the filling of small topographic “sinks” 

or single cell depressions that inhibit continuous flow across 

a DEM. This enforces continuous drainage by raising the 

elevation of a single cell depression to the “pour” level of its 

neighbors using a standard GIS hydrologic function.

We used the TIN to generate a one-meter resolution DEM 

that has not been checked for 1-m accuracy, and thus, GIS 

products based on this topographic data should be interpreted 

as preliminary. Such resolution and accuracy, however, are 

needed for forecasting debris flow inundation where flow 

widths can be expected to be less than 10 meters.

Background for Flow Volume Assessment for 

Scottsburg, Oregon

Some constraints on applicable flow volumes are provided by the work of Ketcheson and Froelich (1978), who 

completed a landslide inventory in the Mapleton Ranger District of the Oregon Coast Range and found that initial volumes 

of slope failures ranged from 1.5 to 150 m3 for 104 events 

during a ten-year period (1966-1976). Although these initial 

volumes cannot be used to estimate maximum flow volume, 

they do provide a minimum flow-volume estimate.

Harvey and Squier (unpub. data, 1998) describe two 

debris flows that occurred within the same sub-basin as the 

Hwy 38 MP 13 event and describe evidence observed in aerial 

photographs for the Scottsburg area. In January 1990, a flow 

blocked the highway but did not enter the Umpqua River. In 

December 1992, a flow deposited 500-600 m3 of material on 

the highway. Aerial photographs taken in 1986 indicate recent 

Figure 16. A, Shaded relief map constructed from a DEM, overlaid with orange where slopes exceed 30 degrees. The red box identifies 

the test basin where the modified LAHARZ program is applied. B, Example of locations (red cells) meeting initiation criteria along 

potential flow paths.

Umpqua River

0 500

Meters

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0 50 Meters
Debris-Flow Hazard Delineation by using DEMs and GIS 25

debris flows in adjacent sub-basins also reached the highway. 

Aerial photographs from 1997 indicate that the fatal 1996 

debris flow event was accompanied by multiple events within 

the upper reaches of the same subbasin and on adjacent slopes 

outside of the sub-basin. Many of those flows reached the 

highway. Total flow volumes, total runout lengths, and initiation locations were not recorded except for the single fatal 

event. The initiation site for the 1996 event was ~45 m from 

the ridgeline (roughly sketched on a 1:2,000 scale map) and 

the runout length was about 400 m.

Harvey and Squire (unpub. data, 1998) note that the 

November 1996 storm that produced the debris flows was 

preceded by 5 inches of precipitation in a 24-hour period and 

a total of 6 inches in 2 days. Harvey and Squier (unpub. data, 

1998) indicate that this 2-day intensity of rain was the greatest in the past 48 years, and their recurrence analysis indicates 

that the storm event was a 50-year storm.

LAHARZ Application

One basin (red box, fig. 16A) was selected from the 

5 km2-area of LIDAR coverage to run trial applications. One 

of the November 1996 debris flows emerged from this basin 

along the Umpqua River and pushed a traveling car off the 

road and into the Umpqua River channel.

Combining the three criteria for debris-flow initiation 

(see section on “Data Input”) identifies hundreds of potential 

initiation sites within the study basin (fig. 16B). Each potential initiation cell, shown in red, represents one square meter. 

A total of 42 contributing source areas (≥103 m2 each) were 

identified within the basin (shades of green, fig. 17).

The selected potential flow volumes are 103, 103.5, 104, 

and 104.5 m3 based on historic volumes for this region of the 

Coast Range and a rough estimate of available erodible material. Reports about the Scottsburg area suggest that several 

debris flows in the last two decades had volumes of ~103 m3. 

This minimum flow volume may represent events of roughly 

decadal frequency, and the larger flow volumes would represent less frequent events. In the study basin the longest potential flow path is ~800 meters and the entrainment swath might 

be 30-50 meters in width with one or two meters of erosion, 

yielding a conservative upper limit for flow volume ~104.5 m3.

For the selected series of flow volumes (103, 103.5, 104, 

and 104.5 m3), the inundation-area equations from table 6, 

A=0.1V2/3 and B=20V2/3, predict cross-sectional areas (A) of 10, 

22, 47, and 100 m2, and planimetric areas (B) of 2,000; 4,300; 

9,300; and 20,000 m2. With the aid of LAHARZ software, the 

four pairs of successively larger predicted A- and B- values 

produce a set of four nested inundation zones. One set of inundation zones is produced for every initiation point (fig. 18A). 

Several amalgamated inundation-hazard zones are shown in 

figure 18B. Merging all the hazard zones from each of the initiation points creates coalesced hazard zones (fig. 19) for each 

postulated flow volume. The resulting debris-flow hazard map 

for the whole basin is shown in figures 19 and 20.

Figure 17. Shaded relief map of upper half of the test basin. Green-shaded polygons illustrate minimum contributing source areas 

that define the start of potential flow paths (blue). Flow paths are not perennial stream channels, but are the paths of steepest descent 

below an area of potential flow initiation. The image on the right shows the location of the upper half of the test basin (also fig. 16A) and 

the prevalence of steep slopes in the test basin as indicated by the presence of the orange overlay where slopes exceed 30 degrees.

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0 50

Meters

Umpqua River

0 500

Meters
26 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

B

A

Figure 18. Examples of nested, inundation-hazard zones computed for four flow volumes (orange is 103 m3, yellow is 

103.5 m3, green is 104 m3, and brown is 104.5 m3). A, Inundation-hazard zones computed downstream from a single initiation point within a 

flow path. B, Amalgamated inundation-hazard zones computed from seven initiation points (red cells along blue flow paths).
Debris-Flow Hazard Delineation by using DEMs and GIS 27

Figure 19. Debris-flow inundation-hazard map computed for four flow volumes ranging from 103 to 104.5 m3. Hazard zones show the 

predicted inundation limits for debris flows that start anywhere within the basin where the initiation criteria are met. The spikiness of 

the terminus of the LAHARZ hazard zones as the hypothetical flows spill out of the constricted channel are a result of the limitation of 

flow directions across gridded elevation data.

Hazard Map Summary

The modeled hazard zones do not predict the runout of a 

particular debris flow but instead delineate the paths likely to 

be inundated by a suite of debris flows with various volumes. 

The four nested hazard zones are colored from “cool” to “hot” 

to reflect which areas are most hazardous because they are 

subject to inundation by even the smallest debris flows. In 

general, hazard is greatest along channel thalwegs (fig. 20), 

and it decreases with distance and elevation above the valley 

floor. Moreover, bracketing flow volumes and using graded 

shades of color generates a hazard map that conveys visual 

uncertainty. The nested hazard zones are equivalent to error 

bars where the increments between flow volumes (100.5) conservatively exceed the statistical errors inherent in the inundation equations (0.32–0.45). The nested hazard zones imply that 

wide paths (sometimes overlapping between adjacent channels) near channel heads are less likely to be inundated than 

are narrow paths. 

The ragged edges of the computed inundation zones 

result from using a computational grid having only four potential transect directions (NW-SE, N-S, NE-SW, W-E) in which 

to calculate any cross-section. Furthermore, the computational 

methodology and, therefore, the hazard map do not account 

explicitly for changes in flow volume (due to entrainment or 

deposition) or run-up potential because the same predicted 

A-value is used to compute the inundation cross-section along 

the entire length of a potential debris-flow path. 
28 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

A

B

Figure 20. Oblique perspectives of the test basin with debris flow-hazard zone overlays, view to the southeast. A, Oblique aerial 

view. B, Up-valley view from the north bank of the Umpqua River, Oregon.
References Cited 29

Conclusions

The main objective of this work is to provide a statistically tested and calibrated model that can be used to forecast inundation by future debris flows and rock avalanches. 

To accomplish this objective, a database consisting of flow 

volumes, maximum inundated cross-sectional areas, and total 

inundated planimetric areas was compiled for 64 nonvolcanic 

debris flows ranging in volume from 101 to 107 m3 and for 

143 rock avalanches ranging from 105 to 1011 m3 in volume. 

The database complements the lahar inundation-area database 

of Iverson and others (1998) for events that range between 105

and 1010 m3 in volume.

Power-law equations of the form Area = α·Volume 2/3

with calibrated α-coefficients provide a good fit 

(r2 = 0.76 – 0.91) to the debris-flow and rock-avalanche data 

with standard errors ranging from 0.45 to 0.32. The resulting inundation-area equations for debris flows are A = 0.1 

V2/3 and B = 20 V2/3, and the inundation-area equations for 

rock avalanches are A = 0.2 V2/3 and B = 20 V2/3, where V is 

flow volume, A is inundated cross-sectional area, and B is 

inundated planimetric area. These equations are similar to a 

set of inundation-area equations (A = 0.05 V2/3 and B = 200 

V2/3) developed for lahars by Iverson and others (1998). The 

equations imply no scale dependence of the runout process 

for rock avalanches, debris flows and lahars, but the differing 

α-coefficients of the equations indicate that the bulk mobilities of different types of flow can differ. Specifically, the 

α-coefficients imply that rock avalanches and nonvolcanic 

debris flows are less mobile than lahars because they inundate 

planimetric areas roughly ten times smaller than do lahars of 

similar volume. The maximum cross-sectional area inundated 

by rock avalanches is, on average, four times greater than that 

of lahars and twice as large as that of debris flows of the same 

volume, although the statistical support for this distinction 

is weak. The similarity of the inundation-area equations for 

relatively small, nonvolcanic debris flows and much larger 

rock avalanches indicates that these two types of flows may 

have strong mechanical similarities, even though the processes 

operate on different scales.

Implementation of the inundation-area equations within 

the GIS-based program, LAHARZ, provides a repeatable, 

objective way to produce hazard maps. Use of a range of 

hypothetical flow volumes that span orders of magnitude provides graphical output that depicts uncertainty in the predicted 

inundation areas. Not knowing the volume of the next event 

and, therefore, the inundation limits for that volume, is tolerable where hazard is assessed in terms of relative likelihood of 

inundation for a range of flow volumes instead of the inundation limits of the next event.

A strength of this methodology compared to traditional 

hazard-mapping techniques is that a broad range of possible 

volumes is considered in delineating maximum inundation 

areas. Rather than making predictions of future runout based 

on the maximum extent of deposits of past events, this method 

allows for the possibility that future events will be larger than 

those in the historical or geologic record. A limitation of this 

methodology is that flow dynamics and run-up potential where 

granular mass flows encounter channel bends or obstacles are 

not considered. Another limitation is that events representing 

statistical outliers (defined by the curves of 99-percent level of 

confidence for prediction) will not be predicted accurately.

Quantifying recurrence intervals for specific flow volumes in a frequency-magnitude analysis for a particular region 

could assign probabilities to each flow volume and, therefore, 

assign annual likelihood that a specific postulated flow would 

reach a certain point down valley. Until such work is undertaken, postulating a range of future flow volumes provides an 

indication of the range of inundation possibilities.

Acknowledgments

The authors thank USGS colleagues Dennis Helsel 

and Susan Cannon for insightful reviews of this manuscript. 

Professor Albyn Jones of Reed College provided additional 

assistance in the statistical review of this work. Steve Schilling 

(USGS) wrote the original version of the computer program, 

LAHARZ, which was applied in this project. Michelle Howell 

collected a significant portion of the rock avalanche data. 

We thank Andrew Fountain and Scott Burns (Portland State 

University) for serving on the first author’s Master’s thesis 

committee and supervising phases of this project. Jason Hinkle 

(ODF) and Jon Hofmeister (DOGAMI) provided field guidance in the area around Scottsburg, Oregon.

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Appendix A—Data Table 33

Appendix A—Data Table Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year * V A B DF 1 10,000,000 - - 1,900,000 Upper Lillooet River British Columbia, Canada 900 B.P. DF 2 3,000,000 7,000 - - Klattasine Creek southern Rockies, British Columbia, Canada 1971-1973 DF 3 3,000,000 3,000 200,000 Devastation Cr. British Columbia, Canada 1931 DF 4 2,000,000 - - 1,300,000 Caraballeda Fan/ Sierra de Avila Vargas State, Venezuela December 1, 1999

DF 5 1,600,000 1,000 - - Sesa Landslide/ Grigna 

valley

Bienno, northern Italy 1993

DF 6 1,200,000 300 - - Capricorn Creek Mount Meager, British 

Columbia, Canada

1998

DF 7 1,000,000 200 570,000 Ophir Creek Washoe County, Nevada, 

USA

May, 1983

DF 8 1,000,000 400 - - Turbid Cr. British Columbia, Canada 1984DF 9 200,300 - - 72800 Shadow Canyon vicinity of Boulder, 

Colorado, USA

DF 10 200,000 - - 70,000 Capricorn Cr. British Columbia, Canada 1972DF 11 195,000 3,700 160,000 Bullock Creek Mt Thomas, New Zealand April, 1978DF 12 180,000 300 90,000 St. Peter's Dome Columbia Gorge, Oregon, 

USA

November, 2001

DF 13 162,700 - - 59200 Fern Canyon vicinity of Boulder, 

Colorado

DF 14 150,000 40 - - Sourgrass Debris Flow Sierra Nevadas, California January 1, 1997

Name of Event/ Location
34 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

DF 15 136,000 - - 77,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1978

DF 16 92,000 230 140,000 Hummingbird Creek Mara Lake, British 

Columbia, Canada

July 11, 1997

DF 17 90,000 4,200 50,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1946

DF 18 87,000 - - 52,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1984

DF 19 80,000 - - 40,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1925

DF 20 80,000 90.0 100,000 West Dodson Columbia Gorge, Oregon, 

USA

DF 21 76,000 300 - - Polallie Creek Mount Hood, Oregon December 25, 1980DF 22 63,000 77 - - Pierce Creek southern British Columbia November 28, 1995DF 23 60,000 - - 35,000 Hot Springs Cr. British Columbia, Canada 1984DF 24 55,000 48 - - Wahleach A British Columbia, CanadaDF 25 50,000 80 - - Hope Creek southern Rockies, British 

Columbia, Canada

November 8, 1995

DF 26 50,000 32 21,000 Boundary Cr. British Columbia, Canada 1987DF 27 25,000 50 18,000 Boundary Cr. British Columbia, Canada 1989DF 28 24,000 - - 12,000 Cathedral Mountain southern Rockies, B.C. 1962
Appendix A—Data Table 35

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

DF 29 20,000 78 10,000 Canyon Cr. British Columbia, Canada 1990DF 30 20,000 70 7,000 Lower Ryan R. tributary British Columbia, Canada 1984DF 31 20,000 400 - - Mt. Currie gully British Columbia, Canada 1989DF 32 20,000 48 - - M-Creek British Columbia, CanadaDF 33 20,000 47 - - Charles Creek British Columbia, CanadaDF 34 17,000 30.0 16,000 Mayflower Tenmile Range, Colorado August 18, 1961DF 35 10,000 - - 5,000 No Good Cr. British Columbia, Canada 1990DF 36 10,000 55 8,000 Canyon Cr. British Columbia, Canada 1987DF 37 9,990 50.4 2,180 Black Cr. Olympics, WashingtonDF 38 7,000 - - 5,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1982

DF 39 6,000 16 - - McGillivray Cr. gully British Columbia, Canada 1989DF 40 5,790 19.4 1,540 Marlow#1 Coast Range, Oregon, USADF 41 5,000 - - 5,000 Cathedral Mountain southern Rockies, British 

Columbia, Canada

1984

DF 42 5,000 12 8,600 Newton Canyon Santa Monica Mountains, 

California

December 29, 1965
36 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

DF 43 5,000 37 7,500 Boundary Cr. British Columbia, Canada 1988DF 44 4,800 22.8 2,400 Slide Cr. San Bernardinos, CaliforniaDF 45 3,500 20 - - Fountain Ridge gully British Columbia, Canada 1989DF 46 3,390 23.0 2,110 SlumpAcres Coast Range, Oregon, USADF 47 3,000 44 1,570 Tom McDonald Creek California (Redwood Creek 

basin), USA

December 16, 1982

DF 48 2,300 15.0 - - Oddstad California, USADF 49 2,000 11 - - Lillooet R. east fan British Columbia, Canada 1989DF 50 1,050 30 1,100 New ZealandDF 51 690 20.0 492 SlumpAcres 2 Coast Range, Oregon, USADF 52 660 38.8 621 Marlow#3 Coast Range, Oregon, USADF 53 660 5.0 - - Big Bend California, USADF 54 610 11.0 - - Yosemite site California, USADF 55 300 3.3 2,000 B1 California, USADF 56 200 5.6 - - Angel B gully British Columbia, Canada 1990
Appendix A—Data Table 37

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

DF 57 200 3 - - United KingdomDF 58 150 - - 260 Old Topanga Canyon Santa Monica Mountains, 

California

January 26, 1969

DF 59 135 20.6 240 Marlow#4 Coast Range, Oregon, USADF 60 100 15 100 No Good Cr. British Columbia, Canada 1988DF 61 100 2.7 600 N32 CanadaDF 62 27.87 0.9 200 Levan debris flow Utah, USADF 63 10 0.7 200 N2 CanadaDF 64 10 0.5 250 USGS flume experiments USA

 * Dates are year A.D. unless specified as year B.P. (years before present). Ref# Reference code and number indicates landslide type and key # for Appendix B/ data bibliography. DF = nonvolcanic debris flow; RA = rock avalanche; 

LA = lahar.

 - - Information is not available in source report.
38 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 1 12,000,000,000 - - 51,000,000 Flims AlpsRA 2 45,000,000,000 2,000,000 675,000,000 Shasta California, USA 300,000-380,000 B.P.RA 3 28,000,000,000 - - 300,000,000 Popocatepetl Mexico PleistoceneRA 4 25,000,000,000 - - 1,085,000,000 Raung Java QuaternaryRA 5 18,000,000,000 - - 400,000,000 Avachinsky Kamchatka PleistoceneRA 6 16,000,000,000 - - 400,000,000 Peteroa South AmericaRA 7 15,000,000,000 - - 480,000,000 Socompa South America HoloceneRA 8 15,000,000,000 - - 200,000,000 Antuco South AmericaRA 9 15,000,000,000 - - 1,400,000,000 Meru AfricaRA 10 12,600,000,000 - - 840,000,000 Wrangell Alaska, USA 200,000 B.P.RA 11 12,000,000,000 - - 1,550,000,000 Volcan de Colima Mexico 4,028 B.P.RA 12 8,100,000,000 - - 150,000,000 Chimborazo South America PleistoceneRA 13 8,000,000,000 - - 80,000,000 Tungurahua South America 3,000 B.P.RA 14 7,500,000,000 - - 250,000,000 Egmont, (1) New Zealand 23,000 B.P.
Appendix A—Data Table 39

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

RA 15 7,100,000,000 - - 1,150,000,000 Mawenzi AfricaRA 16 7,000,000,000 - - 200,000,000 Drum USA PleistoceneRA 17 3,500,000,000 800,000 170,000,000 Roque Nublo Canary Islands PlioceneRA 18 3,000,000,000 - - 80,000,000 Calbuco South America HoloceneRA 19 2,900,000,000 - - 175,000,000 Galunggung Indonesia 23,000 B.P.RA 20 2,800,000,000 - - 80,000,000 Jocotitlan Mexico 10,000 B.P.RA 21 2,500,000,000 - - 9,000,000 Engelberg AlpsRA 22 2,500,000,000 - - 64,000,000 Mount St. Helens Washington, USA 1980RA 23 2,100,000,000 - - 12,000,000 Kofels AlpsRA 24 2,000,000,000 - - 28,000,000 Siders (Sierre) AlpsRA 25 2,000,000,000 - - 90,000,000 Asama JapanRA 26 2,000,000,000 1,600,000 -- Usoi Landslide Dam Pamir Mountains, Tajikistan 1911RA 27 1,800,000,000 - - 70,000,000 Iriga Philippines 1628RA 28 1,500,000,000 - - 98,000,000 Shiveluch Kamchatka 1964
40 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 29 1,300,000,000 - - 65,000,000 Iwaki JapanRA 30 1,500,000,000 - - 34,000,000 Bandai Japan 1888RA 31 1,000,000,000 64,000 2,600,000 Mayunmarca Rockslide & 

Debris Flow

Andes Mountains, Peru April 25, 1974

RA 32 1,000,000,000 - - 14,500,000 Fernpass AlpsRA 33 1,000,000,000 - - 45,000,000 Mombacho Central AmericaRA 34 1,000,000,000 - - 20,000,000 San Pedro-Pellado South America HoloceneRA 35 900,000,000 - - 6,800,000 Kandertal AlpsRA 36 800,000,000 - - 8,800,000 Glarnisch-Guppen AlpsRA 37 800,000,000 - - 27,000,000 Popa Burma PleistoceneRA 38 800,000,000 - - 30,000,000 Bezymianny Kamchatka 1956RA 39 600,000,000 - - 4,300,000 Totalp AlpsRA 40 600,000,000 - - 6,400,000 Dejenstock AlpsRA 41 550,000,000 - - 11,600,000 Monte Spinale AlpsRA 42 500,000,000 - - 30,000,000 Augustine, (2) West Island Alaska, USA 500 B.P.
Appendix A—Data Table 41

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

RA 43 500,000,000 - - 40,000,000 Sierra Velluda South America HoloceneRA 44 500,000,000 - - 25,000,000 Soufriere, W.I. Guadeloupe 3,000 B.P.RA 45 498,000,000 - - 4,660,000 Maligne Lake Canadian Rocky MountainsRA 46 469,000,000 - - 12,000,000 Marocche im Sarcatal AlpsRA 47 400,000,000 - - 8,000,000 Parpan-Lenzerheide AlpsRA 48 400,000,000 - - 3,600,000 Lago di Molveno AlpsRA 49 400,000,000 - - 11,000,000 Eibsee AlpsRA 50 400,000,000 - - 96,000,000 Cotopaxi, South America < 20,000 B.P.RA 51 360,000,000 - - 16,000,000 Dobratsch (2) AlpsRA 52 350,000,000 - - 120,000,000 Egmont, (2) New Zealand 6,570 B.P.RA 53 340,000,000 - - 15,000,000 Unzen (Mayu-yama) Japan 1792RA 54 300,000,000 - - 21,000,000 Augustine, (1) Burr Point Alaska, USA 1883RA 55 285,000,000 - - 1,900,000 Vaiont Alps 1963RA 56 250,000,000 - - 5,200,000 Lago di Tovel Alps
42 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 57 250,000,000 - - 800,000 Cayley, (2) Canada 4,800 B.P.RA 58 230,000,000 - - 10,000,000 Myoko Japan 7,780 B.P.RA 59 224,000,000 - - 4,560,000 Chaski Bay Crater Lake, Oregon, USARA 60 210,000,000 - - 13,200,000 Tschirgant AlpsRA 61 180,000,000 - - 4,000,000 Bormio AlpsRA 62 170,000,000 - - 7,000,000 Masiere di vedane AlpsRA 63 170,000,000 - - 8,000,000 Dobratsch (1) AlpsRA 64 165,000,000 - - 1,000,000 Lago di Poschiavo AlpsRA 65 150,000,000 - - 3,500,000 Lavini de Marco AlpsRA 66 150,000,000 - - 15,000,000 Abimes de Myans AlpsRA 67 150,000,000 - - 8,000,000 Chaos Crags USA 1650RA 68 150,000,000 - - 15,000,000 Callaqui South AmericaRA 69 150,000,000 - - 10,000,000 Augustine (3) Alaska, USA ~1700RA 70 140,000,000 - - 2,500,000 Obersee Alps
Appendix A—Data Table 43

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

RA 71 140,000,000 - - 18,000,000 Papandayan Indonesia 1772RA 72 120,000,000 - - 1,700,000 Oeschinensee AlpsRA 73 115,000,000 - - 1,840,000 Cal de la Madeleine AlpsRA 74 100,000,000 120,000 22,500,000 Huascaran Peru 1970RA 75 100,000,000 - - 1,000,000 Am Saum AlpsRA 76 100,000,000 - - 5,000,000 Oberterzen AlpsRA 77 100,000,000 - - 2,400,000 Mallnitz AlpsRA 78 91,000,000 - - 9,300,000 Lastarria South America prehistoricRA 79 86,000,000 - - 1,550,000 Medecine Lake Canadian Rocky MountainsRA 80 80,000,000 - - 4,000,000 Pletzachkogel AlpsRA 81 80,000,000 - - 3,580,000 Lofer AlpsRA 82 68,000,000 - - 1,600,000 Lac Lauvitel AlpsRA 83 56,500,000 - - 2,700,000 Obernbergtal AlpsRA 84 56,000,000 12,000 6,750,000 Ontake Japan
44 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 85 55,000,000 - - 8,520,000 Mt. Cook New Zealand 1991RA 86 54,000,000 15,000 6,000,000 Mageik USA historicRA 87 50,000,000 - - 2,000,000 Pontives AlpsRA 88 50,000,000 - - 2,300,000 Marquartstein AlpsRA 89 50,000,000 - - 2,200,000 Diablerets Alps 1714, 1749RA 90 49,400,000 190,000 700,000 Madison Canyon Wyoming, USA 1959RA 91 40,000,000 - - 1,000,000 Monte Avi AlpsRA 92 40,000,000 - - 1,000,000 Kleines Rinderhorn AlpsRA 93 39,100,000 - - 3,630,000 Mt. Kitchener Canadian Rocky MountainsRA 94 38,000,000 - - 1,260,000 Danger Bay, west Crater Lake, Oregon, USARA 95 35,000,000 - - 4,000,000 Goldau Alps 1806RA 96 34,000,000 - - 1,150,000 Danger Bay, east Crater Lake, Oregon, USARA 97 30,000,000 - - 700,000 Haslensee AlpsRA 98 30,000,000 - - 5,000,000 Dobratsch Alps
Appendix A—Data Table 45

Flow Volume

Maximum Inundated 

Cross-sectional Area

Total Inundated 

Planimetric Area

Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V A B

Name of Event/ Location

RA 99 30,000,000 - - 850,000 Voralpsee AlpsRA 100 30,000,000 - - 1,100,000 Torbole AlpsRA 101 30,000,000 13,000 3,000,000 Frank Slide Turtle Mountain, Alberta, 

Canada

April, 1903

RA 102 29,500,000 - - 1,700,000 Haiming AlpsRA 103 25,000,000 28,000 1,300,000 North Long John rock 

avalanche

Inyo Mountains, Owens Valley, California

prehistoric

RA 104 25,000,000 - - 1,200,000 San Giovanni AlpsRA 105 21,000,000 - - 790,000 St Andre AlpsRA 106 20,000,000 - - 450,000 Mordbichl AlpsRA 107 20,000,000 - - 500,000 Lago di Alleghe AlpsRA 108 20,000,000 - - 1,000,000 Kals AlpsRA 109 17,500,000 - - 700,000 Monte Corno AlpsRA 110 16,000,000 - - 700,000 Brione AlpsRA 111 16,000,000 - - 410,000 Grand Clapier AlpsRA 112 15,000,000 - - 6,000,000 Huascaran Peru 1962
46 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 113 15,000,000 - - 800,000 Disentis AlpsRA 114 15,000,000 - - 1,000,000 Biasca AlpsRA 115 14,000,000 - - 600,000 Ludiano AlpsRA 116 14,000,000 - - 720,000 Llao Bay Crater Lake, Oregon, USARA 117 13,000,000 - - 950,000 Hintersee AlpsRA 118 13,000,000 - - 580,000 Eagle Point, west Crater Lake, Oregon, USARA 119 12,100,000 - - 8,250,000 Sherman Glacier USA 1964RA 120 12,000,000 - - 1,000,000 Lago de Antrona AlpsRA 121 11,000,000 8,000 5,000,000 Rainier Washington, USA 1963RA 122 10,000,000 - - 2,200,000 Mount Munday Canada 1997RA 123 10,000,000 - - 580,000 Elm Alps 1881RA 124 8,500,000 - - 880,000 Oberes Vallesinella AlpsRA 125 8,000,000 - - 600,000 Val Brenta Alta AlpsRA 126 7,000,000 - - 720,000 Melkode Alps
Appendix A—Data Table 47

Ref #

Flow Volume

(cubic meters)

V

Maximum Innundated

Cross-sectional Area

(square meters)

A

Total Innundated 

Planimetric Area

(square meters)

B

Name of Event/Location Region/State/Country Year*

RA 127 6,000,000 - - 470,000 North Nahanni District of Mackenzie, NWT, Canada 1985RA 128 6,000,000 - - 430,000 Tucketthutte AlpsRA 129 5,000,000 - - 200,000 Prayon AlpsRA 130 5,000,000 12,000 600,000 Cayley, (1) Canada 1963RA 131 4,820,000 - - 300,000 Beaver Flats south Canadian Rocky MountainsRA 132 4,130,000 - - 300,000 Beaver Flats north Canadian Rocky MountainsRA 133 4,000,000 - - 400,000 Fionnay AlpsRA 134 4,000,000 - - 400,000 Adams, USA (1) Washington, USA 1921RA 135 3,000,000 - - 250,000 Winkelmatten AlpsRA 136 3,000,000 - - 700,000 Adams, USA (2) 1997RA 137 2,400,000 - - 580,000 Cloudcap Bay Crater Lake, OregonRA 138 1,820,000 1,000 550,000 Puget Peek USA 1964RA 139 2,100,000 - - 137,000 Jonas Creek south Canadian Rocky MountainsRA 140 4,500,000 - - 226,000 Jonas Creek north Canadian Rocky Mountains
48 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches Flow Volume Maximum Inundated Cross-sectional Area Total Inundated Planimetric Area Ref # (cubic meters) (square meters) (square meters) Region/ State/ Country Year *

V

A

B

Name of Event/ Location

RA 141 400,000 - - 200,000 Fidaz AlpsRA 142 150,000 - - 75,000 Haltenguet AlpsRA 143 100,000 - - 100,000 Felsberg Alps* Dates are year A.D. unless specified as year B.P. (years before present).Ref # indicates landslide type and key # for bibliography. DF = non-volcanic debris flow; RA = rock avalanche; LA = lahar.
Appendix B—Data Bibliography 49

Appendix B—Data Bibliography

Data References for Appendix A, Debris Flows

DF 1 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 2 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Clague, J. J., Evans, S. G., and Blown, I. G., 1985, A debris flow triggered by the breaching of a moraine-dammed lake, Klattasine Creek, British Columbia: Canadian Journal of Earth Sciences, v. 22, no. 10, p. 1492-1502.

DF 3 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 4 Wieczorek, G. F., Larsen, M. C., Eaton, L. S., Morgan, B. A., and Blair, J. L., 2001, Debris-flow and flooding hazards associated with the December 1999 storms in coastal Venezuela and strategies for mitigation: U.S. Geological Survey Open File 

Report 01-0144.

DF 5 Crosta, G. B., 2001, Failure and flow development of a complex slide: the 1993 Sesa landslide: Engineering Geology, v. 59, 

p. 173-199.

DF 6 Bovis, M. J., and Jakob, M., 2000, The July 29, 1998, debris flow and landslide dam at Capricorn Creek, Mount Meager 

Volcanic Complex, southern Coast Mountains, British Columbia: Canadian Journal of Earth Sciences, v. 37, p. 1321-1334.

DF 7 Glancy, P. A., and Bell, J. W., 2000, Landslide-Induced Flooding at Ophir Creek, Washoe County, Western Nevada, May 30, 

1983: U.S. Geological Survey Professional Paper 1617.

DF 8 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 9 Miller, H.F., 1979, Debris flows in the vicinity of Boulder, Colorado: Boulder, Colorado, University of Colorado M.S. Thesis, 93 p.

DF 10 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 11 Pierson, T. C., 1980, Erosion and Deposition by Debris Flows at Mt Thomas, North Canterbury, New Zealand: Earth Surface 

Processes, v. 5, p. 227-247.

DF 12 Janssen, K., and Marshall, C., 2003, unpublished report prepared for Fall 2002 Environmental Geology taught by Scott 

Burns, Debris flow along 

I-84.

DF 13 Miller, H.F., 1979, Debris flows in the vicinity of Boulder, Colorado: Boulder, Colorado, University of Colorado M.S. Thesis, 93 p.

DF 14 DeGraff, J. V., 1997, Geologic Investigation of the Sourgrass Debris Flow, Calaveras Ranger District, Stanislaus National 

Forest: U.S. Department of Agriculture; Forest Service, FS 6200-7.

DF 15

Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 16 Jakob,M., Anderson, D., Fuller,T., Hungr,O. and Ayotte, D., 2000. An Unusually Large Debris Flow at Hummingbird Creek, 

Mara Lake, British Columbia. Canadian Geotechnical Journal Vol. 37, no. 5, p.1109-1125.

DF 17 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.
50 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

DF 18 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 19 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 20 Iverson, R.M., Schilling, S.P., Vallance, J.W., 1998, Objective delineation of lahar-inundation hazard zones: GSA Bulletin, 

vol. 100, no. 8, p. 972-984.

DF 21 Gallino, G. L., and Pierson, T. C., 1985, Polallie Creek debris flow and subsequent dam-break flood of 1980, East Fork Hood 

River basin, Oregon, U.S. Geological Survey Water-Supply Paper 2273.

DF 22 Jakob, M., Hungr, O., and Thomson, B., 1997. Two debris flows with anomalously high magnitude. Debris Flow Hazards 

Mitigation, Mechanics, Prediction and Assessment. Procs., The First International Conference on Debris Flow Hazards 

ASCE, C.L.Chen, Ed., 

DF 23 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 24 Thurber Consultants Ltd, 1985, Debris torrent assessment, Wahleach and Floods, Highway 1, Hope to Boston Bar Creek 

summit, Coquihalla Highway. Report to B.C. Min. Transportation and Highways, Victoria, B.C.

DF 25 Jakob, M., Hungr, O., and Thomson, B., 1997. Two debris flows with anomalously high magnitude. Debris Flow Hazards 

Mitigation, Mechanics, Prediction and Assessment. Procs., The First International Conference on Debris Flow Hazards 

ASCE, C.L.Chen, Ed., San Francisco, pp. 382-394.

DF 26 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 27 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 28 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 29 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 30 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 31 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 32 Thurber Consultants Ltd and Ker Priestman & Associates Ltd, [no date], Site visit to debris torrent facilities at Charles 

Creek, Harvey Creek, Magnesia Creek, Alberta Creek, Lions Bay, B.C. Field Guide.

DF 33 Thurber Consultants Ltd and Ker Priestman & Associates Ltd, [no date], Site visit to debris torrent facilities at Charles 

Creek, Harvey Creek, Magnesia Creek, Alberta Creek, Lions Bay, B.C. Field Guide.

DF 34 Curry, R.R., 1966, Observations of alpine mudflows in the Tenmile Range, Colorado: Geological Society of America Bulletin, v. 77, p. 771-776.

DF 35 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 36 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.
Appendix B—Data Bibliography 51

DF 37 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 38 Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 39 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 40 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 41

Clague, J. J., and Evans, S. G., 1994, Formation and Failure of Natural Dams in the Canadian Cordillera: Geological Survey 

of Canada, Geological Survey of Canada Bulletin 464.

Jackson, E., Jr., Hungr, O., Gardner, J. S., and Mackay, C., 1989, Cathedral Mountain Debris Flows, Canada: Bulletin of the 

International Association of Engineering Geology, v. 40, p. 36-54.

DF 42 Campbell, R. H., 1975, Soil Slips, Debris Flows, and Rainstorms in the Santa Monica Mountains and Vicinity, Southern 

California, U.S. Geological Survey Professional Paper 851.

DF 43 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 44 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 45 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 46 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 47 Walter, T., unpublished data, 1983, Memorandum: Subj: Debris slide of December 16, 1982.

DF 48 Howard, T.R., Baldwin, J.E., and Donley, H.F., 1988, Landslides in Pacifica, California, caused by the storm, in Landslides, 

floods, and marine effects of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological 

Survey Professional Paper 1434, p. 163-183.

DF 49 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 50 McDonnell, J.J., 1990, The effect of macropores on debris flow initiation: Quarterly Journal of Engineering Geology, vol. 23, 

p. 325- 332.

DF 51 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 52 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 53 Howard, T.R., Baldwin, J.E., and Donley, H.F., 1988, Landslides in Pacifica, California, caused by the storm, in Landslides, 

floods, and marine effects of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological 

Survey Professional Paper 1434, p. 163-183.

DF 54 Howard, T.R., Baldwin, J.E., and Donley, H.F., 1988, Landslides in Pacifica, California, caused by the storm, in Landslides, 

floods, and marine effects of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological 

Survey Professional Paper 1434, p. 163-183.

DF 55 Owens, I.F., 1972, Morphological characteristics of alpine mudflows in the Nigel Pass area, in Slaymaker, O., and McPherson, H.J., eds., Mountain Geomorphology: Vancouver, Tantalus Research, p. 93-100.

DF 56 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 57 Bevin, K. and E.F. Woods, 1983, Catchment geomorphology and the dynamics of runoff contributing areas: Journal of Hydrology, vol. 65, p. 139-158.

DF 58 Campbell, R. H., 1975, Soil Slips, Debris Flows, and Rainstorms in the Santa Monica Mountains and Vicinity, Southern 

California, U.S. Geological Survey Professional Paper 851.
52 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

DF 59 Stock, J.D., 2001, unpublished data in personal communication to R.M.Iverson, 1/16/2001, Berkeley, California.

DF 60 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p.

DF 61 Owens, I.F., 1972, Morphological characteristics of alpine mudflows in the Nigel Pass area, in Slaymaker, O., and McPherson, H.J., eds., Mountain Geomorphology: Vancouver, Tantalus Research, p. 93-100.

DF 62 Cannon, S.H., 1989, An approach for estimating debris flow runout distance, in Proceedings Conference XX, International 

Erosion Control Association, Vancouver, B.C.

DF 63 Owens, I.F., 1972, Morphological characteristics of alpine mudflows in the Nigel Pass area, in Slaymaker, O., and McPherson, H.J., eds., Mountain Geomorphology: Vancouver, Tantalus Research, p. 93-100.

DF 64 Major, J.J., 1996, Experimental studies of depositionby debris flows: Process, characteristics of deposits, and effects of porefluid pressure [Ph.D. dissert.]: Seattle, University of Washington, 341 p.

Iverson, R.M., 1997, The physics of debris flows: Reviews in Geophysics, v. 35, p. 245-296.
Appendix B—Data Bibliography 53

Data References for Appendix A, Rock Avalanches

RA 1 Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. Halfte. Tauchniz Leipzig.

RA 2 Crandell, D.R., Miller, C.D., Glicken, H.X., Christiansen, R.L., and Newhall, C.G., 1984, Catastrophic debris avalanche 

from ancestral Mount Shasta volcano, California: Geology, vol. 12, p. 143-146.

Crandell, D.R., 1989, Gigantic debris avalanche of Pleistocene age from ancestral Mount Shasta volcano, California, and 

debris-avalanche hazard zonation: USGS Bulletin 1861.

RA 3 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 4 Siebert, L., Bronto, S., Supriatman, I., Mulyana, R., 1996, Massive debris avalanche from Raung Volcano, Eastern Java: 

EOS, vol. 77, p. S291.

RA 5 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 6 Haller, M J, Mendia, J E, and Ostera, H A, 1991, Mapa preliminar de riesgo en la vertiente Argentina del Volcan Peteroa; 

Preliminary map of risk on the Argentinian slopes of Peteroa Volcano in Proceedings for Congreso Geologico Chileno, 

Vina del Mar, Chile, Aug. 5-9, 1991, vol.6, pp.355-358.

RA 7 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 8 Thiele, R., Moreno, H., Elgueta, S., Lahsen Azar, A., Rebolledo, S., Petit-Breuilh, M.E., 1998, Evolucion geologico-geomorfologica cuaternaria del tramo superior del valle del rio Laja; Quaternary geological-geomorphological evolution of the 

uppermost course of the Rio Laja Valley: Revista Geologica de Chile, vol.25, no.2, pp.229-253.

RA 9 Guest, N.J., Leedal, G.P., 1956, The volcanic activity of Mount Meru: Records of the Geological Survey of Tanganyika 

1953, Rec., v. 3, Geological Survey of Tanganyika, Tanzania, p. 40-7.

RA 10 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 11 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Stoopes, G.R., Sheridan, M.F., 1988, Giant debris avalanches from the Colima Volcanic Complex, Mexico- Implications for 

long-runout landslides (>100 km) and hazard assessment: Geology, vol. 20, p. 299-302.

RA 12 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 13 Hall, M.L., Robin, C.R., Beate, B., Mothes, P., Monzier, M., 1999, Tungurahua Volcano, Ecuador- Structure, eruptive history, and hazards: Journal of Volcanology and Geothermal Research, vol. 91, p. 1-21.

RA 14 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 15 Omenge, J.M., Mosley, P.N., 1995, Evidence for catastrophic flank collapse of Mt. Kilimanjaro and implication for geological hazard assessment, in Proceedings of 10th Conference of the Geological Society of Africa; GSA 95 international 

conference, Nairobi, Kenya, Oct. 9-13, 1995, Conference Programme, vol.10, pp.99-100.

RA 16 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 17 Cacho, L.G., Iez-Gil, J.L., Arana, V., 1994, A large volcanic debris avalanche in the Pliocene Roque Nublo Stratovolcano, 

Gran Canaria, Canary Islands: Journal of Volcanology and Geothermal Research, vol. 63, p. 217-229.

RA 18 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 19 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.
54 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

RA 20 Siebe, C., Komorowski, J.C., Sheridan, M.F., 1992, Morphology and emplacement of an unusual debris avalanche deposit at 

Jacotitlan volcano, Central Mexico: Bulletin of Volcanology, vol. 54, p.573-589.

RA 21 Arbenz, P., 1934, Helvetische Region, in ed. Schweiz Geol. Kommission, Geologischer Fuhrer der Schweiz, Wepf & Cie, 

Basel.

RA 22 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Rowley, P.D., Kuntz, M.A., MacLeod, N.S., 1981, Pyroclastic-flow deposits: USGS Professional Paper 1250, p. 489-512.

Voight, B., Janda, R.J., Glicken, H., and Douglass, P.M., 1983, Nature and mechanics of Mount St. Helens rockslide-avalanche of 18 May 1980: Geotechnique, vol. 33, p. 243-273.

RA 23 [Ampferer, 1939] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 24 [Lacger, 1903] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 25 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 26 Schuster, R.L., 2002. Usoi Landslide Dam and Lake Sarez, Pamir Mountains, Tajikistan in Proceedings for Annual Conference of Geological Society of America, Denver, Colorado, October 27-30, 2002.

RA 27 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Aguila, L., Newhall, C.G., Miller, C.D., and Listanco, E., unpublished document, Reconnaissance geology of a large debris 

avalanche from Iriga Volcano, Philippines.

RA 28 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 29 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 30 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Schuster, R.L., and Crandell, D.R., 1984, Catastrophic debris avalanches from volcanoes in Proceedings of the Fourth Annual Conference on Landslides, Toronto.

RA 31 Kojan, E., and Hutchinson, J. N., 1978, Mayunmarca rockslide and debris flow, Peru, in Voight, B., ed., Rockslides and 

Avalanches: Amsterdam, Netherlands (NLD), Elsevier Sci. Publ. Co., p. 315-361.

RA 32 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S. 

RA 33 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 34 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 35 [Turnau, 1906] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 36 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 37 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 38 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Siebert, L., 1996, Hazards of Large Volcanic Debris Avalanches and Associated Eruptive Phenomena in Monitoring and 

Mitigation of Volcano Hazards: Washington D.C., Springer-Verlag Berlin Heidelberg, p. 541-572.
Appendix B—Data Bibliography 55

RA 39 [Bornhauser, 1950] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 40 Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. Halfte. Tauchniz Leipzig.

RA 41 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 42 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Siebert, L., Beget, J.E., Glicken, H., 1995, The 1883 and late-prehistoric eruptions of Augustine volcano, Alaska: Journal of 

Volcanology and Geothermal Research, vol. 66, p. 367-395.

RA 43 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 44 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 45 Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an introduction in Rockslides and Avalanches, 1, B. 

Voight, ed., Elsevier Publishing Company, p. 2-63.

RA 46 [Trevisan, 1941] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 47 [Arbenz, 1934] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 48 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 49 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S. 

RA 50 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 51 Till, A., 1907, Das Naturereignis von 1348 und die Bergsturze des Dobratsh, Mitteilungen du K.K. Geographischen Gesellschaft in Wein 50 Wein.

RA 52 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 53 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 54 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Siebert, L., Beget, J.E., Glicken, H., 1995, The 1883 and late-prehistoric eruptions of Augustine volcano, Alaska: Journal of 

Volcanology and Geothermal Research, vol. 66, p. 367-395.

RA 55 Muller, L., 1964, The rock slide in the Vaiont Valley: Felsmechanik und Ingenieur-geologie, vol. 2, p. 148-212.

RA 56 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 57 Evans, S.G., and Brooks, G.R., 1991, Prehistoric debris avalanches from Mount Cayley volcano, British Columbia: Canadian Journal of Earth Science, vol. 28, no. 9, p.xxx.

RA 58 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 59 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake 

revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey 

Geologic Investigations Series I-2790.
56 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

RA 60 Heuberger, H., 1968, Die Alpengletscher im Spat und Postglazial: Eine chronologische Ubersicht, Eiszeitalter und Gegenwart, vol. 19, p. 270-275 Ohringen (Hohenlohe’sche Buchhandl).

RA 61 [Turrer, 1962] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 62 [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 63 [Aichinger, 1951] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 64 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 65 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 66 Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des amenagements hydrauliques: Masson et Cie, Paris, 343 pp.

RA 67 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 68 Naranjo, J.A., Young, S.R., Moreno, H., 1998, Mitigation of volcanic risk in the Biobio River basin, Chile in Proceedings for 

Cities on volcanoes, International meeting on Cities on volcanoes, Rome and Naples, Italy, June 28-July 4, 1998.

RA 69 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

Siebert, L., Beget, J.E., Glicken, H., 1995, The 1883 and late-prehistoric eruptions of Augustine volcano, Alaska: Journal of 

Volcanology and Geothermal Research, vol. 66, p. 367-395.

RA 70 [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 71 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 72 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 73 Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des amenagements hydrauliques: Masson et Cie, Paris, 343 pp.

RA 74 Plafker, G. and Erickssen, G.E., 1978, Nevados Huascaran Avalanches, Peru in Rockslides and Avalanches, 1, B. Voight, ed., 

Elsevier Publishing Company, p.277-314.

RA 75 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S. 

RA 76 Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. Halfte. Tauchniz Leipzig.

RA 77 [Hammer, 1927] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 78 Naranjo, J.A., and Francis, P., High velocity debris avalanche at Lastarria volcano in the north Chilean Andes: Bulletin of 

Volcanology, vol. 49, p. 509-514.

RA 79 Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an introduction in Rockslides and Avalanches, 1, B. 

Voight, ed., Elsevier Publishing Company, p. 2-63.

RA 80 Klebelsberg, R., 1935, Geologie von Tirol.- 872 S., 1 Kt., 11 Beil.; Berlin (Borntraeger)[Leidlmair, 1956] compiled in Li 

Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., 

Bd. 27, heft. 24, p. 472-482.

RA 81 [Leidlmair, 1956] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 82 Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des amenagements hydrauliques: Masson et Cie, Paris, 343 pp.

RA 83 [Pashinger, 1953] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.
Appendix B—Data Bibliography 57

RA 84 Inokuchi, T., 1985, The Ontake Rock Slide and Debris Avalanche Caused by the Naganoken-Seibu Earthquake in Proceedings IVth International Conference and Field Workshop on Landslides, Tokyo, 10 p.

Nagaoka, Masatoshi, 1987, Geomorphological Characteristics and Causal Factors of the 1984 Ontake Landslide Caused by 

the Naganoken-Seibu Earthquake: Bulletin of the Geographical Survey Institute, vol. 31, p. 72-89.

RA 85 Hancox, G.T., Chin, T.J., and McSaveney, M.J., 1991, Immediate report, Mt. Cook Rock Avalanche, 14 December 1991, 

Report by the New Zealand Department of Scientific and Industrial Research, 23 December 1991.

RA 86 Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of 

Volcanology, vol. 49, p. 435-459.

RA 87 Fuganti, A., and V.G. Antonio, 1969, Previsioni sul progettato traforo ferroviario del Brennero in base studio geologico del 

tracciato: Convegno Int. sui problemi tecnini nella costruzione di gallerie, Torino, 1969.

RA 88 [Broili, 1914] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 89 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 90 Hadley, J.B., 1978, Madison Canyon Rockslide, Montana, USA in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier 

Publishing Company, p. 167-180.

RA 91 [Grasso, 1968] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 92 Furrer, E., 1962, Der Bergsturz von Bormio: Vierteljahrsschrift der Naturforschenden Gesellschaft in Zürich, vol. 107, p. 

233-242.

RA 93 Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an introduction in Rockslides and Avalanches, 1, B. 

Voight, ed., Elsevier Publishing Company, p. 2-63.

RA 94 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake 

revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey 

Geologic Investigations Series I-2790.

RA 95 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 96 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake 

revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey 

Geologic Investigations Series I-2790.

RA 97 [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 98 Till, A., 1907, Das Naturereignis von 1348 und die Bergsturze des Dobratsh, Mitteilungen du K.K. Geographischen Gesellschaft in Wein 50 Wein.

RA 99 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 100 [Penck, 1901] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 101 Cruden, D.M. and O. Hungr, 1986, The debris of Frank Slide and theories of rockslide-avalanche mobility: Can. J. Earth 

Science, vol. 23, 425-432.

RA 102 Heuberger, H., 1966, Gletschergeschichtliche Untersuchungen in den Zentralalpen zwischen Sellrain und Ötztal: Wissenschaftliche Alpenvereinshefte, heft 20, 126 pp.

RA 103 Blair, T.C., 1999, Form facies and depositional history of the North Long John rock avalanche, Owens Valley, California: 

Canadian Journal of Earth Science, vol. 36, p. 855-870.

RA 104 Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut de l’ere chretienne a nos jours, in Materiaux 

pour l’Etude des Calamites, Societe de Geographie Geneve, vol. 32, p. 271-340.

RA 105 [Onde, 1938] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.
58 Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches

RA 106 [Klebelsberg, 1935] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 107 Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut de l’ere chretienne a nos jours, in Materiaux 

pour l’Etude des Calamites, Societe de Geographie Geneve, vol. 32, p. 271-340.

RA 108 [Cornelius, 1936] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 109 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 110 [Cadisch, 1953] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 111 [Bourdier, 1961] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 112 Plafker, G. and Erickssen, G.E., 1978, Nevados Huascaran Avalanches, Peru in Rockslides and Avalanches, 1, B. Voight, ed., 

Elsevier Publishing Company, p.277-314.

RA 113 [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 114 Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut de l’ere chretienne a nos jours, in Materiaux 

pour l’Etude des Calamites, Societe de Geographie Geneve, vol. 32, p. 271-340.

RA 115 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 116 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake 

revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey 

Geologic Investigations Series I-2790.

RA 117 [Gillitzer, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 118 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake 

revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey 

Geologic Investigations Series I-2790.

RA 119 McSaveney, M.J., Sherman Glacier Rock Avalanche, Alaska, USA in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier 

Publishing Company, p. 197-258.

RA 120 [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift 

fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 121 Crandell, D.W, and Fahnestock, R.K, 1965, Rockfalls and avalanches from Little Tahoma Peak on Mount Rainier Washington: Geological Survey Bulletin 1221-A, Washington, 30 p.

RA 122 Evans, S.G., and Clague, J.J., 1998, Rock avalanche from Mount Munday, Waddington Range, British Columbia, Canada: 

Landslide News, no. 11, p. 23-25.

RA 123 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 124 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: 

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 125 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall:

 Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 126 [Schmit-Thome, 1960] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall:

 Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 127 Evans, S.G., Aitken, J.D., Wetmiller, R.J., and Horner, R.B., 1987, A rock avalanche triggered by the October 1985 North

 Nahanni earthquake, District of Mackenzie, N.W.T.: Canadian Journal of Earth Science, vol. 24, p. 179-184.
Appendix B—Data Bibliography 59

RA 128 [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall:

Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 129 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S. 

RA 130 Clague, J.J., and Souther, J.G., 1983, The Dusty Creek landslide on Mount Cayley, British Columbia: Canadian Journal of

 Earth Science, vol. 19, p. 524-539.

RA 131 Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an introduction in Rockslides and Avalanches, 1, B. 

Voight, ed., Elsevier Publishing Company, p. 2-63.

RA 132 Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an introduction in Rockslides and Avalanches, 1, B. 

Voight, ed., Elsevier Publishing Company, p. 2-63.

RA 133 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S. 

RA 134 Vallance, J.W., 1999, Postglacial lahars and potential hazards in the White Salmon River system on the southwest flank of

 Mount Adams, Washington: USGS Bulletin No. 2161.

RA 135 Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche Alpenvereinshefte 25: 230 S.

RA 136 Iverson, R.M., written communication, August 1997: fieldnotes.

RA 137 Ramsey, D.W., Robinson, J.E., Dartnell, P., Bacon, C.R., Gardner, J.V., Mayer, L.A., Buktenica, M.W., 2002, Crater Lake

 revealed; Using GIS to visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey

 Geologic Investigations Series I-2790.

RA 138 Hoyer, M., 1971, Puget Peak Avalanche, Alaska: Geological Society of America Bulletin, vol. 82, p. 1267-1284.

RA 139 Jakob, M., 2008, unpublished data in personal communication to J.W. Vallance, 20 June 2008, Vancouver, British

 Columbia, Canada.

RA 140 Jakob, M., 2008, unpublished data in personal communication to J.W. Vallance, 20 June 2008, Vancouver, British

 Columbia, Canada.

RA 141 [Niederer, 1941] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall:

 Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

RA 142 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich.

RA 143 [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for predicting the extent of a major rockfall: Zeitschrift

 fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.
Griswold—Mobility Statistics and Automated Hazard Mapping for Debris Flows and Rock Avalanches—Scientific Investigations Report 2007–5276