THESIS APPROVAL

The abstract and thesis of Julia Pamela Griswold for the Master of Science in Geology were presented June 11, 2004, and accepted by the thesis committee and the department.

COMMITTEE APPROVALS: ______Andrew G. Fountain, Chair

______Richard M. Iverson

______Scott F. Burns

______J. Alan Yeakley Representative of the Office of Graduate Studies

DEPARTMENT APPROVAL: ______Michael L. Cummings, Chair Department of Geology ABSTRACT

An abstract of the thesis of Julia Pamela Griswold for the Master of Science in

Geology presented June 11, 2004.

Title: Mobility Statistics and Hazard Mapping for Non-volcanic Debris Flows and

Rock Avalanches.

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,

2/3 rock avalanches, and lahars with diverse volumes (V). The equations A=α1V and

2/3 B=α2V indicate that the maximum valley cross-sectional area (A) and total valley planimetric area (B) likely to be inundated by a flow depend only on the flow volume and topography of the flow path. Testing of these equations involves determining whether they satisfactorily fit data for documented flows, and calibration entails determining best-fit values of the coefficients α1 and α2. This thesis describes statistical testing and calibration of the equations using field data compiled from many sources, and it describes application of the equations to delineation of debris-flow hazard zones in the Coast Range of southern Oregon.

Separate inundation-area equations are appropriate for debris flows, rock avalanches, and lahars, because statistical tests demonstrate that data describing A, B, and V for these types of flows are derived from distinct parent populations. For all flow types, the dependence of A and B on V is described better by power-law equations than by linear, quadratic, or exponential equations. Moreover, F-tests show that power laws with exponents equal to 2/3 produce fits that are effectively indistinguishable from the best fits obtained using adjustable power-law exponents.

Calibrated values of the coefficients α1 and α2 provide a scale-invariant index of the relative mobility of rock avalanches (α1 = 0.2, α2 = 20), non-volcanic 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 non-volcanic debris flow of the same volume.

The utility of the calibrated debris-flow inundation equations A=0.1V2/3 and

B=20V2/3 was 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 southern Oregon. This application required knowledge of local geology to specify a suitable range of prospective debris-flow volumes and required development and use of a new algorithm for identification of prospective debris-flow source areas in finely dissected terrain.

2

MOBILITY STATISTICS AND HAZARD MAPPING FOR

NON-VOLCANIC DEBRIS FLOWS AND ROCK AVALANCHES

by

JULIA PAMELA GRISWOLD

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE in GEOLOGY

Portland State University 2004 ACKNOWLEDGEMENTS

I have many to thank but there are two to whom I am especially grateful. Dick Iverson was the lead advisor for this thesis directing the scope of the thesis, providing generous guidance, and spending many hours bringing order and sense to my writing. Steve Schilling provided the technical training and support, supplied the review for the GIS-related portions of the thesis, and bore the brunt of my daily GIS questions. I was very fortunate to be made apart of their research efforts and to work alongside such first rate scientists. Good humor, creative solutions, fieldwork projects (especially helping at the USGS flume experiments), and boxes of donuts made for a productive and thought-provoking couple of years. It has been an undeserved privilege working with Dick and Steve, and I thank them whole-heartedly for the experience and for opening many doors for me. Both Dick and Steve arranged USGS student contract positions that supported and trained me through my thesis. I also would like to acknowledge Jason Hinkle (Oregon Dept. of Forestry) and Jon Hofmeister (Oregon Dept. of Geology and Mineral Industries) who helped early on sharing their ideas and experience. Visiting the lower Umpqua River valley together stressed the consequences of debris flow-related fatalities, structural damage, and channel impacts. Their professionalism, enthusiasm, and friendship are greatly appreciated. Jason also provided the Scottsburg LIDAR. I’m indebted to Scott Burns for drawing me out to Portland and for his kind support throughout my coursework, my participation with AEG, and in reviewing my thesis. Scott’s untiring commitment to his students and his professional participation in AEG are greatly appreciated. I am especially grateful to him for slipping me extra tickets to AEG functions! Big thanks also go to Andrew Fountain and Alan Yeakley for serving on my thesis committee. Andrew provided the valuable link between the USGS and PSU. A cheer goes out to Martin Streck and Nancy Eriksson for their help, interest, and kindness during my four years at PSU. Among the many PSU students, there are a special few who enjoyed arguing the finer points of geology over a pint: Michelle i Cunico, Aaron Fox, Brent Gaston, Hiram Henry, Kenny Janssen, Robin Johnston, Meg Lunney, Jason Taylor and Susan Wacaster. Thanks to all the colorful characters at CVO who create a work place of ideas and who supplied empty threats and horror stories of what might happen if I never finished. The good wishes and coffee pot runneth over. Lastly and ultimately, hugs and kisses to my entire Woodbury family. Without my family’s help, this masters endeavor would have been more difficult, if not impossible. And I can’t forget my constant desktop companion, Pinatubo the cat.

ii TABLE OF CONTENTS Acknowledgements ...... i List of Tables...... iv List of Figures ...... v Chapter 1: Introduction ...... 1 Description of the Physical/ Geological Phenomena ...... 2 Specific Objectives...... 5 Chapter 2: Runout Prediction Methods ...... 7 Historic and Geologic Evidence...... 7 Physically Based Models...... 8 Empirical Models ...... 9 Statistical Models Constrained by Physical Scaling Arguments...... 11 Chapter 3: The Database ...... 14 Data Quality ...... 16 Chapter 4: Discrimination between Datasets ...... 19 Chapter 5: Regression Analysis ...... 25 Comparison of Various Regression Models...... 25 Analysis of Variance ...... 27 Results of the F-test...... 30 Summary and Interpretation of Statistical Results...... 43 Chapter 6: Debris-Flow Application using DEMs and GIS...... 45 Data Input...... 46 Debris-Flow Test Area – Scottsburg, Oregon ...... 49 History of the Scottsburg area ...... 49 Topography and geology...... 50 Topographic dataset: acquisition and description ...... 51 Flow volume assessment for Scottsburg ...... 52 LAHARZ application ...... 54 Hazard Map Evaluation...... 59 Chapter 7: Conclusions ...... 61 Future Work and Improvements...... 63 References ...... 65 Appendix A/ Data Table...... 70 Appendix B/ Data Bibliography...... 87

iii LIST OF TABLES

Table 1. Table of variance (s2) and mean ( x ) used to compute the F-test and t-test statistics. Parameters are flow volume (V), cross-sectional area (A), and planimetric area (B)...... 22 Table 2. Comparison table for the F-test. In all cases, the tabulated F-value (α = 0.05) is less than the computed F-statistic, and thus, all variances are statistically different at the 95% level of confidence. [DF, debris flow; RA, rock avalanche; LA, lahar]...... 23 Table 3. Comparison table for Welch’s (1938) t-test. The tabulated t-values are less than the computed t-statistic for acceptable levels of confidence. [DF, debris flow; RA, rock avalanche; and LA, lahar]...... 24 Table 4. Coefficients of determination (r2) for the regression models for debris flow datasets of flow volume - cross-sectional area (V, A) and flow volume - planimetric area (V, B). Rock avalanche r2 values are similar but generally lower...... 26 Table 5. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Debris Flow Data...... 35 Table 6. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Debris Flow Data...... 36 Table 7. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Rock Avalanche Data ...... 37 Table 8. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Rock Avalanche Data ...... 38 Table 9. Summary table of the calibrated best-fit regression equations (Model 1). ...39 Table 10. Summary table of the calibrated specified 2/3-slope regression equations (Model 2). Only one significant digit is reported for the α-coefficients...... 39 Table 11. The F-statistic evaluates the comparison between the specified slope models and the best-fit regression model. In all cases, the specified 2/3-slope models have tabulated F-value less than the computed F-statistic, and thus, the best-fit regression models do not fit the data significantly better than the 2/3-slope models for (α = 0.05 for debris flows, α = 0.01 for rock avalanches)...... 39

iv LIST OF FIGURES

Figure 1. Idealized inundation limits of a surge front as a granular flow passes down- valley from a source area. The maximum valley cross section, A, inundated at four transects (black and yellow) and the total inundated planimetric area, B (dashed yellow line). Photograph is from the 20 July 2003 debris flow at Minamata in Kyushu, Japan (Sidle and Chigira, 2004)...... 5 Figure 2. Diagram showing lahar source area and runout path. Where the downstream topography and flow volume are known, inundation limits for A and B can be computed. Figure modified from Iverson et al., 1998...... 12 Figure 3. All data for debris flows, rock avalanches, and lahars with flow volume as the independent variable. All data and data sources are tabulated in Appendices A and B...... 15 Figure 4. Debris flow data (V-B) plotted with best-fit regressions. To better view most of the data and regression curves, the axes are clipped...... 26 Figure 5. Debris flow data and three regression models (DF: A1, A2, A3)...... 33 Figure 6. Debris flow data and three regression models (DF: B1, B2, B3) ...... 33 Figure 7. Rock avalanche data and three regression models (RA: A1, A2, A3)...... 34 Figure 8. Rock avalanche data and three regression models (RA: B1, B2, B3)...... 34 Figure 9. Lahar data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (following Helsel and Hirsch, 1992). Lahar plots are taken from Iverson et al., 1998...... 40 Figure 10. Debris flow data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (following Helsel and Hirsch, 1992)...... 41 Figure 11. Rock Avalanche data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (following Helsel and Hirsch, 1992)...... 42 Figure 12. Shaded-relief map with highways and logging roads for an area west of the town of Scottsburg in the central Coast Range of Oregon ...... 50 Figure 13. Topographic map and mapped stream sections of debris flow inundation (blue) to perennial streams. The darkened box highlights the DEM area along the Umpqua River. The purple is the extent of the Oregon Department of Forestry’s research area (Robinson et al., 1999)...... 53

v Figure 14. a) The DEM is overlaid with a grid where orange indicates slopes that exceed 30º. The red box identifies the test basin to be tested using the modified LAHARZ program. b) Initiation locations (red cells) on potential flow paths. ..54 Figure 15. Green shades are minimum contributing source areas that define the start of potential flow paths (blue)...... 56 Figure 16. a) Two sets of inundation zones for four flow volumes (orange is 103 m3, lime green is 103.5 m3, green is 104 m3, and brown is 104.5 m3) from single initiation points (red cells along blue flow paths) and b) Assembled hazard zones from many sets of inundation zones that delineate areas of relatively high hazard (orange) to low (brown)...... 56 Figure 17. Preliminary debris-flow hazard assessment map for four flow volumes between 103 – 104.5 m3. These are the predicted inundation limits for debris flows that start anywhere within the basin where three initiation criteria are met...... 57 Figure 18. a) Oblique perspective of the basin with debris flow-hazard zone overlays looking to the southeast. b) Up-valley perspective as viewed from the north bank of the Umpqua River...... 58

vi CHAPTER 1: INTRODUCTION

Debris flows and rock avalanches are two types of mass flows intermediate between hyperconcentrated stream flow and dry granular flows (Iverson, 1997).

Distinguishing between various forms of flow-type landslides has traditionally been based on physical characteristics such as grain size, material properties and textures, magnitude, deposit morphology, percent clay, and water content. These characteristics differ within and between landslide types such that no set of characteristics provides a definitive classification. This thesis discriminates between debris flows and rock avalanches on the basis of inundation-area statistics by employing an approach like that used by Iverson et al. (1998) for lahars. Establishing the difference in inundation-area relationships will aid in understanding flow mobility and in delineating hazard zones likely to be inundated by lahars, non-volcanic debris flows, and rock avalanches (defined on pages 3 - 5).

The lahar area-inundation equations developed by Iverson et al. (1998) are currently used to map hazard zones in areas adjacent to volcanoes in North and

Central America. Schilling (1998) implemented the set of lahar equations in

LAHARZ, a Geographic Information System (GIS)-based program that facilitates rapid calculation and delineation of the hazard zones for a range of probable flow volumes and any specified flow-path topography. This thesis expands the use of this program with some modifications for the hazard mapping of non-volcanic debris flows and rock avalanches.

1 Description of the Physical/ Geological Phenomena

Mass flows on Earth’s surface range between dry, unsorted granular rock avalanches and water-saturated debris flows and hyperconcentrated stream flow

(Iverson and Vallance, 2001). Flows are described by many terms in common practice and in the scientific literature, but my nomenclature is restricted to rock avalanche, debris flow (non-volcanic), and lahar (volcanic debris flow). In this section, I 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. The motion is influenced 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 broader, less steep slopes. Deposits may include massive hummocks composed of coarse debris and partially saturated sediment (Crandell, 1989). Volumes of well- documented rock avalanches generally exceed 106 m3. At Mount Shasta in California, one prehistoric rock avalanche is estimated to have a volume ~45 km3 (>1010 m3)

(Crandell, 1989). The large block sizes and volume of 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 (ed.) (1979), Eisbacher and Clague (1984), Ui et al. (1986), Siebert (1984), Ui (1983), and Siebert et al. (1987).

2 Debris flows initiate in steep drainages (generally > 30º) where sufficient unconsolidated material and water are available. The water-saturated material liquefies during initial slope failure or down slope entrainment and stays fluid due to the persistence of high pore-fluid pressure, which is facilitated by fine matrix sediment

(Iverson et al., 1997). The initial slope failure and subsequent entrainment of shallow soils, colluvium, alluvium, or poorly consolidated bedrock and the presence of surface water (e.g. ponded water, snow, ice, runoff, outbreak flood) or shallow groundwater

(perched or return flow) provide the necessary conditions for debris flows to form.

Local topographic features generally play a role in focusing water flow on a slope where failure will occur. These topographic features include gullies, swales, hollows or convergent slopes and also include constructed features such as road prisms.

Focusing of shallow groundwater is also influenced by stratigraphy that may aid saturation and development of high pore-water pressures. Termination of debris flow motion downvalley on lesser slopes of stream channels, alluvial fans, or other shallow- sloped broad area commonly results in a coarse depositional snout and bounding levees with a liquefied interior of finer material (Iverson, 1997). Additional descriptions and reports on debris flows are provided by Iverson (1997), Iverson et al.

(1997), Major (1996), Fannin and Rollerson (1993), Takahasi (1991), and Johnson

(1984).

Hazardous debris flows are characterized by their proximity to people and structures, which are often built in runout areas. Non-volcanic debris flows rarely exceed volumes of 106 m3, and although they are commonly smaller than rock

3 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’.

Lahars are debris flows that originate on the flanks of volcanoes where abundant, loose rubble material 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 the high degree of fragmentation of the volcanic rubble makes for a readily erodible and clay-rich material (Vallance and

Scott, 1997). The failed volcanic material incorporates large volumes of water, ice, and snow to reach full saturation. Lahars generally inundate the lower flanks of volcanoes and the downstream reaches of major tributaries that originate on those flanks. Volumes of well-documented lahars generally exceed non-volcanic debris- flow volumes and are typically >105 m3. Observed runout distances of lahars indicate that they are typically more mobile than rock avalanches. For example, lahars originating on Mount Rainier have traveled more than 120 km to Puget Sound, filling the White River system to depths greater than 100 meters (Vallance and Scott, 1997).

Owing to the speed with which these flows travel, lahars can arrive at heavily populated, down-valley communities with little warning. Additional descriptions and reports on lahars are provided by Vallance and Scott (1997), Crandell and Mullineaux

(1967, 1975), Janda et al. (1981), Major (1984), and Pierson (1985).

4 Specific Objectives

The first objective for this thesis is to assemble a dataset 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 non-volcanic debris flows and rock avalanches (Figure 1).

Figure 1. Idealized inundation limits of a surge front as a granular flow passes down- valley from a source area. The maximum valley cross-section, A, inundated at four transects (black and yellow) and the total inundated planimetric area, B (dashed yellow line). Photograph is from the 20 July 2003 debris flow at Minamata in Kyushu, Japan (Sidle and Chigira, 2004).

5 A second objective is to use the database to determine whether planimetric and cross-sectional areas inundated by non-volcanic debris flows, rock avalanches, and lahars form distinct statistical populations as functions of flow volume. If the populations are distinguishable, then I will test the hypothesis that the scale invariant inundation-area equations (Iverson et al., 1998),

2/3 A=α1V (1)

2/3 B=α2V , (2) with calibrated α parameters derived from inundation-area statistics, predict the extent of runout paths for non-volcanic debris flows and rock avalanches.

To implement these predictive equations, I will modify the LAHARZ computer program with the new inundation-area equations and modify the flow initiation criteria to extend the use of the software to non-volcanic debris flows and rock avalanches. In GIS, I will create a hazard map for a range of hypothetical flow volumes in a drainage basin prone to debris flows in a geographic area where precise topographic control is available. Finally, I will compare the hazard map generated from the LAHARZ program for non-volcanic debris flows to other published studies and efforts to delineate hazard zones for that field area.

6 CHAPTER 2: RUNOUT 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 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 constrained by physical scaling arguments.

Method (4) is adopted in this thesis, as described below. First however, the context for use of this method is clarified by proving a brief synopsis of methods (1), (2), and

(3).

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 (e.g. Scott et al., 1998). Although this method is useful, 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 becomes more serious as the number of documented past events in a particular area becomes smaller. The problem can be particularly severe where a dearth of documentation of past events results from limited historical record-keeping and geological fieldwork. A second problem with 7 this forecasting method is reliance on geological inference and consequent lack of reproducibility. Different geologists may examine the same historic 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.

Physically Based Models

Physically based models for predicting inundation by mass flows have varying degrees of sophistication, but all such models are built on a foundation of physical conservation laws. The most elementary models invoke only one-dimensional momentum conservation for a translating point mass (i.e., 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, there 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 (e.g., debris flows, Iverson, 1997) or their volumes exceed about 1 million cubic meters (Hsu, 1975; Scheidegger, 1973).

One approach to remedying the failing of the Heim (1932) model involves use of resistance formulae other than Coulomb friction. For example, viscosity coefficients or fixed yield-strength coefficients have been suggested as alternatives to tan φ (e.g., Voight et al., 1983; Johnson, 1984; Dade and Huppert, 1998). A

8 significant problem with this approach is that the importance of such resistance coefficients is not supported by experimental data (Iverson, 2003).

Basal fluid pressure can be invoked as means of modifying Coulomb friction

(e.g., Shreve, 1968; Sassa, 1988). Pore-pressure effects are well supported by 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 simply assigned a basal pore-pressure distribution that fits experimental observations

(e.g., Iverson, 1997), but it is not clear that this approach can be applied to field phenomena.

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 (e.g.,

Iverson and Denlinger, 2001; Denlinger and Iverson, 2001). However, such 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 et al., 2004). 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 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 equation depends systematically on avalanche volume (e.g., Scheidegger,

9 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 is apparent to even casual observers.

Some authors have used empirically calibrated limits for debris-flow stoppage to predict debris-flow progress through successive channel cross-sections. Benda and

Cundy (1990) use an empirical model based on channel junction angles (≥70º) and channel gradients (<20º) to predict termination for most debris flows. Fannin and

Rollerson (1993) use 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) use slope geometry and net changes in volume for successive sections of a channel to determine whether a debris flow is entraining or depositing.

Changes in flow volume are assessed for each section until the cumulative volume is zero. Other authors report that flow volume and runout path lengths are proportional

(for debris flows: Cannon, 1989; for rock avalanches: Kilburn and Sorenson, 1998).

For example, Kilburn and Sorenson (1998) conclude that L=aV1/2 where a = [3-40] is a calibrated coefficient. This equation lacks dimensional homogeneity, however.

For rock avalanches, Li Tianchi (1983) related volume to planimetric area, log(Area) = 1.8807 + 0.5667 · logV, (r2 = 0.8831, σ = 0.2806), and with statistical methods, produced a pair of prediction curves for runout length and width for a given vertical relief and volume. Vallance and Scott (1997) observed a dimensionally sound relationship between planimetric areas of inundation to volume (B3/2 ∝ V). Dade and

10 Huppert (1998) proposed a physical basis for predicting inundated planimetric areas

2/3 2 for rock avalanches, area ~ V , and used statistical regression to confirm the /3-

2 exponent. Hungr (1990) also observed the /3-exponent for rockfalls and rock avalanches. These authors used dimensional considerations to strengthen the validity of their empirical equations.

Statistical Models Constrained by Physical Scaling Arguments

A hybrid approach was developed by Iverson et al. (1998) and applied to forecasting inundation by lahars downstream from volcanoes. Subsequent sections of this thesis extend this approach to forecasting inundation by rock avalanches and non- volcanic debris flows.

The approach of Iverson et al. (1998) utilized scaling arguments to postulate that both the total planimetric area (B) and the maximum valley cross-sectional area

(A) inundated by a passing lahar will be proportional to flow volume raised to the 2/3 power. Statistical analysis of a dataset comprising 36 lahars at nine volcanoes was then used to test and confirm the validity of the 2/3 power laws. Optimal values of the proportionality coefficients were determined using regressions of log-transformed data, yielding the predictive equations

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

B = 200 V 2/3 for planimetric area (4)

These equations have quantitative confidence limits, which were determined by statistics associated with the regression analysis.

11 Together, equations (3) and (4) suffice to delineate inundation limits, provided that V is known and the topography downslope or downstream from the lahar source area is known (Figure 2). Indeed, relative to other empirical methods, a key advantage of the Iverson et al. (1998) method is that it makes full use of three-dimensional topographic constraints for forecasting inundation.

Figure 2. Diagram showing lahar source area and runout path. Where the downstream topography and flow volume are known, inundation limits for A and B can be computed. Figure modified from Iverson et al. (1998). Because flow volume V is the independent variable in the method of Iverson et al. (1998), forecasts of inundation limits generally postulate a range of prospective V values, and inundation limits A and B are calculated for the range of postulated V’s.

This procedure results in a nested set of prospective inundation limits, which reflect uncertainties about the volumes of future flows to descend a particular path. Selection of appropriate V values depends on geological knowledge available to assign recurrence probabilities to various V’s (Iverson et al., 1998). Commonly, however, 12 such data are unavailable, and geological inferences provide the basis for selecting prospective flow volumes.

A related challenge involves identification of source areas. Iverson et al.

(1998) identified prospective lahar source areas as all locations within any valley that drains the upper slopes of a volcano. Upper slopes were defined 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 these two points. Generally, H/L values between 0.1 and 0.3 are well suited for identifying prospective lahar source areas on volcanoes. However, it is more difficult to identify prospective source areas in dissected terrain that is not characterized by a single dominant feature such as a volcano. Therefore, a new method for identification of source areas of non-volcanic debris flows in finely dissected terrain is described in Chapter 6.

13 CHAPTER 3: THE DATABASE

Diverse data sources and geographic locations (with diverse climate, bedrock, vegetation, etc.) are used to build a robust dataset. Events were selected to represent the general nature of debris flows and rock avalanches and not to be biased towards one region. Documented runout paths of debris flows and rock avalanches of prehistoric and historic events were found in the scientific literature, unpublished reports and maps, and personal communications or field notes. Evidence for maximum inundated cross-sectional area includes high flow marks indicated by strandlines, levees, embedded gravels or the height of stripped bark in the trunks of adjacent trees, and the height of log jams. Evidence for total inundated planimetric area is 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 does not include the extent of subsequent flooding or hyperconcentrated flow.

Flow volume, V, is the independent variable. Pairing of data on inundated cross-sectional area (A) or planimetric area (B) with flow volume data is necessary.

Ideally, all three parameters are known for a particular event. All data pairs for lahars

(Iverson et al., 1998), debris flows, and rock avalanches are plotted in Figure 3. Data and data sources are tabulated in Appendix A and B, respectively.

Atypical examples were excluded from the dataset. For example, an event in which multiple debris flows coalesced such that parameters for a single event were obscured was excluded. Also excluded were events that followed in rapid

14

Cross-sectional Area vs. Flow Volume

Planimetric Area vs. Flow Volume

Figure 3. All data for debris flows, rock avalanches, and lahars with flow volume as the independent variable. All data and data sources are tabulated in Appendices A and B. 15 succession such that flow path 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 were also excluded from the dataset.

Data Quality

The greatest limitation to data collection is the availability of precise surveys or maps. Reconstructing the necessary inundation parameters (V, A, and B) is commonly complicated by lack of accurate knowledge of topography before and immediately after an event. Surveys made shortly after an event that record high flow marks and surveys that include detailed topographic maps of the land surface from before and after an event are most useful. However, because the motivation behind the various papers and reports on debris flows and avalanches differed, the type and quality of data varied tremendously.

Not all reports included in the database explicitly stated the results of measurements and surveys or reported the inundation parameters A, B, and V. The quality and quantity of other information dictated whether or not it could be used to reconstruct the inundation parameters. Reports fell into three categories. Those 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/trail crossings, and maximum and average flow widths in well-constrained channels. These dimensions were used to calculate one or

16 more of the inundation parameters in a piece-wise fashion. In these cases, calculations 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, my calculations from these descriptive reports were included in the database.

The third category of report either reported detailed measurements that could not be used to reconstruct the inundation parameters objectively and reproducibly or only one of the three inundation properties (V, A, B) could be reconstructed. Reports from this third category were excluded from the database.

If reports included maps of the deposit without a quantitative assessment of inundation parameters, a simple method for determining the area B was used and entailed overlaying a fine grid on the map, counting the boxes within the mapped extent, and using the scale of the map to calculate the total area. The same procedure is used to determine cross-sectional area A for any drawn 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 includes only one significant digit for each volume and area entry. Values of volume and area are generally accurate to only one or two significant digits due to the following factors: 1) scale at which surveys were done and maps produced; 2) erosion of deposits and loss of reconstructable evidence between the time of the event and the study; and 3) inferences from descriptive reports were used to reconstruct the inundation parameters where they were not specifically reported. It is important to

17 point out that precision greater than one significant digit would have little importance in this study as the data are examined on logarithmic scale plots and in power-law equations. The order of magnitude of the data is most important.

Many of the data come from events along the west coast of the United States and Canada, Central America, southern Europe, and Japan. Efforts were made, however, to include all the continents and to represent various climates and geologic settings. Data for a total of 64 debris flows (44 volume-planimetric area pairs and 50 volume-cross-sectional area pairs), and 143 rock avalanches (142 volume-planimetric area pairs and 12 volume-cross-sectional pairs) are included. Rock avalanche volumes range between 105 and 1011 m3 (cubic meters), which correspond respectively to the

Felsberg event (Heim, 1921) and the Flims event (Jackli, 1957). Debris flow volumes range between 101 and 107 m3 (cubic meters), which correspond to U.S.G.S. debris flow flume experiments (Iverson et al., 1992) and the 900 years B.P. (before present)

Upper Lillooet River debris flow in British Columbia (Jordan, 1994).

18 CHAPTER 4: DISCRIMINATION BETWEEN DATASETS

The second objective of this thesis is to test whether the data populations for debris flows, rock avalanches, and lahars are statistically different. If the datasets are distinct, then different sets of predictive equations are warranted for debris flows and rock avalanches. Testing whether two populations are different is generally done with the combined procedures of the F-test and t-test (Davis, 1986, p. 61, p. 67). The t test establishes the probability that two populations have different sample means. The null and alternate hypotheses for the t test where µ is the mean of the data sample are:

Null hypothesis, H0: µ1 = µ2 Alternate hypothesis, H1: µ1 ≠ µ2 (where 1 and 2 are subscripts indicating different populations)

The null and alternate hypotheses for the F test where s2 is the variance of the data sample are:

2 2 Null hypothesis, H0: s1 = s2 2 2 Alternate hypothesis, H1: s1 ≠ s2 (where 1 and 2 are subscripts indicating different populations)

The standard F-test determines the probability that two samples from the same normally distributed population have different variances by comparing the computed F statistic (Equation 5) to a tabulated distribution of the F statistic (Davis, 1986, p. 67).

The computed F value is given by

2 s F = 1 (5) 2 s2

19 where s2 is the variance and the subscripts denote the two different sample groups

2 2 where s1 > s2 . The degrees of freedom (df) are the number of data points in excess of the number of parameters to be calibrated (here, 1) are determined for both sample groups. Pairs of degrees of freedom for the two sample groups correspond to a single tabulated critical value of F for an acceptable level of confidence (Davis, 1986).

Based on the number of data points (m) and the number of parameters to be calibrated,

df = m − 1 (6)

The t-test establishes the probability that two samples are drawn from populations with different means. The standard t-test requires that the samples have similar variances, which at preliminary observation (Figure 3), these datasets do not.

The Welch's t-test (heteroscedastic t-test) assumes unequal variances. For Welch’s test, t is computed using

x − y t = (7) s 22s 12+ mn where x and y are sample means and m and n are the number of data points in sample groups 1 and 2, respectively (Welch, 1938; Keselman et al., 2004). The computed t-statistic is compared to a tabulated t-value that is identified by a corresponding pair of degrees of freedom for the two sample groups for an acceptable level of confidence (Davis, 1986). With inhomogeneous variances and unknown standard deviations, the degrees of freedom are not known exactly but can be approximated using

20 2 ⎛ s 2 s 2 ⎞ ⎜ 1 + 2 ⎟ ⎜ m n ⎟ df = ⎝ ⎠ 2 2 (8) ⎛ 2 ⎞ ⎛ 2 ⎞ ⎜ s1 ⎟ ⎜ s2 ⎟ ⎜ m ⎟ ⎜ n ⎟ ⎝ ⎠ + ⎝ ⎠ m −1 n −1

If the statistical tests show that two population samples have different means and variances for an acceptable level of confidence, then the two populations are regarded as statistically different. Comparing two datasets at a time, three flow types

(debris flow, rock avalanche, and lahar) and three parameters (flow volume, cross- sectional area, and planimetric area) require nine comparison tests. Table 1 lists the variances and means for each of the nine population samples. Table 2 compares the computed F-statistic, the degrees of freedom, and the tabulated F-value (e.g. Davis,

1986). The samples have statistically different variances if the computed F-statistic is greater than the tabulated F-value. In all nine comparisons, the variances of the samples are shown to be statistically different at the 95% level of confidence.

21 Table 1. Table of variance (s2) and mean ( x ) used to compute the F-test and t-test statistics. Parameters are flow volume (V, m3), cross-sectional area (A, m2), and planimetric area (B, m2). s 2 Debris Flows x V 1.88 x 1012 3.92 x 105 A 1.66 x 106 4.44 x 102 B 1.20 x 1011 1.15 x 105

Rock Avalanches V 1.30 x 1020 2.88 x 109 A 4.53 x 1011 3.74 x 105 B 5.72 x 1016 7.77 x 107

Lahars V 6.76 x 1017 2.61 x 108 A 1.24 x 109 1.43 x 104 B 1.13 x 1016 4.61 x 107

Next, we systemically compare the computed t-statistic and the tabulated t value for the computed degrees of freedom and the probability associated with rejecting the null hypothesis (Table 2). Eight out of the nine comparisons show that the means of the samples are statistically different with more than an 85% level of confidence, and one comparison shows the means to be different with a 70% level of confidence. The lower level of confidence means a higher probability of error (30%).

Considering the desired task to find a more practical method to forecast runout of typical events, the probability of error is decidedly acceptable.

22 Table 2. Comparison table for the F-test. In all cases, the tabulated F-value (α = 0.05) is less than the computed F-statistic, and thus, all variances are statistically different at the 95% level of confidence. [DF, debris flow; RA, rock avalanche; LA, lahar] Groups for F-statistic Degrees of Freedom Tabulated Comparison (computed) (group 1 and 2) F-value (group 1 and 2) α = 0.05 Flow Volume (V) RA, DF 6.91 x 107 141, 62 1.40 RA, LA 1.92 x 102 141, 25 1.58 LA, DF 3.60 x 105 25, 62 1.81 Cross-sectional Area (A) RA, DF 2.73 x 105 11, 48 2.51 RA, LA 3.66 x 102 11, 16 2.70 LA, DF 7.46 x 102 16, 48 2.12 Planimetric Area (B) RA, DF 4.75 x 105 140, 42 1.47 RA, LA 5.07 140, 25 1.58 LA, DF 9.37 x 104 25, 42 1.86

23 Table 3. Comparison table for Welch’s (1938) t-test. The tabulated t-values are less than the computed t-statistic for acceptable levels of confidence. [DF, debris flow; RA, rock avalanche; and LA, lahar]

Groups for Degrees of t-statistic Tabulated Level of Comparison Freedom (computed) t-value confidence (group 1 and 2) (group 1 and 2) for tstat>tvalue Flow Volume (V) RA, DF 141, 62 3.03 1.98 95%

RA, LA 141, 25 2.71 1.98 95%

LA, DF 25, 62 1.65 1.48 85%

Cross-sectional Area (A) RA, DF 11, 48 2.00 1.78 90%

RA, LA 11, 16 1.93 1.78 90%

LA, DF 16, 48 1.67 1.51 85%

Planimetric Area (B) RA, DF 140, 42 3.88 1.98 95%

RA, LA 140, 25 1.11 1.04 70%

LA, DF 25, 42 2.25 2.06 95%

Having demonstrated within a reasonable level of confidence that the populations of debris flows, rock avalanches, and lahars are statistically different in terms of their inundation-areas, the next objective of the thesis can be discussed.

24 CHAPTER 5: REGRESSION ANALYSIS

The next objective is to test whether the power-law Equations 1 and 2

2/3 2/3 (A=α1V and B=α2V ), similar to those used by Iverson et al. (1998) to predict the inundation areas of lahars, can also be used for debris flows and rock avalanches. To pass the tests, the power-law regression must be shown to provide a good fit to the data, and the 2/3-exponent identified on the basis of dimensional considerations must be shown to fit the data nearly as well as a best-fit exponent. Moreover, the error in prediction must be shown to be acceptable. For this purpose the data on flow volume, cross-sectional area, and planimetric area are no longer considered separately.

Instead, this chapter discusses paired datasets that relate cross-sectional area, A, and planimetric area, B, to flow volume, V.

Comparison of Various Regression Models

Diverse regression curves (exponential, linear, quadratic, cubic, and power law) are fit to the data using least-squares methods to test whether the power-law curve provides the best fit. For these regressions, coefficients of determination (r2) are listed for debris flows in Table 4. An example of one of the paired datasets (debris flow volume vs. planimetric area) is plotted in Figure 4 with the various best-fit regressions to give a visual estimate of fit. Although both the cubic polynomial and power law forms are good fits to the data, there is no physical basis for a cubic polynomial model. Moreover, a cubic polynomial has more adjustable parameters

25 than a power-law model and is, therefore, less desirable (Jefferys and Berger, 1992).

Therefore, the power-law regression model best suits the data.

Table 4. Coefficients of determination (r2) for the regression models for debris flow datasets of flow volume - cross-sectional area (V, A) and flow volume - planimetric area (V, B). Rock avalanche r2 values are similar but generally lower. Regression model V, A V, B Exponential 0.37 0.23 Linear 0.47 0.78 Quadratic 0.53 0.81 Cubic polynomial 0.59 0.93 Power law 0.78 0.92

Figure 4. Debris flow data (V-B) plotted with best-fit regressions. To better view regression curves and most of the data, the axes are clipped.

26 Constant power-law exponents (constant slopes on log-log plots) imply a fractal scaling. Geometric fractals are said to be scale invariant (Peitgen et al., 1992).

Scale invariance suggests that for a wide domain of flow volumes, the range of inundated areas will scale consistently with volume raised to an appropriate power.

Clearly, however, there is a practical limit below which the equations will 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 log-transformed versions of the equations would imply a non- zero y-intercept where volume is equal to zero, and this is impossible. This doesn’t mean that the equations don’t hold true but that they operate over a finite range of volumes.

Analysis of Variance

The next issue to examine is whether the proposed 2/3-power-law exponent with a calibrated α-coefficient is a viable alternative to a power-law equation with an exponent and coefficient derived from best-fit regression. An additional question is whether there is a significant relationship between the dependent and independent variables. Answers to both questions rely on the outcome of an analysis of variance

(ANOVA) and F-test statistics.

The power-law equations are log-transformed before testing to take advantage of linear regression. Use of log-transformed data also acknowledges that data scatter is likely to scale with data magnitude. Following log-transformation, the equations to be tested and calibrated become the following:

27 log A = logα1 + β1 logV (9)

logB = logα2 + β2 logV (10) where the β-coefficients (slopes) are both hypothesized to be 2/3.

The best-fit regressions (Model 1) for these equations have two parameters

(slope and y-intercept) to be calibrated using a standard procedure of minimizing the residual sum of squares. The specified 2/3-slope regressions (Model 2) and the specified zero-slope regression (Model 3) have one parameter to be calibrated. The two questions to be answered are (1) Does the specified 2/3-slope model fit the data significantly better than a specified zero-slope model (a horizontal line where there is no relationship between the variables)? and (2) If the best-fit regression equations more closely fit the data, is there a significant difference between that model and the specified 2/3-slope model? If the regressions are shown to have no significant difference using the coefficient of determination, r2, and a variation on the standard F- test, then the 2/3-slope model is a suitable representation of the data.

The data and three models are plotted in Figures 5 - 9. The statistical results are listed in Tables 5 - 8 under column headings DF A1 for debris flow cross-sectional area – flow volume dataset Model 1; DF B2 for debris flow planimetric area – flow volume dataset Model 2; etc. For debris flows and rock avalanches with two datasets each and three statistical models, there are twelve cases. For comparison, the calibrated equations for all twelve cases for debris flows and rock avalanches are listed together with the six equations for lahars from Iverson et al. (1998) in Tables 9 and 10.

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

⎛SSsloped_models ⎞ (11) r2 =−1 ⎜⎟⎜⎟ ⎝⎠SSzero_slope_model where SS is the residual sum of squares. The r2 is calculated for only the non-zero slope models (Models 1 and 2 in Tables 5 - 8). Sum of squares for the best-fit model is necessarily less than those of the specified slope models. The extent to which r2 exceeds zero indicates how well the sloped models surpass the zero-slope model in describing the data. All cases have r2 values greater than 0.77 (Tables 5 - 8), and we can conclude that there is a significant relationship between the inundation-area variables and flow volume.

The F-test is used to compare the specified slope models (Models 2 and 3 in

Tables 5- 8) against the best-fit regression model (Model 1). The null and alternate hypotheses state that

H0: The specified-slope regression model (Model 2 or Model 3) fits the data as well as the best-fit regression model (Model 1). H1: The specified-slope regression 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 to compare the null-hypothesis models (specified 2/3 or zero slope) against the best-fit regression model (Weisburg, 1985):

(SSnull − SSregression ) ()DF − DF F = null regression (12) ⎛ SS ⎞ ⎜ regression ⎟ ⎝ DFregression ⎠

29 where SS is the residual sum of squares. The F-statistic value is compared against a tabulated value based on an F distribution (e.g. Haan, 1977). A probability of the F statistic falling within the F distribution is determined. A desirable probability might be greater than 95% confidence level, that in rejecting one of the models, we do not falsely accept a bad model. In this case, rejecting one of the null hypotheses means that the fixed slope model reflects a different linear relationship than the regression line which best fits the data. The key test is whether we can reject Model 3 (specified zero slope) but accept Model 2 (specified 2/3 slope). That test determines whether the

2/3 slope adequately predicts the data to the selected confidence level, but that the fixed slope does not. Table 11 lists the statistical parameters needed in the F-test.

Results of the F-test

For debris flows, the computed F-statistics for the specified 2/3-slope models are smaller than the tabulated F-values at the 95% degree of confidence. We conclude that we cannot reject the null hypothesis for both DF A2 and DF B2, i.e. that there is no evidence that the best-fit regression fits the data significantly better than the specified 2/3-slope models. The computed F-statistics for the zero slope models (DF

A3 and DF B3) are far greater than the tabulated F-values. We make the obvious conclusion that the null hypothesis is rejected, i.e. that the best-fit regressions provide a better fit to the data than a horizontal line.

The rock avalanche models have similar F-test results. At the 99% degree of confidence, the computed F-statistics are less than the tabulated F-values for the specified 2/3-slope models but are greater than the F-values for the specified zero-

30 slope models. We conclude that we fail to reject the null hypothesis for Model 2 but can make the rejection for Model 3. Thus, there is again no evidence that the best-fit regressions fit the data better than the specified 2/3-slope models. For both debris flows and rock avalanches, we know that the differences between the best-fit regression models and the 2/3-slope models are slight and for the purposes of forecasting natural hazards, we adopt the 2/3-slope models as acceptable fits to the data.

Next, we examine the error and uncertainty of the 2/3-slope models. The standard error, σ, of these models (Tables 5 - 8) and the 95% confidence interval curves for prediction (Figures 9 - 11) describe the uncertainty of predicting areas inundated by future flows if volume is known (Helsel and Hirsh, 1992). The factors of error (10σ ) for predicting inundation areas (A, B) for rock avalanches (10σ = 2.7, 2.8) are larger than for debris flows (10σ = 2.7, 2.1) and lahars (10σ = 2.2, 1.9). The standard errors of the models indicate that the regression trends are accurate for a factor of <3. For example, the inundation-area equations predict that a debris flow of

103 m3 would inundate a planimetric area of 2,000 m2. The confidence-interval curves for regression (inner set, Figure 10) dictate that the error associated with fitting the power-law regression to the data would actually determine that inundated planimetric area ranges between 1,000 – 3,000 m2. This inner pair of curves neck in the midst of the data points because the regression line is better constrained here than at the fringes of the data population where the pair of curves flare outwards. The 95% confidence interval for prediction (outer set of curves, Figure 10) for the same flow volume,

31 however, indicates a wider range, between 400 – 7,000 m2. Intervals for 95% confidence in prediction for typical events have a wide spread across the population because of the data scatter associated with these large-scale natural events.

32

Figure 5. Debris flow data and three regression models (DF: A1, A2, A3)

Figure 6. Debris flow data and three regression models (DF: B1, B2, B3)

33

Figure 7. Rock avalanche data and three regression models (RA: A1, A2, A3).

Figure 8. Rock avalanche data and three regression models (RA: B1, B2, B3).

34 Table 5. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Debris Flow Data

Models for prediction of cross-sectional area of inundation, A Variable Best-fit regression Specified 2/3 slope Specified zero slope (Model DF-A1) (Model DF-A2) (Model DF-A3) Calibrated Slope of 0.59 0.67 0 the Line

Calibrated Intercept -0.66 -0.97 1.6 of Line at log V =0

α coefficient log-1(-0.66) = 0.22 log-1(-0.97) = 0.11 log-1(1.6) = 44 Number of data 50 50 50 pairs (N)

Residual Degrees of 48 49 49 Freedom (DF)

Residual Sum of 9.0 9.6 41 Squares (SS)

Residual Mean 0.19 0.20 0.84 Square (MS)

Standard Error of 0.43 0.44 0.91 Model (σ)

Coefficient of 0.78 0.77 0.0 determination (r2)

F statistic NA 3.2 170 (comparison to model A1)

35

Table 6. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Debris Flow Data

Models for prediction of planimetric area of inundation, B Variable Best-fit regression Specified 2/3 slope Specified zero slope (Model DF-B1) (Model DF-B2) (Model DF-B3) Calibrated Slope of 0.73 0.67 0 the Line

Calibrated Intercept 1.0 1.3 4.0 of Line at log V =0

α coefficient log-1(1.0) = 10 log-1(1.3) = 19 log-1(4.0) = 9,000 Number of data 44 44 44 pairs (N)

Residual Degrees of 42 43 43 Freedom (DF)

Residual Sum of 4.2 4.5 50 Squares (SS)

Residual Mean 0.099 0.10 1.2 Square (MS)

Standard Error of 0.31 0.32 1.1 Model (σ)

Coefficient of 0.92 0.91 0.0 determination (r2)

F statistic NA 3.7 470 (comparison to model A1)

36

Table 7. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Rock Avalanche Data

Models for prediction of cross-sectional area of inundation, A Variable Best-fit regression Specified 2/3 slope Specified zero slope (Model RA-A1) (Model RA-A2) (Model RA-A3) Calibrated Slope of 0.71 0.67 0 the Line

Calibrated Intercept -1.0 -0.64 4.7 of Line at log V =0

α coefficient log-1(-1.0) = 0.10 log-1(-0.64) = 0.23 log-1(4.7) = 52,000 Number of data 13 13 13 pairs (N)

Residual Degrees of 11 12 12 Freedom (DF)

Residual Sum of 1.9 2.3 12 Squares (SS)

Residual Mean 0.17 0.19 0.98 Square (MS)

Standard Error of 0.41 0.44 0.99 Model (σ)

Coefficient of 0.84 0.80 0.0 determination (r2)

F statistic NA 2.7 58 (comparison to model A1)

37

Table 8. Parameters and Analysis-of-Variance Statistics for Alternative Linear Models of Log-Transformed Rock Avalanche Data

Models for prediction of planimetric area of inundation, B Variable Best-fit regression Specified 2/3 slope Specified zero slope (Model RA-B1) (Model RA-B2) (Model RA-B3) Calibrated Slope of 0.75 0.67 0 the Line

Calibrated Intercept 0.73 1.4 6.8 of Line at log V =0

α coefficient log-1(0.73) = 5.3 log-1(1.4) = 24 log-1(6.8) = 6,000,000 Number of data 142 142 142 pairs (N)

Residual Degrees of 140 141 141 Freedom (DF)

Residual Sum of 27 28 130 Squares (SS)

Residual Mean 0.19 0.20 0.92 Square (MS)

Standard Error of 0.44 0.45 0.96 Model (σ)

Coefficient of 0.79 0.79 0.0 determination (r2)

F statistic NA 6.2 540 (comparison to model A1)

38 Table 9. Summary table of the calibrated best-fit regression equations (Model 1).

Debris flows A=0.22V0.59 B=10V0.73 (models DF-A1 and DF-B1) Rock avalanches A=0.10V0.71 B=5.3V0.75 (models RA-A1 and RA-B1) Lahars (Iverson et al., 1998) A=0.062V0.65 B=110.V0.69 (models LA-A1 and LA-B1)

Table 10. Summary table of the calibrated specified 2/3-slope regression equations (Model 2). Only one significant digit is reported for the α-coefficients. Debris flows A=0.1 V2/3 B=20 V2/3 (models DF-A2 and DF-B2) Rock avalanches A=0.2 V2/3 B=20 V2/3 (models RA-A2 and RA-B2) Lahars (Iverson et al., 1998) A=0.05 V2/3 B=200 V2/3 (models LA-A2 and LA-B2)

Table 11. The F-statistic evaluates the comparison between the specified slope models and the best-fit regression model. In all cases, the specified 2/3-slope models have tabulated F-value less than the computed F-statistic, and thus, the best-fit regression models do not fit the data significantly better than the 2/3-slope models for (α = 0.05 for debris flows, α = 0.01 for rock avalanches). Computed F-statistic

Degrees of Probability Tabulated specified specified Freedom of Error F-value 2/3-slope zero-slope Debris Flows

V,A 1, 48 0.05 4.0 3.2 170

V, B 1, 42 0.05 4.0 3.7 470

Rock Avalanches

V, A 1, 11 0.01 9.7 2.7 58

V, B 1, 140 0.01 6.8 6.2 540

39

Figure 9. Lahar data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (Helsel and Hirsch, 1992). Lahar plots are taken from Iverson et al. (1998).

40

Figure 10. Debris flow data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (Helsel and Hirsch, 1992).

41

Figure 11. Rock Avalanche data and best-fit regression line (solid) with 95% confidence interval for regression (inner pair of dashed curves) and 95% confidence interval for prediction (outer pair of dashed curves) (Helsel and Hirsch, 1992).

42 Summary and Interpretation of Statistical Results

Data for three granular-flow processes have been shown to form statistically distinct populations of flow volumes, maximum inundated cross-sectional areas, and total inundated planimetric areas. Distinct populations warrant individual predictive equations.

Power-law equations best relate areas of inundation to flow volume, and equations of the form Area =α ⋅ Volume2/3 are adopted based on dimensional and statistical considerations. Statistical testing and empirical calibration show that the fit of power-law equations with a specified 2/3-slope is indistinguishable from that of best-fit regression equations at the 95% or 99% level of confidence for debris flows and rock avalanches, respectively.

Empirically calibrated power-law equations show that the areas inundated by debris flows, rock avalanches, and lahars can be predicted for a specified flow volume within a quantified and acceptable degree of error. According to the coefficients of the power-law formulae (Table 10), lahars inundate a planimetric area roughly ten times greater than do debris flows or rock avalanches of the same volume. Rock avalanches inundate valley cross-sectional areas four times greater than a lahar of equal flow volume. Debris flows inundate channel cross sections at an intermediate level between lahars and rock avalanches. This behavior of rock avalanches suggests a relatively resistive, bulkier cross-sectional surge front whereas lahars flow more efficiently and fluidly. The explanation for this difference is perhaps in the degrees of saturation or the grain-size distribution of volcanic rubble. Non-volcanic debris flows

43 appear to be a middle-member between lahars and rock avalanches. A general observation (e.g. Vallance and Scott, 1997) that lahars travel more fluidly than the blockier, more resistive flows of debris flows and rock avalanches is, therefore, supported by the inundation equations.

44 CHAPTER 6: DEBRIS-FLOW APPLICATION USING DEMS AND GIS

To create a hazard zonation map using inundation-area equations (Table 10) requires: (1) identifying potential source areas, (2) selecting multiple potential flow volumes, (3) using inundation-area equations to calculate the predicted A and B values, (4) selecting 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, and (5) displaying the delineated planimetric areas as overlapping “nested” hazard zones that reflect uncertainty and relative levels of hazard posed by the suite of potential flow volumes. Predicted A and B values can be calculated and placed on a basemap manually, but the process has been fully automated in a GIS-based program that calculates thousands of successive cross- sections automatically. This automation is convenient especially for high resolution data covering large areas and also ensures reproducibility. The GIS-based software,

LAHARZ, is written in the Arc Info Macro Language (AML) (Schilling, 1998). The equations and software have been used to construct dozens of volcano hazard assessment maps around the world (e.g. Schilling et al., 2001, Gardner et al., 2004;

Vallance et al., 2001; Scott et al., 2001) and used and compared to other methods of runout prediction in academic theses (e.g. Sheridan et al., 1999; Haapala, in review,

2004; Sorenson, 2003). This chapter summarizes the data requirements and programming modifications necessary to adapt LAHARZ for debris flows and rock avalanches.

45 Data Input

Data input for LAHARZ includes flow volumes, topographic data, a suite of supplementary hydrologic data derived from the topography, and some threshold values that bracket critical slope and define flow-path initiation. Determination of flow volumes requires a rough estimate of available surficial material on a slope and historical perspective of the range of volumes that a given topographic area can produce. For example, the Coast Range in Oregon would not likely produce a rock avalanche with a volume of 10 km3 because the relief and geologic setting cannot generate a flow volume much greater than 0.001 km3. Ideally, the range of volumes should span 2 or 3 orders of magnitude to include a range of probability of occurrence.

The volumes range from smaller (more frequent) to larger (less frequent) to depict areas of relative inundation hazard. Areas that are inundated more frequently have greater hazard associated with them. Overlaying nested areas of relative inundation hazard mimics the use of error bars for a single zone where the increment between successive zones is based on statistical error. Once these areas are delineated, they will be depicted as nested hazard zones that grade from smaller, more hazardous areas to larger, less hazardous areas. A gradation from high to low hazard also implies uncertainty by suggesting that any given flow volume has a unique predicted runout area ± the runout area from the closest larger and smaller nested hazard zones. For example, flow volumes for Mount Rainier are 109.5, 109, 108.5, 108, and 107.5 m3

(Iverson et al., 1998). They span two orders of magnitude in increments of 100.5 m3, which conservatively exceeds the statistical errors, and the series of computed

46 inundation areas rank from low (less frequent inundation) to high (frequent inundation) hazard.

Choosing adequate resolution and precision of topographic data depends on the size of event that could be generated. For example, the digital elevation model (DEM) used in the lahar hazard assessment at Mount Rainier in Washington has 62.5-m grid cells for a map extent spanning much of western Washington (Iverson et al., 1998).

This resolution is adequate for large-volume lahars that inundate broad areas, as potential inundation areas are significantly greater than the resolution of the grid cell size. On the other hand, small-volume debris flows, may have flow widths of less than 10 meters and would require cell size resolution and accuracy on the order of a couple meters.

In LAHARZ, topographic data are used to calculate supplementary grids of flow direction and flow accumulation. These grids together are used to define the thalweg of a flow path. A selected minimum threshold for contributing area defines the initiation of a stream and mimics the sensitivity of the landscape to the generation of overland flow. For example, in a pumice-rich area or arid landscape, streams develop below larger contributing watersheds than streams that initiate on less pervious slopes or wetter climates. The minimum default value for contributing source area for lahars is typically 1,000 10-meter grid cells or 104 m2. This value most often matches published maps of perennial streams. Lahar inundation begins when a stream passes beyond the “H/L cone” (Schilling, 1998). Similar flow path definitions

47 are used for debris flows and rock avalanches, but selection of inundation onset requires modification.

Debris-flow inundation begins where three criteria are met: (1) A specified minimum source area funnels overland flow and shallow groundwater from an upland area to a single outlet point. An initial failure from within this contributing source area could mobilize along an issuing computer-generated flow-path thalweg. The default minimum contributing source area for debris flows is 103 m2. This value matches expectation of available material for an initial failure, expectation of focused intermittent flow during storms, and evidence of initial failure scars and the onset of debris flow inundation in site-specific reports (e.g. Harvey and Squier, 1998). (2) A selected minimum slope of 30º along a flow path defines onset of debris flow mobilization and inundation. This slope is based on observation that debris flows generally initiate on steep slopes >30º (Iverson et al., 1997). (3) Slopes must be uniformly steep about a flow path. Here, uniformly steep is defined as 95% of grid cells in a 100-m2 area centered on a flow path cell must exceed 30º. This maintains continuity of flow and prohibits inclusion of small failures that move a short distance downslope and terminate on a patch of shallow slopes. Criteria (1) and (2) serve to identify many potential initiation cells in a DEM, and Criterion (3) puts a limitation on the number of cells that define the starting points for debris flow inundation. The specifications listed here are default settings for a modified LAHARZ, but are adjustable once sensitivity analysis is performed for a study area.

48 Initiation criteria for rock avalanches, as yet, are less objective because the process is less dependent on the watershed of the source area. Flow-path thalwegs are defined for minimum contributing source areas of 50 m2, and manual identification of starting points for inundation requires knowledge of the location and extent of the potential failure. To date, runout areas have been generated only for well-identified source areas. From the initiation points for debris flows and source-area locations for rock avalanches, LAHARZ follows the network of flow paths cell by cell calculating channel profiles for all flow volumes to each predicted cross-sectional value at each cell until the total planimetric area for each volume is depleted.

Debris-Flow Test Area – Scottsburg, Oregon

History of the Scottsburg area

In the central Oregon Coast Range, the town of Scottsburg (Figure 12) and the

Rock Creek-Hubbard Creek watershed (a.k.a. Stump Acres) in southern Douglas

County became infamous between November 17-19, 1996 when two debris flows caused five fatalities. 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,

J. and D. Tims, 1996; Harvey, A. and B. Squier, 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 season (Figure 13).

49 Topography and geology

The map area (~5 km2), characterized by narrow, dissected valleys and sharp ridges, spans a section of Highway 38 and the Umpqua River at ~40 river kilometers from the coast. The elevation ranges from 390 to ~3 meters msl (above mean sea level) at the downstream area of the Umpqua River. Several unnamed perennial streams flow along lower portions of the valleys. The average and maximum slopes for the map area are 35° and 77° if flat areas (river and roads) are excluded.

Figure 12. Shaded-relief map with highways and logging roads for an area west of the town of Scottsburg in the central Coast Range of Oregon The entire map area is underlain by the Tyee Formation, a late Eocene, eastwardly dipping, rhythmically bedded sandstone and siltstone (Baldwin, 1961).

Where the sandstone dominates, ridgelines are sharp; and where the siltstone

50 dominates, ridges are moderately rounded. The weakly consolidated bedrock is easily weathered and eroded, and yields a succession of highly dissected ridges and small valleys. Based on the ODF Storm Impacts Study (Robinson et al., 1999) and the State of Oregon’s Emergency Management Plan (OEMP, 2000), the highest debris flow/torrent hazard lies 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° can produce rapidly moving landslides, regardless of geologic unit. In citing high-risk designations, the ODF uses lower slope criteria in the Tyee Formation than for other geologic types (Robinson et al., 1999).

Topographic dataset: acquisition and description

LIDAR (LIght Detection And Ranging) data acquired by ODF on November 9,

1997 for Scottsburg, Oregon were collected as part of a pilot study for ODF in their damage assessment of the impacts of the torrential storms 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 high resolution and accuracy X, Y, and Z locations (e.g.

Schickler and Thorpe, 2001).

The topographic data had been processed to eliminate the dense canopy to derive elevation of the bare earth surface. The extent of the study area excludes less dense data coverage around the periphery and therefore avoids coarse interpolation and false slopes. Areas exist within the DEM where the elevation data captured are

51 sparse 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.

I used these data to generate a one-meter resolution DEM that has not been checked for 1-meter accuracy, and thus, GIS products based on this topographic data should be interpreted as preliminary. Such resolution and accuracy, however, are needed for debris flow inundation prediction where flow widths can be expected to be less than ten meters.

Flow volume assessment for Scottsburg

Ketcheson and Froelich (1978) completed an inventory in the Mapleton

Ranger District of the Oregon Coast Range and found that initial volumes of slope failures ranged from 1.5 – 150 m3 for 104 events over a ten-year period (1966-1976).

These initial volumes cannot be used to estimate total flow volume, however they do provide a minimum flow volume estimate.

Harvey and Squier (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 of 1990, a flow blocked the highway

52 but did not enter the Umpqua River. In December of 1992, a flow deposited 500-600 m3 of material on the highway. Aerial photographs taken in 1986 indicate recent

Figure 13. Topographic map and mapped stream sections of debris flow inundation (blue) to perennial streams. The darkened box highlights the DEM area along the Umpqua River. The purple is the extent of the Oregon Department of Forestry’s research area (Robinson et al., 1999). 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 sub-basin 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 are lacking except for the single fatal event.

53 Initiation 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 600 m.

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 over 2 days (Harvey and Squier, 1998). They indicate that this 2-day intensity of rain was the greatest in the past 48 years. Their recurrence analysis indicates that the storm event was a

50-year storm.

LAHARZ application

One basin (red box, Figure 14a) was selected from the 5 km2 LIDAR area to run trial tests. The November 1996 debris flow emerged from this basin to push a traveling car off the road.

Test Basin

b)

a)

Figure 14. a) The DEM is overlaid with a grid where orange indicates slopes that exceed 30º. The red box identifies the test basin to be tested using the modified LAHARZ program. b) Initiation locations (red cells) on potential flow paths. 54 Combining the three criteria for initiation identifies hundreds of potential sites for initiation within the basin (Figure 14b). Each red potential initiation cell is only one square meter. A total of 42 contributing source areas (≥103 m2 each) were identified (shades of green, Figure 15). The selected range of 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 material (longest flow path is ~800 meters and entrainment swath might be 30-50 meters for a one or two meter erosion depth and so a conservative upper limit for flow volume is ~104.5 m3). For this series of flow volumes, the inundation area equations from Table 10, 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, respectively. The four pairs of successively larger predicted A- and B- values produce one set of nested inundation zones. One set of inundation zones is produced for every initiation point. Two sets are shown in Figure

16a. Merging all of the sets for each of the initiation points creates ‘coalesced’ hazard zones (Figure16b). The debris flow hazard assessment map for the whole basin is shown in Figure 17 and Figure 18.

55

Figure 15. Green shades are minimum contributing source areas that define the start of potential flow paths (blue).

Figure 16. a) Two sets of inundation zones for four flow volumes (orange is 103 m3, yellow is 103.5 m3, green is 104 m3, and brown is 104.5 m3) from single initiation points (red cells along blue flow paths) and b) Assembled hazard zones from many sets of inundation zones that delineate areas of relatively high hazard (orange) to low (brown). 56 Hwy 38

Flow Volume 103 m3

103.5 m3

104 m3

104.5 m3

Figure 17. Preliminary debris-flow hazard assessment map for four flow volumes between 103 – 104.5 m3. These are the predicted inundation limits for debris flows that start anywhere within the basin where three initiation criteria are met. 57 N

a)

b)

Figure 18. a) Oblique perspective of the basin with debris flow-hazard zone overlays looking to the southeast. b) Up-valley perspective as viewed from the north bank of the Umpqua River.

58 Hazard Map Evaluation

The modeled hazard zones do not predict the runout of a particular debris flow but delineate the maximum swath likely to be inundated by an average debris flow.

The methodology and therefore the GIS procedure do not incorporate changes to flow volume (entrainment or deposition) or run-up potential. That is, the same predicted

A-value is used to compute the inundation cross-section along the entire length of a potential debris-flow path. This does not mimic a dynamic flow because the volume during early stages of flow (near the head of a channel) may be considerably less than the achieved volume at the toe of a slope. The delineated hazard areas are, therefore, conservative.

The four nested hazard zones are colored from “hot” to “cool” colors to reflect which areas are designated more hazardous. Hazard is greatest along channel thalwegs (Figure 17) and decreases with distance and elevation above the channel valley. An effect of this depiction suggests that the wide swaths (sometimes overlapping between adjacent channels) near channel heads are less likely to be inundated.

The “spiked” edges of the inundation zones are a function of using grid data having only eight potential directions (NW, N, NE, etc.) in which to calculate any cross-section. Bracketing flow volumes and using graded shades of color makes a visually effective hazard map that illustrates uncertainty. The nested levels of hazard are equivalent to error bars where the increments between flow volumes reflect statistical errors.

59 Evidence from reports on the Scottsburg area suggests that several debris flows in the last two decades had volumes of ~103 m3. The range of flow volumes selected to run LAHARZ begin at this volume and span two orders of magnitude. The minimum flow volume may be a decadal or half-century frequency event and the larger flow volumes in that range would represent the less frequent events.

60 CHAPTER 7: CONCLUSIONS

A database of flow volumes, maximum inundated cross-sectional areas, and total inundated planimetric areas is compiled for 64 debris flows ranging in volume between 101 and 107 m3 (cubic meters) and 143 rock avalanches ranging between 105 and 1011 m3. The database of lahar inundation-area parameters collected by Iverson et al. (1998) includes events that range between 105 and 1011 m3. This sample of worldwide data provides statistical evidence that the populations of lahars, debris flows, and rock avalanches are distinguishable in terms of flow volume and inundation areas.

The main objective of this thesis is to provide a statistically tested and calibrated model that can be used to forecast inundation by debris flows and rock avalanches. The database is large and robust enough to provide a statistically valid result. The hypothesized relationship Area = α·Volume 2/3 with calibrated α- coefficients provides a good fit (r2 = 0.76 – 0.91) to the data with standard errors ranging from 0.45 – 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. 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 et al.

(1998). The equations imply no scale dependence of runout process, whereas previous estimates involving runout descent to length ratios (H/L) indicate runout is scale dependent.

61 The α-coefficients of these inundation-area equations reflect the typical bulk mobility of each type of flow, and they imply that rock avalanches and non-volcanic 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 of a rock avalanche is, on average, four times greater than that of lahars of the same volume.

Implementation of the inundation-area equations within the GIS-based program, LAHARZ, provides a repeatable, objective way to produce hazard maps.

Where DEMs with adequate resolution and accuracy are available, the suite of equations will expand the application of LAHARZ to include rock avalanches and debris flows. Use of a range of hypothetical flow volumes that span orders of magnitude provides graphical output that depicts uncertainty in our ability to predict 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 rather than predicting the flow path of the next event.

A strength of this methodology over 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 predicted areas of inundation are not physically based, that

62 the motion and run-up potential of granular mass flows is not considered around river bends or against obstacles. The predicted zones reflect the maximum swath that an average flowing landslide will pass through for a given volume. A final limitation is that events representing statistical outliers of a population (defined by the curves of

95% level of confidence for prediction) will not be accurately predicted.

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 given volume would reach a certain point down valley. Until that level of work is undertaken, postulating a range of flow volumes nonetheless gives a rough sense of the range of inundation possibilities.

Future Work and Improvements

Two topics in this thesis that deserve further consideration and need improvement are the initiation criteria for debris flows and rock avalanches in

LAHARZ and the need for a magnitude-frequency study for the Scottsburg area. The procedure for predicting runout from a source area is robust and objective. The criteria for identifying potential initiations are also objective and have undergone sensitivity analysis for the Scottsburg topographic dataset, but they are not tested against other susceptibility models.

A calibrated relationship between recurrence interval and flow volume for the

Scottsburg area would improve the usefulness of the hazard map for risk management officials. The smallest volume used in the Scottsburg hazard map represents roughly a

63 decadal event. Mapping older, larger volume flows in the Coast Range and identifying the frequency of similar volume events would improve our ability to constrain annual probability of occurrence.

Small volume rock avalanche events are seldom recorded because of their relative frequency, lack of consequence, and/or limited access. Whereas volcanic and non-volcanic debris flows span volumes from 10 – 109.5 m3, rock avalanche events of less than one million cubic meters are generally not documented. Scaling factors may become evident when smaller volume events are included in the database.

As LIDAR data become more affordable and available, hazard assessments for

<106 m3 debris flows will be more feasible. For now, access to topographic data of adequate resolution and accuracy is the limiting factor in the production of such debris-flow hazard maps.

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69

APPENDIX A/ DATA TABLE

70 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA 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 1971-1973 Columbia, Canada 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 Vargas State, Venezuela December 1, 1999 Avila DF 5 1,600,000 1,000 - - Sesa Landslide/ Grigna Bienno, northern Italy 1993 valley DF 6 1,200,000 300 - - Capricorn Creek Mount Meager, British 1998 Columbia, Canada DF 7 1,000,000 200 570,000 Ophir Creek Washoe County, Nevada, May, 1983 USA DF 8 1,000,000 400 - - Turbid Cr. British Columbia, Canada 1984

DF 9 200,300 - - 72800 Shadow Canyon vicinity of Boulder, Colorado, USA DF 10 200,000 - - 70,000 Capricorn Cr. British Columbia, Canada 1972

DF 11 195,000 3,700 160,000 Bullock Creek Mt Thomas, New Zealand April, 1978

DF 12 180,000 300 90,000 St. Peter's Dome Columbia Gorge, Oregon, November, 2001 USA DF 13 162,700 - - 59200 Fern Canyon vicinity of Boulder, Colorado 71 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B DF 14 150,000 40 - - Sourgrass Debris Flow Sierra Nevadas, California January 1, 1997

DF 15 136,000 - - 77,000 Cathedral Mountain southern Rockies, British 1978 Columbia, Canada DF 16 92,000 230 140,000 Hummingbird Creek Mara Lake, British July 11, 1997 Columbia, Canada DF 17 90,000 4,200 50,000 Cathedral Mountain southern Rockies, British 1946 Columbia, Canada DF 18 87,000 - - 52,000 Cathedral Mountain southern Rockies, British 1984 Columbia, Canada DF 19 80,000 - - 40,000 Cathedral Mountain southern Rockies, British 1925 Columbia, Canada 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, 1980

DF 22 63,000 77 - - Pierce Creek southern British Columbia November 28, 1995

DF 23 60,000 - - 35,000 Hot Springs Cr. British Columbia, Canada 1984

DF 24 55,000 48 - - Wahleach A British Columbia, Canada

DF 25 50,000 80 - - Hope Creek southern Rockies, British November 8, 1995 Columbia, Canada DF 26 50,000 32 21,000 Boundary Cr. British Columbia, Canada 1987

DF 27 25,000 50 18,000 Boundary Cr. British Columbia, Canada 1989 72 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B DF 28 24,000 - - 12,000 Cathedral Mountain southern Rockies, B.C. 1962

DF 29 20,000 78 10,000 Canyon Cr. British Columbia, Canada 1990

DF 30 20,000 70 7,000 Lower Ryan R. tributary British Columbia, Canada 1984

DF 31 20,000 400 - - Mt. Currie gully British Columbia, Canada 1989

DF 32 20,000 48 - - M-Creek British Columbia, Canada

DF 33 20,000 47 - - Charles Creek British Columbia, Canada

DF 34 17,000 30.0 16,000 Mayflower Tenmile Range, Colorado August 18, 1961

DF 35 10,000 - - 5,000 No Good Cr. British Columbia, Canada 1990

DF 36 10,000 55 8,000 Canyon Cr. British Columbia, Canada 1987

DF 37 9,990 50.4 2,180 Black Cr. Olympics, Washington

DF 38 7,000 - - 5,000 Cathedral Mountain southern Rockies, British 1982 Columbia, Canada DF 39 6,000 16 - - McGillivray Cr. gully British Columbia, Canada 1989

DF 40 5,790 19.4 1,540 Marlow#1 Coast Range, Oregon, USA

DF 41 5,000 - - 5,000 Cathedral Mountain southern Rockies, British 1984

73 Columbia, Canada Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B DF 42 5,000 12 8,600 Newton Canyon Santa Monica Mountains, December 29, 1965 California DF 43 5,000 37 7,500 Boundary Cr. British Columbia, Canada 1988

DF 44 4,800 22.8 2,400 Slide Cr. San Bernardinos, California

DF 45 3,500 20 - - Fountain Ridge gully British Columbia, Canada 1989

DF 46 3,390 23.0 2,110 SlumpAcres Coast Range, Oregon, USA

DF 47 3,000 44 1,570 Tom McDonald Creek California (Redwood Creek December 16, 1982 basin), USA DF 48 2,300 15.0 - - Oddstad California, USA

DF 49 2,000 11 - - Lillooet R. east fan British Columbia, Canada 1989

DF 50 1,050 30 1,100 New Zealand

DF 51 690 20.0 492 SlumpAcres 2 Coast Range, Oregon, USA

DF 52 660 38.8 621 Marlow#3 Coast Range, Oregon, USA

DF 53 660 5.0 - - Big Bend California, USA

DF 54 610 11.0 - - Yosemite site California, USA

DF 55 300 3.3 2,000 B1 California, USA 74 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B DF 56 200 5.6 - - Angel B gully British Columbia, Canada 1990

DF 57 200 3 - - United Kingdom

DF 58 150 - - 260 Old Topanga Canyon Santa Monica Mountains, January 26, 1969 California DF 59 135 20.6 240 Marlow#4 Coast Range, Oregon, USA

DF 60 100 15 100 No Good Cr. British Columbia, Canada 1988

DF 61 100 2.7 600 N32 Canada

DF 62 27.87 0.9 200 Levan debris flow Utah, USA

DF 63 10 0.7 200 N2 Canada

DF 64 10 0.5 250 USGS flume experiments USA 75 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 1 12,000,000,000 - - 51,000,000 Flims Alps

RA 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 Pleistocene

RA 4 25,000,000,000 - - 1,085,000,000 Raung Java Quaternary

RA 5 18,000,000,000 - - 400,000,000 Avachinsky Kamchatka Pleistocene

RA 6 16,000,000,000 - - 400,000,000 Peteroa South America

RA 7 15,000,000,000 - - 480,000,000 Socompa South America Holocene

RA 8 15,000,000,000 - - 200,000,000 Antuco South America

RA 9 15,000,000,000 - - 1,400,000,000 Meru Africa

RA 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 Pleistocene

RA 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. 76 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 15 7,100,000,000 - - 1,150,000,000 Mawenzi Africa

RA 16 7,000,000,000 - - 200,000,000 Drum USA Pleistocene

RA 17 3,500,000,000 800,000 170,000,000 Roque Nublo Canary Islands Pliocene

RA 18 3,000,000,000 - - 80,000,000 Calbuco South America Holocene

RA 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 Alps

RA 22 2,500,000,000 - - 64,000,000 Mount St. Helens Washington, USA 1980

RA 23 2,100,000,000 - - 12,000,000 Kofels Alps

RA 24 2,000,000,000 - - 28,000,000 Siders (Sierre) Alps

RA 25 2,000,000,000 - - 90,000,000 Asama Japan

RA 26 2,000,000,000 1,600,000 -- Usoi Landslide Dam Pamir Mountains, 1911 Tajikistan RA 27 1,800,000,000 - - 70,000,000 Iriga Philippines 1628

RA 28 1,500,000,000 - - 98,000,000 Shiveluch Kamchatka 1964 7 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 29 1,300,000,000 - - 65,000,000 Iwaki Japan

RA 30 1,500,000,000 - - 34,000,000 Bandai Japan 1888

RA 31 1,000,000,000 64,000 2,600,000 Mayunmarca Rockslide & Andes Mountains, Peru April 25, 1974 Debris Flow RA 32 1,000,000,000 - - 14,500,000 Fernpass Alps

RA 33 1,000,000,000 - - 45,000,000 Mombacho Central America

RA 34 1,000,000,000 - - 20,000,000 San Pedro-Pellado South America Holocene

RA 35 900,000,000 - - 6,800,000 Kandertal Alps

RA 36 800,000,000 - - 8,800,000 Glarnisch-Guppen Alps

RA 37 800,000,000 - - 27,000,000 Popa Burma Pleistocene

RA 38 800,000,000 - - 30,000,000 Bezymianny Kamchatka 1956

RA 39 600,000,000 - - 4,300,000 Totalp Alps

RA 40 600,000,000 - - 6,400,000 Dejenstock Alps

RA 41 550,000,000 - - 11,600,000 Monte Spinale Alps

RA 42 500,000,000 - - 30,000,000 Augustine, (2) West Island Alaska, USA 500 B.P. 78 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 43 500,000,000 - - 40,000,000 Sierra Velluda South America Holocene

RA 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 Mountains

RA 46 469,000,000 - - 12,000,000 Marocche im Sarcatal Alps

RA 47 400,000,000 - - 8,000,000 Parpan-Lenzerheide Alps

RA 48 400,000,000 - - 3,600,000 Lago di Molveno Alps

RA 49 400,000,000 - - 11,000,000 Eibsee Alps

RA 50 400,000,000 - - 96,000,000 Cotopaxi, South America < 20,000 B.P.

RA 51 360,000,000 - - 16,000,000 Dobratsch (2) Alps

RA 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 1792

RA 54 300,000,000 - - 21,000,000 Augustine, (1) Burr Point Alaska, USA 1883

RA 55 285,000,000 - - 1,900,000 Vaiont Alps 1963

RA 56 250,000,000 - - 5,200,000 Lago di Tovel Alps 79 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B 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, USA

RA 60 210,000,000 - - 13,200,000 Tschirgant Alps

RA 61 180,000,000 - - 4,000,000 Bormio Alps

RA 62 170,000,000 - - 7,000,000 Masiere di vedane Alps

RA 63 170,000,000 - - 8,000,000 Dobratsch (1) Alps

RA 64 165,000,000 - - 1,000,000 Lago di Poschiavo Alps

RA 65 150,000,000 - - 3,500,000 Lavini de Marco Alps

RA 66 150,000,000 - - 15,000,000 Abimes de Myans Alps

RA 67 150,000,000 - - 8,000,000 Chaos Crags USA 1650

RA 68 150,000,000 - - 15,000,000 Callaqui South America

RA 69 150,000,000 - - 10,000,000 Augustine (3) Alaska, USA ~1700

RA 70 140,000,000 - - 2,500,000 Obersee Alps 80 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 71 140,000,000 - - 18,000,000 Papandayan Indonesia 1772

RA 72 120,000,000 - - 1,700,000 Oeschinensee Alps

RA 73 115,000,000 - - 1,840,000 Cal de la Madeleine Alps

RA 74 100,000,000 120,000 22,500,000 Huascaran Peru 1970

RA 75 100,000,000 - - 1,000,000 Am Saum Alps

RA 76 100,000,000 - - 5,000,000 Oberterzen Alps

RA 77 100,000,000 - - 2,400,000 Mallnitz Alps

RA 78 91,000,000 - - 9,300,000 Lastarria South America prehistoric

RA 79 86,000,000 - - 1,550,000 Medecine Lake Canadian Rocky Mountains

RA 80 80,000,000 - - 4,000,000 Pletzachkogel Alps

RA 81 80,000,000 - - 3,580,000 Lofer Alps

RA 82 68,000,000 - - 1,600,000 Lac Lauvitel Alps

RA 83 56,500,000 - - 2,700,000 Obernbergtal Alps

RA 84 56,000,000 12,000 6,750,000 Ontake Japan 81 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 85 55,000,000 - - 8,520,000 Mt. Cook New Zealand 1991

RA 86 54,000,000 15,000 6,000,000 Mageik USA historic

RA 87 50,000,000 - - 2,000,000 Pontives Alps

RA 88 50,000,000 - - 2,300,000 Marquartstein Alps

RA 89 50,000,000 - - 2,200,000 Diablerets Alps 1714, 1749

RA 90 49,400,000 190,000 700,000 Madison Canyon Wyoming, USA 1959

RA 91 40,000,000 - - 1,000,000 Monte Avi Alps

RA 92 40,000,000 - - 1,000,000 Kleines Rinderhorn Alps

RA 93 39,100,000 - - 3,630,000 Mt. Kitchener Canadian Rocky Mountains

RA 94 38,000,000 - - 1,260,000 Danger Bay, west Crater Lake, Oregon, USA

RA 95 35,000,000 - - 4,000,000 Goldau Alps 1806

RA 96 34,000,000 - - 1,150,000 Danger Bay, east Crater Lake, Oregon, USA

RA 97 30,000,000 - - 700,000 Haslensee Alps

RA 98 30,000,000 - - 5,000,000 Dobratsch Alps 82 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 99 30,000,000 - - 850,000 Voralpsee Alps

RA 100 30,000,000 - - 1,100,000 Torbole Alps

RA 101 30,000,000 13,000 3,000,000 Frank Slide Turtle Mountain, Alberta, April, 1903 Canada RA 102 29,500,000 - - 1,700,000 Haiming Alps

RA 103 25,000,000 28,000 1,300,000 North Long John rock Inyo Mountains, Owens prehistoric avalanche Valley, California RA 104 25,000,000 - - 1,200,000 San Giovanni Alps

RA 105 21,000,000 - - 790,000 St Andre Alps

RA 106 20,000,000 - - 450,000 Mordbichl Alps

RA 107 20,000,000 - - 500,000 Lago di Alleghe Alps

RA 108 20,000,000 - - 1,000,000 Kals Alps

RA 109 17,500,000 - - 700,000 Monte Corno Alps

RA 110 16,000,000 - - 700,000 Brione Alps

RA 111 16,000,000 - - 410,000 Grand Clapier Alps

RA 112 15,000,000 - - 6,000,000 Huascaran Peru 1962 83 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 113 15,000,000 - - 800,000 Disentis Alps

RA 114 15,000,000 - - 1,000,000 Biasca Alps

RA 115 14,000,000 - - 600,000 Ludiano Alps

RA 116 14,000,000 - - 720,000 Llao Bay Crater Lake, Oregon, USA

RA 117 13,000,000 - - 950,000 Hintersee Alps

RA 118 13,000,000 - - 580,000 Eagle Point, west Crater Lake, Oregon, USA

RA 119 12,100,000 - - 8,250,000 Sherman Glacier USA 1964

RA 120 12,000,000 - - 1,000,000 Lago de Antrona Alps

RA 121 11,000,000 8,000 5,000,000 Rainier Washington, USA 1963

RA 122 10,000,000 - - 2,200,000 Mount Munday Canada 1997

RA 123 10,000,000 - - 580,000 Elm Alps 1881

RA 124 8,500,000 - - 880,000 Oberes Vallesinella Alps

RA 125 8,000,000 - - 600,000 Val Brenta Alta Alps

RA 126 7,000,000 - - 720,000 Melkode Alps 84 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 127 6,000,000 - - 470,000 North Nahanni Dist. Of Mackenzie, NWT, 1985 Canada RA 128 6,000,000 - - 430,000 Tucketthutte Alps

RA 129 5,000,000 - - 200,000 Prayon Alps

RA 130 5,000,000 12,000 600,000 Cayley, (1) Canada 1963

RA 131 4,820,000 - - 300,000 Beaver Flats south Canadian Rocky Mountains

RA 132 4,130,000 - - 300,000 Beaver Flats north Canadian Rocky Mountains

RA 133 4,000,000 - - 400,000 Fionnay Alps

RA 134 4,000,000 - - 4,000,000 Adams, USA (1) Washington, USA 1921

RA 135 3,000,000 - - 250,000 Winkelmatten Alps

RA 136 3,000,000 - - 700,000 Adams, USA (2) 1997

RA 137 2,400,000 - - 580,000 Cloudcap Bay Crater Lake, Oregon

RA 138 1,820,000 1,000 550,000 Puget Peak USA 1964

RA 139 1,640,000 - - 4,660,000 Jonas Creek south Canadian Rocky Mountains

RA 140 1,230,000 - - 2,590,000 Jonas Creek north Canadian Rocky Mountains 85 Maximum Inundated Total Inundated Flow Volume Cross-sectional Area Planimetric Area Ref # (cubic meters) (square meters) (square meters) Name of Event/ Location Region/ State/ Country Year * VA B RA 141 400,000 - - 200,000 Fidaz Alps

RA 142 150,000 - - 75,000 Haltenguet Alps

RA 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. 86

APPENDIX B/ DATA BIBLIOGRAPHY

87 DATA REFERENCES FOR APPENDIX A, DEBRIS FLOWS

Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 1 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. 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. DF 2 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. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 3 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. 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 DF 4 1999 storms in coastal Venezuela and strategies for mitigation: U.S. Geological Survey Open File Report 01-0144. Crosta, G. B., 2001, Failure and flow development of a complex slide: the DF 5 1993 Sesa landslide: Engineering Geology, v. 59, p. 173-199. Bovis, M. J., and Jakob, M., 2000, The July 29, 1998, debris flow and landslide dam at Capricorn Creek, Mount Meager Volcanic Complex, DF 6 southern Coast Mountains, British Columbia: Canadian Journal of Earth Sciences, v. 37, p. 1321-1334. Glancy, P. A., and Bell, J. W., 2000, Landslide-Induced Flooding at Ophir DF 7 Creek, Washoe County, Western Nevada, May 30, 1983: U.S. Geological Survey Professional Paper 1617. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 8 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Miller, H.F., 1979, Debris flows in the vicinity of Boulder, Colorado: DF 9 Boulder, Colorado, University of Colorado M.S. Thesis, 93 p. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 10 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Pierson, T. C., 1980, Erosion and Deposition by Debris Flows at Mt Thomas, DF 11 North Canterbury, New Zealand: Earth Surface Processes, v. 5, p. 227- 247. Janssen, K., and Marshall, C., 2003, unpublished report prepared for Fall DF 12 2002 Environmental Geology taught by Scott Burns, Debris flow along I-84. Miller, H.F., 1979, Debris flows in the vicinity of Boulder, Colorado: DF 13 Boulder, Colorado, University of Colorado M.S. Thesis, 93 p. DeGraff, J. V., 1997, Geologic Investigation of the Sourgrass Debris Flow, DF 14 Calaveras Ranger District, Stanislaus National Forest: U.S. Department of Agriculture; Forest Service, FS 6200-7.

88 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. DF 15 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. Jakob,M., Anderson, D., Fuller,T., Hungr,O. and Ayotte, D., 2000. An DF 16 Unusually Large Debris Flow at Hummingbird Creek, Mara Lake, British Columbia. Canadian Geotechnical Journal Vol. 37, no. 5, p.1109-1125. 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. DF 17 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. 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. DF 18 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. 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. DF 19 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. Iverson, R.M., Schilling, S.P., Vallance, J.W., 1998, Objective delineation of DF 20 lahar-inundation hazard zones: GSA Bulletin, vol. 100, no. 8, p. 972-984. Gallino, G. L., and Pierson, T. C., 1985, Polallie Creek debris flow and DF 21 subsequent dam-break flood of 1980, East Fork Hood River basin, Oregon, U.S. Geological Survey Water-Supply Paper 2273. Jakob, M., Hungr, O., and Thomson, B., 1997. Two debris flows with anomalously high magnitude. Debris Flow Hazards Mitigation, DF 22 Mechanics, Prediction and Assessment. Procs., The First International Conference on Debris Flow Hazards ASCE, C.L.Chen, Ed., Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 23 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Thurber Consultants Ltd, 1985, Debris torrent assessment, Wahleach and Floods, Highway 1, Hope to Boston Bar Creek summit, Coquihalla DF 24 Highway. Report to B.C. Min. Transportation and Highways, Victoria, B.C. Jakob, M., Hungr, O., and Thomson, B., 1997. Two debris flows with anomalously high magnitude. Debris Flow Hazards Mitigation, DF 25 Mechanics, Prediction and Assessment. Procs., The First International Conference on Debris Flow Hazards ASCE, C.L.Chen, Ed., San Francisco, pp. 382-394. 89 Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 26 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 27 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. 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. DF 28 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. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 29 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 30 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 31 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Thurber Consultants Ltd and Ker Priestman & Associates Ltd, [no date], Site DF 32 visit to debris torrent facilities at Charles Creek, Harvey Creek, Magnesia Creek, Alberta Creek, Lions Bay, B.C. Field Guide. Thurber Consultants Ltd and Ker Priestman & Associates Ltd, [no date], Site DF 33 visit to debris torrent facilities at Charles Creek, Harvey Creek, Magnesia Creek, Alberta Creek, Lions Bay, B.C. Field Guide. Curry, R.R., 1966, Observations of alpine mudflows in the Tenmile Range, DF 34 Colorado: Geological Society of America Bulletin, v. 77, p. 771-776. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 35 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 36 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Stock, J.D., 2001, unpublished data in personal communication to DF 37 R.M.Iverson, 1/16/2001, Berkeley, California. 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. DF 38 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. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 39 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Stock, J.D., 2001, unpublished data in personal communication to DF 40 R.M.Iverson, 1/16/2001, Berkeley, California. 90 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. DF 41 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. Campbell, R. H., 1975, Soil Slips, Debris Flows, and Rainstorms in the Santa DF 42 Monica Mountains and Vicinity, Southern California, U.S. Geological Survey Professional Paper 851. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 43 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Stock, J.D., 2001, unpublished data in personal communication to DF 44 R.M.Iverson, 1/16/2001, Berkeley, California. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 45 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. Stock, J.D., 2001, unpublished data in personal communication to DF 46 R.M.Iverson, 1/16/2001, Berkeley, California. Walter, T., unpublished data, 1983, Memorandum: Subj: Debris slide of DF 47 December 16, 1982. 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 DF 48 of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological Survey Professional Paper 1434, p. 163-183. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 49 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. McDonnell, J.J., 1990, The effect of macropores on debris flow initiation: DF 50 Quarterly Journal of Engineering Geology, vol. 23, p. 325- 332. Stock, J.D., 2001, unpublished data in personal communication to DF 51 R.M.Iverson, 1/16/2001, Berkeley, California. Stock, J.D., 2001, unpublished data in personal communication to DF 52 R.M.Iverson, 1/16/2001, Berkeley, California. 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 DF 53 of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological Survey Professional Paper 1434, p. 163-183. 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 DF 54 of the storm January 3-5, 1982, in the San Francisco Bay Region, California: U.S. Geological Survey Professional Paper 1434, p. 163-183. Owens, I.F., 1972, Morphological characteristics of alpine mudflows in the DF 55 Nigel Pass area, in Slaymaker, O., and McPherson, H.J., eds., Mountain Geomorphology: Vancouver, Tantalus Research, p. 93-100. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 56 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. 91 Bevin, K. and E.F. Woods, 1983, Catchment geomorphology and the DF 57 dynamics of runoff contributing areas: Journal of Hydrology, vol. 65, p. 139-158. Campbell, R. H., 1975, Soil Slips, Debris Flows, and Rainstorms in the Santa DF 58 Monica Mountains and Vicinity, Southern California, U.S. Geological Survey Professional Paper 851. Stock, J.D., 2001, unpublished data in personal communication to DF 59 R.M.Iverson, 1/16/2001, Berkeley, California. Jordan, P., 1994, Debris flows in the southern Coast Mountains, British DF 60 Columbia: dynamic behaviour and physical properties: University of B.C. PhD thesis, 260 p. 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92 DATA REFERENCES FOR APPENDIX A, ROCK AVALANCHES

Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. RA 1 Halfte. Tauchniz Leipzig. 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. RA 2 Crandell, D.R., 1989, Gigantic debris avalanche of Pleistocene age from ancestral Mount Shasta volcano, California, and debris-avalanche hazard zonation: USGS Bulletin 1861. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 3 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Bronto, S., Supriatman, I., Mulyana, R., 1996, Massive debris RA 4 avalanche from Raung Volcano, Eastern Java: EOS, vol. 77, p. S291. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 5 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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 RA 6 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. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 7 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Thiele, R., Moreno, H., Elgueta, S., Lahsen Azar, A., Rebolledo, S., Petit- Breuilh, M.E., 1998, Evolucion geologico-geomorfologica cuaternaria RA 8 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. Guest, N.J., Leedal, G.P., 1956, The volcanic activity of Mount Meru: RA 9 Records of the Geological Survey of Tanganyika 1953, Rec., v. 3, Geological Survey of Tanganyika, Tanzania, p. 40-7. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 10 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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 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. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 12 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459.

93

Hall, M.L., Robin, C.R., Beate, B., Mothes, P., Monzier, M., 1999, RA 13 Tungurahua Volcano, Ecuador- Structure, eruptive history, and hazards: Journal of Volcanology and Geothermal Research, vol. 91, p. 1-21. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 14 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Omenge, J.M., Mosley, P.N., 1995, Evidence for catastrophic flank collapse of Mt. Kilimanjaro and implication for geological hazard assessment, in RA 15 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. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 16 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Cacho, L.G., Iez-Gil, J.L., Arana, V., 1994, A large volcanic debris avalanche in the Pliocene Roque Nublo Stratovolcano, Gran Canaria, Canary RA 17 Islands: Journal of Volcanology and Geothermal Research, vol. 63, p. 217-229. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 18 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 19 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebe, C., Komorowski, J.C., Sheridan, M.F., 1992, Morphology and RA 20 emplacement of an unusual debris avalanche deposit at Jacotitlan volcano, Central Mexico: Bulletin of Volcanology, vol. 54, p.573-589. Arbenz, P., 1934, Helvetische Region, in ed. Schweiz Geol. Kommission, RA 21 Geologischer Fuhrer der Schweiz, Wepf & Cie, Basel. 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: RA 22 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. [Ampferer, 1939] compiled in Li Tianchi, 1983, A mathematical model for RA 23 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Lacger, 1903] compiled in Li Tianchi, 1983, A mathematical model for RA 24 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 25 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459.

94

Schuster, R.L., 2002. Usoi Landslide Dam and Lake Sarez, Pamir Mountains, RA 26 Tajikistan in Proceedings for Annual Conference of Geological Society of America, Denver, Colorado, October 27-30, 2002. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. RA 27 Aguila, L., Newhall, C.G., Miller, C.D., and Listanco, E., unpublished document, Reconnaissance geology of a large debris avalanche from Iriga Volcano, Philippines. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 28 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 29 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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 Schuster, R.L., and Crandell, D.R., 1984, Catastrophic debris avalanches from volcanoes in Proceedings of the Fourth Annual Conference on Landslides, Toronto. Kojan, E., and Hutchinson, J. N., 1978, Mayunmarca rockslide and debris RA 31 flow, Peru, in Voight, B., ed., Rockslides and Avalanches: Amsterdam, Netherlands (NLD), Elsevier Sci. Publ. Co., p. 315-361. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 32 Alpenvereinshefte 25: 230 S. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 33 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 34 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. [Turnau, 1906] compiled in Li Tianchi, 1983, A mathematical model for RA 35 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. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 37 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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., 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.

95

[Bornhauser, 1950] compiled in Li Tianchi, 1983, A mathematical model for RA 39 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. RA 40 Halfte. Tauchniz Leipzig. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 41 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. RA 42 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. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 43 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 44 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 45 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. [Trevisan, 1941] compiled in Li Tianchi, 1983, A mathematical model for RA 46 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Arbenz, 1934] compiled in Li Tianchi, 1983, A mathematical model for RA 47 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 48 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 49 Alpenvereinshefte 25: 230 S. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 50 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Till, A., 1907, Das Naturereignis von 1348 und die Bergsturze des Dobratsh, RA 51 Mitteilungen du K.K. Geographischen Gesellschaft in Wein 50 Wein. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 52 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 53 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459.

96

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., 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. Muller, L., 1964, The rock slide in the Vaiont Valley: Felsmechanik und RA 55 Ingenieur-geologie, vol. 2, p. 148-212. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 56 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Evans, S.G., and Brooks, G.R., 1991, Prehistoric debris avalanches from RA 57 Mount Cayley volcano, British Columbia: Canadian Journal of Earth Science, vol. 28, no. 9, p.xxx. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 58 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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 RA 59 visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey Geologic Investigations Series I-2790. Heuberger, H., 1968, Die Alpengletscher im Spat und Postglazial: Eine RA 60 chronologische Ubersicht, Eiszeitalter und Gegenwart, vol. 19, p. 270- 275 Ohringen (Hohenlohe'sche Buchhandl). [Turrer, 1962] compiled in Li Tianchi, 1983, A mathematical model for RA 61 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for RA 62 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Aichinger, 1951] compiled in Li Tianchi, 1983, A mathematical model for RA 63 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 64 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. Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des RA 66 amenagements hydrauliques: Masson et Cie, Paris, 343 pp. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 67 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. 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, RA 68 International meeting on Cities on volcanoes, Rome and Naples, Italy, June 28-July 4, 1998.

97

Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. RA 69 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. [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for RA 70 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 71 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. RA 72 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich. Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des RA 73 amenagements hydrauliques: Masson et Cie, Paris, 343 pp. Plafker, G. and Erickssen, G.E., 1978, Nevados Huascaran Avalanches, Peru RA 74 in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p.277-314. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 75 Alpenvereinshefte 25: 230 S. Heim, A., 1921, Geologie der Schweiz. Band 2: die Schweizer Alpen 1. RA 76 Halfte. Tauchniz Leipzig. [Hammer, 1927] compiled in Li Tianchi, 1983, A mathematical model for RA 77 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Naranjo, J.A., and Francis, P., High velocity debris avalanche at Lastarria RA 78 volcano in the north Chilean Andes: Bulletin of Volcanology, vol. 49, p. 509-514. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 79 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. Klebelsberg, R., 1935, Geologie von Tirol.- 872 S., 1 Kt., 11 Beil.; Berlin (Borntraeger)[Leidlmair, 1956] compiled in Li Tianchi, 1983, A RA 80 mathematical model for predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Leidlmair, 1956] compiled in Li Tianchi, 1983, A mathematical model for RA 81 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Gignoux, M., and Barbier, R., 1955, Geologie des barrages et des RA 82 amenagements hydrauliques: Masson et Cie, Paris, 343 pp. [Pashinger, 1953] compiled in Li Tianchi, 1983, A mathematical model for RA 83 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

98

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. RA 84 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. Hancox, G.T., Chin, T.J., and McSaveney, M.J., 1991, Immediate report, Mt. RA 85 Cook Rock Avalanche, 14 December 1991, Report by the New Zealand Department of Scientific and Industrial Research, 23 December 1991. Siebert, L., Glicken, H., Tadahide, U., 1987, Volcanic hazards from RA 86 Bezymianny- and Bandai- type eruptions: Bulletin of Volcanology, vol. 49, p. 435-459. Fuganti, A., and V.G. Antonio, 1969, Previsioni sul progettato traforo RA 87 ferroviario del Brennero in base studio geologico del tracciato: Convegno Int. sui problemi tecnini nella costruzione di gallerie, Torino, 1969. [Broili, 1914] compiled in Li Tianchi, 1983, A mathematical model for RA 88 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. Hadley, J.B., 1978, Madison Canyon Rockslide, Montana, USA in RA 90 Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 167-180. [Grasso, 1968] compiled in Li Tianchi, 1983, A mathematical model for RA 91 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Furrer, E., 1962, Der Bergsturz von Bormio: Vierteljahrsschrift der RA 92 Naturforschenden Gesellschaft in Zürich, vol. 107, p. 233-242. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 93 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. 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 RA 94 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. 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 RA 96 visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey Geologic Investigations Series I-2790. [Bruckner, 1901] compiled in Li Tianchi, 1983, A mathematical model for RA 97 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Till, A., 1907, Das Naturereignis von 1348 und die Bergsturze des Dobratsh, RA 98 Mitteilungen du K.K. Geographischen Gesellschaft in Wein 50 Wein. RA 99 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich. 99

[Penck, 1901] compiled in Li Tianchi, 1983, A mathematical model for RA 100 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Cruden, D.M. and O. Hungr, 1986, The debris of Frank Slide and theories of RA 101 rockslide-avalanche mobility: Can. J. Earth Science, vol. 23, 425-432. Heuberger, H., 1966, Gletschergeschichtliche Untersuchungen in den RA 102 Zentralalpen zwischen Sellrain und Ötztal: Wissenschaftliche Alpenvereinshefte, heft 20, 126 pp. Blair, T.C., 1999, Form facies and depositional history of the North Long RA 103 John rock avalanche, Owens Valley, California: Canadian Journal of Earth Science, vol. 36, p. 855-870. Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut RA 104 de l'ere chretienne a nos jours, in Materiaux pour l'Etude des Calamites, Societe de Geographie Geneve, vol. 32, p. 271-340. [Onde, 1938] compiled in Li Tianchi, 1983, A mathematical model for RA 105 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Klebelsberg, 1935] compiled in Li Tianchi, 1983, A mathematical model for RA 106 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut RA 107 de l'ere chretienne a nos jours, in Materiaux pour l'Etude des Calamites, Societe de Geographie Geneve, vol. 32, p. 271-340. [Cornelius, 1936] compiled in Li Tianchi, 1983, A mathematical model for RA 108 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 109 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Cadisch, 1953] compiled in Li Tianchi, 1983, A mathematical model for RA 110 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Bourdier, 1961] compiled in Li Tianchi, 1983, A mathematical model for RA 111 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Plafker, G. and Erickssen, G.E., 1978, Nevados Huascaran Avalanches, Peru RA 112 in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p.277-314. [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for RA 113 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Montandon, F., 1933, Chronologie des grands eboulements alpins, du debut RA 114 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.

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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 RA 116 visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey Geologic Investigations Series I-2790. [Gillitzer, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 117 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. 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 RA 118 visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey Geologic Investigations Series I-2790. McSaveney, M.J., Sherman Glacier Rock Avalanche, Alaska, USA in RA 119 Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 197-258. [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for RA 120 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Crandell, D.W, and Fahnestock, R.K, 1965, Rockfalls and avalanches from RA 121 Little Tahoma Peak on Mount Rainier Washington: Geological Survey Bulletin 1221-A, Washington, 30 p. Evans, S.G., and Clague, J.J., 1998, Rock avalanche from Mount Munday, RA 122 Waddington Range, British Columbia, Canada: Landslide News, no. 11, p. 23-25. RA 123 Heim, A., 1932, Bergsturz und Menschenleben, Fretz & Wasmuth, Zurich. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 124 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 125 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. [Schmit-Thome, 1960] compiled in Li Tianchi, 1983, A mathematical model RA 126 for predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. 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, RA 127 District of Mackenzie, N.W.T.: Canadian Journal of Earth Science, vol. 24, p. 179-184. [Schwinner, 1912] compiled in Li Tianchi, 1983, A mathematical model for RA 128 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 129 Alpenvereinshefte 25: 230 S. Clague, J.J., and Souther, J.G., 1983, The Dusty Creek landslide on Mount RA 130 Cayley, British Columbia: Canadian Journal of Earth Science, vol. 19, p. 524-539.

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Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 131 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 132 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 133 Alpenvereinshefte 25: 230 S. Vallance, J.W., 1999, Postglacial lahars and potential hazards in the White RA 134 Salmon River system on the southwest flank of Mount Adams, Washington: USGS Bulletin No. 2161. Abele, G., 1974, Bergstürze in den Alpen: Wissenschaftliche RA 135 Alpenvereinshefte 25: 230 S. RA 136 Iverson, R.M., written communication, August 1997: fieldnotes. 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 RA 137 visualize and analyze postcaldera volcanoes beneath Crater Lake, Oregon: U.S. Geological Survey Geologic Investigations Series I-2790. Hoyer, M., 1971, Puget Peak Avalanche, Alaska: Geological Society of RA 138 America Bulletin, vol. 82, p. 1267-1284. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 139 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. Voight, B., and Pariseau, W.G., 1978, Rockslides and avalanches, an RA 140 introduction in Rockslides and Avalanches, 1, B. Voight, ed., Elsevier Publishing Company, p. 2-63. [Niederer, 1941] compiled in Li Tianchi, 1983, A mathematical model for RA 141 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. [Jackli, 1957] compiled in Li Tianchi, 1983, A mathematical model for RA 143 predicting the extent of a major rockfall: Zeitschrift fur Geomorphologi N.F., Bd. 27, heft. 24, p. 472-482.

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