Holocene History of Deep-Seated Landsliding in the North Fork

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2-10-2017 Holocene History of Deep-Seated Landsliding in the North Fork Stillaguamish River Valley From Surface Roughness Analysis, Radiocarbon Dating, and Numerical Landscape Evolution Modeling

Adam M. Booth Portland State University, [email protected]

Sean R. LaHusen University of Washington-Seattle

Alison R. Duvall University of Washington-Seattle

David R. Montgomery University of Washington-Seattle

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Citation Details Booth A.M., LaHusen S.R., Duvall A.R., Montgomery D.R. 2017. Holocene History of Deep-Seated Landsliding in the North Fork Stillaguamish River Valley From Surface Roughness Analysis, Radiocarbon Dating, and Numerical Landscape Evolution Modeling. Journal of Geophysical Research: Earth Surface, 122(2):456-472.

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Journal of Geophysical Research: Earth Surface

RESEARCH ARTICLE Holocene history of deep-seated landsliding in the North 10.1002/2016JF003934 Fork Stillaguamish River valley from surface roughness Key Points: analysis, radiocarbon dating, and numerical • Surface roughness is a proxy for landslide age over thousand-year time landscape evolution modeling scales • In the NFS River valley, landslide Adam M. Booth1 , Sean R. LaHusen2 , Alison R. Duvall2, and David R. Montgomery2 deposits with ages of ~1000 cal years fl B.P. are most abundant, re ecting 1Department of Geology, Portland State University, Portland, Oregon, USA, 2Department of Earth and Space Sciences, changing climate and tectonics • Topographic wavelength-dependent University of Washington, Seattle, Washington, USA smoothing of surface roughness is consistent with soil transport theory Abstract Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of landslides on landscape evolution. While landslide inventories can map spatial Supporting Information: • Supporting Information S1 distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of landslides to analyze temporal patterns. Here we quantify the record of the Correspondence to: Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River A. M. Booth, valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the [email protected] ages of more than 200 deep-seated landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show Citation: that roughness systematically decreases with age as a function of topographic wavelength, consistent with Booth, A. M., S. R. LaHusen, A. R. Duvall, and D. R. Montgomery (2017), Holocene models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency history of deep-seated landsliding in the at ~1000 calibrated (cal) years B.P., with very few landslide deposits older than 7000 cal years B.P. or younger North Fork Stillaguamish River valley than 100 cal years B.P., likely reflecting a combination of preservation bias and a complex history of changing from surface roughness analysis, radiocarbon dating, and numerical climate, base level, and seismic shaking in the study area. Most recent landslides have occurred where landscape evolution modeling, channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral J. Geophys. Res. Earth Surf., 122, 456–472, channel migration is a primary control on the location of large deep-seated landslides in the valley. doi:10.1002/2016JF003934.

Received 22 APR 2016 1. Introduction Accepted 21 JAN 2017 Accepted article online 27 JAN 2017 Quantifying the magnitude, timing, and spatial distribution of a region’s landslides is essential to determine Published online 10 FEB 2017 their contribution to landscape evolution. Specifically, landslides ranging over many orders of magnitude in size control the erosion rate and topographic form of mountain ranges [Hovius et al., 1997; Larsen and Montgomery, 2012] and regulate the size, amount, and longevity of sediment delivered to channels [Costa and Schuster, 1988; Miller and Benda, 2000; Korup et al., 2004; Ouimet et al., 2007]. Because landslides often occur in response to specific triggers, such as prolonged or intense rainfall [Caine, 1980] or earthquakes [Keefer, 1984], spatial and temporal patterns of landsliding may also provide insight into a region’s climatic and tectonic history [e. g. Reneau et al., 1990; Jibson, 1996; Trauth et al., 2003; Borgatti and Soldati, 2010; Stock and Uhrhammer, 2010; Mackey and Quigley, 2014]. Large, deep-seated landslides, defined here as those having failure planes deeper than the rooting depth of trees, may be particularly useful for interpreting a landscape’s long-term history beyond decadal time scales because their deposit morphology can persist for millennia. Active landslide deposits tend to have sharp, well-defined displaced blocks bounded by internal scarps, hummocky topography, and closed depressions. With time, these features become more subdued, poorly defined, or otherwise modified by nonlandslide processes [Keaton and DeGraff, 1996]. In the western United States, the transformation of a landslide from active to relict, characterized by only weak or subtle landslide features, can take 10 kyr or more, suggesting that landslide morphology can be useful for approximately dating landslides over the Holocene [McCalpin, 1984]. Landslide inventory maps [Wieczorek, 1984; Guzzetti et al., 2012] often use deposit morphology and crosscutting relationships to assign relative ages to landslides [Burns and Madin, 2009; Haugerud, 2014; Pierson et al., 2016]. Absolute dating of deep-seated landslides has shown that they tend to occur more frequently during wetter or more variable climatic periods [Bovis and Jones, 1992; Panizza et al., 1996; Hermanns

©2017. American Geophysical Union. et al., 2000; Trauth et al., 2003; Soldati et al., 2004; Prager et al., 2008; Borgatti and Soldati, 2010], during periods All Rights Reserved. of high seismicity [Hermanns et al., 2000], or during periods of rapid base level lowering [Bilderback et al.,

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 456 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

2014]. Moreover, structural, lithologic, climatic, fluvial, and tectonic conditions all influence the spatial distribution of landslides [Hermanns and Strecker, 1999; Roering et al., 2005; Mackey and Roering, 2011; Safran et al., 2011; Scheingross et al., 2013]. Constructing detailed deep-seated landslide chronologies for a region relies on precise absolute age dating of large numbers of landslides. Radiocarbon dating of organic material from within a landslide deposit is the most frequently used technique, but it can be problematic for several reasons. Foremost, dateable material may be absent, and if material is present, it may represent the entrainment of older organic material with ages that predate the landslide event [Panek, 2014]. This possibility is especially likely in heavily vegetated areas with abundant dead organic material in the landslide path [Dufresne et al., 2010]. Furthermore, many deep-seated landslides can be persistently active for centuries or longer [Bovis and Jones, 1992; Mackey et al., 2009] and may repeatedly reactivate after periods of dormancy. It therefore may be unclear what specific landslide event a radiocarbon date documents. As a result of these limitations, the most thorough studies relying on radiometric methods have dated at most a few tens of deep-seated landslides [Borgatti and Soldati, 2010], and radiometric dates are best suited to landslides that occurred as a single or short- duration event. In this study, we minimize many of these limitations by conducting work in the North Fork Stillaguamish (NFS) valley, Washington, United States, where many slope failures have occurred as discrete events that can be dated with radiometric techniques to within a few hundred years [LaHusen et al., 2016]. In the central part of the NFS valley, the 2014 Oso landslide killed 43 people due to its high mobility [Keaton et al., 2014; Iverson et al., 2015; Wartman et al., 2016]. Although that site had been intermittently active with smaller landslides for several decades [Miller and Sias, 1998], the 2014 failure began as a large, extremely rapid debris avalanche-flow [Iverson et al., 2015], with its deposit recording a multistaged event that occurred over several minutes [Keaton et al., 2014]. Numerous deep-seated landslides in the NFS valley have similar morphology to the Oso slide, implying that they also failed primarily as discrete, large events. Radiocarbon dating of organic debris found within a landslide deposit should therefore have a high likelihood of dating discrete landslide events at this study site, allowing us to develop an empirical age-roughness relationship that we apply to the hundreds of landslides identified in the valley. Surface roughness of landslide features measured from high-resolution topographic data at length scales greater than ~1 m may generally reflect multiple properties of a landslide including its size, type, activity, material properties, boundary conditions, runout, and age [Goetz et al., 2014]. Landslides are typically rougher than the surrounding stable terrain, which has enabled automatic delineation of large numbers of landslide features [Booth et al., 2009; Tarolli et al., 2010; Van Den Eeckhaut et al., 2012]. Additionally, differences in roughness have been used to objectively identify parts of a landslide complex with different mechanical properties or movement styles [McKean and Roering, 2004; Glenn et al., 2006]. While different measures of surface roughness perform comparably in delineating landslide deposits [Berti et al., 2013], the conditions for which roughness might reflect landslide age, and which measures of surface roughness best indicate age, remain to be determined. Recently, Goetz et al. [2014] demonstrated that three different measures of surface roughness showed little to no correlation with landslide age in the Swabian Alb, Germany, over a timespan of ~200 years. Those authors suggested that the small sample size of 12 landslides and short time period of analysis may have caused confounding factors such as landslide reactivation or lithologic variation to obscure a potential age-roughness relationship. However, over longer time scales of hundreds to thousands of years, with larger sample sizes, and by focusing on landslides with similar failure mechanisms that occurred in a similar substrate, we expect surface roughness to track landslide age in the NFS valley [LaHusen et al., 2016]. Determining a robust age-roughness relationship and evaluating its performance require a large data set of landslides with both absolute ages and crosscutting relationships to assess relative ages. Quantifying how surface roughness changes with time can also illuminate landscape evolution by near-surface soil transport, which is a primary process that smooths topography with time. This study combines landslide mapping, surface roughness analysis, radiocarbon dating, and numerical modeling to analyze spatial and temporal patterns of landsliding along a 25 km stretch of the NFS valley known to have a high concentration of landslides [Dragovich et al., 2003a, 2003b; Haugerud, 2014; Badger, 2015; LaHusen et al., 2016]. We first present a map of 218 deep-seated landslides based on lidar interpretation and field verification. We then evaluate the performance of three measures of topographic roughness calculated at different spatial scales to correctly identify relative landslide ages based on crosscutting relationships,

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 457 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 1. (a and b) Overview maps of the study region including major faults and rivers and (c) landslide map of the study area, with each landslide deposit colored according to its surface roughness and corresponding predicted age (sections 3.2 and 4).

as well as to reflect landslide absolute age determined by radiocarbon dating. These absolute ages define an empirical age-roughness model, which we use to place bounds on the hillslope transport coefficient, a fundamental constant in a widely applied nonlinear hillslope evolution model. We conclude by analyzing the pattern of landsliding in space and time in the context of fluvial processes, Holocene climate change, and regional seismicity.

2. Background and Study Area The NFS River drains the western Cascade foothills into Puget Sound (Figures 1a and 1b) and flows through a landscape bearing a strong imprint of late Pleistocene glacial processes. The Puget Lobe [Bretz, 1913] of the Cordilleran Ice Sheet advanced southward into the Puget lowlands and adjacent valleys of the western Cascades ~19,000 cal years B.P. and reached the NFS valley ~18,000 cal years B.P. [Porter and Swanson, 1998]. At times, the Puget Lobe blocked west-flowing valleys, forming ice-dammed lakes [Mackin, 1941; Booth, 1986; Porter and Swanson, 1998]. This process deposited a series of advance glaciolacustrine sediments consisting of silt to silty fine sand, coarser advance outwash deposits, and till in the NFS valley [Dragovich et al., 2003a, 2003b]. These sediments now underlie the lower portions of hillslopes lining both sides of the valley. After reaching its maximum extent ~16.8 kyr B.P. [Porter and Swanson, 1998], the Puget Lobe began to retreat, streams draining around its margin deposited sand and gravel as recessional outwash, and less extensive and finer grained recessional glaciolacustrine sediments were deposited in glacially dammed lakes [Booth, 1986]. These sediments underlie the upper portions of hillslopes and the broad terraces on both sides of the NFS valley, such as Whitman Bench to the northwest of the Oso landslide (Figure 1c). The pattern of permeable, unconsolidated sediments above less permeable, consolidated layers is common throughout the Puget lowlands, and the resulting perched groundwater generates large numbers of landslides in the region [Tubbs, 1974]. On this glacial backdrop, several additional processes such as changes in base level and stream power, climate, and earthquakes might also relate to the timing and spatial arrangement of landslide deposits currently preserved in the NFS valley [e.g., Palmquist and Bible, 1980]. As the Puget Lobe retreated past the NFS valley ~16,400 cal years B.P. [Porter and Swanson, 1998], marine water reentered the Puget Lowlands, and the regional drainage network shifted from draining southwest to the Pacific through the modern Chehalis River to draining northwest through the Strait of Juan de Fuca. The NFS River rapidly incised through ~70 m of glacial sediments by 12,500 cal years B.P. at Arlington, Washington, near the western end of our study area, and up to ~200 m of sediments in the central part of

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the study area, as isostatic uplift exceeded eustatic sea level rise [Thorson, 1981; Beechie et al., 2001]. Also at ~12,500 cal years B.P., a lahar from Glacier Peak diverted the Sauk, Suiattle, and upper Skagit Rivers from flowing through the NFS valley to instead flow north through the Skagit River valley, reducing the drainage area of the NFS River (Figure 1b) [Beget, 1982; Tabor et al., 2001; Booth et al., 2003]. From 12,500 to 5500 cal years B.P. relative sea level, the position of the river’s mouth and its base level remained fairly constant [Mathews et al., 1970; Booth, 1987; Beechie et al., 2001], and the river has only incised 4 m since 11,700 cal years B.P. [LaHusen et al., 2016]. This incision history is relevant to landsliding because the rapid drop in base level following ice retreat generated local relief, potentially initiating landslides, while the subsequent reduction in drainage area would have reduced the NFS River’s ability to transport sediment delivered to the channel by landslides. Following deglaciation, a relatively cool climate promoted the establishment of subalpine conifer forests in western Washington, which lasted until ~10,000 cal years B.P. [Barnosky, 1981; Leopold et al., 1982; Benda et al., 1992]. Around that time, the climate became warmer and drier, and vegetation patterns shifted to become dominated by Alder (genus: Alnus) and Douglas fir(Pseudotsuga)[Barnosky, 1981; Leopold et al., 1982; Cwynar, 1987]. The warm and dry periods persisted until ~6000 cal years B.P., after which cooler and wetter conditions promoted the establishment of modern forest composition with an expansion of Cedar (Cupressaceae) and Hemlock (Tsuga) and a reduction in Alder (Alnus)[Leopold et al., 1982; Cwynar, 1987; Brubaker, 1991]. The landslides analyzed in this study are deep-seated with failure planes below the rooting depth of trees, so changes in precipitation and forest composition likely affected conditions at their failure planes by altering recharge to the deeper groundwater system. The postglacial NFS valley has likely experienced moderate or more intense ground shaking (Modified Mercalli Intensity (MMI) of V or greater), which is an approximate lower bound for triggering landslides [Keefer, 1984], caused by earthquakes on three crustal faults that have been active in the Quaternary [Czajkowski and Bowman, 2014] as well as on the Cascadia subduction zone (Figure 1a). The Cascadia subduction zone has produced 19 full or partial margin ruptures over the past 10 kyr [Goldfinger et al., 2012], many of which probably caused strong shaking (MMI of VI) in the study area (Washington State Seismic Hazards Catalog (https://fortress.wa.gov/dnr/seismicscenarios/)). The record of offshore turbidite deposits at numerous sites and tsunami deposits in coastal areas suggests that the most recent of the full margin rupture earthquakes occurred at A.D. 1700, ~480 cal years B.P., ~800 cal years B.P., ~1200 cal years B.P., ~1500 cal years B.P., and ~2500 cal years B.P. [Atwater et al., 2003; Nelson et al., 2006; Goldfinger et al., 2012]. The Seattle fault zone has also produced a cluster of late Holocene earthquakes that probably caused moderate shaking in the study area, the best documented of which occurred 1050–1020 cal years B.P. [Atwater and Moore, 1992; Bucknam et al., 1992; Schuster et al., 1992; Atwater, 1999; Nelson et al., 2003]. The southern Whidbey Island fault zone is closer to the study area and potentially caused strong to very strong shaking in the study area ~3000 cal years B.P. and more recently than ~2730 cal years B.P. [Kelsey et al., 2004; Sherrod et al., 2008]. The Darrington Hills-Devil Mountain fault zone runs through the central part of the study area, crossing the Whitman Bench, and possibly caused intense shaking ~2000 cal years B.P. [Personius et al., 2014].

3. Landslide Mapping and Surface Roughness Analysis 3.1. Landslide Inventory Map We first created a landslide map of the NFS valley (Figure 1c) for landslides occurring only in glacial sediments in order to minimize the potential influence of different substrates on landslide style and morphology. We based our mapping on visual interpretation of lidar-derived slope and hillshade maps, following Schulz [2004] and Burns and Madin [2009]. We identified landslides clearly visible as hummocky deposits of displaced blocks below arcuate head scarps at a scale of 1:12,500, which allowed us to refine the boundaries of previously mapped landslides [Dragovich et al., 2003a, 2003b; Haugerud, 2014; Badger, 2015; LaHusen et al., 2016] and in many cases to distinguish multiple landslides within one previously mapped landslide complex. In such landslide complexes, we distinguished individual landslides when there were clear head and lateral scarps that separated the lower landslide from the upper landslide within the complex. Although scarps were used to identify landslides, we outlined only deposits in the map in order to focus our surface roughness analysis and numerical modeling on

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 459 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 2. Landslide and valley characteristics as a function of distance east of the study site boundary. (a) Valley width (left axis, open black circles) and glacial terrace relief on the north (right axis, light gray points) and south (right axis, dark gray points) sides of the valley. (b) Normalized histograms showing the fraction of landslides calculated by the number of landslides (hachured bars) or the area of landslides (gray bars).

this single morphologic unit. Furthermore, to avoid potential bias, we completed all mapping prior to calculating surface roughness. While collecting samples for radiocarbon dating, we verified the lidar- based mapping in the field at those locations by confirming the presence of head and lateral scarps at slide boundaries. Lidar data available through the Puget Sound LIDAR Consortium (http:// pugetsoundlidar.ess.washington.edu/) were collected in 2003, 2013, and 2014 after the Oso landslide, and the extent of the two most recent, 3 ft (0.9144 m) resolution data sets defined the study area. The landslide inventory map shows 218 deep-seated landslides (Figure 1c). The smallest landslide deposit visible at the mapping scale has an area of ~400 m2; the modal landslide size, defined by the peak of the his- togram of landslide areas, is ~8000 m2, and the largest landslide in the valley, the Rowan landslide, has an area of 1.7 km2. To quantify the spatial pattern of these landslides, we plotted their frequency as a function of distance east of the study site boundary and compared this to valley width and glacial terrace relief (Figure 2). Width was defined as the north-south distance across the valley at an elevation of 75 m above the NFS River, and relief was measured by subtracting the elevation of the terraces from the elevation of the river on both the north and south sides of the valley. The distribution of landslides as a function of east-west distance is bimodal and broadly higher in the central part of the valley where glacial terrace relief is also high, especially to the north of the river, and where valley width is narrow. The two main peaks in the distribution at ~8 km and ~12 km are centered on the locations where two major tributary streams, Brooks Creek and Rollins Creek, respectively, join the NFS River from the north, while the lower number of and area affected by glacial landslides between these peaks correspond to a prominent bedrock knob on the north side of the valley just west of the Rowan landslide (Figure 3) [Dragovich et al., 2003b]. The distribution tapers off at either end of the study area where the valley widens and the glacial terraces are absent or lower than in the central part of the valley.

3.2. Surface Roughness Analysis and Relative Ages After a landslide occurs, near-surface soil transport and incision by overland flow alter the deposit morphology with time. While soil transport tends to smooth sharp features and decrease topographic roughness, incision by overland flow can create and maintain sharp-edged gully features, thereby increasing roughness, as quantified on alluvial fan surfaces of different ages [Frankel and Dolan, 2007]. To define a monotonic relationship between landslide age and surface roughness, we therefore excluded gullies from our roughness analysis. We identified gullies by using arbitrary thresholds of drainage area and

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 460 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 3. Surfaces used for Fourier analysis in Figure 4 (thick black outlines around Whitman Bench, Headache Creek, Rowan, and Oso) and locations of radiocarbon samples (black and white points) with dates ±1σ uncertainties in calibrated years B.P. from this study (Table 2) and from LaHusen et al. [2016]. Landslide polygons include examples of gullies and roads excluded from the roughness analysis.

topographic curvature, defined as the Laplacian of elevation, that qualitatively provided a good match to features clearly identifiable as gullies in the lidar data (Figure 3). Pixels with drainage areas of >2790 m2, measured by using the D-infinity algorithm [Tarboton, 1997], and convex-up curvatures of >0.09 m 1, measured over a smoothing length scale of 7 m by using the wavelet transform and Mexican hat wavelet [Lashermes et al., 2007; Booth et al., 2009], correspond well to the gully network on most landslide deposits. To minimize misclassification of short, quasi-linear depressions between landslide hummocks as gullies, we applied the additional constraint that more than 10 pixels tentatively identified as gully pixels must be continuously connected in order for them to be defined as a gully. We also excluded roads and other human modifications to the landscape that crossed the landslide polygons. Last, we applied a buffer of 10 m radius to all roads and gullies and excluded these areas from the surface roughness analysis (Figure 3). We evaluated the performance of three surface roughness metrics calculated at two different spatial scales based on their ability to correctly identify relative age relationships of overlapping landslide polygons and to track known absolute ages. To inform our selection of spatial scales, we first computed the two- dimensional Fourier transform of the topography for a reference surface unaffected by landslides, the Whitman Bench glacial terrace, and three landslides with known absolute ages: Headache Creek, Rowan, and Oso (Figure 3), using a 28 m moving window. The Fourier transform measures the variance of the topography in each window as a function of spatial frequency, thereby providing a measure of surface roughness as a function of spatial scale [Rayner, 1972; Perron et al., 2008; Booth et al., 2009]. At all four sites, topographic variance increases with spatial wavelength (Figure 4a). Relative to the reference surface, the three landslides all have higher variance at all spatial scales, and the younger landslides tend to have higher variance than the older landslides. Normalizing each landslide’s variance by dividing it by that of the reference surface (Figure 4b) highlights that these effects are most pronounced for spatial wavelengths ≥10 m, approximately the size of the smaller landslide hummocks and displaced blocks, where the variance of the landslide surface topography is significantly higher (at a 99% significance level) when compared to the reference surface. Furthermore, topographic variance decreases with landslide age most rapidly at the shortest length scales and more slowly at the longest length scales, consistent with hillslope evolution by diffusive hillslope processes [Culling, 1965; Mudd and Furbish, 2007].

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 461 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Based on the Fourier analysis, we defined one surface roughness metric as the sum of the topographic variance over spatial scales from 10 to 30 m. The additional metrics of surface roughness were the standard deviation of slope (SDS) measured in a moving window and the average magnitude of the curvature measured at specific spatial scales by using the 2-D continuous wavelet transform and Mexican hat wavelet [Lashermes et al., 2007; Booth et al., 2009]. We chose not to use metrics that directly take into account variability in aspect [e.g., McKean and Roering, 2004] because those metrics tend to produce anomalously high roughness values for relatively flat surfaces such as the large bench-like deposits of the older landslides in our study area. We computed roughness metrics at the shortest spatial scales possible given the digital elevation model (DEM) resolution, 3 m for SDS and 4 m for wavelet-based curvature, in order to capture the rapid decrease of roughness with landslide age at the shortest spatial scales, as well as at a longer length scale of Figure 4. (a) Power spectra generated from the Fourier transform of the Oso, Rowan, and Headache Creek landslide deposits, with the 15 m to capture the more pronounced power spectrum of Whitman Bench as a reference, nonlandslide changes in roughness with landslide age at surface. (b) Power spectra of the landslides normalized by that of those spatial scales. The roughness metrics Whitman Bench. were calculated for each pixel in the entire DEM, and areas near the study area boundaries contaminated by edge effects were excluded from analysis. The roughness of each landslide polygon was then defined as the mean roughness of all pixels in the polygon, excluding the buffered gully and road pixels. We then identified 134 crosscutting relationships between pairs of overlapping landslide deposits and determined whether or not each roughness metric correctly identified the younger landslide as rougher than the older landslide in the pair. The roughness metrics taken at shorter spatial scales correctly classified the highest numbers of crosscutting relationships, with wavelet-based curvature at a 4 m length scale getting 90% correct, and SDS in a 3 m diameter window getting 87% correct (Table 1). SDS in a 15 m diameter window performed next best with 84% of crosscutting relationships correctly characterized, while the other longer-spatial-scale metrics performed more poorly at 69% correct for the Fourier transform at 10–30 m wavelengths and at 65% correct for the wavelet-based curvature at a 15 m spatial scale. The major differences in performance between short- and long-spatial-scale measurements arose mainly from the smallest landslides in the inventory, where crosscutting relationships were clear, but surface roughness of the two adjacent landslides was often quite similar.

Table 1. Evaluation of Surface Roughness Metrics for Estimating Landslide Relative and Absolute Ages Surface Roughness Metric (and Spatial Scale)

Fourier Transform Standard Deviation of Standard Deviation of Wavelet-Based Wavelet-Based (10–30 m) Slope (3 m) Slope (15 m) Curvature (4 m) Curvature (15 m)

Number (and %) of crosscutting 93 (69%) 117 (87%) 113 (84%) 120 (90%) 87 (65%) relationships correct RMSE absolute age misﬁt (years)a 1600 2000 1700 1800 1200 aRMSE absolute age misﬁt is the root-mean-square error between the seven known and predicted ages shown in Figure 5.

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 462 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Table 2. Sample Locations, Descriptions, and Radiocarbon Dates Sample Location(s) Radiocarbon Age(s) Calibrated Age(s) Landslide or Feature Dated (Long; Lat) Sample Material (B.P. ± 1σ Error) (cal B.P. ± 1σ Error)

Skaglund Hill landslide 121.860; 48.267 Wood 253 ± 29 233 ± 79 121.860; 48.267 Wood 192 ± 30 144 ± 144 121.860; 48.268 Wood 231 ± 28 229 ± 77 Unnamed landslide 121.864; 48.279 Wood 1330 ± 31 1243 ± 53 Unnamed landslide 121.864; 48.280 Wood 557 ± 26 580 ± 45 Terrace postdating 121.827; 48.284 Wood 3812 ± 27 4198 ± 52 C-Post Road landslide 121.827; 48.284 Wood 3727 ± 24 4073 ± 52 Rowan landslidea 121.879; 48.272 Wood 572 ± 37 588 ± 64 121.877; 48.272 Wood 307 ± 30 382 ± 82 121.869; 48.274 Wood 722 ± 24 674 ± 21 121.869; 48.274 Wood 693 ± 24 624 ± 59 121.876; 48.283 Bark 368 ± 23 410 ± 100 121.876; 48.283 Wood 426 ± 29 430 ± 96 Headache Creek landslidea 121.850; 48.287 Bark 5304 ± 28 6089 ± 94 121.849; 48.286 Bark 5371 ± 28 6149 ± 130 121.848; 48.286 Bark 5138 ± 27 5870 ± 113 Fluvial terrace buried by 121.858; 48.274 Wood 10,103 ± 37 11,700 ± 286 Rowan landslidea aDates from Rowan, Headache Creek, and the Fluvial Terrace Buried by Rowan are From LaHusen et al. [2016].

Since each roughness metric was calculated in a moving window of a given width, each pixel’s roughness value was inﬂuenced by neighboring pixels, such that pixels outside a landslide polygon affected the calcula- tion of roughness near the boundary of that polygon. These border effects were most pronounced for small landslides and large window sizes, which could explain the poorer performance of the long-spatial-scale roughness metrics.

4. Absolute Ages To quantify the timing of landslides in the NFS valley, we defined an empirical relationship between surface roughness and absolute age for seven landslides based on 17 radiocarbon dates as well as an age of 0 for the Oso landslide (Table 2 and Figure 3). We used the following previously determined average calibrated radiocarbon ages: 518 cal years B.P. for the Rowan landslide, 6000 cal years B.P. for the Headache Creek landslide, and 11,700 cal years B.P. from a fluvial terrace for the oldest landslide at the site [LaHusen et al., 2016]. The six radiocarbon dates from different locations on the Rowan landslide have a relatively narrow range of 382 ± 82 to 674 ± 21 cal years B.P., suggesting that despite the possibility of landslide reactivations and incorporation of organic material from previous events into the deposit, radiocarbon dating may constrain the most recent failure date to within a few hundred years. For the Skaglund Hill landslide complex, we obtained three dates of 233 ± 79, 229 ± 77, and 144 ± 144 cal years B.P. from wood exposed by gully incision in the upper (233 cal years B.P.) and lower (229 and 144 cal years B.P.) parts of the landslide complex (Table 2 and Figure 3). These three ages all overlap within their uncertainties and can be reasonably interpreted in several different ways. The first interpretation is that the landslide complex formed from a single event, or multiple events spaced closely in time, that occurred more recently than ~300 cal years B.P.. On the other hand, if the complex is correctly mapped as two separate landslides, the dates could record only the younger, lower landslide. In this interpretation, the age of the wood found in the upper, older landslide could relate to more recent gully erosion processes occurring around the time of the lower landslide or to formation of the lower landslide’s head scarp, which is just a few meters from the sample site location. We favor mapping the landslide complex as two separate events based on the clearly defined head scarp of the lower slide and consistent with previous interpretations [Haugerud, 2014; LaHusen et al., 2016] and assign an age of 230 cal years B.P. to the lower slide. In any case, dividing the landslide complex into one or two slides did not result in statistically significant differences between the resulting age-roughness models. We also obtained two dates of 1243 ± 53 and 580 ± 45 cal years B.P. from two different samples found in a gully wall that defines the border between two unnamed landslides to the northeast of the Rowan

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 463 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 5. Landslide age versus mean roughness of the landslide deposit, quantified by the 2-D wavelet transform at a 15 m smoothing length scale. The black diamonds are radiocarbon dates from previous work (open symbols) and dates from this study (filled symbols, Table 2). The grey diamond is the age of a terrace representing a minimum age for the adjacent landslide. Uncertainties in the horizontal are described in section 4, and uncertainties in the vertical are approximately the symbol size. The dashed grey line is an exponential fit to the seven landslide ages. The open circles are numerical modeling results for different values of the hillslope transport coefficient, K (equation (1)), using the Oso landslide deposit as the initial condition (supporting information).

landslide (Figure 3 and Table 2). The older age likely documents wood that was buried by the older, larger landslide to the northeast of the sample location and then exposed by movement of the younger, smaller landslide just west of the sample location, similar to the way the Oso landslide exposed wood previously buried by the neighboring Headache Creek landslide [Keaton et al., 2014; LaHusen et al., 2016]. The younger age likely documents the smaller landslide, which overrides the Rowan landslide deposit and falls within its range of age estimates, indicating that it may have occurred in conjunction with that landslide. Two samples of wood obtained from a terrace on the north side of the NFS River and east of the Oso landslide have ages of 4198 ± 52 and 4073 ± 52 cal years B.P. (Table 2 and Figure 3). The terrace postdates the large C- Post Road landslide directly to its north and therefore constrains only the minimum age of that landslide. As such, we do not use this date to define the age-roughness curve. Despite the better performance of the two short-length-scale roughness metrics at identifying relative ages (Table 1 and section 3.2), they do not perform as well in explaining the absolute age data. To quantify this tendency, we fit an exponential curve to each set of age versus roughness data and determined the misfit between the data and the best fit exponential model (Table 1). The 15 m length-scale wavelet-based curvature performed the best, followed by the other two long-length-scale roughness metrics, while the short-spatial-scale roughness metrics resulted in the highest misfits. The longer wavelength metrics likely performed best because those length scales correspond to the sizes of the dominant roughness elements, such as hummocks and displaced blocks, being analyzed on the landslide deposits (Figure 4). Additionally, the dated landslides are relatively large, so their average roughness values are less sensitive to pixels near their boundaries compared to smaller landslides, which limited the accuracy of the long-spatial-scale metrics in predicting relative ages (section 3.2). Because the wavelet-based curvature at a 15 m length scale performed better than the other metrics, we adopted it as the preferred roughness metric for predicting absolute age in this study. The resulting empirical age-roughness model is the exponential function A = 27440 exp(4320R), where A is landslide age and R is landslide deposit roughness quantified by the 2-D wavelet transform (Figure 5). Specifically, R is the average squared curvature of all pixels in a landslide polygon measured at a 15 m smoothing length scale.

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 464 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Quantitatively defining the uncertainty of a landslide’s age predicted by this age-roughness model is challenging because defining landslide age, in general, involves qualitative interpretation of the failure style. Nonetheless, three quantitative estimates of uncertainty can provide guidance in estimating the uncertainty in age predicted by surface roughness. First, the multiple radiocarbon dates from the Rowan and Headache Creek landslides (Figure 3) suggest that uncertainty in using radiocarbon to date a single landslide event at the study site is approximately ±150 years, similar to the range of ages expected to be found in mature Pacific Northwest forests [Franklin and Waring, 1980]. We consider this range a best case scenario for a landslide that likely failed as a rapid, single event. Second, measuring surface roughness from lidar data is an additional source of uncertainty, which we quantified by comparing the roughness of overlapping areas of the 2013 and 2014 lidar data sets, excluding areas that experienced real topographic change. Subtracting the roughness of each pixel in the 2013 data from that of the 2014 data gives a distribution of residuals that is centered on zero and has an interquartile range of ±0.0002 m 2 (horizontal error bars in Figure 5). When translated to age via the exponential fit, this spread predicts a relative uncertainty on landslide age of ~90%, which we view as extremely conservative given the good performance of surface roughness in predicting relative ages (Table 1). Third, the root-mean-square error on the exponential fit is ~1200 years (Table 2), and the misfit of individual points on the age roughness curve tends to increase with landslide age, ranging from near zero to 2300 years (Figure 5). Since this fit implicitly reflects the underlying uncertainties in both surface roughness and radiocarbon age, we suggest that it provides the most reasonable estimate of uncertainty on landslide age predicted by the age- roughness model. We therefore recommend that the younger ages should be interpreted to have uncertainties of ~150 years, which grow to several thousand years for the oldest ages in the data set (Table S1 in the supporting information).

5. Numerical Surface Roughness Evolution Modeling The exponential form of the age-roughness model is consistent with hillslope evolution driven by near- surface soil disturbance processes such as bioturbation, rain splash, or freeze-thaw cycles. Hillslope sediment ﬂux (per unit contour width), qs, tends to increase nonlinearly with slope such that ∇z q ¼ K s 2 (1) 1 ðÞjj∇z =Sc

where K is a transport coefficient, ∇z is the gradient of elevation, and Sc is a critical gradient where sediment flux becomes infinite [Roering et al., 1999]. We incorporated equation (1) into a mass balance framework in order to numerically solve for the evolution of landslide deposit surface elevation with time (supporting infor-

mation) for different values of K (Figures 5 and 6). We used a fixed value of 1.25 for Sc, which is the average slope of the head scarp exposed by the Oso landslide and a typical value calibrated for soil-mantled hillslopes in the Pacific Northwest [Roering et al., 1999]. In each model run, the surface of the Oso landslide was the initial condition, and roughness averaged over the modeled landslide deposit was recorded at intervals of 1000 years over the course of the 13 kyr model run. Values for the transport coefficient, K, of 1.5 × 10 3 m2 yr 1 and 1.1 × 10 2 m2 yr 1 approximately bound the data of the empirical age-roughness relationship (Figure 5). The range of K values is consistent with previous estimates based on topographic analysis of hillslopes in the Pacific Northwest and other forested regions [Fernandes and Dietrich, 1997; Roering et al., 1999; Hurst et al., 2012] and more broadly in agreement with estimates from scarp and shoreline degradation studies [Avouac, 1993; Hanks, 2000]. However, the younger landslide ages tend to plot along the modeled age-roughness curve for a large K value, while the older landslide ages tend to plot along a lower K value curve. This suggests that the model assumption of temporally constant K does not fully explain the data. Instead, changes in climate and vegetation, as discussed earlier in section 2, may have caused the efficiency of hillslope sediment transport processes to systematically increase over the Holocene, or K may decrease over millennia as vegetation becomes reestablished on an initially bar- ren landslide deposit. An example model run with K = 2.9 × 10 3 m2 yr 1 illustrates how landslide morphology changes with time (Figure 6). Over the first 1 kyr of the simulation, most of the short-length-scale roughness visible in the lidar data has already been smoothed. Then, smoothing proceeds more slowly for the larger

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 465 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 6. (a–e) Slope maps of the surface evolution of the Oso landslide predicted by a nonlinear model of hillslope sediment ﬂux (equation (1)). Figure 6a is 2014 lidar data, while Figures 6b–6e are model results. For comparison, (f) a lidar- derived slope map of the landslide directly southeast of the Oso landslide with a similar size and shape and a predicted age of ~5000 cal years B.P. is presented.

roughness elements, such as the displaced blocks in the central part of the landslide deposit. By the end of the model run the Oso deposit surface is visually similar to those of the oldest landslides in the study site with similar size, geometry, and long runout behavior, and quantitatively has a similar value of mean surface roughness (Figure 6f).

6. Temporal Patterns of Landsliding To analyze the temporal pattern of landsliding in the NFS valley we assigned the age predicted by the exponential ﬁt to the age-roughness curve (Figure 5) to each of the landslide deposits in the study area (Figure 1a). We then plotted the predicted age of each landslide against its distance to the east of the study site boundary to clearly visualize the pattern of landsliding in space and time (Figure 7). The distribution of landslides in time shows a general increase in the number and area of landslide deposits toward the present, with several local maxima (Figure 7b). Few very old landslides are preserved in the valley, with only four landslide deposits (2% of mapped landslides) predicted to have ages older than 7000 cal years B.P.. These deposits tend to be large and located relatively far from the active channel (Figure 1c), suggesting a preservation bias caused by ﬂuvial erosion of older landslide deposits or remobilization of older landslide deposits by more recent landslides. Half of the landslides have occurred more recently than 1100 cal years B.P., but few mapped landslides are predicted to have historic ages. For example, only 18 landslides (7% of mapped landslides), including Oso, are younger than 100 cal years B.P., and excluding Oso, these recent landslides account for less than 2% of the total mapped landslide deposit area. These young landslides are generally small, and most of them occur either in narrow tributary valleys or at the outsides of meander bends of the NFS River (Figure 1c). Additionally, in the most recent millennium, there are two subtle peaks with both a large number and area of landslide deposits at ~900–1300 cal years B.P. and more recently at ~500–700 cal years B.P. (Figure 7b).

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 466 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

Figure 7. (a) Bubble plot showing the predicted age versus location of all landslides in the study area, with symbol area proportional to landslide deposit area and background shading highlighting relative climatic conditions (section 2). (b) Normalized histograms showing the fractional number (hachured bars) and area (gray bars) of landslides in 100 year bins of landslide age.

7. Discussion 7.1. What Does a Landslide Age Mean? In this study, we assume that each landslide deposit in the inventory represents a discrete event with an age that can be determined from radiocarbon dating of material contained in the deposit or empirically estimated based on its surface roughness. However, many landslides likely remobilized older landslide deposits, and furthermore, we do not know definitively whether they occurred as rapid, discrete events or instead actively deformed at slower rates, or in multiple stages, possibly over millennia. If a landslide creeps or is intermittently active for a period of time prior to a large, rapid failure, radiocarbon dating of organic material found in the deposit should be considered a maximum age for the main event [Panek, 2014], while surface roughness would likely reflect the most recent event. If a landslide continues to creep or intermittently reactivate after a large, rapid failure, it may incorporate fresh organic material, with radiocarbon ages that post- date the large event, and surface roughness would again likely reflect the most recent phase of active movement. If deformation is ongoing in the present, surface roughness might more strongly correlate with relative activity among different landslides or different parts of a landslide complex [McKean and Roering, 2004; Glenn et al., 2006]. In terms of interpreting our age-roughness curve (Figure 5), the observation that radiocarbon age might reflect any phase of landslide movement, while surface roughness likely reflects the most recent phase of movement or relative activity, could explain the considerable variability around the general relationship between landslide radiocarbon age and surface roughness. Despite these potential complications, we believe that assigning a single, representative age to each landslide in our study area offers a reasonable first-order approximation for the following reasons. First, we mapped landslides at a scale that allowed us to distinguish individual slides within larger landslide complexes to minimize complications due to potential differences in relative activity. Second, differencing lidar DEMs from 2003, 2013, and 2014 reveals no signs of active, slow, creeping deformation for any of the landslides in the study area. While it is possible that these landslides may slowly deform or creep under different climatic or tectonic scenarios than those of the last decade, the observation that they are not currently active suggests

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 467 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

that their surface roughness reﬂects the amount of time since they were last active rather than relative activity. Second, multiple radiocarbon dates from different locations on the Rowan (six dates) and Headache Creek (three dates) landslides span ~200–400 years (Figure 3), implying that each of these landslides likely occurred as a single event or multiple events that were closely clustered in time. Most of the other landslides in the study area are morphologically similar to those two landslides, suggesting that they failed in a similar man- ner. Third, the behavior of the Oso landslide site prior to and including the 2014 failure supports the interpretation that many landslides in the valley occurred as closely spaced events. That site experienced punctuated episodes of movement since the early twentieth century [Miller and Sias, 1998], but the larger 2014 failure overprinted the surface morphology and remobilized the deposits of those previous events. If that sequence of events had happened in the past, we expect that radiocarbon dating of wood found in the deposit would most likely reﬂect the largest, most recent event, with an uncertainty of several hundred years resulting from the previous movement episodes and from the range of ages corresponding to inner versus outer growth rings of trees. While these factors support the use of surface roughness as a proxy for landslide age in the NFS valley, applying the technique to other regions where landslides creep, are intermittently active, or occur in highly variable substrates might require more detailed radiocarbon dating or may not always be viable.

7.2. Conceptual Model of Landsliding in the North Fork Stillaguamish River Valley Based on our estimated spatial and temporal patterns of landsliding, we propose a conceptual model for the Holocene evolution of landsliding in the NFS valley in the context of hillslope-channel coupling, and climatic and tectonic forcing. First, immediately following retreat of the ice sheet ~16,400 cal years B.P., rapid base level drop established steep hillslopes up to ~200 m in height by ~12,000 cal years B.P.. While generation of this relief would likely have set the stage for widespread landsliding, very few of these oldest landslides are preserved because of more recent overprinting from fluvial processes, valley wall retreat, or remobilization by more recent landslides. Typical lateral channel migration rates in the region on the order of several myr 1 [O’Connor et al., 2003] and an average valley width of several kilometers suggest a time scale for era- sure of landslide deposits on the order of 1 kyr for the NFS valley. Most of the large, old landslide deposits that have not been eroded by lateral channel migration are preserved where terraces buffer them from the active floodplain. After the initial pulse of relief generation, slowing isostatic rebound, capture of the NFS River’s headwaters by the Skagit River at ~12,400 cal years B.P., and a shift to a warmer and drier climate at ~10,000 cal years B.P., all would have inhibited additional relief generation and probably slowed the rate of deep-seated landsliding. Very few landslide deposits from this time period remain in the landscape, suggesting that preservation bias likely plays a role. However, the lack of preserved landslide deposits persists until ~7000 cal years B.P., which was approximately the end of the relatively warm and dry climatic regime, suggesting that lower precipitation might also have played a role in limiting the number of landslides occurring in the early Holocene. From ~7000 cal years B.P. to ~1000 cal years B.P. the number of landslides preserved in the valley increases dramatically, probably reflecting a combination of better preservation of landslide deposits and a real increase in landslide frequency. We argue that the increase is real since it does not continue to the present (Figure 7b), as would be expected for preservation bias in sedimentary deposits in general [Sadler, 1981; Schumer et al., 2011]. Interestingly, the peaks in landslide frequency at ~500–700 and ~900–1300 cal years B.P. result from landslides that are distributed along much of the length of the valley rather than clustered in space (Figure 7b). We infer that regional climatic events or seismic shaking could explain this pattern, as opposed to more localized hillslope-channel interactions. Cascadia subduction zone earthquakes are possible candidates for triggering widespread landsliding at those times, although less precisely dated earthquakes on other crustal faults in or near the study area also have overlapping dates, and the relatively large uncertainties on landslide ages prevent us from tying specific landslides to specific earthquakes. If Cascadia subduction zone earthquakes did in fact trigger widespread landsliding in the study site, the relative shortage of large, historic landslides could then reflect a lack of hillslopes primed for failure, if most marginally stable slopes failed during or within a few years after the most recent large earthquake [Dadson et al., 2004; Lin et al., 2008; Hovius et al., 2011]. Instead, localized hillslope-channel interactions have dominated the pattern of landsliding over the last few hundred years [e.g., Miller, 1995; Miller and Sias, 1998; T. Dunne, personal communication, 2017], with landslides occurring where fluvial processes directly interact with the toes of hillslopes, such as at the outsides of meanders of the NFS River and in its laterally confined tributary drainages.

BOOTH ET AL. LANDSLIDING IN THE N. FORK STILLAGUAMISH 468 Journal of Geophysical Research: Earth Surface 10.1002/2016JF003934

This implies that hillslope-channel systems where lateral erosion is dominant may have long response times that decouple landsliding from the vertical incision rate, contrasting with widely applied threshold slope models in which landslide frequency is tightly coupled to vertical incision with short response times [Burbank et al., 1996; Larsen and Montgomery, 2012]. As such, large landslides still have the potential to occur in the NFS valley and will likely continue as long as lateral movement of the river interacts with the high-relief glacial terraces flanking the valley. Dramatic changes in climate and vegetation in the study area over the Holocene likely affected not only landslide occurrence but also the rate at which diffusive hillslope processes modified the surface roughness of landslide deposits. The surface roughness of the older landslide deposits dating from >1000 cal years B.P. decreases gradually with time and is best modeled with a transport coefficient of K = 0.003 m2 yr 1. However, the roughness of the young landslide deposits dating from <1000 cal years B.P. decreases rapidly with time, and the trend is most consistent with a relatively high modeled hillslope transport coefficient of K = 0.01 m2 yr 1 (Figure 5). This threefold inferred increase in K likely reflects an increase in soil mixing and transport due to bioturbation resulting from changing regional climate and changing vegetation on a fresh, bare landslide deposit as it becomes recolonized. The magnitude of this increase in K is comparable to or slightly larger than the estimated difference between grassland and forested ecosystems [Gabet et al., 2003; Hughes et al., 2009], which suggests that the change from open-canopy to closed-canopy forest ~6000 cal years B.P. probably explains only part of the predicted change in K and that the succession of vegetation on fresh landslide deposits also plays a role.

8. Conclusions We derived a Holocene history of deep-seated landsliding in the NFS valley, Washington State, by defining an empirical relationship between landslide deposit surface roughness and landslide age, estimated for a limited number of landslides by radiocarbon dating. We tested three different surface roughness metrics calculated over a range of length scales and determined that wavelet-based curvature measured at a 15 m smoothing length scale performed the best at predicting absolute landslide ages. An exponential model provided a reasonable fit to the age-roughness data, and the smoothing of surface roughness with time is consistent with disturbance-driven soil transport with a transport coefficient of ~0.003–0.01 m2 yr 1. Very few landslide deposits have ages older than 7000 cal years B.P., after which the frequency of landsliding increases steadily until ~1000 cal years B.P.. We suggest that this increasing trend represents a combination of preservation bias and a change from relatively warm and dry to relatively cool and wet climate, with recent peaks in landslide occurrence possibly caused by seismic triggers. Few of the 218 mapped landslides have historic ages, and their locations are concentrated in areas of local hillslope-channel interaction. This suggests that the persisting conditions of steep, high relief slopes composed of glacial sediment adjacent to an actively meandering channel will continue to promote landsliding until the thick package of glacial sediment is either evacuated or redeposited with a lower slope in the valley bottom. Acknowledgments We thank J. McKean and an anonymous reviewer for providing constructive criticism in their reviews, as well as References Editor J. Buffington and Associate Editor Atwater, B. F. (1999), Radiocarbon dating of a Seattle earthquake to A.D. 900–930 [abs.], Seismol. Res. Lett., 70(2), 232. K. Michaelides for their thorough Atwater, B. F., and A. L. Moore (1992), A tsunami about 1000 years ago in Puget Sound, Washington, Science, 258(5088), 1614–1616, comments that improved the quality of doi:10.1126/science.258.5088.1614. the manuscript. We acknowledge the Atwater, B. F., M. P. Tuttle, E. S. Schweig, C. M. Rubin, D. K. Yamaguchi, and E. Hemphill-Haley (2003), Earthquake recurrence inferred from 2014 and 2015 Processes in the Surface paleoseismology, in The Quaternary Period in the United States, vol. 1, edited by A. R. Gillespie, S. C. Porter, and B. F. Atwater, Environment class at Portland State pp. 331–350, Elsevier Ltd., Amsterdam, Netherlands. University, as well as Jon Weatherford Avouac, J. P. (1993), Analysis of scarp profiles—Evaluation of errors in morphologic dating, J. Geophys. Res., 98, 6745–6754, doi:10.1029/ and Lev Bakin, for preliminary landslide 92JB01962. reconnaissance mapping. Radiocarbon Badger, T. C. (2015), SR 530 MP 35 to 41 Geotechnical Study, Washington State Dep. of Transportation. dating was supported by a GSA student Barnosky, C. W. (1981), A record of late Quaternary vegetation from Davis Lake, southern Puget Lowland, Washington, Quat. Res., 16, 221–239. grant and UW Quaternary Research Beechie, T. J., B. D. Collins, and G. R. Pess (2001), Holocene and recent geomorphic processes, land use, and salmonid habitat in two north Center and Earth and Space Sciences Puget Sound river basins, Geomorphic Processes Riverine Habitat, Water Sci. Appl., 4,37–54. grants to S.R.L. We thank Susan Wisehart Beget, J. E. (1982), Postglacial volcanic deposits at Glacier Peak, Washington, and potential hazards from future eruptions, U. S. Geol. Surv. for the help with sample collection. The Open-File Rep. 82-830. data used in the study are available in Benda, L., T. J. Beechie, R. C. Wissmar, and A. Johnson (1992), Morphology and evolution of salmonid habitats in a recently deglaciated river the tables, figures, supporting basin, Washington State, USA, Can. J. Fish. Aquat. Sci., 49(6), 1246–1256. information, and through the Puget Berti, M., A. Corsini, and A. Daehne (2013), Comparative analysis of surface roughness algorithms for the identification of active landslides, Sound Lidar Consortium. Geomorphology, 182,1–18, doi:10.1016/j.geomorph.2012.10.022.

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