Geologic and physiographic controls on bed-material yield, transport, and channel morphology for alluvial and bedrock rivers, western Oregon

Jim E. O’Connor1,†, Joseph F. Mangano1,2, Scott W. Anderson1,3, J. Rose Wallick1, Krista L. Jones1, and Mackenzie K. Keith1 1U.S. Geological Survey, Oregon Water Science Center, 2130 SW 5th Avenue, Portland, Oregon 97201, USA 2Department of Geosciences, Colorado State University, Fort Collins, Colorado 80523, USA 3Department of Geography, University of Colorado–Boulder, Boulder, Colorado 80309, USA

ABSTRACT geometry and sediment supply. At the scale (Stanford and Ward, 1993; Yarnell et al., 2006). of western Oregon, the physiographic and Most fundamentally, the distinction relates to the The rivers of western Oregon have di- lithologic controls on the balance between balance between bed-material supply and river verse forms and characteristics, with channel bed-material supply and transport capacity transport capacity (Gilbert, 1877, 1914; Howard, substrates ranging from continuous alluvial exert far-reaching infl uence on the distribu- 1980; Whipple, 2004). Rivers in which the long- gravel to bare bedrock. Analysis of several tion of alluvial and nonalluvial channels and term channel transport capacity exceeds bed- measurable morphologic attributes of 24 val- their consequently distinctive morphologies material supply (termed supply- or detachment- ley reaches on 17 rivers provides a basis for and behaviors—differences germane for un- limited rivers) will typically fl ow over bedrock comparing nonalluvial and alluvial channels. derstanding river response to tectonics and beds for part or much of their courses. Where Key differences are that alluvial reaches have environmental perturbations, as well as for supply meets or exceeds transport capacity greater bar area, greater migration rates, and implementing effective restoration and moni- (transport-limited rivers), channel beds are typi- show systematic correlation among variables toring strategies. cally formed of a continuous mantle of alluvial- relating grain size to bed-material transport bed material. capacity. We relate these differences between INTRODUCTION This categorization, however, masks sub- channel types to bed-material transport rates stantial complexity. As summarized by Church as derived from a coupled regional analysis of The rivers of western Oregon have channel (2002, 2006) and Lisle (2012), the morphology empirical sediment yield measurements and beds ranging from fully alluvial to bedrock. A and transport conditions of alluvial channels physical experiments of clast attrition dur- local history of in-stream gravel mining in con- involve interrelations among fl ow, channel and ing transport. This sediment supply analysis junction with an ongoing permitting process valley characteristics, sediment supply, and sedi- shows that overall bed-material transport for continued mining have prompted a series of ment grain size. These interrelations commonly rates for western Oregon are chiefl y con- investigations of bed-material production, trans- create conditions of bed-material fl ux, channel trolled by (1) lithology and basin slope, which port, and channel morphology across this spec- form, bed elevation, and bed-sediment textures are the key factors for bed-material supply trum of channel types in western Oregon (Wal- such that the bed material entering the system is into the stream network, and (2) lithologic lick et al., 2010, 2011; Jones et al., 2011, 2012a, balanced, at decadal to millennial time scales, control of bed-material attrition from in- 2012b, 2012c). These studies, expanded upon by that exiting, i.e., the graded river of Mackin transport abrasion and disintegration. This and synthesized here, show the importance of (1948). This system, classically depicted by the bed-material comminution strongly affects (1) geologic and physiographic controls on bed- Lane-Borland balance between stream energy bed-material transport in the study area, re- material production and in-stream gravel fl ux; and sediment fl ux (Lane, 1955), has been subject ducing transport rates by 50%–90% along and (2) the differences between fully alluvial to more than a century of scrutiny because of the the length of the larger rivers in the study channels and those that locally fl ow on bedrock many pragmatic implications of predicting allu- area. A comparison of the bed-material in terms of predicting transport rates, bed-mate- vial channel behavior and morphology in con- transport estimates with the morphologic rial characteristics, and channel morphology. sequence of changing environmental conditions. analyses shows that alluvial gravel-bed chan- Channels with bedrock beds and margins nels have systematic and bounding relations Alluvial, Bedrock, and have also been studied extensively, but chiefl y between bed-material transport rate and at- Mixed-Bed Channels for their broad role in pacing valley incision and tributes such as bar area and local transport landscape evolution (summarized by Turowski, capacity. By contrast, few such relations are The distinction between alluvial and bedrock 2012). Finer-scale studies have mostly focused evident for nonalluvial rivers with bedrock or channels has broad implications regarding long- on bedrock channel forms (summarized by mixed-bed substrates, which are apparently term channel incision (Howard, 1980; Whipple, Wohl, 1998; Whipple, 2004; Richardson and more infl uenced by local controls on channel 2004; Turowski et al., 2008a, 2008b; Turowski, Carling, 2005), erosional processes (Whipple 2012), channel morphology (Montgomery et al., 2000; Wohl and Merritt, 2001; Johnson and et al., 1996; Montgomery and Buffi ngton, 1997; Whipple, 2007; Goode and Wohl, 2010a), and †E-mail: [email protected] Tinkler and Wohl, 1998), and physical habitat transport conditions (Goode and Wohl, 2010b;

GSA Bulletin; March/April 2014; v. 126; no. 3/4; p. 377–397; doi:10.1130/B30831.1; 11 fi gures; 4 tables; Data Repository item 2014049; Published online 7 January 2014.

For permission to copy, contact [email protected] 377 © 2014 Geological Society of America O’Connor et al.

Hodge et al., 2011). Few “bedrock” channels, The regional geology is important to our ents less than 0.005 and would be considered however, have continuous bedrock beds; most analysis, and we have aggregated existing map- transport or response reaches in the Montgom- have patches, locally extensive, of alluvium in ping into six main lithologic groupings (Fig. 1). ery and Buffi ngton (1997) categorization. Study and fl anking the channel, leading to the terms The Paleozoic and Mesozoic rocks of the tec- reaches were defi ned on the basis of broad-scale “mixed-bed” or “semi-alluvial” channels (How- tonically accreted Klamath terrane underlie geomorphic characteristics, with boundaries ard, 1980, 1998; Lisle, 2012; Turowski, 2012). much of the southwestern part of the study area. typically corresponding to major confl uences, The degree of alluvial cover has been hypoth- Uplifted Tertiary marine sediments constitute changes in valley confi nement, and extent of esized to modulate bedrock erosion (Gilbert, the Coast Range sedimentary province underly- tidal infl uence. The 24 separate reaches summa- 1877; Sklar and Dietrich, 2001; Finnegan et al., ing much of the western part of the study area. rized here do not include the tidal reaches iden- 2007; Turowski et al., 2007). Only recently, Tertiary marine volcanic rocks within the Coast tifi ed in these previous studies. The short fl uvial however, have studies focused on the alluvial Range and the Columbia River Basalt Group reaches of Hunter Creek and the Chetco, Wil- characteristics of channels that locally or con- in the northern part of the study area have been son, and Miami Rivers defi ned in our previous tinuously fl ow on bedrock (Chatanantavet and grouped together into what we term the Coast studies (Wallick et al., 2010; Jones et al., 2011, Parker, 2008; Goode and Wohl, 2010b; Hodge Range volcanics province. The axis of the Cas- 2012c) have been aggregated in this assess- et al., 2011). cade Range is underlain by Quaternary volcanic ment so that each one of these rivers is repre- In this study, we directly compare alluvial rocks of the High Cascades province, and these sented by single valley reaches. The resulting and bedrock channels of western Oregon, and rocks are fl anked to the east and west by Tertiary 24 study reaches range from <5 km to as long we relate their distribution and character to volcanic rocks grouped into the Western Cas- as 115.7 km (Table 1). The Umpqua and Rogue basic controls on bed-material fl ux and trans- cades lithologic province. Basins, broad valleys, River basins have the most reaches, refl ecting port capacity. These differences and distinc- and coastal plains are underlain by Quaternary distinct morphological differences along these tions have implications for understanding river sediment. large and long rivers and their major tributaries, response to tectonics and environmental pertur- Our analysis is based on morphologic obser- while several of the shorter coastal rivers each bations, as well as for implementing effective vations from 24 valley reaches within the 17 consist of a single reach. All of the study reaches restoration and monitoring strategies. rivers . Analyzed reaches span the spectrum of have gravel bed material. fully alluvial to bedrock (Fig. 2). Our measure- Western Oregon Study Area and ments of channel and bar area, channel migration Valley Reach Classifi cation Analysis Summary rates, channel slope, and bar sediment texture are evaluated with respect to (1) fi eld-based assess- Each reach was defi ned as alluvial or non- This synthesis derives from studies of sev- ment of their alluvial versus bedrock character, alluvial solely on the basis of the presence or eral individual rivers and river basins in western and (2) estimates of local mean annual bed- absence of continuous alluvial cover on the Oregon. We have investigated channel condi- material fl ux. Local bed-material fl ux is derived channel bed as evident from aerial photographs tions and bed-material transport rates for the from a regional empirical analysis of bed-mate- (Table DR11) and fi eld observations (Fig. 2; Chetco and Umpqua River basins of southwest- rial production paired with a separate experi- Table 1). In accordance with Lisle’s (2012) ern Oregon (Wallick et al., 2010, 2011). Addi- ment-based analysis of bed-load clast comminu- identifi cation of this critical threshold condition, tionally, we have completed reconnaissance tion. We test these estimated bed-material fl uxes reaches in which patches of in-channel bedrock evaluations of Hunter Creek (Jones et al., 2011), against several independent assessments of bed- were exposed to suffi cient extent to locally con- Rogue River basin (Jones et al., 2012a), Coquille material supply and transport. We then discuss trol the river profi le were judged nonalluvial. River basin (Jones et al., 2012b), and the fi ve the specifi c implications of these results, fi rst by Most of the nonalluvial reaches are “mixed bed” rivers entering Tillamook Bay and the Nehalem comparing the Rogue and Umpqua Rivers, and or “semi-alluvial” in the terminology of How- River (Jones et al., 2012c) (Fig. 1). In total, these then by broader regional assessment and com- ard (1998) and Lisle (2012). For two reaches, studies encompass 17 rivers in western Oregon parison of the distribution and morphology of the North Umpqua River and the Coast Range (Table 1). alluvial and nonalluvial valley reaches. Finally, reach of the main-stem Umpqua River, the All of these main-stem rivers drain into we develop broader inferences regarding over- channel fl ows on bedrock for the majority of its the Pacifi c Ocean or estuarine bays (Fig. 1). all physiographic and geologic controls on the length (Fig. 2C). These reaches probably meet The Rogue and Umpqua Rivers, the largest of distribution of alluvial and nonalluvial channels, most workers’ defi nitions of bedrock channels. the study area at 13,390 km2 and 12,103 km2, concluding with management implications. While the distinction between “bedrock” and respectively (Table 1), have sources in the Cas- “mixed bed” somewhat arbitrarily subdivides cade Range of southern Oregon; the others MORPHOLOGICAL ANALYSIS— the 12 nonalluvial reaches, the other 12 reaches begin in the Coast Range or Klamath Mountains METHODS AND RESULTS classifi ed as “alluvial” were distinctive in that of western Oregon and northwestern California. they all had continuous alluvial cover on their The Hunter Creek basin, with a drainage area The morphologic measurements enable beds. Because some of the alluvial reaches were of 115 km2, is the smallest for which we have assessment of the regional variety of rivers and locally confi ned by bedrock margins, they would detailed measurements. All of these rivers are controls on their characteristics. The valley be classifi ed as bedrock rivers by Turowski et al. subject to the cool and wet maritime climate of reaches (Table 1) were defi ned by the studies (2008b), but their delineation as alluvial in this the Pacifi c Northwest. All have been affected to of Wallick et al. (2010, 2011) and Jones et al. study refl ects the pragmatic considerations from some degree by the typical Pacifi c Northwest (2011, 2012a, 2012b, 2012c) and include the land-use perturbations such as dams, timber contiguous portions of the 17 rivers in which 1GSA Data Repository item 2014049, supplemen- harvest (and splash-damming), fi re, in-stream the channel beds are composed of, or are locally tary tables and fi gures, is available at http:// www and fl oodplain gravel mining, placer mining, fl anked by, substantial accumulations of mod- .geosociety .org /pubs /ft2014 .htm or by request to and local channel and fl oodplain development. ern alluvium. All of these reaches have gradi- editing@ geosociety .org.

378 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

124°W 123°W 122°W 121°W 120°W EXPLANATION Lithologic Provinces Quaternary sediment High Cascades Grays River Western Cascades Salmon RiverR Coast Range (sedimentary) Kalama River White 46°N Coast Range (volcanic) and Columbia River basalt Pacific ive Klamath r Columbia River Ocean Nehalem River ver Western Oregon bed-material and Ri Tualatin River WilsonWilson River morphology study basins Clackamas River er Sediment yield study basins JohnJo Day River Nestucca River hn Lakes and reservoirs Day Streams

Deschutes RiverRiv 45°N Rive Bed-material sampling site r Metolius Attrition experiment sampling site SantiamSantiam RivRiver River Yaquina River er Sediment yield measurement site Willamette River

AlseaAlsea River Crooked River River

Siuslaw River e enzi 44°N McKenzieMcK Washington River Seattle Row River Umpqua River Portland

I

CoquilleCoqu Range d ille Range River a h

ascade Coast Oregon o C 43°N Klamath Mountains

SpragueSprag River River ue Ri ver Nevada Rogue River Illinois RiverRiver Study area ApplegatApplega e River California Lost River te River San Francisco R iver Chetco River 42°N Williamson River Smith RiverSmithRiver KlamathKlamat RiverSmithRiverSmithh RiverRiver River River

Pit RiverRiver 025 50 100 Kilometers Redwood Creek

41°N TrinityT R 0 25 50 100 Miles rinity R iver Mad RiveRiver

r

Eel RivRiverer

SacramentoSa River c ram

ento River 40°N Base map modified from U.S. Geological Survey digital data, various resolutions. Geology modified from Ludington and others, (2007) and Ma and others (2009), various scales. Projection: NAD83 UTM, Zone 10N.

Figure 1. Regional map showing grouped lithologic units, sediment yield measurement sites and contributing basins, sample loca- tions for attrition experiments, and basin boundaries for the western Oregon gravel transport and channel morphology studies of Wallick et al. (2010, 2011) and Jones et al. (2011, 2012a, 2012b, 2012c). Specifi c site locations are provided in supplementary Tables DR2, DR3, and DR8 (see text footnote 1). Lithologic groupings are provided in supplementary Table DR7 (see text footnote 1).

Geological Society of America Bulletin, March/April 2014 379 O’Connor et al.

regulatory and geomorphic perspectives that ux (1) they can be categorized from fi eld inspection and aerial photographs, (2) the channels could 8552 9949 3396 1919 5061 1917 (t/yr)** 21,462 25,352 15,146 23,426 34,692 63,035 19,677 68,965 plausibly incise within decadal time scales in 111,226 113,007 120,292 272,843 444,404 100,425 132,007 225,962 165,172 145,594 response to a reduction in bed-material supply Bed-material fl (including attrition) or increase in transport capacity, and (3) bed- material transport is likely capacity limited 9 8 9 9 8 9 8 9 8 9 9 9 8 8 8 8 9 9 9 9 01×66.3 01× 01×56. # and, consequently, can be reliably estimated by †† †† †† †† .D.N .D.N

/yr) empirical fl ow-based sediment rating curves or 3 3 7.8 (m by bed-material transport capacity relations. discharge 3 Average annual Average Reach and Site Measurements § 1 0.1 7.0 4.1 4.1 .1 For each of the 24 reaches, we mapped and

(m/yr) measured the low-fl ow wetted channel, chan-

Migration rate nel centerline, and all exposed gravel bars with areas greater than 300 m2 at a scale of 1:3000 )

2 using 0.5 and 1 m resolution ortho-imagery col- 5 06.0 90.0 89.0 31.0 0.0 lected during summer low-fl ow periods in 2005 (km and 2009 by the National Agriculture Imagery Total bar area Total Program (Fig. 3; Table 1; Tables DR1 and DR2

resolution and acquisition dates are provided in supplementary Table DR1 Table resolution and acquisition dates are provided in supplementary [see footnote 1]). Additionally, we mapped the 64100.0 09100.090.1 0610 04100.06 2040 † e 1). geomorphic fl oodplain for each reach, defi ned 0 0 .081 .041 (m/m) as the lateral extent of Holocene-era fl uvial reaches. Reach slope

ysis area noted in supplementary Table DR2 (see text footnote 1). Table ysis area noted in supplementary processes, on the basis of available geology, soils, and topography and reconnaissance fi eld 2 3. 0 inspection, as described in Wallick et al. (2011) . .1 . 1 1 1 (m/m) for the Umpqua River. From this mapping, we Sinuosity evaluated 2005–2009 channel centerline migra- tion rates relative to the fl oodplain centerline for the intervening period in the manner of 62 5 9 38 3 5 2 1 (m)

width O’Connor et al. (2003b). Bed-material textures at 93 bars located in 19 Average wetted Average of the study reaches were sampled during the summers of 2008–2010 (Table DR3 [see foot- note 1]). For each sampling site, we employed TABLE 1. SUMMARY OF ANALYSIS REACHES ANALYSIS OF 1. SUMMARY TABLE 454 522 86 6 65 oodplain a modifi ed grid technique (Kondolf et al., 2003) 2 (m) 7 4 2 width to measure 200 surface clasts by template (Fed- eral Interagency Sediment Project U.S. SAH-97 Average fl Average Gravelometer) at 0.3 m increments along two parallel 30 m tapes. Bed-material substrate 761 06 42 0 6 )* 9 8 2

5 4 88 was sampled at 45 of the surface-material sam- 2 area (km pling sites by removing the surface layer from Drainage a 1 m2 area at the grid center and then collect- ing ~40–60 L of sediment, such that the larg- 3.0 5. 1.5 3.9 3.0 est particles did not constitute more than ~5% 6 1 2 (km) of the total sample volume. Most bed-material Centerline reach length substrate samples were analyzed by the U.S. Geological Survey (USGS) Sediment Labora- tory in Vancouver, Washington, where samples ima s s n k ihcl io i l s r

a were dried and weighed in half-phi intervals. n iM e r i i l lI T M K Some substrate samples, particularly those in the Galice valley reach of the Rogue River, were fi eld sieved. Sampling sites were predominantly located at bar apices, though vegetation, bedrock outcrops, and other factors dictated that some bars were sampled at other locations. For sites in

Morphological characteristics were digitized from National Agriculture Imagery Program ortho-imagery from 2005 and 2009; image Morphological characteristics were digitized from National the Chetco and Umpqua River drainages, we also i e s si ih ksar ugo N.D.—not determined. m o Centerline migration rate measured relative to floodplain axis for period 2005–2009; only calculated alluvial and mixed-bed DR5 (see text footnot Table Mean annual discharge determined from U.S. Geological Survey gauging station records; supplementary Supplementary Table DR4 shows basis for reach-slope values (see text footnote 1). Table Supplementary conducted partial lithologic classifi cation of bed- c aiM Note: *Determined at downstream end of reach. **As determined for calculation point(s) representative for the reach; includes the effects bed-material trapping by dams; anal **As determined for calculation point(s) representative the reach; includes effects † # § †† ni l i l lI K (see text footnote 1). Bedrock reaches Umpqua Coast Range 115.7 10,490 505 104 1.03 0.00081 0.59 N.D. 6.57 × 10 Alluvial reaches Chetco Chetco 12.2 900 336 51 1.10 0.00126 0.63 3.8 2.01 × 10 Mixed-bed reaches Applegate Upper Applegate 15.0 1370 422 26 1.06 0.00360 0.07 0.6 4.57 × 10 River Reach name WilsonNehalem Wilson Nehalem 4.0S. Umpqua 6.8 494N. Umpqua 1840 Roseburg 823 North Umpqua 314 75.9 47.0 33 4660 51 3520 1.26 783 1.06 0.00124 468 0.00129 0.14 56 0.13 72 1.2 1.22 1.4 1.06 0.00107 1.05 × 10 0.00186 1.03 2.37 × 10 0.32 1.2 N.D. 2.44 × 10 3.30 × 10 T R ApplegateRogueS. Fk. Coquille Lower ApplegateTillamook Broadbent 41.6 Lobster Creek 30.7 1990 37.2Rogue TillamookRogueS. Fk Coquille 13,310 640 651 10.2N. Fk. CoquilleS. Umpqua Grants Pass 261Umpqua 356 Powers 110 Gravelford Galice 33 25.4 Days Creek 18.5 369 14.6 Valley Garden 95 6470 24 88.8 47.5 1.18 18.8 490 750 0.00280 10,290 1.07 1960 1.31 721 14 8930 0.00070 2.97 0.00092 92 287 126 480 1.24 1196 2.35 69 0.35 3.2 0.00220 23 16 54 1.9 32 1.04 1.4 6.29 × 10 0.03 83 0.00140 1.06 1.20 1.02 8.79 × 10 1.10 6.91 × 10 0.6 0.00270 0.00031 1.15 0.00200 0.33 0.00246 0.00096 0.27 0.01 0.94 N.D. 0.84 1.0 0.09 0.8 0.6 1.1 3.03 × 10 0.6 1.2 6.91 × 10 × 10 8.35 5.12 × 10 9.13 × 10 5.74 × 10 Md. Fk. Coquille Bridge 15.4 800 110 24 1.07 0.00147 0.04 0.7 × 10 6.64 Hunter Creek Hunter 12.4 110 175 18 1.19 0.00210material 0.24 samples 1.1 (Wallick et N.D. al., 2010, 2011).

380 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

A B C

Figure 2. Photographs of alluvial, mixed, and bedrock rivers within the western Oregon study area. (A) View downstream of a portion of the Lobster Creek reach of the Rogue River. Channel fl ows over continuous alluvial gravel bed. (B) View upstream of a portion of the Roseburg reach of the South Umpqua River. Channel fl ows over a mixed bed of bedrock (indicated by arrows) and patchy gravel alluvium. (C) View upstream of a portion of the Coast Range reach of the Umpqua River where the bed is predominantly bedrock.

Channel slope was measured at both the reach Alluvial reaches have scaled bar areas (relative D* = (W·S)/D50, (1) scale and locally about each bed-material sam- to low-fl ow channel area) of 0.35–2.16, with a pling site over a distance of 20 channel widths mean of 0.83. By contrast, nonalluvial reaches where W is reach-averaged wetted width at low

(Tables DR2 and DR4 [see footnote 1]). Elevation have scaled bar areas of 0.03–0.59, with a mean fl ow (Table 1), D50 is the median bar-surface data underlying the slope calculations included of 0.24. For the analyzed reaches, a scaled-bar- grain size, and S is local slope measured at each light detection and ranging (LiDAR) topographic area criterion of 0.3 would have correctly clas- bed-material sampling site (Table DR3 [see surveys, local thalweg and water-surface profi le sifi ed 21 of the 24 reaches into alluvial or non- footnote 1]). Because channel width presum- surveys, and elevation information from USGS alluvial categories. ably scales with fl ow depth, D* is analogous to 7.5 minute quadrangles (commonly in the form Centerline migration rates were also gener- the dimensionless shear stress variable known of USGS 10 m digital elevation models). For ally greater for the alluvial reaches. The migra- as the Shields number, τ*, reaches in which gauging records existed or for tion rates for alluvial channels, scaled by the τ ρ ρ ρ which fl ow could be estimated from upstream reach-average low-fl ow channel width, ranged * = ( gdS)/[( s – )gD], (2) and downstream gauges on the same river system from 0.02 yr–1 to 0.11 yr–1 (mean 0.05 yr–1) for –1 ρ ρ (Table DR5 [see footnote 1]), we also determined the 2005–2009 period, compared to <0.02 yr where is fl uid density, s is clast density, g is the average annual water fl ow (Table 1). to 0.04 yr–1 (mean 0.02 yr–1) for the nonalluvial gravitational acceleration, d is fl ow depth, and D

reaches (Fig. 3E). The two alluvial reaches with is a characteristic particle diameter such as D50. General Patterns and Correlations the lowest migration rates, the Illinois River and These formulations show that τ* and D* both the Lobster Creek reach of the Rogue River, are essentially express the ratio of fl uid shear stress These observations and measurements enable both closely confi ned by valley margins (Fig. on bed-material particles to particle weight. empirical evaluation of regional patterns among 3B), probably inhibiting lateral migration. For alluvial rivers in steady state, where

channel morphology, channel migration, bed Median particle diameter (D50) for the 93 bar transport capacity and grain size interact in rela- sediment texture, and fl ow conditions (Figs. surfaces ranged from 12 to 205 mm (Fig. 3F; tion to sediment supply (Mackin, 1948; Lane, 3 and 4). An evident factor from comparison Table DR3 [see footnote 1]). In general, the non- 1955) to maintain steady transport conditions, among measured parameters is that many mor- alluvial reaches had coarser bars (two-sample D* should correlate with measures of sediment phologic and texture characteristics are inde- t-test; p = 0.023). Grain size on nonalluvial supply, such as bar area (Fig. 4C). Conversely pendent of the alluvial or nonalluvial character reaches was weakly but signifi cantly correlated for nonalluvial rivers, the generally higher val- of the reach. Across both categories, slope var- with local slope (R2 = 0.18; p < 0.001),2 whereas ues of D* and the absence of correlation are ied from 0.0003 to 0.004, channel width varied no such correlation was shown by the alluvial consistent with these nonalluvial reaches having

from 13 m to 104 m, and fl oodplain width varied reaches (Fig. 4B). Armoring (ratio of surface D50 transport capacities in excess of bed-material

from 92 m to 1196 m, without signifi cant differ- to subsurface D50) varied from 1 to 5.5 among sediment supply. ences between the alluvial and nonalluvial cat- the 45 sites for which subsurface samples were A correlation involving all reach types is that egories. Also, slope against drainage-area plots collected, but with no evident difference between of bar area with channel migration rate (Fig. do not clearly discriminate between alluvial and the alluvial and nonalluvial reaches (Fig. 3F). 4D), with the alluvial reaches generally having nonalluvial reaches; they actually show that In the absence of local hydraulic information, both greater bar areas and migration rates. This most of the nonalluvial reaches are associated a measure of local transport capacity relative to fi nding accords with active bar formation and with larger drainage areas (Fig. 4A), thereby grain size, D*, was calculated on the basis of the lateral channel movement being highly inter- contrasting with many other environments nondimensional formulation, related processes (Church, 1992). The bar-rich (Howard and Kerby, 1983; Montgomery et al., but laterally confi ned Illinois River is a dis- 1996; Massong and Montgomery, 2000). 2All correlations are based on linear regression of tinct outlier. The main obvious difference between alluvial log-transformed values, with p ≤ 0.05 judged signifi - Taken together, plan-view morphologic and and nonalluvial reaches is in bar area (Fig. 3D). cant for scaling coeffi cients. bed-texture characteristics of Oregon coastal

Geological Society of America Bulletin, March/April 2014 381 O’Connor et al.

0.005 Chetco Hunter Applegate Lower Illinois Rogue Lobster Creek SF Coquille Broadbent Wilson Miami Tillamook Trask Kilchis Nehalem Upper Applegate Rogue Grants Pass Rogue Merlin Rogue Galice SF Coquille Powers MF Coquille Bridge Gravelford NF Coquille Creek S. Umpqua Days S. Umpqua Roseburg Umpqua Garden Valley Umpqua Coast Range North Umpqua A 0.004 Alluvial Mixed-bed Bed- 0.003 rock (m/m) 0.002

Reach Slope 0.001 0.000 1.4 B 1.3 1.2 (m/m)

Sinuosity 1.1 1.0 1200 C 1000 Low-flow channel 800 Floodplain

(m) 600 Width 400 200 0 2.5 D 2.0 ) 2 1.5 /m 2 1.0 (m 0.5 Scaled Bar Area 0.0 0.12 E 0.10 0.08

(m/m) 0.06

0.04 NO DATA NO DATA

Rate 0.02

Scaled Migration 0.00 250 F 200 150 (mm)

50 100 D NO DATA NO DATA NO DATA Bar Surface 50 0 6 G 5 4 3

(mm/mm) 2 NO DATA NO DATA NO DATA NO DATA NO DATA NO DATA NO DATA

Armoring Ratio 1 Trask Miami Illinois Kilchis Hunter Wilson Chetco Nehalem Tillamook Rogue Galice Rogue Merlin North Umpqua Upper Applegate Lower Applegate Lower MF Coquille Bridge Rogue Grants Pass SF Coquille Powers Rogue Lobster Creek S. Umpqua Roseburg Umpqua Coast Range SF Coquille Broadbent NF Coquille Gravelford NF Coquille S. Umpqua Days Creek S. Umpqua Days Umpqua Garden Valley

Figure 3. Summary plots of morphologic attributes of the 24 study reaches, categorized into alluvial, mixed bed, and bedrock. Box plots for the bar-surface grain-size measurements show distribution of median grain-size values for valley reaches for which we made multiple measure- ments. Reaches in each category are ordered south to north. Boxes indicate median, 25th, and 75th percentile values, with attached whiskers indicating 10th and 90th percentile values. Supporting data are tabulated in supplementary Tables DR2 and DR3 (see text footnote 1).

382 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon rivers span a wide range of conditions. Some Figure 4. Scatter plots of selected morpho- aspects, particularly bar area, particle size, logic and grain-size observations for al- 0.01 and migration rates, appear to differ between luvial, mixed-bed, and bedrock channels A a priori defi ned transport-limited alluvial and in western Oregon study area. Support- 0.005 supply-limited nonalluvial (mixed-bed and ing data are tabulated in supplementary bedrock) reaches. Other characteristics, such as Tables DR2 and DR3 (see text footnote 1). 0.002 slope, channel width, and channel confi nement, (A) Slope-area plot. The absence of clear show little relation to alluvial or nonalluvial separation between alluvial and nonalluvial 0.001 status. For the alluvial rivers in the study area, reaches, and the preponderance of nonallu- Slope (m/m) indicators of bed-material transport rates, such vial reaches with relatively larger drainage 0.0005 Alluvial as channel migration rate and transport capac- areas are strong evidence that factors be- Mixed-bed ity (as measured by D*), are strongly correlated sides fl ow and channel slope control the dis- 0.0002 Bedrock with bar area, as would be expected for trans- tribution of alluvial and nonalluvial reaches. port-limited systems. (B) Median grain size of bar-surface particle 0.0001 All of these observations and relations, how- counts in relation to channel slope, showing 10 100 1000 10000 100000 Drainage Area (km2) ever, are in the absence of information on actual a signifi cant (P < 0.001) positive correlation 500 bed-material transport rates. Consequently, we (and 95% confi dence limits) between slope B 0.38 D50 = 594S (nonalluvial reaches) expand the analysis by developing reach-spe- and grain size for mixed-bed and bedrock Adjusted R2 = 0.18, P<0.001 cifi c estimates of annual bed-material transport channels. No such correlation is evident for 200 from regional measurements of bed-material alluvial channels. This plot has more points than the other ones of this fi gure because supply and transport in combination with esti- 100 mates of particle comminution during fl uvial some valley reaches had several grain-size transport. measurements, with local slope determined 50 for each measurement site. (C) Scaled bar BED-MATERIAL TRANSPORT— area (normalized by low-fl ow channel area) Median Grain Size (mm) Grain Size Median METHODS AND RESULTS with respect to D* (= [W·S]/D50), a measure 20 of local transport capacity relative to grain size, showing signifi cant positive correlation We combine a regional analysis of bed-mate- 10 rial supply with experiments on clast comminu- among alluvial channels. Horizontal un- 0.0001 0.0002 0.0005 0.001 0.002 0.005 0.01 tion to give spatially explicit determinations of certainty bars show standard deviation of Local Slope (m/m) average annual bed-material fl ux for the rivers reach observations where fi ve or more mea- 10 C Scaled Bar Area = 0.82(D*)0.57 of the study area. For this analysis, we defi ne surements were made, and entire range for (alluvial reaches only) 2 bed material as clasts of 0.5 mm diameter and reaches with fewer bar-surface grain-size ) Adjusted R = 0.50, P<0.01 2 /m greater, consistent with d of the 45 sampled bar measurements. No uncertainty bars indicate 2 16 1 substrates ranging from 0.6 to 28 mm (median only a single measurement for the reach. 1.6 mm) (Table DR3 [see footnote 1]). We (D) Relation of scaled bar area to migration assume steady-state conditions—meaning no rate (scaled by mean channel width), show- net changes in storage—and describe average ing signifi cant positive correlation among all annual conditions for all aspects of the analy- study reaches. 0.1 Scaled Bar Area (m sis, recognizing that some features of these fl uvial systems refl ect historical conditions and processes and that actual transport conditions 0.01 vary tremendously from year to year. majority of these were derived from reservoir 0.1 0.2 0.5 1 2 5 10 or catch-basin surveys, but some sites, such D* Bed-Material Yield as Oak Creek, Chetco River, and Smith River, 10 were based on bed-load sampling in combina- D Bed material entering the fl uvial network tion with either a sediment rating curve or appli- )

2 Illinois R.

was estimated from empirical relations between cation of bed-load transport equations. These /m 2 measured bed-material fl ux and physiographic, bed-material yield measurements were derived 1 geologic, and climatic properties of contribut- from basins ranging from 0.6 km2 to 6906 km2 ing basins. The approach is similar to the sedi- (median area 230 km2) and represented dura- ment yield studies of Hooke (2000), O’Connor tions of 1–95 yr. Contributing areas were Scaled Bar Area = 0.77 et al. (2003a), and Aalto et al. (2006), although adjusted for the presence of upstream dams. We 0.1 4.46(Scaled Migration Rate)

Scaled Bar Area (m (all observations) it focuses on bed material (grain size > 0.5 mm) did not use data from landslide surveys or other Adjusted R2 = 0.15, P<0.05 rather than total clastic yield. similar indirect approaches for estimating basin From existing literature, we assembled 34 yield or in-stream bed-material transport. observations of bed-material transport in rivers These measurements have considerable 0.01 0.01 0.02 0.05 0.1 0.2 of western Oregon and northern California uncertainty. Because of the diffi culty and Scaled Migration Rate ((m/yr)/m) from which we could derive annual transport uncertainty in measuring bed load, and then rates (Table 2; Table DR6 [see footnote 1]). The extrapolating to estimate annual loads (Gomez,

Geological Society of America Bulletin, March/April 2014 383 O’Connor et al.

1991), values based on bed-load measurement programs are optimistically accurate to within a factor of two in the best of circumstances, )6002(secneicSretawllitSseYseY ) )0002(s )0 )0 )0002(secneicSretawllitSseYseY )9002(rolyaTdnaraakyDseYseY 0 )a )a 0 0 0 such as for the Chetco River (Wallick et al., )2991(jedaMdnaelsiLoN ) )0991(ffloWdnatnarGoN )0991(ffloWdnatnarGoN ) 0 02( 0 )6002(.late.cnIGFMseYseY 29 0 3 3 2 2 99 (secn ( 002(. 002(. )0102(.latekcillaWseYseY (2006) 2010). Uncertainties in estimating bed-material 9 003a) s s 1( 1 ecneicSretawllitSseYseY e e (ffl )9002(hcetarteTseYoN )9002(hcetart )9 ces (2000) c c )4002(relpmaWseYseY )4002(relpmaWseYseY )4002 jeda from the reservoir surveys may be even greater, )8 ) ne n l l 0 )1002(irarreFseYoN 1002(s ei ences (2006) ei atero atero o 0 i

0 owing to survey errors and uncertainties in addi- 02( cSretaw cSretawllitSs cS (2004) W 2 (hce M (relpmaWseYseY d r tion to assumptions or limited knowledge of d n k et n n nor et al. (2003a) n cind at r a awll no no tart Corp Corp (2004) e

fi deposit volume density, the component of the i narG b el ll c (1998) University (2009, written commun.) C C it et it ’Ose ’Ose si a m . Klingeman, Oregon State eTseYoN e deposit that is bed material, and reservoir trap P L Pse A J.E. O’Connor, personal observation J.E. O’Connor, S SseYse S T effi ciency. seY seYseY s s s o o eYo eY e e Our empirical analysis was premised on N N Y Y Y YoN Y analysis Source Included basin factors such as physiography, climate, in residual and geology being the major factors control- seY s s s oN oN oN o oN oN oN ling bed-material supply to the fl uvial system. eY e eY N N Y Y

model Similar to the analyses O’Connor et al. (2003a) regression Included in and Aalto et al. (2006), potential physiographic and climatic predictor variables were calculated /yr) 0.41gnitar/gnilpmasdaol-deB0.1581,598,4082,955 2.5 9.21gnitar/gnilpmasdaol-deB2.75 2.05 8. 6.534gnit 5.022snoitauqe/gnilpmasdaol-deB9.10 0. 6.12 0.9gnitar/gnilp 0. 1. 4.05 7.801snoitauqe/gnilpmasdaol-deB5.62 4.41yevrusatledriovreseR1 6 7.11 7.41gnita 0.45 9. 0.13 5.89 6. 0.84 6.4 8.21yevrusatledriovreseR8.972424,459, 1.01yevrusatledriovreseR7.2 3 .4 for the contributing area of each measurement 4.0 Yes Yes Stillwater Sci 463g 4 3g 61 7 12yev yield 2 5 location (Table 2; Table DR6 [see footnote 1]). (t/km

Bed-material Also, for each contributing area, a categorical Table DR6 (see text footnote 1). Table variable of dominant geology was determined y n n it it ary e v a a a y yevrusriovreseR ye y yevrusriovreseR y yevrusriovreseR y yevrus yevrusriovreseR yevrusriovreseR yevrusriovreseR yevrusriovreseR from the classifi cation of regional lithologies r r r/ r/gnilpma r/ r/gnilpmasdaol-deB1.24 e evr e ev u u g g v v v s s r rusriovreseR r rusriovreseR ni ni (Fig. 1; Table DR7 [see footnote 1]), with domi- u u u l l at atle sri sri s l pma pma r r e i iov nance defi ned as the geologic unit of greatest m d d o o o asdaol-deB6.04 ri ri vrese vrese v s s s r r spatial extent. o ov e e d daol-de d v s s r res a a e eR es ol ol The resulting best-fit power-law model - - R R R eR0. eR9. d d eB eB (Fig. 5) predicts unit bed-material yield as a B function of average basin slope and the pres- ence or absence of Klamath terrane rocks as the ) Measurement method 7. 3. 3. 8.4 0. 4.6 0. 1. 4.001905 1.46415,008,4308,065 1.9 3.0 1.1671663,610,5829 3. 3. 4.73 2 . 5495 55 71 14 6 10 856143 4 63129 0931342, 00242 dominant lithologic unit, 7 86181, 09 01 5247 1 75298,7 area (km 7 7 18 7 4 7 3 3537,199,4 6 17

Contributing 2.2727 (0.356·KT ) Qbm = 450·S × 10 , (3) 0 7 60 40 7 0 511,466,4398,104 2 15, 74, 7 1 84, 6 13, 8 98,6 1,93 2 6,82 4,7 6,6 7,59 2,63 9, 0,23 5 8,7 3 ,609, , , ,097,4387,645 70 27 8 69 827, 88 530,56 4 010,52 (m) 46, 88, 48,4 97,432 9 49, 49,4977, where Qbm is annual unit bed-material fl ux in 9, 9,4 5,471 5, 8, 8,496 6 7,433 9, 7,4 ,4927,604 Northing 4 4 4 4 4 4 4 4 4 4 tonnes (t) per square kilometer per year, S is 8 0 8 5 6 7 1 0 8 5 9 0 5 1 mean basin slope (dimensionless, derived from Location 1 55,3 2 1 43,1 97, 00, 43,0 3 22,2 7 82, 7 4 2 50,76 6, 0,67 3,18 0,2 4,0 6,63 3,8 3,48 9, 7 5 4 , , , ,1 USGS 1/3 arc-second resolution topographic 305 94 77 045 56 36 055 655 365 (m) UTM, Zone 10N 7 14 3 6 65 55 3 8 Easting 575,790 4,903,240 13.7 Reservoir survey 54.6 Yes Yes Stillwater Sciences 635,126 4,931,722 5597.7 Reservoir delta survey 2.2 Yes Yes O’Connor et al. (2 4 5 6 4 5 6 5 5 4 4 5 5 5 5 data), and KT represents the categorical vari- able of the dominant lithologic unit, for which

TABLE 2. SUMMARY OF REGIONAL BED-MATERIAL YIELD OBSERVATIONS BED-MATERIAL OF REGIONAL 2. SUMMARY TABLE the value equals 1 if the dominant lithology is the Klamath terrane and equals 0 for all other dominant lithologic units. The model coeffi cient has been adjusted by a factor of 1.145, using the “smearing estimate of bias” associated with the n n n i isabettemalliW,ekaLggaHyrneH,ke i nisa

nisa distribution of residuals to account for the pre- s s maDno absetu abma n i

b diction bias resulting from back transformation b s nisa riovreseRtiorteD,mrAmaitnaS,re ab r au n s i i etuhc o s n it from logarithmic units (Newman, 1993). The i qpm i vrese htama a b s h sreviD2.oNo naS, nisabeizn nisa nisa 199 be )riovreser( cse abma

ettema overall result is easily interpretable in that bed- rio i s Uhtro zn r 1 b b eD, i l D, 1 R

–5 material yield is everywhere strongly related to v o m eizneKcM eizneK res K, 99 it e rA ri s v ekaLeetekoT,reviRauqpmU l e r 4 r l KcM gn r naS iov i o iov k 1– e basin slope, and that bed material yield is ~2.3 91,eka v N,eka irp e h a s e reseR W, s Lo e 54 n RkroFhtroN,reviRsamakca r KcM,3d r e i e , u ,m r times greater when the dominant lithology of e cM,1 ri k Rl i e s kaLkeerCtso S bnet lo lom nisa 9 s s a o a l o rAt iM 1 eR a vreseRaneroD,reviRwoR eR L b vreseRhtimS LpmutS,r , the basin is Klamath terrane rocks, which prob- ,k L doS,reviRau t meL,r r 2dehsretawswerdnAJH e bett e h e ll e r eulBtakeer i c o u etaG ive i de rra gir L,revi v e iR c rOt ably refl ects its more intense deformation his- ainrofil ow e v o iR, h h B, ema h e nir s sret H, o

a tory compared to the other lithologic groups. r cO, v l revi i a , Clearwater forebays 1 and 2, North Umpqua basin 553,733 4,788,914 48.5 Reservoir survey 20.5 Yes Yes Stillwater Scien norI, , Deschutes River arm, Lake Billy Chinook e r revi et B, , Trail Bridge Reservoir, McKenzie arm, basin Bridge Reservoir, Trail , 576,258 4,903,592 42.1 Reservoir survey P, Ra R s l k e l e liW,ke a awsw a , Metolius arm, Lake Billy Chinook, Deschutes basin 621,607 4,941,628 822.2 Reservoir delta survey 7.2 Yes Yes

v This relation was strengthened by judicious eer revi L, v k au iRsamakc k A i C,rev w R r erCsnigg eer u , eer reviReugoR R k , R ev k v s qpmU qpm q revi h iRmaitnaS

eer removal of outliers (Fig. 5; Table DR6 [see Cd C r s eer pmUhtroN s iR werd R eta ama Ctuowo Coco ubne doowde Complete information on derivation of mass estimates, period record, and predictor variable values is provided in supplement e i r deko e oo

C footnote 1]). Five outliers had clear reasons for r R Rht Cn Uhtro d o w y nA C cte n raelC htim kca ht h w t n tr ier im AJ k

n removal from the analysis. In particular, the h de r o ne o Note: alC y aO cO J h r o o l l cS l lF H S McKenzie River R R C J C H Crooked River, Crooked River arm, Lake Billy Chinook, Deschutes basinCrooked River, 637,827 4,930,421 4166.6 Reservoir delta survey 1.5 Yes Yes O’Con S Camp/Dutch and Scotch Creeks, Iron Gate Reservoir, Klamath basinCamp/Dutch and Scotch Creeks, Iron Gate Reservoir, 546,483 4,647,026 97.7 Reservoir delta survey 25.1 Yes Yes Pacifi Smith River, Trail Bridge Reservoir, Smith Arm, McKenzie basin Smith Bridge Reservoir, Trail Smith River, River and measurement site Deschutes River N N B Metolius River N Clearwater River C B C two Redwood Creek measurements were from

384 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

200 used in the fi nal regression. Moreover, its inclu- Redwood Creek at Blue Lake sion reduces the signifi cance of the Klamath variable and the overall model. Consequently, Redwood Creek at Orick

/yr), we judge the model based on slope and the pres- 2 150 ence or absence of Klamath terrane rocks (Fig. Bed-material yield * (10–0.356(Klam)) = 450*(Slope)2.27 Adjusted R2 = 0.83, P<0.001 5) as the best overall model for predicting bed- material yield in western Oregon with the avail- 100 able bed-material fl ux information. The results of this sediment yield analysis for western Oregon are very similar to those of Aalto et al. (2006) for the Bolivian Andes, where 50 multiple regression analyses involving several H.J. Andrews similar predictor variables resulted in a fi nal Specific Sediment Yield (t/km model in which slope and geology explained 90% of the variance in sediment yield in the 0 (adjusted for Klamath Terrane geology factor) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Bolivian Andes. These results also concur with Mean Basin Slope (m/m) an earlier analysis the Deschutes River basin (which includes some of the data used in this Observations used to determine model analysis), in which sediment yield was strongly Removed from model, but included in residual analysis correlated to basin slope and drainage density, Not included in model or residual analysis which in turn correlated to local geologic units (O’Connor et al., 2003a). Figure 5. Bed-material yield observations and selected power-law model (and 95% confi dence limits). Yield values are adjusted for Bed-Material Comminution Klamath terrane geology factor to enable plotting against mean ba- sin slope of contributing watershed. Measurement site information, The bed-material yield analysis provides references, and all values are given in Table 2 and supplementary a means of estimating the mass of bed mate- Table DR6 (see text footnote 1). rial entering the fl uvial system. However, bed material moving downstream is constantly diminished by particle attrition or comminution. highly disturbed watersheds where sediment The fi nal regression model was based on Although particle breakage and abrasion do not fl uxes have been historically higher than natural the 22 retained observations and had a coeffi - reduce the total sediment fl ux, these processes (Nolan and Janda, 1995). The three low yields cient of determination (R2) value of 0.83 (P < transform bed material into size fractions small from the H.J. Andrews Experimental Forest are 0.001). Both the slope coeffi cient and Klamath enough to be transported in suspension. Such from very small (1 km2 or less) and densely for- variable scaling factor have P values less than comminution likely contributes to the common ested watersheds for which the short measure- 0.1. Residuals, including the visual outliers, are observation of downstream fi ning of bed mate- ment periods did not include any signifi cant uncorrelated with other trial parameters, with rial in many fl uvial systems (Sternberg, 1875; mass movements, which episodically contrib- the exception of drainage density (Table 3; Fig. Shaw and Kellerhals, 1982; Kodama, 1994a, ute bed-material sediment in these watersheds. DR1 [see footnote 1]). While drainage density 1994b; Attal and Lavé, 2006), although selective Seven additional observations were removed was positively correlated with total sediment transport may be a factor in some environments from the regression analysis on the basis of yield for the Deschutes River basin in central (Brierley and Hickin, 1985; Paola et al., 1992; being visual outliers and derived from less reli- Oregon (O’Connor et al., 2003a), the correla- Ferguson et al., 1996; Hoey and Bluck, 1999). able measurements, but these were retained in tion in this analysis is negative with this larger We assessed bed-material attrition by a series the analysis of residuals. data set, and it is driven by the visual outliers not of tumbler experiments. We collected fi ve bed-

TABLE 3. PREDICTOR VARIABLES AND RESIDUAL CORRELATION P values from Spearman correlation between residuals of regression and predictor variables Variable Regression data set (22 observations) Regression data set and visual outliers (29 observations) Area (km2 *) 98.0 49.0 *)m/m(epolS 74.0 33.0 *)m(noitavelenaeM 37.0 46.0 *)m(noitavelemumixaM 56.0 87.0 lemuminiM *)m(noitave 35.0 12.0 *)m(feileR 34.0 13.0 Mean precipitation (mm)† 24.0 26.0 Drainage density (km/km2)§ 36.0 50.0 Length of record (yr)# 28.0 12.0 Note: All variable values are given in supplementary Table DR6 (see text footnote 1). *Calculated from U.S. Geological Survey National Elevation 1/3 arc-second data set, available at http://nationalmap.gov/index.html. †Calculated from gridded (30 arc-second) mean annual precipitation estimates for the period 1971–2000 provided by the PRISM Climate Group, available at http://www.prism.oregonstate.edu. §U.S. Geological Survey National Hydrography Data Set, 1:24,000 scale digital vector data obtained from http://nhd.usgs.gov/data.htm. #Period represented by bed-material flux observations; complete information in supplementary Table DR6 (see text footnote 1).

Geological Society of America Bulletin, March/April 2014 385 O’Connor et al.

(–αx) material samples of ~10 L each from active Wx = W0 ·e , (4) are higher than many other experimental studies gravel bars for each of fi ve bedrock geologic and span similar values as determined in the few provinces determined in the simplifi ed classi- where Wx is the mass (in grams, g) of a particle fi eld cases where downstream size reduction

fi cation of regional lithologies (Fig. 1; Tables after traveling distance x (km), W0 is the initial can be confi dently attributed to abrasion (Fig. DR8 and DR9 [see footnote 1]). An exception particle mass (g), and α is the mass loss coeffi - 7B; Shaw and Kellerhals, 1982; Mikoŝ, 1994). was that only four samples could be collected cient (km–1). Higher α values signify higher Consequently, we use the results of the tumbler for the High Cascades province because of rates of abrasion and conversion of bed mate- experiments without adjustment to approximate sparse bed material in streams draining this ter- rial to material expected to be transported as actual bed-material attrition rates, although this rain. We excluded the Quaternary sedimentary part of the suspended load. While the resulting supposition remains unverifi ed. unit from the attrition analysis since the clasts rates specifi cally pertain to the loss of gravel- forming this unit presumably originate from the size material, they are closely equivalent to bed- Regional Bed-Material Flux other fi ve lithologic provinces. Samples were material mass loss (sizes greater than 0.5 mm) collected at sites where the contributing basin because the discarded wear products were dom- Combining the bed-material yield estimates was solely composed of the targeted geologic inantly fi ne sand, silt, and clay. with our tumbler-derived attrition rates allows province,3 and each primary sample was from a The multiple analyses of each geologic prov- for spatially explicit predictions of bed-material separate basin. Collection sites were distributed ince indicate distinct rates of attrition among the fl ux in western Oregon and far northwestern throughout the range of the study area (Fig. 1) lithologic types, while samples from the same California. Our approach was to calculate annual and had basin areas ranging from 3.4 km2 to terrains—even those from widely dispersed bed-material yield with Equation 3 for each 12 1665 km2 (median 65 km2). sites—are mutually consistent (Fig. 7A; Table digit hydrologic unit code watershed (“HUC12”; Multiple types of samples were prepared DR8 [see footnote 1]). Splits of individual description at http:// water .usgs .gov /GIS /huc and analyzed (Table DR8 [see footnote 1]), but samples and samples including wider ranges of .html; accessed 5 June 2013) from the watershed the standard sample introduced to the tumbler grain sizes gave similar results as the standard boundary data set at http:// datagateway .nrcs .usda consisted of ~2000 g of gravel evenly divided samples (Fig. 7B). .gov/ (accessed 17 May 2011). The median basin among four 1/2ϕ size classes: 16–22.6 mm, The hardest rocks—those losing mass at the size for the 1042 HUC12 basins in our study area 22.6–32 mm, 32–45 mm, and 45–64 mm. This lowest rates—were those from the High Cas- is 66 km2, which is smaller than the 390 km2 distribution was a simplifi ed approximation of cades geologic province (Figs. 6 and 7). These median area of the 22 basins of sediment yield subsurface bed material sampled from gravel are chiefl y young unweathered basalts and observations underlying the bed-material yield bars within the study area. basaltic andesites resulting from Quaternary estimates but well within the range of basin sizes Each sample was dried and weighed and then Cascade Range volcanism. Of similar resistance with bed-material yield observations. The annual placed with 2 L of water into a Lortone QT 12 to abrasion were the metamorphic Paleozoic and bed-material supply calculated in this manner for rotary tumbler. Each run was periodically halted, Mesozoic rocks of the Klamath terrane, mainly each HUC12 basin is assumed to enter the fl uvial and the sample was drained, dried, and sieved. exposed in southwestern Oregon and northwest system (as derived from USGS 1/3 arc-second Size classes greater than 2 mm were individu- California. Slightly softer are the Tertiary volca- [10 m] resolution topographic data) at the basin ally weighed at 1/2ϕ intervals and then returned nic rocks—chiefl y basalts—of the Cascade and outlet. Downstream from the entry point, the to the tumbler with 2 L of clean water. We did Coast Ranges. By far the softest rocks were the annual fl ux of bed material from the contribut- not retain sand (2 mm diameter) and fi ner grains, Tertiary sedimentary rocks of the Coast Range. ing basin was calculated on the basis of the litho- and the mass of these materials was included in These rocks lost more than half of their gravel- logic composition of the basin, the lithologic- the total mass loss of the experiment. For most sized mass each kilometer (0.54 km–1), a rate 80 specifi c attrition rates derived from the tumbler trials, each sample was initially tumbled for 1 h times greater than rocks of the adjacent Klamath experiments, and distance traveled. All of these followed by two 2 h runs and three 3 h runs, or terrane, and as much as 1000 times greater than calculations assume steady-state conditions with else stopped when the sample had lost 25% of a sample of quartzite clasts (Fig. 7B). As dis- no net changes in bed-material storage along the its initial mass (Table DR9 [see footnote 1]). For cussed in more detail in subsequent sections, this hydrologic network. samples from the Coast Range sediments litho- discrepancy is probably the single most impor- By summing contributions from each basin logic province, exceptionally rapid comminution tant factor explaining why rivers draining Coast and accounting for attrition, we calculated total required measurements at 5 min intervals. Time Range sedimentary rocks have bedrock channels annual bed-material fl ux at 1045 locations, in the tumbler was converted to distance traveled and long fl uvial estuaries as they approach the providing spatially explicit predictions of bed- by multiplying the 60 cm interior circumference Pacifi c Ocean. material fl ux for all streams draining one or of the tumbler by the 32 revolutions-per-minute Translating these experimental tumbler results more HUC12 watersheds (Fig. 8; Table DR10 speed of the tumbler, giving 1.152 km/h. into actual mass-loss rates along the rivers of [see footnote 1]). This analysis predicts a wide Following Sternberg (1875), a mass-loss our study area requires assuming that transport range of annual bed-material fl uxes: as much as coeffi cient (α) was determined for each sample in the tumbler can be related to actual river 572,000 t/yr just downstream of the confl uence on the basis of simple exponential linear regres- transport distances. Some experiments have of the Rogue and Illinois Rivers, to less than sion of the mass measurements with distance shown that the true particle travel distance in 1000 t/yr at several basin outlets, particularly in (Fig. 6; Tables DR8 and DR9 [see footnote 1]). the tumbler may be less than half of that cal- the low-relief coastal plains fl anking the Pacifi c The relation takes the form of, culated from the tumbler circumference (Mikoŝ Ocean, the broad volcanic uplands of parts of and Jaeggi, 1995). Consistent with this, sev- the High Cascade lithologic province, and eral authors have suggested that experimental within the lowlands of the Willamette Valley. 3For a single sampling site (MFC-1 of Table DR8) of multiple upstream lithologic groups, a sample of devices underestimate abrasion rates in rivers, As expected from the empirical yield and attri- Coast Range volcanic clasts was separated from a bar as reviewed by Attal and Lavé (2009). Never- tion relations, topography and lithology strongly composed of both volcanic and sedimentary rocks. theless, our tumbler-determined abrasion rates infl uence regional patterns of predicted bed-

386 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

Time Tumbled (h) 02468101214 2200

2000 α = 0.001 α = 0.004

α = 0.007 Figure 6. Plots of representative clast attrition experiments, 1800 showing one experiment and derived mass-loss coeffi cient, α = 0.011 α, in units of km–1, for each lithologic province. Complete α = 0.013 tumbler experimental results are tabulated in supplemen- Mass (g) 1600 tary Table DR9 (see text footnote 1). EXPLANATION Quartzite High Cascades 1400 Klamath Terrane α = 1.206 Western Cascades Coast Range volcanics Coast Range sediments 1200 0 2 4 6 8 1012141618 Calculated Travel Distance (km)

EXPLANATION Standard samples Geometric mean of standard samples Duplicate samples from the same stream Bed load and corresponding standard samples Quartzite samples, this study 10 A B Shaw and Kellerhals (1982) field values Standard samples Other (lithologies: quartzite, limestone, samples granitics, and <10% sandstone) Mikos (1994) field values (lithologies: limestone, quartzite, Figure 7. Summary results of attrition experi- calcite, and <15% sandstone) ments from this study and other fi eld-based

1 1 measurements. Complete experimental re- sults from analyses conducted in this study 0.541

) are tabulated in supplementary Table DR9

–1 (see text footnote 1). (A) Results of standard- sample analyses for each lithologic terrain in the western Oregon study area, showing 0.1 0.1 mean value used for bed-material fl ux cal- culations. (B) Results of duplicate and ad- ditional analyses (quartzite clasts and wider

Mass loss coefficient (km grain-size range for bed-material samples) 0.016 in addition to the fi eld-based clast attrition 0.01 0.010 0.01 rates documented by Shaw and Kellerhals 0.007 0.006 (1982) and Mikoŝ (1994). Standard and bed- material sample compositions are reported in supplementary Table DR8 (see text foot- note 1).

0.001 0.001 Klamath Klamath Mikos (1994) High Cascades High Cascades Western Cascades Western Western Cascades Western Quartzite (Klamath) Coast Range Volcanic Coast Range Volcanic Coast Range Sedimentary Coast Range Sedimentary Geologic Province or Study Name Shaw and Kellerhals (1982)

Geological Society of America Bulletin, March/April 2014 387 O’Connor et al.

124°W 123°W 122°W EXPLANATION Lithologic Terrains AstoriaAstoriaAstoriaAstoria Quaternary sediment 46°N High Cascades Western Cascades Coast Range sediments NehalemNehalem R. WASHINGTON Coast Range volcanics and Columbia River basalt Columbia R. Klamath Terrane Portland Sediment Transport Network WilsonWilson R. Scoggins Dam Upper Bull " " Run Dam Features 0 Bed material flux (excluding trapping by dams), ca R. River Mill Dam " ClackamasClackam R. Pacific in tonnes per year NestucNestucca R. " 572,000 Ocean North Fork Dam as R. amette R. " Timothy 0 Bed material flux (including Lake Dam 45°N WillametteWill R. trapping by dams), Siltez R. in tonnes per year Salem 499,000 ntiam R. North SantiamSa R. " Major dam included in model South Santiam R. " HUC12 basins Newport Sa Detroit Reservoir Dam Corvallis nti City/town am R.

" Green Peter Lake Dam " Foster Reservoir Dam "Trail Bridge Fern Ridge " Blue River Dam Reservoir Dam Lake Dam " " Washington Siuslaw R. Cougar Reservoir Dam Seattle Eugene 44°N Middle Fork Willamette R. Bend " Fall Creek Reservoir Dam " rk WLookout Point Lake Dam illamett Portland " I CottageUmpqua Grove R. Lake Dam " Dorena e " Study d R. Oregon Lake Dam area a Hills Creek Reservoir Dam h o

Winchester Dam " " Coquille R. " Lemolo Lake Dam Nevada Soda Springs Dam California

43°N San Francisco

" Galesville Reservoir Dam

Rogue R. " Lost Creek Reservoir Dam

Illinois R. " Gold Savage Rapids Dam 015 30 60 Kilometers Medford Beach Klamath Falls

" Emigrant Lake Dam 0 15 30 60 Miles " 42°N Applegate Lake Dam

Sm CALIFORNIA itht R. Base map modified from U.S. Geological h R.SR. Survey digital data, various resolutions. Geology modified from Ludington and others, (2007) and Ma and others (2009), various scales. Projection: NAD83 UTM, Zone 10N.

Figure 8. Summary results of bed-material fl ux estimates for the western Oregon study area, with bed-material fl ux indicated by line width, based on calculations for 1045 HUC12 basins, as tabulated in supplementary Table DR10 (see text footnote 1). Results are shown for scenarios of no bed-material trapping by dams (maroon color), and assumed complete bed-material trapping by the 25 major dams in the study area (blue). Lithologic terrains are defi ned in Figure 1 and supplementary Table DR7 (see text footnote 1). Calculation nodes do not exactly correspond to dam locations, so effects of dams are generally attributed to next downstream calculation point.

388 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon material fl ux (Fig. 8). The greatest bed-material Figure 9. Results of Monte Carlo experi- Coefficient of Variation fl ux is predicted for the basins of southwestern ments investigating sources of uncertainty 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Oregon and northern California, which drain the in bed-material fl ux calculations. Each case 600 A All variables; Klamath Mountains, refl ecting the elevated yield assessed 500 trials for each of 1045 calcula- mean coefficient of variation = 0.69 from the Klamath terrane lithologic province, tion points. The primary conclusion from 400 the high local slopes (mean slope = 0.40 ± 0.10 these experiments is that uncertainty in [m/m]), and the low attrition rate for Klamath the bed-material yield relation is the major 200 terrane clasts. Relatively high bed-material trans- overall source of uncertainty in the sedi- port is predicted for streams draining the Western ment fl ux calculations. Results are summa- 0 600 Cascades and Coast Range volcanic lithologic rized in supplementary Table DR11 (see text Coefficient of variation associated with un- B certainty in bed-material yield relation provinces, also a result of the high basin slopes footnote 1). (A) Coupled yield and attrition (equation 3); mean = 0.66 (0.29 ± 0.11 and 0.25 ± 0.17 [m/m], respectively) model, showing distribution of total coeffi - 400 and the composition of materials, which are cient of variation associated with estimates resistant to abrasion. Areas of low predicted bed- of average annual bed-material transport at 200 material fl ux include those drainage basins domi- each calculation point. (B) Isolated analysis nated by Quaternary sediments (mean slope 0.02) of coeffi cient of variation associated with 0 1000 and the Coast Range sediments lithologic prov- the bed-material yield model of Equation 3. C ince, where bed-material yield from each basin (C) Isolated analysis of coeffi cient of varia- 800 Coefficient of variation associated with is predicted to be high because of high average tion associated with the attrition estimates. uncertainty in attrition rates; mean = 0.06 basin slopes (0.29 ± 0.11 [m/m]), but bed-mate- (D–F) Analyses of the coeffi cient of variation rial fl ux diminishes rapidly downstream because associated with the individual parameters of 600 of exceptional attrition rates. the bed-material yield model of equation. 400 Uncertainty in Bed-Material Predictions 200 Uncertainty in these estimates derives from and northern California study area captures all the combined uncertainty in the yield estimates bed-material entering upstream reservoirs, we 0 and the attrition rates. On the basis of the esti- can use the combined bed-material yield and 600 D Coefficient of variation associated mated uncertainties of the sediment yield model comminution calculations to estimate the down- with exponent in bed-material pro- (coeffi cients and exponents) and the mass-loss stream effects of dams on bed-material fl ux 400 duction relation (equation 3); = 0.34 coeffi cients (from the multiple analyses), we con- (Fig. 8; Table DR10 [see footnote 1]). In total, ducted Monte Carlo experiments of 500 trials for bed-material supply is cut off from ~28% of the 200 each of several scenarios. Incorporating all the study area. Of the large basins, most affected is model uncertainties for bed-material yield and the Willamette River, where 34% of the basin 0 Number of Calculation Points 1200 attrition rates, the coeffi cient of variation for the no longer contributes bed material to the main- E best-fi t calculated bed-material fl ux at each of the stem Willamette River at its mouth. However, Coefficient of variation 1000 associated with coefficient 1045 calculation points ranged from 0.48 to 2.1, the downstream effects on bed-material fl ux are in bed-material production with an average of 0.69 (Fig. 9; Table DR11 [see even greater than this percentage would indi- relation (equation 3); = 0.46 footnote 1]). Separate Monte Carlo analyses of cate; for example, our analysis indicates that 800 the infl uence of the uncertainty of the individual dams have reduced the peak bed-material fl ux coeffi cients and exponents in the regression mod- on the Willamette River by 64%—from 199,000 600 els show that the uncertainty in the bed-material t/yr without dams just downstream of the San- yield model (Eq. 3) accounts for ~90% of total tiam River confl uence, to 72,000 t/yr. Dams on 400 prediction uncertainty (Fig. 9), with the coeffi - the Coast Fork Willamette, Middle Fork Willa- cient, exponent, and Klamath terrane factor (for mette, Santiam, and Clackamas Rivers have the 200 basins where the Klamath terrane is the dominant largest apparent effects on overall bed-material lithology) all contributing about equally to the fl uxes. For the Willamette River, because of 0 calculated uncertainty. This analysis nevertheless variable attrition rates and dam locations, the 1000 F Coefficient of variation associated likely underestimates total uncertainty because effects of dams diminish downstream, such with coefficient of Klamath Terrane of (1) our strengthening of the regression model that at the Willamette River confl uence with 800 factor (equation 3); mean = 0.43 (for basins where Klamath Terrane by eliminating outliers and (2) employment of the Columbia River, our calculations indicate is dominant) the unverifi ed assumption that the tumbler-deter- that the bed-material fl ux has declined 58% as 600 mined attrition rates directly correspond to rates a consequence of impoundment, from 112,000 along the rivers of the study. to 47,000 t/yr. Other large rivers, such as the 400 Umpqua and Rogue Rivers, also have dams Effects of Dams that trap bed material, but the overall effects 200 become small downstream (Fig. 8). While this 0 The overall analysis does not account for analysis shows regional patterns for large rivers 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 bed-material trapped by dams. By assuming that of western Oregon, similar fi ner-scale analyses Coefficient of Variation each of 25 major dams in the western Oregon incorporating better information on locations

Geological Society of America Bulletin, March/April 2014 389 O’Connor et al. and effi ciency of bed-material trapping may be t useful to examine effects of bed-material trap- ping for individual rivers and river reaches. 48.0 21.1 65.0 53.0 64.0 98.0 1 ux estimate

Additionally, these calculations do not account 1. 1 3.65 for river incision or widening owing to reduced 0.03 0.13 bed-material supply, which may partly compen- ([t/yr]/[t/yr]) sate upstream supply loss. Ratio of recruitment and bed-material fl extraction values to best-fi 761,2 761,281203,74622,111 181,4 999,43565,11253,52 95 991,13633,21624,32 811,74125,71296,43 Comparison with Other Measurements # 4,3127259499 84% (t/yr) 8 1 quantile The bed-material yield, attrition, and fl ux 1203,74622,11 calculations lead to several testable predictions 676325 ux estimates fllairetam-deB 16% and comparisons. Because most of the dams (t/yr) quantile have been in place several decades, most of the t

following comparisons employ fl ux estimates 5 8 (t/yr) 21,462 9524 35,319 adjusted for trapping of bed material by the 25 model Best-fi 1 113,007 52,042 176,575 major dams in the study area. 165,172 70,838 274,246 The most direct comparison would be with 5 direct measurements of bed-material transport. 002– .D.N .D.N 8002 8002 8002 5002 Most such available measurements have been 3 002; 2001 2006 incorporated into the bed-material yield model 2009 1

(Table 2), but annual volumes of gravel bar 0020 Minimum year replenishment at surveyed in-stream mining sites provide an indication of minimum trans- alyses. .D.N .D.N 593,0170024 19 6975 4062 port rates. For the eight reaches for which such § 2, 66 survey information is available (Table 4), vol- (t/yr) umes of gravel-bar deposition have ranged from Minimum 0.03 to 3.1 (median = 0.56) of the predicted snoitarepogninimleva .D.N**.D.N .D.N.D.N 9 6 60020 60028994 991 bed-material transport for the surveyed reach 0 0 year (assuming a deposit density of 2.1 t/m3). While 2 this range is large, the South Fork Coquille River Maximum 3 is the only one for which measured gravel depo- 4 80 2005 9210 97,4 05 59,8 34,71207, § sition exceeded the predicted bed-material fl ux. ,12211,41 r 2 8 (t/yr) g For Hunter Creek and the Chetco River, 145 Maximum m surveyed gravel recruitment volumes are not a e rt

as complete as reports of mined volumes. For s -nimorf 876, 16 005 699, § 5 both of these alluvial reaches with extensive 0, 9 3 3 321 019 0 (t/yr) 9 3

in-stream gravel mining, mined volumes over Average s

9–20 yr have equated to ~1.1 times the pre- noi . t 3 dicted bed-material fl ux (Table 4). In particular a vr esb for Hunter Creek, where there is little evidence 002 0 8 90 70 allick et al. (2010, 2011) and Jones et al. (2011, 2012b, 2012c). and Jones et al. (2011, et al. (2010, 2011) allick 1 0 † –6 O 02– 02– 02 of net incision or aggradation over the last sev- 02– 600 90 0 –6002 0 0 1 0 eral decades (Jones et al., 2011), this average 5 2 2 2;40 99 00 0 removal volume may approach the bed-material 02 2003–2009 21,877 68,980 2006 6430 1 2 TABLE 4. SUMMARY OF GRAVEL BAR SURVEYS ASSOCIATED WITH IN-STREAM MINING SITES ASSOCIATED BAR SURVEYS OF GRAVEL 4. SUMMARY TABLE fl ux into the reach, which in turn closely equates 02 to the predicted fl ux from the yield and attrition routing. The situation may be similar for the 024– 1 9 4 52 3 Chetco River, but evidence of incision and bar- 12 of years Period area loss since the 1970s (Wallick et al., 2010) Number indicates net bed-material loss that may partly 1 5 2004–2006; 2008–2009 3800 1 4 2 1 coincide with the 2000–2008 time period for 1 of sites* which mined gravel volumes slightly exceeded Number the predicted average annual fl ux. Summarizing the mining site measurements, aside from the South Fork Coquille River, the replenishment and mined volume measurements are consistent with the estimated bed-material fl uxes.

Bed-material yield, in addition to fi ne sedi- Recruitment and extraction values are summarized from W m ret oc o elah i nosl s i cte t m ment and solute loads, constitutes the total h Analysis period based on completeness of survey records. Original volume measurements converted to tonnes on basis of 2.1 t/m Flux values account for attrition and bed-material trapping by dams; 16th 84th percentile on basis of Monte Carlo an n e c ai **N.D.—not determined or applicable. † § # Note: *For some reaches, the number of reporting sites varied during assessed period record. i li uH h h eN South Umpqua Roseburg 3–5 7 Reported recruitment Reported extraction C S. Fk. Coquille Broadbent 4–8 13 1996–2006; 2009 78,378 144,690 1999 28,640 C K South Umpqua Days Creek River and reach W landscape denudation. From our bed-material M

390 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

yield analysis (in this case, ignoring bed-mate- Range sedimentary rocks in this reach, which emptying into the Pacifi c Ocean in southwestern rial trapping by dams), we can estimate verti- ranges from 0.12 to 0.25 (Wallick et al., 2011). Oregon. Although the headwaters of both basins cal erosion rates associated with bed-material For basins with more distinctive rock types, are in the High Cascades lithologic province, production for each HUC12 watershed. For evaluation of clast abundance could be a strong and both have tributaries and headwater areas in the Coast Range and Klamath terrane litho- test of this approach to estimating bed-material the Western Cascade Range lithologic province, logic provinces, these rates range from 0.05 to transport rates, and even serve as a basis for inde- the Rogue River basin has much more of its area 0.13 mm/yr, assuming a rock density of 2.60 pendent estimates of bed-material supply, fl ux, within the Klamath terrane (Fig. 1). By contrast, t/m3 (Fig. DR2 [see footnote 1]). These values or attrition, as demonstrated by Mueller (2012) the lowermost 200 km of the Umpqua River are ~10%–30% of regional uplift rates (Bier- for gravel-bed rivers in the Rocky Mountains. fl ows mostly through rocks of the Coast Range man et al., 2001; Heimsath et al., 2001; Van- Although none of these evaluations of the bed- sedimentary province (Figs. 1 and 10). Laningham et al., 2006), a plausible value of material fl ux predictions provides compelling The predicted bed-material fl uxes along these bed-material production relative to total load confi rmation of their accuracy, all are generally river corridors refl ect these similarities and dif- if the Oregon Coast Range is in approximate consistent. More systematic testing, particularly ferences (Fig. 10). For both river systems, esti- steady state with respect to erosion and uplift, as of bed-material fl ux rates and bed-material com- mated bed-material fl ux increases downstream proposed by Montgomery (2001). The areas of position, would provide stronger evaluation of within the High Cascades and Western Cas- highest predicted erosion associated with bed- both the yield and attrition components of the cades terrains, and annual transport rates climb material production are in the Klamath Moun- analysis. Additionally, the Monte Carlo analyses substantially for both rivers upon entering the tains in southwestern Oregon and northwestern indicate that overall improvement of the predic- Klamath terrane lithologic province. For the California, the steeper parts of the Coast Range, tions would chiefl y result from better statistical South Fork Umpqua and main-stem Umpqua and in the dissected Western Cascades. models of bed-material yield. Given the appar- River system, the maximum estimated bed- The particle comminution modeling allows ent importance of lithology, suffi cient obser- material fl ux is 242,000 t/yr at RK 259 of the predictions of the mass of suspended load attrib- vations to discriminate yields from specifi c South Fork Umpqua River, with bed material utable to abrasion and the transformation of bed lithologic provinces in addition to the Klamath mainly derived from the Klamath terrane (Fig. material to suspended load. The most complete terrane would probably improve estimates. 10B). The predicted fl ux at a similar position suspended load records in the region are for the on the Rogue River, which has not yet entered streamfl ow measurement site on the Umpqua DISCUSSION: BED-MATERIAL the Klamath terrane, is much lower—~40,000 River near Elkton (USGS streamfl ow station TRANSPORT AND CHANNEL t/yr (Fig. 10A). However, downstream, the pat- 14321000). This station accounts for 79% of GEOMORPHOLOGY terns diverge in concert with the different rock the 12,103 km2 basin, and it recorded an aver- types feeding into the two rivers. The Rogue age suspended load 3,200,000 t/yr for the period The bed-material transport results, although River, which enters and then continues through 1956–1973 (Curtiss, 1975). By comparison, the improvable and requiring more testing, do Klamath terrane rocks, increases its bed-mate- predicted mass of bed-material loss due to attri- explain many aspects of regional and local rial fl ux to 572,000 t/yr just downstream of the tion at that location—determined by subtracting channel morphology. For example, the diver- confl uence of the 2564 km2 Illinois River at RK the predicted bed-material fl ux (with dams) with gent characters of the Rogue and Umpqua Riv- 43.8. The Illinois River, which is entirely within attrition from that without attrition—is 533,000 ers are explained by the lithologies that they the Klamath terrane, contributes an estimated t/yr; equivalent to 17% of the measured average drain. Additionally, the transport estimates 273,000 t/yr at its mouth—constituting nearly annual suspended load. clarify the distribution of alluvial and nonallu- half of the Rogue River’s total. By contrast, the The lithologic-dependent fl ux calculations vial channels in western Oregon and correlate Umpqua River, which enters the Coast Range also provide predictions of the lithologic com- with measurable aspects of channel and valley- sediments at about RK 220, 37 km downstream position of bed material at any location in the bottom morphology. More broadly, this exami- of the location of peak bed-material fl ux, gener- fl uvial network. This prediction is challenging nation of the settings and morphologies of the ally loses bed material downstream as attrition to test in the region because of the diffi culty in diverse channels draining western Oregon and exceeds supply, declining to 78,000 t/yr at its distinguishing clasts among some lithologic northwestern California allows for general con- entrance to the estuary at RK 44. The 961 km2 provinces, especially those of the volcanic ter- clusions regarding bed-material supply and river Smith River, which is entirely within the Coast rains. However, the distinctive sandstones and form and processes, including the distribution of Range sedimentary province and enters the siltstones of the Coast Range sedimentary rocks alluvial and nonalluvial channels and their key Umpqua River at RK 16, contributes only ~8000 allow partial evaluation. For the Umpqua River, morphologic attributes. t/yr of bed material to the lower Umpqua River. we measured the abundance of sandstones and These differences in predicted bed-material siltstones from samples of 400 surface clasts at The Umpqua and Rogue Rivers, fl ux accord with river character. The lower each of 12 bars between river kilometer (RK) a Tale of Two Lithologies Rogue River is fl oored and fl anked by gravel 175.5 and 46.7 (Wallick et al., 2011). The frac- bars to the Pacifi c Ocean (Fig. 2A). Tidal infl u- tion of sedimentary rock clasts relative to all Comparison of the Umpqua and Rogue River ence on the Rogue River is short, only extending clast types ranged from 0.026 to 0.086 (0.038 basins illustrates how predicted bed-material 6.7 km inland from the Pacifi c, indicating that ± 0.022). The predicted fraction of Coast Range fl ux is affected by variations in lithology and bed-material supply has kept pace with Holo- sedimentary clasts for six fl ux calculation points its resulting controls on bed-material yield and cene sea-level rise (Jones et al., 2012b). By con- between RK 166.6 and 56.2 ranges from 0.010 downstream comminution (Fig. 10). The Rogue trast, the lower Umpqua River fl ows on bedrock to 0.044 (0.030 ± 0.014). These values are not River basin drains 13,390 km2, slightly larger with few gravel accumulations (Fig. 2C), until signifi cantly different (two-sample t-test, P > than the 12,103 km2 of the Umpqua River. The reaching tidal infl uence, which extends 44 km 0.05), and both are much lower than the fraction basins have adjacent headwaters in the Cascade upstream (Wallick et al., 2011). The long fl uvial of the contributing basin underlain by the Coast Range and fl ow through the Coast Range before estuary of the Umpqua River indicates that bed-

Geological Society of America Bulletin, March/April 2014 391 O’Connor et al.

1000 A 900 Rogue River Figure 10. Estimated bed- material flux, assuming no 800 Klamath Terrane Western High Cascades Cascades trapping by dams, in relation 700 to dominant surrounding geol- 600 EXPLANATION ogy for the (A) Rogue River Bed-material flux estimates 2 (13,390 km ) and (B) Umpqua 500 Best fit River (12,103 km2) basins of 16th percentile Monte Carlo 400 Applegate River southwestern Oregon. This 84th percentile Monte Carlo Major Confluence comparison emphasizes the 300 importance of the distribution 200 Bear Creek of rock types within the basin. 100 The abundance of Coast Range Illinois River sedimentary rocks susceptible 0 B Umpqua and South Fork Umpqua Rivers to abrasion in the lower part 500 Klamath Terrane of the Umpqua River basin re- 400 Coast Range Sediments North Fork High Umpqua River Cascades sults in substantial downstream Cow Creek decrease in bed-material fl ux. 300 Smith River

Supporting data are tabulated Bed-material flux (thousands of tonnes per year) 200 by HUC-12 basin in supple- 100 mentary Table DR10 (see text footnote 1). 0 0 100 200 300 400 Distance from mouth (km)

material accumulation has not kept up with Holo- size nor, surprisingly, channel migration rate to the other major lithologic provinces. Particu- cene sea-level rise, resulting in a partly drowned correlates with scaled bed-material transport larly high bed-material yields from the Klamath river valley near its Pacifi c Ocean confl uence. (Figs. 11A and 11B). By contrast, scaled bed- terrane likely are due to intense deformation material transport rate correlates strongly with associated with multiple episodes of Meso- Bed-Material Flux and River Morphology both scaled bar area and D*, but only for the zoic and Early Tertiary accretion, predisposing alluvial reaches (Figs. 11C and 11D). In both these hard rocks to physical weathering and More broadly, the bed-material fl ux estimates cases, these relations bound most observations, production of gravel-sized bed material. As a allow further examination of the reach and site with the alluvial reaches providing an upper consequence of high bed-material yield rates, morphometry. For these correlations, we scaled bound to the bar-area measurements and a lower the rivers of southwest Oregon and northwest the reach-specifi c best-fi t bed-material fl ux esti- bound to the reach-averaged D* values. These California have alluvial channels and high bed- mates (adjusted for the presence of dams) by results parallel the morphometric relations material transport rates. annual fl ow volume (Table 1). These values, shown in Figure 4, whereby bar area seemingly The Western Cascades also supply substantial essentially an annual bed-material concentra- responds sensitively to bed-material fl ux, par- bed material. This bed material is not as hard as tion, were determined for calculation points ticularly for alluvial reaches. that from the Klamath terrane, but the large area representative of each analysis reach, typically of the Western Cascades in conjunction with near the downstream end (Table DR2 [see foot- Role of Geology and Physiography steep slopes result in signifi cant bed-material note 1]). To nondimensionalize this parameter, production. Accordingly, the large rivers drain- annual bed-material fl ux was converted to min- As is known for many rivers, it is evident ing large areas of the Western Cascades, includ- eral volume by assuming a density of 2.6 t/m3. from this analysis that regional geology imparts ing the Coast and Middle Forks of the Willa- Because not all valley reaches had satisfactory an overriding infl uence on the character of mette River, the McKenzie River, the Santiam streamfl ow measurement data, only 20 of the 24 gravel-bed rivers in western Oregon and north- River, the Molalla River, and the Clackamas reaches could be analyzed in this manner.4 ern California. This study clarifi es that this River, are predominantly alluvial with gravel Some reach characteristics correlate with infl uence comes about by two distinct avenues: beds in their downstream reaches. It is bed mate- local estimates of bed-material fl ux, while others its control on bed-material yield, and its control rial from this lithologic province that has been do not. For example, neither bar-surface grain on downstream clast comminution. primarily trapped by dams in the study area that reduces bed-material volume in the main-stem 4Bed-material fl ux estimates for all basins could be Bed-Material Yield Willamette River by as much as 64%. scaled by other fl ow measures, such as the 0.5 annual Regionally, the accreted and uplifted Klamath These fi ndings are consistent with many other exceedance fl ow, derived from regional regressions terrane, affi xed to western North America dur- studies documenting the control of lithology and employed in StreamStats (http:// water .usgs .gov /osw ing the late Mesozoic Era and Early Tertiary physiography on sediment yield and channel /streamstats), but these regressions for western Ore- gon rely on slope as a predictor variable and conse- Period, is an exceptional source of gravel bed morphology. Although most studies investigating quently are not independent of the bed-material fl ux material (Figs. 1, 8, and 10), contributing about physiographic and lithologic factors contributing estimates. four times the bed material per unit area relative to sediment yield or landscape denudation have

392 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon focused on total clastic yield (commonly based on Figure 11. Scatter plots of estimated bed- measurements of suspended load), several point material fl ux in relation to selected mor- to slope and lithology as primary controls (Cull- phologic and grain-size observations for Scaled Bed-material Flux (m3/m3) ing, 1960; Ahnert, 1970; Milliman and Syvitski, alluvial, mixed-bed, and bedrock channels. 10–8 10–7 10–6 10–5 10–4 10–3 200 1992; Summerfi eld and Hulton, 1994; O’Connor Horizontal uncertainty bars for the fl ux val- A et al., 2003a; Aalto et al., 2006; Andrews and ues show the 16th and 84th percentile values from the Monte Carlo analyses. Flux values Antweiler, 2012; Mueller, 2012). Fewer studies 100 have looked specifi cally at production of bed include effects of bed-material trapping by material; two recent studies that have evaluated dams. Supporting data are tabulated in sup- bed-material production—Mueller (2012) and plementary Tables DR2 and DR3 (see text 50 (mm) Andrews and Antweiler (2012)—both document footnote 1). (A) Median bar-surface grain 50 D strong lithologic infl uence on supply. In particu- size. Vertical uncertainty bars show stan- lar, Andrews and Antweiler (2012) showed that dard deviation of multiple particle counts Alluvial bedrock lithology, basin relief, and mean annual for reaches with fi ve or more measurements, 20 Mixed-bed precipitation “are all highly signifi cant predictors and entire range for reaches with fewer bar- Bedrock of mean annual sedi ment fl uxes” for California surface grain-size measurements. The ab- 10 coastal river basins. Mueller’s (2012) analysis of sence of vertical uncertainty bars indicates 0.2 Rocky Mountain rivers documented the strong only a single particle size measurement B infl uence of basin lithology on bed-material fl ux, for the reach. (B) Scaled migration rate. with little infl uence of other basin factors such (C) Scaled bar area, showing significant 0.1 as slope, precipitation, drainage density, and positive correlation (and 95% confi dence basin relief. limits) with bed-material fl ux for alluvial

Similar to the studies of Ahnert (1970), Pinet channels. (D) D* (= [W·S]/D50), a measure 0.5 and Souriau (1988), Riebe et al. (2001), Aalto of local transport capacity relative to grain et al. (2006), and Mueller (2012), our analysis size, showing signifi cant positive correlation (m/yr/m) shows no additional explanatory power provided with bed-material fl ux for alluvial channels. by employing mean precipitation as an addi- Rate Scaled Migration 0.02 tional predictor variable (Table 3; Fig. DR1 [see footnote 1]). In part this may be because precipi- tation strongly correlates to terrain factors such western Oregon and northwestern California 0.01 as elevation and slope in western Oregon (Daly study area, the large disparity in attrition rates 10 C et al., 2008), resulting in precipitation, slope, and among the different rock types is a primary fac- Scaled Bar Area = 5310(Flux)0.81 bed-material yield all being highly correlated. tor controlling regional river conditions. (alluvial reaches only) Adjusted R2 = 0.90, ) This contrasts with Andrews and Antweiler’s The lithologic control of particle comminu- 2 P<0.001 /m (2012) analysis of coastal California rivers, in tion and its effects on bed-material characteris- 2 1 which the best model of mean annual sediment tics have been described by Krumbein (1941), yield was approximately linearly proportional to Shaw and Kellerhals (1982), Kodama (1994a), mean annual precipitation in addition to a strong and Attal and Lavé (2006, 2009), among oth- dependence on basin relief. ers. Recent work has investigated related river 0.1

network controls and implications (for example, Scaled Bar Area (m Bed-Material Comminution Collins and Dunne, 1989; Pizzuto, 2005; Attal Our analysis indicates that bed-material fl ux and Lavé, 2006; Sklar et al., 2006; Chatananta- and, consequently, channel morphology are vet et al., 2010; Mueller, 2012). Together, these 0.01 strongly affected by particle comminution. This studies have shown, similar to this study, that 10 fi nding is amplifi ed in the study area by the in certain settings, lithologic control on particle D exceptionally low resistance of the Coast Range attrition affects the bed-material fl ux and trans- 5 sediments to breakdown. For the Umpqua River, port conditions, downstream patterns in bed- which has ~28% of the total basin underlain by material size distribution, and the lithological 2 the soft Coast Range sedimentary lithologic ter- distribution of bed material.

rain, 94% of bed material erodes into sand and Our results also point to a little-made distinc- D* 1 fi ner material during fl uvial transport, substan- tion between rock hardness with respect to its tially decreasing bed-material fl ux, resulting resistance to in-transit breakdown and its pro- 0.5 in bedrock channel boundaries, and boosting pensity to produce bed material. This is particu- D*= 19,500(Flux)0.91 suspended sediment loads, and perhaps promot- larly the case for Klamath terrane rocks, which 0.2 (alluvial reaches only) ing fl oodplain building by enhanced overbank have signifi cantly higher yield rates but com- Adjusted R2 = 0.74, P<0.01 deposition. However, even for rivers predomi- minution rates similar to other rock types in the 10–8 10–7 10–6 10–5 10–4 10–3 nantly draining rock types resistant to attrition, region. The high yield rates of the Klamath ter- Scaled Bed-material Flux (m3/m3) such as the Rogue River, particle comminution rane likely owe to pervasive fracturing and joint- reduces calculated bed-material fl ux by more ing that accompanied tectonic amalgamation, than half along the length of the river. In the creating conditions of relatively weak hillslope-

Geological Society of America Bulletin, March/April 2014 393 O’Connor et al. scale strength, whereas abrasion works slowly controls imposed by bedrock geometry and out- Much of the recent research on bedrock on mineral or grain boundaries within the small crops (Lisle, 1986, 2012; O’Connor et al., 1986). and mixed-bed channels has been motivated but coherent metasedimentary and metavolcanic Within the alluvial reaches, the positive cor- by understanding the interactions between clasts that have entered the channel. The Coast relation between D*, the measure of local trans- alluvial cover and channel incision in bedrock Range sediments behave oppositely; bed-mate- port capacity relative to grain size, and scaled (reviewed by Turowski, 2012). The results here rial yield rates are unexceptional, but commi- bar area (Fig. 4C) and the strong correlation indicate that locations of alluvial and bedrock nution rates are high for the weakly cemented between estimated bed-material fl ux and bar reaches, and consequently areas of bedrock inci- siltstones and sandstones. area (Fig. 11C) indicate that the alluvial chan- sion and long-term landscape evolution, can be nels in the study area have, to a certain extent, partly controlled by the spatial distribution of Alluvial and Nonalluvial Rivers adjusted their morphometry to transport capac- lithology and physiography and its effects on ity. These results indicate “graded” channels bed-material production, transport, and com- The geologic and physiographic factors on as hypothesized by Mackin (1948) and quan- minution down the fl uvial network. As noted bed-material production and downstream com- tifi ed in Lane’s (1955) equality relating fl ow, by Duvall et al. (2004), these types of lithologi- minution within the northern California and sediment fl ux, sediment caliber, and slope. The cally controlled network effects on bed material Oregon study area control the distribution of positive correlation between scaled fl ux and D* can complicate the linkages among rock uplift, alluvial and nonalluvial channels. The distinc- also accords with observations that the threshold the distribution of bedrock and alluvial chan- tion between channel types represents an impor- Shields values important for controlling channel nels, channel incision, and resulting broad-scale tant threshold in channel behavior (Howard, morphology and bed texture increase with scaled channel morphologic characteristics such as 1980; Lisle, 2012) affecting several geomorphic bed-material transport rates and channel lability profi le concavity. For rivers such as the Umpqua and ecological processes and conditions, and it (Church, 2006); here, the scaling factor of 0.91 River, where more than 90% of the bed material has pragmatic implications for river manage- indicates the relation is nearly linear. The wide may disintegrate into suspended load, down- ment. Within the study area, alluvial channels range of migration rates among alluvial reaches stream river morphology and concavity may have greater bar area, greater migration rates, is in part infl uenced by bed-material fl ux (and its have little connection with basin-scale tectonic and fi ner bar textures (Figs. 3 and 4). For allu- control on bar area), but some reaches, such as controls. These network effects are in addition vial channels, scaled bar area correlates posi- the Illinois River, are laterally confi ned by val- to the local lithologic complications identifi ed tively with measures of transport capacity and ley walls to the extent that migration rates are by VanLaningham et al. (2006) in their analy- estimated bed-material fl ux, whereas such cor- relatively low (Figs. 3 and 4). sis of river concavity and morphology for the relations are not evident for nonalluvial reaches The correlations for alluvial rivers between Oregon Coast Range, for which they concluded (Figs. 4C and 11C). bed-material fl ux and (1) bar area and (2) D* “inverting tectonic information from distribu- These differences accord with understand- appear to be limiting relations bounding the tions of channel types and river profi le concav- ing of alluvial and bedrock channel behavior. observations from nonalluvial reaches (Figs. ity in tectonically active mountain belts depends The absence of a well-defi ned critical gradient 11C and 11D). For the case of bar area, this indi- on isolating lithologic and other variables inde- separating nonalluvial from alluvial channels in cates a systematic and maximum limiting rela- pendently.” the slope–drainage area plot (Fig. 4A) indicates tion between scaled bar area and bed-material that factors such as lithology or hydrology exert fl ux, also consistent with the “sediment stage” CONCLUSIONS AND APPLICATIONS important infl uence (Montgomery et al., 1996). concept of Lisle and Church (2002). The mini- In this study area, lithology-controlled sediment mum limiting relation between bed-material Our studies were motivated by specifi c man- supply and in-channel attrition are probably the fl ux and D*, which scales with the Shields agement issues pertaining to bed-material sup- primary factors obscuring the drainage area– number, indicates relatively lower and system- ply and its relation to channel morphology. slope threshold between alluvial and nonalluvial atically variable Shields number values for the Results are relevant to several aspects of these channels. alluvial rivers compared to higher and non- issues: Foremost, our coupled analysis of bed- Lateral channel mobility for gravel-bed rivers systematic values for the nonalluvial reaches. material yield and in-channel attrition gives test- requires bar building (Church, 1992), with rates Consequently, bed material is probably more able site-specifi c estimates of annual bed-mate- of migration in part controlled by bar growth. frequently entrained in the nonalluvial reaches rial fl ux—a notoriously diffi cult-to-measure Meander rates in general scale with bar area, than in the alluvial reaches. but important attribute for gravel-bed rivers. regardless of channel type (Fig. 4C), but bar area For nonalluvial rivers, our morphologic mea- Although these estimates have large uncer- strongly correlates with bed-material fl ux for surements and bed-material transport analyses tainty, and annual values probably vary tremen- only the alluvial reaches (Fig. 11C). The scaling indicate few systematic reach-scale patterns or dously from year to year, they provide bounds factor of 0.81 in this relation indicates a nearly correlations (Figs. 4 and 11), supporting the for management of fl uvial gravel resources. The linear correspondence between scaled fl ux and view that the distinction between alluvial and analysis applied here also allows for estimating scaled bar area. This latter observation is con- nonalluvial channels is an important threshold the effects of bed-material trapping by dams sistent with “sediment stage,” an index of the for fl uvial systems. For nonalluvial reaches, it and other structures, thereby providing context volume of sediment in the active channel (Lisle, appears that local hydraulic and sediment sup- for restoration strategies designed in consider- 2012), correlating with transport rates for alluvial ply conditions strongly infl uence morphologic ation of bed-material transport. This approach is channels (Lisle and Church, 2002). The absence characteristics and transport conditions, possi- transferable to other regions where independent of correlation of bar area and bed-material fl ux bly indicated by the weak correlation between measures of bed-material yield can be obtained. for the nonalluvial reaches is consistent with local slope and D50 (Fig. 4B). Little distinction is Also, our results are also consistent with the transport capacity exceeding supply. In non- evident between reaches categorized as “mixed idea that aspects of local bed-material fl ux may alluvial reaches, we hypothesize that bars form bed” and “bedrock” among the morphologic be estimated from the lithologic composition of primarily as a consequence of local hydraulic characteristics and relations. the bed material (Mueller, 2012).

394 Geological Society of America Bulletin, March/April 2014 Alluvial and bedrock rivers, western Oregon

Our results also confi rm the fundamental vary considerably with the areal extent of allu- downstream fi ning in bedrock streams with lateral input : Water Resources Research, v. 46, W02518, doi: behavioral differences between alluvial and vial cover (Hodge et al., 2011). Additionally for 10.1029 /2008WR007208 nonalluvial rivers. While our defi ning criteria nonalluvial rivers, transport capacity equations Church, M., 1992, Channel morphology and typology, in directly relate to aspects of channel behavior— for bed-material transport likely only provide Calow, P., and Petts, G.E., eds., The Rivers Handbook: Oxford, UK, Blackwell, p. 126–143. particularly the potential for reach-scale inci- maximum limiting transport estimates (Wal- Church, M., 2002, Geomorphic thresholds in riverine land- sion—the morphological relations also indicate lick et al., 2011), and better estimates of bed- scapes: Freshwater Biology, v. 47, p. 541–557, doi: other key distinctions with management impli- material fl ux must derive from consideration of 10.1046 /j.1365 -2427 .2002 .00919.x. Church, M., 2006, Bed material transport and the morphol- cations. In particular for alluvial channels, bar bed-material supply. ogy of alluvial river channels: Annual Review of Earth area scales strongly with estimated bed-material and Planetary Sciences, v. 34, p. 325–354, doi:10.1146 ACKNOWLEDGMENTS /annurev .earth .33 .092203 .122721. transport rates, as well as transport capacity (as Collins, B.D., and Dunne, T., 1989, Gravel transport, gravel indicated by D*). This observation suggests This work was funded by the U.S. Army Corps of harvesting, and channel-bed degradation in rivers drain- measurements of bar area can serve as both a Engineers, Oregon Department of State Lands, and ing the southern Olympic Mountains, Washington, U.S.A.: Environmental Geology, v. 13, p. 213–224. predictor of bed-material supply, consistent the U.S. Geological Survey. Charles Cannon, Danial Polette , Norman Buccola, Ester Duggan, Rachel Culling, W.E.H., 1960, Analytical theory of erosion: The with the “sediment stage” concept of Lisle and Journal of Geology, v. 68, p. 336–344, doi:10.1086 Peavler, Tara Chestnut, Steve Sobieszczyck, Terrence Church (2002), as well as a monitoring mea- /626663. Conlon, and Xavier Rodriquez Lloveras assisted with Curtiss, D.A., 1975, Sediment Yields of Streams in the sure for reach-scale trends in bed-material fl ux. fi eld measurements. Esther Duggan and Eric Staut- Umpqua River Basin: U.S. Geological Survey Open- Other morphologic characteristics appear less berg assisted with the tumbler experiments. Tana File Report, 1 plate with text. sensitive. In particular, our measurements indi- Haluska and Charles Cannon provided geographic in- Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, formation systems assistance. Discussions with Gor- M.K., Taylor, G.H., Curtis, J., and Pasteris, P.P., 2008, cate few strong correlations between sediment don Grant, Janine Castro, Peter Klingeman, Joshua Physiographically sensitive mapping of climatological supply and armoring and grain size, indicating Roering, Thomas Lisle, Erich Mueller, Peter Wilcock, temperature and precipitation across the conterminous United States: International Journal of Climatology, that these attributes may be affected by clast and John Pitlick helped shape many of the conclu- v. 28, p. 2031–2064, doi: 10.1002 /joc .1688. attrition (Attal and Lavé, 2006) or refl ect local sions presented here. 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