Morphologically based model of bed load transport capacity in a headwater stream

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110,, doi: /2003jf000117, 2005 Morphologically based model of bed load transport capacity in a headwater stream Erich R. Mueller and John Pitlick Department of Geography, University of Colorado, Boulder, Colorado, USA Received 17 December 2003; revised 14 March 2005; accepted 31 March 2005; published 25 June [1] Field measurements of channel and bed material properties are used to develop a network-based model of bed load transport capacity in Halfmoon Creek, a high-gradient stream in the Rocky Mountains of Colorado. The formerly glaciated watershed contains self-formed reaches that are generally disconnected from hillslopes and major sediment perturbations, strung together by a sequence of bedrock and cascade channels. Measurements of channel geometry, slope, and bed sediment texture (surface and substrate) were taken in 27 reaches with gradients ranging from 0.6 to 6% to examine interactions among key variables affecting bed load transport. The analysis shows that the grain size distribution of the substrate is relatively uniform throughout the basin; however, the distinction between surface and substrate texture varies systematically with channel gradient and shear stress. Reaches with high shear stress are characterized by coarse surface layers, with a high threshold for transport, limiting the mobility of the sediment supplied. In reaches with lower gradient and shear stress the difference between surface and substrate texture is less, increasing both the frequency of transport and the mobility of the bed load. While it might appear that the steeper reaches of Halfmoon Creek have a high capacity for carrying fine-medium gravel, the relative mobility of these sizes is affected by the presence of very coarse sediment on the bed surface. As a result, modeled estimates of bed load transport rate indicate that average annual bed load sediment yields increase downstream nearly linearly with discharge, suggesting a simple scaling of bed load transport capacity with discharge and drainage area. Citation: Mueller, E. R., and J. Pitlick (2005), Morphologically based model of bed load transport capacity in a headwater stream, J. Geophys. Res., 110,, doi: /2003jf Introduction [2] Headwater streams are frequently described as complex systems that evolve over time in response to a mix of geomorphic processes [Knighton, 1998; Wohl, 2000]. Hillslope transport of sediment from landslides and debris flows may dominate sediment supply in some watersheds, locally forcing changes in channel gradient and grain size [Grant et al., 1990; Montgomery and Buffington, 1997; Brummer and Montgomery, 2003]. Elsewhere, fluvial sediment transport dominates and channel morphology reflects the orderly transfer of water and sediment according to established principles [Emmett, 1975; Pizzuto, 1992; Whiting et al., 1999]. These and many other observations have led to generalized conceptual models and stream classification systems in which the headwater reaches of rivers are characterized as being primarily erosional, whereas intermediate or distal reaches are considered transitional to depositional [Schumm, 1977; Church, 1992; Montgomery and Buffington, 1997]. [3] Modeling erosion and sediment transport across these different domains is problematic, and perhaps only loosely Copyright 2005 by the American Geophysical Union /05/2003JF000117$09.00 tied to position within the drainage basin. In headwater streams, the most important conceptual issue concerns the distinction between alluvial channels in which the sediment transport capacity balances the sediment supply and nonalluvial channels in which the capacity exceeds the supply. There is a tendency to lump mountain streams into the latter category for two reasons: (1) Channel gradients are generally high (>2%), and therefore the shear stress or stream power available to move sediment appears to be quite high [Montgomery and Buffington, 1997]; and (2) the sediment seen on the bed (cobbles and gravels) is typically much coarser than the sediment collected during short sampling intervals (bed load), and is potentially coarser than the material sampled from the substrate (bulk bed material). Observations that the bed load is finer than the substrate [Church et al., 1991; Lisle, 1995; Wathen et al., 1995; Whiting et al., 1999; Habersack and Laronne, 2001; Ryan and Emmett, 2002; Church and Hassan, 2002] imply that the load is derived from sources other than the bed, and that transport rates are driven more by sediment supply than by flow conditions [Dietrich et al., 1989; Buffington and Montgomery, 1999b]. However, a number of other field studies and data sets indicate that transport in headwater streams is closely coupled to flow properties and bed material characteristics [Milhous, 1973; Parker et al., 1of14

2 1982; Andrews, 1984, 1994; Lisle, 1995; Powell et al., 2001; Torizzo and Pitlick, 2004]. It appears, therefore, that the range of conditions in headwater basins is quite large, and it is not evident that bed load transport data, by themselves, convey the information needed to analyze watershed-scale sediment supply. [4] In this paper we propose an alternative approach for assessing sediment transport capacity in headwater streams. We start with the premise that the volume or mass of bed load carried in alluvial rivers is governed by a handful of interrelated factors, including the water discharge, the channel geometry (width and depth), the slope, and the grain sizes available for transport [Gilbert, 1914; Mackin, 1948; Rubey, 1952; Henderson, 1966; Ferguson, 1986; Carson and Griffiths, 1987; Howard et al., 1994]. None of the factors above can be excluded in assessing the sediment transport capacity of a stream system. It is particularly important to include grain size as a variable because of its importance in scaling the transport rate and in defining thresholds for motion. The distinction between surface and substrate (or subsurface) grain sizes is of further importance in gravel bed rivers because local coarsening of the bed surface limits the mobility of finer sizes that would otherwise move quite easily [Parker, 1990]. Modifications of bed surface textures provide a mechanism for channels to adjust to changes in transport capacity and sediment supply [Dietrich et al., 1989; Lisle et al., 1993; Buffington and Montgomery, 1999a, 1999b; Wilcock, 2001; Lisle and Church, 2002], and, in the absence of major sediment inputs, may result in downstream fining within sedimentary links [Rice, 1999] or reaches of rapidly declining shear stress [Ferguson et al., 1996]. As the variables needed to estimate sediment transport rates (width, depth, slope, grain size) are readily measured through a channel network, we propose that their joint variation should reflect changes in sediment transport capacity. We focus here on bed load transport because of its importance in determining channel form in headwater streams. Our approach and analysis is consistent with transport-based theories for hydraulic geometry [Parker, 1979; Pizzuto, 1992], and takes advantage of the fact that channel properties are not nearly as variable in space or time as is the process of bed load transport. [5] Linkages between channel morphology, grain size and sediment transport capacity are investigated here using measurements from Halfmoon Creek, a headwater stream located in the Sawatch Range of central Colorado. Halfmoon Creek contains a mix of self-formed and forced (nonalluvial) reaches. This setting is typical of mountain streams where channel and sediment properties change rapidly over short distances. Because of the glacial history of the basin, alluvial stream reaches are typically disconnected from hillslopes, with infrequent debris fans distributed relatively evenly throughout the basin. Our measurements emphasize geomorphic conditions in selfformed reaches with average gradients of less than about 10%. These reaches are identified in the field by the presence of alluvial bed and bank materials where fluvial processes are the primary controls on morphology. The selfformed reaches are discontinuous, but they occur throughout the watershed, including the lowest-order tributary segments. The forced reaches are associated with glacial steps or moraines. These reaches are very steep (>10%), and act effectively as conduits that pass sand- and gravel-sized sediment to adjoining alluvial reaches. Estimates of the mass of bed load transported through the alluvial reaches are computed from a bed load transport relation and a dimensionless flow frequency curve. These estimates are then used to assess changes in the mass balance of sediment through the stream network. We hypothesize that if the stream network is approximately in steady state, and if the sediment supply is roughly uniform throughout the basin, then the sediment input to the channel system should scale linearly with drainage area, and each alluvial channel segment will have adjusted its hydraulic geometry and slope to carry proportionally that much more sediment than the segment immediately above. 2. Study Area [6] Halfmoon Creek is located in the Sawatch Mountains of central Colorado (Figure 1). The drainage basin ranges in elevation from 4400 to 2990 m, and includes the two highest peaks in Colorado (Mt. Elbert and Mt. Massive). The entire basin has been glaciated. Talus slopes and bedrock exposures of Precambrian gneiss and schist occur throughout the headwaters (>3500 m elevation); the lower basin includes open, U-shaped valleys bordered by recessional and lateral moraines, which are now forested. The setting is typical of alpine basins in Colorado, where Quaternary glaciation has had a primary influence on basin morphology. Annual peak flows in Halfmoon Creek are derived from snowmelt runoff, resulting in a regular interannual hydrograph with a mean annual flow of 0.85 m 3 /s and a mean annual flood of 8.0 m 3 /s, as recorded at the USGS gauge near the downstream end of the study area (period of record ). Summer thunderstorms are common but they are of short duration and typically do not produce much runoff. At the furthest downstream site (Figure 1) the creek drains an area of 64 km 2 and the flow is unregulated. [7] Stream sediment is derived from localized bank erosion and mass wasting of hillslopes, glacial moraines and debris fans. Overall, the basin is relatively stable against erosion, and the prior effects of glaciation limit the coupling between hillslope processes and channel morphology in a number of places [Church, 2002]. Nevertheless, there is trace evidence of small hillslope failures resulting from localized storms in the basin, and evidence of active sediment transport along most of the main channel. Bed load sediment yields have not been measured, but deposits left in a retention basin at the outlet of the basin suggest the annual sediment yield is on the order of 10 m 3 /yr. [8] Measurements were taken in 27 alluvial reaches along the main channel and in three tributary valleys, herein differentiated as South, Middle, and North Halfmoon creeks (Figure 1) (see also Figure 5 in section 4.1). The study sites are located at closely spaced intervals (every km) along the main stem and tributary segments. Site characteristics vary considerably (Table 1). In general, the study reaches can be discriminated between those occurring in hanging valleys, which are separated from the main valley by cascade-type channels at glacial steps, and those occurring in the main valley itself (Figure 2). Although many of the reaches are bordered by forest, woody debris is not 2of14

3 Figure 1. Location map of study sites within the Halfmoon Creek basin generated from 10-m DEMs. Numbers represent the 27 study reaches. Note distinct lateral moraines downstream from site 7. prominent in the channel. Twelve of the study sites are located in tributary valleys. These sites are located in plane bed reaches that are steep, but otherwise, self-formed (Figure 2a). Fifteen study sites lie along the main channel of Halfmoon Creek. These sites are located in alluvial plane bed reaches or pool riffle reaches where the gradients are somewhat less (Figure 2b). Downstream of the confluence with the south fork, the slope of the main channel decreases consistently as the creek flows from steep, forested terrain into a wide, sparsely forested alluvial valley through which the stream meanders. The valley then narrows where a small tributary, Elbert Creek, has produced an alluvial fan and a steepening of the valley profile. Downstream of the fan, the valley again opens up, allowing the stream to meander across a floodplain (Figure 2c). The east end of the valley is bordered more closely by lateral moraines which restrict the channel and steepen the gradient through the two downstream sites. 3. Methods [9] Basin-wide trends in sediment transport capacity were determined by calculating the annual bed load sediment yield at each of the 27 study sites. Consistent measurements of channel and bed material properties were made at each site in order to estimate parameters for calculating transport rates (details of the calculations are discussed below). In addition to the field data, a key element of the analysis includes the development of an empirical relation for adjusting the threshold for bed load transport in steep streams. [10] The study sites were located in relatively straight reaches to limit complications arising from changes in channel curvature and topography. A minimum of three cross sections was surveyed with a level at each site in order to determine the bank-full channel geometry. Measurements of both the bed and water surface profile were made over an approximate length of ten times the channel width in order to determine the reach average slope. As statistical tests of the regression coefficients indicated there was no significant difference in bed and water surface slopes (p < 0.05), the latter were used to approximate the energy slope. Bank-full channel dimensions were determined by a distinct break in bank slope. Measurements of bank-full depth at the left bank and right bank were generally similar (within 5 cm), with the lower value being used in calculations to define a bank-full hydraulic radius and width (Table 1). [11] Surface sediment samples were taken at each site using a variation of the Wolman [1954] method, with a minimum of 100 particles sampled at random. Particle sizes were measured at 1/2-phi intervals using a metal template (gravelometer). Bulk samples of the subsurface sediment were also obtained at all but one site in order to determine the ratio of surface to subsurface grain size fractions, degree of bed armoring, sorting, and trends in downstream fining. Bulk samples were large enough to ensure that the largest grain was no more than 5% of the total weight and usually much less [Church et al., 1987]. The coarse fraction (>32 mm) of the subsurface samples was sieved in the field and the fine fraction (<32 mm) was sieved in the lab, again at 1/2-phi intervals. Graphical plots of grain size versus cumulative frequency were used to determine individual 3of14

4 Table 1. Study Site Characteristics a Site Dist., km A, km 2 Q b,m 3 /s S D 50,m D 50s,m R, m B, m t, N/m 2 t* Q s, t/yr N.A N N N N M M M M S S S S a Dist. is the distance downstream, A indicates the drainage area, Q b is the bank-full discharge estimated from the continuity equation, S is the slope, D 50 is the median grain size of the surface bed material, D 50s is the median grain size of the subsurface bed material, R is the bank-full hydraulic radius, B is the bank-full width, t is the bank-full shear stress, t* is the bank-full dimensionless shear stress, and Q s is the modeled annual sediment yield. percentiles (D 84, D 50, D 16 ) of the grain size distribution of each sample (Table 1). [12] Average annual bed load sediment yields were estimated for each site by calculating the instantaneous bed load transport rate for each of several increments of discharge (discharge classes), then summing those values to get the annual load. As the procedure for estimating annual loads is somewhat involved, it is perhaps useful at this point to lay out the individual steps: (1) Bed load transport rates were calculated using an empirical transport function based on excess shear stress; (2) the transport function was modified to include an adjustment for the threshold shear stress, which accounts for differences in flow properties in high-gradient streams; (3) the frequencies of sedimenttransporting events were determined from a dimensionless flow duration curve, developed from gauging station records and an empirical relation for discharge as a function of drainage area; and last, (4) bed load transport rates were calculated for each increment of discharge, then weighted by the frequency of that discharge, p(q), and summed to get the average annual bed load sediment yield. Comments on the assumptions and uncertainties of the analysis are given in the discussion which follows. [13] The estimates of bed load transport are based on the equation of Parker [1979], which can be written in a general form as: q* ¼ 11:2 ðt* t* c Þ4:5 t* 3 ð1þ where q* is a dimensionless transport parameter, t* is the dimensionless shear stress, and t c * is the critical dimensionless shear stress. We chose to leave t c * as free parameter rather set it to a specific value (0.03) as was done originally [Parker, 1979]. The dimensionless shear stress is t t* ¼ ; ð2þ ðr s rþgd 50 where t is the boundary shear stress, r s and r are the densities of sediment and water, respectively, g is the gravitational acceleration, and D 50 is the median grain size of the bed sediment. The transport parameter can be expanded as follows: q b q* ¼ ðs 1ÞgD 3 0:5 ; ð3þ 50 where q b is the volumetric transport rate per unit width, and s is the specific gravity of sediment, assumed to be Combining (1) and (3) yields an equation for the total volumetric bed load transport capacity, Q s ¼ 11:2 ðt* t* c Þ4:5 t* 3 ðs 1ÞgD 3 0:5B; 50 ð4þ where B is the channel width. Written in this form (4) illustrates that downstream variations in bed load transport capacity are driven by the interaction of four main variables: t*, t c *, D 50, and B. The effect of these interactions is evident in most streams and rivers, where, for example, downstream reductions in grain size are generally accom- 4of14

5 Figure 2. Characteristic examples of study reaches. (a) Downstream view of site N3, north fork of Halfmoon Creek. Site is located in a broad glacial valley at an elevation of 3473 m; S = and B =3.8 m. (b) Upstream view of site 12, main channel of Halfmoon Creek. Site is located in a forested area at an elevation of 3105 m; S = and B = 8.3 m. (c) Upstream view of site 3, main channel of Halfmoon Creek. Mt. Elbert in the background. Site is located in an open meadow at an elevation of 3031 m; S = and B =9.7m. panied by increases in B and/or increases in the excess shear stress, t* t c *. If the stream system is approximately in steady state, the variables on the right-hand side of (4) should vary in ways that minimize differences in transport s /@x, otherwise there would be clear evidence of aggradation or degradation. It is also important to note that (4) includes two nonlinearities, thus bed load transport computations are very sensitive to uncertainties in the individual variables. Among the variables in (4) we were most concerned with uncertainty in the estimates of t* and t c *. Methods for estimating these parameters are discussed below; however, it is worth noting that, in the case of gravel or cobble bed rivers, there is a strong theoretical and empirical basis for suggesting that the difference between t* and t c * should be relatively small over the range of channel-forming flows [Parker, 1979; Andrews, 1984; Pizzuto, 1992; Parker and Toro-Escobar, 2002; Pitlick and Cress, 2002]. [14] Site-specific estimates of t* and t c * were found using two empirical relations, one developed from measurements of channel properties in Halfmoon Creek, the other developed from an analysis of bed load transport measurements taken elsewhere. The estimates of t* are derived from a basin-wide relation for the bank-full dimensionless shear stress, t b *, obtained from field measurements of the bank-full hydraulic radius, reach average slope and bed surface D 50, with one site excluded (S4, Table 1); that relation is t b * ¼ 2:14S þ 0:034; with r 2 = 0.91 and p [15] Estimates of the threshold Shields stress, t c *, were made for each site using a relation developed specifically for high-gradient gravel and cobble bed stream channels [Mueller et al., 2005]. This relation is based on an analysis of flow and bed load transport measurements taken in 45 streams and rivers in the western USA and Canada. The analysis focused on variations in the threshold for bed load transport, which arise from changes in flow properties and bed structure as channel gradient and relative roughness increase [Bathurst et al., 1987; Jarrett, 1984; Wiberg and Smith, 1991; Church et al., 1998; Buffington and Montgomery, 2001]. For each of these data sets, Mueller et al. [2005] plotted the relation between dimensionless bed load transport rate and dimensionless shear stress, and, following the procedure of Parker et al. [1982], estimated the reference dimensionless shear stress, t r *, associated with a small, nonzero ð5þ 5of14

6 calculated values of bank-full discharge were then pooled with data from other gauged gravel bed streams in Colorado [Andrews, 1984; Torizzo and Pitlick, 2004] to form a regional relation between Q b and drainage area, A. Figure 3 shows a plot of the combined data, fit with the relation, ^Q b ¼ 0:35A 0:725 ; ð8þ Figure 3. Regional relation between measured bank-full discharge, Q b, and drainage area used to predict downstream changes in discharge for Halfmoon Creek. transport rate. The resulting estimates of t r * were then correlated to reach average slope, giving t r * ¼ 2:18S þ 0:021; with r 2 = 0.70 and p For the purposes of the present study, we assume t r * t c *. It is worth noting that even though equations (5) and (6) were developed independently of each other, they form essentially parallel lines, suggesting the difference between t b * and t c * is nearly constant over a wide range of channel slopes. [16] The next step involved formulation of two intermediate relations linking the estimates of t* derived from field measurements to the probability density function for flow frequency, which was derived from the gauge record. The first intermediate relation is used to predict the bank-full discharge at individual sites on the basis of drainage area. Initial estimates of Q b were made for each site using measured values of bank-full width and hydraulic radius, R, and calculated values of bank-full velocity, U (i.e., Q b = UBR). The estimates of velocity were based on a flow resistance relation developed specifically for Halfmoon Creek from current-meter measurements of discharge, taken over a range of flows ( m 3 /s) at 11 sites [Mueller, 2002]. Four flows were measured at six of the main channel sites and two flows were measured at one main channel site and four of the tributary sites, yielding data for a range of depths and slopes. Values of the Darcy-Weisbach friction factor, f = 8gRS/U 2, were computed for each discharge measurement, and then correlated to relative roughness, R/ D 84, where D 84 is the particle size for which 84% of the sediment is finer. A least squares fit of these data yields 1 p ffiffi ¼ 2:26 log f R þ 1:00 D 84 with r 2 = 0.70 and p 0.001, excluding outliers from one tributary site where very large boulders produced unusually high values of f. Over the range of observed flows the flow resistance relation for Halfmoon Creek is very similar to a previous relation formulated by Limerinos [1970]. The ð6þ ð7þ with r 2 = 0.88 and p 0.001; A is the drainage area, in square kilometers, and ^Q b is the bank-full discharge expected for a given drainage area. We use the fitted relation for Q b rather than the cross section based estimates because this reduces inconsistencies arising from anomalously high or low values of the calculated bank-full flow velocity. [17] The second intermediate relation is used to specify the frequency of individual discharges, Q i, relative to the bank-full discharge, Q b. To do this we developed a dimensionless flow duration curve based on the record of daily flows at the Halfmoon Creek gauge (record length = 55 yr). Daily discharge values were grouped into 40 intervals, and the interval values, Q i, were normalized by the bank-full Q at the gauge (7 m 3 /s). Figure 4 shows the upper end of the normalized flow frequency distribution, fit with the following exponential relation, 4:85 Qi=Qb pq ð i =Q b Þ ¼ 44:7e ð Þ : ð9þ This equation was then used to determine the frequency of individual discharges at each site, assuming that the shape of the flow duration curve does not vary with drainage area. This is essentially the same as assuming that the same unit hydrograph applies to each site. This is a relatively good assumption for small basins in snowmelt-dominated regions because the timing and intensity of runoff tend to follow very consistent patterns, which are reflected by regional similarities in the statistical distributions of flood peaks and daily streamflows [Pitlick, 1994; Gupta and Dawdy, 1995; Torizzo and Pitlick, 2004]. Figure 4. Relation between flow frequency and scaled discharge, Q i /Q b, for flows greater than 1/2 bank-full Q, as determined from the USGS gauge record. 6of14

7 biggest source of error is associated with the estimates of t b * and t c *; small differences in these variables can produce large differences in transport rate, and the choice of individual values is, therefore, very important. The choice of sediment transport equation also has some influence on the results; we used the Parker [1979] relation because it gave reasonable estimates for the total sediment yield at the outlet of the basin (170 t/yr, which compares favorably with our visual estimate of the annual accumulation of sediment in a settling pond located above a diversion structure near the downstream study site). In contrast, the Meyer-Peter and Müller [1948] equation gave sediment yields exceeding 1000 t/yr, which would be unreasonable for a stream of this size in this area. The potential error introduced by using a standardized flow-frequency relation for the entire basin is unknown, and, of course, there is some error associated with the field data; however, the uncertainty in these measurements and assumptions is probably small in comparison to the errors noted above that carry over into the transport calculations. Figure 5. (a) Longitudinal profile of Halfmoon Creek basin. (b) Downstream changes in reach-averaged slope. [18] Next, we formulated an equation for estimating individual values of t* at each site for each increment of discharge, Q i. This equation has the form of an at-a-station hydraulic geometry relation, with the local Shields stress increasing as a power function of discharge, t* ¼ ^t b * Q 0:5 i ; ð10þ ^Q b where ^t b * is found with (5) and ^Q b is found with (8). The exponent of 0.5 in (10) is the average of 12 values obtained by Torizzo and Pitlick [2004] in their analysis of the relation between flow and bed load transport in gravel bed streams in Colorado; the value of 0.5 is also consistent with the exponent for depth in a typical at-a-station hydraulic geometry relation [Leopold and Maddock, 1953]. Equation (10) has the desirable property that t*! ^t b *asq i! ^Q b. [19] Instantaneous bed load transport rates, Q s, were calculated for each site and each increment of discharge using (4), (6), and (10), and the measured values of D 50 and B. The calculated values of Q s, were then weighted by the annual frequency of that discharge, p(q i /^Q b ), and summed to estimate the annual bed load sediment yield. [20] Given the number of assumptions and steps used in the analysis, it is worth commenting on some of the potential sources of error and uncertainty. Probably the 4. Results 4.1. Channel Slope [21] The longitudinal profile of Halfmoon Creek is concave overall (Figure 5a), but marked by several distinct inflections associated primarily with glacial steps at tributary junctions [MacGregor et al., 2000], but also debris fans and moraines. Headwater segments of the creek include short alluvial reaches separated by cascades, which appear as steps in the profile. The most abrupt change in gradient occurs in the area from 4 to 6 km downstream where the three tributary forks come together. The creek then flows through an open alluvial valley and the profile is quite smooth. At a distance of 9.5 km there is another small inflection in the profile where the creek is incising through glacial deposits and a debris fan produced by Elbert Creek. Below that point the valley again opens up, and for another 4 km, the slope decreases systematically downstream. In the lower 1 km of the study area, the creek is more confined by lateral moraines near the downstream extent of glaciation, resulting in another increase in slope. [22] In general, average gradients of tributary channel reaches are higher than main channel reaches (Table 1). A plot of average gradients of the study sites (Figure 5b) shows that slopes of the tributary reaches generally exceed 0.025, whereas slopes of the main channel reaches are typically less than As noted above, systematic decreases in slope are interrupted in several locations by glacial steps associated with tributary junctions and confinement between moraines and debris fans Bed Material [23] Median grain size of the surface bed material, D 50,in Halfmoon Creek varies from to m (Table 1 and Figure 6). Bed surface samples from the tributary channel reaches show little systematic variation or downstream reduction in grain size (Figure 6), whereas samples from the main channel show systematic fining of the bed surface in two separate reaches, from km and km (Figure 6). Downstream fining in these reaches mimics the rapid changes in slope noted in Figure 5, which reflect local 7of14

8 Figure 6. Downstream changes in the median grain size of the surface and subsurface bed material. Solid points are surface D 50, and open points are subsurface D 50. flattening of the valley profile. Rice and Church [1998] describe such reaches as sedimentary links where changes in grain size are punctuated by sediment inputs from tributaries or hillslopes. For purposes of comparison we fit the surface grain size data within these reaches with separate exponential functions: x xo D ¼ D o exp ð ð Þ=LÞ ð11þ where D o is the grain size at the start of a fining sequence, x x o is the distance downstream from the start of a fining sequence, x o, and L is the decay length scale in kilometers. This is equivalent to the original Sternberg [1875] equation where L equals 1/a, and a is the fining parameter (km 1 ). Table 2 compares regression estimates of L and a derived for surface and subsurface fining relations within links, as well as the basin as a whole. The results show that the surface D 50 within the two main stem sedimentary links fines very rapidly over short distances (L = 3 and 9, and a = 0.33 and 0.11, respectively, p < 0.01, Table 2). The estimates of a for these two links are comparable to sedimentary links in active gravel bed rivers where localized sediment supply and effects of past glaciation sometimes force abrupt transitions in valley morphology and slope [Ferguson and Ashworth, 1991; Knighton, 1998; Morris and Williams, 1999; Rice, 1999]. In contrast to the individual links, the basin-wide trend in surface D 50 in Halfmoon Creek is weak (L = 83; a = 0.012), and the relation is not statistically significant (p = 0.23). [24] Downstream fining of the subsurface sediment is subtle compared to that of the surface sediment discussed above. The median grain size of the subsurface sediment, D 50s, ranges from to m (Table 1). A plot of the downstream trends in D 50s (Figure 6) displays minor inflections at some of the same points noted above. However, fining below these points is not strong; values of L and a for the subsurface D 50 within individual links are not statistically significant, nor is the coefficient for the basin-wide relation (p > 0.2, Table 2). [25] Figure 7 shows the full grain size distribution of all sediment samples, differentiated by reach average gradient. We differentiated the samples to examine connections between channel gradient and size separation of the individual fractions. The split was made at S = 2.5% because it is approximately at this point where we start to see major differences in hydrodynamic processes related to slope and relative submergence (R/D 84 ); this split also results in roughly equal sample sizes. Close inspection of the data in Figure 7 suggests that the differences in sediment samples from high- and low-gradient reaches mostly result from variations in the coarser size fractions. To evaluate the significance of these variations we performed a series of t tests comparing differences among the individual grain size parameters (D 84, D 50, and D 16 ). The results are presented in Figure 8, which shows a series of box plots comparing size parameters of surface and subsurface samples, differentiated by gradient. Figure 8a indicates that the average surface D 84 of high- and low-gradient reaches differs by 46 mm (38%), which is statistically significant (t = 3.56, p = ). Figure 8b shows that there is also a difference in the average surface D 50 of high- and low-gradient reaches, but the separation is much less (11 mm or 18%) and the statistical significance of the difference is lower (t = 2.39, p = 0.025). Figure 8c shows that there is no significant difference in the average D 16 of the surface sediment (t = 0.79, p = 0.44). Figures 8d 8f compare parameters of the subsurface size distributions. Figure 8d indicates that the average subsurface D 84 of high- and low-gradient reaches differs by 17 mm (23%), which is marginally significant (t = 2.06, p = 0.05). Figures 8e and 8f comparing values of the subsurface D 50 and D 16 show that there is no significant difference (t = 0.52, p = 0.61; t = 1.52, p = 0.14) between samples taken in high- and low-gradient reaches Channel Morphology and Hydraulics [26] The average bank-full width, B, of Halfmoon Creek increases from about 3 m in headwater reaches to about 11 m in lower reaches (Table 1 and Figure 9a). Over the same distance, the average bank-full hydraulic radius, R, increases from about 0.3 to 0.5 m (Table 1, Figure 9b). The data follow more irregular trends in the middle of the study area where the slope and grain size are changing rapidly. From 5.3 to 8.5 km downstream, for example, there is a decrease in bank-full R and a slight increase in bank-full Table 2. Comparison of Surface and Substrate Fining Relations for Halfmoon Creek a Link n L a r 2 F p km Surface km Overall Substrate km km Overall a The length scale, L (km), and fining coefficient, a (km 1 ), were determined from linear regression of ln-transformed values of grain size versus distance; n is the number of values, r 2 is the coefficient of determination, F is the value of the F distribution, and p is the significance probability. 8of14

9 Figure 7. Full grain size distribution of all sediment samples. (a) Surface and (b) subsurface samples taken in high-gradient reaches (S > 2.5%). (c) Surface and (d) subsurface samples taken in low-gradient reaches (S < 2.5%). The dashed shaded lines represent the average grain size distributions of the highgradient reaches. B coinciding with fining through the upper sedimentary link and a transition in planform from straight plane bed reaches to more sinuous pool riffle reaches. This trend reverses at site 7 (9.3 km downstream) where the Elbert Creek fan confines the valley and supplies coarser bed material. The trends of decreasing bank-full R and increasing bank-full B are crudely repeated through the lower sedimentary link (from 9.3 to 13.3 km) again without much change in water discharge. [27] Downstream trends in the bank-full shear stress, t b, and the bank-full Shield s stress, t b *, are plotted in Figure 10. Coding the individual values by reach average slope, S, shows that gradient has a disproportionate influence on the local estimates of both t b and t b *. Shear stress and channel gradient are clearly correlated (as t is proportional to RS), however, as is apparent in the field data, variations in shear stress (or Shields stress) are driven much more by changes in slope than changes in depth (or grain size). This also suggests that these slope-driven changes in shear stress interact with mobile fractions of the bed sediment to give the textural trends discussed earlier. These interactions are examined in Figure 11, which plots four separate relations between the bank-full shear stress and sediment size, measured in terms of two parameters (D 84 and D 50 ) of the surface and subsurface sediment. Among the four parameters, the surface D 84 is most highly correlated to t b (Table 3); the subsurface D 84 and the surface D 50 are likewise correlated with t b, although the trends are not quite as strong as for the surface D 84 (Table 3). Note that the relations for the subsurface D 84 (Figure 11a) and surface D 50 (Figure 11b) nearly coincide, suggesting that, for a given t b, the coarser fractions of the bed substrate actively exchange with average-sized particles on the bed surface. The last relation shown at the bottom of Figure 11 indicates that there is essentially no correlation between the subsurface D 50 and t b. The significance probability of this relation ( p =0.11; Table 3) suggests that the slope of the line is not 9of14

10 Figure 8. Box plots illustrating the median, quartiles, minimum, maximum, and outliers in (a) surface D 84, (b) surface D 50, (c) surface D 16, (d) subsurface D 84, (e) subsurface D 50, and (f) subsurface D 16 between high- and low-gradient reaches. significantly different from zero, meaning, the variation in subsurface D 50 is independent of the bank-full shear stress. [28] The relations shown in Figure 11 suggest that the separation between intermediate and coarse size fractions becomes increasing large as the shear stress increases. These differences are particularly evident in comparing the substrate D 50 with the size parameters of the bed surface: in reaches with relatively low shear stress (t < 50 N/m 2 ), the substrate D 50 differs from the surface D 50 by a factor of about 2, whereas in reaches with high shear stress (t > 150 N/m 2 ), the difference exceeds a factor of 3. Thus, while it might appear that the steeper reaches of Halfmoon Creek should have a high capacity for carrying fine to medium gravel, the relative mobility of these sizes is affected by the presence of very coarse gravels and cobbles on the bed surface. Although, the proportion of surface layer grains moving during high flows is probably never very large, it was evident during field sampling that many of the coarsest grains on the bed were loose and likely to move at high flow. The substrate exists presumably because there is exchange between the sediment in motion and the sediment stored in the bed. Exchange between the bed load and the substrate cannot occur unless there is some movement of the surface layer grains, and as suggested below, this appears to occur at all of the study sites at least a few days per year Sediment Transport Model [29] Modeled annual sediment yields are plotted in relation to bank-full discharge in Figure 12. Although the modeled values exhibit appreciable scatter, the overall trend is consistent with the expectation that sediment transport capacity should increase monotonically downstream. These data were fit with a power law relation: Q s ¼ 23:2^Q 0:96 b ; ð12þ Figure 9. radius. Downstream trends in (a) width and (b) hydraulic 10 of 14

11 sediment yield suggests that the alluvial reaches convey sediment in some near-equilibrium steady state where the total bed load increases in proportion to the water discharge. 5. Discussion [31] The principal goals of this research were to investigate interactions among channel and bed material properties within a headwater stream, and then use those properties to model basin-wide trends in annual bed load sediment yield. The results indicate that in spite of significant variations in channel morphology and sediment texture, the modeled sediment yields define a clear trend, with bed load transport capacity increasing nearly linearly with discharge. A number of empirical relations and assumptions went into the development of the model, thus the question arises: Is it possible to validate the results in any way? Since we did not measure bed load at any of the sites, we cannot assess the accuracy of the estimates directly. However, a reasonable comparison can be made using downstream relations for suspended sediment, for which there are several examples. Emmett [1975] measured suspended sediment at 20 stations in the upper Salmon River drainage basin in Idaho. He used the at-a-station relations for suspended sediment to formu- Figure 10. Downstream trends in (a) bank-full shear stress, t, and (b) bank-full dimensionless shear stress, t*, with data stratified by slope. with r 2 = 0.92 and p 0.001, where Q s is in metric tons per year. The observed trend in Q s reflects potentially complex interactions among the variables B, D, and t* t c * in (4). In high-gradient reaches with coarse bed material, instantaneous unit transport rates (load per unit width per unit time) tend to be very high; however, since a relatively high discharge is required to exceed the estimated threshold Shield s stress, ^t *, c bed load transport in these reaches occurs perhaps only a few days per year, and as a result, the average annual load is not particularly high. In low-gradient reaches with finer bed material, ^t * c is exceeded more often and sediment is moved perhaps many days per year. The frequency of sediment transporting flows ranges from just under 3 days/yr at the steepest site, to nearly 18 days/yr at the lowest-gradient site. The variations in flow frequency are primarily related to the nonlinear relation between Q i /Q b and t* because at steeper sites where ^t * c is higher, a slightly higher unit discharge is necessary to initiate motion. The effects of low shear stress and small grain size in low-gradient reaches are thus more than made up for by an increase in the frequency of bed load transport, resulting in proportionally higher annual sediment yields. [30] Interestingly, the estimated annual sediment yield increases nearly linearly with discharge (Figure 12), suggesting a simple scaling of sediment yield as discharge increases downstream. Despite variations in grain size, width, flow frequency, and channel gradient, the modeled Figure 11. Relation between bank-full shear stress, t, and (a) surface and subsurface D 84 and (b) surface and subsurface D 50, where solid and open symbols represent high- and low-gradient reaches, respectively. 11 of 14

12 Table 3. Linear Regression Statistics of Grain Size Versus Bank- Full Shear Stress for the Surface and Subsurface D 50 and D 84 for Data in Figure 11 a Grain Size a b r 2 p D D 50s D D 84s a The slope is a, and the intercept of the of the regression equations is b; the coefficient of determination is r 2 ; and the significance probability is p. late a downstream relation for load as a function of bankfull discharge, Q ss ¼ 0:89Q 0:75 b ð13þ where Q ss is the suspended sediment load, in tons per day, and Q b is the bank-full discharge, in cubic feet per second. Leopold and Maddock [1953] developed flow and suspended sediment relations for 20 rivers in the western USA, and suggested that the exponent in (13) should have a value of Since sediment load equals water discharge times concentration, an exponent of less than 1.0 indicates that sediment concentration is decreasing downstream, perhaps because of floodplain deposition. The exponent of 0.96 in our relation implies that concentration is roughly constant downstream, as expected in a system where water and sediment are supplied in equal proportions. Similarly, Rickenmann [2001] found that bed load volumes were nearly linearly proportional to effective runoff volumes from bed load sampling in 19 mountain streams, 13 of which had gradients above 5%. [32] The downstream trend in bed load sediment yield of Halfmoon Creek reflects a trade-off between the frequency and intensity bed load transport, controlled in large part by adjustments in bed surface texture. We found that while there was not much variation in the texture of the substrate sediment, there was a clear correlation between the bed surface texture and the local shear stress. It appears, therefore, that the effects of grain hiding and exposure, caused by changes in surface texture, may either reduce or enhance the mobility of the bed load as it moves through high- and low-gradient reaches. In the same way that Ferguson and Ashworth [1991] describe longitudinal changes on the Allt Dubhaig in Scotland, we interpret the downstream trends in surface texture (fining or coarsening) as a mobile bed response that maintains equilibrium transport through reaches of rapidly changing shear stress. If the bed load grain size distribution in this stream is at all similar to the substrate (which is consistent throughout the basin), then changes in surface layer texture and transport frequency may serve to equalize transport rates through individual reaches, as proposed by Parker et al. [1982]. [33] Variations in bed sediment texture have also been described in the literature as a response to changes in the sediment supply. Results from a number of laboratory experiments using sediment feed flumes generally indicate that the bed becomes coarser as the sediment supply decreases [see Lisle and Church, 2002, and references therein] or finer as the supply increases [Dietrich et al., 1989; Lisle et al., 1993; Buffington and Montgomery, 1999b]. The surface-based transport model of Parker [1990] likewise suggests that the surface texture will become finer or coarser as necessary to balance the sediment supply. However, experiments conducted by Wilcock et al. [2001] showed that, if anything, the bed surface becomes coarser as discharge and transport rate are increased. The contrast in results stems from differences in the operation of sediment feed versus sediment-recirculating flumes in which the size distribution of the sediment supplied either stays the same (feed) or evolves over time (recirculating). Natural streams are hybrids of these two systems: streams are likely to respond to changes in sediment supply through evolution of the bed surface texture (sediment feed), but as the bed evolves, the sediment output to downstream reaches eventually becomes disconnected from the initial input (recirculating). Clearly, dramatic increases in sediment supply would likely alter sediment textures and sediment yields in the study basin, with the extent of this effect dictated by the size and caliber of the input. Yet given the relative infrequency of channel-impacting debris flows and landslides, conditions in Halfmoon Creek are probably more analogous to a sediment-recirculating system than a sediment feed system. Glaciation has left a strong imprint on the landscape, producing a mix of reaches with locally high slope and shear stress, coupled to reaches in broad alluvial valleys. The feed to any particular reach is therefore determined largely by the output from the reaches upstream. The data clearly show that there is not much difference in the D 50 of the substrate throughout the basin, suggesting that the sediment supplied from hillslopes and channel bed sources has a similar size distribution. However, the coarser fractions of the substrate and of the bed surface clearly vary with the local shear stress. It appears that the shear stress and grain size vary jointly in order to balance the frequency and intensity of bed load transport; adjacent reaches with Figure 12. Modeled annual sediment yield, Q s, plotted versus estimated bank-full discharge, ^Q b, for each study reach. 12 of 14