Downstream fining, selective transport, and hillslope influence on channel bed sediment in mountain streams, Colorado Front Range, USA

Geomorphology 239 (2015) 91–105

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Downstream fining, selective transport, and hillslope influence on channel bed sediment in mountain streams, Colorado Front Range, USA Foeke Menting a, Abigail L. Langston b, Arnaud J.A.M. Temme a,c,⁎ a b c

Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands Geological Sciences, University of Colorado, Boulder, CO, USA Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

a r t i c l e

i n f o

Article history: Received 25 September 2014 Received in revised form 15 March 2015 Accepted 16 March 2015 Available online 24 March 2015 Keywords: Front Range Downstream fining Downstream sorting Channel bed material Hillslope processes

a b s t r a c t Channel bed sediment is composed of a combination of upstream and local hillslope sources. Factors that influence downstream changes in size, sorting, and lithological characteristics of bed material are complex. In order to better understand this complexity, we measured grain sizes and channel geometry on five streams draining the Colorado Front Range. The studied streams flow from steeply incised canyons in the mountains onto the gently sloping Colorado High Plains. Downstream fining occurs at the scale of the entire study reach (~30 km), but not in each individual stream. Differences between streams are likely related to watershed size and discharge characteristics. At smaller spatial scales within each stream, the fining pattern is less clear and is disrupted by lateral inputs from locally steep hillslopes in the incised canyons of the Front Range. However, the exclusion of local hillslope influence by distinguishing among individual lithologies in the channel bed material that are not present in the local hillslopes shows that fining also occurs over these smaller spatial scales. In the absence of steep hillslopes adjacent to the channels, bed sediment becomes a better mix of lithologies from the whole watershed rather than dominated by local hillslope sediment influx. We propose four dominant factors that control the channel bed characteristics in the study area: inheritance, hydrodynamics, lithologically controlled sediment abrasion rates, and differences in denudation rates throughout the watershed with associated differences in hillslope steepness. Combining these factors leads to a conceptual model that explains our observations and illustrates the complexity of the behaviour of bed material in steep mountainous regions and their bounding basins. Distinguishing among the different lithologies in the bed material was instrumental in decomposing and understanding the complex trends in this system. © 2015 Elsevier B.V. All rights reserved.

1. Introduction An understanding of erosional processes in bedrock channels is necessary in order to predict the response of the rest of the landscape to changes in climate or tectonic uplift (e.g., Whipple, 2001, 2004; Wobus et al., 2006a). Bedrock erosion rates in channels are controlled by the bed cover and the transport capacity of the river that moves the eroded materials downstream (Gasparini et al., 2004; Sklar and Dietrich, 2004). Extreme flood events and the size distribution of the channel bed material determine the occurrence and scale of bedrock erosion events: floods provide the stream power to set the bed cover in motion and abrade the channel bed, while the size distribution of the bed cover determines how much stream power is needed to set the sediment in motion and start the erosion process (Sklar and Dietrich, 2004; Wobus et al., 2006a; Siddiqui and Robert, 2010; ⁎ Corresponding author at: Department of Environmental Sciences, PO Box 47, 6700AA Wageningen, The Netherlands. Tel.: +31 317 484445. E-mail addresses: [email protected] (A.L. Langston), [email protected] (A.J.A.M. Temme).

http://dx.doi.org/10.1016/j.geomorph.2015.03.018 0169-555X/© 2015 Elsevier B.V. All rights reserved.

DiBiase and Whipple, 2011). Therefore understanding the characteristics and behaviour of the channel bed material in mountainous regions is important to accurately predict the response of the channel bed to large and small flood events and to inform landscape evolution models (e.g., Coulthard et al., 1999; Schoorl et al., 2000; Tucker et al., 2001). Channel bed material behaves predictably at large spatial scales. The most important patterns are downstream fining, which was first described in the nineteenth century (Sternberg, 1875), and downstream sorting (e.g., Brierley and Hickin, 1985; Komar, 1987). Downstream fining is attributed to the abrasion of individual grains during transport (e.g., Gomez et al., 2001) and to the selective transport of smaller grain sizes associated with downstream decreases in stream power (Ferguson et al., 1996; Hoey and Ferguson, 1997). Selective transport also results in downstream sorting because the preferential transport of the fine fraction of heterogeneous bed material results in a better sorting of the bed material (Hoey and Ferguson, 1997). However, at smaller spatial scales, these general patterns are not always observed. At spatial scales of tens of kilometres, abrasion and selective sorting are often not the dominant processes that influence the size distribution of channel bed material. Many studies have

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shown an absence of downstream fining or even downstream coarsening in mountainous areas, which is usually attributed to lateral inputs (Rice and Church, 1998; Gomez et al., 2001; Davey and Lapointe, 2007; Rengers and Wohl, 2007; Mureşan, 2009) and to changes in channel geometry that influence the stream power (Rengers and Wohl, 2007; Heitmuller and Hudson, 2009; Pike et al., 2010). Both causes were invoked to explain downstream coarsening in Brummer and Montgomery (2003). Lateral inputs can include sediment flux from hillslopes or tributaries that introduce coarser and less-sorted sediment into a trunk channel (Grimm et al., 1995). The effect of lateral inputs can be partially mitigated by studying channel sections between two tributary junctions, which are termed sedimentary links (Rice and Church, 1998). Rice and Church (1998) found fining patterns within sedimentary links in two gravel-bed rivers in the Rocky Mountains in Canada (1–17 km), but not at the full reach scale (N 100 km). This setting had clear tributary inputs disturbing the size patterns, without significant other lateral influences. Conversely, on the Rio Chagres in Panama, Rengers and Wohl (2007) only found downstream fining on the scale of the whole channel reach (40 km) and not within sedimentary links. The lack of fining within sedimentary links was attributed to strong influences from lateral inputs such as landslides and to a smaller disturbing effect of tributaries. Nontributary additions of sediment were also recognized by Davey and Lapointe (2007), working in the Canadian Ste Marguerite River, and by Benda (1990) in the Oregon Coast Range. Benda (1990) mentioned debris flows, and Davey and Lapointe (2007) mentioned steeply sloped canyon reaches as sources of sediment in these settings. Changes in slope, width, or discharge can strongly influence local hydraulic properties of the channel and thus influence the channel bed composition at that location in the channel (Krumbein, 1942; Wobus et al., 2006a,b; Rengers and Wohl, 2007). Finer bed material can be attributed to decreased stream power from lower channel slopes (e.g., Hoey and Ferguson, 1997) or from wider channel sections (e.g., Rengers and Wohl, 2007). Discharge characteristics of the stream also significantly influence the channel bed composition (Ashworth and Ferguson, 1986; Whiting et al., 1999; Snyder et al., 2003; Lenzi et al., 2004; Hassan, 2005). Heitmuller and Hudson (2009) suggested that rivers with very variable flow characteristics may not show clear downstream fining or sorting. This could also apply to mountainous streams with strong differences in discharge throughout the year, particularly those dominated by flashy summer rainstorms in semiarid regions (Jarrett and Costa, 1983). Several landscape models currently simulate the downstream evolution of channel bed material (e.g., Hoey and Ferguson, 1994; Gasparini et al., 2004; Sklar and Dietrich, 2004), but empirical and field data sets are still needed to determine when these models are appropriate. Studies show varying results regarding which processes exert the strongest controls on the downstream evolution of channel bed material. These differences are usually attributed to differences in channel properties and environmental conditions such as topography, lithology, and climate (Rice and Church, 1998; Gomez et al., 2001; Brocard and Van der Beek, 2006; Rengers and Wohl, 2007; Mureşan, 2009; Pike et al., 2010). For example, Gomez et al. (2001) attributed observed fining of bed material in a gravel river in New Zealand mainly to a downstream decrease in channel gradient, whereas Pike et al. (2010) attributed coarsening in headwaters and fining in downstream reaches of mountain streams in Puerto Rico mainly to hydrological and transport processes, as well as to local bedrock lithology. More knowledge about how topography, lithology, and climate influence channel bed material is therefore needed to improve the applicability of landscape evolution models to different settings. In order to better understand and potentially predict the relative influence of channel properties, lateral inputs, and discharge characteristics on channel bed material, detailed data sets from a diverse range of landscapes are needed. A valuable data set would describe a series of connecting channels with different geometry and discharge

characteristics so that the existing theories of the effects of these factors can be tested at various scales. In this study, our objective is to provide and analyse such a data set from the Colorado Front Range. In the semiarid Front Range, streams flow through a steep mountainous environment and subsequently enter a gently sloping landscape on the High Plains. We test whether downstream fining and sorting exist at several spatial scales within the study area and use the lithological composition of the bed material to determine primary sources of bed sediment and to calculate sediment abrasion rates. This data set allows us to determine the dominant processes that control the channel bed composition in this part of the Colorado Front Range and potentially for other steep, mountainous landscapes. 2. Study site Streams draining the Colorado Front Range flow through steep mountain environments and onto the low-relief High Plains (Fig. 1), allowing us to observe how bed sediment characteristics change from a steep, high-energy environment to a low-relief environment. The Front Range lies at the eastern edge of the Rocky Mountains where the high elevation core of the Rocky Mountains abruptly transitions to the High Plains. The mountains in the study area consist mainly of a combination of granitic and metamorphic lithologies of early and middle Proterozoic age (Fig. 2), with peaks that reach over 4000 m at the Continental Divide. The highest reaches of the study area were extensively glaciated during glacial intervals, and a few small remnants of these glaciers remain today. Volcanic outcrops in mountainous areas of the study site are remnants of the Ignimbrite Flare-Up, a period of high volcanic activity in the western part of the United States about 40–25 million years ago (Tweto and Sims, 1963). The abrupt transition from the mountains to the plains is characterized by spectacular sedimentary outcrops, rising almost vertically out of the plains. These sedimentary layers consist of the Fountain Formation (300–280 Ma), Lyons Sandstone (280–250 Ma), and Dakota Sandstone (100 Ma) (Kellogg et al., 2008; Cole and Braddock, 2009). The underlying lithology of the plains beyond the mountain front consists of soft sedimentary bedrock units, primarily the Cretaceous-aged Pierre Shale. In the past ~ 5 Ma, the erosion of ~ 500 m of sediment from the Denver Basin in the High Plains has caused base level of the streams to fall, inciting a series of knickpoints that have been making their way into the granitic core of the Front Range (Anderson et al., 2006). Below the knickpoints, the streams have incised steep canyons; locally the canyons can be as narrow as 50 m and as deep as 300 m (Schildgen et al., 2002). In order to determine how channel properties and bed sediment characteristics change as streams flow from the mountains to the plains, we measured channel geometry and conducted point counts at 61 locations along five creeks in the study area: Left Hand Creek (LH), Fourmile Creek (FM), North Boulder Creek (NBC), Middle Boulder Creek (BC), and South Boulder Creek (SBC) (Fig. 1). Fourmile Creek and North and Middle Boulder Creeks are all within the Boulder Creek watershed. All of the studied streams except Lefthand Creek originate at or close to the Continental Divide and were glaciated during the last glacial maximum (locally called Pinedale, e.g., Benedict, 1981). Fourmile Creek and North Boulder Creek are situated entirely in the mountains and drain into Middle Boulder Creek. Since the 1840s streams draining the Colorado Front Range (including South Boulder Creek, Boulder Creek, and Lefthand Creek) have been heavily impacted by human activities, especially from mining, deforestation, road construction, flow control structures, and water diversion (Wohl, 2006). Hundreds of individual placer and lode mines were present in the study area, especially along Fourmile Creek and Lefthand Creek, beginning in the 1870s, but large-scale mining in the study area had ceased by the 1920s (Murphy, 2006). Timber harvesting in the watersheds following the mining boom and increased frequency of wild fires (Murphy, 2006) likely resulted in increased sediment flux from deforested hillslopes to the channels (Pierce et al., 2004;

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Fig. 1. Location of the Colorado Front Range and the channels that were surveyed with the sampling locations and catchments indicated. Left Hand Creek is purple, Middle Boulder Creek is green, North Boulder Creek is yellow, Fourmile Creek is red, and South Boulder Creek is blue. Channel sections with a catchment size larger than 16 km2 are drawn. Dams and their reservoirs are located roughly 3 km upstream from B1 and 7 km upstream from S1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. Geological map of the study area, with the sampling locations indicated as well as the boundary between mountains and plains, determined by the eastern boundary of the Fountain Formation (after Kellogg et al., 2008; Cole and Braddock, 2009).

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Fig. 3. The spatial scales used in this study. Level 5 is not displayed; it concerns individual sampling locations. The green line indicates the mountain front. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Istanbulluoglu and Bras, 2005). However, most of the hillslopes in the study area have recovered and are once again forested with pinus ponderosa and pinus contorta (Veblen et al., 2000). No apparent

impacts from historic mining activities on the current stream properties persist, such as increased sedimentation of fine-grained material (James, 1999).

Fig. 4. Longitudinal channel profiles extracted from a 9 m DEM (Gesch, 2007) of all the studied channels with the D16, D50, and D84 of each sampling location along the channel indicated on the secondary y-axis. D50 grain size is indicated by black circles and D16 and D84 grain sizes are indicated by the upper and lower error bars. The red triangles indicate the location of the knickpoints in the mountains, and in the plains they indicate the divide between upstream locations and downstream locations. The vertical, dashed, black lines in LH, SBC, and BC indicate the mountain front. The horizontal grey lines in Left Hand Creek (LH) and Middle Boulder Creek (BC) show the extent of the sedimentary links (SL) investigated in this study. Note that not all horizontal and vertical axes are scaled equally.

F. Menting et al. / Geomorphology 239 (2015) 91–105

Many of our sampling locations are near roads, as the roads often follow the streams. The current roads, which have been in place since the 1950s, constrict water flow through the canyon in many places and restrict lateral channel movement. Sediment supply to streams near roads may be impacted through increased addition of fine material from traction sand used during the winter, erosion on unpaved gravel stretches of road, and more frequent mass movement on hillslopes near roads (Larsen and Parks, 1997). Flow control structures and water diversions significantly influence the hydrology of the study streams by affecting the magnitude, frequency, and duration of stream discharge (e.g., Graf, 1999). Flow control structures on Middle Boulder Creek and South Boulder Creek were built in the early–mid-twentieth century and are primarily used to store water for municipal water use during the spring snow melt and provide flood control for the streams. Water diversions from the study streams for agricultural and municipal water use have the largest impact on stream properties. All of the study streams that flow onto the plains have significant water diversions, and stream discharge can be reduced by as much as two-thirds of water flowing in the mountain sections above the diversions (Murphy et al., 2003; Colorado Division of Water Resources, 2014). The decreased discharge on the plains has resulted in a narrowed channel and the establishment of vegetation on banks that are no longer flooded on a yearly basis (Johnson, 1998; Surian, 1999). 3. Methods The field survey was conducted during nine days of sampling in the period between 26 July 2013 and 13 August 2013. This narrowly preceded the rain event that caused major flooding in the study streams and much damage and loss of life between 9 September and 16 September 2013. Sampling locations were spaced with fairly regular intervals of about 2 km to maximize the spatial extent of sampling locations and to include the Front Range and parts of the High Plains. The boundary between the Front Range and the High Plains was defined as the easternmost outcrop of the Fountain Formation. Near this boundary, sampling locations were spaced at shorter intervals of 500 to 1000 m in order to capture more precisely the transition of the channels from a confined, mountainous environment to the relatively flat, open environment of the High Plains. At each location, current channel width, bankfull width, channel crosssectional profile, channel slope, and the size (b-axis) and lithology of at least 100 randomly selected stones in the channel bed were measured. Bankfull width was defined as the distance between the points on both banks where the steeper bank changed into the flatter floodplain. Channel slope was measured with a laser range finder over a distance of about 20 m, centred on the sampling location. The 100 stones at each location were randomly selected using the Wolman pebble count method (Wolman, 1954). The measured size classes ranged from b 8 to N300 mm with half Φ (log2) intervals. Phi is a common unit in grain size analyses (Wentworth, 1922) and provides equal differences between size classes based on consecutive halvings of the diameter. Five lithological classes were distinguished: granite/diorite (g), quartzite (q), other metamorphic (m), sedimentary

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(s), and volcanic (v) rocks. These classes were chosen because they represent the major lithologies in the catchments and because they are easy to distinguish in the field. No lithology was recorded for the particles b 8 mm. During the field survey we were limited to sampling locations where reaching and crossing the creeks was possible. This limitation has introduced sampling bias towards the wider and calmer parts of the creeks. The majority of the sampling locations were situated in plane-bed channel reaches; and a few locations were situated in step-pool or cascadetype channel reaches (Montgomery and Buffington, 1997), largely in mountainous channel sections with steeper slopes. In the step-pool and cascade channel reaches, the sampling was mainly conducted in the pool sections of the channel. This variation in sampling locations may nevertheless have led to an underestimation of grain size in the mountainous parts of the research area. Also, all three authors conducted the sampling, which may have introduced an operator bias (Marcus et al., 1995; Wohl et al., 1996; Daniels and McCusker, 2010). We have not been able to quantify and correct for this bias for practical reasons: we maximized sampling locations instead of repeated observations. Downstream fining rate was calculated with linear regression and expressed in mm change in b-axis per km. For comparison with experimentally derived fining rates that are expressed in mass percentage change per km (Attal and Lavé, 2006), the volume of stones was calculated from the measured b-axes, assuming equal density for all lithologies and perfectly spherical stones. The effect of the assumption of perfect roundness was explored quantitatively for a range of possible mass-loss mechanisms and stone shapes, resulting in the conclusion that the assumption leads to a possible underestimation of volume and mass-loss between 0% and 30% (see material). 3.1. Scale levels Five spatial scale levels were used to investigate fining, sorting, and lithological distribution (Fig. 3). At the largest scale level (level 1), all sampling locations in the mountains were grouped and compared to all the sampling locations in the plains. At level 2, mountain and plains locations were also grouped and compared, but for each watershed individually. Level 3 zooms in to the mountains and the plains separately. In each creek, the mountain locations were grouped according to their position above or below the knickpoint, as this is where the largest differences were expected because of changes in hydraulic conditions downstream of the knickpoint, such as channel steepening. In the plains, locations were also divided into two groups, with the downstream group containing three locations and the upstream group the other locations (Fig. 4). This division was made based on the assumption that the locations that are farther downstream clearly are no longer a mountain stream. Scale level 4 zooms in to individual sedimentary links. Sedimentary links were defined as channel sections with a relatively low increase in drainage area between subsequent sampling locations, and the boundaries of the sedimentary links were defined by clear jumps in the size of the drainage area caused by tributaries. Five such channel sections were identified, all within the mountains: three in Left Hand Creek (SL1, SL2, SL3) and two in Middle Boulder Creek (SL4, SL5, Fig. 4). Finally, scale level 5 looked at individual sampling locations

Table 1 Results of the statistical test for fining, using the Mann–Whitney-U test for levels 1, 2, and 3 and linear regression for level 4; statistical significance is indicated with p-values (Fin. indicates fining, Cor. indicates coarsening and 0 indicates no change). Spatial scale

Fining (Fin.), Stable (0), Coarsening (Cor.)

Level 1 Level 2

Fin. (p = 0.0039) BC + FM + NBC Fin. (p b 0.0001) BC-M Fin. (p = 0.0316) SL1 Cor. (p = 0.238)

Level 3 Level 4

BC-P Fin. (p = 0.0008) SL2 0 (p = 0.9)

SBC Fin. (p = 0.0025) FM 0 (p = 0.0939) SL3 Cor. (p = 0.133)

LH-M Cor. (p = 0.0006) SL4 0 (p = 0.998)

LH 0 (p = 0.350) LH-P 0 (p = 0.105) SL5 Fin. (p = 0.482)

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where the lithology of channel material was compared on the one hand to the lithology of the upstream catchment and on the other hand to the lithology of slopes immediately overlying the location. 3.2. Statistics At the three largest scales, the Mann–Whitney U test (Mann and Whitney, 1947) was used to determine whether stone sizes and the level of sorting differed substantially between groups of locations. At scale level 4 (sedimentary links), linear regression was used to determine the effect of downstream distance on the median stone size (D50) and the level of sorting. Linear regression was performed on mm-scale data and therefore corresponds to logarithmic regression on the Φ scale. Sorting was quantified with the sorting coefficient derived by Heitmuller and Hudson (2009) from Prothero and Schwab (1996): pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sorting ¼ D84 =D16 . We used Phi values for D84 and D16, making the range of the sorting coefficient in this study 1.00–1.66, with 1.66 indicating extremely poor sorting and 1 indicating extremely good sorting. Linear regression was also used to derive downstream fining rates per lithological class for 12 sections of sedimentary links (i) without local hillslope source for the lithology class within that channel section, (ii) that contained at least four locations, and (iii) where the count of the lithological class at each location was 5 or higher. This resulted in overall and lithology-specific fining rates and in overall sorting rates. Lithologyspecific sorting was not calculated because of low stone counts leading to high uncertainty in the estimates of D84 and D16. At individual locations (scale level 5), the Χ2 test (Pearson, 1900) was used to test whether the distribution of lithologies in the channel bed equals their relative surface area in the local hillslopes directly overlying the sampling location and whether it equals their relative surface

area in the entire upstream watershed. Standard GIS functionality was used to determine the distribution of lithologies in the watersheds upstream of the sampling locations using a 9-m DEM (Gesch, 2007) and two geological maps (Kellogg et al., 2008; Cole and Braddock, 2009). The local hillslope area was approximated as the difference in watershed surface at a sampling location and the watershed surface of a location 100 m upstream from that sampling location and was also calculated using standard GIS functionality. The accuracy of this approach is a function of the quality of the geological maps (Kellogg et al., 2008; Cole and Braddock, 2009). One limitation in this respect is the area of alluvium/colluvium indicated on the geological map (Fig. 2). Alluvium/colluvium was not considered a lithology in our area calculations, and it was therefore indirectly assumed to be a perfect lithological mix of the remainder of the watershed. Also, where lithologies are indicated, the geological maps are not entirely without error. For instance, in Fourmile Creek at the most upstream sampling locations, the channel bed material consisted mainly of granite (N50%), while the geological map showed almost no such geological units in the upstream area (~1% granite; Fig. 2). We assumed that the geological maps lacked detail in this area and possibly in other areas too. It was, however, beyond the scope of this study to produce an improved geological map, and we assumed that lacking detail did not affect our results elsewhere. 4. Results 4.1. Trends in downstream fining at multiple scales The results of the statistical analyses of downstream fining are presented in Table 1 with the corresponding grain size distributions

Fig. 5. Grain size distributions (GSD's) on which the Mann–Whitney-U tests have been performed. Upstream groups are represented by the red lines with triangular data points, the downstream groups are represented by the blue lines with round data points. Graph A shows downstream fining for spatial scale level 1. Level 2 GSD's are shown in B–D: B and C show downstream fining, D shows no clear fining pattern but does suggest downstream mixing (decrease in sorting). Level 3 GSD's are shown in E–I: E and F show downstream fining, H downstream coarsening and G and I show no clear fining patterns although G does suggest downstream mixing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

F. Menting et al. / Geomorphology 239 (2015) 91–105 Table 2 Downstream sorting in the five sedimentary links under investigation (for their position in the channels, see Fig. 4). Left Hand Creek

Length (km) Nr. of locations Sorting

Boulder Creek

SL1

SL2

SL3

SL4

SL5

6.2 4 Sorting

8.7 5 Mixing

4.3 5 Mixing

6.4 4 Stable

9.4 6 Sorting

for levels 1–3 in Figs. 4 and 5. To assess the differences in grain size at the largest scale for this study, the cumulative grain size distributions for all points in the mountains and all points in the plains were compared. At this largest scale level, grain size distributions in the mountains and the plains share a D50 value of 64 mm (Fig. 5A), but the D16 and D84 in the plains are one size class smaller than in the mountains, 8 vs. 11 mm and 128 vs. 180 mm, respectively. This results in a statistically significant fining trend in the bed sediment as streams flow from the mountains to the plains (p = 0.0039) (Table 1). When comparing mountains to plains for each watershed individually (scale level 2), Boulder Creek and South Boulder Creek exhibit downstream fining (p b 0.01). In terms of grain size values, this means that D16, D50, and D84 values for Boulder Creek in the mountains are 11, 64 and 180 mm, respectively, and the same values are all one size class smaller in the plains: 8 mm, 45 mm and 128 mm (Fig. 5B). In South Boulder Creek D16, D50, and D84 values in the mountains are 22.6, 64 and 128 mm, respectively, and in the plains 22.6, 45 and 128 mm. Only the D50 value differs by one size class (Fig. 5D), but the difference between the size distributions as a whole is large enough to be significant (p = 0.0025). Lefthand Creek, however, shows no statistically significant difference in size distributions of mountain and plains material at this scale level (p = 0.350). In terms of D16, D50, and D84 values there is no difference between the mountains and the plains, 8, 64 and 180 mm, respectively (Fig. 5D). At scale level 3 (Fig. 5E to I), Middle Boulder Creek shows fining within the mountains (p = 0.0316) and within the plains (p b 0.01). The D50 within the mountains is 64 mm upstream and downstream of the knickpoint, but the cumulative size distributions differ significantly (Fig. 5E). In the plains the D50 is 45 mm up and downstream of the break, but again the two size distributions as a whole differ significantly (Fig. 5 F). Fining was unexpected in the mountains because of the increase in channel slope downstream of the knickpoint. Lefthand Creek does show the expected downstream coarsening trend in the mountains, as D50 increases from 32 mm upstream to 45 mm downstream of the knickpoint (Fig. 5H). In the plains section of Lefthand Creek, fining

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is suggested but not statistically significant (p = 0.11) (Fig. 5I). In Fourmile Creek, which is entirely in the mountains, no downstream fining has been shown at this spatial scale but rather a downstream decrease in sorting (Fig. 5G). Because all South Boulder Creek mountain points are situated downstream of the knickpoint, fining trends were not investigated for this creek at this scale level. At scale level 4, only one of the five sedimentary links (SL5) shows downstream fining (Table 1) but with a low statistical significance. In the other sedimentary links, coarsening or no clear change in size distributions was observed. The results of the statistical test for sorting show no sorting effect at scale levels 1, 2, or 3. Only at the sedimentary link scale level is some sorting present (Table 2), albeit with low statistical significance. 4.2. Downstream fining for individual lithologies In most cases downstream fining was observed for lithologies that were not present in the local hillslopes, even when fining was not apparent in the total bed material (Table 3). Small fining rates were found for granitic lithologies (b−3.0 mm diameter change/km), with Lefthand Creek even indicating coarsening (0.7 mm/km). In contrast, metamorphic lithologies in most channel sections show large fining rates (on average −8.1 mm/km); but the rates are highly variable between different channel sections, and coarsening in the metamorphic fraction also occurs (in BC3). For volcanic lithologies, similarly large fining rates were found. This suggests that metamorphic and volcanic lithologies have larger fining rates than granitic lithologies — weaker rocks appear to fine faster. In most cases the fining rates for a particular lithology are larger than the overall fining rate of all channel bed material over the same channel section (Table 3). The lithologies without local source area seem to fine at a higher rate than the overall bed material, which includes lithologies with a local source area. This shows that in most cases some fining occurs in the mountain reaches, which is not clearly visible when looking at all bed material at the same time. 4.3. Relationship between bed sediment composition and watershed geology The channel bed material reflects the local and the regional lithology to some extent (Fig. 6). For instance, the transition through the sedimentary ridges is clearly represented by an increase in sedimentary rocks in the channel bed for Boulder Creek, South Boulder Creek, and Lefthand Creek; but at the same time, metamorphic rocks are present in the downstream reaches of all streams, although metamorphic bedrock is typically only found high in the watershed (Fig. 6). The importance of local lithology and the lithological composition of the

Table 3 Lithology-specific fining rates versus the fining rates of the total bed material for different channel sections, sorted by lithologies and fining rates; negative values indicate fining, positive values indicate coarsening.a Creek

Lithology

Nr of locations

Length of channel section (km)

Average Count

Lithology Fining rate (mm/km)

R2

p

Total bed Fining rate (mm/km)

Difference: (Lith-total) (mm/km)

Lithology: mass%/km

Total: mass%/km

BC SBC LH LH SBC BC1 BC2 FM BC3 SBC BC FM

G G G M M M M M M Q V V

9 5 6 4 5 4 5 5 11 5 8 6

7.5 7.8 8.4 2.4 3.9 6.4 9.4 7.6 9.2 9.0 7.0 9.4

54 43 58 8 8 10 11 17 11 12 14 11

−3.0 −0.7 0.7 −18.9 −11.0 −7.6 −6.8 −2.8 1.5 −4.8 −8.1 −7.0

0.30 0.04 0.01 0.41 0.75 0.89 0.67 0.21 0.09 0.27 0.40 0.70

0.13 0.74 0.82 0.36 0.06 0.06 0.09 0.44 0.37 0.37 0.09 0.04

−1.7 −2.0 −1.1 −14.0 −5.6 0.0 −1.1 4.1 −0.6 −3.3 −3.3 −1.5

−1.4 1.2 1.8 −4.9 −5.5 −7.6 −5.8 −6.9 2.1 −1.6 −4.8 −5.5

−13 −3 2 −51 −43 −23 −21 −13 10 −21 −25 −29

−11 −9 −5 −52 −17 0 −5 30 −4 −13 −21 −10

a Fining rates and their R2 and p values are the results of a linear regression on D50 values (mm) for the particular lithology. Negative values in the ‘Difference’ column indicate stronger fining for an individual lithology than for the total channel material. BC1, 2, and 3 indicate three different reaches in Middle Boulder Creek of which BC1 is the most upstream and BC 3 the most downstream.

98 F. Menting et al. / Geomorphology 239 (2015) 91–105 Fig. 6. Geological maps of the watersheds of Left Hand Creek (A), Boulder Creek (B) and South Boulder Creek (C). The pie charts indicate for each sampling location the geological distribution of the channel bed material (%). The radius of each slice indicates the D50 (mm).

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Fig. 6 (continued).

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Fig. 6 (continued).

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Fig. 7. Chi-square p-values for all locations, based on a test of equality between channel bed composition and total upstream watershed composition. The colours indicate the channel of each sampling location (LH = purple, BC = green, NBC = yellow, FM = red, SBC = blue). An increase in p-values when going from the mountains to the plains is clearly visible, although the p-values remain extremely small (note the log scale of the y-axis). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

channel bed can also clearly be seen in Lefthand Creek. Near sample locations L3, L4, and L5, the percentage of volcanic material in the channel bed increases to about 5–15% because of the local volcanic source; but farther downstream, where volcanic units are no longer present in the local watershed, the volcanic contribution in the channel bed declines to only a few percent.

However, the hypothesis that channel material is a perfect mix of upstream watershed lithology can be rejected for most locations on the basis of the Χ2 test with a 95% confidence interval (Fig. 7). Only two locations pass the Χ2 test (N4 and B9), against 59 locations where the hypothesis is rejected. Chi-square p-values do decrease downstream (Fig. 7), suggesting that even though total watershed lithology and

Fig. 8. Counts of granite, metamorphic, and volcanic material in the channel bed; the percentage in the upstream watershed and in the local hillslopes for Boulder Creek (BC, A–C), Left Hand Creek (LH, D–F) and Fourmile Creek (FM, G–I). On the second y-axis the local hillslope is shown, calculated as the average of the six steepest hillslopes over a distance of 200 m for each location in a 9-m DEM: to both sides of the creek at the sampling location and 100 m upstream and downstream.

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channel bed lithology are not the same, the channel bed composition is a better representation of the lithology of the total watershed once the watershed gets larger and when the creek flows through the plains. The hypothesis that the local hillslope lithology equals the channel bed material lithology was confirmed for only one location (L2). This shows that the lithology of the local hillslopes cannot be a sole predictor of the variability and distribution of lithologies observed in the channel bed. Clearly, neither the lithology of the total watershed nor the lithology of the local hillslopes alone predicts the lithology found in the channel bed. The composition of the channel bed material is a combination of the total upstream watershed lithology and the local hillslope lithology. In other words, upstream fluvial and local hillslope sources for the material exist. However, total upstream watershed lithology generally becomes a better predictor for the composition of bed material when going downstream (Fig. 7). This raises the question of what drives the downstream decrease in local hillslope input. We hypothesize that steeper slopes in the mountains cause higher erosion rates and more local contributions to channel bed material. In general, steep hillslopes that coincide with high local lithology counts result in a strong representation of hillslope lithology in the channel bed (Fig. 8). This is most clearly visible where the drop in hillslope steepness at the mountain front reduces the local hillslope input and shifts the channel counts towards the total watershed lithology. Apparently, hillslope steepness plays an important role as it

facilitates hillslope processes. From our research, this appears to happen only where local hillslopes are over about 30%. No clear differences are visible in the effect of different hillslope values larger than 30%. A closer look allows the distinction of three cases in the relationship between channel bed lithological composition and local/watershed lithological composition (Fig. 8). First, the most common observation is that channel bed composition is a mix of total and local lithology, as is the case for Boulder Creek (Fig. 8A,B,C) and for granite and metamorphic material in Fourmile Creek (Fig. 8G,H). Second, and less commonly, a lithology is present in larger amounts than either the total or the local lithology, for example granite in Lefthand Creek (Fig. 8D) Third, occasionally, a lithology is present in smaller amounts than the total and the local lithology, for example, metamorphic rocks in Lefthand Creek (Fig. 8E). Channel bed composition that is between total and local lithology seems logical and can be explained by a mixing of the total watershed lithology and local hillslope lithology. For instance, for the granite in Boulder Creek (Fig. 8A), steep hillslopes (N30%) coincide with the presence of granite in the local hillslopes, resulting in a percentage of granite in the channel bed between that of the total watershed and the local hillslopes. In the absence of steep local hillslopes, the percentage of granite in the channel bed is closer to that of the total watershed. On the other hand, metamorphic lithologies are absent in this part of the watershed of Boulder Creek (Fig. 8B), which results in percentages of metamorphic material between that of the total watershed and that of

Fig. 9. Conceptual model of the four processes at work in the Colorado Front Range. It shows two different lithologies, ‘square grey’ and ‘round white’, with different fining rates (0.5 and 1 mm per step, respectively). Inheritance (1) indicates that each particle will travel through the whole channel from its source area (a, c or d) down to its location of final deposition (downstream of e). This causes similarity between neighbouring locations (pie charts a–e). In each step, all particles fine according to the fining rate of their lithology (2). Hillslope processes (3) occur in the steep section of the mountains (4.2: Middle Catchment). In this way, denudation rates (4) ultimately control which parts of the watershed contribute to forming of the channel bed material. In this conceptual model only the ‘round white’ lithology is present in the steep local hillslopes and thus only ‘round white’ material is added to the channel (c, d). This causes an increase in ‘round white’ particles in the channel bed (pie charts c, d). Because the fresh material is coarse, this causes a lower fining rate for ‘round’ particles in these sections of the channel than without local sources, visible when comparing the D50 differences between a and b (no local addition) and between b–e.

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the local hillslopes. Also here, a decrease in local hillslopes moves the percentage of metamorphic stones in the channel bed closer to the total watershed percentage. Lithologies that are present in larger percentages in the channel bed than in both the total and local geology indicate an overrepresentation in the channel bed (Fig. 8D). A channel bed composition that is smaller than both the total and local lithology indicates an underrepresentation. Both of these phenomena occur in Lefthand Creek: granite is mainly overrepresented whereas metamorphic is mainly underrepresented. 5. Discussion Our results highlight the dominant processes at work in controlling the size and composition of the channel bed material in the sampled creeks. First, results of the D16 and D84 values confirm the occurrence of downstream fining at the large spatial scale, as first seen by Sternberg (1875) and also found in other surveys in mountainous regions (e.g., Rengers and Wohl, 2007; Pike et al., 2010). The fining of channel bed material as streams flow from the mountains to the plains can be attributed to hydrodynamic control on grain size distributions and to downstream abrasion of bed sediment. Hydrodynamic control on the stream includes channel properties, such as channel slope and channel width, as well as decreasing discharge in the plains segments as a result of significant water diversions in all study streams. Channel slopes in the mountain segments are higher than on the plains segments (Fig. 4) and channels are narrower in the mountain segments as a result of both lithologic control on valley width (Fig. 2) and artificial narrowing of the channel by road construction. Higher slopes and narrower channels allow the transport of larger grain size fractions in the mountain segments. Water diversions from the plains segments of the study streams reduce the magnitude and duration of peak flows, especially during spring snow melt, decreasing the transport capacity of the stream and potentially resulting in increased deposition of bed material at the diversions and in sediment that is finer than expected downstream of the diversions. Because even D50 and D84 grain sizes fine gradually moving from the lower mountain segments to the plains (Fig. 4), rather than abruptly at flow diversions within the plains, we expect that water diversions play a small role in grain size characteristics. This may be attributed to discharge events associated with summer storms that are not as impacted by diversions but that often reach the discharge magnitude of typical spring runoff and can be much higher (Colorado Division of Water Resources, 2014). A large flood event, such as the flooding on the Front Range during September 2013, erases any effect of flow diversions on grain size distributions (Langston, 2014). The artificial fining as a result of the diversions could result in an overestimation of abrasion rates, but should not affect the comparison of faster downstream abrasion rates of metamorphic and volcanic material compared to granitic material. We may have underestimated fining rates at the largest spatial scale because of the sampling bias towards calmer sections in the steppool and cascades in the mountain segments, which leads to the preferential sampling of finer bed materials. At smaller scale levels, downstream fining is less often found, something also previously observed in mountainous regions (e.g., Rengers and Wohl, 2007; Mureşan, 2009; Pike et al., 2010). This can be attributed to lateral inputs from hillslope processes (Fig. 8), as seen in previous studies (Benda, 1990; Rice and Church, 1998; Davey and Lapointe, 2007). Detailing the forcing of this input, our results suggest that steep local slopes enhance the delivery of locally derived lithology to the stream bed, overwhelming the signal of upstream lithological contributions (Fig. 8). Further demonstration of hillslope influence comes from the fact that, in most cases, overall bed sediment fining rates are smaller than fining rates of the individual lithologies in the bed sediment that are not represented in the local hillslopes (Table 3). In Lefthand Creek, the absence of fining despite water diversions from the channel that would promote fining may be explained by its small catchment size

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and the resulting hydraulic characteristics. At the mountain front, the Boulder Creek watershed is roughly twice the size of the Lefthand Creek watershed. Moreover, the headwaters of Lefthand Creek are not glaciated and flow in the stream is more sensitive to rain storms, which have the potential to produce much higher discharge than a typical spring snow melt (Jarrett and Costa, 1983). The analysis of fining for individual lithologies shows that fining occurs regularly within the mountain reaches of the channels, which is not clearly observed in the total channel bed material. After unit conversion, our fining rates for granite and metamorphic rock (Table 3) are about an order of magnitude larger than experimentally derived abrasion rates for granitic and metamorphic gneiss material (Kodama, 1994a,b; Lewin and Brewer, 2002; Attal and Lavé, 2006, 2009). Because the effect of assumptions in our unit conversion would only be an underestimation of fining rates, this strongly suggests that in the creeks that we studied, selective transport, not abrasion, is the main process affecting grain size distribution. Downstream sorting, which also results from selective transport, could not be tested for individual lithologies owing to limited stone counts. This is a potential target for future research with a larger number of stone counts. This analysis was only possible by investigating the behaviour of a single lithology in subsections of sedimentary links where that lithology has no local source areas. We suggest that this is a valuable method to filter local hillslope influences out of the data and reveal patterns that would otherwise be hidden by the complexity of the behaviour of the rest of the bed material. This is particularly useful in steep mountainous areas where lateral inputs are common and is more useful than sedimentary links in these types of environments. 5.1. Conceptual model Based on the results presented here, we distinguish four factors that control the size and composition of the material in the bedrock channels in the study area and perhaps more widely: inheritance, hydrodynamic control, lithology-specific fining, and spatial variation in erosion rates. The first, most fundamental factor is inheritance. All eroded material will leave the mountains through the creeks, and all material will have to pass each channel section at some point in time. Therefore some expression of the upstream watershed must be present in the channel bed, i.e., inheritance: the lithological composition of one location is to some degree similar to the composition of its upstream neighbouring location. Our data clearly support this at the smallest scale because in most cases, sampling locations show only minor differences in lithological composition with their neighbour (Figs. 5 and 7). At the larger scale, moving downstream from the mountains and into the plains, the lithological composition increasingly looks like the lithological composition of the total watershed (Figs. 6 and 7). When leaving the erosive bedrock channels in or downstream of depositional basins, this would no longer be the case as connectivity breaks down (e.g., Attal et al., 2008). Inheritance also explains why material can be overrepresented relative to the local hillslope and to the overall catchment lithology: if local hillslopes upstream of a location have provided large amounts of material, that material will pass downstream locations. We observed this in Lefthand Creek for granite. Similarly, underrepresentation can occur if the bed material has not yet experienced an effect of hillslopes providing a previously unrepresented lithology — as observed in Lefthand Creek for metamorphic lithology. The second factor is the hydrodynamic control on the channel bed, which drives the movement of bed material with different transport thresholds, ultimately resulting in fining and sorting. Discharge characteristics are crucial because they largely determine stream power and thus the size fraction of the bed material that can be moved. The absence of downstream fining in Lefthand Creek despite water diversions that promote sediment fining may be

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explained by the small catchment size and the resulting hydraulic characteristics. At the mountain front, the Boulder Creek watershed is roughly twice the size of the Lefthand Creek watershed, making Lefthand Creek more sensitive to intense bursts of precipitation (Costa, 1987; Pitlick, 1994), which have the potential to produce much higher discharge than a typical spring snow melt (Jarrett and Costa, 1983) and transport larger grain sizes. Heitmuller and Hudson (2009) hypothesized that more variable discharge results in the absence of clear patterns in downstream fining of the bed material. Our observations support this hypothesis. The third factor is differential fining of lithologies. Previous experimental and field studies have emphasized that the lithology of the clast exerts the strongest control on sediment abrasion rates (Kodama, 1994a,b; Lewin and Brewer, 2002; Attal and Lavé, 2006, 2009). Fining will generate smaller sizes for particles that have been transported farther from their source location (Table 3), eventually ending up in the b8-mm size fraction and becoming lithologically indeterminate in our study. This lack of lithological determination for the b8-mm fraction could have introduced uncertainty in our lithological comparison of the channel bed to the total watershed, as certain lithologies fine faster than others. Generally, fining rates for metamorphic and volcanic lithologies are higher than for granitic lithologies, as observed here and as expected from previous studies (Bradley et al., 1972; Attal and Lavé, 2006). This factor explains why more and larger granite stones were found in the channel bed than would be expected based on other factors (Fig. 8). The fourth factor that influences channel bed composition is spatial variation in slope and erosion rates. At small spatial scale, where local hillslopes are steep, the lithological composition of the channel bed moves away from the total watershed lithology (as would be the case with pure inheritance) and closer to the local hillslope lithology (Fig. 8). The effect of local hillslopes on channel bed composition is also visible when comparing fining rates of individual lithologies to those of the total bed material (Table 3). Our analysis suggests that the threshold slope above which hillslope sediment flux dominates channel bed material is about 30% in the study area, although we did not study hillslope activity itself in detail. Rengers and Wohl (2007) studied the effect of local slopes as well, by assuming that they are correlated to narrower channels where coarser stones were observed. At larger spatial scale, variation in denudation rates across the watershed results in an unequal contribution from different source areas (Schildgen et al., 2002; Dethier and Lazarus, 2006). This is likely the case in this transient landscape that is responding to Ma-scale knickpoint migration, as well as to repeated glacial/interglacial cycles (Anderson et al., 2006). In particular, the mountains forming the Continental Divide in the west and the steeply incised canyons to the east should contribute more material per unit surface area than the relatively low-relief area between them (Birkeland et al., 2003). The four factors combine into a conceptual model of the processes at work and how they influence the channel bed composition (Fig. 9). Based on this simple model, we imagine that an increase in the number of different lithologies with different fining properties and an increase in geological variation rapidly changes the conceptually simple system into one with complex dynamics. Distinguishing among the different lithologies in the channel bed sediment can be one of the keys to unravelling these complex systems (e.g., Rice, 1999). Landscape evolution models have long been used to simulate the evolution of mountain ranges, focussing on the incision of rivers as the main large-scale control (Attal et al., 2008; Goren et al., 2014). These models are based on continuity equations for mass and partly energy expenditure by flowing water, include expressions for channel geometry, and sometimes track several different grain sizes (e.g., Van De Wiel et al., 2007). This makes it possible to simulate the factors inheritance, hydrodynamic control, and variation in denudation rates. Selective transport, resulting in downstream sorting and fining, should emerge from these factors. However, to our knowledge, no spatially

explicit model so far has introduced fining by abrasion or lithological differences in abrasion rates. Including these processes to landscape evolution models in order to assess their effect over a range of boundary conditions remains a frontier and may provide testable predictions for future observations. 6. Conclusions This study provides evidence for several dominant processes influencing the channel bed composition in the steep mountainous landscape of the Colorado Front Range and the gently sloping landscape of the High Plains. We show that fining occurs at the largest spatial scale (10–30 km) but that it is less common at the smaller spatial scales (2–10 km). No clear sorting effects are observed. The channel bed material consists of a mix of materials from upstream areas and from local hillslopes, with the contribution from local hillslopes probably depending on the steepness of local hillslopes. Isolating channel sections for close investigation by looking at single lithologies and their source location proved to be a valuable method for extracting patterns in downstream fining, which would otherwise be hidden by the complexity of the behaviour of all bed materials consisting of a mix of different lithological rock types with different source locations. Four dominant factors controlling the channel bed lithology and size characteristics were identified. These factors are inheritance, hydrodynamic control, lithology-specific fining, and spatial variation in erosion rates. These factors combine into a conceptual model of the behaviour of the bed material. The model shows that a relatively simple system can lead to complex patterns in the bed material. It also shows that discerning between lithological units and source areas is very useful when trying to understand and decompose these patterns. We encourage future research in mountainous areas to focus more on the specific behaviour of different lithologies and their source areas. Acknowledgements The authors acknowledge the kind and helpful reviews of three anonymous reviewers and the advice of Ellen Wohl and Richard Marston. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.geomorph.2015.03.018. References Anderson, R.S., Riihimaki, C.A., Safran, E.B., MacGregor, K.R., 2006. Facing reality: Late Cenozoic evolution of smooth peaks, glacially ornamented valleys, and deep river gorges of Colorado's Front Range. Geol. Soc. Am. Spec. Pap. 398, 397–418. Ashworth, P., Ferguson, R., 1986. Interrelationships of channel processes, changes and sediments in a proglacial braided river. Geogr. Ann. Ser. A Phys. Geogr. 361–371. Attal, M., Lavé, J., 2006. Changes of bedload characteristics along the Marsyandi River (central Nepal): implications for understanding hillslope sediment supply, sediment load evolution along fluvial networks, and denudation in active orogenic belts. Tectonics, climate, and landscape evolution 398, p. 143. Attal, M., Lavé, J., 2009. Pebble abrasion during fluvial transport: experimental results and implications for the evolution of the sediment load along rivers. J. Geophys. Res. Earth Surf. 114 (F4) (2003–2012). Attal, M., Tucker, G., Whittaker, A., Cowie, P., Roberts, G.P., 2008. Modeling fluvial incision and transient landscape evolution: influence of dynamic channel adjustment. J. Geophys. Res. Earth Surf. 113 (F3) (2003–2012). Benda, L., 1990. The influence of debris flows on channels and valley floors in the Oregon Coast Range, USA. Earth Surf. Process. Landf. 15 (5), 457–466. Benedict, J.B., 1981. The Fourth of July Valley: Glacial Geology and Archeology of the Timberline Ecotone. Center for Mountain Archeology Ward, CO. Birkeland, P., Shroba, R., Burns, S., Price, A., Tonkin, P., 2003. Integrating soils and geomorphology in mountains—an example from the Front Range of Colorado. Geomorphology 55 (1), 329–344.

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