COARSE PARTICULATE ORGANIC MATTER TRANSPORT IN TWO ROCKY MOUNTAIN STREAMS

Kristin Bunte, Research Scientist, Colorado State University, Engineering Research Center, 1320 Campus Delivery, Fort Collins, Colorado 80523, kbunte@engr.colostate.edu; Kurt W. Swingle, Environmental Scientist; 630 Iris Ave., Boulder, Colorado 80304, kskb@ix.netcom.com; Jens M. Turowski, Research

Scientist, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany, jens.turowski@gfz-potsdam.de and at Swiss Federal Research Institute WSL,

Mountain Hydrology and Mass Movements, Züricherstrasse 111, CH-8903 Birmensdorf, Switzerland; Steven R. Abt, Professor emer., Colorado State University, Engineering Research Center, 1320 Campus

Delivery, Fort Collins, Colorado 80523, sabt@engr.colostate.edu; Daniel A. Cenderelli, Fluvial Geomorphologist/Hydrologist, USDA Forest Service, National Stream and Aquatic Ecology Center, 2150

Centre Ave., Fort Collins, CO 80526, dcenderelli@fs.fed.us. Abstract Samples of coarse particulate organic matter (CPOM) were collected over one-month snowmelt highflow seasons in two high-elevation, subalpine, streams in the Rocky Mountains. Bedload traps developed for sampling gravel bedload were found to be suitable samplers for CPOM transport. When flow overtopped bedload traps, CPOM transport rates were adjusted to estimate and add the unsampled CPOM portion traveling higher in the water column; when nets overfilled, CPOM transport rate were adjusted for the reduction in captured transport. CPOM transport rates were well related to flow in consecutive samples but showed pronounced hysteresis over the diurnal fluctuations of flow, between consecutive days, and over the rising and falling limbs of the high-flow season. Hysteresis effects require intensive sampling and establishing separate rating curves for all rising and falling limbs in order to compute annual CPOM load. Annual CPOM export for the two streams was 2.7 and 4 kg/ha/year, but both streams exported 6.5 and 6.6 kg/ha/year in the forested portion of the watershed.

INTRODUCTION Coarse particulate organic matter (CPOM) is comprised of particles 1-100 mm, and those particles play an important role as a food source for benthic organisms (e.g., Fisher and Likens, 1973). Furthermore, fluvial transport of CPOM is one of the forms in which carbon is exported from a basin. CPOM typically contributes about 2.5-10% of total carbon export (Wallace et al., 1995, 1997; Richardson et al., 2005), and knowledge of CPOM export is needed when establishing watershed carbon budgets that account for input, consumption, retention, and export. Dry CPOM supplied to a stream (e.g., as litter fall) tends to be transported on the surface over relatively short distances (up to several 100 m, Jones and Smock, 1991; Webster et al., 1994; Wallace et al., 1995) before becoming trapped around obstacles where particles become waterlogged, sink, and accumulate on the stream bottom. Submerged CPOM experiences carbon leaching, microbial colonization (conditioning), shredding by invertebrates, and physical abrasion (Foucreau et al., 2013). In snowmelt regimes, when CPOM deposits are re-entrained at the beginning of high-flow season, fully waterlogged CPOM from the channel bed, together with some dry CPOM that had been stranded along the banks, is transported through the watershed and exported. Studies of annual CPOM export are rare from high elevation Rocky Mountain streams where large tracts of forested land are located in the US (Webster and Meyer 1997; Turowski and Hilton, 2013). This study makes a start towards filling this gap. Because CPOM is lightweight (compared to inorganic bedload) and easily entrainable by flow, one may postulate that transport rates of waterlogged CPOM are influenced by the interplay between (local) flow hydraulics and the dynamics of storage and release from local retention within the streambed and along the banks; hence transport is variable over time and with discharge. Several publications have suggested that CPOM transport rates respond very quickly to increased flow rates (Beschta, 1981; 1983a; Perry and Rose, 1984; Estep and Beschta, 1985; Wallace et al., 1995; Kiffney et al., 2000; Johnson et al., 2006; Beschta and Jackson, 2008; Turowski et al., 2013; Turowski and Hilton, 2013), whereas Angradi (1991) found no such relation. However, despite the relative importance of CPOM in the carbon budget and concerted efforts (Webster and Meyer, 1997; Richardson, 1992; Richardson et al., 2005, 2009), temporal dynamics of CPOM transport over the annual highflow season have not been adequately described. Difficulties in measuring the downstream conveyance of CPOM have posed the biggest problem to advancing in-sights into its transport dynamics. CPOM export from a basin can be quantified by excavating and analyzing the annually accumulated material in debris basins (Bilby, 1981), but that practice reveals no insights into transport dynamics. The collection of all mobile CPOM in a screened sampling box under an overfall and emptying the box episodically, biweekly or daily during high flows (Wallace et al., 1995; Johnson et al., 2006), is a workable practice

mailto:kbunte@engr.colostate.edumailto:kskb@ix.netcom.commailto:jens.turowski@gfz-potsdam.demailto:sabt@engr.colostate.edumailto:dcenderelli@fs.fed.us

reserved to small streams draining catchments of a few ha in size with discharges of a few liters/second. In larger streams, only portions of the CPOM transport can be sampled. Placing drift nets into the water column for minutes or hours at a time, episodically throughout the year (e.g., Newbern et al., 1981; Perry and Rose, 1984; Angradi, 1991; Jones and Smock, 1991; Kiffney et al., 2000) is limited to tranquil flows. Beschta (1981, 1983a) and Beschta and Jackson (2008) collected CPOM during stormflow events as by-catch in bedload samples in a 7.6 by 7.6 cm opening Hel-ley-Smith sampler with a 0.2 mm mesh-width net. However, those measurements are limited to the lower 7.6 cm of the water column and miss organic matter carried higher up. Turowski et al. (2013) collected CPOM transport in a high-energy mountain torrent, sampling the entire water column using bedload traps 0.2 m high (Bunte et al. 2007, 2008, 2010). They present the only transport relation known so far. In this study we: 1) demonstrate use of bedload traps for sampling CPOM transport in high-energy but wadeable streams during a high-flow season, 2) document CPOM transport and its variable relations to water flow (hysteresis) over a snowmelt high-flow season, and 3) provide estimates of annual CPOM export in two high elevation Rocky Mountain streams.

STUDY SITES Study sites CPOM samples were collected in conjunction with gravel bedload sampling in two cobble-bed streams in the Rocky Mountains. The studies were conducted in 2001 and 2002 (Bunte, 2001; Bunte and Swingle, 2003), about 10 years before the widespread onset of pine beetle kill in the central Rocky Mountains. Both study streams have snowmelt runoff regimes and daily fluctuations of flow. See Table 1 for site and basin characteristics.

Table 1: Study site and basin characteristics for the two study streams.

1) From Google Earth images; 2) individual trees or tree groups discernible;

Little Granite Creek East St. Louis Creek Study site locations Gros Ventre Range, 31 km SE of

Jackson Hole, WY Fraser Experimental Forest, 71

km NW of Denver, CO Site coordinates 43º19’17.40”N; 110º 30”41.77”E 39º53’12.97”N; 105º52’38.42”WMain flow direction: headwaters; main stem SW; SE NW; N Min. and max. basin elevation (m) 2030 to 3220 2,889 to 3890 Drainage basin area (km2) 13.09 8.03 1) % unforested 38 (above treeline) 38 (33 above tree line + 5 too dry)1, 2) % partially forested, open canopy 41 - 1) % fully forested, (almost) closed canopy 21 62 Stream gradient (m/m) at site, over reach 0.012, 0.018 0.03, 0.093 Stream width at study site (m) 5.0 3.5 Stream length incl. tributaries (km) 14.5 6.0 Reach stream morphology plane-bed; forced pool-riffle seq. step-pool; occ. forced pool-riffle Bed surface D16, D50, and D84 sizes (mm) 23, 67, 138 15, 108, 258 Bankfull flow (m3/s) 2.83 0.764 Main lithology sandstone, shale, limestn., granite granite and schist

Little Granite Creek The Little Granite Creek study site (Figure 1a) in NW Wyoming is located 3.3 km above the confluence with Granite Creek. Downstream of Pleistocene glaciated headwater areas, the valley is typically con-fined by forested hillslopes (Lodgepole Pine (Pinus contorta), Engelmann Spruce (Picea engelmannii), Douglas Fir (Pseudotsuga menziesii)), but opens to a 45-50 m wide meadow 135 m upstream from the sampling site. The stream is incised about 1.1 m into the valley fill, keeping most high flows confined to the channel. The sampling site was located within a 25-m long straight run. Flow stage elevation was digitally recorded in 15-minute increments. Flow velocity was measured almost daily at about 20 verticals across the channel with an electromagnetic current meter at 0.6 of the flow depth to compute discharge and establish a hydrograph. Bankfull flow (Qbkf) was estimated from field evidence and from the long-term flow record of a near-by stream adjusted by drainage area of the study stream. East St. Louis Creek East St. Louis Creek is a steep, cobble-bed mountain stream (Figure 1b) in the Fraser Expe-rimental Forest (http://www.fs.usda.gov/main/fraser/about/site-description) in central Colorado. The drainage basin is covered by glacial till, and near-stream valley slopes are typically gentle. Most of the basin is located in subalpine forest (Engleman spruce, Subalpine fir (Abies lasiocarpa), Lodgepole pine); there are a few quaking aspen (Populus tremuloides), alder (Alnus incana), and willows (Salix species). The streambed is typically incised by about 1 m

http://www.fs.usda.gov/main/fraser/about/site-description

which prevents extensive overbank flooding. The sampling site was located in a short and locally wide plane-bed section a few meter upstream of a weir pond where discharge is recorded continuously at a crested weir. Bankfull flow was taken as the 1.5-year recurrence interval flow of a long-term flow record.

b. a. a) b)

Figure 1: Upstream view of the Little Granite Creek (a) and East St. Louis Creek (b) study sites.

METHODS AND ANALYSES Sampling with bedload traps and the sampling scheme Bedload traps (Bunte et al., 2004, 2007, 2008, 2010) that were used for sampling CPOM transport together with gravel bedload in this study (Bunte, 2001; Bunte and Swingle, 2003) have the shape of a shoebox without a bottom placed on its long side. The aluminum frame is 0.2 m high, 0.3 m wide, and 0.1 m deep; a net 1 to 1.6 m long with a 3.6 mm mesh width is attached to the frame (Figure 2a). The frame is placed onto a 0.41 by 0.35 m aluminum ground plate that is installed flush with the average height of the bed and anchored to the stream bottom with two metal stakes that are pounded into the bed. Adjustable straps attached to the frame are tied around the metal stakes and hold the sampler in place. This set-up allows bedload traps to be deployed for sampling times ranging from minutes to hours depending on transport rates and fill level of the net. For emptying, the string that closes the net at its downstream end is untied, the contents are shaken into a large bucket, and the string is re-tied for another round of sampling. Samples can thus be taken back-to-back during a field day, while the sampler frames remain fastened on the ground plates (Figure 2b). Four traps were installed about 1 m apart across the stream at the two study sites. Sampling covered an 8-10 hour period between the daily falling limbs of flow in the late morning and the daily rising limbs up to or just past daily peak flow in the evening. Bedload traps were removed from the stream bed overnight to avoid overfilling them.

a) b)

Figure 2: Bedload traps installed at E. St. Louis Cr. (a); Retying the net after emptying the contents into buckets (b).

At Little Granite Cr., the 2002 snowmelt hydrograph had two distinct peaks, one on May 21 (85% Qbkf) and one on June 1 (110% Qbkf) (Figure 3a). CPOM sampling started at 21% Qbkf on the first rising limb with 1-hour samples and continued daily until the end of the first discharge peak two weeks later. Over the second discharge peak, samples were collected alternately over intervals of 10 and 60 minutes. Sampling extended over another two-week period until flow had decreased to 40% Qbkf on the second falling limb. Five to eight samples were collected each day, totaling 92 samples over the four-week high-flow season. At East St. Louis Cr., all CPOM samples were collected over 1-hour periods, starting at 44% of Qbkf a week into the high-flow season, covering peak flow that was 70% of Qbkf, and ending 17 days later at 36% of Qbkf (Fig. 3b). Two to seven samples were collected per day on most days, totaling 73 samples. Figure 3: Peakflow portion of the annual hydrograph (blue line) with measured CPOM transport rates (yellow dots), those extrapolated for times of daily peak and low flow (red dots), and the interpolated continuous record of CPOM transport rates (red line) at Little Granite Creek (left) and East St. Louis Creek (right). Note different scales of plots. CPOM composition Most of the CPOM collected in the bedload traps consisted of small cones from coniferous trees, cone scales, coniferous needles, small twigs and sticks (typically 1-3 cm in diameter, 10-30 cm long), leaves (mainly willow), small, spongy pieces of wood, and occasional hard pieces of bark. CPOM was dark brown in color and appeared as thoroughly soaked mulch from the forest floor. Dry needles were more frequent early in the high-flow season; at higher flows, sticks joined the CPOM mixture. Later in the high-flow season, rain, sleet, and hail washed, dislodged, and tore off dry needles and the green tips of coniferous trees that were included in the CPOM mix together with parts of fresh herbaceous plants. The CPOM composition in the study streams is similar to the CPOM the authors have collected during bedload measurements in several other Rocky Mountain streams. The density of waterlogged CPOM is slightly higher than that of water. When water flow was still clear during mo-derate high flows, the authors observed CPOM to travel as bedload, rolling, sliding, and bouncing along the bed sur-face, especially the plump conifer cones and soaked wood pieces. At larger flows, CPOM moved in larger hops and in near-bed suspension; small organic particles moved as suspended load within the water column, while dry sticks floated. As flow approached bankfull flow conditions, the water was turbid, and the channel bed was not visible. Field processing The combined sample volumes of wet CPOM and gravel bedload reached up to 20 liters. Early in the highflow season, sample volumes were comprised mostly of CPOM, but the CPOM portion decreased to less than half the volume later in the high-flow season. Samples collected near peak flow in the two study streams are shown in Figure 4. CPOM and gravel bedload were typically separated in the field to keep bagged sample volumes manageable and to minimize the number of bags with rotting CPOM to be dealt with later. Samples smaller than 1 liter were poured into a metal bowl and stirred with water to allow gravel bedload particles to sink; CPOM was then removed from the top by carefully decanting the wet organics into a bucket and helping the process along with a rubber spatula (Figure 5a). The action of stirring with water and decanting was repeated several times until all CPOM was removed. Large CPOM and gravel bedload samples were handled either in small batches as described above or pre-processed in a 20-liter bucket: after stirring with plenty of water and settling of the gravel particles, CPOM was removed from the top using a large wire-mesh spoon (Figure 5b). After repeating the process several

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Little Granite Creek,2002

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East St. Louis Creek,2001

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times, the remainder of the sample—bedload containing some CPOM—was separated in a bowl as described above. The volume of wet CPOM samples was determined in a clear 500 ml measuring cup or in a graded 10-liter bucket.

b)

a)

Figure 4: CPOM samples collected at high flow in the four traps at Little Granite (a) and East St. Louis Creek (b).

b)a)

Figure 5: Field separation of CPOM from inorganic bedload in small (a) and large samples (b). Computation of wet volumetric and dry mass CPOM transport rates Wet volumetric CPOM transport rates (QCPOM vol; l/hour) were computed individually for each stream width segment (w1 to w4) represented by one of the traps because trap spacing and sampling time differed slightly between traps. The cross-sectional wet volumetric CPOM transport rate is obtained from the sum over all width segments:

QCPOM vol = V1 · w1ws · ts1 +

V2 · w2ws · ts2 +

V3 · w3ws · ts3 +

V4 · w4ws · ts4 (1)

V is the CPOM volume per trap, ws is the trap width, and ts is the sampling duration. The summed stream sections equal the active stream width. A dozen CPOM samples from the two streams were dried to constant weight in a low temperature oven. The dry mass transport rate QCPOM (g/hr) is obtained from multiplying the wet volumetric trans-port rate by the mean dry weight of 112 g/l at E. St. Louis Cr. (std. dev. of 11.2 g) and 93.3 g/l at Little Granite Cr. Adjustment for unsampled CPOM passing over the traps Flow sometimes overtopped the bedload traps—that extend 0.2 m above the bed—leaving near-surface CPOM transport unsampled. At Little Granite Cr., the two central

traps became overtopped at 20% Qbkf and the two lateral ones at 30% Qbf. The maximum overflow height reached 0.22 m during the brief periods of peak flow. To estimate the unsampled CPOM portion, a vertical concentration profile of CPOM needs to be assumed. If the concentration of waterlogged CPOM particles (CCPOM [g/m3]) is highest near the stream bottom, and given a near logarithmic velocity profile with the fastest flow (v [m/s]) near the water surface, CPOM transport rates per stream section (QCPOM [g/m2s] = CCPOM v) are approximately even throughout the water column (Figure 6a). If, by contrast, CCPOM is roughly even vertically through the water column, QCPOM is highest near the water surface (Figure 6b). If large amounts of CPOM (e.g., many dry sticks) float near the water surface, while waterlogged CPOM transport is concentrated near the bottom, a much larger portion of CPOM transport is missed by not sampling the top of the water column (Figure 6c).

Flow

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th

CCPOM; v, QCPOM

QCPOM v CCPOM b)

Flow

dep

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QCPOM v CCPOM a)

Flow

dep

th

CCPOM; v, QCPOM

QCPOM v CCPOM c)

Figure 6: Interplay between CCPOM, v, and QCPOM. Downward increasing trend of CCPOM causes a vertically approx. even QCPOM profile (a). Vertically even CCPOM causes highest QCPOM near the stream surface (b). Downward increasing CCPOM together with high near-surface concentration also cause the highest QCPOM near the surface.

Based on visual field observation of where within the water column CPOM is transported, this study assumed a downward increasing trend for CCPOM with a vertically even distribution QCPOM (Figure 6a) in which case the unsampled portion of CPOM transport is directly proportional to the height of flow overtopping the traps htop. Overtopping heights were recorded at various flows Q for all traps, and htop was described as a logarithmic function of Q where x and m are regression coefficients. Htop = x (log Q) + m (2) Once Q started overtopping one of the traps, overtop-adjusted transport rates QCPOM adj were computed from measured transport rate QCPOM meas as

QCPOM adj =

htrap +

htophtrap · QCPOM meas (3)

where the trap height htrap which included the frame and the net thickness is 218 mm. During the brief peak flow periods, the overtop-adjusted cross-sectional QCPOM adj rates at Little Granite Creek were up to twice the sampled values. At East St. Louis Creek, flows did not exceed 70% Qbkf, and the sampling site was also considerably wider than the average stream width. Only one of the four traps became overtopped at 35% Qbkf, and the maximal htop was 6 cm at the highest measured flow. The overtopping adjustment increased cross-sectional CPOM transport rates by less than 6% for those flows. Adjustment of 1-hour samples at Little Granite Creek During the first rising limb in 2002, almost all samples at Little Granite Cr. were collected over 1-hour sampling times, but 1-hour and 10-minute samples were collected alternately during the first falling limb and the second rising and falling limbs. CPOM transport rates computed from 10-minute samples (QCPOM 10) were generally larger than those computed from 1-hour samples (QCPOM 60) at similar flow (Figure 6a and b), indicating that 1-hour samples had a reduced sampling efficiency. This would occur when the nets overfilled or clogged which reduced through flow and capture rates. Differences in capture rates between sampling times were negligible early in the high-flow season, but QCPOM 10 exceeded QCPOM 60 by a factor of up to two at the highest transport rates near peak flow (Figure 6a). In order to use all samples collected over the highflow season in the analyses of annual CPOM export, the reduced sampling efficiency of the 1-hour samples was account-ed for by adjusting 1-hour transport rates to those that might have been obtained by 10-minute sampling. The 1-hour and 10-minute CPOM samples were collected at alternate times, and because discharge changed quickly between samples, transport rates for the two sampling times cannot be directly related to each other. Instead, CPOM

transport rates for both sampling times were predicted for eight flow values evenly spaced between Qmin and Qmax from the second rising and falling limb CPOM rating curves (Figure 7a). To account for the inherent bias in predictions from a power function with data scatter, each predicted QCPOM 10 and QCPOM 60 was multiplied by a bias correction factor CF. From the several correction functions available (see discussion by Hirsch et al. 1993), the one proposed by Ferguson (1986, 1987) was selected for ease of computation (CF = exp(2.651 SEy2) where SEy is the standard error of the y-estimate.

Figure 7: Relations of overtop-adjusted CPOM transport rates from 1-hour and 10-minute sampling with discharge during the second rising and falling limbs of flow at Little Granite Creek, 2002 (a). Relation between 1-hour and

10-minute QCPOM predicted from CPOM transport relations in panel (a) (b). The relationship between predicted 1-hour and 10-minute QCPOM transport rates QCPOM 60 and QCPOM 10 was well de-fined with an r²=0.99 (Figure 7b). Assuming that this relation established from data collected during the second discharge peak likewise applied to the first discharge peak, the inverse of the regression function in Figure 7b (QCPOM 60adj = 0.666 QCPOM 10 1.155) was used to adjust all 1-hour CPOM transport rates >10 g/min for their reduced sampling efficiency; no adjustment was needed for small samples for which sampling time had no effect on samp-ling efficiency. After adjustments, there were 91 individual CPOM transport rates available over the high-flow season for computations of annual CPOM export at Little Granite Cr. Peak flow (70% Qbf) and maximum QCPOM (81 g/min) were much lower at East St. Louis Cr., so a reduction in sampling efficiency of 1-hour samples was un-likely, and all 73 collected 1-hour samples were used without time adjustment to compute CPOM transport relations. CPOM transport rates and relation to discharge The range of measured CPOM transport rates extended over more than two orders of magnitude in both study streams (Figure 8a and b). Due to pronounced hysteresis, QCPOM scattered over one order of magnitude in both streams for a given flow, and this resulted in a comparatively poor fit of the power function transport relationships QCPOM = a·Q b fitted to all CPOM data (r2 of 0.60 and 0.73) where a is the coefficient and b the exponent. By contrast, power function relations fitted to measured CPOM transport rates and flows for the individual rising and falling limbs were well defined in both study streams (r2 between 0.85 and 0.96; see transport equations in Figure 8a and b), but differ notably among each other and from the relation for all data. The pronounced differences of transport relations between rising and falling limbs of flow makes clear that separate CPOM rating curves needed to be used to compute CPOM load over the high-flow season. At East St. Louis Cr., the exponent b fitted to the relation QCPOM = a·Q b for all data is 4.5, while exponents for indi-vidual rising and falling rating curves range between 6.6 and 7.1. At Little Granite Cr., QCPOM increases less strong-ly with flow, yielding an exponent of 3.5 for all samples, while exponents for individual limbs of flow are within 3.0 and 4.1. The QCPOM transport relations reported here are of similar steepness as the relation established by Turowski et al. (2013) for the Swiss Erlenbach torrent with an exponent b = 4.42, likewise sampled with bedload traps. Computation of seasonal CPOM loads using a rating curve and a summation approach Both a rating curve and a summation approach were employed to compute seasonal and annual CPOM loads. For the rating curve

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10-min, d falling li2n mb:.78QCPOM = 7.36 Q^3

r²=0.96, n = 18, CF=1.014

10-min, 2nd rising limb:.19.077

Little Granite Creek,2002

QCPOM = 11.8 Q^4r²=0.84, n = 11, CF=1

1-hr, 2nd falling limb:Q^3.55

029QCPOM = 6.37 r²=0.88, n = 18, CF=1.

1- , 2nd risihr b:.4 ^3.03

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ng limQCPOM = 15 Qr²=0.94, n = 16, CF=1.07

10-min, 2nd rising: QCPOM = 10.8 Q 4.19 r2=0.84, n=14, CF=1.08

1-hr, 2nd rising: QCPOM = 15.4 Q 3.03 r2=0.94, n=16, CF=1.07 1-hr, 2nd falling:

QCPOM = 6.37 Q 3.55 r2=0.88, n=18, CF=1.03

10-min, 2nd falling: QCPOM = 7.36 Q 3.78 r2=0.96, n=18, CF=1.01

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QCPOM 60 = 1.47 QCPOM 10 0.86r2=0.99, n=16, CF=1.00

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Figure 8: Relations of CPOM transport rates vs. discharge for seasonal rising and falling limbs of flow at Little Granite Creek (a) and East St, Louis Creek (b). All p-values are < 0.0001. Note different plot scales for a) and b).

approach, each flow value of the 15-minute hydrograph was multiplied by the CPOM transport rate (g/min) predict-ed for that flow from the respective power function QCPOM = a·Q b rating curve (Figure 8a and b), multiplied by the Ferguson (1986, 1987) bias correction factor CF, multiplied by 15 to integrate over each 15-minute time increment, and summed over the rising and falling limbs of the high-flow season. Seasonal CPOM loads during the unmeas-ured flows prior to the high-flow season, the post high-flow falling limb, and late summer low flows were computed from a rating curve approach. Zero CPOM transport was assumed for both streams between fall and spring when flows are very low (5-10% of bankfull), and a snow cover shields most of the stream surface from CPOM input. Summation approach The sequence of CPOM fluxes computed from samples collected over the daily rising limbs of flow (and adjusted for sampler overtopping and overfilling where needed) generally followed the diurnal fluctuations of flow. The lowest and highest daily transport rates fell onto the daily minima and maxima of flow at both streams. This correspondence can be used to extend the observed daily trends to peak- and low-flow times at which CPOM was not measured and to estimate daily maximum and minimum QCPOM when needed. CPOM transport rates were then interpolated geometrically (due to power-function relations of QCPOM with Q) between daily low-flows and peak flows to obtain a value for each 15-minute increment. This created a continuous 15-minute estimate of CPOM transport rates over the high-flow season (Figure 3a and b). Multiplication of QCPOM by the time of each 15-minute increment and summing over the sampled time period of the high-flow season yielded the CPOM load for the sampled portion of the highflow season. A rating curve approach was used to compute CPOM loads for the unsampled pre- and post high-flow seasons, extending trends from the CPOM rating curves for the first rising limb and the second falling limb, respectively, to lower flows. At Little Granite Cr., no hydrograph was available for the 80-day remainder of the seasonal falling limb between July 12 and Sept. 30, and a constant value of QCPOM = 0.05 g/min was assumed. Simulating a hydrograph with bankfull flow at East St. Louis Creek Because CPOM transport rates increase with water discharge and hence with the highflow volume, a comparison of annual CPOM export between basins should be referenced to “normal” flow years, defined as years in which flows reach or slightly exceed the bankfull level for a few days. Flows at Little Granite Cr. peaked briefly at bankfull, approaching a normal year. For East St. Louis Cr. that had peaked at only 70% of bankfull flow, the study simulated a hydrograph that peaks at bankfull. To this end, measured peak flow was increased by 0.244 m3/s to reach a bankfull flow of 0.764 m3/s, and a smooth hydrograph was created by tapering off the mathematical flow addition towards the rising and falling limbs. CPOM transport rates were estimated by extrapolating the rating curves established for rising and falling limbs of flow in 2001 (Figure 8b). The predicted transport rates were then applied to each 15-minute time increment and summed over the new hydrograph. The stream’s CPOM supply was considered sufficient to permit extension of the measured East St. Louis Cr. CPOM rating curves to bankfull flow because measured CPOM transport rates during a small flow peak on the falling limb of the hydrograph at the end of the high-flow season were higher than those

1st rising: y= x^3.54·55.01.28

=1.02

= 25; CF=1.08

r² = 0.85, n = 16; CF=

1st falling: yr² = 0.96, n

= x^2.98·15.8= 14; CF

2nd rising: y= x^4.14·12.7r² = 0.91, n

2nd falling: y= x^3.82·6.87r² = 0.90, n = 36; CF=1.03

all data: y= x^3.48·16.2r² = 0.71, n = 91, CF=1.38all 1-hr samples adj. to

10-min sampling time0.1

1

10

100

00

CP

OM

tran

spor

t rat

es (g

/min

)

0.1 1 Discharge (m³/s)

East St. Louis Creek 2001

hflow:

.932

aCr²

start and end of higy = 8047 x^7.08r²=0.93, n=20, CF=1.09

2nd falling:y = 1160 x^6r²=0.88, n=29, CF=1.0

ll data:POM = 280 Q^4.54=0.60, n=74, CF=1.24

1st falling, 2nd rising:y = 1909 x^6.63r =0.92, n=25, CF= .0² 1 3

Qbkf

Start and end of the highflow season: QCPOM = 8047 Q 7.08 r2=0.93, n=20, CF=1.09 1st falling and 2nd rising: QCPOM = 1909 Q 6.63 r2=0.92, n=25, CF=1.03 2nd falling: QCPOM = 1160Q 6.93 r2=0.88, n=29, CF=1.02

East St. Louis C

1st rising 1st falling 2nd rising 2nd falling

r.

All data: QCPOM = 280 Q 4.54 r2=0.60, n=74, CF=1.24

Little Granite Cr. 1st rising: QCPOM = 55.0 Q 3.54 r2=0.85, n=16, CF=1.28

1st falling: QCPOM = 15.9 Q 2.98 r2=0.96, n=14, CF=1.02

2nd rising: QCPOM = 12.7 Q 4.14 r2=0.91, n=25, CF=1.08

2nd falling: QCPOM = 6.87Q 3.92 r2=0.90, n=36, CF=1.03

All data: QCPOM = 16.2Q 3.48 r2=0.71, n=91, CF=1.38

a) b)

predicted from the falling limb rating curves that were based on measurements early on the falling limb. This suggested sufficient CPOM supply to allow CPOM transport rates to increase with increasing flow up to bankfull flow.

RESULTS Annual CPOM export Annual CPOM export at Little Granite Cr. in 2002 computed from the summation approach was 3.6 metric tonnes (t) which represents a yield of 2.7 kg/ha/year. The vast majority (97%) of the annual export occurred during the 1-month high-flow season (Table 2), while only 0.4, 2.8, and 0.2% of the annual CPOM export occurred prior to high flow, after high flow, and later during summer low flows, respectively.

Table 2: CPOM transport rates and loads computed for hydrograph portions at Little Granite Cr. in 2002.

Time period Hydrograph portion Flow range (m3/s)

No. of days

Range of transport rates (g/min)

Seasonal load (t)

% of annual export

April 18 - May 12 prior to high flow 0.08-0.34 24 0.001 – 1.5 *1) 0.013 0.4 May 12 - June 14 high flow 0.25-3.26 33 0.8 – 1458 3) 3.45 96.6 June 14 - July 11 falling limb 0.29-1.17 27 0.06 – 13 *2) 0.101 2.8 July 12 - Sept 30 late summer low Q 0.15-0.29 est. 80 0.05 *2) 0.006 0.2 Oct 1 – April 17 base flow 0.09-0.15 est. 201 0 0 0

2002 annual export (t) entire year 365 3.57 100 Basin yield (kg/ha/yr) entire year 2.73

* estimated by extrapolation from rating curve for 1) first rising limb; 2) second falling limb; 3) based on intensive sampling that facilitated using a summation approach. Annual CPOM export at East St. Louis Cr. was 169 kg during the 2001 low-flow year, with a basin yield of 0.211 kg/ha/year—more than an order of magnitude less than exported from Little Granite Cr. Most of the CPOM export occurred within the high-flow period (82%), although notable amounts (11%) were still supplied during the falling limb (see 2001 in Table 3). Annual CPOM export at East St. Louis Cr. for a simulated bankfull hydrograph (see bf-year in Table 3) reached 3.2 metric tonnes, 19 times more than computed for the low-flow year. While this estimate might have some uncertainty because trends measured in the low-flow rating curves were extrapolated to higher flows (when perhaps supply exhaustion may set in), it shows the important role that runoff plays in annual CPOM export from a basin. Other studies have shown that differences in the CPOM export of more than an order of magnitude between years are not unusual (Bilby, 1981; Wallace et al., 1995). Table 3: CPOM transport rates, loads, and yields for various hydrograph parts at East St. Louis Cr. in 2001 and for

a year with a simulated a hydrograph reaching the bankfull level

Time period Hydrograph part

Days Flow range (m3/s)

Range of transport rates (g/min)

Loads and yields

% of annual export

2001 bf-year 2001 bf-year 2001 bf-yr. 2001 bf-yr.May 15 - June 1 rising limb 16 0.18-0.51 0.18-0.74 0.05-79 1) 0.05-1102 1) 0.0042 1.2 2.5 38 June 1 - June 17 highflow 12 0.34-0.53 0.53-0.76 1.6-99 2) 15.5-1363 2) 0.139 2.0 82 61

June 17 - July 15 falling limb 30 0.17-0.42 0.17-0.53 0.03-19 1) 0.01-15.5 1) 0.026 0.055 15 1.7 July 16 - Sept 30 late sumr. Q 77 0.044-0.19 ~0-0.08 3) ~0-0.08 3) 0.00013 0.05 0.004

Oct 1 - May 14 base flow 230 0.028-0.044 0 0 0 ‘01 ann. export (t) entire year 365 0.169 3.24 100 Yield (kg/ha/yr) entire year 0.211 4.04 1) Based on rating curve for 1st rising limb; 2) based on summation approach and intensive sampling; 3) based on rating curve for all data. Comparison of CPOM export and yield CPOM export was slightly higher at Little Granite Cr. (3.6 t) compared that at East St. Louis Cr. (3.2 t) for the simulated bankfull hydrograph. Per basin area, though, East St. Louis Cr. yielded more CPOM (4.0 t) than Little Granite Cr. (2.7 t). For the forested portion of the basin area, both streams had similar CPOM exports of 6.6 and 6.5 kg/ha/year, a low estimate given that flows just briefly reached bankfull.

While little information exists for coniferous forests (Richardson et al. 2005), mean annual CPOM exports of about 0.4 to 10 kg/ha are reported for deciduous watersheds (Table 4). Per forested watershed area, CPOM export of 6.5 and 6.6 kg/ha/year from the two subalpine Rocky Mountain watersheds is comparable to export from deciduous forests such as in the Atlantic NE Hubbard Brook (e.g., Bilby 1981) and the Brazilian Mato Grosso (Johnson et al., 2006).

Table 4: Comparison of CPOM export reported for various streams with forested watersheds.

Location Coweeta, NC watersheds

C53, C54, C55

Hubbard Brook, NH watersheds W5, W6

Little Granite Cr.,

WY

East St. Louis 1),

CO

Juruena, Mato Grosso, Brazil

Reference Wallace et al. (1995)

Bilby & Likens (1980); Bilby (1981)

This study

Johnson et al. (2006)

Basin area (ha) 5.2 - 55 13 - 22 1309 803 1 - 2 Main forest type deciduous deciduous coniferous deciduous CPOM export (kg/ha/yr) 0.14 - 1.2 7.1 - 15 2) 2.7 4.0 3.7 3) CPOM export per forested watershed area (kg/ha/yr)

0.4 est.4) 10 est. 4) 6.6 5)

6.5 5)

3.7 est. 4)

1) est. for a year with bankfull flow; 2) based on 11 and 12 years prior to removal of debris dams; 3) based on a reported 3.5% portion of CPOC on total carbon export; 4) assuming basin areas are fully forested; 5) 21% basin area are fully forested; of the partially forested area (41%), half is counted to the fully forested area (=21% + 20.5% = 41.5% forested); 38% is above treeline or otherwise unforested; 5) 62% basin area are fully forested; 33% above treeline and 5% otherwise sparely forested.

DISCUSSION AND SUMMARY Bedload traps with a 4-mm mesh were found suitable for sampling the CPOM-gravel bedload mix. Traps that ex-tend through the entire water column over all flows would be desirable for a more complete capture of CPOM trans-port. However, during high flows, taller traps may not be able to withstand the greater force of flow near the top of the water column, possibly causing the traps to bend backwards and become dislocated from the ground plates. To estimate CPOM transport passing above the bedload traps, measuring profiles of the vertical CPOM concentration at increments through the water column at various flows might be useful. Care should be taken not to overfill the nets which may require a reduction in sampling times when loads are high. CPOM transport rates presented here may constitute a conservative estimate because the 4-mm mesh might not representatively sample the 1-4 mm portion of CPOM. However, based on the large amounts of captured conifer needles and needle fragments, elongated particles probably roll over the bed or tumble in the flow rather than go straight like an arrow that might pass straight through the mesh. Sampler nets with 1-mm mesh were also found to be less suitable for collecting CPOM because CPOM easily clogs those nets, causing a reduction in hydraulic and sampling efficiency (Bunte et al. 2015, session 5C). Since gravel bedload and CPOM are jointly transported and jointly captured in the sampler, it makes sense to combine field projects of bedload and CPOM sampling. CPOM transport rates showed seasonal hysteresis that was more pronounced than what has been measured for gravel bedload transport at the study streams. The extremely supply driven (and limited) transport dynamics cause CPOM transport relations to differ drastically between individual rising and falling limbs of the high-flow hydro-graph, as well as between the pre-and post runoff seasons (Bilby and Likens, 1980; Wallace et al., 1995). At Little Granite Cr., the season’s first rising limb QCPOM was 4-5 times higher than QCPOM on the first falling limb, 3-4 times higher than on the second rising limb and about 6 times higher than on the second falling limb (Figure 8a). At East St. Louis Cr., QCPOM during the first rising limb was about 3 times higher than on the shared transport relation for the first falling and second rising limb (Figure 8b) and also about 6 times higher than on the second falling limb. During small rainfall peaks towards the end of the highflow season at East St. Louis Cr., QCPOM approached the relationship of the first rising limb, suggesting that CPOM was still available for transport on the falling limb in this in low-flow year. The quick response of QCPOM to increasing flow in both streams indicates that CPOM is easily entrained and trans-ported, i.e., transport is hydraulically controlled. However, the hysteresis relations, and transport rates that decrease over time, show that the response of CPOM flux to flow is also governed by the availability of CPOM supply which changes over the course of the high-flow season.

As QCPOM is strongly related to flow, 95% of the annual CPOM export was concentrated in the central one-month period of the snowmelt high-flow season in both study streams. Strong hysteresis effects during that period require that CPOM transport be intensively sampled and that separate CPOM rating curves are established for each portion of the typically multi-peaked snowmelt hydrograph. For example, had CPOM at Little Granite Cr. been sampled over only a few consecutive days (i.e., been limited to one of the rising or falling limbs of flow), and only one rating curve (together with bias correction factor CF) been used to compute the highflow CPOM load, computed loads might have varied between 1.7 tonnes (if field sampling was limited to the second falling limb) and 9.7 tonnes (if field sampling was limited to the 1st rising limb) depending on whether the field visit occurred early or late in the high-flow season. By contrast, using the appropriate rating curve for each limb resulted in a CPOM load of 3.5 tonnes which is very close to the annual load of 3.6 tonnes computed from the summation approach. CPOM yields in the two high-elevation Rocky Mountain study streams with subalpine coniferous forests was 2.7 and 4.0 kg/ha/yr for bankfull flow events, but both streams yielded similar amounts (6.6 and 6.5 kg/ha/yr) per forested portion of the basin area. Those Rocky Mountain CPOM yields are similar to export reported for North Eastern deciduous forests (7-15 kg/ha/yr), and the Brazilian Mato Grosso. Due to high inter-annual variability, CPOM export is ideally based on the average of long-term observations. Failing that, it might be advisable to compute CPOM for normal flow years in which bankfull flow is reached or slightly exceeded for a few days. Acknowledgement We thank Sandra Ryan and Mark Dixon for the 2002 flow data from Little Granite Creek and L. Porth, K. Elder, and B. Starr (all USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO) for the 2001 East St. Louis Creek flow data. Field work and part of the data analysis was funded by the USDA Forest Service Stream Systems Technology Center, Fort Collins, CO. Further analyses and part of the manuscript were prepared while K.B. received support from the Swiss Federal Research Institute (WSL) in Birmensdorf, CH. Sandra Ryan and Ellen Wohl (CSU) provided friendly reviews of the manuscript. We thank John Potyondy (USFS ret.) for vision, insight, and long-term support of our field studies.

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DISCUSSION AND SUMMARYREFERENCES