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Keywords:

  • suspended;
  • sediment;
  • carbonate;
  • river;
  • Yukon;
  • load;
  • yield;
  • climate change

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Loads and yields of suspended sediment and carbonate were measured and modeled at three locations on the Yukon, Tanana, and Porcupine Rivers in Alaska during water years 2001–2005 (1 October 2000 to 30 September 2005). Annual export of suspended sediment and carbonate upstream from the Yukon Delta averaged 68 Mt a−1 and 387 Gg a−1, respectively, with 50% of the suspended sediment load originating in the Tanana River Basin and 88% of the carbonate load originating in the White River Basin. About half the annual suspended sediment export occurred during spring, and half occurred during summer-autumn, with very little export in winter. On average, a minimum of 11 Mt a−1 of suspended sediment is deposited in floodplains between Eagle, Alaska, and Pilot Station, Alaska, on an annual basis, mostly in the Yukon Flats. There is about a 27% loss in the carbonate load between Eagle and Yukon River near Stevens Village, with an additional loss of about 29% between Stevens Village and Pilot Station, owing to a combination of deposition and dissolution. Comparison of current and historical suspended sediment loads for Tanana River suggests a possible link between suspended sediment yield and the Pacific decadal oscillation.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] The hydrology of arctic and subarctic regions is undergoing dramatic change as a result of climate warming [Hinzman et al., 2005; Serreze et al., 2000]. Many northern glaciers are receding [Kaser et al., 2006; Arendt et al., 2002], the magnitude and timing of water discharge (Q) is changing [Peterson et al., 2002; McClelland et al., 2006], and precipitation patterns have been altered [Arctic Climate Impact Assessment (ACIA), 2005]. The Yukon River is the largest unregulated river in the U.S., with large areas of continuous and discontinuous permafrost throughout the basin [Brabets et al., 2000]. Permafrost warming, thermokarst formation, and air temperature increases in northern Alaska are well documented [Hinzman et al., 2005]. Q-carbon relations in the Yukon Basin have changed [Striegl et al., 2005, 2007], as may have Q-nutrient relations [Dornblaser and Striegl, 2007]. The Yukon River is well suited to study potential hydrologic responses to climate change, and may provide a basis for predicting responses in other major Arctic rivers.

[3] Suspended sediment discharge is sensitive to climate change [Gordeev et al., 1996]. As conveyors of organic carbon [Striegl et al., 2007], nutrients [Dornblaser and Striegl, 2007], metals and contaminants [Brabets et al., 2000], and carbonates [Eberl, 2004] between the terrestrial and aquatic environments, suspended sediments are critical to understanding land/ocean linkages in the Arctic. Suspended sediments can affect aquatic life in rivers and coastal estuaries by clogging fish gills, burying spawning sites, or by altering benthic habitats [U.S. Environmental Protection Agency, 1977]. Of particular interest is the degree to which suspended nutrients and contaminants reach the open ocean, or are sequestered in floodplains and deltas, or man-made impoundments such as reservoirs. Carbonates play a major role in the global carbon cycle, with about 13% of the total C exported from continents to oceans originating from carbonate minerals [Ludwig et al., 1998; Cole et al., 2007], and can strongly influence solute concentrations in rivers [Frey et al., 2007]. Carbonate dynamics can also influence whether an aquatic system is a source or sink of CO2 to the atmosphere [Semiletov et al., 2007; Liu et al., 2008].

[4] Suspended sediments result from natural erosional processes such as glacial scouring, riverbank erosion, floodplain resuspension, and fires, as well as man-made disturbances such as mining, logging, dams, agricultural runoff, and urbanization [Meade et al., 1990]. In the Yukon River (YR) Basin, most suspended sediments result from natural processes. As many arctic rivers have been dammed [Holmes et al., 2002], it is important to address suspended sediments in relatively undisturbed rivers such as the Yukon when assessing potential climate change impacts in northern watersheds.

[5] Most suspended sediment (SS) data sets for Arctic rivers are limited in their scope and duration, particularly when compared to records of Q [Holmes et al., 2002]. The results reported here focus on the SS and carbonate aspects of a large U.S. Geological Survey water-quality study conducted during water years (WY) 2001–2005 on the YR and two of its major tributaries, the Porcupine and Tanana Rivers. This study was initiated to compile a comprehensive water-quality database for the Yukon River and to investigate potential hydrologic responses to climate change. New data presented from this study supplement earlier SS records compiled by Brabets et al. [2000], and improve the understanding of SS flux with respect to water discharge and of the carbonate content of transported sediment. This paper quantifies current SS and carbonate loads and yields, explores possible trends from the recent past, describes sediment processes and provenance on a seasonal basis, compares new information with earlier estimates of sediment transport in the Yukon River Basin, and discusses potential future responses in SS fluxes owing to climate change.

2. Study Area and Hydrology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[6] The Yukon River flows approximately 3340 km from northwestern Canada through Alaska, USA, draining approximately 853,300 km2 (Figure 1). It is critically important to the Bering Sea, contributing most of its freshwater runoff, dissolved solutes, and sediment load [Lisitsysn, 1969]. The Yukon River Basin varies greatly in topography, climate, geology, permafrost, land cover, and water quality [Brabets et al., 2000]. Mountainous, glaciated terrain occupies small but important SS-generating areas in the headwaters of British Columbia and Yukon Territory, the St. Elias Range in the White River Basin, and the Wrangell and Alaska Ranges in the Tanana River (TR) Basin. The YR normally flows clear until it reaches the White River, from which it receives large amounts of sediment and carbonate. Downstream from the White River, the sediment-laden YR has a secchi depth of about 2 cm.

image

Figure 1. Map of the Yukon River Basin showing U.S. Geological Survey stream gauging and measurement station locations and watershed boundaries. YRP, Yukon River at Pilot Station; YRS, Yukon River near Stevens Village; TR, Tanana River at Nenana; PR, Porcupine River near Fort Yukon; and YRE, Yukon River at Eagle.

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[7] Yukon River at Eagle (YRE) includes flow from all headwater areas in Canada. Yukon River near Stevens Village (YRS), approximately 700 km down river from Eagle, is just downstream from the 34,000 km2 Yukon Flats, an area of extensive bogs and wetlands. Yukon River at Pilot Station (YRP) is the furthest downstream location where the YR can be effectively gauged and sampled before it enters the Yukon Delta. The Porcupine River (PR) is a large tributary draining permafrost-dominated wetlands. The glacier-fed Tanana River has its headwaters in the Alaska Range, and its watershed includes the city of Fairbanks.

[8] Although increases in air temperature and permafrost warming are evident in northern latitudes [Chapman and Walsh, 1993; Clein et al., 2007], precipitation changes are not constant on the pan-Arctic scale. While precipitation has increased in Eurasian watersheds [Peterson et al., 2002], it has decreased in northern Canada [Dery and Wood, 2005]. For the Yukon Basin, recent analysis suggests a decrease in precipitation from 1980 to 2000 [Clein et al., 2007].

[9] The Yukon River hydrograph is characterized by peak Q in late May/early June owing to snowmelt. Often a smaller secondary peak in Q is evident in mid to late August resulting from melting of perennial snowpack and alpine glaciers and/or rain events. During 2001–2005, average annual Q at YR-Pilot was 211 km3 a−1 (Table 1). While Eurasian rivers have shown an upward trend in Q from 1936 to 1999 [Peterson et al., 2002, 2006], there was no significant change in Q for the YR for >30 years prior to 2000 [Walvoord and Striegl, 2007; McClelland et al., 2006].

Table 1. Station Name, Abbreviation, Location, and Annual and Mean Water Discharge for Water Years 2001–2005
StationAbbreviationU.S. Geological Survey StationNorth Longitude, West LatitudeDrainage Area (km2)Site Elevation (m)Q (km3 a−1)2001–2005 Mean Annual Q (km3 a−1)
20012002200320042005
Yukon River at EagleYRE1535600064°47′22″, 141°47′22″294,00025991.174.565.47179.176
Porcupine River near Fort YukonPR1538900066°59′26″, 143°08′16″76,40015810.812.111.19.39.5211
Yukon River near Stevens VillageYRS1545350065°52′32″, 149°43′04″508,4007312310898.996.6108107
Tanana River at NenanaTR1551550064°33′55″, 149°05′30″66,30010323.323.822.424.124.724
Yukon River at Pilot stationYRP1556544761°56′04″, 162°52′50″831,4006223188225190227211

3. Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

3.1. Data Collection and Sample Analyses

[10] From October 2000 through September 2005 (WY 2001–2005), the USGS measured Q, SS and carbonate concentrations, and other water-quality constituents at five stream gauging stations in the YR Basin in Alaska (Figure 1; see also Table 1). A water year is defined as the twelve month period from 1 October through 30 September, and designated by the calendar year in which the period ends. Station descriptions, Q, water chemistry, and suspended sediment concentration data are archived in the USGS National Water Information System (NWIS), and are available from the NWIS web interface http://waterdata.usgs.gov/nwis). Flow characteristics were also summarized by Striegl et al. [2007].

[11] Suspended sediment samples were collected at each station six to eight times per year using the USGS Equal Discharge Increment (EDI) sampling protocol [Edwards and Glysson, 1999] (see http://pubs.usgs.gov/twri/twri3-c2/). One EDI sample was collected each year under ice to characterize late winter base flow conditions, while the remainder of the samples were collected approximately every 3 weeks during the ice-free season from May through September. Samples were processed according to established USGS protocols [Guy, 1969] (see http://pubs.usgs.gov/twri/twri5c1/). SS concentrations were determined at the USGS Cascades Volcano Observatory in Vancouver, Washington using ASTM methods [ASTM International, 2002]. Quantitative mineralogy of the SS was determined by X-ray diffraction (XRD) as described by Eberl [2004], with analyses performed at the USGS laboratory in Boulder, Colorado. The uncertainty in the determination of mineralogy by XRD is 6% (relative error) [Eberl, 2003].

3.2. Loads and Yields

[12] Daily SS loads (kg d−1) were calculated from daily Q and periodic SS concentrations using the FORTRAN Load Estimator (LOADEST) program [Runkel et al., 2004]. For YR-Pilot, the daily Q record did not begin until April 2001. October 2000 to April 2001 Qs were based on long-term average Qs (1976–1996) in order to run the LOADEST program for the complete water years 2001–2005. Given that winter Q at YR-Pilot is small compared to annual Q and that every year's winter flows are estimated (NWIS; see http://waterdata.usgs.gov/nwis), the estimation of WY 2001 winter Q does not substantially affect the results. LOADEST requires at least 12 direct nonzero measurements of flow and concentration over a wide range of Q in order to calculate loads by applying the statistical method of Adjusted Maximum Likelihood Estimation (AMLE). Approximately 30 measurements of SS concentration at each site collected between October 2001 and September 2005 (NWIS; see http://waterdata.usgs.gov/nwis) were used to calculate loads. Given that all of the SS concentrations were uncensored (above detection), the AMLE method converges to MLE (Maximum Likelihood Estimation). For Tanana, for the periods 1966–1970 and 1983–1987, 15 and 23 measurements, respectively, were used to calculate loads. Historical SS data were too sparse to make LOADEST calculations for the other stations studied.

[13] LOADEST centers the Q and concentration data to eliminate colinearity. LOADEST automatically selected one of nine predefined regression models to fit the data on the basis of the Akaike Information Criterion. Model output presents the standard error (SE) and the standard error of prediction (SEP) of loads calculated for the modeled flow period. In addition, R2 of the AMLE, residuals data, and the serial correlation of residuals are output to verify the validity of the model and to permit confirmation that the residuals are normally distributed. Ideally, half of the samples used to calibrate the model should be taken near peak flow and half at the lower end of the hydrograph. While sampling near peak flow was not always possible owing to the uncertainty of the timing in peak flow and logistical constraints, sediment sampling was conducted at between 85 and 98% of peak flow for all stations except Porcupine River.

[14] The coefficient of variation (CV), calculated as the SEP/Mean Load, shows that the SS data were modeled well using LOADEST for every station except Porcupine (Table 2). Owing to the lack of data for periods of high flow, LOADEST was unable to accurately model peak SS discharge in May 2004 for Porcupine. The modeled load would have required a SS concentration of 5700 mg/L for the 5664 m3/s discharge recorded on 21 May. The greatest SS concentration ever measured by the USGS at Porcupine was 882 mg/L on 3 June 1975, at 4673 m3/s (NWIS; see http://waterdata.usgs.gov/nwis). Using the limited data set, LOADEST thus greatly overestimated the SS load during the May 2004 peak. Therefore, a transport curve using the greatest measured SS concentrations from the period of record was used to estimate the peak SS concentration, and then SS load, for May 2004. This allowed us to greatly improve confidence in the SS load estimate for Porcupine.

Table 2. Mean Seasonal Suspended Sediment and Carbonate Concentrations, Loads, and Yields, 2001–2005a
StationSuspended Sediment Concentration (mg L−1)Suspended Sediment Load (Mt)CVSuspended Sediment Yield (gm−2)Carbonate Content of Suspended Sediment (%)Carbone Load as C (Mt)Carbonate Yield as C (g m−2)Runoff (mm)
  • a

    Concentrations ± standard deviation; values in parentheses indicate number of samples; NA, not measured; CV, coefficient of variation of modeled load. Estimated suspended sediment loads are modeled using LOADEST. Loads are in million metric tons (Mt). Summer-autumn 2005 uses estimated flow for 1–31 October 2005.

  • b

    Includes one value <1 mg L−1 that was set to 0.5 mg L−1 to calculate average.

Yukon River at Eagle (YRE)Spring601 ± 111 (12)19 649.2 ± 3.1 (9)0.220.886
 Summer-Autumn608 ± 107 (17)22 7414.5 ± 1.9 (13)0.411.4128
 Winter1.8 ± 0.3 (4)0.07 0.2NA  42
 Annual 4115.5%140 0.642.2256
Porcupine River near Fort Yukon (PR)Spring106 ± 18 (12)1.9 24NANANA71
 Summer-Autumn23 ± 5 (13)0.18 2.4NANANA59
 Winter(1.1 ± 0.3)b (4)0.001 0.02NANANA9
 Annual 2.058.8%27NANANA139
Yukon River near Stevens Village (YRS)Spring491 ± 67 (12)20 397.0 ± 1.3 (11)0.180.481
 Summer-Autumn385 ± 36 (17)17 3312.9 ± 2.1 (15)0.280.697
 Winter9 ± 2 (5)0.29 0.6NA  30
 Annual 377.8%73 0.461.0208
Tanana River at Nenana (TR)Spring1228 ± 240 (13)8.8 1302.2 ± 2.0 (14)0.030.493
 Summer-Autumn1872 ± 242 (16)24 3601.8 ± 0.5 (15)0.060.9206
 Winter19 ± 3 (5)0.25 3.8NA  57
 Annual 336.8%500 0.081.2356
Yukon River at Pilot Station (YRP)Spring431 ± 59 (10)33 392.0 ± 0.3 (10)0.080.193
 Summer-Autumn387 ± 42 (18)35 426.6 ± 2.7 (14)0.300.4121
 Winter4 ± 0 (5)0.46 0.5NA  37
 Annual 687.3%82 0.390.5251

[15] Carbonate loads could not be modeled in LOADEST owing to the lack of under-ice samples with which to ground the model during low-flow periods. Low SS concentrations in winter made it impractical to capture enough sediment to perform mineralogical analyses. Therefore, average seasonal carbonate fractions were multiplied by seasonal SS loads to calculate estimated carbonate loads. Carbonate is defined as the sum of calcium carbonate, magnesium carbonate, and dolomite. While the relative contributions of these three minerals to total carbonate change with site and season, all three have a similar carbon fraction, ranging from 12 to 14% C in carbonate. Therefore, to express carbonate in units of carbon (C), SS loads and yields were multiplied by the average percent C in the suspended carbonates (13%). Tables of carbonate mineralogical data can be found in the work of Schuster [2003, 2005a, 2005b, 2006, 2007].

[16] Yields were calculated by dividing total Q (m3 t−1) or sediment or carbonate load (mass t−1) for a flow period by watershed area (m2). Water yield (runoff) is presented as mm or cm water t−1 uniformly distributed over each basin, and constituent yield is presented as g m−2 t−1.

[17] Seasonal concentrations, loads, and yields of SS and carbonate were calculated by delineating seasons on the basis of hydrology and water chemistry [Striegl et al., 2005, 2007; Dornblaser and Striegl, 2007]. Spring included 1 May to 30 June; summer-autumn, 1 July to 31 October; and winter, 1 November to April 30.

4. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

4.1. Suspended Sediment Concentrations, Loads, and Yields

[18] SS concentrations were greatest in the glacier-influenced Tanana Basin (range = 13–4000 mg/L), and least in the wetland-dominated Porcupine Basin (range = 1–218 mg/L). Seasonally, mean concentrations were greater in spring than summer-autumn for Porcupine, YR-Stevens, and YR-Pilot, owing to spring flushing (Table 2). For YR-Eagle and Tanana, summer-autumn had the slightly greater concentrations, likely owing to mid to late summer melting of perennial snowfields and glaciers in headwater areas. Winter base flow SS concentrations were low at all stations. Mean concentrations of SS in the YR tended to decrease going down river, although YR-Pilot had a mean concentration that was similar to YR-Stevens in summer-autumn owing to the influx of the sediment-laden Tanana, which had mean SS concentrations 2–3 times greater than any of the Yukon main stem stations.

[19] On a mean annual basis, the Tanana contributed nearly 50% of the SS load at YR-Pilot (Table 2), while the Porcupine contributed about 3%. Similar to concentration trends, the loads at Porcupine and YR-Stevens were greatest in the spring, while loads at YR-Eagle and Tanana were greatest in summer-autumn. These trends are illustrated in Figure 2, the LOADEST-modeled daily SS discharge for the five stations. Melting of glaciers and perennial snowpack in the White River Basin above YR-Eagle and in the headwaters of the Tanana caused greater loads later in the season, while in the Porcupine, very little SS was evident once the spring flush was over. At YR-Pilot, summer-autumn SS loads were slightly greater than in spring. While spring loads were slightly greater at YR-Stevens than YR-Eagle, summer-autumn loads were substantially less. Winter loads were generally more than an order of magnitude less than either spring or summer-autumn loads.

image

Figure 2. Average LOADEST-modeled seasonal and annual suspended sediment loads for the five USGS stations, 2001–2005.

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[20] SS yields were by far the greatest in the Tanana (Table 2), with an annual yield 3–18 times greater than any other subbasin. Spring yields were 2–5 times as great, and summer-autumn yields were 5–150 times as great. There was a decrease in spring and summer-autumn yield between YR-Eagle and YR-Stevens in part owing to the inflow of the low-yield Porcupine. Spring yield was virtually unchanged between YR-Stevens and YR-Pilot, with summer-autumn yield increasing slightly, owing to the influence of the high-yield Tanana.

[21] Five years of modeled seasonal average SS yields are plotted against seasonal water yield in Figure 3. Across all subbasins, summer-autumn SS yields increased with increasing water yield (Figure 3a). This does not appear to be the case with spring, as SS yields were variable across a narrow range in water yield. However, if the individual basins are split out from Figure 3a, all subbasins except the Porcupine showed a strong correlation between SS yield and water yield for spring (Figure 3b). The steepest relation between SS yield and water yield occurred in Tanana and YR-Eagle, the basins with the most glacial cover. Summer-autumn SS yields were more independent of water yield than spring SS yields for all sites except Tanana (Figure 3c), which is not surprising, given the mid to late summer glacial melting that occurs in headwater areas. Winter yields were independent of water yield (Figure 3d).

image

Figure 3. Suspended sediment yield versus water yield, 2001–2005, for the five measurement stations: (a) 5-year seasonal means, at each of the five stations; (b) spring yields; (c) summer-autumn yields; and (d) winter yields.

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4.2. Carbonate Fractions, Loads, and Yields

[22] Seasonal percent carbonate is shown in Table 2. It was not possible to determine mineralogy on SS for any season at Porcupine or for any winter sample at the other sites owing to insufficient SS concentrations. Percent carbonate was greatest at YR-Eagle, reflecting inputs from the White River, the main source of carbonate for the entire Yukon River [Eberl, 2004; Brabets et al., 2000]. Glacial discharge in the headwaters of the White increased in mid to late summer, resulting in greater summer-autumn carbonate concentrations. Carbonate percentages decreased with downstream distance, most dramatically between YR-Stevens and YR-Pilot due at least in part to dilution by the low-carbonate Tanana.

[23] Carbonate loads, the product of carbonate fractions and SS loads, also decreased with downstream distance both annually and for the spring season. During summer-autumn, loads at YR-Pilot were slightly higher than at YR-Stevens; while percent carbonate was about half that of YR-Stevens, this was offset by SS loads that were more than double those of YR-Stevens. Winter loads could not be determined, as there were no carbonate percentages for that period. However, if one assumes a winter average carbonate content of 5% for YR-Eagle (the lowest-concentration recorded prior to the peak Q), the winter load would be three orders of magnitude lower than the sum of the spring and summer-autumn loads, and is therefore considered inconsequential in terms of annual load.

[24] Carbonate yields decreased with downstream distance both annually and seasonally (Table 2). Between YR-Eagle and YR-Stevens, and between YR-Stevens and YR-Pilot, yields dropped by about 50%. Tanana, while having the lowest carbonate concentration of any site, was second in yield only to YR-Eagle owing to its high SS concentrations.

5. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

5.1. Sources and Fate of Suspended Sediments and Carbonates

[25] Sediment concentrations and loads for the YR are strongly seasonal and highly variable (Table 2; see also Figure 2), as with most Arctic/subarctic rivers [Holmes et al., 2002]. Annual estimates of SS load for YR-Pilot from 1976 to 96 varied by a factor of 4 [Holmes et al., 2002], while those for YR-Pilot in the USGS 2001–2005 study varied by a factor of 2. Porcupine had the greatest variability in annual SS loads (a factor of >3) while Tanana had the least variability (a factor of <1.5).

[26] Despite the large variability in annual sediment loads, the 68 ± 19 (SD) Mt a−1 average annual load for 2001–2005 at YR-Pilot agrees well with the 54 Mt a−1 load reported by Brabets et al. [2000] and the 60 Mt a−1 load reported by Holmes et al. [2002] for 1976–1996 using sediment transport curves and USGS sediment data. Interestingly, the BQART model developed by Syvitski and Milliman [2007], applied using the YR-Pilot 2001–2005 average Q, watershed area, relief, average basin temperature, glacial coverage, and lithology, predicts a SS load for YR-Pilot of 68 Mt a−1, in exact agreement with the value estimated by LOADEST. SS loads and yields for YR are much greater than for the major Siberian rivers, owing to the alpine glaciers in the YR Basin that discharge large annual sediment loads, and to dams on other rivers such as the Yenisey that trap sediments [Holmes et al., 2002]. The Mackenzie River carries twice the SS load of the YR [Holmes et al., 2002], but has twice the watershed area, resulting in equivalent yields.

[27] Seasonally, most of the sediment load was transported during the open-water season (Table 2; see also Figure 2). For Porcupine, 91% of the annual load occurred during spring flush, while for Tanana, 73% occurred during summer-autumn. For the Yukon main stem sites, the relative loads for spring versus summer-autumn were closer to 50/50. The relative seasonal contributions to Q were somewhat different. For Porcupine, 51% of annual Q occurred in spring, while at Tanana, 58% of annual Q occurred in summer-autumn. For the main stem sites, 47–50% of annual Q occurred in summer-autumn, with 34–39% in spring.

[28] Sediment transport curves can aid in the understanding of SS/Q relations. While typical curves take on a simple power function, deviations can occur which illustrate seasonal differences, or hysteretic relations owing to depletion effects [Meade et al., 1990]. Although transport curve characteristics vary spatially and temporally, the curve for YR-Pilot in 2002 (Figure 4a) is not atypical of the YR main stem sites. From winter base flow conditions, SS concentrations rise with the spring flush, then recede until another pulse of sediment is evident in midsummer. The transport curves for Porcupine and Tanana are less complicated (Figures 4b and 4c).

image

Figure 4. Sediment transport curves. (a) Yukon River at Pilot Station (YRP), 2002. (b) Porcupine River near Fort Yukon (PR), 2001–2005. (c) Tanana River at Nenana (TR), 2001–2005. Note the different instantaneous discharge scale for Figure 4a.

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[29] The seasonality evident in SS concentrations, loads, and transport curves all point to the different sources of sediment in the YR Basin. These sources are important to consider, as source controls the geochemical properties of the sediment, which in turn controls the magnitude of sediment-associated nutrient and contaminant loads [Walling, 2005]. During spring, which includes ice-out and peak Q, the main sources of sediment include bank erosion and resuspension of previously deposited sediments. As peak Qs recede into summer-autumn, glacial melt becomes the dominant source of suspended sediment. This is evident in the greater summer-autumn SS loads at YR-Eagle and Tanana, the two subbasins with the greatest watershed area covered by glaciers. The seasonality of these sources is illustrated in the transport curve at YR-Pilot (Figure 4a), which shows two distinct inputs in spring and midsummer.

[30] This seasonality effect is also clear in the sediment mineralogy. Carbonate fractions and loads (Table 2) showed strong maxima in summer-autumn owing to the melting of glaciers in the headwaters of the White River, Yukon Territory. The White has been shown to be the major source of carbonates to the YR by Eberl [2004], who found that suspended sediments in the YR were only 2% carbonate upstream from the confluence with the White; downstream from the confluence carbonate content increased to 14%. Given that virtually all the carbonate measured at YR-Eagle originates from the White, and that Tanana is the only other appreciable source, approximately 88% of carbonates entering the YR originate in the White (Table 2).

[31] Within basin fate of the suspended sediments and carbonates can be estimated by examining the changes in the modeled loads between stations. The decrease in SS load between YR-Eagle (with the addition of the Porcupine River) and YR-Stevens ranged from 2 to 13 Mt a−1 for 2001–2005 (Ave = 6 Mt a−1, SD = 4 Mt a−1) (Table 2). With the addition of about 1 Mt a−1 from the Chandalar River [Brabets et al., 2000], this suggests that, on average, ∼7 Mt a−1 of sediment was lost between YR-Eagle and YR-Stevens, most likely in the extensive Yukon Flats region. If the sediment load at YR-Stevens is summed with that of Tanana and the Koyukuk River [Brabets et al., 2000], it appears that on average, an additional 4 Mt a−1 was lost between YR-Stevens and YR-Pilot, resulting in an average annual loss of about 11 Mt a−1 between YR-Eagle and YR-Pilot.

[32] The estimated SS load lost to sediment deposition is in good agreement with that of Brabets et al. [2000], although some additional major sources of sediment loss cannot be taken into account. Riverbank erosion can be substantial on the YR, with as much as 2 m of bank eroded per year [Striegl et al., 2007]. This occurs throughout the open-water season, but may be especially rapid during ice-out, when it is too dangerous to sample for SS. Any eroded sediments deposited in floodplains upstream from the next downriver measurement site are unaccounted for in the total loss calculation. In addition, the Porcupine and Tanana sites are located upstream of their confluences with the YR, and therefore do not include sediment entering the rivers from tributaries located between the measurement sites and the river mouths. This would include the Sheenjek and Black Rivers, tributaries of the Porcupine, and the Nenana and Kantishna Rivers, tributaries of the Tanana. These two latter rivers in particular drain high glaciated regions of the Alaska Range, and likely contribute substantial amounts of SS to the Tanana. Also missing from the sediment export calculation is the contribution by bed load transport. While bed load transport has not been measured in the main stem of the Yukon, measurements in the Tanana suggest that bed load transport accounts for only 1–2% of the suspended sediment load [Burrows et al., 1979]. And finally, the proportion of SS that settles out in the Yukon Delta before reaching the Bering Sea is unknown. While it has been estimated that about half of Mackenzie River sediments are transported beyond the Mackenzie Delta into the Arctic Ocean [Macdonald et al., 1998], YR transport to Norton Sound and the Bering Sea could vary considerably.

[33] Carbonate loads also indicate a loss moving down the YR in AK (Table 2). Between YR-Eagle and YR-Stevens, the carbonate load decreased 27% on an average annual basis, with a majority of that decrease occurring during summer-autumn. At YR-Pilot, 29% of the carbonate load from YR-Stevens and Tanana was lost annually. These estimates are in agreement with those of Eberl [2004]. One gap in understanding the source and fate of carbonate for the entire YR is a result of a lack of SS concentration data for the Donjek River, Yukon Territory. The Donjek River originates in the St. Elias Mountains and flows into the White River downstream from the White River gage. Suspended sediment load estimates for the White [Brabets et al., 2000] therefore do not include the Donjek. Given that the Donjek has an annual Q similar to that of the White (Water Survey of Canada; see http://www.wsc.ec.gc.ca/index_e.cfm?cname = main_e.cfm), and that the carbonate content of Donjek River sediments is almost twice that of White River sediments D. Eberl, USGS, unpublished data, 2005), sediment and carbonate loads from the Donjek are probably substantial. However, any losses of Donjek River carbonates in the reach to YR-Eagle cannot be determined at this time.

[34] Carbonate losses are due to a combination of deposition and carbonate dissolution [Eberl, 2004; Striegl et al., 2007]. As a first-order approximation, because ∼10% of the SS load at YR-Eagle is lost in Yukon Flats (Table 2), it could also be assumed that ∼10% of the carbonates are also lost owing to sedimentation, with the remainder of the missing carbonate undergoing dissolution. However, Eberl [2004], using a more rigorous approach involving mineralogical analysis of suspended sediments, concluded that approximately 30% of carbonates undergo dissolution between YR-Eagle and YR-Stevens. While this carbonate weathering process contributes little to total DIC (dissolved inorganic carbon) export, it substantially alters the isotopic composition of DIC, and it has occasionally consumed YR CO2 to less than atmospheric equilibrium [Striegl et al., 2007], changing the YR from a source of atmospheric CO2 to a short-term sink.

[35] On an average annual basis, the YR appears to store millions of metric tons of sediment in floodplains between YR-Eagle and YR-Pilot. It is quite possible that this storage is temporary, however, and that much of this sediment is re-suspended during peak Q every spring. Froese et al. [2005], studying longitudinal valley fill thickness in the middle YR Basin, concluded that this stretch of the river has been in a state of mass balance equilibrium for sediments over the Holocene. The ultimate fate of sediment and carbonate loads remains unclear. While sediment loads at YR-Pilot can be estimated, the extent of sedimentation in the Yukon Delta versus how much sediment reaches the Bering Sea is unknown. This open question has important implications for the ecology of the Bering Sea, as processes in the Delta will dictate the final transport and/or sequestration of sediment-associated organic carbon, carbonates, nutrients, metals, and contaminants.

5.2. Recent Trends From the Tanana River

[36] While only 5.6% of the Tanana Basin is glaciated, it is not surprising that most of the SS in the Tanana originates from these regions [Chikita et al., 2007]. As glaciers recede under a warming climate, one might expect changes in the SS load of rivers such as Tanana. Using the SS record dating back to 1966 (NWIS; see http://waterdata.usgs.gov/nwis), Tanana SS loads were modeled using LOADEST for the water years (WY) 1966–1970, the earliest 5-year period where data are available, as well as WY 1983–1987, a 5-year period intermediate between the earliest records and those of the major 2001–2005 study.

[37] There is a significant difference between the three 5-year periods (Figure 5). On an annual basis, cumulative SS yield versus water yield has the lowest slope for 1966–1970, the greatest slope for 1983–1987, and an intermediate slope for 2001–2005 (Figure 5a; p < 0.0001, ANOVA). Seasonally, this trend is most reflected in summer-autumn (Figure 5c; p < 0.0001). This is not unexpected, as most of the SS load from Tanana occurs during this season (Table 2). Spring (Figure 5b) does not show a significant difference between the three periods, while winter (Figure 5d) shows a significant difference between 1966 and 70 and the two later periods.

image

Figure 5. Cumulative suspended sediment yield versus cumulative water yield, Tanana River at Nenana (TR): (a) annual, (b) spring, (c), summer-autumn, and (d) winter.

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[38] One possible correlate for the computed differences may be climate shifts, often the driving factor for sediment loads after human effects [Syvitski, 2003]. According to Pacific decadal oscillation (PDO) indices, the years 1966–1970 were in a cool phase, the years 1983–1987 were in a warm phase, and the years 2001–2005 appear to encompass a transition between cool and warm (Joint Institute for the Study of Atmosphere and Ocean (JISAO); see http://jisao.washington.edu/pdo). The observed differences in the SS yield/water yield relations (Figure 5a) appear to reflect these PDO phase changes, with 1966–1970 having the least SS yield per given water yield (cool phase), 1983–1987 having the greatest yield relation (warm phase), and 2001–2005 having an intermediate relation (cool to warm transition). Average annual SS yields (Table 3) mirror this trend.

Table 3. Average Annual and Seasonal Water Yields and Suspended Sediment Yields, Tanana River at Nenana ± Standard Deviation
YearsPDO PhaseAverage Spring Water Yield (cm)Average Summer-Autumn Water Yield (cm)Average Winter Water Yield (cm)Average Annual Water Yield (cm)Average Annual Suspended Sediment Yield (g m2 a−1)
1966–1970cool9.3 ± 2.817.9 ± 4.44.5 ± 0.531.7 ± 5.6330 ± 120
1983–1987warm7.8 ± 1.820.4 ± 1.85.5 ± 0.533.7 ± 2.6559 ± 93
2001–2005transition9.3 ± 2.120.6 ± 1.35.7 ± 0.635.6 ± 1.3503 ± 58

[39] In Alaska, the PDO typically manifests itself most strongly in winter, most likely owing to the lack of substantial solar radiation forcing, which results in the advection of warm moist air into the state becoming a major climate driver [Hartmann and Wendler, 2005]. In interior Alaska, wherein the Tanana Basin resides, the 1976 shift between cool and warm phases of the PDO resulted in winter increases in air temperature (+3.1°C), cloudiness (+7%), and snowfall (+21%), although air temperature and total precipitation also increased in summer [Hartmann and Wendler, 2005].

[40] Seasonal water yields for the Tanana seem to mirror these shifts in precipitation. While not statistically significant, average summer-autumn and winter water yields for Tanana appeared to increase between the cool phase 1966–1970 years and the warm phase 1983–1987 years, while spring water yield seemed to decrease (Table 3). The years 2001–2005 saw an increase in average spring water yield, with no change in average summer-autumn or winter water yields over the 1983–1987 period. A positive change in the sediment/water relation between cool and warm PDO phases for summer-autumn and winter (Figures 5c and 5d) is not surprising, given the concurrent increases in air temperature and precipitation. Unfortunately, lack of long-term data from subbasins such as the White River prohibits an examination of other possible climate shift/sediment yield relations.

5.3. Climate Change Implications

[41] Warmer temperatures will likely result in continued melting of glaciers and permafrost, changes in Q and precipitation, a longer open-water season, increased fire frequency, and changes in atmospheric CO2 [Serreze et al., 2000]. Each of these factors has the potential to alter future SS loads. Receding glaciers should initially cause more erosion, as glacial drift left behind becomes available for transport [Meade et al., 1990]. Increased SS loads may be sustained even after glaciers disappear, although eventually the loads will decrease as easily transported material decreases. Given the importance of glaciers in the headwater areas of the YR Basin, any continued shrinkage of glaciers could have a large impact on SS loads in the YR.

[42] While there have been no observed changes in Q for the YR for a >30-year record prior to 2000 [Walvoord and Striegl, 2007; McClelland et al., 2006], net precipitation is expected to increase at high latitudes with continued warming [Cubasch et al., 2001]. Changes in precipitation appear to be the primary driver for changes in Q [McClelland et al., 2004], thus concomitant changes in Q with changes in precipitation would not be unexpected. Given the positive relation between SS yield and water yield (Figures 3 and 5), any increases in precipitation should be accompanied by increases in SS yield. This relation is seen in climate models that predict a 10% increase in SS loads in arctic rivers for every 20% increase in Q [Syvitski, 2002; Gordeev, 2006]. These same models also predict sediment load increases of 22–30% for arctic/subarctic rivers for every 2°C increase in temperature.

[43] In addition to the total change in precipitation, the timing of any changes in precipitation will also be important. Seasonally, spring sees the greatest increase in SS yield with water yield for all subbasins except perhaps Tanana (Figures 3b and 3c), and thus might be expected to see the greatest increases in SS yield from any increases in spring water yield. However, as 50% of the SS load at YR-Pilot comes from the Tanana, summer-autumn changes in water yield could result in substantial changes in SS loads at YR-Pilot as well.

[44] Other factors associated with warmer temperatures will also likely increase SS loads. Melting stores of permafrost will expose long-frozen soils to erosion. A longer open-water season will allow more time for bank erosion, and an increased fire frequency will also open up more forest soil to erosional forces. Further, continued increases in atmospheric CO2 concentrations may lead to greater SS loads. The model of Van Blarcum et al. [1995] predicts a 15% increase in precipitation and a 26% increase in runoff for the YR in a doubled CO2 climate. While their model carries some uncertainty owing to low predictability of current precipitation and runoff, their results are consistent with the general prediction that precipitation and runoff would increase in a doubled CO2 climate [Aerts et al., 2006; Miller and Russell, 1992; Stouffer et al., 1989; Mitchell, 1989].

[45] Finally, linkages between climate variables (i.e., Arctic Oscillation, ENSO, and PDO) and Q and precipitation have been documented [Peterson et al., 2006; Dery and Wood, 2005; Neal et al., 2002; Brabets and Walvoord, 2009], and a possible linkage between SS yield and PDO has been suggested for the Tanana in this study. Thus, an examination of possible linkages should be explored when considering long-term data sets and any potential relation with climate change. However, care must be taken when drawing conclusions on the basis of these linkages, as climate indices are often interrelated [McClelland et al., 2006], and indeed, climate change may be changing the patterns of these indices [Shindell et al., 1999].

[46] As arctic systems are particularly sensitive to change [Serreze et al., 2000], it is important to understand current fluxes across the terrestrial-aquatic continuum, so that future changes in arctic rivers transport processes might be gauged and predicted. However, the size and direction of trends are not constant across the arctic/subarctic [McClelland et al., 2006]. Therefore it is important to gather as much information as possible, from as many systems as possible, and not rely on broad extrapolations across the pan-Arctic region.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[47] Sample collection and laboratory analyses for this study were a team effort by many USGS scientists. We thank all of you for your contributions. Tim Brabets, Greg Koltun, John R. Gray, Alan Shiller, and an anonymous reviewer provided comments on the draft manuscript that greatly contributed to its improvement.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Study Area and Hydrology
  5. 3. Methods
  6. 4. Results
  7. 5. Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
wrcr12097-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
wrcr12097-sup-0002-t02.txtplain text document2KTab-delimited Table 2.
wrcr12097-sup-0003-t03.txtplain text document1KTab-delimited Table 3.

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