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WATER RESOURCES RESEARCH, VOL. 45, W06411, doi:10.1029/2008WR007546, 2009

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Suspended sediment and carbonate transport in the Yukon River Basin, Alaska: Fluxes and potential future responses to climate change Mark M. Dornblaser1 and Robert G. Striegl2 Received 23 October 2008; revised 18 February 2009; accepted 6 April 2009; published 11 June 2009.

[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 a1 and 387 Gg a1, 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 a1 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. Citation: Dornblaser, M. M., and R. G. Striegl (2009), Suspended sediment and carbonate transport in the Yukon River Basin, Alaska: Fluxes and potential future responses to climate change, Water Resour. Res., 45, W06411, doi:10.1029/2008WR007546.

1. Introduction [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 1 2

U.S. Geological Survey, Boulder, Colorado, USA. U.S. Geological Survey, Denver, Colorado, USA.

This paper is not subject to U.S. copyright. Published in 2009 by the American Geophysical Union.

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

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DORNBLASER AND STRIEGL: SEDIMENT TRANSPORT

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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. 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 [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. [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].

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79.1 9.52 108 24.7 227 71 9.3 96.6 24.1 190

2001 – 2005 Mean Annual Q (km3 a1) 2005 2004

65.4 11.1 98.9 22.4 225 74.5 12.1 108 23.8 188 91.1 10.8 123 23.3 223 259 158 73 103 6 294,000 76,400 508,400 66,300 831,400 141°4702200 143°0801600 149°4300400 149°0503000 162°5205000 64°4702200, 66°5902600, 65°5203200, 64°3305500, 61°5600400,

Abbreviation

YRE PR YRS TR YRP

Station

Yukon River at Eagle Porcupine River near Fort Yukon Yukon River near Stevens Village Tanana River at Nenana Yukon River at Pilot station

15356000 15389000 15453500 15515500 15565447

2003 North Longitude, West Latitude

Drainage Area (km2)

Site Elevation (m)

2001

2002

Q (km3 a1)

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[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 a1 (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].

3. Methods

U.S. Geological Survey Station

Table 1. Station Name, Abbreviation, Location, and Annual and Mean Water Discharge for Water Years 2001 – 2005

76 11 107 24 211


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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 d1) 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 longterm 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 3 of 12

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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. [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 t1) or sediment or carbonate load (mass t1) for a flow period

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by watershed area (m2). Water yield (runoff) is presented as mm or cm water t1 uniformly distributed over each basin, and constituent yield is presented as g m2 t1. [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 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, summerautumn 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. [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 highyield 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

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Summer-Autumn Winter Annual

Summer-Autumn Winter Annual Spring



608 ± 107 (17) 1.8 ± 0.3 (4)


387 ± 42 (18) 4 ± 0 (5)

431 ± 59 (10)

1872 ± 242 (16) 19 ± 3 (5)

1228 ± 240 (13)

385 ± 36 (17) 9 ± 2 (5)

491 ± 67 (12)

23 ± 5 (13) (1.1 ± 0.3)b (4)

106 ± 18 (12)

601 ± 111 (12)

Spring

35 0.46 68

24 0.25 33 33

17 0.29 37 8.8

0.18 0.001 2.0 20

22 0.07 41 1.9

19

Suspended Sediment Load (Mt)

7.3%

6.8%

7.8%

58.8%

15.5%

CV

42 0.5 82

360 3.8 500 39

33 0.6 73 130

2.4 0.02 27 39

74 0.2 140 24

64

Suspended Sediment Yield (gm2)

6.6 ± 2.7 (14) NA

2.0 ± 0.3 (10)

1.8 ± 0.5 (15) NA

2.2 ± 2.0 (14)

12.9 ± 2.1 (15) NA

NA NA NA 7.0 ± 1.3 (11)

NA

14.5 ± 1.9 (13) NA

9.2 ± 3.1 (9)

Carbonate Content of Suspended Sediment (%)

0.39

0.30

0.08 0.08

0.06

0.46 0.03

0.28

NA NA NA 0.18

0.64 NA

0.41

0.22

Carbone Load as C (Mt)

0.5

0.4

1.2 0.1

0.9

1.0 0.4

0.6

NA NA NA 0.4

2.2 NA

1.4

0.8

Carbonate Yield as C (g m2)

121 37 251

206 57 356 93

97 30 208 93

59 9 139 81

128 42 256 71

86

Runoff (mm)

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 3) while Tanana had the least variability (a factor of 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

Table 3. Average Annual and Seasonal Water Yields and Suspended Sediment Yields, Tanana River at Nenana ± Standard Deviation Years

PDO Phase

Average 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 a1)

1966 – 1970 1983 – 1987 2001 – 2005

cool warm transition

9.3 ± 2.8 7.8 ± 1.8 9.3 ± 2.1

17.9 ± 4.4 20.4 ± 1.8 20.6 ± 1.3

4.5 ± 0.5 5.5 ± 0.5 5.7 ± 0.6

31.7 ± 5.6 33.7 ± 2.6 35.6 ± 1.3

330 ± 120 559 ± 93 503 ± 58

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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. [47] Acknowledgments. 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.

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M. M. Dornblaser, U.S. Geological Survey, 3215 Marine Street, Suite E-127, Boulder, CO 80303, USA. ([email protected]) R. G. Striegl, U.S. Geological Survey, Box 25046, MS 413, Denver, CO 80225, USA.

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