Main Text

Introduction

Quantitative estimates of annual fluvial suspended sediment yield (hereafter, ‘sediment yield’) are sought by physical scientists as signals of environmental dynamics, by ecologists for their associations with water quality and habitat value, and by engineers for hydro-infrastructure and river system design. Measuring sediment yields presents a challenge, because sediment transfer is inherently variable in space and time (Morehead, Syvitski, Hutton, & Peckham, 2003; Orwin, Lamoureux, Warburton, & Beylich, 2010). Research from glaciated Arctic catchments indicates that sediment transfer typically reflects catchment-scale processes (Hodgkins, Cooper, Wadham, & Tranter, 2003), including meteorological forcing (Lewis & Lamoureux, 2010; Syvitski, 2002), glacial dynamics (Bogen & Bønses, 2003; Gurnell, Hannah, & Lawler, 1996; Hodson & Ferguson, 1999; Hodson, Tranter, Dowdeswell, Gurnell, & Hagen, 1997), and other geomorphological processes (Hasholt et al., 2005). The complexity of meteorological forcing on sediment transfer in glaciated catchments is accentuated by reports of substantial variations through time over recent millennia (Saarni, Saarinen, & Lensu, 2015), and across spatial scales (Striberger et al., 2011). Sediment transfer can vary significantly on inter-annual, decadal, and century scales (Bogen & Bønses, 2003; Gurnell et al., 1996; Lewis, Lafrenière, & Lamoureux, 2012; Lewkowicz & Wolfe, 1994; Orwin & Smart, 2004a; Richards, 1984; Tape, Verbyla, & Welker, 2011), and may reflect landscape evolution over glacial-interglacial cycles (Church & Ryder, 1972; Church & Slaymaker, 1989; Leonard, 1997). Within a single open-channel season, the majority of annual catchment-scale sediment yield can be transported during one, or a few, discrete events (Bogen & Bonses, 2003; Dugan, Lamoureux, Lafrenière, & Lewis, 2009; Fenn, Gurnell, & Beecroft, 1985; Hasholt et al., 2005; Lewis et al., 2012; Schiefer et al., 2017; Østrem, 1975). Sediment availability and exhaustion can greatly affect seasonal sediment transfer (Bogen & Bønses, 2003; Forbes & Lamoureux, 2005; Hodgkins, 1999; Hodgkins et al., 2003; Hodson et al., 1998; Irvine-Fynn et al., 2005). Proglacial instrumentation and sampling programs to directly measure suspended sediment concentrations (SSCs) at daily, or preferably hourly or finer sampling intervals (Orwin et al., 2010), over periods longer than one open-channel season are rare in the Arctic (Hasholt et al., 2005). Consequently, statistical models are relied upon to estimate annual sediment transfer from discontinuous samples of SSC. In Arctic Canada, automated sampling has enabled continuous measurement of SSC fluctuations and estimation of sediment yields using spline curves (Cockburn & Lamoureux, 2008; Favaro & Lamoureux, 2014; Lewis et al., 2012); however, such intensive sampling throughout the full length of the open-channel season is not always feasible. More typically, statistical models comprise simple sediment rating curves, using either discharge (Bogen & Bonses, 2003; Dugan et al., 2009; Fenn et al., 1985; Forbes & Lamoureux, 2005; Hodgkins, 1996; Hodson et al., 1998; Horowitz, 2003; Lamb & Toniolo, 2016; Lewkowicz & Wolfe, 1994; McLaren, 1981; O’Farrell et al., 2009; Rasch, Elberling, Jakobsen, & Hasholt 2000; Østrem, 1975), or, less often, turbidity (Harrington & Harrington, 2013) as the single predictor of SSC. Despite their popularity, failure to adequately account for quasi-autocorrelation has been identified as a pitfall associated with the use of simple sediment rating curves (Hodgkins, 1999; Hodson & Ferguson, 1999). Quasi-autocorrelation arises from shortfalls in formulation of the regression model, including: an incorrect fit, failing to identify the presence of lags, changes in response between the dependent and independent variables, and omitting relevant independent variables (Fenn et al., 1985; Gao, 2008; Hodson & Ferguson, 1999). SSC may be underestimated or overestimated by simple sediment rating curves (Gao, 2008; Walling, 1977), with perturbations smoothed and margin for error reduced as the monitoring period increases (Horowitz, 2003). However, monitoring spanning more than a couple of months of one or two open-channel seasons is rare in remote arctic environments (e.g. Bogen & Bønses, 2003). Statistical methods can be applied to address quasi-autocorrelation, improving the predictive ability of sediment rating curves. For example, separating rating curves according to discrete temporal periods, or stage, have both proven popular, with varied success (Harrington & Harrington, 2013; Hodgkins, 1996; Hodson et al., 1998; Horowitz, 2003; Lewkowicz & Wolfe, 1994; McLaren, 1981; Richards, 1984; Walling, 1977; Østrem, 1975). Multiple-regression models, incorporating hydrological, temporal, and meteorological explanatory variables with optimal lag times, are often preferable over rating-curve-separation because they account for processes that can decouple SSCs from contemporaneous discharge or turbidity fluctuations, and help us understand multifaceted, dynamic processes driving glaciofluvial sedimentation (Hodgkins, 1999; Hodson & Ferguson, 1999; Irvine-Fynn et al., 2005; Richards, 1984; Willis, Richards, & Sharp, 1996). Temporal variables can be used as indicators of sediment availability, including: hysteresis effects, intra-season variability, and seasonal variations. Meteorological variables can also capture temporal variability in SSCs, including: diurnal and longer cycles of solar radiation and temperature affecting melt-related erosion and transfer; and rainfall-induced events generating hillslope erosion and delivery processes, extra-channel erosion, and sediment entrainment with rising discharge. Further, inclusion of meteorological variables in sediment modeling can provide useful information for interpreting past climates from longer sedimentary records, and assessing sensitivity to climate change through hydroclimatic system forecasting (Forbes & Lamoureux, 2005; Gordeev, 2006; Lewis & Lamoureux, 2010; Syvitski, 2002). Despite potential advantages, multiple-regression models are uncommonly used for studying suspended sediment transfer in catchments above the Arctic Circle (Hodgkins, 1999; Hodson & Ferguson, 1999; Irvine-Fynn et al., 2005; Schiefer et al., 2017). In Arctic Alaska (defined hereafter as Alaskan land above the Arctic Circle—66.33°N), even simple sediment rating curves have rarely been constructed (Arnborg et al., 1967; Lamb & Toniolo, 2016; Rainwater & Guy, 1961; Trefry, Rember, & Trocine, 2004). The objective of this paper is to use multiple-regression models to estimate seasonal sediment yields, and interpret physical processes driving these yields, at Lake Peters, northeast Brooks Range, Arctic National Wildlife Refuge, Alaska, a site selected for hydrological and paleoenvironmental research (Benson, Kaufman, McKay, Sciefer, & Fortin 2019; Broadman et al., 2019; Ellerbrook, 2018; Thurston, 2017).

Study Area

Lake Peters (69.32°N 145.05°W) is situated approximately 300 km north of the Arctic Circle, and 70 km from the Arctic Ocean in the Brooks Range, Alaska (Figure 1). Lake Peters catchment (171 km2) is ringed by steep mountains, and glaciated (9%) with some of the largest valley glaciers in Arctic Alaska. Bedrock comprises low-grade metasedimentary and sedimentary rocks, primarily southward-dipping sandstone, semischist, and phyllite, with minor chert and quartzite (Reed, 1968). Terminal cirque and lateral moraines formed during the Little Ice Age (ca. 1200-1850 CE) are conspicuous around the margins of extant glaciers in the headwaters of Lake Peters (Evison, Calkin, & Ellis, 1996). Interpolated climate data for Lake Peters (1980 - 2009) shows mean annual precipitation of 360 mm, and mean January and July monthly temperatures of -22.0°C and 10.5°C, respectively (Stavros & Hill, 2013). Permafrost is known to occur at the bases of hillslopes and in bottoms of river valleys in the Brooks Range (Kanevskiy et al., 2011). Soils are sparse, and vegetation largely consists of arctic grasses, herbs, and riparian shrubs. Above 1300 m, channel-side vegetation is sparse. Lake Peters is the primary source of the Sadlerochit River, which discharges into the Arctic Ocean. Carnivore Creek (128 km2 catchment; 10% glacier coverage based on aerial photos taken in 2016) and Chamberlin Creek (8 km2catchment; 23% glacier coverage) are the primary inflows to Lake Peters (Figure 2), although several minor non-glaciated catchments also flow into Lake Peters over and through large alluvial fans (Figure 1). The Carnivore sub-catchment covers 75% of the total area of the Lake Peters catchment. The eastern side of the Carnivore valley is glacierized, channels are more deeply incised, and side-valley alluvial fans are more compact, compared to the western side. The lower reach of Carnivore Creek flows over a shallow slope with plane bed morphology, surrounded by a terraced floodplain and some periglacial surface features. Chamberlin Creek’s catchment is comparatively steep, with the summit of Mount Chamberlin (2750 m asl) only 4.7 km from Lake Peters. Chamberlin Glacier (1.9 km²) is the third largest glacier in Lake Peters catchment. In the upper catchment, Chamberlin Creek flows over and through moraines; in the mid-catchment the channel has incised into a confined bedrock-controlled valley, with steep step-pools; and downstream of the alluvial fan apex the creek flows over lower-grade step-pools (Figure 3).

Methodology