Climate during the last glacial maximum in the Wasatch and southern Uinta Mountains inferred from glacier modeling

Geomorphology 75 (2006) 300 – 317 www.elsevier.com/locate/geomorph

Climate during the last glacial maximum in the Wasatch and southern Uinta Mountains inferred from glacier modeling Benjamin J.C. Laabs a,*, Mitchell A. Plummer b, David M. Mickelson a a b

Department of Geology and Geophysics, University of Wisconsin, Madison, WI 53706, USA Idaho National Engineering and Environmental Laboraotry, Idaho Falls, ID 83415-2107, USA Received 5 January 2005; accepted 27 July 2005 Available online 9 November 2005

Abstract Recent improvements in understanding glacial extents and chronologies in the Wasatch and Uinta Mountains and other mountain ranges in the western U.S. call for a more detailed approach to using glacier reconstructions to infer paleoclimates than ¨ T methods. A coupled 2-D mass balance and ice-flow numerical modeling approach developed by commonly applied AAR-ELA-A [Plummer, M.A., Phillips, F.M., 2003. A 2-D numerical model of snow/ice energy balance and ice flow for paleoclimatic interpretation of glacial geomorphic features. Quaternary Science Reviews 22, 1389–1406.] allows exploration of the combined effects of temperature, precipitation, shortwave radiation and many secondary parameters on past ice extents in alpine settings. We apply this approach to the Little Cottonwood Canyon in the Wasatch Mountains and the Lake Fork and Yellowstone Canyons in the south-central Uinta Mountains. Results of modeling experiments indicate that the Little Cottonwood glacier required more precipitation during the local Last Glacial Maximum (LGM) than glaciers in the Uinta Mountains, assuming lapse rates were similar to modern. Model results suggest that if temperatures in the Wasatch Mountains and Uinta Mountains were ~ 6 8C to 7 8C colder than modern, corresponding precipitation changes were ~ 3 to 2 modern in Little Cottonwood Canyon and ~ 2 to 1 modern in Lake Fork and Yellowstone Canyons. Greater amounts of precipitation in the Little Cottonwood Canyon likely reflect moisture derived from the surface of Lake Bonneville, and the lake may have also affected the mass balance of glaciers in the Uinta Mountains. D 2005 Elsevier B.V. All rights reserved. Keywords: Uinta mountains; Wasatch Range; Mass balance; Last glacial maximum; Glacial extent

1. Introduction Glacial records in mountain settings provide valuable clues to the frequency and magnitude of climate change during the late Quaternary Period. The areal extent and volume of glacier ice in a drainage basin * Corresponding author. Current address: Geology Department, Gustavus Adolphus College, 800 West College Ave., St. Peter, MN 56082, USA. Tel.: +1 507 933 7442; fax: +1 507 933 6285. E-mail address: [email protected] (B.J.C. Laabs). 0169-555X/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2005.07.026

is dependent on the history of its mass balance. The mass added is generally considered to be most dependent on winter precipitation and the mass lost on summer temperature (Meierding, 1982). Thus, glacier size is dependent on local climate conditions, and reconstructed glacier extent is often considered a proxy for paleoclimate. Glacial mapping in the southern Uinta Mountains (Laabs, 2004) and in the Wasatch Mountains (Richmond, 1965; Madsen and Currey, 1979; Shakun, 2003) allows past ice extents to be reconstructed. By modeling energy balance and ice flow under prescribed

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temperatures and precipitation amounts, glacier reconstructions can ultimately be used to infer climate conditions during past glaciations. The objective of this study is to use two-dimensional, mass-balance and iceflow modeling of past glaciers in Little Cottonwood Canyon (Wasatch Mountains), and Lake Fork and Yellowstone Canyons (southern Uintas) to infer climate conditions during the local LGM. Recent improvements in understanding Pleistocene glacial chronologies in the western US (e.g., Phillips et al., 1996; Sturchio et al., 1994; Licciardi et al., 2001, 2004; Thackray et al., 2004; Gosse et al., 1995a,b; Schildgen et al., 2002; Benson et al., 2004a,b; among others) provide a framework for increasingly detailed studies of past glacial climates. Licciardi et al. (2004) summarize chronological records in the western US, which they interpret to indicate two glacial maxima during the latest Pleistocene: one during the global Last Glacial Maximum (LGM) at ~ 21 cal. ka and one at ~ 17 cal. ka (Licciardi et al., 2004 and references therein). Records in the northern Rocky Mountains suggest that mountain glaciers may have been less extensive during the global LGM, reaching the maximum extents at ~ 17 cal. ka (Licciardi et al., 2004; Thackray et al., 2004). Records in the central Rocky Mountains of southwestern Wyoming and Colorado indicate termination of the local LGM by ~ 17 to 16 cal. ka (Benson et al., 2004a,b; Laabs, 2004). 1.1. Study area The Wasatch Mountains are a north–south trending range in northern Utah that borders on the central Rocky Mountains and the eastern Great Basin (Fig. 1A). Little Cottonwood Canyon is a steep, narrow, glacial-trough valley that drains westward into Great Salt Lake. The Uinta Mountains are an east–west trending range that is generally much higher in elevation than the Wasatch Mountains. Lake Fork and Yellowstone Canyons are broad, gently sloping, glacial-trough valleys that drain southward into the Green River. Mountain glaciers advanced in these ranges while Lake Bonneville expanded in the eastern Great Basin to cover most of western Utah and parts of southern Idaho, western Nevada and northern Arizona (Fig. 1B). During the Pinedale Glaciation, the glacier in Little Cottonwood Canyon attained a maximum length of ~ 19 km, covered an area of 40 km2, and terminated at ~ 1520 m asl (Shakun, 2003). The Lake Fork and Yellowstone Canyon glaciers were much larger during the Pinedale, with respective lengths of 38 and 41 km,

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areas of 232 and 214 km2 and higher terminus elevations of ~ 2256 and ~ 2264 m asl (Laabs, 2004). Glacier reconstructions in these two valleys by Shakun (2003) indicate LGM equilibrium-line altitudes (ELAs) of 2470 m asl in Little Cottonwood Canyon and ~ 3050 m asl in Lake Fork and Yellowstone Canyons. This difference in ELA over a west–east distance of only ~ 100 km (see Fig. 1) suggests a significant paleoclimatic difference between the Wasatch and Uinta Mountains. The timing of the local Last Glacial Maximum (LGM) in each of these basins is constrained by cosmogenic surface-exposure and radiocarbon ages of moraines (Laabs, 2004; Madsen and Currey, 1979) (Fig. 2). 1.2. Description of models The modeling procedure employed in this study, developed by Plummer and Phillips (2003), involves the use of a 2-D snow accumulation/ablation model combined with a 2-D ice-flow model. It allows the amount of accumulation or ablation of snow at any point in a drainage basin to be determined as a function of predefined temperature and precipitation parameters as well as topographic parameters (shading, aspect and avalanching, among others) that can modify radiation balance and accumulation. Net accumulation and ablation are calculated from the energy-balance of the snow surface during the melt season. The 2-D accumulation/ablation distribution becomes the primary input to a second model that simulates glacier growth according to equations for ice flow. Simulated ice extent is, therefore, a function of the climate parameters defined in the energy balance model and the ice-flow parameters in the flow model. This approach allows testing the effects of a range of temperature and precipitation conditions on glacial extent, which can be matched to field data and used to set limits on past glacial climate. Several advantages exist to using this physically based modeling approach to infer paleoclimate from glacial geomorphic features. First, it eliminates some of the assumptions that are typically made in converting glacial geomorphic evidence into an estimate of paleoclimate conditions. These include, for example, the significance of particular glacial features (e.g., cirque floors and lateral moraines) and the accumulation–area ratio of past glaciers. Second, the model directly addresses many of the controls on accumulation and ablation that are overlooked in simpler models, such as the dependence of irradiance on topography and the dependence of annual snow/rain

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Fig. 1. (A) Map of the western US showing the alpine glacier systems examined in this study (dark gray) and the extent of Lake Bonneville during the last glaciation. Arrows indicate directions of seasonal circulation. Modified from Munroe (2001). (B) Landsat image of northeastern Utah. White boxes indicate locations of the Little Cottonwood (LC, in the Wasatch Range) and the Lake Fork (LF) and Yellowstone (YS) Canyons (in the Uinta Range).

partitioning on temperature (e.g., Seltzer, 1994). Third, a relatively dense network of climate stations (SNOTEL stations, coordinated by the Western Regional Climate Center, www.wrcc.dri.edu) in the study areas allows relatively accurate calculation of the modern distribution of temperature and precipitation for each drainage basin (see Table 1); these data provide input to the mass/energy balance model. Finally, the independent and combined effects of increased winter precipitation and decreased summer temperatures on glacier mass balance can be tested, as can the effects of changes in average monthly cloudiness (which affects the amount of incoming solar radiation), changes in the seasonal distribution of precipitation, and changes in average monthly wind speed.

1.3. Previous inferences of LGM climate A paleoclimate study in the Wasatch and Uinta Mountains was pursued because of the unique location and orientation of these mountain ranges, and the existing disparity among proposed climate conditions during the last glaciation in the central Rockies and the Great Basin. During the last glaciation, glaciers in the Wasatch Mountains were adjacent to and immediately downwind (east) of Lake Bonneville, a pluvial lake that covered 47,800 km2 in western Utah. Glaciers in the south central Uinta Mountains were also downwind of Lake Bonneville (Fig. 1), but more than 100 km further east. Porter et al. (1983) summarized glacial records in the Rocky Mountains and suggested that, under drier than

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Fig. 2. Shaded-relief of the Little Cottonwood (bottom), Lake Fork (left) and Yellowstone (right) Canyons (see Fig. 1 for locations of each canyon). Maps were produced from mosaics of 30-m U.S. Geological Survey 7.5-min digital elevation models. The grids were clipped to the boundaries of each drainage basin and resampled to cell sizes of 90 m for Little Cottonwood Canyon and 180 m for the much larger Lake Fork and Yellowstone Canyons. Field-reconstructed ice extents (black lines) for the LGM (from sources noted in the text) were digitized and used for comparing mapped ice extent with modeled ice extent. Dashed arrows indicate ice-flow direction.

modern climate, LGM temperatures in the Rocky Mountains would have been more than 10 8C cooler than modern based mainly on ELA reconstructions. Kaufman (2003) used amino-acid paleothermometry of fossil ostracodes in Lake Bonneville deposits to show that temperatures were 7–138 C cooler than modern during the period ~ 24 to 12 cal. ka and suggested that decreases in effective evaporation brought about by cold summer temperatures in the eastern Great Basin may have led to the rise of Lake Bonneville. Results of regional-scale paleoclimatic and hydrologic modeling of the Lake Bonneville basin by Hostetler et al. (1994) similarly suggest a significant decrease in summer temperature (~ 7 8C to 9 8C cooler than modern) and a small increase in precipitation (~ 13 mm in winter and 7 mm in summer) occurred in this area during the peak of Lake Bonneville (~ 17 cal. ka; Oviatt, 1997). Other studies suggest that climate was not only cooler, but also wetter than modern in the eastern Great Basin and central Rocky Mountains (e.g., Mif-

flin and Wheat, 1979; Hostetler and Benson, 1990; Thompson et al., 1993, and references therein). Benson and Thompson (1987) attribute the rise of Lakes Lahontan and Bonneville to the presence of the southern Cordilleran and Laurentide ice sheets, which displaced the jet stream southward to the northern Great Basin, thereby increasing precipitation in this area. General circulation model simulations of winter and summer climates during the LGM support this hypothesis, and indicate temperatures b 10 8C cooler and precipitation amounts greater than modern (e.g., Bartlein et al., 1998). In addition, Hostetler et al. (1994) suggest that moisture derived from the surface of Lake Bonneville was intercepted by surrounding mountain ranges, including the Wasatch Mountains, and returned to Lake Bonneville. This implies that lake-effect moisture affected glacier mass balance in the Wasatch Mountains. Recent interpretations of the glacial record in the Uinta Mountains also suggest that precipitation

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Table 1 Sources of climate data near Little Cottonwood, Lake Fork and Yellowstone Canyons Canyon

Station name

Sourcea

Location (dd)

Elevation (m asl)

Data used for model inputb

Period of record

Little Cottonwood

Pleasant Grove Louis Meadow Lookout Peak Snowbird Atlamont YS-Altamont Brown Duck Lake Fork Basin Chepeta YS-Altamont Mosby Mountain Lakefork Five Points Lake Chepeta

RAWS SNOTEL SNOTEL SNOTEL WRCC RAWS SNOTEL SNOTEL RAWS RAWS SNOTEL SNOTEL SNOTEL RAWS

N40.438 W.111.758 N40.838 W111.778 N40.838 W111.728 N40.558 W111.658 N40.378 W110.288 N40.548 W110.338 N40.58 W110.588 N40.548 W110.58 N40.818 W110.078 N40.548 W110.338 N40.628 W109.888 N40.608 W110.438 N40.728 W110.478 N40.818 W110.078

1585 2043 2500 2939 1951 2378 3232 3323 3695 2378 2896 3079 3329 3695

W T, P T, P T, P T, P T, W T, P T,P W T, P, W T T, P T, P W

1997–present 2000–present 1953–present 1990–present 1953–present 1983–present 1979–present 1981–present 1998–present 1983–present 1979–present 1980–present 1982–present 1998–present

Lake Fork

Yellowstone

Data from the YS-Alatamont and Chepeta stations were used to compute wind-speed regressions for both Lake Fork and Yellowstone Canyons. a All data were retrieved from www.wrcc.dri.edu. RAWS=Remote Automated Weather Station. SNOTEL=Snowpack Telemetry. WRCC = Western Regional Climate Center. b W = wind, T = temperature, P = precipitation.

amounts were higher at the time of the local LGM. Munroe and Mickelson (2002), Shakun (2003) and Oviatt (1994) reconstructed equilibrium-line altitudes (ELAs) for the LGM in the northern and southern Uinta Mountains. They documented an eastward rise in ELAs across the Uinta Mountains, which is steepest in the area within ~ 50 km of the eastern shore of Lake Bonneville. Munroe and Mickelson (2002) attribute this pattern to significant precipitation derived from the surface of Lake Bonneville in the Wasatch and western Uinta Mountains, with valleys becoming progressively drier downwind in the eastern Uintas. They also used reconstructed ELAs to infer temperature and precipitation conditions and suggest that summer temperature was 5.5–8 8C cooler and precipitation in the western Uintas was greatly increased (up to 10 modern) during the LGM by lake-effect moisture derived from the surface of Lake Bonneville. Disagreement among documented reconstructions of LGM climate in the eastern Great Basin/central Rocky Mountains region calls for additional research in this area. We use the combination of known glacial extents and numerical modeling to test the likely changes in temperature and precipitation that occurred during the LGM. 2. Methods 2.1. Local climate parameterization The snow accumulation/ablation model requires climatic input parameters, primarily modern mean monthly temperature–elevation lapse rates and precip-

itation–elevation gradients that are varied to simulate past glacial climates; these are computed from data recorded by SNOTEL stations (Table 1). Within the range of elevations where SNOTEL data are available, a linear model best describes modern lapse rates and precipitation gradients (Figs. 3 and 4) in each canyon. To account for the significant west–east climatic gradients across the study area, data from three stations near Little Cottonwood and Lake Fork Canyons and four stations near Yellowstone Canyon were used to compute separate lapse rates and precipitation gradients for each basin. Differences in precipitation gradients between the Wasatch and Uinta Mountains (Figs. 3 and 4) reflect the strong orographic barrier of the Wasatch Mountains (Zielinski and McCoy, 1987), where westerly precipitation is intercepted. This effect is particularly noticeable during winter months, when westerly Pacific storm tracks carry moisture to this area most efficiently (Fig. 4A; Mitchell, 1976). Valleys in the Uinta Mountains become increasingly drier leeward (eastward) of the Wasatch Mountains (Fig. 5). During summer months, precipitation gradients are steeper in the southern Uinta Mountains than in the Wasatch Mountains and absolute precipitation amounts are similar (Fig. 4B). Summer lapse rates in the three areas are similar; but the slightly steeper lapse rates in the Uinta Mountains produce significantly cooler temperatures because maximum elevations are much higher than in the Wasatch Mountains (Fig. 3A). Lapse rates are also slightly higher in the Yellowstone Canyon than in the Lake Fork Canyon, and this trend of increasing

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Fig. 3. Monthly lapse rates in the Yellowstone (diamonds, dashed-dotted line), Lake Fork (squares, solid line) and Little Cottonwood Canyons (triangles, dotted line) for October, January, April and July. Rates are computed as least-squares linear-regressions fit to mean monthly temperatures computed from data recorded at SNOTEL and RAWS stations. Regression-line equations for each month (including those not shown here) are in Appendix A1.

lapse rates with increasing downwind distance from the Wasatch appears to be directly related to increasing aridity. Relatively large amounts of summer precipitation in the Uinta Mountains (Fig. 4) reflect the importance of south-southwesterly circulation during summer, which delivers moisture to the southern Uintas from the Gulf of California to produce monsoontype storms. High pressure situated over the Great Basin generally prevents moist air masses from south-southwesterly source regions from reaching the Wasatch Mountains (Mitchell, 1976). In using modern lapse rates to describe modern climate and paleoclimates, we implicitly assume that the controls on the vertical structure of the atmosphere were essentially the same during the LGM as they are today, despite significant decreases in temperature or increases in precipitation. This assumption is somewhat supported by studies of regional climate patterns that suggest that atmospheric circulation in northern Utah at the time of the LGM was similar to modern (Benson and Thompson, 1987; Zielinski and McCoy, 1987). Although differences in the frequency or seasonality of precipitation might alter temperature gradients, such relationships are difficult to predict and are probably

best considered via sensitivity tests. In any case, if winter precipitation in the study area was derived from a westerly source, it is likely that precipitation in the Uinta Mountains during the LGM was limited by efficient capture of westerly moisture by the Wasatch Mountains as it is today (Fig. 5). 2.2. The mass/energy balance model The 2-D, in-the-horizontal-plane, energy balance model employed in this study is described in Plummer and Phillips (2003). The model calculates the annual rate of ice accumulation and ablation for each cell on a digital elevation grid from the equation bn ¼

Z

T N0

T b0

ð P  EÞdt þ

Z

Tb0

ð P  M  E Þdt

ð1Þ

TN0

where b n is the net annual mass balance at any location in the x,y plane, P is snowfall, E is evaporation or sublimation, T is temperature and M is the mass of snow melted. Melting is only calculated during a snowmelt season, which is defined by the period of time when mean monthly temperatures are above zero (in-

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Fig. 4. Monthly precipitation–elevation gradients in the Yellowstone (diamonds, dashed-dotted line), Lake Fork (squares, solid line) and Little Cottonwood Canyons (triangles, dotted line) for October, January, April and July. Rates are computed as least-squares linear-regressions fit to mean monthly precipitation data recorded at WRCC, SNOTEL and RAWS stations. Regression line equations for each month (including those not shown here) are in Appendix A1.

dicated by the second group of terms in Eq. (1)). Sublimation is the only means of loss when mean monthly temperatures are below zero (the first group of terms in Eq. (1)). The rate of melting is calculated

from the shortwave and longwave radiation balances, turbulent energy exchanges, and advective and conductive energy exchanges to a snow surface at the melting point: Q¼RþH LþAþG

Fig. 5. West–east precipitation gradients across the Wasatch and Uinta Mountains at 2600 (diamonds, dashed-dot curve), 3000 (triangles, solid curve), and 3400 m asl (squares, dotted curve) during January (similar trends exist for all winter months; see Laabs, 2004). Points are computed from precipitation–elevation gradients in Little Cottonwood Canyon (LC, Wasatch), the southwestern Uintas (SWU), Lake Fork Canyon (LF), Yellowstone Canyon (YS, central Uintas), and the eastern Uintas. See Fig. 1 for locations of each canyon. Best-fit curves are second-order polynomials.

ð2Þ

where Q is the energy flux available for melting, R is the shortwave and longwave radiation balance, H and L are the sensible and latent heat fluxes, respectively, A is the energy advected with rain and G is the conductive heat flux from the ground. Mass balance is calculated at a monthly time step and integrated over a period of 1 year, or several years, until the net annual balance is constant. Calculation of radiation balance accounts for effects of topographic shading, angle of incidence and albedo, with the latter term dependent in a simple manner on cloudiness and fraction of total snowfall melted. The turbulent energy exchange equations follow a bulk transfer scheme and depend primarily on surface temperature, wind speed and relative humidity. Advective and conductive energy exchanges contribute only a small amount to the mass/energy balance calculation; energy added by advection is dependent entirely on rainfall (which adds heat to snow/ice) and conduc-

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tion is assumed to contribute a small, constant amount of energy. Because annual mass balance is most dependent on temperature and precipitation, the regressions in Figs. 3 and 4 are the primary inputs to the mass balance model. Secondary parameters include mean monthly estimates of relative humidity, cloudiness, and wind speed, and the standard deviation of daily temperatures for each month. Temperature, precipitation, and wind speed data are available at a range of elevations within the vicinity of each basin. The monthly fraction of sky cover was estimated by the number of days with precipitation per month (i.e., sky cover = the number of days with precipitation divided by the number of days/month). Sky cover and relative humidity were determined from local climate data but were more difficult to relate to elevation. These parameters were kept constant for all elevations within each drainage basin. To simulate past glaciers, the climatic input parameters of the accumulation/snowmelt model were adjusted to reflect a range of possible glacial climates (discussed below). The resultant accumulation map was used to determine the shape and extent of the steady-state glaciers that would develop. Potential adjustments to the climatic inputs to the model are limited primarily by our ability to describe modern and past climatic conditions, where little data exist for the latter. In this study, we limited our adjustments to additive variations of temperature (the temperature assigned to a cell based on its elevation minus the applied temperature change) and multiplicative variations of precipitation (the elevation-dependent precipitation times an applied factor, where 1 precipitation = modern precipitation) (Plummer and Phillips, 2003). Thus, the temperature–elevation lapse rate is translated but its gradient remains unchanged while the precipitation–elevation gradient increases when precipitation rates are increased. 2.3. Ice-flow modeling The snow and energy input to each cell of a digital elevation model determines the accumulation and ablation areas for the modeled basin. In the accumulation area, ice builds up and flows outward in the direction of ice-surface slope (usually down hill) into the ablation area until the glacier reaches a steady state. The computed rates of accumulation and ablation determine the net annual mass balance for the basin, and the transfer of mass between these two areas is based on an empirical equation for ice flow via deformation and sliding. The time-dependent flow of ice is deter-

307

mined by solving the following continuity expression for 2-D flow, @qy @h @qx ¼ bn   @t @x @y

ð3Þ

where h = ice surface elevation above datum, b n = net annual mass balance, q = ice discharge per unit width, and x and y are directions of ice flow in the horizontal plane. The flux of ice between adjacent cells ( q x , q y ) is determined by the thickness and the depth-integrated flow velocity, u, the latter computed from equations for ice flow by sliding (u s) and deformation (u d): u ¼ us þ ud ¼ f ð sBÞn þ ð1  f ÞH

2 ð sAÞm 5

ð4Þ

where B and A are coefficients of sliding and deformation, respectively; H is ice thickness and f is a factor used to vary the ratio of sliding to deformation. Following Fastook and Chapman (1989), the exponents n and m are taken as 2 and 3, respectively. The basal shear stress, s, is s ¼ qgHrh

ð5Þ

where q is ice density and g is gravitational acceleration. Eq. (3) is solved using fully explicit five-point finite differences (Plummer and Phillips, 2003). 2.4. Model calibration In parameterizing the model with modern climatologic data, we seek to determine how the climatic conditions associated with past glacial extents differ from modern conditions. The reliability of the calculated differences thus depends on how well the model describes modern conditions, and how well it describes the climatic sensitivity of steady-state alpine glaciers. For example, if the model overpredicts the modern glacial extents, it will underestimate the changes needed to produce larger glaciers. As glaciers no longer exist in the Wasatch and Uintas, however, it is impossible to calibrate the model using modern glaciers in the study areas. Two thin, semi-perennial snowfields, however, exist on shaded headwalls near the summit of the central Uintas (below Mount Lovenia and the Red Castle) and the model does produce a small but positive net annual accumulation rate in those areas. Correspondence of these local ablation minima provides at least some confidence that the model reproduces the modern accumulation/ablation pattern reasonably accurately. Another means of evaluating model accuracy is to compare simulated monthly snowpack thickness to ob-

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served SNOTEL snowpack thickness histories. Simulated spring-season snowpack thicknesses are very similar to SNOTEL measurements of snow-water equivalent (Table 2), but because ablation is primarily a melt-season calculation, and significant spring melting begins in late April to early May, agreement between measured and modeled snowpack in May is most significant among the comparisons in Table 2 (accumulation is largely an input parameter, so that comparisons of winter snowpack thickness are most valuable as a simple check that the input functions are behaving properly). Snow disappears from virtually all of the SNOTEL sites by early to middle June and simulated melting is also complete by the end of June. The monthly time-step currently implemented in the model precludes tuning the timing to any better accuracy than that. Given that this latter comparison evidences a reasonable match at the altitudes of the SNOTEL stations, that the model appears to match locations of local ablation minima at the high elevations in the Uintas and that the temperature and precipitation functions are based on an excellent meteorological data set, we believe that the model reproduces modern climatic conditions well enough to provide a good basis for subsequent estimates of the effects of hypothetical climate shifts. For the ice-flow model, initial values for parameters A and B in Eq. (4) were taken from Plummer and Phillips (2003). Those parameters were subsequently adjusted to provide a good match to LGM glacial extent in full glacial climate simulations for the study area. As is discussed below, the actual flow parameters are not a critical parameter in this study because we force simulated ice thickness to match glacial geomorphic evidence, and do not attempt to use ice thickness as a guide to paleoclimatic conditions.

3. Results 3.1. Sensitivity and uncertainty For a steady-state glacier simulation, the mass balance gradient effectively defines the extent of the glacier, except to the degree that the dviscosityT of the ice alters the shape of the glacier. That is, if the ice deforms or slides under little stress, it can produce a much thinner glacier than one that responds less readily to stress. As glacier thickness affects surface mass balance, differences in ice thickness can also produce differences in glacial extent. If the actual flow characteristics of the dpaleo-iceT were known, this effect could be used to help constrain the mass balance of the glacier because mass balance gradients, which increase with greater accumulation rates, also affect ice thickness. In paleoclimate studies, we cannot know the flow characteristics of the paleoglaciers, but the shape and thickness of the vanished glacier are tightly constrained by trimlines, lateral moraines and other geomorphic evidence. Ice flow parameters were, thus, adjusted to maintain, for each simulation, ice thickness consistent with observations. To improve agreement between modeled and field-reconstructed ice thicknesses in Yellowstone Canyon, ice-flow parameters A and B (Eqs. (2) and (4)) were increased by a factor of 5 above those described in Plummer and Phillips (2003) to 5.0  10 7 and 7.5  10 3, respectively, still well within the range of empirically determined values of these parameters (Table 3). This change reduced modeled ice thickness by 200 m. Parameters A and B were not adjusted for simulations in Lake Fork and Little Cottonwood Canyons because simulated ice thicknesses agreed with field-reconstructed estimates documented in Laabs (2004) and Richmond (1965).

Table 2 Measured and modeled snowpack at SNOTEL stations Month

SNOTEL station

Elevation (m asl)

Drainage basin

Mean measured snowpack (cm swe)a

Modeled snowpack (cm swe)b

April April April April April May May May May May

Snowbird Lake Fork Brown Duck Lake Fork Basin Five Points Lake Snowbird Lake Fork Brown Duck Lake Fork Basin Five Points Lake

2939 3079 3232 3323 3329 2939 3079 3232 3323 3329

Little Cottonwood Yellowstone Lake Fork Lake Fork Yellowstone Little Cottonwood Yellowstone Lake Fork Lake Fork Yellowstone

103.3 34.5 51.8 55.9 46.9 89.7 19.8 47.5 54.4 36.8

87.5–92.4 36.3–38.2 50.6–53.5 59.4–61.2 43.7–45.6 48.0–86.0 15.6–28.0 48.5–50.9 53.8–66.3 33.9–47.0

a

Data were retrieved from www.wrcc.dri.edu. Because the locations of SNOTEL stations are not precisely known, a range of output values from within 200 m of the approximate location of each station is reported. b

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Table 3 Empirically determined coefficients for ice flow Reference Stroeven et al. (1989) Oerlemans, 1988 Schemeitz and Oerlemans, 1997 Huybrechts et al. (1989) Plummer and Phillips (2003) Paterson, 1994 Oerlemans, 1989 This Study—Little Cottonwood and Lake Fork Canyons This Study—Yellowstone Canyon

Ice thickness also affects glacial extent by altering the surface mass balance. The climatic combinations described in Fig. 6 were based on net accumulation/ ablation maps calculated based on the modern topography. Ice advance, however, increases the elevation of the valley surface, which, in turn, increases precipitation but decreases topographic shading. For that reason, Plummer and Phillips (2003) recommend calculating net annual accumulation and ablation on a simulated glacier that matches field evidence of ice thickness and extent. In this study, we took a simpler approach, and calculated accumulation/ablation on the modern topography. This simplification was based on observations that the opposing effects of the ice-altered topography are of similar magnitude, so that the net effect is minor. The degree of this effect was tested in Yellowstone Canyon by simulating ice extent with a monthly temperature depression of 7.2 8C and modern precipitation. The ice surface grid was added to the valley surface grid, and the mass/energy balance model was run on the new surface. Ice extent in the second simulation decreased by less than 2% (or an equivalent temperature change of less than 0.18 C). Similar results were found by running this test in the Lake Fork and Little Cottonwood Canyons with respective temperature depressions of 8.4 8C and 9.1 8C and modern precipitation. This indicates that ice advance did not significantly affect glacier mass balance and that neglecting that effect does not introduce significant errors in estimating past climatic conditions from model results. 4. Discussion 4.1. Model simulations of LGM ice extents For each of the three canyons studied, model simulations define a specific set of climatic scenarios that reproduced observed LGM ice extent (Figs. 6 and 7). Plotting the successful temperature-precipitation conditions for each canyon (Fig. 7) yields a set of non-linear

A (year 1 kPa 3) 8

1.9  10 2.2  10 8 3.0  10 8 8.0  10 8 1.0  10 7 2.1  10 7 3.0  10 6 1.0  10 7 5.0  10 7

B (m year 1 kPa 2) 3.0  10 3 3.2  10 3 7.9  10 3 – 1.5  10 3 – 2.8  10 3 1.5  10 3 7.5  10 3

curves that do not, in general, overlap. The non-linearity essentially reflects that increases in precipitation are here expressed as increases in precipitation gradient. The different trajectories of the curves, on the other hand, reflect the different temperature lapse rates and precipitation gradients of each canyon. Although modern winter precipitation gradients and amounts are greater in the Wasatch than in the Uinta Mountains (Fig. 4), for example, modeled ice extent is greater in the Lake Fork and Yellowstone Canyons than in Little Cottonwood Canyon for any given decrease in temperature and/or change in precipitation (Fig. 7). This suggests that colder temperatures in the upper parts of the Lake Fork and Yellowstone Canyons, relative to Little Cottonwood Canyon, are more significant in determining mass balance than the winter precipitation difference between the two ranges (Figs. 3B and 4A). The separate curves for each canyon emphasize one of the advantages of this modeling approach; (1) the ability to consider glacial response to multiple variables at once, and (2) how local topographic gradients may produce differing climatic sensitivity in relatively nearby canyons. These curves provide an intriguing means of reconstructing regional LGM climatic conditions, even though uncertainties in the input data and potential errors in the models limit our confidence in paleoclimatic inferences based solely on these data. Considering that the LGM glaciers in each of these canyons were contemporary, the simplest interpretation of the associated paleoclimatic conditions would be to assume that a single combination of temperature-precipitation changes explains all three glaciers. If that were true, the curves should intersect at one or more points. The curves in Fig. 7 do not yield a single temperature–precipitation combination, however, that could produce LGM ice extent in all three canyons. Only one combination, a temperature depression of 5 8C and a precipitation increase of 2.5, produces LGM extents in both of the Uinta canyons. This suggests either that climatic conditions in the three different canyons were not uniformly different from modern

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climate in temperature and precipitation, or that the uncertainties involved in this approach are too great to identify the common change. Given that Little Cottonwood Canyon is in a different mountain range, on the shores of the Lake

Bonneville basin, it seems likely, that it experienced a wetter LGM climate than the high Uintas, as it does today, because of the increased potential for lakeeffect snow and rain. On the other hand, given that all three canyons are at approximately the same lati-

Fig. 6. (A) Ice extent during the LGM simulated under four different combinations of temperature depressions (e.g., T—8.4 8C) and precipitation changes (e.g., P  0.5) relative to modern in Yellowstone Canyon. Black lines indicate estimated ice extent during the LGM based on field mapping (from Laabs, 2004). (B) Ice extent during the LGM simulated under four different combinations of temperature depressions (e.g., T—9.9 8C) and precipitation changes (e.g., P  0.5) relative to modern in Lake Fork Canyon. Black lines indicate ice extent during LGM based on field mapping (from Laabs, 2004). (C) Ice extent during the LGM reproduced under four different combinations of temperature depressions (e.g., T—10.4 8C) and precipitation changes (e.g., P  0.5) relative to modern in Little Cottonwood Canyon. Black lines indicate ice extent during the LGM based on field mapping and air-photo interpretations (from Madsen and Currey, 1979; Shakun, 2003).

B.J.C. Laabs et al. / Geomorphology 75 (2006) 300–317

311

Fig. 6 (continued).

tude (N408), temperature depressions in all three canyons would probably have been the same. The trajectories of the successful climatic scenarios in each range are consistent with this hypothesis; larger increases in precipitation are required to reproduce LGM glaciers in Little Cottonwood Canyon than in the other two canyons for any given drop in temperature. For example, given a temperature depression of 7 8C in all three canyons, the Little Cottonwood glacier would have required a 2.5 precipitation change whereas the Lake Fork and Yellowstone glaciers would have required only 1.5 and 1.2 changes in precipitation, respectively (Fig. 7).

While differences in precipitation between the Wasatch Mountains and the Uinta Mountains seem plausible and likely, it is considerably more problematic to posit large precipitation differences between the two canyons in the Uinta Mountains. If we assume that temperature and precipitation differences from modern climate would have been the same throughout the Uintas, and that temperature departures from modern would have been identical throughout the region, we effectively define constraints that are, in this case, satisfied by only one climatic scenario. A temperature depression of ~ 5 8C and a precipitation change of 2.5 modern yields LGM ice extent in the Lake Fork and

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Fig. 6 (continued).

Yellowstone Canyons, while the same temperature depression in Little Cottonwood Canyon requires an accompanying increase in precipitation greater than 3.5 modern. This interpretation of the model results indicates that LGM climate was colder and much wetter throughout this region and, as suggested by Munroe and Mickelson (2002), lake-effect moisture derived from the surface of Lake Bonneville produced an even larger increase in precipitation in the Wasatch Mountains.

We can infer slightly different temperature depressions if we consider that precipitation changes might not have been exactly uniform across the Uintas, even between the nearby Lake Fork and Yellowstone canyons. Strong east–west precipitation gradients already exist in that range (Fig. 5), and subtle differences in storm tracks could have produced non-uniform precipitation changes. Assuming that precipitation changes were not different by more than about 20% still widens the range of possible temperature depressions to about

Fig. 7. Plot of temperature and precipitation combinations that yielded LGM ice extents in model experiments.

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4–6 8C. The warmer end of that range requires an even more dramatic increase in precipitation (~4.5) in Little Cottonwood Canyon, while the colder end reduces the necessary precipitation factor to about 3 modern. Continuing toward slightly more complicated climatic scenarios, reasons exist to consider non-uniform temperature changes within the region. Hostetler et al. (1994) suggest that the presence of Lake Bonneville may have kept local winter temperatures higher and summer temperatures cooler than in surrounding areas. Scenarios with warmer temperatures in the Wasatch than in the Uintas would require even greater precipitation increases in the Wasatch than those mentioned above. Interpreting the model results, based on the exact trajectories of the separate curves, assumes that the inverse-modeled estimates of LGM temperature–precipitation combinations for each canyon are the only possible scenarios. Significant uncertainties exist in these combinations because of a myriad of uncertainties in the data input to the model and because of potential biases in the model calculations. These include the lapse rates and precipitation–elevation gradients; which are based on data from a small number of weather stations; estimates of climate input data that are not available locally (e.g., relative humidity and cloudiness) and our lack of knowledge of how numerous second-order climate variables differed during the LGM. While we have not directly assessed uncertainty in this study, sensitivity tests conducted for basins in the Sierra Nevada, California (Plummer, 2002), suggest that these effects produce uncertainties in the calculated temperature depressions and precipitation factors of about F0.5 8C and F 30%, respectively. Incorporation of this uncertainty in the model results would, thus, expand the curves of Fig. 7 to bands encompassing a wide range of temperature–precipitation combinations, approximately centered on the existing curves. Because the intersection of the Lake Fork and Yellowstone curves forms a rather oblique angle, this would greatly expand the range of combinations that could satisfy the paleoclimate constraints discussed previously, probably even to the extent that many combinations of temperature depression and precipitation change could have produced all three LGM glaciers. Assuming that the intersection of the response curves is largely a function of differing climatic gradients, not just uncertainty, the LGM temperature depression implied by the intersecting curves is ~ 4 8C to 6 8C, with corresponding precipitation increases of ~ 3 to 2 modern in the Uintah Canyons and N 4 to 3 modern in Little Cottonwood Canyon.

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This paleoclimatic estimate is intriguing, in that it appears to reflect a new way of defining the unique paleo precipitation–temperature combination connected with a particular set of glacial geomorphic features. Rather than focus on that result, however, which probably overstates our ability to accurately simulate the slight difference in climatic response between the two basins, we explore now the full range of LGM climatic scenarios that the model suggests are possible, and attempt to better constrain our estimate of LGM temperature depression by examining paleoclimatic reconstructions of several previous studies in light of those results. Previous studies have suggested colder estimates of LGM climate (e.g., Kaufman, 2003) than the  4 8C to  6 8C temperature range that results from applying reasonable climatic constraints to the model results of this study. Kaufman (2003) concluded from amino acid paleothermometry that temperature depression in the Lake Bonneville basin during the period 24 to 12 cal. ka was between 7 8C and 13 8C. Our simulation results indicate that a temperature depression of 7 8C would have been accompanied by no precipitation change in the Yellowstone, 1.5 modern in the Lake Fork Canyons, and 2 modern in Little Cottonwood Canyon. At colder temperatures, our results indicate that a depression of more than ~ 9 8C would have been accompanied by less-than-modern precipitation in all 3 basins, and a depression of more than 10.5 8C would have required 0.5 modern in Little Cottonwood Canyon and less than 0.1 modern in Yellowstone Canyon. Such relative drops in precipitation imply that little moisture was available to glaciers in the central Rocky Mountains during the LGM. This contradicts studies with general circulation models (e.g., Bartlein et al., 1998), however, that show a southward displacement of the polar jet stream during the LGM that would have led to increased precipitation in the northern Great Basin and central Rocky Mountains (e.g., Benson and Thompson, 1987). If these models are correct, precipitation in the Wasatch and Uinta Mountains would probably have been greater than modern during the LGM, so based on our modeling results, temperature depressions of more than 9 8C are unlikely. Yet, because Kaufman’s amino acid data are perhaps the best available paleotemperature proxy for the region, we suggest that a range of temperature depressions that encompasses the cold end of our estimated range and the warm end of Kaufman’s range,  6 8C to  7 8C, is most likely. Corresponding precipitation increases are ~ 2 to 1 in the Yellowstone and Lake Fork Canyons and 3 to 2 in Little Cottonwood Canyon.

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4.1.1. Mass balance gradients The absolute amounts of accumulation and ablation on glaciers and ice-surface mass-balance gradients (i.e., the change in mass balance with elevation) that would have accompanied specific temperature changes have implications for the climatic classification of the reconstructed glaciers (Table 4). Ablation gradients on the modeled Lake Fork and Yellowstone glaciers were extracted by calculating the mass/energy balance on the ice-elevation surface simulated for a given set of temperature/precipitation combinations that produced LGM ice extent. For a temperature depression of 5.5 8C and precipitation 2.3 modern, ablation gradients on these glaciers would have been ~ 6 to 8 mm/m. These gradients for such climatic conditions are at the high end of ablation gradients on modern glaciers in continental mountain settings where glaciers exist under cold and dry climates (e.g., Mayo, 1984), and reflect the relatively steep temperature-elevation lapse rates during summer months that currently occur in the south-central Uinta Mountains (see Fig. 3). A temperature depression of 5.5 8C in Little Cottonwood Canyon, on the other hand, would have been accompanied by a ~ 3 precipitation increase. This implies very high annual snowfall, exceeding 4.5 m of water equivalent precipitation, or more than 13 m of snow at high elevations (~ 3500 m asl). Ablation rates on the Little Cottonwood glacier would also have been very high, with more than 10 m of annual melting at the terminus. These amounts of net annual accumulation and ablation are typical of modern coastal glaciers

Table 4 Reconstructed ablation gradients in the Uinta Mountains Glacial valley

b

Burnt Fork Thompson Creekb Middle Fork Beaver Creekb Other Rocky Mountain valley glaciersc Yellowstone Lake Fork Yellowstone Lake Fork a

Climatea

Ablation gradient (mm of water/m of elevation)

N/A N/A N/A

1.4 2.8 2.1

N/A

1.0–5.0

T—5.1 T—5.1 T—8.4 T—8.4

8C, 8C, 8C, 8C,

P  2.5 P  2.5 P  0.5 P 1

8.0 6.0 5.0 4.0

Temperature and precipitation combinations that yielded LGM ice extent in Lake Fork and Yellowstone Canyons (see Figs. 6 and 7). b Valley in the northeastern Uintas; ablation gradients are from Munroe (2001). c From Leonard et al. (1986), Murray and Locke (1989), and Munroe (2001).

(e.g., Kuhn, 1984), and are perhaps reasonable estimates if, as some previous studies have concluded (e.g., Hostetler et al., 1994; Munroe and Mickelson, 2002), Lake Bonneville was a significant moisture source for glaciers in the Wasatch Mountains. 4.1.2. Comparison with recent studies in the Uinta Mountains As noted above, Munroe and Mickelson (2002) inferred an LGM temperature depression of 5.5 8C from glacier reconstructions in the Uinta Mountains and suggested an important relationship between Lake Bonneville and glaciers in the Uinta Mountains. Our results support their finding that glaciers downwind of Lake Bonneville received more precipitation than modern under such a temperature depression, especially in the case of Little Cottonwood Canyon, where a depression of 5.5 8C would have been accompanied by a ~ 3 precipitation increase. This finding is also supported by results of regional climate modeling described in Hostetler et al. (1994), which suggest that moisture derived from the surface of Lake Bonneville was likely returned to the lake (via surrounding drainages including Little Cottonwood Canyon) to maintain its hydrologic budget. In the south-central Uintas (Lake Fork and Yellowstone Canyons), model results indicate that a temperature depression of 5.5 8C would have been accompanied by ~ 2 more precipitation than modern; this is slightly less than results in Munroe and Mickelson, who estimate that winter accumulation was ~ 3 greater than modern in the north-central Uintas (due north of the Lake Fork and Yellowstone Canyons) during the LGM. 5. Conclusions This study used a relatively new approach to reconstructing paleoclimatic conditions from glacial geomorphic evidence by combining applications of numerical modeling of snow accumulation/ablation and ice flow. This method allows ready accounting of the spatially varying influence of temperature, precipitation, shortwave radiation, and many secondary parameters on glacier mass/energy balance and, ultimately, ice extent. We find this approach significantly aids in exploring the implications of the many potential climate scenarios that could have characterized past glacial climates. In this study, for example, we explored glacial response to numerous changes in temperature and precipitation, and observed that it was significantly tied to the local temperature-eleva-

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tion lapse rate and local topographic precipitation gradient. A broad range of potential temperature and precipitation conditions during the time of the LGM in northern Utah were obtained from modeling of mass/energy balance and ice flow in the Little Cottonwood, Lake Fork and Yellowstone Canyons. Assuming that these temperature–precipitation combinations accurately reflect glacial sensitivity to probable climatic scenarios, a narrow interpretation of the intersection point of the three glacier response curves would indicate that LGM temperature depressions were ~ 4 8C to 6 8C. Given the uncertainties in the calculations, however, we find this estimate premature and we consider it more compelling to interpret these results in combination with other seemingly reliable paleoclimatic data for the region. We, thus, estimate that temperatures during the LGM were ~ 6 8C to 7 8C colder than modern and that precipitation was ~ 2 to 1 modern in the Uinta Mountains and ~ 3 to 2 modern in Little Cottonwood Canyon. This implies that Lake Bonneville enhanced precipitation on downwind glaciers in the Wasatch Mountains. Increased precipitation downwind of the lake may also have influenced the Uinta Mountains; however, our finding that LGM climate included up to ~ 2 modern precipitation is slightly greater than precipitation values estimated for LGM climate elsewhere in the western US (e.g., Porter et al., 1983; Benson and Thompson, 1987). Additional work is needed to improve limits on the temperature and precipitation combinations that

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may have existed during the LGM in northern Utah and to better understand the relationship between Lake Bonneville and glaciers downwind. One possible approach to this would be to model the hydrologic budget of Lake Bonneville concurrently with nearby glaciers (cf. Plummer, 2002) in the western Uintas and other Great Basin and Rocky Mountain ranges. Another way to set limits on temperature and precipitation would be to apply the approach used here to a broader area that includes low-elevation valleys that were not occupied by ice during the last glaciation; the supposition being that excessive temperature depressions or precipitation in model experiments would produce ice in such valleys. Given the spatial and temporal variability of paleoclimate in the western US at the time of the global and local LGM–as suggested here and in previous studies–continued efforts to improve the understanding of glacial chronology will provide an essential framework for these paleoclimatic inferences. Acknowledgments Funding was provided by the Geological Society of America, the Desert Research Institute, and the National Science Foundation (EAR-0345277). Discussions with J. Munroe, S. Hastenrath, J. Knox, B. Singer, and A. Zhu along with reviews by F. Phillips and an anonymous colleague were extremely helpful in preparing early drafts of this paper.

Appendix A. Monthly temperature and precipitation regressions in Little Cottonwood, Lake Fork and Fork and Yellowstone Canyons

Drainage basin

Month

Temperature lapse rate

R2

Precipitation–elevation gradient (cm water/km)

R2

Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood

January January January February February February March March March April April Aprily May May May June

y = 3.4 + 3.7 y = 6.8 + 11.8 y = 6.8 + 11.8 y = 3.4 + 4.7 y = 6.2 + 10.6 y = 6.8 + 11.8 y = 4.5 + 10.1 y = 7.3 + 17.3 y = 7.1 + 16.5 y = 6.7 + 19.4 y = 7.9 + 22.8 = 8.8+24.9 y = 5.6 + 21.3 y = 8.4 + 28.9 y = 7.9 + 27.7 y = 5.6 + 25.2

0.969 0.934 0.934 0.969 0.995 0.934 0.999 0.998 0.857 0.969 0.976 0.971 0.989 0.999 0.932 0.989

y = 13.3x  18.5 y = 5.6x  11.7 y = 5.6x  11.7 y = 12.017.6 y = 6.4x  10.9 y = 6.6x  14.0 y = 7.8x  6.6 y = 5.9x  9.8 y = 7.1x  15.3 y = 8.7x  9.7 y = 5.4x  8.7 y = 6.8x  14.4 y = 9.6x  13.9 y = 4.78.7 y = 5.9x  11.8 y = 8.2x  14.6

0.956 0.986 0.992 0.982 0.997 0.996 0.988 0.999 0.982 0.973 0.999 0.994 0.973 0.999 0.994 0.899

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Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Liitle Cottonwood Lake Fork Yellowstone Little Cottonwood Lake Fork Yellowstone Little Cootonwood Lake Fork Yellowstone

June June July July July August August August September September September October October October November November November December December December

y = 9.5 + 37.5 y = 9.1 + 36.2 y = 5.6 + 25.4 y = 8.4 + 37.9 y = 8.3 + 37.4 y = 5.6 + 29.2 y = 8.4 + 36.9 y = 7.9 + 35.3 y = 5.6 + 24.6 y = 8.4 + 31.9 y = 7.1 + 28.1 y = 3.4 + 13.7 y = 7.3 + 23.3 y = 6.8 + 21.8 y = 3.4 + 7.0 y = 7.3 + 16.3 y = 7.1 + 15.5 y = 3.4 + 3.7 y = 7.3 + 12.2 y = 6.0 + 9.0

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0.999 0.935 0.760 0.999 0.861 0.989 0.999 0.928 0.984 0.999 0.837 0.967 0.998 0.934 0.968 0.998 0.857 0.968 0.998 0.834

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