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Stuck in the Wild—The Hydrology of the Teklanika River (Alaska) in the Summer of 1992

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In late spring of 1992, Christopher McCandless crossed the Teklanika River, west of Healy, Alaska (United States). His summer has been well documented both in the book and the movie ‘Into the Wild.’ In early summer of 1992, he attempted to cross back over the river, but was stopped by high waters and he died later that summer. This paper investigates the hydrologic conditions of the Teklanika River watershed. We consider both climatological conditions and also conditions during the summer of 1992. We run process-based snowpack and runoff models in order to estimate the river hydrograph at the point of Mr. McCandless’ attempted crossing. Our results demonstrate that the Teklanika river is very flashy during the summer, responding rapidly to strong episodic rainfall events. The main snowmelt signal occurred in mid-to-late May, after Mr. McCandless’ first crossing and before his second attempt. The specific day of his attempted re-crossing corresponded to a large runoff event, driven by rainfall. We conclude that Mr. McCandless had unfortunate timing and that, had he tried to cross a day or two earlier or later, the outcome may have been different. This paper is also an opportunity to explore the hydrologic compromises that must be made when trying to study ungauged, or poorly gauged, areas. There is a spectrum of choices regarding input datasets and methodological simplifications and the correct location on that spectrum will depend on the particular watershed the objectives and expectations of the study.
Stuck in the WildThe Hydrology of
the Teklanika River (Alaska) in the
Summer of 1992
David F. Hill
1
,
2
* and Christina Aragon
1
1
Water Resources, Oregon State University, Corvallis, OR, United Sates,
2
Civil and Construction Engineering, Oregon State
University, Corvallis, OR, United States
In late spring of 1992, Christopher McCandless crossed the Teklanika River, west of Healy,
Alaska (United States). His summer has been well documented both in the book and the
movie Into the Wild.In early summer of 1992, he attempted to cross back over the river, but
was stopped by high waters and he died later that summer. This paper investigates the
hydrologic conditions of the Teklanika River watershed. We consider both climatological
conditions and also conditions during the summer of 1992. We run process-based snowpack
and runoff models in order to estimate the river hydrograph at the point of Mr. McCandless
attempted crossing. Our results demonstratethattheTeklanikariverisveryashy during the
summer, responding rapidly to strong episodic rainfall events. The main snowmelt signal
occurred in mid-to-late May, after Mr. McCandlessrst crossing and before his second
attempt. The specic day of his attempted re-crossing corresponded to a large runoff event,
driven by rainfall. We conclude that Mr. McCandless had unfortunate timing and that, had he
tried to cross a day or two earlier or later, the outcome may have been different. This paper is
also an opportunity to explore the hydrologic compromises that must be made when trying to
study ungauged, or poorly gauged, areas. There is a spectrum of choices regarding input
datasets and methodological simplications and the correct location on that spectrum will
depend on the particular watershed the objectives and expectations of the study.
Keywords: snow-melt, energy-balance modeling, glacier runoff, Alaska, river crossings
INTRODUCTION
On 18 June 2020, Bus 142 was airlifted (Figure 1) from the wilderness west of Healy, Alaska
(United States). The bus, which was used for employee housing during road construction efforts, was
abandoned in 1961. After decades in obscurity, the bus became famous upon the publication of Into
the Wild(Krakauer, 1997) and the subsequent movie of the same title. That book tells the story of
Christopher McCandless, who spent the summer of 1992 in the Alaskan wilderness, using the bus as a
basecamp. He crossed the Teklanika River from east to west on April 28. After 10 weeks in the
wilderness, Mr. McCandless attempted to re-cross the river from west to east on July 5.He encountered
impassable conditions and died in August of 1992 due to malnourishment. A recent article (Krakauer
et al., 2015) concluded that consumption of Halpinum(wild potato) seeds contributed to his death.
The legacy of Mr. McCandless is a polarizing one, with some parties appreciative of his sense of
adventure and others disapproving of his lack of planning and resourcefulness. For example, viable
crossing points (a roadway bridge and a cable-car across the river) upstream and downstream were
marked on maps available at that time. Visits to the site of Bus 142 were common in the decades
Edited by:
Wouter Buytaert,
Imperial College London,
United Kingdom
Reviewed by:
John Pomeroy,
University of Saskatchewan, Canada
Heather Best,
U.S. Geological Survey, Alaska,
United States
*Correspondence:
David F. Hill
dfh@oregonstate.edu
Specialty section:
This article was submitted to
Hydrosphere,
a section of the journal
Frontiers in Earth Science
Received: 22 March 2022
Accepted: 21 June 2022
Published: 15 July 2022
Citation:
Hill DF and Aragon C (2022) Stuck in
the WildThe Hydrology of the
Teklanika River (Alaska) in the Summer
of 1992.
Front. Earth Sci. 10:902226.
doi: 10.3389/feart.2022.902226
Frontiers in Earth Science | www.frontiersin.org July 2022 | Volume 10 | Article 9022261
ORIGINAL RESEARCH
published: 15 July 2022
doi: 10.3389/feart.2022.902226
following Mr. McCandlesssummer there and multiple drownings
in the Teklanika River contributed to the decision to remove
the bus.
This article rst reviews the basic uid dynamicsassociated with
crossing a river. The goal for doing this is to provide some general
context and guidelines for how the difculty of crossing a river
scales with the volumetric owrate, or discharge. Next, this article
performs an analysis of the hydrology of the Teklanika watershed.
This is done by investigating the limited stream gauge data in the
vicinity and climatological normals of temperature and
precipitation in the area. Additionally, a process-based modeling
study of the precipitation, snowmelt, and runoff for the basin for
the summers of 1991 and 1992 is carried out. The goal of these
efforts is to answer several questions. Was there anything
anomalous about the summer of 1992 in terms of temperatures
or precipitation? Was the increase in ow that prevented Mr.
McCandless from re-crossing the river a summer-long increase due
to high-elevation snow and glacial melt? Or, was it a short-lived
pulse due to rainfall? In short, this article seeks to add a coda to the
story of 1992 by identifying whether or not Mr. McCandless was
truly stuck in the wild,or simply the victim of unfortunate timing.
BACKGROUND
Specic statistics on river drownings due to attempted crossings
are difcult to obtain. Some studies of mountain fatalities
(Faulhaber et al., 2020;Zürcher et al., 2020) focus more on
climbingaccidents, while others (Farstad and Luttrell, 2020)
focus on drownings associated with watercraft (kayaing, rafting).
One thorough analysis (Heggie and Amundson, 2009) of search
and rescue callouts (SAR) in United States national parks
separated boating incidents from swimmingincidents (21 and
6% of SAR incidents, respectively) but swimming is not exactly
the same as river crossing. One useful study (Peden et al., 2016)of
10 years of Australian river drownings (n= 770) breaks out
fatalities by cause and the swept away(n= 23) category
represents 3% of the loss of life.
From a uid dynamics perspective, what matters most initially
when crossing a river is the Reynolds number (Re) of the ow, the
numerator of which is the product of the ow speed (V) and
characteristic length scale (D), which could be taken to be the
diameter of your leg. If that number is fairly low, there may be a
pattern of alternating swirls downstream, known as a vortex
street. As Re increases, the ow becomes more chaotic, or
turbulent, and it separates as it passes your legs, creating large
wakes downstream. This ow separation produces large drag
forces which scale with V
2
.
What causes a rivers speed to increase? Simply put, more
water owing downhill, or volume discharge Q. Essentially, the
hydraulic patterns (distributions of water elevation and velocity)
are functions of the hydrology (water volumes). Natural rivers,
with irregular cross sections, will have complicated patterns of
velocity, but it is useful here to consider the idealized case of a
wide rectangular channel. In this case, the Manning equations for
velocity and discharge are
Vα
ny2/3S1/2
0(1)
Qα
nby5/3S1/2
0.(2)
In these equations αis a conversion factor that accounts for the
system of units (British vs. metric), nis a roughness parameter, y
is the depth of the ow, bis the width of the ow, and S
0
is the
streamwise slope of the river bed. Combining these equations
shows that Vdepends on Q
2/5
. Thus, doubling the owrate
increases the speed by 30% and increasing the ow by a factor
of 10 increases the speed by a factor of two and a half.
Fast-moving water is not the full story of wading a river,
however. After all, a swift current that is only ankle or shin deep
may be trivial to cross. As the owrate in a river increases, not
only does the speed increase, but the water elevation, or stage, also
increases to accommodate the greater volume passing by. If we
solve (2) for y, we see that it depends on Q
3/5
. So, if the owrate
doubles, the stage goes up by 50%. If the owrate goes up by a
factor of 10, the stage quadruples.
This increase in stage matters for two reasons. First, drag
forces of water owing past an object depend on the frontal area
of the object. If you are knee deep in a owing river, that frontal
area is a rectangular area equal to the width of your calf multiplied
by the distance from your knee to the ground. And, multiply this
by two, assuming you have both legs in the water. So, as the depth
increases, this frontal area increases, and so does the drag.
Reducing this frontal area will reduce the drag, so turning
FIGURE 1 | Bus 142 being airlifted by the Alaska Army National Guard;
18 June 2020. Source: Wikimedia Commons.
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Hill and Aragon Stuck in the Wild
your body sideways to the rivers current can make it easier to
cross. And, there is the secondary benet of allowing you to get
into a strong stance to resist the ow.
To provide just one sample calculation, consider a person
50 cm wide, who is standing in 1 m of water and facing the
current, with legs together. Assuming a drag coefcient of 1.5,
which is suitable for an elliptical cross section, with its minor axis
parallel to the current, the force due to a current of 1 m per second
is roughly 375 N, or 80 pounds. Many people might be able to
resist that, but a doubling of the current to 2 m per second would
quadruple this force to over 300 pounds.
The second reason that an increase in stage matters has to do
with buoyancy and friction (Figure 2). The object on the left has
its weight (W) perfectly balanced by the upward force (N) from
the ground. Due to friction between the object and the ground,
there is a maximum friction force F
f
that is proportional to N. As
long as the drag force F
d
is less than this friction force, the object
will stay put. At right, the object is now partially submerged. The
key difference here is the balance of forces in the vertical
direction. The weight is now countered by the sum of a
buoyancy force (F
b
) and a smaller upward force (N) from the
ground. Since Nis reduced, the maximum frictional force is also
reduced, meaning it takes much less drag force to dislodge the
object.
In every way, therefore, increases in owrate stack the odds
against attempts to cross a river. The increases in owrate raise
the stage of the river and increase its speed, both of which increase
the drag forces experienced by a person wading across. And,
increases in stage submerge a greater portion of the persons
body, which reduce the ability to resist the drag. Drag forces vary
linearly with uid density and water is 1,000 times more dense
than air. Anyone who has been buffeted around by strong wind
gusts during a storm should recognize that even modest ows of
water, combined with uncertain footing or slippery rocks, can
quickly make a river crossing impossible.
METHODS
Study Site
The Teklanika river (Figure 3) has its headwaters at the toe of the
Cantwell Glacier, just east of Mount Pendleton in the Alaska Range
in Denali National Park. This stretch of the Range, which spans east
to west from the Denali Massif to the Hayes Range is fantastically
straight, as if drawn with a ruler. It is cut on its western side by the
Muldrow Glacier, spilling off of the northeastern anks of Denali
itself. It is breached near its middle by the Nenana River, which
parallels Highway 3 (Parks Highway), on its way north through the
town of Healy. Rivers on the south aspect of this wall of peaks are
short lived, quickly gathering into the Chulitna River, as it heads
south towards Cook Inlet. Rivers draining the north aspect have
much longer paths to take. The two main forks (Figure 4)ofthe
Toklat, the Teklanika, and Sanctuary River all gather up individual
drainages like capillaries into veins as they spill down wide glacial
valleys. Once they leave the Alaska Range behind, gradients ease and
currents slow.
The Teklanika itself ows from its headwaters to Cathedral
Mountain, which it skirts on its eastern anks before passing the
Denali Park Road and a National Park Service campground on its
eastern bank. Further north, the river passes through a tight gorge
west of Mt. Wright and then gathers up Sanctuary River. The
Teklanika next joins up with the Savage river just after the
crossing of the Stampede Trail. Further north, as it passes
Comma Lake, the Teklanika slows dramatically and changes
from a heavily braided river to a highly sinuous river with
bends that double back on themselves amid countless remnant
oxbows. Eventually the Teklanika joins up with the Nenana River
and then the Tanana River, before joining the Yukon river on its
journey to the Chukchi Sea.
Both Figures 3 and 4show various points of interest. The
Stampede Trail trailhead near Eightmile Lake is where Mr.
McCandless was dropped off in late April of 1992. The trail,
which amounts to a much deteriorated road from the 1960s, rst
crosses the Savage River before crossing the Teklanika River. The
former location of Bus 142 is shown, in addition to the location of
a historic (19641974) United States Geological Survey river
gauging location (Site 15518350). Figure 3 also shows two
watersheds. The USGSwatershed is the land draining to the
gauge location and has an area of about 1,250 km
2
. The
Teklanikawatershed is the land draining to the intersection
of the Stampede Trail and the Teklanika River and this has an
area of 800 km
2
, or 64% that of the USGS watershed. The
Teklanika watershed ranges in elevation from about 500 to
2,200 m, with an average elevation of 1,100 m.
USGS Gauge Data
Alaska is a challenging state, in terms of long-term gauging
datasets. Remote terrain, harsh conditions, and sheer scale
FIGURE 2 | Forces on an unsubmerged (left) and partially submerged (right) object.
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Hill and Aragon Stuck in the Wild
mean that many basins are not gauged with the same coverage as
those in the conterminous United States. As noted above, the
USGS maintained a gauge on the Teklanika River (15518350) for
the water years 1965 through 1974. A brief summary of the ow
data are provided by The Mangi Environmental Group (2005)
and there is also a study (Schalk, 2005) that uses these limited data
to estimate ood return periods.
Figure 5 shows hydrographs, or plots of daily river ows, from
the 10 years of available USGS data. The gray shaded area spans
the minimum and maximum values for each day of the year, and
the black line shows the average value over the period of record.
The irregular nature of the black line is due to averaging over only
10 years, which is a relatively short period, and also to the fact that
the river is quite ashy in nature. The blue line shows a smoothed
version of this, which is a better representation of a much longer-
term average. From the hydrograph, it is clear that cold winter
temperatures keep ows very low. A strong increase in ows is
observed from mid-April through October, which is due to a
combination of snowmelt and precipitation patterns. These
summer ows are about 10 times greater than the winter
FIGURE 3 | Overview (inset) and closeup view of the Teklanika River watershed in south-central Alaska.
FIGURE 4 | View looking south at the Teklanika watershed and other river basins draining the north side of the Alaska Range.
Frontiers in Earth Science | www.frontiersin.org July 2022 | Volume 10 | Article 9022264
Hill and Aragon Stuck in the Wild
ows. Over the period of record the average river ow at the
gauge was 19.3 m
3
s
1
, which amounts to a runoff depth of
486 mm. The average of the highest ow seen each year over
this period was 240 m
3
s
1
. Finally, the standard deviation, which
is a measure of the variability or ashinessof the ow, was
29 m
3
s
1
.
Relevant and recent discussion of the hydrology of Alaska
rivers is provided by Curran and Biles (2021). They categorize
rivers into three broad classes, based upon the timing of peak ow
and also the main drivers of ow. Rain-dominated watersheds
(category 1) tend to have peaks both in May and October. Snow
dominated watersheds (category 2) see peak ows in May/June
and often a second peak in September or October. Finally, high-
elevation melt watersheds (category 3) see a broad peak that spans
summer months and is due to high-elevation snow melt and also
ice-melt from glacier surfaces. Curran and Biles (2021) go on to
subdivide (A, B, C, etc.) these classes based upon ner-scale
details of the timing and magnitudes of hydrographs within each
category. A complete inventory of the gauges studied is provided
in an accompanying data release and the Teklanika watershed
(draining to the USGS gauge) is classied as 3B.
Teklanika Watershed Climate and Weather
Streamow is the byproduct of many physical processes.
Precipitation, snowmelt, inltration of water into the soil,
evaporation, and other complex processes partition and direct
water both in space and in time. The two most signicant drivers
of streamow are the patterns of precipitation and temperature.
The Scenarios Network for Alaska and Arctic Planning (SNAP)
project distributes gridded monthly time series, downscaled to a
spatial resolution of 771 m, based on the Climate Research Unit
(Harris et al., 2014) (CRU; Version 3.1) datasets. Figure 6 shows
the variation in the mean annual precipitation (19802009) for
much of Alaska, and also for the Teklanika watershed. Coastal
Alaska is one of the wettest places on Earth, with the coastal
mountain ranges receiving in excess of 8,000 mm of precipitation.
Interior Alaska is much drier, and the Teklanika watershed
receives between 400 and 800 mm of precipitation per year,
with a spatial average of 580 mm. The USGS watershed
averaged 550 mm of precipitation over the 19802009 period
and 573 mm over the 19651974 period. This works out to a
runoff ratio of 84%, based on the gauge data described above. This
is consistent with previous studies (Beamer et al., 2016) in Alaska
which looked at sublimation and evaporative uxes that remove
water from a watershed before it is able to run off.
The seasonal variations of mean monthly temperature and
precipitation, averaged over the Teklanika watershed, are shown
in Figure 7. The precipitation data show the presence of strong
summer rains, and the mean annual precipitation averaged over
the watershed is 580 mm for the 19802009 period and 600 mm
for the 19651974 period. If these precipitation volumes ran off
uniformly over the year, they would correspond to streamows of
14.7 and 15.2 m
3
s
1
for the two time periods. These are less than
the average ow of 19.3 m
3
s
1
at the USGS Gauge since that gauge
captures the Savage River as well. If we apply simple area-scaling
to the USGS data, the average river ow of the Teklanika
watershed for the 19651974 period is estimated to be about
12.4 m
3
s
1
. Finally, note that the temperature data are nearly in
phase (slightly ahead) with the precipitation data and show
above-freezing temperatures from April through September.
Model Description
In order to model snowpack processes and runoff across the
Teklanika watershed, we used a collection of process-based
models that have been used widely in high-latitude locations
dominated by snow and ice, including Alaska (Beamer et al.,
2016,2017;Crumley et al., 2019,2021). Only a brief summary is
presented here, with readers directed to the original citations for
additional details.
MicroMet (Liston and Elder, 2006b)isrst used to
interpolate weather data (either from discrete stations, or
gridded re-analysis cells), including temperature,
precipitation, relative humidity, and wind speed and
direction, to the high resolution grid used for the model.
Following interpolation, individual adjustments are made to
each variable. These are done using known relationships
between weather variables and topography. Solar and
longwave radiation are calculated with sub-models, based on
temperature, humidity, and topographic aspect and slope.
Next, SnowModel (Liston and Elder, 2006a) is used to evolve
the snowpack over time. This is done using an energy-balance
approach rather than relying on statistical approaches or proxy
methods such as temperature index equations. Processes in
SnowModel include, among others, accumulation of snow
precipitation, wind transport of snow, sublimation, and
snowpack ripening and melt. SnowModel does not contain
glacier dynamics, but it does model the melt of glacier ice
after the seasonal snowpack has melted away. In this sense, it
is an appropriate model for capturing the hydrologic response of
a glaciated environment for a given year.
Finally, HydroFlow (Liston and Mernild, 2012;Mernild and
Liston, 2012) was used to route rainfall, snowmelt, and ice-
melt across the landscape. It does this through solution of
coupled equations for fast-response and slow-response ow
and it produces a time series of streamow at all model grid
cells.
Evapotranspiration (ET) was estimated in the following way.
First, recall from the gauge data that the runoff ratio for the
FIGURE 5 | USGS streamow data at the Teklanika gauge from
19641974.
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Hill and Aragon Stuck in the Wild
Teklanika watershed was 84%. Next, we analyzed the MODIS
MOD16A2GF.061 dataset (Mu et al., 2011), which is an 8-day
composite, 500 m resolution product based on the Penman-
Monteith (Monteith, 1965) equation. The MODIS data
showed that the annual variation of ET was essentially
Gaussian, with a peak at the beginning of July. For the
Teklanika watershed, the annual average ET from the MODIS
data was about 350 mm. Compared to the precipitation input
from the SNAP dataset, this would produce a runoff ratio of only
40% instead of the observed 84%. Therefore, our approach was to
preserve both the runoff ratio suggested by the gauge data and the
seasonal variation of water abstraction by ET suggested by the
MODIS data. To do this, 16% of the annual precipitation volume
for our model runs was t to a Gaussian curve and subsequently
removed as ET during the runoff modeling.
Figure 8 shows the model domain used in this study. Both the
Teklanika watershed and the USGS watershed are shown for
reference, but following results will focus on the former.
Model Forcing Data
The model framework described above requires elevation, land
cover, and weather data. The elevation data were obtained (US
Geological Survey, 2020)fromthe1/3rdarc-secondDigital
Elevation Model (DEM), which is part of the USGS National
Map 3DEP Data Collection. Land cover data were obtained
from the 2011 Alaska National Land Cover Database (NLDC)
(Homer et al., 2015). These datasets are shown in Figures 9A,B.
One of the landcover classicationsispermanentsnow/iceand
Figure 9C shows the ice (glacier) cover in the southwest corner
of the Teklanika watershed. The ice cover from the NLCD
dataset differs signicantly from the Randolph Glacier
Inventory (Version 6) (RGI Consortium, 2017), and
Figure 9D shows the ice mask representing the union of
those datasets. The RGI is generally regarded as a superior
product, as it is based on manual inspection and digitization
while the NLCD is based on automated processing. The NLCD
dataset shows 8.6 km
2
ofglaciercoverintheTeklanika
watershed, and the RGI dataset shows 20 km
2
.Model
simulations were carried out with both ice masks, in order to
determine the sensitivity of the summer runoff to the glacier
cover.
For an additional point of context, a portion of the
1951 USGS 1:250000 scale quadrangle for Healy, Alaska is
shown in Figure 10. This map indicates fairly comparable
glacier cover at the headwaters of the Teklanika, but much
more glacier cover at the headwaters of Sanctuary River, which
joins the Teklanika upstream of the Stampede crossing.
Digitizing the glacier areas of this historic map reveals a
cover of 31.6 km
2
. Note that the Savage River basin has no
glacier cover in any of these datasets.
Finally, weather forcing data were obtained from the Climate
Forecast System (CFS) reanalysis product (Saha et al., 2010)
which has a 6-h temporal resolution and a spatial resolution
FIGURE 6 | Mean annual precipitation for Alaska and (inset) the Teklanika watershed for the period 19802009.
FIGURE 7 | Climatologies of mean monthly precipitation and mean
monthly temperature averaged over the Teklanika watershed for the period
19802009.
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Hill and Aragon Stuck in the Wild
of approximately 40 km. As noted above, MicroMet distributes
data, either from in-situ stations or from reanalysis grid cell
centers, to the high-resolution model grid.
Model Calibration and Conguration
Direct calibration of the Teklanika watershed is complicated by
the fact that the period for which streamow is available does not
overlap with the period of record of the CFS reanalysis product.
Therefore, for this study, model parameters were selected based
on previous comparable studies (Beamer et al., 2016,2017;
Crumley et al., 2019,2021) in Alaska. Calibration for those
studies was based on glacier mass balance data, streamow
data, and snow telemetry data. One of the calibration sites was
the Gulkana Glacier, providing some representation to the
processes of the eastern Alaska Range.
With these parameters, a model simulation was carried out
from 1 October 1990 through 30 September 1992. The model
time step was 6 hours, but model output was saved at a daily time
step. Note that this model time step is too coarse to adequately
capture the diurnal variations in streamow that are observed in
some Alaskan rivers. The model spatial resolution was set at
100 m.
RESULTS
Since runoff starts off largely with the interactions between
precipitation and temperature, Figure 11 shows the daily
precipitation and temperature, averaged over the Teklanika
watershed, for the 1992 water year. The total precipitation for
the year is 681 mm. This is consistent with the monthly
precipitation grids of Hill et al. (2015) that showed that water
year 1992 had about 20% more precipitation than average. A
wetter than normal year will certainly contribute to increased
streamow, but the more important question is how the
precipitation for that year was distributed over time and how
quickly the temperatures for that year melted out the snowpack.
Note that the mean daily temperature rises above freezing in mid-
May, and drops back below freezing in mid-September. Also, the
precipitation time-series shows numerous signicant events from
the start of June through September. When compared to the
previous year, temperatures in summer 1992 rose above freezing
much later (1 month) and much more steeply, and summer rains
were much more intense.
The results for snow cover in the model domain show that
October storms quickly blanket the entire domain and depths
steadily accumulate throughout winter and early spring. After
mid-May (Figure 12), the snow quickly retreats to only the
highest elevations in the domain, and by mid-September 1992,
snow has returned over much of the area. The average (averaged
over the Teklanika watershed) depth of snow water equivalent
(SWE) is shown in Figure 13. What is remarkable about this
gure is that in just 14 days, starting on May 17, the average SWE
drops from 22 to 2 cm. This is due to the steeply rising
temperatures seen in Figure 11.
Figure 14 provides the estimated hydrograph for the
Teklanika Watershed. For various points of comparison, the
hydrograph from 1991 is shown in addition to 1992. Also,
gray shading is used to show the envelope of values observed
for the USGS watershed over the period 19641974 (see
Figure 5). Remember that the Teklanika watershed is 64% the
size of the USGS watershed. Next, note that the hydrograph
presented is for the case where the RGI glacier mask was used.
Recall that the RGI mask showed greater glacier cover in the
Teklanika watershed. A simulation with the NLCD glacier mask
(not shown) was also done. During the summer time, it was found
that the streamow from the RGI simulation was about 5 m
3
s
1
greater than the NLCD simulation. Also recall the historic ice
cover shown in Figure 10, which was larger still. It is therefore
expected that summer ows during the period of record of the
USGS gauge would have been further elevated, compared to the
current simulations.
FIGURE 8 | Model domain (gray shaded region) and watersheds of
interest.
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Hill and Aragon Stuck in the Wild
FIGURE 9 | Model domain elevation model (A) and land cover classications (B). Zoomed in views of the southwest corner of the Teklanika watershed land cover
are shown in (C) for the NLCD dataset and (D) for the RGI dataset.
FIGURE 10 | A portion of the 1951 USGS 1:250000 quadrangle for Healy, Alaska. The eld of view is zoomed in on the upper reaches of the Teklanika watershed.
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Hill and Aragon Stuck in the Wild
The mean discharge in water year 1992 is 16.0 m
3
s
1
for the
RGI simulation, which amounts to a runoff depth of 630 mm. The
peak discharge is 260 m
3
s
1
. The green line shows the date of
5 July, and that day corresponds to a high ow of nearly
110 m
3
s
1
. The ows on 3 and 7 July, respectively, are 15 and
50 m
3
s
1
.
FIGURE 11 | Daily precipitation (water equivalent) and temperature, averaged over the Teklanika watershed, for the 1992 water year.
FIGURE 12 | Distributions of snow depth in the model domain and the Teklanika watershed (red boundary) during late spring 1992.
FIGURE 13 | Average depth of snow water equivalent (SWE) over the
Teklanika watershed during the 1992 water year.
FIGURE 14 | Estimated streamow for the Teklanika watershed during
spring and summer of 1991 and 1992. The envelope (19641974) of values
for the larger USGS watershed is also shown.
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Hill and Aragon Stuck in the Wild
The hydrograph for the summer of 1992 is atter than the 10-
years average shown in Figure 5. Part of this is due to reduced
glacier cover, from the 1960s to present day, which reduces
summer ows. A larger part of it is due to the rapid warming
observed in May which quickly melted out much of the
snowpack. The hydrograph for summer 1991 demonstrates a
more sustained summer ow due to much more gradual warming
and snowmelt. It also shows less variability since rainfall events
during summer 1991 were less intense.
In summary, the hydrographs estimated in this study show
three important results. First, the crossing of the Teklanika river
by Mr. McCandless on 28 April 1992 was likely enabled by an
extended cold spell, which signicantly delayed the streamow
increase associated with the spring snowmelt. Second, streamow
variability in the summer of 1992 was larger than normal due to
the spring snowmelt quickly dissipating, and due to intense
rainfall events. Finally, the attempted re-crossing of the
Teklanika River on 5 July 1992 corresponded with a large
runoff event that rose and fell very quickly.
DISCUSSION
There are several important limitations of and qualications
about this study that should be made clear. First, it is
impossible to predict the actual patterns of water elevation
and velocity at the Stampede Trail crossing of the Teklanika
River in July of 1992. To do so would require an accurate
hydraulic model which, in turn, would require accurate
information about river cross sectional shape. In braided
Alaskan rivers, cross sections constantly evolve in response to
strong currents and a heavy sediment load, so the cross sections of
1992 are long gone. This article has largely applied hydrologic
modeling techniques, which focus more on water quantities, and
less on exact patterns.
Second, this study was not able to resolve the diurnal
uctuations in discharge that can be observed in some
Alaskan rivers. Lignite Creek (USGS Station 15518080;
category 2B) is a good example of this. Inspection of the 15-
min data during the summer of 1992 at that site shows strong
diurnal uctuations in late May. These uctuations become quite
small in later summer, as the spring snowmelt pulse dissipates
and the hydrograph becomes dominated by strong peaks
associated with rainfall. The CFSv2 data used to force the
model for this study were on a 6-h time step and this
temporal resolution is too coarse to capture these diurnal signals.
Most signicantly, this study is complicated by the lack of
contemporaneous in-situ datasets. Hydrologic studies require
weather, elevation, and various land characteristic datasets as
forcing, and snow telemetry and streamow datasets for
calibration and validation. Unfortunately, there was no
temporal overlap between the gauge data and the weather
reanalysis data (CFS/CFSv2 period of record is 1979 - present)
used in this study. This prevented direct calibration and indirect
calibration and simplications were necessary.
The 10 years of USGS data on the Teklanika (Figure 5) were
invaluable to this study and to understanding the hydrologic
characteristics of the watershed at that time. The facts that the
estimated hydrographs for 1991 and 1992 (Figure 14) are lower
than the 19651974 average, and that the 1992 peak ow seems
anomalously high are the largest potential criticisms of this work
and therefore deserve greater discussion.
To investigate the relative magnitudes of peak ows in
1991 and 1992, in the absence of gauge data for the Teklanika,
it is useful to consider the gauge records from other USGS
stations. Of course, there is no perfect proxy watershed,
sharing similar location, elevation, size, and other
characteristics. The Chena River (15493000; category 2B) east
of Fairbanks has an area of 2,390 km
2
, but lacks the high
elevations of the Teklanika watershed. Its hydrograph in
1992 shows a later and much strong spring snowmelt than in
1991. The same is true for the Salcha River (15484000; 5,600 km
2
;
category 2B) southeast of Fairbanks and Lignite Creek (15518080;
125 km
2
; category 2B). In this way, these watersheds reinforce
some of the features in Figure 14. Watersheds such as the Nenana
River at Healy (15518040; 5,380 km
2
; category 3B) are appealing
points of comparison since they include high mountains in the
Alaska Range, but the Nenana has discharge data for 1991 only
(not 1992). It is worth noting that long-term water surface
elevation (stage, not discharge) data for the Nenana are
available through the Alaska-Pacic River Forecast Center of
the National Weather Service. These stage data (Supplementary
Figure S1A) show comparable peaks in 1991 and 1992, but they
also conrm a delayed and very rapid rise in the river in
1992 compared to 1991. The Nenana also shows a broader,
more consistent hydrograph in summer months, due to its
larger size and its higher mean elevation than the Teklanika.
To address the differences in hydrograph shape between the
model results for the Teklanika watershed and the observations
for the USGS watershed, we looked at several things. First, it
should be noted that climatological (long-term averages)
hydrographs are not static. As an illustration of this, we
considered (Supplementary Figure S1B) the Little Susitna
(15290000; 158 km
2
; category 3B) for two different 30-years
periods. Note that the more current hydrograph has
signicantly less ow in the summer months than the historic
one. This could be due to a number of inuences, including
reduced extent of the Mint Glacier and warmer temperatures.
Additionally, the spring freshet occurs about a week earlier in the
current hydrograph compared to the historic one.
We also applied our modeling workow to the Little Susitna
watershed in order to gain an understanding of its ability to
reproduce discharge in a gauged watershed. This domain is again,
not a perfect proxy for the Teklanika, as it is at the southern end of
the Talkeetna Range, and not part of the Alaska Range. However,
it has temporally overlapping snow telemetry and streamow
data, and both it and the Teklanika are 3B catchments. Modeled
and observed discharges are shown in Supplementary Figure
S1C. The results show that the model is able to capture many of
the peaks in the runoff and the overall performance metrics (R
2
=
0.84, NSE = 0.7) are fairly good.
The limitations raised in this discussion do not necessarily
discount the present ndings. In ways, the appropriate tool for a
study scales with ambition, i.e., the type and the accuracy of the
Frontiers in Earth Science | www.frontiersin.org July 2022 | Volume 10 | Article 90222610
Hill and Aragon Stuck in the Wild
information sought. The goal of this study was not to pinpoint the
precise discharge on 5 July 1992. Rather, it was to investigate how
the hydrology of 1992 compared to other years and to see how the
discharge on 5 July compared to the days prior and after. And,
connecting these relative changes in streamow to hydraulic
estimates (Equations 1,2) of how velocity and stage vary with
streamow is a useful second step on a path towards
understanding the literal ups and downs of the Teklanika River.
To return to Chris McCandlesssummer, it is clear that the
spring snowmelt in 1992 was delayed and rapid, making it
possible for him to reach Bus 142 at the end of April.
Following this rapid snowmelt, the river rose and fell every
week or so, in response to rainfall events. Attempting to re-
cross the river on July 5th therefore appears to have been a case of
bad timing by Mr. McCandless. Had he returned to the river just a
few days earlier or later, the conditions for crossing may have
been more favorable.
DATA AVAILABILITY STATEMENT
Publicly available datasets were analyzed in this study. This data
can be found here: USGS Streamow data for the Teklanika River:
https://waterdata.usgs.gov/nwis/inventory/?site_no=15518350. USGS
Streamow data for the Little Susitna River: https://waterdata.
usgs.gov/nwis/inventory/?site_no=15290000. Downscaled
(SNAP) 771m monthly precipitation grids: http://ckan.snap.
uaf.edu/dataset/historical-monthly-and-derived-precipitation-
products-771m-cru-ts. Snow Telemetry (SNOTEL) data for
Independence Mine: https://wcc.sc.egov.usda.gov/nwcc/site?
sitenum=1091. MODIS 8-days evapotranspiration data: https://
opendap.cr.usgs.gov/opendap/hyrax/MOD16A2GF.006/contents.
html. 1/3 arc-second digital elevation model: https://data.usgs.
gov/datacatalog/data/USGS:3a81321b-c153-416f-98b7-cc8e5f0e17c3.
NLCD 2011 Alaska data: https://www.mrlc.gov/data/nlcd-2011-
land-cover-alaska-0. RGI (version 6) glacier cover: https://www.
glims.org/RGI/. Climate Forecast System (CFS) data: https://
www.ncei.noaa.gov/data/climate-forecast-system/access/reanalysis/
6-hourly-ux/.
AUTHOR CONTRIBUTIONS
DH developed the idea for this study and carried out initial model
analysis and manuscript writing. CA helped develop many of the
modeling tools that were used in the nal work and also
contributed to data acquisition, model calibration, and other
aspects.
ACKNOWLEDGMENTS
Thank you Fiona for renewing my interest in this story.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/feart.2022.902226/
full#supplementary-material
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Conict of Interest: The authors declare that the research was conducted in the
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A comprehensive study of the Gulf of Alaska (GOA) drainage basin was carried out to improve understanding of the coastal freshwater discharge (FWD) and glacier volume loss (GVL). Hydrologic processes during the period 1980-2014 were modeled using a suite of physically based, spatially distributed weather, energy-balance snow/ice melt, soil water balance, and runoff routing models at a high-resolution (1 km horizontal grid; daily time step). Meteorological forcing was provided by the North American Regional Reanalysis (NARR), Modern Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR) data sets. Streamflow and glacier mass balance modeled using MERRA and CFSR compared well with observations in four watersheds used for calibration in the study domain. However, only CFSR produced regional seasonal and long-term trends in water balance that compared favorably with independent Gravity Recovery and Climate Experiment (GRACE) and airborne altimetry data. Mean annual runoff using CFSR was 760 km3 yr-1, 8% of which was derived from the long-term removal of stored water from glaciers (glacier volume loss). The annual runoff from CFSR was partitioned into 63% snowmelt, 17% glacier ice melt, and 20% rainfall. Glacier runoff, taken as the sum of rainfall, snow, and ice melt occurring each season on glacier surfaces, was 38% of the total seasonal runoff, with the remaining runoff sourced from nonglacier surfaces. Our simulations suggests that existing GRACE solutions, previously reported to represent glacier mass balance alone, are actually measuring the full water budget of land and ice surfaces.
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Introduction: Most recreational whitewater fatalities are caused by fixed underwater entrapment or by "flush drowning," an obscure term frequently associated with high-volume rivers, continuous rapids, cold water, and a lack of prolonged underwater entrapment. Although entrapment drowning is typically associated with submersion hypoxia, flush drownings likely involve diverse mechanisms of death; as such, a concise definition is elusive. This said, certain risk factors may be predictively associated with flush drownings. We attempt to further characterize causes of fatal river accidents and possible effects of water temperature on injury pattern. Methods: We reviewed river mortality data collected from the American Whitewater Association accident database comparing fatal whitewater accident trends in the Rocky Mountain region versus the Southeastern United States. We limited data from the Southeast to the months of June through August to create a warm water cohort. We then divided lethal accidents into flush drowning, entrapment submersion, or miscellaneous events, defining each category in specific terms. Results: Flush drownings were more common in the Rocky Mountains than in the Southeast subgroup and involved older victims on average than entrapment drowning or miscellaneous events. Entrapment drownings were common in both regions, primarily occurring at fallen trees or rock formations. Conclusions: Flush drownings appear to occur more frequently in older persons. Although hypothetical, the relative increase in flush drowning in the Rocky Mountains might partly be the result of colder water temperatures. If the cause of flush drowning is better understood, safety in whitewater recreation may be improved.