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Impacts of Historic Climate Variability on Seasonal Soil Frost in theMidwestern United States
TUSHAR SINHA
School of Life Sciences, Arizona State University, Tempe, Arizona
KEITH A. CHERKAUER AND VIMAL MISHRA
Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana
(Manuscript received 17 December 2008, in final form 30 November 2009)
ABSTRACT
The present study examines the effects of historic climate variability on cold-season processes, including
soil temperature, frost depth, and the number of frost days and freeze–thaw cycles. Considering the impor-
tance of spatial and temporal variability in cold-season processes, the study was conducted in the midwestern
United States using both observations and model simulations. Model simulations used the Variable In-
filtration Capacity (VIC) land surface model (LSM) to reconstruct and to analyze changes in the long-term
(i.e., 1917–2006) means of soil frost variables. The VIC model was calibrated using observed streamflow
records and near-surface soil temperatures and then evaluated for streamflow, soil temperature, frost depth,
and soil moisture before its application at the regional scale. Soil frost indicators—such as the number of frost
days and freeze–thaw cycles—were determined from observed records and were tested for the presence of
significant trends. Overall trends in extreme and mean seasonal soil temperature from 1967 onward indicated
a warming of soil temperatures at a depth of 10 cm—specifically in northwest Indiana, north-central Illinois,
and southeast Minnesota—leading to a reduction in the number of soil frost days. Model simulations in-
dicated that by the late-century period (1977–2006), soil frost duration decreased by as much as 36 days
compared to the midcentury period (1947–76). Spatial averages for the study area in warm years indicated
shallower frost penetration by 15 cm and greater soil temperatures by about 38C at 10-cm soil depth than in the
cold years.
1. Introduction
Many recent studies of observation records have sug-
gested that climate variability has increased and climate
change has accelerated in the recent years. For instance,
global and regional surface temperatures have increased
in the twentieth century, with the greatest warming oc-
curring in the last three decades (WMO 2006). The fre-
quency of extreme temperature and precipitation events
has also increased; specifically, the earth has experienced
11 of its top 12 warmest years in the last 12 yr (1995–2006)
based on the instrumental record available since 1850
(Alley et al. 2007). In the Northern Hemisphere, there
has been a significant reduction in mean snow cover
area (e.g., Dye and Tucker 2003; Mote et al. 2005), while
the maximum area covered by seasonally frozen ground
has decreased by about 7% since the 1900s, and spring
snowmelt has occurred earlier (Lemke et al. 2007; Stewart
et al. 2005; Hodgkins and Dudley 2006). Easterling (2002)
found that the number of days with subfreezing air tem-
perature has decreased by four days per year in the United
States during the period of 1948–99, while in recent
decades the frost-free season based on air temperature
has started 11 days earlier in the northeastern United
States (Cooter and Leduc 1995). The air temperature
frost-free season length has increased more in the west-
ern United States than in the eastern United States
(Easterling 2002; Kunkel et al. 2004). While most of the
studies were dedicated to studying the effect of climate
variability at larger scales (Easterling 2002; Kunkel
et al. 2004; Frauenfeld et al. 2007), fewer studies have
concentrated on regional scales (Cooter and Leduc 1995;
Kling et al. 2003). Cold-season processes are associated
Corresponding author address: Tushar Sinha, School of Life
Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ
85287-4501.
E-mail: [email protected]
VOLUME 11 J O U R N A L O F H Y D R O M E T E O R O L O G Y APRIL 2010
DOI: 10.1175/2009JHM1141.1
� 2010 American Meteorological Society 229
with a high degree of spatial (e.g., northern Minnesota
versus southern Indiana) and temporal (e.g., onset–thaw
of soil frost) variability in the midwestern United States,
which underscores the need for a study dedicated to this
region.
Climate variability and climate change may directly
influence cold-season variables. For instance, winter soil
temperature is directly governed by the increase in
both air temperature and precipitation—mostly snow-
fall (Zhang et al. 2001, 2003). Hardy et al. (2001) sug-
gested that the shifting of regional climate toward lower
snow accumulations and shorter durations of snow on
the ground will result in deeper soil freezing and more
days with soil frost in temperate forests. Recently, an
observational study found that decreasing snow cover
has led to cooler soil temperatures in northern Indiana
(Sinha and Cherkauer 2008). However, increases in
frozen ground are short-term effects of limited spatial
extent (Zhang 2005), as increasing air temperatures
eventually overwhelm changes in insulation due to the
snowpack. For instance, although winter air tempera-
ture increased by 48–68C at Irkutsk, Russia, during the
past century, the soil temperature has increased by up to
98C because of an increase in early winter snowfall and
early snowmelt in spring (Zhang et al. 2003). Thus,
changes in cold-season soil temperatures and its derived
variables, such as the number of soil frost days, are influ-
enced by changes in air temperature and winter precipi-
tation as snow.
Historic observations in the midwestern United States
indicate that climate variability has increased during
the past century, which is likely to reinforce spatial–
temporal variability in regional cold-season processes.
The northern regions of the Midwest have warmed by
more than 28C, whereas the southern portions of the
Ohio River Valley have cooled by about 0.58C during
the past century (Easterling and Karl 2001). This region
has also experienced an increase in annual precipitation
by up to 20% with about double the frequency of heavy
precipitation events in the latter part of twentieth cen-
tury than in the early part of the century (Easterling and
Karl 2001; Kunkel et al. 1998). In addition, the mid-
western United States experiences uncertainties in the
frequency and severity of cold-season storms that affect
flooding in late winter and early spring, the number of
soil frost days and its time of occurrence, and soil tem-
peratures. Therefore, we addressed the following ques-
tions in the present study: (i) How significant were the
trends–changes associated with cold-season variables
(soil frost, number of frost days and freeze–thaw cycles)
in the midwestern United States? (ii) How has climate
variability influenced regional cold-season variables in
the past century? (iii) How sensitive were the responses
of cold-season variables during the extreme climatic
years (e.g., warm–cold and wet–dry)? We addressed
these questions by integrating observations and model-
ing forced with long-term meteorological observations.
2. Background
To determine how climate variability affects soil frost,
cold season descriptive variables were determined from
both observed and simulated records. The selected sim-
ulated soil frost variables were calibrated and evaluated
with observations before being used to extend the time
series for the past century and the entire study area using
the Variable Infiltration Capacity (VIC) model.
a. VIC model
The VIC model (Liang et al. 1994, 1996; Cherkauer
and Lettenmaier 1999) is a large-scale hydrologic model
that has been used for large watersheds (.10 000 km2)
and has performed well under different climatic re-
gimes (e.g., Liang et al. 1996; Wood et al. 1997; Lohmann
et al. 1998a,b; Nijssen et al. 2001a,b; Cherkauer and
Lettenmaier 2003; Bowling et al. 2003a,b). The details of
the VIC model are described in Liang et al. (1994, 1996)
and Cherkauer and Lettenmaier (1999), and only a brief
description of the details is provided here. Distinguish-
ing characteristics of the model include its represen-
tation of the spatial subgrid variability of infiltration
through the Variable Infiltration Capacity curve, and it
use of a nonlinear Arno parameterization for baseflow
recession (Abdulla et al. 1999). The horizontal land sur-
face is classified into any number of user-defined vege-
tation classes based upon the fraction of the grid cell it
occupies. The model can be driven by daily meteorologi-
cal variables, and it predicts surface temperature, evapo-
transpiration, runoff, snow water equivalent (SWE), and
other variables for each grid cell. Runoff and baseflow
can be routed to the basin outlet from each grid cell
using a stand-alone routing model developed by Lohmann
et al. (1998a,b).
Updates to the representation of cold-season pro-
cesses in the VIC model are detailed in Cherkauer and
Lettenmaier (1999) and Cherkauer et al. (2003), though a
brief description is provided here for clarity. Cherkauer
and Lettenmaier (1999) developed a frozen soil algo-
rithm that solves for thermal fluxes at a user-defined
number of soil nodes through the soil column and pre-
dicts soil water and ice content for each soil layer. In
addition, the moisture fluxes are adjusted by the pres-
ence of ice. Calculating fluxes using both ice and liquid
soil moisture makes the soil appear wet, which reduces
infiltration, while drainage is reduced when only liquid
soil moisture is used.
230 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
The model employs a two-layer snow algorithm, in
which a thin surface layer is used to estimate energy
exchange with atmosphere and the bottom layer acts as
storage to simulate deeper snowpacks (Cherkauer and
Lettenmaier 1999). The snow algorithm was originally
designed for use within the Distributed Hydrology Soil
Vegetation Model (DHSVM; Storck and Lettenmaier
1999). Cherkauer et al. (2003) added explicit represen-
tation of snow interception in the canopy to improve
representation of snow accumulation and ablation in
forested regions. Canopy intercepted snow is removed
through sublimation, mass release, and meltwater drip.
The model, as used for this study, assumed a spatially
uniform snow cover within each vegetation type, which
suggests an underestimation of snowmelt rates under
thin and discontinuous snow. It also assumes a new snow
density of 100 kg m23. Densification of older snowpacks
is based on Snow Thermal Model 89 (SNTHRM.89;
Jordan 1991). The snowpack surface albedo curve pa-
rameters are based on snow age and season (Bras 1990).
Maximum temperature when precipitation can occur as
only snow was set to 0.258C, whereas the minimum
temperature when precipitation can fall completely as
rain was defined as 20.258C.
The VIC model version 4.1.0 r3 was used in this study
with the finite difference soil thermal solution described
by Cherkauer and Lettenmaier (1999) using a constant
bottom boundary temperature. The thermal damping
depth was set to 10 m, and the model was allowed to spin
up for 2 yr.
b. Study area
The study area consists of the following six states in
the midwestern United States: Minnesota (MN), Iowa
(IA), Wisconsin (WI), Illinois (IL), Michigan (MI), and
Indiana (IN; Fig. 1). These regions were selected be-
cause the spatial and temporal variability in seasonal soil
frost is likely to vary with air temperature, which in-
creases from north to south, and precipitation, which
increases from the northwest to the southeast. This area
comprises parts of the upper Mississippi River basin, the
Ohio River basin, and the Great Lakes drainage basin.
The area consists of the rolling forested landscapes of
southern Illinois, and Indiana; a central region of the
relatively flat farmland that produces mostly corn and
soybean; and the evergreen and deciduous forests of
northern Wisconsin, Minnesota, and Michigan (Easterling
and Karl 2001). In winter, the region is influenced by cold
Arctic air masses, which move into the region, resulting in
frequent storms and windy conditions. The total annual
precipitation varies from as low as 640 mm in the western
Minnesota and Iowa to more than 1020 mm along the
Ohio River. Annual average temperatures vary from 2.758C
in the north to 158C in the south.
3. Methods and data
a. Observational data
1) DAILY SOIL TEMPERATURES
Data for soil temperatures were obtained from sites
that have collected at least 10 years of consistent soil
temperature at a depth of 10 cm. The longest period for
which consistent soil temperature data were available
was 1966 to 2006. Data since 1982 were obtained from
the National Climatic Data Center’s (NCDC) Summary
of the Day (SOD) dataset (available online at http://
www.ncdc.noaa.gov/oa/ncdc.html) and the Illinois State
Water Survey (WARM 2008). Data prior to 1982 were
available only as paper records, and they were digitized
from the Climatological Data for five of the six states
within the study domain (U.S. Department of Com-
merce 1966–1982). Maximum and minimum soil tem-
peratures at a depth of 10 cm were taken using a Palmer
dial-type temperature sensor, which has a precision of
FIG. 1. Map showing the locations of 1) selected sites that have
been collecting soil temperatures: (a) MOR, (b) WAS, and (c)
Lamberton (LAM) in MN; (d) Chatham (CHA) and (e) northwest
Michigan (NMI) in MI; (f) Hancock, WI (HAN); (g) DEC, (h)
AME, and (i) Burlington (BUR) in IA; ( j) Freeport (FRE), (k)
Urbana (URB), and (l) Orr (ORR) in IL; (m) WAN, (n) West
Lafayette (WLA), and (o) Dubois (DUB) in IN; and 2) calibrated
watersheds in the region: ANOKA–Mississippi River at Anoka,
MN; MINNR–Minnesota River at Jordan, MN; IOWAR–Iowa
River at Wapello, IA; CHIPR; GRAND–Grand River at Grand
Rapids, MI; ILLIR–Illinois River at Valley City, IL; and WABAS.
Stars represent streamflow gauging stations.
APRIL 2010 S I N H A E T A L . 231
0.58C (Schaal et al. 1981). All the measurements for
these sites were taken under a bare soil surface (refer to
the Appendix A). Data for the Illinois sites were col-
lected by the Illinois State Water Survey and represent
soil temperatures at 10 cm below a grass surface.
2) WEEKLY FROST DEPTH AND SNOW DEPTH
Weekly frost and snow depths were obtained from the
National Snow and Ice Data Center (NSIDC) for se-
lected sites in Minnesota during the two cold seasons
of 1975/76 and 1979/80 for comparison of observed and
simulated variables. The frost depths were measured us-
ing frost tubes (Haugen and King 1998).
3) MONTHLY STREAM FLOW DATA
Monthly stream flow data were obtained since 1982
for selected basins monitored by the U.S. Geological
Survey (USGS) based on criteria described in section 3e.
These data were useful for model calibration and eval-
uation. Several gauged watersheds within the study do-
main were selected for calibration (Fig. 1).
b. Model input data
1) SOIL DATA AND VEGETATION DATA
Soil parameters were processed from a multilayer soil
characteristics dataset at a 1-km resolution for the con-
terminous United States (CONUS-SOIL; Miller and
White 1998). The CONUS-SOIL data are based on the
original State Soil Geographic database (STATSGO).
Land use parameters were acquired from the Land Data
Assimilation System (LDAS) project (Mitchell et al.
2004; Maurer et al. 2002). Additional parameters re-
quired for the frozen soil algorithm were taken from
Mao and Cherkauer (2009). The land use map was pro-
cessed using vegetation classifications based on 1-km
resolution satellite data from 1992 to 1993 (Hansen et al.
2000). These data have been gridded to a resolution
of 1/8th-degree latitude and longitude for the contermi-
nous United States. Monthly leaf area index (LAI)
data for each vegetation type and each 1/88 grid cell have
been obtained from Myneni et al. (1997). Vegeta-
tion library parameters were obtained from Mao and
Cherkauer (2009).
2) EXTENSION OF DAILY GRIDDED
METEOROLOGICAL DATA
Long-term records of daily precipitation and maxi-
mum and minimum air temperature from 1915 to 2006
were obtained from the NCDC’s SOD dataset and
gridded at a spatial resolution of 1/88 latitude by longi-
tude (about 14 km 3 11 km) for the midwestern United
States using the methodology of Hamlet and Letten-
maier (2005). This gridding methodology is applicable
for long-term trend analysis, as it accounts for changes
in meteorological station location and temporal in-
consistencies, which otherwise can lead to spurious
trends in precipitation and temperature. Hamlet and
Lettenmaier (2005) found that despite acceptable stream-
flow simulations through the VIC model for recent de-
cades, streamflow prior to 1950 was strongly biased in
comparison with that after 1950 due to temporal hetero-
geneities in the driving data. Hamlet and Lettenmaier
(2005) suggested a methodology to make temporal cor-
rections based upon monthly U.S. Historical Climatology
Network (HCN) records and topographic corrections
based upon monthly precipitation maps produced by
the Parameter-elevation Regressions on Independent
Slopes Model (PRISM). This methodology has been
tested and verified for the state of California and has
now been applied to the midwestern United States start-
ing in 1915 for this study. Daily wind speeds since 1949
were obtained from the National Center for Atmospheric
Research–National Centers for Environment Prediction
(NCAR–NCEP) reanalysis project (Kalnay et al. 1996),
whereas the daily wind speed prior to 1948 was derived
from post-1949 wind climatology as described in Hamlet
and Lettenmaier (2005).
c. Data processing
1) SOIL TEMPERATURE
Daily observed soil temperature data from selected
stations were subjected to quality-checking procedures
to identify missing dates, inconsistencies, and unrea-
sonably high diurnal ranges in soil temperatures (Sinha
and Cherkauer 2008). The erroneous data were replaced
with ‘‘no data’’ values for making comparisons. Years
missing more than 10% of observed values during the
cold season were eliminated to avoid any biases in trend
analysis. Cold-season statistics were computed for a pe-
riod from September through May, which incorporates
the earliest observed occurrence of soil frost and the
latest observed thaw at all the sites in the region. The
following cold-season variables were estimated from
the daily time series of maximum and minimum soil
temperatures (Tsoil) at a depth of 10 cm as described in
Sinha and Cherkauer (2008):
(i) Seasonal mean maximum Tsoil is determined by
calculating the mean of daily maximum Tsoil data
series during the cold season.
(ii) Seasonal mean minimum Tsoil is determined by
calculating the mean of daily minimum Tsoil data
series during the cold season.
232 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
(iii) Extreme minimum temperature is determined by
calculating the extreme value of daily minimum Tsoil
data series during the cold season.
(iv) Annual soil frost days are estimated by counting
the number of days when the soils are frozen dur-
ing the cold season.
(v) Annual freeze–thaw cycles are estimated by count-
ing the number of times soil temperature changes
between frozen and thawed states during the cold
season. Details provided in Sinha and Cherkauer
(2008) are summarized for clarity. A freeze event
occurs when at least daily minimum Tsoil on the
first day is at or below 08C and both daily maximum
and minimum Tsoil on the subsequent day are also
at or below 08C. Similarly, a thaw event occurs
following a freeze event when both maximum and
at least one minimum Tsoil on two consecutive days
are greater than 08C. A freeze event, followed by
a thaw event at a given soil depth is defined as one
freeze–thaw cycle. A threshold value of 20.258C
(instead of 08C in the case of observed data) was used
to estimate freeze–thaw cycles from model-simulated
daily maximum and minimum soil temperatures.
This step is useful to make similar comparisons from
frost variables computed from observed soil tem-
peratures. The observed soil temperature sensor
has a precision of 0.58C, which implies that any
fluctuation of less than 0.258 around 08C will not be
measured by the sensor.
(vi) Onset day of soil frost is determined by computing
the first day since 1 September when soil is frozen.
(vii) Last thaw day is determined by computing the last
day of soil frost since 1 September.
2) FROST DEPTH
Among the stations measuring frost depth in the study
region, only data from the Minnesota sites passed quality
checks for consistency and continuity and were included
in this analysis.
3) CLIMATE VARIABILITY IN THE GRIDDED
METEOROLOGICAL FORCING DATA
Climate variability for the study area was assessed
by estimating extreme temperature and precipitation
events during the cold season derived from the daily
gridded meteorological forcing data for precipitation
and air temperature. Mean air temperatures were cal-
culated by averaging daily maximum and minimum
air temperature over the cold season, from September
through May, for the entire region. Seasonal anomalies
were normalized for both precipitation and average air
temperature (Fig. 2) using
Xnormalized
5(X
t�X
mean)
(Xmax�X
min)
, (1)
where Xnormalized is the normalized time series, Xt is the
time series for the cold season, Xmean is the mean, and
Xmax is the maximum value and Xmin is the minimum
value of the time series. A threshold value of 0.3 was
selected to define multiple extreme events that occurred
during the past century to distinguish into warm and wet
years based upon air temperature and precipitation,
respectively (Fig. 2). This means that the years with
values greater than 0.3 were considered as warm based
on average air temperature and considered as wet based
upon precipitation. Similarly, a threshold of 20.3 was
selected to identify cold and dry years. Only one year—
1917—met the criteria of a cold year, whereas other
extreme events were spread over multiple years. Sea-
sonal statistics were analyzed to study the effects of
climate variability on selected soil frost indicators.
d. Observed data analysis
Data analysis was performed in two stages: 1) data
series were tested to identify the presence of any sig-
nificant autocorrelations, which is the correlation of a
variable with itself over successive time intervals, and 2)
data series were tested to identify significant trends in
soil frost variables using the Mann–Kendall test. The
presence of autocorrelation increases the possibility of
detecting trends even if they are absent (Rao et al. 2003).
FIG. 2. Cold-season normalized anomalies of gridded meteoro-
logical forcing data from 1917 to 2006 averaged over the entire
study area for (a) precipitation (PRCP) and (b) mean air temper-
ature. The continuous line indicates a significant trend; the dashed
line indicates a nonsignificant trend. The Mann–Kendall slope of
trend line is indicated.
APRIL 2010 S I N H A E T A L . 233
In the case of significant autocorrelation in a data series,
the effective number of independent observations de-
creases and thus accounting for it reduces the chances of
detecting spurious trends.
Data series with more than 10 years of observations
remaining, after data quality checks, were tested for
significant trends using the Mann–Kendall test, as it is
applicable for 10 or more observations. This test has
been widely used for trend analysis in hydrologic studies
and was applied in a similar form used by Hirsch et al.
(1992). The details of the test were described in Sinha
and Cherkauer (2008). In cases of significant autocor-
relation, a modified Mann–Kendall test (Hamed and
Rao 1998) was used that calculates the autocorrelation
between the ranks of the data after removing the ap-
parent trend, as described in Sinha and Cherkauer (2008).
The trend analysis was performed for the soil frost var-
iables described in section 3c for the following periods
to maximize the number of stations measuring consis-
tent soil temperature at a depth of 10 cm: (i) 1967–2006,
(ii) 1974–2006, (iii) 1983–2006, and (iv) 1991–2006.
e. Modeling Strategy
The VIC model was calibrated using observed stream-
flow from 1982 to 1992 and seasonal average daily soil
temperature at a depth of 10 cm. The model was evalu-
ated for streamflow from 1993 to 2003 as well as aver-
age seasonal soil temperature, soil moisture (three sites
in Illinois), and frost depth (three sites in Minnesota)
based upon data availability. Direct comparisons of
trends in model-derived soil frost variables (such as soil
frost days and extreme seasonal daily Tsoil) have not
been made with those estimated from the observed data
at selected sites, as trends are difficult to identify in model-
derived variables. However, the calibrated model was used
to compare the general patterns of selected soil frost
variables using Spearman rank correlation coefficient
r before it was used to quantify long-term changes in
cold-season parameters for the study domain.
1) CALIBRATION AND EVALUATION OF
STREAMFLOW
Seven major watersheds were selected that were min-
imally influenced by major dams and reservoirs (Fig. 1).
Since land use was based upon 1992/93 satellite data, the
10 years prior to this period (1982–91) were selected as
the calibration period, whereas the subsequent 10 years
(1994–2003) were selected as the evaluation period. This
minimized the effects on streamflow due to land use
change. The VIC model was calibrated on a watershed
basis using observed streamflow measurements. Cali-
bration was performed to improve the agreement of ob-
served and simulated discharge volumes and the shape of
hydrographs. A single set of model parameters that
control infiltration, runoff, and baseflow were obtained
for the entire study domain, to minimize the effect of
parameters change between watersheds on any spatial
analysis. Figure 3 shows the mean monthly hydrographs
for the seven watersheds for the entire length of the
calibration and validation periods.
The following accuracy indicators were used to eval-
uate the model performance:
d Nash–Sutcliffe efficiency (E) as proposed by Nash and
Sutcliffe (1970) was calculated as (Krause et al. 2005):
E 5 1��
n
i51(O
i� P
i)2
�n
i51(O
i�O)2
, (2)
where Oi and Pi are the observed and simulated
discharge at month i, respectively; O is the mean of
observed discharge, and n is the number of monthly
observations.
d Index of agreement (d) was proposed by Willmott
(1981) to overcome the insensitivity of E to differ-
ences in the observed and predicted means and vari-
ances (Legates and McCabe 1999). It was defined as
the ratio of the mean square error and the potential
error (Gaile and Willmott 1984) and was calculated as
(Krause et al. 2005)
d 5 1��
n
i51(O
i� P
i)2
�n
i51(jP
i�Oj1 jO
i�Oj)2
. (3)
The E and the d were applied to monthly streamflows for
the period 1982–2003 (Fig. 3). Values of E varied from
0.27 for the Chippewa River at Durand, WI (CHIPR), to
0.84 for the Wabash River at Mt. Carmel, IN (WABAS),
and the values of d varied between 0.81 and 0.96 for the
CHIPR and WABAS, respectively.
2) CALIBRATION AND EVALUATION OF SOIL
TEMPERATURES
Fifteen sites, with soil temperature observations at
10 cm soil depth, were selected to calibrate and evaluate
model performance. The key soil parameters—such as
Ds (which is the fraction of maximum velocity of base-
flow Dsmax from the lowest soil layer where nonlinear
baseflow begins), parameter describing the variation of
saturated hydraulic conductivity with soil moisture in
the three soil layers, and average soil temperature used
234 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
as the bottom boundary for soil heat flux solutions—were
adjusted during the calibration process. The Spearman
rank correlation coefficient r was selected to compare
relationships between simulated and observed soil frost
variables, because it does not require any assumption
about the frequency distribution of the variables; instead,
it uses the ranks of the data to estimate the correlation
between the variables. It is computed using
FIG. 3. Comparison of monthly observed (Obs) and simulated (Sim) streamflow from 1982 to
2003 at the seven calibrated watersheds. Shown are E (%) and d (%) between observed and
simulated streamflow over the entire period.
APRIL 2010 S I N H A E T A L . 235
r 5
�n
i51R(x
i)R(y
i)� n
n 1 1
2
� �2
�n
i51R(x
i)2� n
n 1 1
2
� �2" #0.5
�n
i51R(y
i)2 � n
n 1 1
2
� �2" #0.5
,
(4)
where R(x) and R(y) are the ranks of a pair of variables
(x and y), with each containing n observations.
4. Results and discussion
a. Observational data analysis for soil frost variables
Trends in observed soil temperature records were
assessed for four periods to maximize the length of re-
cord, as well as the number of active stations, since the
majority of stations came online in the 1980s and 1990s.
Trend analysis indicated that a higher percentage of
stations show statistically significant trends in soil frost
variables for the period of 1967 to 2006 than for any
other period (Table 1). However, this period also had
the fewest available stations. The percentage of stations
showing significant trends in soil frost variables varied
over the different periods considered. Most of the sig-
nificant trends were observed in extreme minimum Tsoil
and seasonal mean maximum Tsoil for all the periods
under consideration. Interestingly, among significant
trends, the magnitude of the Mann–Kendall slope for
extreme minimum Tsoil was higher than the correspond-
ing slopes in mean seasonal Tsoil for all the periods
(Tables 2–5), indicating greater changes in extreme min-
imum Tsoil. The greatest increase in extreme minimum
Tsoil of 0.688C since 1983 as well as the largest decrease
in mean maximum Tsoil (20.528C since 1991) were ob-
served at Morris, MN (MOR; Tables 4 and 5), indicat-
ing that variability in cold-season soil temperatures has
decreased. This site also experienced the greatest rate
of increase in annual number of soil frost days from
1991 to 2006.
During the periods since 1967 and 1974, trends that
were statistically significant indicated a warming in ex-
treme and mean seasonal Tsoil—specifically in northwest
Indiana, north-central Illinois, and southeast Minnesota—
leading to a reduction in the number of soil frost days
[Figs. 4(1a)–4(4b)]. However, periods since 1983 and 1991
indicated a decrease in soil temperatures at a limited
number of sites, such as Morris, where soil frost days in-
creased with time. Significant negative trends in soil tem-
peratures were identified only for the shorter duration
records. For instance, Chatham, MI (CHA), and Farmland,
IN (FRM), have experienced a reduction in extreme mini-
mum Tsoil, with Farmland, IN, also experiencing a reduction
in seasonal mean Tsoil from 1983 to 2006. The mixture of
statistically significant positive and negative trends was
indicative of the dominance of localized controls over
climate signals on seasonal soil frost variables for shorter
periods (i.e., since 1983 and since 1991, respectively).
Higher slopes with significant trends since 1991 in-
dicate that for this shorter period, soil frost variables
have generally experienced more rapid change than in
the past (Table 5). In a few cases, when the trends were
not significant, the direction of change was sensitive
to the period in consideration. For example, at Waseca,
MN (WAS), the direction of change reversed for the
number of soil frost days when computed since 1974 to
that computed since 1983 [Figs. 4(4b) and 4(4c)]. The
1970s were characterized by cold winters in the mid-
western United States, which may have caused greater
soil frost days during early 1970s. Despite the increase
in soil frost days since 1983, the overall direction of
change since 1974 indicated a decrease at WAS, where
the changes were statistically insignificant.
b. Comparison of observations withsimulated variables
Calibration and evaluation of the VIC model typically
focuses on streamflow statistics; however, to use the
model for analysis of soil frost processes, additional
evaluations of soil temperature and frost depth were
performed. Fifteen sites were selected for evaluation of
observed and simulated cold-season variables (Fig. 1).
For the evaluations, all sites, except for the three in
Iowa, made use of daily maximum and minimum Tsoil
data to compute seasonal statistics. The Iowa sites re-
corded only a single daily temperature measurement
taken at the time of observation; hence, these sites were
unable to capture the daily extreme values observed by
the other sites. This implies that the seasonal extremes
and frost dynamics were underrepresented at those sites.
Cold-season mean daily Tsoil comparisons indicated
that the VIC model generally captured overall patterns
TABLE 1. Percentage of stations with statistically significant
trends in soil frost variables for different periods at a significance
level of 5%. The number of stations available for each period is
provided in parentheses.
Time period
1967–2006 1974–2006 1983–2006 1991–2006
Variable (5) (8) (21) (36)
Mean max Tsoil (8C) 80 25 29 08
Mean min Tsoil (8C) 60 25 19 14
Extreme min Tsoil
(8C)
60 25 29 25
Frost days 40 13 29 14
Frost–thaw cycles 40 25 19 19
236 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
of the observed daily Tsoil at most of the selected sites
(Fig. 5); however, there were differences in absolute
values. Here r between the observed and simulated
mean daily Tsoil ranged from 0.75 for Dubois, IN (DUB),
to 20.27 for MOR, with 7 out of the 12 sites having r $
0.5 (excluding the three IA sites because of different
observed Tsoil format). The general patterns in soil
temperature were captured by the model at most sites,
except at MOR and Decorah, IA (DEC), where the
Spearman rank correlations were negative. Since the
Iowa sites had a different format for the observed Tsoil,
the simulated mean daily Tsoil were expected to be
colder than observed Tsoil recorded at a specific time of
a day. The Morris, MN, site, on the other hand, expe-
rienced the highest variability in observed Tsoil among
all the sites, as described earlier, resulting in larger
differences in simulated and observed Tsoil. The dis-
crepancy in absolute values between observed and
simulated Tsoil may be due to differences in meteoro-
logical data and soil data at the selected sites. The
regional simulations were conducted using the 1/8th-
degree gridded meteorological forcing data, which
were spatially interpolated to the center of the grid cell
using neighboring meteorological stations and may
differ from the actual observations of daily precipitation
and air temperatures at selected point locations. The
model further disaggregated daily meteorological data to
subdaily scale, which affected snowfall timings. In ad-
dition, the model assumes a maximum (minimum)
temperature of 0.258 (20.258C) when precipitation falls
as snow (completely as rain). This may cause differences
between observed and simulated snow depth, resulting
in changes in cold-season daily average Tsoil. Further-
more, the VIC model simulations make use of average
soil conditions for the grid cell, which may not capture
site-specific conditions despite forcing the VIC grid cell
with the same vegetation type as the observational site.
Overall, the VIC-simulated daily Tsoil were deemed
acceptable, as the observed patterns were simulated at
more than half of the selected sites and thus the model
was used as an analysis tool to estimate long-term
changes in soil frost variables.
Simulated snow depths and soil frost depths were
evaluated with observations at three sites in Minnesota
using observational data for the cold seasons of 1975/76
and 1979/80 (Fig. 6). Snow depth and frost depth were
well simulated for 1979/80 at the Morris and Waseca sites,
whereas the underestimation of snow depth at Lamberton
resulted in deeper soil frost penetration [Figs. 6(1c)–6(3d)].
Overestimation of snow depth at Waseca for the 1975/76
season led to an underestimation of frost depth, because
of an increase in the insulation effect of snow at the
ground surface. A smaller overestimation of snow cover
at the Lamberton site during 1975/76 did not have a
significant effect on soil frost penetration, which was
slightly deeper than observed [Fig. 6(2b)]. Since the dis-
tribution of precipitation between rain and snow rela-
tive to air temperature is dependent on geographical
locations, snow depth is difficult to simulate (Fassnacht
and Soulis 2002). Additionally, the VIC model is re-
constructing subdaily precipitation and air tempera-
ture from daily values averaged for an 1/8th-degree grid
cell, which further affects the occurrence of snow. With
these caveats in place, the simulation of snow and soil
frost depths by the calibrated VIC model were deemed
satisfactory.
TABLE 2. Stations showing significant trends in soil frost variables from 1967 to 2006. The values indicate the Mann–Kendall slope.
Numbers in parentheses indicate the p value tested for significance; p , 0.05 is statistically significant and N indicates the absence of
significant trend at a 5% significance level.
Stations Mean max Tsoil Mean min Tsoil Extreme min Tsoil Frost days Frost–thaw cycles
CHA N N N N 0.12 (0.04)
WAS 0.06 (0.003) 0.10 (,0.001) 0.23 (0.004) 20.84 (0.005) N
URB 0.06 (0.003) 0.04 (0.003) 0.12 (0.003) N N
WLA 0.07 (,0.001) 0.09 (,0.001) 0.18 (0.05) 21.38 (,0.001) 20.04 (,0.001)
DUB 0.08 (,0.001) N N N N
TABLE 3. Stations showing significant trends in soil frost variables from 1974 to 2006. The values indicate the Mann–Kendall slope, and the
number in parentheses indicate the p value tested for significance; N same as in Table 2.
Stations Mean max Tsoil Mean min Tsoil Extreme min Tsoil Frost days Frost–thaw cycles
MOR N N 0.36 (0.006) N N
WAS N 0.04 (0.030) N N N
WLA 0.09 (,0.001) 0.12 (,0.001) 0.19 (0.003) 21.60 (0.001) 20.06 (0.044)
DUB 0.11 (,0.001) N N N 20.09 (0.004)
APRIL 2010 S I N H A E T A L . 237
As a final evaluation, the calibrated model was used to
estimate the same soil frost indicators computed from
observed soil temperature records. Spearman correlation
coefficients between the annual numbers of soil frost
days estimated from observations and simulations ranged
from 20.05 at Wanatah, IN (WAN), to 0.86 at Ames, IA
(AME), with 5 out of 15 sites indicating r $ 0.50 (Fig. 7).
Three sites had r # 0, indicating significant differences
between observed and simulated soil frost days, most
likely related to the non-site-specific meteorology and
calibration mentioned previously. Generally, the model
predicted a higher number of soil frost days than ob-
servations. In contrast, there were fewer fluctuations
in freeze–thaw events estimated from simulated Tsoil
compared to those estimated from observed Tsoil. This
resulted in r # 0 for 4 out of 12 sites (excluding IA sites)
for the number of freeze–thaw cycles (Fig. 8). It is harder
to predict freeze–thaw cycles than the number of soil
frost days, as soil temperature sensors may not observe
small fluctuations around 08C because of their limited
precision and unknown accuracy, especially around 08C.
To study the effects of climate variability on temporal
and spatial variability in cold-season processes, simula-
tions for the entire period (since 1917) as well as ob-
served daily soil temperature were sorted into cold and
warm and wet and dry years as described in section 3c.
Because only one year (1917) met the criteria of cold
year, and it did not overlap with observations, years 1978
TABLE 4. Same as Table 3, but for 1983–2006.
Stations Mean max Tsoil Mean min Tsoil Extreme min Tsoil Frost days Frost–thaw cycles
MOR N N 0.68 (0.025) 2.18 (0.048) N
LAM N N N 2.88 (0.045) N
CHA N N 20.14 (0.040) N N
DEC 0.09 (0.032) N N N N
NAS N 0.13 (0.004) N 22.88 (0.033) N
CAS 0.16 (0.004) N N N N
AME N N 0.37 (0.009) N N
ATL N N N 20.22 (0.017) N
BUR N 0.21 (,0.001) 0.19 (,0.001) 20.08 (,0.001) 20.07 (,0.001)
WLA 0.08 (0.015) 0.10 (0.001) N N N
FRM 20.14 (0.002) 20.16 (,0.001) 20.20 (0.011) 2.00 (0.005) 0.14 (0.005)
OOL N 0.10 (0.003) N N N
DUB 0.13 (0.002) 20.06 (0.019) 0.32 (0.003) 21.00 (0.004) 20.21 (,0.001)
TABLE 5. Same as Table 3 but for 1991–2006.
Stations Mean max Tsoil Mean min Tsoil Extreme min Tsoil Frost days Frost–thaw cycles
MOR 20.52 (0.004) N N 9.00 (0.004) N
DEC N N N 7.8 (0.038) N
NAS N N N N 0.50 (0.011)
KAN N N N N 0.38 (0.021)
AME N N N N 20.60 (0.047)
ATL N N 0.44 (0.027) N 20.33 (0.035)
BUR N N 0.11 (,0.001) N N
FRE N N 0.25 (0.038) 20.25 (0.014) N
STC N 0.16 (0.006) 0.50 (0.020) N N
PEO 0.15 (0.020) N N N N
BON N N N 1.86 (0.033) N
ORR N 0.12 (0.019) N N N
SPR N 0.09 (0.031) N N N
BEL N N 0.14 (0.022) N N
FAI N 0.13 (0.011) N N N
INA 20.19 (0.006) N N N N
WAN N N N 23.00 (0.030) N
WIN N N 20.40 (0.022) N N
FRM N 20.15 (0.022) 20.35 (0.018) N 0.33 (0.018)
OOL N N 0.14 (0.031) 0.14 (0.027) N
DUB N N 20.43 (0.037) 0.33 (0.011) N
238 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
FIG. 4. Observed trends in the cold-season variables showing (1) mean maximum Tsoil (8C), (2) mean minimum Tsoil
(8C), (3) extreme minimum Tsoil (8C), (4) number of frost days, and (5) number of freeze–thaw (F/T) cycles for
the following periods: (a) 1967–2006, (b) 1974–2006, (c) 1983–2006, and (d) 1991–2006. Symbols represent station
locations. Filled triangles indicate that trends are significant at the 5% significance level and empty triangles indicate
nonsignificant trends. Triangles show the direction of trends.
APRIL 2010 S I N H A E T A L . 239
and 1979 were used to represent cold years for com-
parisons. The differences between observed and simu-
lated Tsoil were within 15% for at least half the sites for
cold, wet, and dry climatic divisions (Table 6), whereas
the model did not performed as well in warm years.
Differences for warm years were still within 20% for five
out of eight sites; therefore, the model performance was
deemed acceptable to capture the long-term means in
daily Tsoil for all climatic divisions and was used as an
analysis tool to quantify climate variability effects.
c. Cold-season sensitivity to snow cover and airtemperature under extreme conditions
To understand the role of snow cover and air tem-
perature on soil temperature and soil frost development,
observations of air temperature and precipitation since
1917 were used to sort simulated variables into warm,
cold, wet, and dry years as described in section 3c. Time
series of a representative year for each climate type were
compared with air temperature for four locations across
the study domain (Fig. 9). For all sites and climate con-
ditions, once the soil was frozen, daily changes in soil
temperature were smaller than for air temperature; when
snow exceeded a threshold depth [;(5–10 cm)], changes
in daily near-surface soil temperature became very small.
This is in agreement with the findings of Nikol’skii et al.
(2002), who found that the top 10 cm of snow cover had
a strong insulation effect on soil surface temperature.
The minimum value of soil temperature is strongly cor-
related to the timing of snow accumulation. When snow
accumulates soon after the occurrence of freezing air
temperatures [e.g., Figs. 9(2c) and 9(2d)] and remains
throughout the winter, minimum soil temperatures are
close to 08C. If snow accumulation occurs later, then
FIG. 5. Cold-season time series of observed and simulated mean daily soil temperatures at a depth of 10 cm for the
following sites: (a) MOR, (b) LAM, (c) WAS, (d) CHA, (e) NMI, (f) HAN, (g) DEC, (h) AME, (i) BUR, ( j) FRE,
(k) URB, (l) ORR, (m) WAN, (n) WLA, and (o) DUB. Observations at the three sites in IA (DEC, AME, and
BUR), unlike the other states, are for a specific time during a day and do not provide information about daily
extremes.
240 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
FIG. 6. Daily time series of observed (red) and simulated (black) variables (cm) snow (snow
depth) and frost depth (F. Depth) and observed thaw depth (blue, in cm) at the following sites
in MN: (1) MOR, (2) LAM, and (3) WAS for the two cold seasons of 1975/76 and 1979/80.
APRIL 2010 S I N H A E T A L . 241
soil temperatures will be colder even under warm [e.g.,
Fig. 9(2a)] or wet [Fig. 9(3c)] climate conditions. When
little to no snow is present during the winter, air tem-
perature is the dominant control on soil temperature
and freezing [e.g., Fig. 9(1d)]; however, snow cover clearly
is the dominant controlling factor, as the colder tem-
peratures during a wet winter [Fig. 9(1c)] do not result
in colder soils than a slightly warmer winter without
snow [Fig. 9(1d)].
d. Spatial and temporal analysis
Subsequent to calibration and evaluation, the VIC
model was used to extend the spatial and temporal
analysis of soil frost. Model simulations starting in 1917
indicated that the annual number of soil frost days de-
creased significantly from north to south in the region
[Fig. 10(1a)], with the average number of soil frost days
ranging from 160 in northern Minnesota to about 4 days
in southern Illinois and Indiana. The average number of
freeze–thaw cycles at a depth of 10 cm was highest in
northern Indiana and lower Michigan, the central parts of
Wisconsin and Illinois, and in southern Iowa [Fig. 10(2a)].
The central regions of Wisconsin and lower Michigan
were under agricultural or mixed use conditions based
upon the land use data used for this study. Agricultural
lands are more susceptible to soil frost than forests be-
cause without a canopy, agricultural fields are more ex-
posed to cold air and surface residues. The lack of a
canopy does lead to greater accumulation of snow in
agricultural fields; however, that snow is more likely to be
redistributed by wind, and it is fully exposed to the sun so
that it melts more quickly. Thus agricultural fields were
more exposed and left with less insulation for longer
periods of time than forested environments, which re-
sulted in higher numbers of freeze–thaw cycles. In con-
trast, western Minnesota and northern Wisconsin were
mostly forested, which reduced the depth of ground snow
but maintained it over longer periods of time. Snow cover
insulates the soil surface from changes in air temperature
and thus reduces the frequency of freeze–thaw cycles.
Furthermore, in the northern regions of the study do-
main, the presence of more persistent below-freezing
FIG. 7. Same as Fig. 5, but for annual time series of number of soil frost days estimated from observed and simulated
soil temperatures at a depth of 10 cm.
242 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
temperatures resulted in fewer freeze–thaw cycles. The
number of freeze–thaw cycles was also limited in south-
ern Illinois and Indiana because the warmer air temper-
atures resulted in fewer days with soil frost and thus fewer
opportunities to complete freeze–thaw cycles.
The timing of the onset of soil frost and last spring thaw
both varied across the study area. The earliest occurrence
of soil frost in the region, on an average, took place
around early November in northern Minnesota, whereas
in the southern regions frost occurred around early to
mid-December [Fig. 10(3a)]. The last thaw in spring
occurred around early February in the southern regions
and around the end of April in the northern regions
[Fig. 10(4a)].
To study temporal variability, changes in soil frost var-
iables were calculated between 30-yr groups represent-
ing early- (1917–46), mid- (1947–76) and late-century
(1977–2006) periods. By studying the differences during
early-century and midcentury periods with respect to the
late-century period, systematic biases—such as the effects
of annual variations that are not generally well repre-
sented in the model-simulated variables—were removed.
Changes in the number of soil frost days in the late
century with respect to the middle and early century in-
dicated a decrease in soil frost days in most of the
FIG. 8. Same as Fig. 5, but for annual time series of number of F/T cycles estimated from observed and simulated soil
temperatures at a depth of 10 cm.
TABLE 6. Obs and Sim average cold-season daily soil tempera-
ture during extreme climatic years (warm, cold, wet, and dry) for
sites measuring soil temperatures (8C) since 1970. Values in bold
indicate that the difference between Obs and Sim soil temperatures
were within 15% of each other.
Warm (8C) Cold (8C) Wet (8C) Dry (8C)
Site Obs Sim Obs Sim Obs Sim Obs Sim
MOR 3.06 3.99 20.58 3.88 2.19 3.04 2.97 4.02
WAS 6.80 7.36 5.3 4.44 5.13 4.75 5.34 5.63
LAM 6.99 4.60 2.56 4.85 5.64 4.84 6.77 5.57
CHA 1.53 3.80 4.90 5.16 4.64 4.02 4.74 4.73URB 10.63 9.22 3.88 7.62 7.72 8.78 9.02 8.52
WAN 9.91 8.23 8.17 7.91 6.39 7.97 8.96 7.03
WLA 10.11 8.63 6.78 6.55 8.0 8.06 7.56 7.37DUB 11.39 9.41 9.26 7.95 8.56 8.62 10.05 8.23
APRIL 2010 S I N H A E T A L . 243
northern and central regions while an increase was ob-
served in southern Illinois [Fig. 10(1b)] and the upper
peninsula of Michigan [Fig. 10(1c)]. The decrease in soil
frost was by as much as 24 days in lower Michigan and 18
days in southern Minnesota, southern Wisconsin, and
the northern regions of Illinois and Indiana [Fig. 10(1c)].
Most of these regions are agricultural lands, which are
more exposed to changes in air temperature. Despite
these reductions, the number of freeze–thaw cycles was
unaffected in most of the region in the late century as
compared to the early-century period [Fig. 10(2b)]. In
comparison, by midcentury, regions in the north indi-
cated an increase or no change in freeze–thaw cycles
while regions in the south indicated a decrease [Fig. 10(2c)].
Increases in soil temperatures may have resulted in
more fluctuations around 08C in the north while pro-
viding fewer opportunities for soil frost development in
the south.
In the late-century period, fewer sites experienced an
earlier onset of frost in central and southern Illinois,
north-central Minnesota, and in the upper peninsula of
Michigan [Fig. 10(3c)]. During this period, both the first
occurrence of frost and the last thaw changed by as much
as 18 days [Fig. 10(4c)]. Interestingly, the area that dis-
played a delay in the onset day of soil frost between the
late- and early-century periods was larger than the re-
gion that experienced the same change between the late-
and midcentury periods [Figs. 10(3b) and 10(3c)]. These
sites were generally those where the number of soil frost
days decreased [Figs. 10(1b) and 10(1c)]. On the other
hand, the last day of thawing in the late-century period
occurred earlier in most of the region [Figs. 10(4b) and
FIG. 9. Daily values of observed air temperature (8C), simulated soil temperature (8C) and simulated snow depth (cm) in (1) northwest
MN (48.81258N, 96.18758W), (2) northern WI (46.06258N, 91.06258W), (3) southern IA (41.56258N, 94.06258W), and (4) southern IN
(38.81258N, 86.68758W), and the following cold seasons: (a) warm, (b) cold, (c) wet, and (d) dry. Simulated soil temperatures and snow
depth were used because of the lack of observations at the selected sites during extreme climatic years.
244 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
FIG. 10. Spatial plot indicating (a) annual average values since 1917, (b) late-century (1977–2006) minus early-
century conditions (1917–46), and (c) late-century minus midcentury conditions (1947–76) for the following
variables: (1) frost days (number), (2) F/T cycles (number), (3) onset day of soil frost, and (4) last thaw day.
APRIL 2010 S I N H A E T A L . 245
10(4c)], by as much as 27 days in lower Michigan and
southern Illinois. This indicates a reduction in soil frost
duration between the mid- and late-century periods by
as much as 36 days—specifically in southeast Minnesota,
northeast Iowa, and north-central Indiana.
Simulations from 1917 indicate that average winter
soil temperatures at a depth of 10 cm for the months of
December, January, and February ranged from 288C in
the north to 128C in the south [Fig. 11(1a)]. In spring
(March–May), the monthly average temperature varied
from about 18 to about 128C [Fig. 11(2a)]. Frost depths
were greatest in the northern regions and increased to
their maximum values in the spring [Figs. 11(3a) and
11(4a)]. This may be due to the lower-than-average SWE
during spring in comparison to the winter season
[Figs. 11(5a) and 11(6a)]. In winter, higher average
monthly SWE was indicative of more days with snow
cover and deeper snow, which further insulated the soil
surface and decreased soil frost penetration. Furthermore,
the development of soil frost continued from winter into
spring, leading to increased frost depths.
Winter soil temperatures increased in the northern part
of the study domain with respect to the early-century
period, while the central part of the study domain expe-
rienced warming between the middle and late century
[Figs. 11(1b) and 11(1c)]. In contrast, northeastern Min-
nesota and the upper peninsula of Michigan underwent
a cooling trend in winter soil temperatures during the
latter part of the twentieth century. This may be due to
a decrease in average monthly winter SWE, which was
indicative of reduced snow cover in these regions of
Minnesota and Michigan [Fig. 11(5c)]. This also led to
increased frost depth during the winters of the late-century
period with respect to midcentury [Fig. 11(3c)]. During
spring, most of the northern regions experienced an in-
crease in soil temperature, leading to reductions in frost
depth in northern Minnesota and western Wisconsin
[Figs. 11(4b) and 11(4c)]. Winter SWE has increased in
southern-central Illinois and Indiana, southern Minne-
sota and northeast Iowa by up to 9% in the late cen-
tury in comparison to the midcentury while spring SWE
has decreased in the northern region, southeastern
Wisconsin, and northern Michigan by 9% [Figs. 11(5c)
and 11(6c)]. The decrease in spring SWE coincided with
the regions that were classified as agricultural and mixed
use. Agricultural regions have higher snow accumula-
tions on the ground surface than forested sites because
trees intercept snowfall, which reduces snow accumu-
lation. Similarly, the regions where SWE has increased
during winter were mostly under agricultural areas.
Cold-season soil temperatures in warm years varied
from 08C in the north to 128C in the south, while in the
cold years temperatures ranged from 258C in the north
up to 98C in the south, an average decrease of 38C (Fig. 12).
Warm years had shallower frost penetration, which may
even be shallower by up to 100 cm at a few sites in northern
Minnesota in comparison to the cold years [Figs. 12(2a)
and 12(2b)]. Cold-year soil frost was deeper, specifically
in most of Minnesota and northern Wisconsin, with av-
erage depths for the entire region exceeding by 15 cm
than those simulated for warm years. Cold years also
distinguished themselves from warm years through higher
snow accumulations. The variability in SWE during cold
years was higher, with a greater number of sites expe-
riencing extreme values of SWE, than in the warm years
(Fig. 13). However, total precipitation in the cold years
was lower than in warm years.
In wet years, soil temperatures were warmer than in
the dry years (Figs. 12c and 12d), but the spatial average
of soil temperatures over the entire study domain for
both wet and dry years were similar (5.068 and 4.988C,
respectively). Wet years experienced higher SWE than
dry years, resulting in reduced depth of frost penetra-
tion, as expected. The median precipitation in wet years
was about 30% higher than that for dry years (Fig. 13).
Although soil temperature changes generally followed
patterns similar to those of air temperature during wet
and dry years, regions with higher SWE, such as the
upper peninsula of Michigan, experienced warmer soil
temperatures than the corresponding air temperatures
because of the insulation effect of snow cover. The fre-
quency and severity of extreme temperature and pre-
cipitation events are likely to be enhanced even more in
the future than has occurred in the past, resulting in
more dramatic changes in cold-season processes.
5. Conclusions
This study focused on the effects of historic climate
variability on cold-season variables in the midwestern
United States, using both observations and model sim-
ulations. Soil frost indicators—such as the number of
frost days and freeze–thaw cycles—were determined
from observed records for different periods based upon
the availability of data and were tested for the presence
of significant trends. We used the VIC model to re-
construct long-term historic time series of cold-season
variables and then analyzed trends and associated spa-
tial and temporal variability. We also analyzed the hy-
drologic response to extreme warm, wet, cold, and dry
years to understand the effect of climate variability on
seasonal soil frost. The primary findings of this study are
summarized as follows:
d An analysis of observed data indicated that the per-
centage of stations showing significant trends and the
246 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
FIG. 11. Spatial plots indicating (a) seasonal average since 1917, (b) late-century
(1977–2006) minus early-century conditions (1917–46), and (c) late-century minus
midcentury conditions (1947–76) in Tsoil (8C), frost depth (cm), and SWE (cm). The
odd numbers represent winter (December–February) and even numbers represent
spring (March–May).
APRIL 2010 S I N H A E T A L . 247
FIG. 12. Cold seasonal averages for the following selected soil frost variables: 1) Tsoil (8C), 2) frost depth (cm), and
3) SWE (mm), 4) PRCP (mm), and 5) monthly air temperature Tair (8C) with the following years: (a) warm, (b) cold,
(c) wet, and (d) dry.
248 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
direction of those trends varied for the different ob-
servational periods considered. A higher percentage
of stations experienced statistically significant trends
in soil frost variables since 1967 in comparison to the
shorter periods considered in this study. Overall
trends in extreme and mean seasonal Tsoil from 1967
and from 1974 to present have generally indicated
a warming in soil temperatures, specifically at north-
west Indiana, north-central Illinois, and southeast
Minnesota, leading to a reduction in the number of
soil frost days. The record is mixed on shorter time
scales (since 1983 and since 1991), indicating localized
effects dominate the regional climate signal in this
region.d Analysis of daily time series of simulated soil tem-
perature and snow depth at four sites indicated that
snow is the dominant control on soil temperatures.
When significant snow (depth . 5 cm) is present, soil
temperatures are relatively constant and do not reach
the extreme minimums of a soil directly exposed to
cold air temperatures. Actual soil temperatures under
snow are controlled by the timing of snow accumula-
tion, such that soil temperatures can be substantially
colder in years with late accumulation or discontinu-
ous snow cover even when winter air temperatures are
higher. With limited to no snow cover, soil tempera-
tures are highly correlated to air temperatures.d The duration of soil frost has decreased by as much as
36 days in the late-twentieth-century period (1977–
2006) as compared to the midcentury (1947–76) period
in southeast Minnesota, northeast Iowa and north-
central Indiana, increasing the length of growing sea-
son. In most of the central and southern regions, the
length of the growing season increased by about two
weeks in the late twentieth century as compared to the
midcentury period. These changes in soil frost dura-
tion suggest a shift in the time of sowing and fertilizer
applications earlier in the spring by two weeks. In
contrast, soil frost days have increased by two weeks
between the middle and end of the twentieth century
in the upper peninsula of Michigan, where the onset of
frost also now occurs two weeks earlier. The central
regions of Wisconsin and lower Michigan have experi-
enced increased freeze–thaw cycles in the late twentieth
FIG. 13. Box plots showing (top) frost depth, (middle) SWE, and (bottom) PRCP for cold seasons representing:
warm, cold, wet, and dry years. The tops and bottoms of the boxes indicate the 25th and 75th quartiles, respectively;
the lines in the middle represent the median value, the whiskers indicate the maximum and minimum values in the
distribution, and crosses indicate values that fell outside of 1.5 3 the interquartile range and were therefore classified
as outliers.
APRIL 2010 S I N H A E T A L . 249
century relative to the early- and midcentury periods.
Therefore, these regions may experience increased po-
tential for soil erosion due to enhanced freezing and
thawing over winter months.d Historically, soil temperatures in warm years were
greater by up to 38C than those in cold years with
shallower frost penetration by 15 cm on average. Cold
years were typically influenced by deeper snowpacks
(higher SWE totals), which led to shallower penetra-
tion of soil frost, specifically in Minnesota and north-
ern Wisconsin. This indicates that snow accumulation
played a key role in soil frost formation and seasonal
dynamics. During the cold season, the extreme wet
years experienced especially high SWE, resulting in
further reduced penetration of soil frost and warmer
soil temperatures compared to dry years.
Long-term model simulations provided useful infor-
mation on spatial and temporal patterns of cold-season
variables for the entire six-state study domain. However,
there are some limitations of this study. This study did
not account for the feedback between vegetation and
climate, as vegetation parameters were based on 1992/93
land cover data. This implies that the model results are
not as adaptive to real-world changes in agricultural and
natural vegetation. The model was also not configured
to represent the effects of urbanization or the lakes and
wetlands that are common in the northern parts of the
study domain. Representation of the earlier-mentioned
processes may improve estimations of various water and
energy balance components in the model, reducing un-
certainties in estimation of freeze–thaw cycles and cold-
season processes.
Acknowledgments. We are thankful to NASA for
providing funding for this research through the Grant
NNG04GP13P. This manuscript is Purdue Climate
Change Research Center (PCCRC) paper 0901.
APPENDIX A
Location and Soil Types for Sites Collecting Soil Temperatures at 10-cm Depth
Stations Latitude (8N) Longitude (8E) Elevation (m) Soil Soil cover
Morris WC Exp (MN) 45.58 295.87 347.5 Forman clay loam Bare
Lamberton SW Exp (MN) 44.23 295.32 348.7 Nicollet silty clay loam Bare
Waseca Exp (MN) 44.07 293.53 351.4 Nicollet clay loam Bare
Chatham Exp Frm (MI) 46.35 286.93 268.2 Rocky loam Sod
Hancock Exp Frm (WI) 44.12 289.53 328.0 Plainfield loamy sand Bare
NW Michigan Res Frm (MI) 44.88 285.68 249.9 — Bare
Decorah 2 N (IA) 43.30 291.80 262.1 Fayette silt loam Bare
Nashua 2 SW (IA) 42.93 292.57 315.5 Kenyon loam Bare
Kanawha (IA) 42.93 293.80 361.2 Nicollet loam Bare
Oelwein 2 S (IA) 42.65 291.92 307.8 Bixby loam Cultivated
Castana (IA) 42.07 295.83 442.0 Ida silt loam Bare
Ames 8 WSW (IA) 42.02 293.77 335.0 Clarion loam Bare
Atlantic 1 NE (IA) 41.42 295.00 353.6 Marshal silt loam Bare
Burlington radio KBUR (IA) 40.82 291.17 214.3 Grundy silt loam Bare
Freeport (IL) 42.28 289.67 265.0 Dubuque Grass
Stcharles (IL) 41.90 288.37 226.0 — Grass
De Kalb (IL) 41.85 288.85 265.0 Flanagan/drummer Grass
Stelle (IL) 40.95 288.17 213.0 Monee Grass
Monmouth (IL) 40.92 290.73 229.0 — Grass
Peoria (IL) 40.70 289.52 207.0 Clinton Grass
Urbana (IL) 40.08 288.23 219.0 Flanagan silt loam Grass
Bondville (IL) 40.05 288.22 213.0 Flanagan/Elburn Grass
Orr (IL) 39.80 290.83 206.0 Clarksdale Grass
Springfield (IL) 39.52 289.62 177.0 Ipava Grass
Brownstown (IL) 38.95 288.95 177.0 Cisne Grass
Olney (IL) 38.73 288.10 134.0 Bluford Grass
Belleville (IL) 38.52 289.88 133.0 Wier Grass
Fairfield (IL) 38.38 288.38 136.0 Cisne Grass
250 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11
REFERENCES
Abdulla, F. A., D. P. Lettenmaier, and X. Liang, 1999: Estimation
of the ARNO model baseflow parameters using daily stream-
flow data. J. Hydrol., 222, 37–54.
Alley, R. B., and Coauthors, 2007: Summary for policymakers.
Climate Change 2007: The Physical Science Basis, S. Solomon
et al., Eds., Cambridge University Press, 18 pp.
Bowling, L. C., and Coauthors, 2003a: Simulation of high-latitude
hydrological processes in the Torne-Kalix basin: PILPS Phase
2(e) 1: Experiment description and summary intercomparisons.
Global Planet. Change, 38, 1–30.
——, and Coauthors, 2003b: Simulation of high-latitude hydro-
logical processes in the Torne-Kalix basin: PILPS Phase 2(e) 3:
Equivalent model representation and sensitivity experiments.
Global Planet. Change, 38, 55–71.
Bras, R. A., 1990: An Introduction to Hydrologic Science. Addison-
Wesley, 643 pp.
Cherkauer, K. A., and D. P. Lettenmaier, 1999: Hydrologic effects
of frozen soils in the upper Mississippi River basin. J. Geophys.
Res., 104, 19 599–19 610.
——, and ——, 2003: Simulation of spatial variability in snow and
frozen soil. J. Geophys. Res., 108, 8858, doi:10.1029/2003JD003575.
——, L. C. Bowling, and D. P. Lettenmaier, 2003: Variable in-
filtration capacity cold land process model updates. Global
Planet. Change, 38, 151–159.
Cooter, E. J., and S. K. Leduc, 1995: Recent frost date trends in the
north-eastern USA. Int. J. Climatol., 15, 65–75.
Dye, D. G., and C. J. Tucker, 2003: Seasonality and trends of snow-
cover, vegetation index, and temperature in northern Eurasia.
Geophys. Res. Lett., 30, 1405, doi:10.1029/2002GL016384.
Easterling, D. R., 2002: Recent changes in frost days and the frost-
free season in the United States. Bull. Amer. Meteor. Soc., 83,1327–1332.
——, and T. R. Karl, 2001: Potential consequences of climate
variability and change for the midwestern United States. Climate
change impacts on the United States: The potential conse-
quences of climate variability and change, National Assess-
ment Synthesis Team Foundation Rep., 167–188.
Fassnacht, S. R., and E. D. Soulis, 2002: Implications during tran-
sitional periods of improvements to the snow processes in the
Land Surface Scheme—Hydrological model WATCLASS.
Atmos.–Ocean, 40, 389–403.
Frauenfeld, O. W., T. Zhang, and J. L. McCreight, 2007: Northern
hemisphere freezing/thawing index variations over the twen-
tieth century. Int. J. Climatol., 27, 47–63.
Gaile, G. L., and C. J. Willmott, 1984: On the evaluation of model
performance in physical geography. Spatial Statistics and
Models, Kluwer, 443–460.
Hamed, K. H., and A. R. Rao, 1998: A modified Mann-Kendall
trend test for autocorrelated data. J. Hydrol., 204, 182–196.
Hamlet, A. F., and D. P. Lettenmaier, 2005: Production of tempo-
rally consistent gridded precipitation and temperature fields for
the continental United States. J. Hydrometeor., 6, 330–336.
Hansen, M. C., R. S. Defries, J. R. G. Townsheed, and R. Sohlberg,
2000: Global land cover classification at 1 km spatial resolution
using a classification tree approach. Int. J. Remote Sens., 21,
1331–1364.
Hardy, J. P., and Coauthors, 2001: Snow depth manipulation and
its influence on soil frost and water dynamics in a northern
hardwood forest. Biogeochemistry, 56, 151–174.
Haugen, R., and G. King, 1998: Seasonal frost depths, midwestern
USA. Circumpolar Active-Layer Permafrost System (CAPS), ver-
sion 1.0. National Snow and Ice Data Center, Boulder, CO, digital
media. [Available online at http://nsidc.org/data/ggd498.html.]
Hirsch, R. M., D. R. Helsel, T. A. Cohn, and E. J. Gilroy, 1992:
Statistical treatment of hydrologic data. Handbook of Hy-
drology, D. R. Maidment, Ed., McGraw-Hill, 17.1–17.55.
Hodgkins, G. A., and R. W. Dudley, 2006: Changes in late-winter
snowpack depth, water equivalent, and density in Maine,
1926-2004. Hydrol. Processes, 20, 741–751.
Jordan, R., 1991: A one-dimensional temperature model for a snow
cover: Technical documentation for SNTHERM.89. U.S.
Army Corps of Engineers, CRREL Special Rep. 91-16, 61 pp.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-
analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.
Kling, G. W., and Coauthors, 2003: Confronting Climate Change in
the Great Lakes Regions: Impacts on our Communities and
Ecosystems. The Union of Concerned Scientists and the
Ecological Society of America Rep., 92 pp.
Krause, P., D. P. Boyle, and F. Base, 2005: Comparison of different
efficiency criteria for hydrologic model assessment. Adv. Geo-
sci., 5, 89–97.
Kunkel, K. E., and Coauthors, 1998: An expanded digital daily
database for climatic resources applications in the midwestern
United States. Bull. Amer. Meteor. Soc., 79, 1357–1366.
——, D. R. Easterling, K. Hubbard, and K. Redmond, 2004: Tem-
poral variations in frost-free season in the United States: 1895–
2000. Geophys. Res. Lett., 31, L03201, doi:10.1029/2003GL018624.
Legates, D. R., and G. J. McCabe Jr., 1999: Evaluating the use of
‘‘goodness-of-fit’’ measures in hydrologic and hydroclimatic
model validation. Water Resour. Res., 35, 233–241.
APPENDIX A. (Continued)
Stations Latitude (8N) Longitude (8E) Elevation (m) Soil Soil cover
Ina (IL) 38.15 288.90 128.0 Cisne Grass
Carbondale (IL) 37.72 289.23 137.0 Parke Grass
Dixon Springs (IL) 37.45 288.67 165.0 Grantsburg Bare
Prairie Heights (IN) 41.63 285.20 301.8 — Bare
Wanatah 2 WNW (IN) 41.45 286.93 224.0 Tracy sandy loam Bare
Columbia City 1 S (IN) 41.15 285.48 271.0 Blount silt loam Grass
Winamac 2 SSE (IN) 41.03 286.58 210.3 — Bare
West Lafayette 6 NW (IN) 40.48 287.00 217.9 Russell silt loam Bare
Farmland 5 NNW (IN) 40.25 285.15 294.1 Pewamo silty clay loam Bare
Tipton 5 SW (IN) 40.22 286.12 272.8 Brookston silty clay loam Bare
Oolitic Exp Frm (IN) 38.88 286.55 198.1 Bedford silt loam Bare
Dubois Forage Frm (IN) 38.45 286.70 210.3 Zanesville silt loam Bare
APRIL 2010 S I N H A E T A L . 251
Lemke, P., and Coauthors, 2007: Observations: Changes in snow,
ice and frozen ground. Climate Change 2007: The Physical
Science Basis, S. Solomon et al. Eds., Cambridge University
Press, 337–384.
Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A
simple hydrologically based model of land surface water and
energy fluxes for general circulation models. J. Geophys. Res.,
99, 14 415–14 428.
——, ——, and ——, 1996: One-dimensional statistical dynamic
representation of subgrid spatial variability of precipitation in
the two-layer variable infiltration capacity model. J. Geophys.
Res., 101, 21 403–21 422.
Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier,
1998a: Regional scale hydrology: I. Formulation of the VIC-
2L model coupled to a routing model. Hydrol. Sci. J., 43,131–142.
——, ——, ——, and ——, 1998b: Regional scale hydrology: II.
Application of the VIC-2L model to the Weser River, Ger-
many. Hydrol. Sci. J., 43, 143–158.
Mao, D., and K. A. Cherkauer, 2009: Impacts of land-use change on
hydrologic responses in the Great Lakes region. J. Hydrol.,
374, 71–82.
Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and
B. Nijssen, 2002: A long-term hydrologically based dataset of
land surface fluxes and states for the conterminous Unites
States. J. Climate, 15, 3237–3251.
Miller, D. A., and R. A. White, 1998: A conterminous United
States multilayer soil characteristics dataset for regional cli-
mate and hydrology modeling. Earth Interactions, 2. [Avail-
able online at http://EarthInteractions.org.]
Mitchell, K. E., and Coauthors, 2004: The multi-institution North
American Land Data Assimilation System (NLDAS): Utiliz-
ing multiple GCIP products and partners in a continental
distributed hydrologic modeling system. J. Geophys. Res., 109,1–32.
Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier,
2005: Declining mountain snowpack in western North America.
Bull. Amer. Meteor. Soc., 86, 39–49.
Myneni, R. B., R. R. Nemani, and S. W. Running, 1997: Estimation
of global Leaf Area Index and absorbed par using radiative
transfer models. IEEE Trans. Geosci. Remote Sens., 35, 1380–
1393.
Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through
conceptual models part I—A discussion of principles. J. Hy-
drol., 10, 282–290.
Nijssen, B., G. M. O’Donnell, D. P. Lettenmaier, D. Lohmann, and
E. F. Wood, 2001a: Predicting the discharge of global rivers.
J. Climate, 14, 3307–3323.
——, R. Schnur, and D. P. Lettenmaier, 2001b: Global retrospec-
tive estimation of soil moisture using the variable infiltration
capacity land surface model, 1980–93. J. Climate, 14, 1790–1808.
Nikol’skii, A. A., E. E. Roshchina, and O. V. Soroka, 2002: Snow
cover as a factor of winter ecology of small mammals in the
steppes zone. Dokl. Biol. Sci., 383, 158–160.
Rao, A. R., K. H. Hamed, and H.-L. Chen, 2003: Time domain
analysis. Nonstationarities in Hydrologic and Environmental
Time Series, Kluwer Academic Publishers, 27–54.
Schaal, L. A., J. E. Newman, and K. A. Scheeringa, 1981: Climatol-
ogy of soil temperatures in Indiana. Department of Agronomy,
Agricultural Experiment Station, Purdue University, Station
Bulletin 307, 87 pp.
Sinha, T., and K. A. Cherkauer, 2008: Time series analysis of freeze
and thaw processes in Indiana. J. Hydrometeor., 9, 936–950.
Stewart, I. T., D. R. Cayan, and M. D. Dettinger, 2005: Changes
toward earlier streamflow timing across western North America.
J. Climate, 18, 1136–1155.
Storck, P., and D. P. Lettenmaier, 1999: Predicting the effect of
a forest canopy on ground snow pack accumulation and ab-
lation in maritime climates. Proc. 67th Western Snow Conf.,
South Lake Tahoe, CA, Western Snow Conference, 1–12.
U.S. Department of Commerce, 1966–1982: Indiana, Michigan,
Illinois, Iowa, Minnesota, Wisconsin: Climatological Data,
Vols. 71–87, Environmental Data and Information Service,
National Climatic Data Center.
WARM, cited 2008: Illinois State Water Survey. [Available online at
http://www.isws.illinois.edu/warm/datatype.asp.]
Willmott, C. J., 1981: On the validation of models. Phys. Geogr., 2,
184–194.
WMO, 2006: Statement on the status of the global climate. WMO
Rep. 998, 12 pp. [Available online at http://www.wmo.ch/
index-en.html.]
Wood, E. F., D. P. Lettenmaier, X. Liang, B. Nijssen, and
S. W. Wetzel, 1997: Hydrologic modeling of continental-scale
basins. Annu. Rev. Earth Planet. Sci., 25, 279–300.
Zhang, T., 2005: Influence of the seasonal snow cover on the
ground thermal regime: An overview. Rev. Geophys., 43,
RG4002, doi:10.1029/2004RG000157.
——, R. G. Barry, D. Gilichinsky, S. S. Bykhovets, V. A. Sorokovikov,
and J. Ye, 2001: An amplified signal of climatic change in soil
temperatures during the last century at Irkutsk, Russia. Climatic
Change, 49, 41–76.
——, M. Serreze, R. G. Barry, D. Gilichinsky, and A. Etringer,
2003: Climate change: Evidence from Russian historical soil
temperature measurements. Geophysical Research Abstracts,
Vol. 5, Abstract 1485. [Available online at http://www.cosis.net/
abstracts/EAE03/01485/EAE03-J-01485.pdf.]
252 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11