Introduction
The critical zone (CZ) is the surficial layer of the planet that extends
from the top of the vegetation canopy to the base of aquifers (Chorover et
al., 2011; Brandley et al., 2007). Within its boundaries, complex
interactions between air, water, biota, organic matter, soils, and rocks take
place that are critical for sustaining life on Earth (Brandley et al.,
2007). The CZ has been conceptualized and studied as a weathering engine or
reactor where interacting chemical, physical, and biological processes drive
weathering reactions (Anderson et al., 2007; Chorover et al., 2011). Over
long timescales, the CZ has evolved in response to climatic and tectonic
forces and has been recently influenced by human activities (Steffen et al.,
2007). Understanding how climate and land use changes affect CZ structure
and related processes has become a priority for the scientific community due to
the implications it may have on the functioning of life-supporting
resources. It has been hypothesized by the researchers from the Jemez River
basin (JRB) – Santa Catalina Mountains (SCM) critical zone observatory
(CZO) (http://criticalzone.org/catalina-jemez/) that a quantification of the
inputs of the effective energy and mass transfer (EEMT) to the CZ can
provide insight about its structure and function (Chorover et al., 2011). CZ
areas that receive greater EEMT influxes have been shown to have greater
structural organization as well as more dissipative product removal
(Rasmussen et al., 2011; Zapata-Rios et al., 2015a). The opposite has been
observed in regions with less EEMT.
EEMT is a variable that quantifies energy and mass transfer to the CZ (Rasmussen et al., 2011).
EEMT integrates within a single variable the energy and mass associated with water that percolates into the CZ
(Eppt), and reduced carbon compounds resulting from primary production
(Ebio) (Rasmussen et al., 2011). It has been demonstrated that other
possible energy fluxes to the CZ, such as potential energy from transport of
sediments, geochemical potential of chemical weathering, external inputs of
dust, heat exchange between soil and atmosphere, and other sources of energy
coming from anthropogenic sources, are orders of magnitude smaller (Phillips,
2009; Smil, 1991; Rasmussen et al., 2011; Rasmussen, 2012). Therefore the
two dominant terms embodied in EEMT are Eppt and Ebio, and only the energy
associated with water and carbon is considered in the EEMT quantification.
Energy from both water and net primary productivity is essential for CZ
processes altering soil genesis, mineral dissolution, solute chemistry, and
weathering rates (among others) (Birkeland, 1974; Neilson, 2003).
Previous research has shown that EEMT can become a tool to predict regolith
depth, rate of soil production, and soil properties (Rasmussen et al., 2005, 2011;
Pelletier and Rasmussen, 2009a, b; Rasmussen and
Tabor, 2007). For instance, strong correlations were found between EEMT,
soil carbon, and clay content in soils on igneous parent materials from
California and Oregon (Rasmussen et al., 2005). Furthermore, transfer
functions were successfully determined between EEMT and pedogenic indices,
including pedon depth, clay content, and chemical indices of soil alteration
along an environmental gradient on residual igneous parent material
(Rasmussen and Tabor, 2007). EEMT has also been incorporated in geomorphic
and pedogenic models on granitic rocks to describe landscape attributes and
regolith thickness (Pelletier and Rasmussen, 2009a, b). Rasmussen and Tabor (2007)
demonstrated that regolith depth on stable low-gradient slopes
increased exponentially with increasing EEMT. Similarly, Pelletier et al. (2013)
found that high EEMT values are associated with large above-ground
biomass, deeper soils, and longer distance to the valley bottoms across
hillslopes in the Santa Catalina Mountains in southern Arizona. More
recently, EEMT estimations have been strongly correlated with water transit
times, water solute concentrations, and dissolution of silicates on a
rhyolitic terrain in northern New Mexico (Zapata-Rios et al., 2015a). In these
studies, the main constituents of EEMT (Eppt and Ebio) were
quantified as an average value, based on climate records from long-term
regional databases as these variables exert first-order controls on
photosynthesis and effective precipitation (Rasmussen et al., 2011; Chorover
et al., 2011).
It is still uncertain how climate variability influences CZ structure,
function, and the timescales of these changes (Chorover et al., 2011; Brooks
et al., 2015). Climate variability might directly influence changes in the
transfer of mass and energy to the CZ as climate has a direct control on
both Eppt and Ebio. In the mountains of the southwestern United
States, a large percentage of annual precipitation falls as snow, which is
stored during the winter and released as snowmelt during the spring (Clow,
2010). The water from the winter snowpack constitutes the main source of
regional water supplies and the largest component of runoff (Bales et al.,
2006; Nayak et al., 2010). The regional snowpack has been documented to be
declining in the southwestern US (Mote et al., 2005; Clow, 2010) and
alterations to the snowpack are likely to produce changes in vegetation,
impact water availability (Bales et al., 2006; Harpold et al., 2012;
Trujillo et al., 2012), and influence inputs of EEMT. For instance,
significant increasing trends in air temperature and decreasing trends in
winter precipitation in the last decades have been documented in the upper
Rio Grande region in northern New Mexico (Harpold et al., 2012).
The objective of this study was to evaluate climate variability and its
influence on the temporal changes of water partitioning and EEMT at the
catchment scale in a semi-arid CZ over the last few decades. This
investigation took place in the upper part of the JRB in
northern New Mexico, a basin dominated by a forest cover and limited human
infrastructure. Micro-climate variability was studied based on daily records
from two SNOTEL stations using records from 1984 through 2012. Water
availability and EEMT were estimated during the same time period, based on
precipitation and temperature from the Parameter-elevation Regressions
on Independent Slopes Model (PRISM), empirical daily observations of
catchment-scale discharge, and satellite-derived net primary productivity
(MODIS).
(a) Relative location of study area within the northwestern state
of New Mexico, (b) upper JRB, ∼1200 km2,
delimited above the USGS gauge station “Jemez River near Jemez” (USGS
08324000) based on a 10 m digital elevation model (DEM).
Methods
Study site
The Jemez River is a tributary of the upper reach of the Rio Grande and is
located between Jemez and the Sierra Nacimiento mountains in northern New Mexico
(Fig. 1a). Its headwaters originate within the 360 km2 Valles Caldera
National Preserve which contains 30 % of the total basin surface (Fig. 1b).
The upper JRB is located at the southern margin of the
Rocky Mountain ecoregion between latitudes 35.6 and
36.1∘ N and longitudes -106.3 and -106.9 W. The basin is characterized by a mean elevation of 2591 m and a
gradient in elevation ranging from 1712 to 3435 m. Based on a 10 m digital
elevation model, the catchment drains 1218 km2 above the US
Geological Survey (USGS) gauge “Jemez River near Jemez” (35.66∘ N and
106.74∘ W; USGS 08324000), located at an elevation of 1712 m.
The basin has a predominant south aspect and a mean catchment slope of
13.7∘. The geology consists of rocks of volcanic origin with
predominant andesitic and rhyolitic compositions that overlie tertiary to
Paleozoic sediments along the western margin of the Rio Grande rift
(Shevenell et al., 1987). Common soil types in the basin include Aridisols,
Alfisols, Mollisols, and Inceptisols (Allen et al., 1991, 2002).
Precipitation has a bimodal pattern with 50 % of annual precipitation
occurring during the winter months (primarily as snow) from October to April
and originates from westerly frontal systems. The remaining 50 % of
precipitation falls as convectional rainfall during the monsoon season
between July and September (Sheppard, 2002). According to the National Land
Cover Database (NLCD), the basin is a forested catchment with 79 % under
evergreen, deciduous, and mixed forest cover and only 0.5 % of area covered
by development and agriculture (http://www.mrlc.gov/nlcd06_leg.php) (Table 1).
Land use classification of the JRB area. 79.7 % of
the total basin is covered by forest, according to the National Land Cover
Database (NLCD; http://www.mrlc.gov/nlcd06_leg.php).
Land use class
Area (km2)
%
Evergreen forest
847.7
69.60
Deciduous forest
92.6
7.61
Mixed forest
29.8
2.44
Grassland/herbaceous
128.0
10.51
Shrub/scrub
85.0
6.98
Pasture/hay
1.8
0.14
Barren land (rock, sand, clay)
1.3
0.10
Developed
6.1
0.50
Cultivated crops
0.1
0.01
Wetlands
25.2
2.07
Open water
0.4
0.03
Total
1218.0
100.00
Climatological stations
There are two Natural Resources Conservation Service snow telemetry
(SNOTEL) stations within the study area with long-term records since 1980
(http://www.wcc.nrcs.usda.gov/snow/; Fig. 1b). The Quemazon
station is located at an elevation of 2896 m (35.92 ∘ N and
106.39∘ W) and the Señorita Divide no. 2 station is located at an
elevation of 2622 m (36.00∘ N and 106.83∘ W). The
stations collect real-time precipitation, snow water equivalent (SWE), air
temperature, soil moisture and temperature, and wind speed and direction.
Air temperature records began at the Señorita Divide no. 2 in 1988 and at
the Quemazon station in 1989. There are no stations with long-term records
for the lower part of the basin.
Climate variability
Climate variability was studied based on 13 variables from the two SNOTEL
stations, derived from daily air temperature, precipitation, and maximum
SWE, following a similar methodology and data processing procedure as in
Harpold et al. (2012). The variables analyzed were winter, summer, and annual
air temperature (∘C); annual and winter precipitation (mm);
maximum SWE (mm); maximum SWE to winter precipitation ratio (-); 1 April
SWE (mm); first day snow cover (water year day); last day snow cover
(water year day); length of snow on the ground (number of days); and SM50,
which is the day of the year in which half of the snowpack melts (number of
days). Climate records for data analysis were aggregated by water year (from
1 October to 30 September). Winter season was considered to be
between October and April and summer season between May and September. The
analysis of climate was conducted from 1984 as a starting year to avoid the
anomalous wet years recorded at the beginning of 1980s that were caused by
the Pacific Decadal Oscillation (PDO) and El Niño–Southern Oscillation
(ENSO) (Harpold et al., 2012; and references therein). The presence of a
monotonic increasing or decreasing trend in the 13 climate variables
recorded at the two individual stations was evaluated from 1984 through 2012
by applying the nonparametric Mann–Kendall test (MKT) with a α=0.10
level of significance and the nonparametric Sen's slope estimator of a
linear trend (Yue et al., 2012; Sen, 1968).
EEMT estimation
Energy from both water and net primary productivity are essential on CZ
processes altering soil genesis, mineral dissolution, solute chemistry, and
weathering rates (among others) (Birkeland, 1974; Neilson, 2003; Anderson et
al., 2007). In this investigation EEMT was calculated as the sum of
Eppt and Ebio (Eq. 1). We applied two different methods
to estimate Eppt and Ebio. Following a similar
methodology described in Rasmussen and Gallo (2013), the term EEMTemp was
empirically estimated at the catchment scale based on baseflow estimations
and average basin-scale net primary productivity (NPP) derived from MODIS
satellite data. In comparison, EEMTmodel was estimated at the
catchment scale based on long-term climate records from PRISM, developed by the
climate group at Oregon State University
(http://www.wcc.nrcs.usda.gov/ftpref/support/climate/prism/) and
described in Rasmussen et al. (2005, 2011). PRISM is a weighted regression
technique that accounts for physiographic factors affecting climate
variables,
and it has been extensively used in the US (Daly et al., 1994, 2002). The
assumption of this study is that the 800 m PRISM data will provide a reasonable
spatial estimation of basin-scale precipitation.
EEMT=Eppt+EbioJm-2s-1
EEMTemp
Upper JRB precipitation and air temperature from 1984 through
2012 was obtained using PRISM data at an 800 m spatial resolution (Daly
et al., 1994, 2002). Daily discharge data were available from
1984 through 2012 from the USGS Jemez River near Jemez gauge station
(http://waterdata.usgs.gov/nwis). The upper Jemez River has not
been subjected to flow regulation, and almost 60 % of the annual discharge
occurs during the snowmelt period between March and May. Daily discharge
records were normalized by catchment area, and mean daily discharge was
aggregated into water years.
Precipitation (P) on the land surface was partitioned between quickflow (S)
and catchment wetting (W). S represents water that directly contributes to
streamflow discharge as a response to precipitation events; thus this amount
of water is not transferred to the CZ. W is the total amount of
water that infiltrates the soil, of which a portion is available for
vaporization (V) including vegetation uptake. The remaining portion of W
flows though the CZ and contributes to baseflow (U). V was
estimated at the annual scale as the difference between P and discharge (Q).
Q was separated between S and U using a one-parameter low-pass filter (Lyne
and Hollick, 1979; Arnold and Allen, 1999; Eckhardt, 2005; Troch et al.,
2009) (Eq. 2).
Uk=aUk-1+1-a2Qk-Qk-1Uk≤Qk,
where a is a filter parameter set to 0.925. This filter was passed twice,
backward and forward in time, to improve the partitioning of U and S at the
beginning of the time series. After this, daily values of Q, U, and S were
integrated to annual timescales. Alterations in snowmelt timing were
evaluated with Q50, which indicates the day of the water year when
50 % of the total annual discharge is recorded at the catchment outlet
(Clow, 2010; Stewart et al., 2004).
The term Eppt_emp (energy input through precipitation)
was calculated as stated in Eq. (3) based on estimations of U and mean
PRISM-derived air temperature at the catchment scale (Rasmussen et al.,
2011; Rasmussen and Gallo, 2013).
Eppt=U⋅Cw⋅ΔT(Jm-2s-1)
In Eq. (3), Cw is the specific heat of water (4187 J kg-1 K-1)
and ΔT is the difference in temperature between ambient
temperature and 0 ∘C, calculated as Tambient-Tref
(273.15 ∘K).
Site and meteorological information for the SNOTEL Quemazon and
Señorita Divide no. 2 stations located at high elevations in the upper
part of the JRB.
Mean air
Mean
temperature (∘C)
precipitation (mm)
Station
Station
Elevation
Latitude
Longitude
Active
Year1
Winter2
Year1
Winter2
Max
ID
name
(m)
(∘ N)
(∘ W)
since
SWE (mm)
708
Quemazon
2896
35.92
-106.39
1980
3.98
-0.87
700.78
347.45
242.53
744
Senorita Divide no. 2
2622
36.00
-106.83
1980
4.23
-0.90
685.98
422.87
239.20
Note: The analysis of precipitation since water year (WY) 1984. 1 Water Year: 1 October–30 September; 2 Winter: 1 October–31 March.
Temperature data availability since 1989 for the Quemazon and 1988 for the Senorita Divide no. 2 station
Net primary productivity
Mean annual NPP at the catchment scale was estimated at a 1 km spatial
resolution for the years 2000–2012 using data MOD17A3 from MODIS
(Zhao and Running, 2010) (http://modis-land.gsfc.nasa.gov/npp.html).
Ebio was calculated as
indicated in Eq. (4) and presented in Rasmussen et al. (2011) and
Rasmussen and Gallo (2013).
Ebio=NPP⋅hbioJm-2s-1,
where hbio is the specific biomass enthalpy and equivalent to
22 kJ m-2 s-1 (Lieth, 1975; Phillips, 2009). As MODIS data were only
available from the year 2000 onwards, single and multivariate linear
regression analysis were estimated with the objective of finding a
statistical model to extend Ebio_emp records back to
1984. Using a similar approach as Rasmussen and Tabor (2007), linear
regressions were used between Ebio_emp and climate variables from the
SNOTEL stations and the entire basin.
EEMTmodel
Eppt_model was calculated based on estimations of
effective precipitation (Peff) which is defined as the amount of water
that enters the CZ in excess of evapotranspiration and is available to flow
through the CZ (Rasmussen et al., 2005; Eq. 5).
Eppt_model(i)=Peff(i)⋅Cw⋅ΔT,
where Peff(i) is the monthly effective precipitation calculated as the
difference between monthly PRISM precipitation and monthly potential
evapotranspiration, calculated using the Thornthwaite equation (Rasmussen et
al., 2005; Thornthwaite, 1948). Peff, calculated as the difference
between monthly precipitation and potential evapotranspiration, has been
traditionally used in soil water balances (Arkley, 1963). Cw and
ΔT are the same parameters described in Eq. (3).
Eppt(i) model was calculated on a monthly basis only for the months
when precipitation is larger than evapotranspiration (Peff(i)>0), and these values were integrated in water years. Ebio_model was
estimated as indicated in Eq. (4), and NPP was calculated following an
empirical relationship based on air temperature (Eq. 6; Lieth, 1975).
NPP(i)=30001+e1.315-0.119Ta⋅days(i)365daysyear
NPP(i) is the monthly NPP in g m-2 yr-1, and Ta is the monthly air
temperature. Days(i) over the number of days in a year is an NPP time
correction. Similar to Eq. (5), Ebio_model was
calculated only for the months where Peff(i)>0. For a detailed description of
EEMT, see Rasmussen et al. (2005, 2011, 2015), Rasmussen and Tabor (2007), and
Rasmussen and Gallo (2013).
The EEMTmodel quantification presented in Chorover et al. (2011) has a
relative mean prediction error of ∼25 % relative to the
predicted value. However, we are using mean trends in EEMT at this catchment-scale study, so we believe that even though the EEMT calculation may have
errors, the mean trends presented in this investigation are close to the true
values.
Water availability, water partitioning, and climate controls on water
availability
A trend analysis was conducted using data from 1984 through 2012 on each
component of the water partitioning analysis (P, Q, U, S, V, W, Q50),
and EEMT using the nonparametric MKT and the Sen's slope
estimator of a linear trend with a α=0.10 level of significance
(Yue et al., 2012; Sen, 1968). Relationships between climate, hydrological
variables, and EEMT were examined by simple and multiple linear regression
analysis with parameters fit through a least-square iterative process (Haan,
1997).
Climatic time series trends for the Quemazon and Señorita
Divide no. 2 SNOTEL stations from 1984–2012. A trend in the precipitation
time series was evaluated with the MKT and Sen's slope
estimator. Trends were considered statistically significant at p≤ 0.1. The
results showed an increasing trend in winter, summer, and annual temperature
in the two stations. Annual and winter precipitation, maximum SWE, and
1 April SWE decreased in both stations during the 29 years
analyzed. The last day of snow cover decreases significantly only at the
Quemazon station. No significant trend was observed for the SWE : winter P
ratio, duration of snowmelt SM50, and length of snow on the ground.
Quemazon
Señorita Divide no. 2
Variable
Q Sen's slope
Sig a
Q Sen's slope
Sig a
estimator
estimator
Winter temp.
0.13
∗∗∗∗
0.10
∗∗
Summer temp.
0.10
∗∗∗
0.10
∗∗∗
Annual temp.
0.14
∗∗∗∗
0.12
∗∗∗∗
Annual precip. (mm)
-6.98
∗∗∗
-7.32
∗∗
Winter precip. (mm)
-4.16
*
-5.94
∗∗
Max SWE (mm)
-3.31
∗
-3.47
∗
SWE : winter P ratio
-0.005
-0.002
1 April SWE
-6.05
∗∗
-5.44
∗
Max SWE (day)
-0.57
∗∗
-0.33
SM50 (days)
-0.02
0.12
First day snow cover (day)
-0.50
0.17
Last day snow cover (day)
-0.65
∗∗
-0.31
Snow on ground (days)
-0.12
-0.60
a Statistical significance;
∗ P< 0.1; ∗∗ P< 0.05;
∗∗∗ P< 0.01; ∗∗∗∗ P< 0.001.
Water partitioning
Mean precipitation in the JRB from 1984 to 2012 was 617 mm,
with observed extreme values of 845 mm in 1985 and 336 mm in 2002. During
the analysis period, winter precipitation represented 54 % of total annual
precipitation. Mean annual precipitation at the catchment scale correlated
significantly with the mean annual precipitation recorded at the Quemazon
(R2=0.45; p< 0.0001) and Señorita Divide no. 2 stations (R2=0.73;
p< 0.0001). In this same timeframe average, minimum and maximum basin-scale
temperatures were 6.1, -1.5, and 13.6 ∘C, respectively. In
general, January was the coldest and July was the warmest month. Basin-scale
mean annual and winter temperatures indicated a statistically significant
increasing trend of 0.5 and 0.4 ∘C decade-1 (not
shown). Mean annual temperature in the JRB significantly
correlated with the mean annual temperature recorded at the Quemazon
(R2=0.29; p< 0.006) and Señorita Divide no. 2 stations (R2=0.67;
p< 0.0001) (not shown).
Mean river basin discharge during the study period was 0.15 mm day-1, and the
maximum and minimum historical streamflow discharges were 2.97 and 0.008 mm day-1,
respectively. In the 29 years of daily discharge records, 90 % of
the time discharge surpassed 0.03 mm day-1 and 10 % of the time exceeded
0.38 mm day-1. Peak discharge occurred between March and May and 58 % of the
annual discharge flowed between these months.
From 1984 to 2012, 3 % of annual precipitation became quickflow
and contributed directly to the streamflow discharge (3 % P; standard
deviation SD = 1.2 % P). As a result, 97 % of the annual
precipitation (SD = 1.2 % P) infiltrated and was available for
vegetation uptake. This 97 % of annual precipitation is further
partitioned between vaporization and baseflow. The amount of water vaporized
into the atmosphere represented 91 % of the annual precipitation
(SD = 3.4 % P). Baseflow corresponded to 6.1 % of the annual
precipitation (SD = 2.2 % P) and represented the largest component of
discharge (73.2 % Q; SD = 5.4 % Q). Quickflow represented the
remaining 26.8 % of annual discharge (SD = 5.4 % Q).
Precipitation and water partitioning at the upper Jemez River
catchment scale. There was a significant decreasing trend quantified by the
Mann–Kendall test (MKT) in the JRB precipitation and all the
components of the water partitioning. For instance, precipitation at the
catchment scale decreased during the last 3 decades at a rate of 6.17 mm
per year and discharge at 1.76 mm yr-1. Q50 indicated that
discharge is occurring 4.3 days earlier per decade.
There was a significant decreasing trend in precipitation and all the water
partitioning components in the upper JRB, as quantified by the
MKT (Fig. 2). Precipitation in the basin decreased at
a rate of -61.7 mm decade-1 (p=0.02) (Fig. 2a), while discharge decreased at
a rate of -17.6 mm decade-1 (p=0.001) (Fig. 2b). The two components of
discharge, baseflow and quickflow, decreased at a rate of -12.4 mm
(p< 0.001) and -5.1 mm (p=0.005) decade-1,
respectively (Fig. 2c, d). Water loss by vaporization decreased -45.7 mm decade-1
(p=0.04; Fig. 2e) and wetting decreased -56.7 mm decade-1
(p< 0.02; Fig. 2f). In addition to the decreasing
trend in the amount of basin-scale discharge, Q50 showed that 50 % of
annual discharge is occurring 4.3 days earlier per decade (p=0.03).
(a) Positive linear correlation between precipitation in the upper
JRB and annual NPP in the upper JRB-derived from
MODIS; (b) linear correlation between baseflow and annual NPP in the upper
JRB. Forest productivity is water limited in the upper JRB. Other variables such as annual, winter, and summer air
temperature did not correlate with NPP.
EEMT
EEMTemp
Using the available 2000–2012 remote-sensing data, mean MODIS NPP
was found to be 450 g C m-2 (SD = 57.1 g C m-2). Using these
13 years of data, no trend in the mean annual NPP for the upper JRB was found. However, mean annual NPP was positively correlated with
basin-scale precipitation (R2=0.56; p=0.003) and baseflow
(R2=0.41; p=0.02) (Fig. 3). These results indicated that forest
productivity in the upper JRB is primarily limited by water
availability, since other climate variables recorded at the two SNOTEL
stations were not good predictors of NPP. As with any spatial and temporal
regression between climate and MODIS data, there are potential errors
associated with forest disturbance, interannual lag effects, and interseason
variability of water availability and other factors. We also note that the
significant relationship, albeit with variability and error, likely captures
these effects on this timescale of the study when no large-scale
disturbance occurred.
From 1984 through 2012, mean Eppt_emp was
1.03 MJ m2 yr-1 (SD = 0.49 MJ m2 yr-1) and mean
Ebio_emp was 9.89 MJ m2 yr-1
(SD = 1.26 MJ m2 yr-1). Multivariate regression analysis
indicated that precipitation at the Quemazon station and the upper JRB
were the best predictors of Ebio_emp (R2=0.66;
p=0.06). Using this multivariate linear regression model,
Ebio_emp data were extrapolated for the years 1984–1999. Using the
combined data set from extrapolated and measured Ebio_emp the mean annual
Ebio_emp was 10.8 MJ m2 yr-1
(SD = 1.37 MJ m2 yr-1) for the period from 1984 to 2012.
Mean EEMTemp was 11.83 MJ m2 yr-1 (SD = 1.74 MJ m2 yr-1)
and Ebio_emp represented 92 % (SD = 0.03 %) of the total EEMTemp during the study
period.
(a) EEMTemp and EEMTmodel showed similar
significant decreasing trends from 1984 to 2012 of 1.2 and 1.3 MJ m-2 yr-1;
(b) EEMTemp and EEMTmodel showed a significant
linear correlation.
EEMTmodel
From 1984 through 2012 mean Eppt_model was
0.1 MJ m2 yr-1 (SD = 0.07 MJ m2 yr-1) and mean
Ebio_model was 6.72 MJ m2 yr-1
(SD = 2.33 MJ m2 yr-1). During this same period, mean
EEMTmodel was 6.82 MJ m2 yr-1
(SD = 2.38 MJ m2 yr-1) and Ebio_model
represented 99 % (SD = 1.2 %) of the total EEMTmodel.
EEMTemp was, on average, 1.7 times larger than EEMTmodel.
Both EEMTemp and EEMTmodel showed a significant linear
correlation (R2=0.42; p=0.0002) and a similar decreasing trend of
1.2 MJ m2 decade-1 (p≤ 0.01) and
1.3 MJ m2 decade-1 (p≤ 0.05), respectively (Fig. 4).
Detailed estimations of EEMTemp and EEMTmodel and their
components can be found in Table S1 (in the Supplement).
Figure 5 highlights changes of EEMT in the upper JRB in relation to water
availability from 1984 to 2012. EEMT was positively correlated to annual
baseflow, increasing during wet years and decreasing during dry years.
Discussion
Climate variability
Global climate is changing and the instrumental records in the southwestern
US for the last 3 decades indicate a decline in precipitation and
increasing air temperatures in the region (Hughes and Diaz, 2008; Folland et
al., 2001). Global climate models further predict drier conditions and a more
arid climate for the 21st century in this region (Seager et al., 2007). For
instance, according to low- and high-emission scenarios, global climate models indicate a substantial increase in
air temperature between 0.6 and 2.2 ∘C and 1.3 and 5.0 ∘C
for the period 2021–2050 and by end of the 21st century, respectively
(Barnett et al., 2004; Cayan et al., 2013). An increase in winter temperature
of about 0.6 ∘C decade-1 was reported from 1984 to 2012 at a
regional level in the upper Rio Grande basin (Harpold et al., 2012). In line
with these other studies, we found that mean annual and winter air
temperature in the upper JRB have increased 0.5 and
0.4 ∘C decade-1, respectively.
Relationship between water availability and EEMT. Baseflow and
EEMT showed a positive linear correlation. As water availability in the
JRB decreases, indicated by baseflow, EEMT also decreases.
Changes in climate have been found to be a predominant influence in snowpack
decline as opposed to changes in land use, forest canopy, or other factors
(Hamlet et al., 2005; Boisvenue and Running, 2006). There are high-confidence
predictions that snowpacks will continue to decline in northern New Mexico
through the year 2100, and projections of snowpack accumulation for
mid-century (2041–2070) show a marked reduction for SWE of about 40 %
(Cayan et al., 2013). Harpold et al. (2012) found a decrease in annual
precipitation and maximum SWE for the upper Rio Grande basin of -33 and
-40 mm decade-1, respectively. In this study, a clear decreasing
trend in annual, winter precipitation, and maximum SWE was observed in records
from 1984 to 2012 in the two high-elevation SNOTEL stations. Records in this
study showed approximately twice the rate of decrease in annual precipitation
and a smaller decrease in maximum SWE of about 7 mm decade-1, compared to
the regional results from Harpold et al. (2012). Harpold et al. (2012) report
that SM50 (-2 days decade-1), snow cover length
(-4.2 days decade-1), day of maximum SWE
(-3.31 days decade-1), and last day of snow cover
(-3.45 days decade-1) for the Rio Grande basin showed statistically
significant trends. However, based on our analysis from the individual SNOTEL
stations, these variables did not show any statistically significant trends.
Changes in discharge and evapotranspiration
Decreasing trends in discharge ranging from 10 to 30 % are expected
during the 21st century for the western US (Milly et al., 2005), and maximum
peak streamflow is expected to happen 1 month earlier by 2050 (Barnett et
al., 2005). Furthermore, it has been reported that streamflow in snowmelt-dominated
river basins is more sensitive to wintertime increases in
temperature (Barnett et al., 2005). In this study, we have found that
50.5 % of annual streamflow occurred between (April) and beginning of the
summer (June). This result is congruent with other studies in snowmelt-dominated
systems in the region (Clow, 2010). Previous research in the
southwest has found that the timing of snowmelt is shifting to early times
ranging from a few days to weeks (Stewart et al., 2004; Mote et al., 2005;
McCabe and Clark, 2005). For instance, Clow (2010) reports that in southern
Colorado rivers, there is a trend toward earlier snowmelt that varied from
4.0 to 5.9 days decade-1 and 1 April SWE decreased between 51 and
95 mm per decade. In this study, it was found that snowmelt timing in the
upper JRB occurred 4.3 days decade-1 earlier and 1 April
SWE decreased between 54 and 60 mm decade-1.
The spatial and temporal variability in total evapotranspiration may exhibit
significant variability (Tague and Peng, 2013), and contrasting
evapotranspiration trends' directions have been reported in different studies
around the world (Barnett et al., 2005). In the JRB, a snow-dominated system,
the decrease in vaporization (45 mm decade-1) is
likely a result of the mismatch of the timing of energy and water fluxes.
While plant water demand remains relatively low, earlier snowmelt may reduce
evapotranspiration by reducing plant/atmospherically available water later
during the growing season when demand is higher (Barnett et al., 2005). The
decrease in vegetation biomass related to water availability, indicated from
the MODIS data at this basin, can also significantly contribute to alter
transpiration water losses. An increase in forest water-use efficiency (ratio
of water loss to carbon gain) with increasing concentrations of carbon
dioxide can also contribute as another cause to the decrease of
evapotranspiration fluxes (Keenan et al., 2013). Modeling studies conducted over 100 years support our finding that evapotranspiration has been decreasing
in the west arid area of the US (Liu et al., 2013). However, evapotranspiration may increase
with temperature in some snow-dominated systems if stored soil or groundwater
remains available to plants, either locally or at downslope locations (Goulden
et al., 2012; Brooks et al., 2015).
Forest productivity
Reduced carbon compounds resulting from primary production are a fundamental
energy component of EEMT (Rasmussen et al., 2011). Modeling and empirical
studies indicate that mountain forest productivity in the southwest is
sensitive to water and energy limitations (Christensen et al., 2008; Tague et
al., 2009; Anderson-Teixeira et al., 2011; Zapata-Rios et al., 2015b;
Zapata-Rios, 2015). Trujillo et al. (2012) found that the satellite derived Normalized Difference Vegetation Index (NDVI) greening increased
and decreased proportionally to the changes in snowpack accumulation along a
gradient in elevation in the Sierra Nevada, while Zapata-Rios et al. (2015b)
and Zapata-Rios (2015) found similar results across a gradient of energy
created by aspect differences at higher elevations in the Jemez Mountains.
Furthermore, energy limitations to productivity have been observed in colder
sites at high elevations (Trujillo et al., 2012; Anderson-Teixeira et al.,
2011; Zapata-Rios et al., 2015b; Zapata-Rios, 2015). Since the mid-1980s
increases have been documented in wildfires and tree mortality rates in high-elevation
forests due to an increase in spring and summer temperatures and
decrease in water availability (Westerling et al., 2006; Van Mantgem et al.,
2009). Results from this study indicated that in the upper JRB,
forest productivity was primarily responding to water availability (Fig. 3).
EEMT variability
All of the above results indicate that the JRB is highly
susceptible to changes in climate that can affect water availability and
ecosystem productivity which impacts EEMT. Rasmussen et al. (2005) estimated
low rates of EEMTmodel< 15 MJ m-2 yr-1 for
the majority of the continental US and demonstrated that Ebio was
the dominant component of EEMT, with contributions above 50 % of total
EEMT in soil orders associated with arid and semi-arid regions. Regions
dominated by Ebio corresponded to regions facing water limitation
and where Ebio accounted for up to 93 % of the total energy and
carbon flux to the CZ (Rasmussen et al., 2011; Rasmussen and Gallo, 2013). In
semi-arid regions vaporization represents over 90 % loss of annual
precipitation (Newman et al., 2006), while groundwater recharge accounts for
less than 10 % of annual precipitation (Scanlon et al., 2006). Under
these conditions, little water remains for CZ processes in
semi-arid regions. Other studies have found that the contributions of
Ebio can be 3–7 orders of magnitude larger than other
sources of energy influxes to the CZ (Phillips, 2009; Amundson et al., 2007).
In this study, we confirmed that for the upper JRB,
Ebio was the dominant term from the total EEMT, and Eppt
contributions were small.
A comparison of EEMTmodel and EEMTemp in 86 catchments
across the US, characterized by having minimum snow influence, indicated that
model and empirical values were strongly linearly correlated (R2=0.75;
p< 0.0001) and EEMTmodel values were larger than
EEMTemp (Rasmussen and Gallo, 2013). One limitation of the
EEMTmodel method is that it calculates energy during the months
when air temperature is above 0 only and assumes no energy associated with
precipitation falling as snow. In a snowmelt-dominated system like the upper
JRB, where snowmelt is the main source of water availability to
ecosystems (Bales et al., 2006), EEMT estimations based only on climate data
will likely underestimate the energy transfer to the CZ. Therefore, using
EEMTemp methodology may be more suitable for snowmelt-dominated
systems. In this study, we found the expected linear correlation between
EEMTmodel and EEMTemp (R2=0.42;
p< 0.001); however, EEMTmodel values were smaller than EEMTemp
values. Although the two methods used in this study to calculate EEMT
indicated different absolute values of EEMT, the rates of decrease of EEMT
per decade are congruent with each other
(EEMTemp=1.2 MJ m2 decade-1; EEMTmodel=1.3 MJ m2 decade-1) (Fig. 5).
While the correlation between EEMT and CZ landscape structure does not
necessitate causation, previous work has shown that these correlations are
widespread and strong, and thus, EEMT has significant predictive ability
(Pelletier and Rasmussen, 2009a, b; Rasmussen and Tabor, 2007; Rasmussen et
al., 2005, 2011; Pelletier et al., 2013; Zapata-Rios et al., 2015a). Although
we do not know the exact timescale of CZ change (Brooks et al., 2015), we
believe the rates of EEMT change found in the upper JRB
between 1.2 and 1.3 MJ m2 decade-1 can be significant for
CZ processes. These rates of EEMT change could represent an upward
movement of more arid, lower EEMT systems to higher elevations. For instance,
in a study conducted in a similar semi-arid region in the Santa Catalina
Mountains located in southern Arizona, Rasmussen et al. (2015)
estimated differences in EEMT of about 25 MJ m2 yr-1 between the
upper elevation (2800 m) covered by mixed conifer forest and low elevation
(800 m) covered by a dry semi-arid desert scrub ecosystem. These changes in
EEMT along the 2000 m elevation gradient in the SCM are equivalent to a
difference of 1.25 MJ m2 yr-1 per 100 m in elevation change.
The rates of EEMT change, every 100 m along the SCM elevation gradient, are
similar to observed rates of EEMT change per decade for the entire JRB.
Along this elevation gradient contrasting vegetation, soil
characteristics, regolith development, chemical depletion, and mineral
transformation have been observed between lower and high elevations on
similar granitic parent material (Whittaker et al., 1968; Lybrand et al.,
2011; Lybrand and Rasmussen, 2014; Holleran et al., 2015). Mollisols and
carbon-rich soils have been characterized in convergent areas of higher EEMT
versus weakly developed Entisols in lower EEMT landscape positions (Lybrand
et al., 2011; Holleran et al., 2015). Furthermore, Rasmussen et al. (2015)
determined differences of 3.9 MJ m2 yr-1 between contrasting
north- and south-facing slopes at a similar elevation. In areas with similar
EEMT, north-facing slopes have soils characterized by greater clay and carbon
accumulation (Holleran et al., 2015). According to topographic wetness,
differences of 0.9 MJ m2 yr-1 were determined between water-gaining
and water-losing portions of the landscape (Rasmussen et al., 2015).
It is still uncertain how the CZ evolves over time and how climate, lithology,
and biota influence the function of the CZ (Chorover et al., 2011). We
postulated that a measure of the energy inputs into the CZ drive CZ
evolution, and their quantification can be related to functions and processes within the
CZ. The energy inputs and mass transfer have been integrated in a single and
transferable metric (EEMT), quantified as water and carbon fluxes that can be
easily transferred and quantified in different ecosystems and regions around
the world (Rasmussen and Tabor, 2007; Rasmussen et al., 2011). This allows the comparison of
energy inputs to the CZ in a broad range of sites, climates, and
ecosystems. EEMT can be used as a tool to provide an initial identification
of landscape locations subjected to higher energy influx (as a result of
water and reduced carbon throughputs) or locations where EEMT is changing
over time, as it has been indicated in the present study. Consistent changes
in EEMT can be indicators of alteration in the function of the CZ, such as
weathering process, hydrochemical, and hydrologic response (among others). In
regions where temperature, precipitation, water availability, and vegetation
are changing, a quantification of EEMT can provide an initial assessment and a
metric to evaluate changes in the CZ. The EEMT model has a limitation in that
it does not provide information on how energy is distributed within the CZ
and does not provide mechanistic insight into CZ processes. However, it can
be used to identify research sites for further instrumentation and measuring
of CZ processes. Although the quantification of EEMT using the methodologies
applied in this study is suitable for large spatial scales, it is limited in
that it does not take into account small-scale variabilities induced by
topography in solar energy, effective precipitation, NPP, and redistribution
of water by differences in microtopography. Therefore, EEMT estimations at
small scales (pedon to hillslopes) need to follow a different approach, as
indicated in Rasmussen et al. (2015).
Summary
We investigated how changes in climate in the southwest affect the trends in
water availability, vegetation productivity, and the annual influxes of EEMT
to the CZ. This investigation took place in the 1200 km2 upper JRB,
a semi-arid basin in northern New Mexico, using records from
1984 to 2012. Results at the two SNOTEL stations indicated clear increasing
trends in temperature and decreasing trends in precipitation and maximum SWE.
Temperature changes include warmer winters
(+1.0–1.3 ∘C decade-1), and generally warmer year-round
temperatures (+1.2–1.4 ∘C decade-1). Precipitation changes
include a decreasing trend in precipitation during the winter
(-41.6–51.4 mm decade-1), during the year
(-69.8–73.2 mm decade-1), and maximum SWE
(-33.1–34.7 mm decade-1). At the upper JRB, all the
water-partitioning components showed statistical significant decreasing
trends including precipitation (-61.7 mm decade-1), discharge
(-17.6 mm decade-1), and vaporization (-45.7 mm decade-1).
Similarly, Q50, an indicator of snowmelt timing, is occurring
-4.3 days decade-1 earlier. Basin-scale precipitation (R2=0.56;
p=0.003) and baseflow (R2=0.41; p=0.02) were the strongest controls
on NPP variability, indicating that forest productivity in the upper JRB
is water limited. This study showed a positive correlation
between water availability and EEMT. For every 10 mm of change in baseflow,
EEMT varies proportionally in 0.6–0.7 MJ m-2 yr-1. From
1984 to 2012, changes in climate, water availability, and NPP have influenced
EEMT in the upper JRB. A decreasing trend in EEMT of
1.2–1.3 MJ m-2 decade-1 was calculated in this same time frame.
Although we cannot determine the timescales of change, these results
suggest an upward migration of CZ/ecosystem structure on the order of
100 m decade-1, and that decadal-scale differences in EEMT are similar
to the differences between convergent/hydrologically subsidized and
planar/divergent landscapes, which have been shown to be very different in
vegetation and CZ structure. As the landscape moves towards a drier and
hotter climate, changes in EEMT of this magnitude are likely to influence
CZ processes.