Journal of Hydrology 519 (2014) 1939–1952
Contents lists available at ScienceDirect
Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Groundwater level response in U.S. principal aquifers to ENSO, NAO,
PDO, and AMO
Amber Jean M. Kuss a, Jason J. Gurdak b,⇑
a
b
University of California, Santa Cruz, Department of Environmental Studies, 1156 High Street, Santa Cruz, CA 95064, USA
San Francisco State University, Department of Earth & Climate Sciences, 1600 Holloway Ave, San Francisco, CA 94132, USA
a r t i c l e
i n f o
Article history:
Received 1 March 2014
Received in revised form 26 July 2014
Accepted 25 September 2014
Available online 5 October 2014
This manuscript was handled by Peter K.
Kitanidis, Editor-in-Chief, with the
assistance of Roseanna M. Neupauer,
Associate Editor
Keywords:
Groundwater
Climate variability
ENSO
NAO
PDO
AMO
s u m m a r y
Groundwater will play an important role in society’s adaptation to climate variability and change. Therefore, it is particularly important to understand teleconnections in groundwater with interannual to
multidecadal climate variability because of the tangible and near-term implications for water-resource
management. Here we use singular spectrum analysis (SSA), wavelet coherence analysis, and lag
correlation to quantify the effects of the El Niño Southern Oscillation (ENSO) (2–7 year cycle), North
Atlantic Oscillation (NAO) (3–6 year cycle), Pacific Decadal Oscillation (PDO) (15–25 year cycle), and
Atlantic Multidecadal Oscillation (AMO) (50–70 year cycle) on precipitation and groundwater levels
across the regionally extensive Central Valley, Basin and Range, and North Atlantic Coastal Plain principal
aquifers (PAs) of the United States (U.S.). Results are compared to recent findings from a similar climate
variability study of the High Plains aquifer to provide the first national-scale assessment of the effects of
interannual to multidecadal climate variability on groundwater resources in U.S. PAs. The results indicate
that groundwater levels are partially controlled by interannual to multidecadal climate variability and
are not solely a function of temporal patterns in pumping. ENSO and PDO have a greater control than
NAO and AMO on variability in groundwater levels across the U.S., particularly in the western and central
PAs. Findings and methods presented here expand the knowledge and usable toolbox of innovative
approaches that can be used by managers and scientists to improve groundwater resource planning
and operations under future climate uncertainty.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
Interannual to multidecadal climate variability partially
controls precipitation distribution in space and time, drought frequency and severity, snowmelt runoff, streamflow, and other
hydrologic processes that profoundly affect surface-water
resources (Ropelewski and Halpert, 1986; Cayan and Webb,
1992; Dettinger and Cayan, 1994; Enfield et al., 2001; Ghil, 2002;
Dettinger et al., 2000, 2002; McCabe et al., 2004; Labat, 2008,
2010; Vicente-Serrano et al., 2011; Ionita et al., 2012). However,
the effects of interannual to multidecadal climate variability on
recharge rates and mechanisms and other subsurface hydrologic
processes that affect groundwater quantity and quality are largely
unknown in most aquifers of the United States (U.S.) (Gurdak et al.,
2009) and other regions of the world (Green et al., 2011; Treidel
et al., 2012). High-frequency (synoptic to seasonal) climate variability creates short-term hydrologic responses, but groundwater
⇑ Corresponding author. Tel.: +1 415 338 6869.
E-mail addresses: [email protected] (A.J.M. Kuss), [email protected] (J.J. Gurdak).
http://dx.doi.org/10.1016/j.jhydrol.2014.09.069
0022-1694/Ó 2014 Elsevier B.V. All rights reserved.
levels and recharge are partially controlled by complex interactions of low frequency (interannual to multidecadal) climate
variability (Dickinson et al., 2004; Hanson et al., 2004; Pool,
2005; Fleming and Quilty, 2006; Gurdak et al., 2007; Anderson
and Emanuel, 2008; Holman et al., 2009, 2011; Clark et al., 2011;
Figura et al., 2011; Perez-Valdivia and Sauchyn, 2011; Tremblay
et al., 2011; Venencio and Garcia, 2011; Perez-Valdivia et al.,
2012). Improved understanding of the long-term fluctuations in
groundwater availability that is dominated by low frequency climate variability is essential for best informed management and
policy decisions, particularly within the context of the increasing
use of groundwater for human consumption and irrigation
(Wada et al., 2010) and the uncertainty of climate change and
related impacts on groundwater quantity and quality (Hanson
et al., 2006; Holman, 2006; Earman and Dettinger, 2011; Stoll
et al., 2011; Gurdak et al., 2012).
The complex nature of climate variability on all temporal scales,
including interannual to multidecadal is a major obstacle in the
reliable identification of global change caused by human activities
(Ghil, 2002). Recent discussions about adopting nonstationary
1940
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
models for water-resource risk assessment and planning is
motivated by climate change and natural low-frequency climate
variability (Milly et al., 2008). The four leading atmospheric–ocean
circulation systems that affect North American interannual to multidecadal climate variability are the El Niño Southern Oscillation
(ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) (Ghil,
2002; McCabe et al., 2004). The timing and phase relation of these
quasiperiodic climate cycles have teleconnections with hydrologic
variability across much of the U.S. (McCabe et al., 2004; Hanson
and Dettinger, 2005; Gurdak, 2008).
In this paper we quantify the teleconnections between ENSO,
NAO, PDO, and AMO and the groundwater resources of principal
aquifers (PAs) along a west–east transect of the U.S. based on
observed anomalies in the Pacific and Atlantic Oceans. PAs are
regionally extensive aquifers and aquifer systems of national significance because of their high productivity or use and are critically
important sources of potable water (USGS, 2003). The primary
objectives of this paper are to characterize nonstationary patterns
in climate variability and lag correlations to spatiotemporal trends
in long-term records of precipitation (55–89 years) and groundwater levels (41–93 years) (Table 1) of the Central Valley aquifer
(52,000 km2), Basin and Range aquifer system (700,000 km2), and
North Atlantic Coastal Plain aquifer system (130,000 km2)
(Fig. 1). Some results from these aquifers are compared to major
findings from a recent and similar climate variability study of the
High Plains aquifer (450,000 km2) (Gurdak et al., 2007) that is
located in the central U.S. (Fig. 1). This paper is the first to provide
national-scale trends on the effects of interannual to multidecadal
Table 1
Hydrologic time-series site information.
Study ID
Agency site ID
Period of record
Years
CV P1
CV GW1
47292
401059122102801
1951–2009
1937–2004
58
67
N
696
128
CV P2
CV GW2
45385
391028121312501
1920–2009
1947–2003
89
56
1068
114
CV P3
CV GW3
49200
382458121455801
1926–2009
1951–2004
83
53
996
104
CV P4
CV GW4
45032
380238121091301
1927–2009
1962–2004
82
42
984
354
CV P5
CV GW5
41244
352228119295201
1940–2009
1937–2004
69
93
828
128
BR P1
BR GW1
266779
393737119514801
1950–2009
1966–2007
59
41
708
138
BR P2
BR GW2
264950
393310114475001
1931–2009
1948–2006
78
58
936
438
BR P3
BR GW3
426135
393143111523301
1941–2009
1935–2007
68
72
816
1951
BR P4
BR GW4
262243
361843115161001
1950–2009
1943–2007
59
64
708
1847
BR P5
BR GW5
264436
360528115094201
1951–2009
1965–2007
58
42
696
1923
NA P1
NA GW1
307633
405308072553101
1938–2009
1945–2006
71
61
852
2119
NA P2
NA GW2
307134
405743072425701
1931–2009
1954–2007
78
53
936
544
NA P3
NA GW3
283181
402553074271701
1948–2009
1944–2005
61
61
732
649
NA P4
NA GW4
72730
391949075410701
1948–2009
1957–2004
61
47
732
564
NA P5
NA GW5
188000
382329075263701
1954–2009
1967–2010
55
43
660
683
CV, Central Valley; BR, Basin and Range; NA, North Atlantic Coastal Plain; P,
precipitation site; GW, groundwater level site; N, number of data points.
climate variability on groundwater resources in U.S. PAs, and
advances
current
understanding
needed
for
effective
water-resource management and policy decisions under increasing
climate uncertainty.
2. Background
2.1. Climate variability
Climate variability is characterized in terms of anomalies,
which are defined as the difference between the current climate
conditions and the mean state, which is representative of ‘‘normal’’
conditions that are computed over many years (Hurrell et al.,
2003). Natural climate variability occurs on many different spatiotemporal scales and is created due to interplay between multiple
variables including (but not limited to) sea-level pressure (SLP)
anomalies, pressure height variations, fluctuations in wind speeds,
variations in the Earth’s orbit, and volcanic eruptions (Ghil, 2002).
Climate indices are generally created using a variety of anomalies
at different locations, such as changes in the mean distribution of
SLP at two locations, the deviation from mean sea-surface temperatures (SSTs) at multiple locations, variations in oceanic wind
strength, or a combination of multiple variables (Ghil, 2002;
Hurrell et al., 2003).
2.1.1. El Niño Southern Oscillation (ENSO)
The ENSO is a 2–7 year quasiperiodic phenomenon that results
from large-scale interactions between the tropical and subtropical
portions of the Pacific and the Indian Ocean basins, which results in
variations in pressure, temperature, and precipitation patterns
throughout the U.S. and other regions of the world (Ropelewski
and Halpert, 1986; Diaz and Markgraf, 1992; Hanson et al.,
2006). We use the Multivariate ENSO Index (MEI) (Wolter and
Timlin, 2011) to measure the temporal extent and the phase designation of ENSO (Fig. 2a). The positive (negative) MEI is related to
the positive (negative) ENSO phase. The MEI is based on multiple
variables of the Comprehensive Ocean-Atmospheric Data Set
(COADS), and represents a weighted average of SLP, zonal and
meridional winds, SST, air temperature, and total cloudiness
(Wolter and Timlin, 2011).
During the positive ENSO (El Niño) phase, the equatorial Pacific
experiences abnormally low SLP in the east and increased SLP in
the west, allowing for the warm waters of the western Pacific to
migrate eastward, thus creating a shift in the jet stream
(Ropelewski and Halpert, 1986). A U.S. coast-to-coast continuity
of increased precipitation (especially in the winter months of
December–February) from North Carolina to California has been
observed during the positive ENSO phase, with stronger correlations in the southwest and central U.S. (Ropelewski and Halpert,
1986; Kiladis and Diaz, 1989; Kurtzman and Scanlon, 2007). During a negative (La Niña) phase, SLPs increase and SSTs decrease
in the eastern equatorial Pacific, creating opposing temperature
and precipitation patterns across the U.S (Ropelewski and
Halpert, 1986; Kiladis and Diaz, 1989; Diaz and Markgraf, 1992).
Additionally, winter precipitation is generally above-normal in
the Pacific Northwest and below-normal in the Southwestern
U.S. during the negative ENSO phase, while the opposite conditions
are generally observed during the positive ENSO phase (Kiladis and
Diaz, 1989). This dipole signature in the winter precipitation of the
western U.S. is influenced by the phasing of the Pacific Decadal
Oscillation (PDO) (Brown and Comrie, 2004).
2.1.2. The North Atlantic Oscillation (NAO)
The NAO is a north–south dipole of pressure anomalies, with
one anomaly centered over Greenland and the other anomaly
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
1941
Fig. 1. Map showing the location of the Central Valley, Basin and Range, and North Atlantic Coastal Plain aquifer systems; and the five co-located precipitation and
groundwater level sites in each aquifer (Table 1). Findings from a recent climate variability study of the High Plains aquifer (Gurdak et al., 2007) is used here to complete the
east–west gradient of Principal Aquifers (PAs) across the United States. The findings and site information for the High Plains sites 1–6 is detailed by Gurdak et al. (2007).
Fig. 2. (a) The monthly Multivariate El Niño Southern Oscillation (ENSO) index (Wolter and Timlin, 2011), (b) Hurrell station-based annual North Atlantic Oscillation (NAO)
index (Hurrell, 1995), (c) monthly Pacific Decadal Oscillation (PDO) index (Mantua et al., 1997), and the (d) Kaplan SST, unsmoothed Atlantic Multidecadal Oscillation (AMO)
index (Enfield et al., 2001).
spanning the central latitudes of the Atlantic between 35° and 40°
N (Hurrell, 1995). The positive phase NAO is characterized by
below normal geopotential heights and pressures in the North
Atlantic and above normal heights and pressures over eastern
U.S. and western Europe, and vice versa during the negative phase
NAO (Hurrell, 1995). The NAO index (Fig. 2b) is the difference
between normalized mean winter (December–March) SLP anomalies between Lisbon, Portugal and Stykkisholmur, Iceland (Hurrell,
1995). The NAO has a dominant quasiperiodic oscillation of
3–6 years with a less significant 8–10 year mode (Hurrell et al.,
2003). During the positive NAO, enhanced westerly flow across
the Atlantic moves warm moist air over Europe and the eastern
U.S., creating an increase in winter storms (Hurrell, 1995;
Ottersen et al., 2001; Hurrell et al., 2003). While the majority of
the positive precipitation signals are seen in Europe, weaker positive correlations are observed in the eastern U.S. (Hurrell et al.,
2003).
2.1.3. The Pacific Decadal Oscillation (PDO)
Decadal to interdecadal variability in atmospheric circulation,
specifically the wintertime Aleutian Low pressure system and SSTs
from 20° N poleward, is often associated with the PDO (Mantua
et al., 1997). The PDO is indexed with monthly northern Pacific
SST residuals (the difference from the observed anomalies and
the monthly mean global average SST anomaly) (Fig. 2c) (Mantua
and Hare, 2002). Fluctuations in the PDO index over the 20th century were most energetic in two general periodicities – one from
15 to 25 years and the other from 50 to 70 years (Mantua and
Hare, 2002). In this paper, we evaluate only the 15- to 25-year periodicity of the PDO.
The climate anomalies connected to the PDO are somewhat
similar to the ENSO with comparable shifts in the jet stream
(Mantua and Hare, 2002). The positive phases of the PDO are associated with warm dry periods in the Pacific Northwest and cool
wet periods in the southwestern U.S. (Mantua and Hare, 2002).
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A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
Regime shifts in the PDO also affect the phase and the occurrence
of the ENSO (Brown and Comrie, 2004; Gutzler et al., 2002). During
the negative (positive) PDO, there is a greater occurrence of negative (positive) ENSO events (Gutzler et al., 2002).
2.1.4. The Atlantic Multidecadal Oscillation (AMO)
The AMO is an atmospheric–ocean phenomenon with a periodicity of 50–70 years that arises from variations in SSTs in the Atlantic Ocean associated with variations in the strength of the
thermohaline circulation (Kerr, 2000; Enfield et al., 2001). The
AMO is indexed with a ten-year running mean of Atlantic SSTs
from 0 to 70° N, with a peak variation of 0.4 °C determining a phase
shift (Fig. 2d) (Enfield et al., 2001). The AMO is associated with
variations in air temperature and precipitation regimes across
the U.S., with a positive (negative) phase producing decreased
(increased) rainfall (Enfield et al., 2001; McCabe et al., 2004). The
AMO may also modulate the strength and occurrence of NAO
(Peings and Magnusdottir, 2014) and ENSO cycles, creating a weakened (strengthened) El Niño during the positive (negative) AMO
phase (McCabe et al., 2004). Coupled influences of the PDO and
the AMO may also produce extensive spatial and temporal fluctuations, with coincident positive AMO and negative PDO increasing
the occurrence of droughts in the U.S. (McCabe et al., 2004).
2.2. Site description
The Central Valley, Basin and Range, and North Atlantic Coastal
Plain aquifer systems are located along a west–east transect of the
contiguous U.S. (Fig. 1). The aquifers are generally unconfined and
consist of unconsolidated sand, gravel, and silt, and represent some
of the most important groundwater resources in the U.S. for agriculture, industry, and domestic uses (Maupin and Barber, 2005).
McMahon et al. (2006, 2007) and Gurdak et al. (2007) provide a
detailed description of the High Plains aquifer (Fig. 1).
The Central Valley aquifer located in California (Fig. 1) is a single, heterogeneous aquifer system that is generally unconfined in
the upper hundred meters and confined at depth by overlapping
discontinuous clay beds, primarily in the southern portion
(Planert and Williams, 1995; Faunt et al., 2009). Climate is Mediterranean and Steppe, with hot summers and mild winters
(Bertoldi et al., 1991). Approximately 85% of the annual precipitation occurs from November to April, with a steady decrease in
overall precipitation from the north (average 584 mm) to the south
(average 152 mm) (Bertoldi et al., 1991). Pumping to support irrigation has affected groundwater levels, with major declines in
much of the aquifer (Maupin and Barber, 2005).
The Basin and Range aquifer system, located primarily in Arizona, California, Nevada, New Mexico, and Utah, includes about
120 alluvium-filled basins interspersed between mountain ranges
(Fig. 1) (Planert and Williams, 1995). The basins are generally
unconsolidated to moderately consolidated, well to poorly sorted
beds of gravel, sand, silt, and clay deposited on alluvial fans, flood
plains, and Pleistocene lakes (Planert and Williams, 1995). The climate is arid, with average annual precipitation ranging from
approximately 102 to 203 mm in the basins and 406–508 mm in
the mountain ranges (Sheppard et al., 2002). Hot summers and
large evapotranspiration rates, particularly in lower altitudes, limit
recharge to an estimated 5% of precipitation (Planert and Williams,
1995). The Basin and Range aquifer system is ranked 4th among the
PAs in terms of total withdrawals, and approximately 81% of withdrawals support irrigated agriculture (Maupin and Barber, 2005).
The North Atlantic Coastal Plain aquifer system, located in
Delaware, Maryland, New Jersey, New York, North Carolina, and
Virginia consists of six regional unconfined and confined aquifers
(Trapp and Horn, 1997). This study focused on wells in the surficial
aquifer that consists of unconsolidated sand and gravel and is the
uppermost aquifer in the North Atlantic Coastal Plain aquifer
system (Trapp, 1992). Average annual temperature is 11°°C and
average annual precipitation is 1044 mm (Polsky et al., 2000).
The North Atlantic Coastal Plain aquifer system is ranked 13th
among the PAs in terms of total withdrawals (Maupin and
Barber, 2005).
3. Methods
The time series evaluated here include the previously mentioned MEI, NAO, PDO, and AMO indices, groundwater levels
(1933–2009) from monitoring wells in the U.S. Geological Survey
(USGS) National Water Information System (NWIS) (USGS, 2012),
and precipitation data (1920–2009) from NOAA (2012) meteorological stations (Table 1). A total of 5 monitoring wells in each PA
were selected based on the length and completeness of the
water-level records and to represent a range of hydrogeologic conditions in the uppermost and unconfined aquifer units. In order to
assess interannual to interdecadal climate variability, we selected
wells that had at least 40 years of water-level records (Table 1).
Although our site selection criteria required at least annual
water-level values with no multi-year gaps in the records, the
majority of the wells have at least quarterly water-level data
(Table 1). One long-term (>40 years) meteorological station was
required to be co-located within 24 km of each monitoring well.
The 5 co-located meteorological stations and monitoring wells
were selected to characterize the general spatial patterns in each
PA and not meant to be an exhaustive representation of the
heterogeneity in each PA. The co-located wells and meteorological
stations are identified by the aquifer name (CV, Central Valley; BR,
Basin and Range; and NA, North Atlantic Coastal Plain), location
(labeled 1–5 from north to south) (Fig. 1), and site type (P, precipitation; and GW, groundwater) (Table 1). Because long-term
groundwater pumping records are not publically available, we
use a simulated groundwater-pumping time series (1962–2003)
from the Central Valley aquifer that was developed by Faunt
et al. (2009) to quantify the effects of interannual to multidecadal
climate variability on groundwater pumping. Simulated groundwater-pumping time series are not publically available for the
other PAs in this study.
3.1. Pre-processing
Time series analysis has been used to assess long-term variations in hydrologic variables (Enfield et al., 2001; Hanson et al.,
2004, 2006; McCabe et al., 2004; Gurdak et al., 2007). Using data
pre-processing methods outlined by Hanson et al. (2004), we interpolated each time series with a monthly spline to integrate any
irregular sampled records, and then converted these data into
cumulative departures series from the period of record using the
monthly mean. Next, the residuals of the monthly cumulative
departure series were obtained by subtracting a regression-fitted
low-order (cubic) polynomial. The overall shape of the low-order
polynomial represents temporal trends (or responses) in the
hydrologic time series to larger climatic cycles or periods of
anthropogenic effects (Hanson et al., 2004). Finally, the residuals
are normalized by the historic mean to facilitate statistical comparisons between various data types and are referred to as normalized
departures (unitless). The primary goal of the data-processing
steps is to remove red noise prior to using singular spectrum analysis (SSA) to identify temporal structure in the time series that
have a statistically significant difference from red noise. The
data-processing steps also remove much of the long-term,
multidecadal anthropogenic signals in the groundwater-level time
series, such as long-term land-use change and the implementation
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
of improved irrigation technology, and annual anthropogenic signals, such as crop rotation and other irrigated-agricultural practices (Hanson et al., 2004; Gurdak et al., 2007). Following the
pre-processing steps, the time series were processed using SSA to
extract temporal structures from the noisy data and using wavelet
analysis to characterize periodicity as a function of time, as
described in detail next.
3.2. Singular spectrum analysis (SSA)
Using the methods outlined by Hanson et al. (2004) and Gurdak
et al. (2007), we applied the SSA–MTM toolkit (Dettinger et al.,
1995; Ghil et al., 2002) for time series frequency analyses using
SSA, which is a modified form of principal component analysis
(PCA) in the vector space of delay coordinates for a time series.
SSA incorporates a data-adaptive method to analyze short-noisy
series and extract the dominant frequencies of a time series
(Vautard et al., 1992). SSA has been widely applied to geophysical
data sets of various lengths and to identify quasi-periodic oscillations, for example ENSO and other interannual to multidecadal
coupled oceanic–atmospheric phenomenon (Vautard et al., 1992).
We used SSA to decompose the detrended and normalized hydrologic time series into temporal principal components (PCs) that
represent the projection of the original time series onto empirical
orthogonal functions (EOFs) (Vautard et al., 1992; Ghil et al.,
2002). The phase information of the time series, oscillatory modes,
and noise are reconstructed by using linear combinations of the
PCs and EOFs to create the reconstructed components (RCs). No
information is lost during the reconstruction process because the
sum of the individual RCs equals the original time series. The variability in most hydrologic time series can be adequately described
in terms of the first 10 RCs (with decreasing variance from one to
ten) (Hanson et al., 2004).
We applied SSA to identify the first 10 RCs that represent statistically significant oscillatory modes within each of the individual
climate index, precipitation, groundwater level, and simulated
pumping time series. The reader is referred to Kuss (2011) for
the SSA results of all 10 RCs for each time series. We used the
Chi-Squared significance test (Allen and Smith, 1996) on each of
the first 10 RCs to identify those statistically significant oscillations
(Ha) against a red-noise null hypothesis (H0). The SSA–MTM toolkit
user guide provides details about the theory and application of ChiSquared significance test (Dettinger et al., 1995; Ghil et al., 2002).
For each individual time series, we group and sum only the statistically significant RCs according to the following period ranges:
2–7 year (ENSO-like), 3–6 years (NAO-like), 15–25 years (PDO-like),
and >25 years (>PDO). PDO fluctuations over the 20th century were
most energetic in two general periodicities – one from 15 to
25 years and the other from 50 to 70 years (Mantua and Hare,
2002). Here we evaluate only the 15- to 25-year periodicity of
the PDO. Because the periodicity of the AMO is indistinguishable
from the lower frequency mode (50–70 years) of PDO variability,
we use the naming convention proposed by Hanson et al. (2004)
and we refer to all periods of climate variability greater than
25 years as greater than PDO (>PDO). By summing RCs based on
similar period ranges, we create composite RCs that represents
the statistically significant oscillatory modes within each of the
raw hydrologic time series that are consistent with ENSO, NAO,
PDO, or >PDO periodicity (Hanson et al., 2004; Gurdak et al.,
2007). The statistically significant composite RCs are used in all
subsequent wavelet and lag correlations analyses.
3.3. Wavelet analysis
We use wavelet analysis to complement the SSA because many
hydrologic time series are nonstationary and have temporal
1943
variations in both amplitude and frequency that can be analyzed
using wavelet transforms (Torrence and Compo, 1998; Grinsted
et al., 2004; Labat et al., 2000; Labat, 2005, 2008; Holman et al.,
2011). The use of wavelet coherence can provide important insight
in how the strength of groundwater teleconnections varies through
time (Holman et al., 2011). We applied wavelet analyses on the
composite RCs from the climate indices, precipitation, and groundwater level time series using a MATLAB script developed by
Grinsted et al. (2004) and outlined in Holman et al. (2011). To calculate coherence between two series, the wavelet power spectrum
of each time series was used (Torrence and Compo, 1998). Important features of the wavelet power spectrum are identified as significant at the 5% level, which indicates a 95% confidence level of
coherence between the two series, identifying the relation
between climatic and hydrologic variables (Torrence and Compo,
1998; Newman et al., 2003; Grinsted et al., 2004; Holman et al.,
2011).
We use the continuous wavelet transform (CWT) that is localized in time (Dt) and frequency (Dx) to identify the periodicities
and phases of cycles within a single time series (Grinsted et al.,
2004; Holman et al., 2011). For the best balance of time and frequency, the Morlet wavelet is used (Torrence and Compo, 1998;
Grinsted et al., 2004). The CWT establishes the power spectrum
of each precipitation, groundwater level, and climate index before
the cross-correlation of the series is performed (Grinsted et al.,
2004).
After the CWT, we used the cross wavelet transform (XWT) to
identify the cross wavelet power of two time series against the
background power spectra for each of the series (Torrence and
Compo, 1998; Grinsted et al., 2004). Because errors are present
at the beginning and end of the finite wavelet power spectrum, a
cone of influence (COI) is used to identify the region of the wavelet
spectrum where these edge effects need to be excluded (Torrence
and Compo, 1998; Grinsted et al., 2004).
While the XWT identifies areas of high common power, the
wavelet coherence (WTC) measures the cross-correlation between
two series as a function of frequency as a quantity between 0 and 1
and identifies spectral coherence of the two series even if there is a
low common power when each series is localized in time–frequency space (Torrence and Compo, 1998). Although the spectral
coherence of the two series is highlighted, the temporal coherence
is diminished as compared to the XWT (Labat, 2005; Grinsted et al.,
2004). In order to test the significance of the WTC, 1000 randomly
constructed synthetic series are created using Monte Carlo methods (Grinsted et al., 2004).
The three methodological steps of CWT, XWT, and WTC help
establish the coherence in the temporal viability of the climate
indices, precipitation, and groundwater levels. Although the three
steps were necessarily followed, we present only the results and
discussion on the WTC, which provides the most robust outcomes
of interest to the reader (Holman et al., 2011). Kuss (2011) presents
detailed results and discussion from the CWT and XWT.
3.4. Lag correlations
Correlation coefficients measure the strength of association
between two variables (Helsel and Hirsch, 2002) and when a system has a delayed response to some forcing or a common delayed
response to an additional variable. To calculate the lag correlations
and their statistical significance, we used the USGS Hydrologic and
Climatic Analysis Toolkit (HydroClimATe), which is a computer
program to assess relations between variable climatic and hydrologic time-series data (Dickinson et al., 2014). Using a priori expectations from regional hydroclimatology documented in the
literature, we report the strongest and statistically significant
(95% confidence interval) lag correlation coefficients (unitless)
1944
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
and corresponding phase lags (year) between the climate indices
and composite RCs from the precipitation time series and between
the composite RCs from the co-located precipitation and groundwater level time series. As explained by Dickinson et al. (2014),
the phase lags between many complex physical processes are generally undetermined, but the lag correlation analysis is valuable in
providing insight into the system without using a dynamic, numerical model.
4. Results and discussion
4.1. Hydroclimatic teleconnections with ENSO, NAO, PDO, and >PDO
The results of the SSA indicate that all the hydrologic time series
contain variations that are partially consistent with ENSO, NAO,
PDO, and >PDO. The precipitation and groundwater level time series in the Central Valley have two dominant modes of variability
consistent with PDO and ENSO (Fig. 3a). The amount of variance
attributed to the PDO-like cycle ranged from 26.4% to 83.0% and
was consistently larger in the precipitation (range 38.6–83.0%,
average of 68.7%) than the co-located groundwater levels (range
26.4–53.1%, average 34.5%) (Fig. 3a). The second largest amount
of variance in precipitation and groundwater levels (range
7.3–20.5%, average 13.3%) was attributed to the ENSO-like cycles
(Fig. 3a). These findings indicate the importance of lowerfrequency PDO forcings with lesser controls by ENSO and are
consistent with findings from Gurdak et al. (2007) that groundwater levels can be substantially influenced by PDO variability. No
SSA modes of variability with periodicities consistent with NAO
or >PDO were identified in precipitation or groundwater levels
sites in the Central Valley (Fig. 3a).
Groundwater levels in the Central Valley aquifer are affected by
withdrawals for irrigation (Faunt et al., 2009). We evaluated the
response in groundwater pumping to climate variability and to further explore the hypothesis that groundwater levels are partially
controlled by climate variability and are not solely a function of
temporal patterns in pumping. The results of SSA indicate that
no statistically significant RCs are present within the simulated
pumping time series (Faunt et al., 2009), which indicates that the
simulated pumping is not substantially controlled by climate variability. A more robust analysis on additional groundwater pumping time series is needed to more definitively evaluate pumping
responses to interannual to multidecadal climate variability. However, it is well documented that agricultural land-use patterns in
the Central Valley are dynamic and may vary gradually or abruptly
in time because of the cumulative effect of urbanization and development, free-market trends and changing crops types, resource
limitations, particularly water, and climate variability (Faunt
et al., 2009). These complex and changing land-use patterns partially control agricultural groundwater demand within the context
of individual farming practices and crop selection. The SSA findings
support these complexities and indicate that the 2- to 7- and 15- to
Fig. 3. The percent variance (%) and period (years) of the combined reconstructed components (RC) for the precipitation and groundwater level time series are shown for the
(a) Central Valley aquifer, (b) Basin and Range aquifer system, and (c) North Atlantic Coastal Plains aquifer system. GW denotes groundwater site and P denotes precipitation
site.
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
25-year cycles within the groundwater levels are responses to
ENSO and PDO. The results of the statistically significant lag correlations, presented below, further support these teleconnections
with groundwater levels.
The SSA results from the Basin and Range aquifer system indicate multiple modes of variability (Fig. 3b). The PDO-like cycle
was consistently observed as the leading mode of variability in
all the sites except for two (BR P2 and BR GW2) and contributed
about 20–77.2% of the variability in all Basin and Range sites
(Fig. 3b). At BR P2 and BR GW2, the >PDO contributed to the greatest amount of site variability (66.1% and 47.3%, respectively).
Although the length of the time series can be a limiting factor in
identifying low-frequency cycles, BR P2 and BR GW2 have moderate record lengths (1931–2009 and 1948–2006, respectively) and
many sites have considerably longer records. Therefore, the length
of the record does not appear to be a controlling factor in observing
the low frequency >PDO cycle at BR P2 and GW2. The ENSO-like
cycle is also observed in all of the Basin and Range sites except
for BR GW2, and contributes 5.8–27.1% of the variance in the Basin
and Range sites (Fig. 3b). Excluding the BR GW2, the ENSO-like
cycle contributes to an average of 13.5% in the variance of the precipitation and groundwater sites (Fig. 3b).
There is an apparent shift in the dominant cycle present in the
hydrologic time series of the North Atlantic Coastal Plain as compared to the Central Valley and Basin and Range aquifer systems
(Fig. 3c). The >PDO cycle is observed at three of the precipitation
sites in the North Atlantic Coastal Plain (NAP2, NAP3, and NAP4),
and contributes to the greatest amount of variance in each record
with 63%, 70.7%, and 65.5%, respectively (Fig. 3c). Similar to Basin
and Range sites with >PDO-like variations, the record lengths at
sites NAP2, NAP3, and NAP4 are not the longest in this study.
The North Atlantic Coastal Plain sites have PDO-like cycles that
contribute about 20–77.7% of the variability (Fig. 3c). A 2- to 7-year
signal contributes 4.1–51.2% (NA P3 and NA GW2, respectively) of
the variance at the North Atlantic Coastal Plain sites, and may be
attributed to the ENSO or the NAO (Fig. 3c). The ENSO 2- to 7-year
periodicity is longer than the NAO 3- to 6-year periodicity, but the
NAO also has an observed 8- to 10-year periodicity (Kiladis and
Diaz, 1989; Hurrell et al., 2003). Previous studies have also shown
coherent ENSO effects in the Mid-Atlantic regions of the U.S.
(Ropelewski and Halpert, 1986). We use the wavelet analysis and
the lag correlations to identify the relative influence of the ENSO
and (or) NAO on the 2- to 7-year cycle in the precipitation and
groundwater levels in the North Atlantic Coastal Plain, as described
below.
4.2. Coherence between climate indices, precipitation, and
groundwater levels
The wavelet transforms support the SSA findings and indicate
significant coherence between ENSO, NAO, PDO, and >PDO in many
of the precipitation and groundwater levels. The WTC from the
Central Valley aquifer (Fig. 4), Basin and Range aquifer system
(Fig. 5), and North Atlantic Coastal Plain aquifer system (Fig. 6)
are discussed next.
All sites in the Central Valley have moderate to strong (0.5–1)
coherence with ENSO in the WTC at the 3- to 7-year period range
(Fig. 4a). At CV P1, coherence at the 5% significance level from 3- to
7-years in the WTC is predominantly from the early-1970s to the
mid- to late-1980s, which is consistent with the strong 1976–
1977 negative ENSO event and the 1982–1983 extreme positive
ENSO event (Fig. 2a). At CV GW1, coherence at the 5% significance
level from 3 to 7 years in the WTC is from the late-1970s to late1980s, which is approximately 4- to 5-years lagged behind the
5% significance level in the P1 WTC (Fig. 4a) due to the lag between
the arrival of precipitation and the travel time of water through the
1945
vadose zone to the water table. The temporal lags between ENSO
coherence in the precipitation and ENSO coherence in the groundwater levels are further quantified in Section 4.3. At sites 1 and 5, a
5% significant coherence in the 3- to 7-year periodicity is observed
and an approximate 5-year temporal lag in the groundwater
coherence behind the precipitation coherence to ENSO (Fig. 4a).
Additionally, moderate to strong coherence is observed at most
sites (with the exception of GW3 and P4), however the results do
not show a 5% significant coherence. Moderate to strong coherence
at the 9- to 24-year period is also apparent in the WTC of the precipitation and groundwater levels at all Central Valley sites (with
the exception of GW2, GW3, and P5) (Fig. 4b), which is consistent
with the periodicity of the PDO. The extent of the 5% significance in
precipitation that is consistent with PDO varies across the Central
Valley, with no 5% significance found at sites P1, P5, GW2, GW3,
and GW4. The northern locations (sites 1 and 2) have more temporally discrete 5% significance that spans the 1970s to 2000s and
centers on the 1980s (Fig. 4b), which was a period of persistent
positive phase PDO (Fig. 2c) associated with cool wet conditions
in the southwestern U.S. Sites 3 and 4 located near the San Francisco Bay and Delta have 5% significance that spans the 1930s to
present day (Fig. 4b). For sites 1–3, the 5% significance level in
groundwater levels that is consistent with PDO tends to be less
apparent, shorter in duration, and often temporally lagged behind
the 5% significance level in the co-located precipitation sites
(Fig. 4b). The WTC for NAO and >PDO were not presented because
the Central Valley precipitation and groundwater levels do not
have statistically significant RCs with periodicities consistent with
NAO or >PDO.
Results of the WTC from the Basin and Range aquifer system
(Fig. 5) have many similarities to the WTC from the Central Valley
aquifer. All sites in the Basin and Range have moderate to strong
(0.5–1) coherence with ENSO in the 3- to 7-year period range
(Fig. 5a) and PDO in the 8- to 24-year period range (Fig. 5b). However, unlike the Central Valley, site 2 in the Basin and Range has
coherence with >PDO in the precipitation and groundwater levels
(Fig. 5c). The coherence between precipitation and groundwater
levels and PDO has two general patterns somewhat similar to those
from the Central Valley. There is strong coherence at longer (16–
25 year) periodicities from about the 1940s and 1950s to present
in sites P2 and GW4, while sites P1, P3, GW3, and P5 have strong
coherence at shorter (8–14 years) periodicities (Fig. 2c). Strong
coherence consistent with >PDO was present at site P2, particularly
from the 1940s to 1960s (Fig. 5c), which is consistent with the last
full positive phase of AMO (Fig. 2d). However, the coherence of the
>PDO signal is observed outside the cone of influence. The phase
arrows in the precipitation and groundwater level WTC points to
the left (out-of-phase), which is consistent with drought across
much of the U.S. and especially the southwestern U.S. that associated with the positive AMO phase (McCabe et al., 2004).
Results of the WTC from the North Atlantic Coastal Plain aquifer
system (Fig. 6) indicate moderate to strong (0.5–1) coherence with
ENSO in the 3- to 7-year period range (Fig. 6a) (with the exception
of GW1 and P2), NAO in the 3- to 7-year period range (Fig. 6b), PDO
in the 8- to 24-year period range (Fig. 6c) (with the exception of
GW2), and with >PDO (Fig. 6d). The North Atlantic Coastal Plain
sites generally have a greater influence of climate oscillations from
the Atlantic Ocean than from the Pacific Ocean; including coherence with NAO at all 5 sites and with >PDO at 3 sites (Fig. 6b
and d). Each of the North Atlantic Coastal Plain sites has strong
coherence and in some cases (notably at sites P3 and GW5) 5% significance level from 2 to 7 years, which is consistent with ENSO
and NAO. WTCs were not computed with the NAO in the Basin
and Range or the Central Valley due to the low probability of
influences from the NAO in the western U.S. (Hurrell et al.,
2003). Whereas the Central Valley and Basin and Range sites had
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Fig. 4. Wavelet coherence (WTC) between precipitation and groundwater levels at the Central Valley aquifer sites 1–5 and the (a) El Niño Southern Oscillation (ENSO) and (b)
Pacific Decadal Oscillation (PDO). Note that spectral power is dimensionless, the thick black lines are the 5% significance level, and the less intense colors indicate the cone of
influence (COI). The phase angle (shown with black arrows) identifies the phase relation between two series, with a right-pointing arrow indicating an in-phase relation and a
left-pointing arrow indicating an anti-phase relation, and arrows pointing up or down show that one time series is leading the other by 90° (Grinsted et al., 2004; Holman
et al., 2011). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
5% significance coherence with ENSO centered on the 1980s, most
of the North Atlantic Coastal Plains have weak to moderate coherence during the 1980s (Fig. 6a). Most of the North Atlantic Coastal
Plain sites have 5% significance coherence with NAO that is centered on the mid- to late-1980s (Fig. 6b), which is consistent with
the shift from the 1940s to 1970s negative phase to a positive
phase in the 1980s with the greatest NAO index values in 1983,
1989, and 1990 (Fig. 2b) (Hurrell and Van Loon, 1997). These
WTC findings indicate relatively less ENSO and relatively more
NAO influence on the precipitation, groundwater levels, and
recharge to aquifers along the North Atlantic Coast as compared
to those aquifers in the western U.S. Moderate to strong coherence
(0.5–0.8) with >PDO is more apparent in the precipitation (sites
2–4) than groundwater levels of the North Atlantic Coastal Plain,
particularly from the 1940s to 1970s (Fig. 6d). Unlike the >PDO
coherence in the Basin and Range, the phase angle in the North
Atlantic Coastal Plain WTC generally are right-pointing indicating
an in-phase relation between the >PDO cycle and the precipitation
(Fig. 6d), which is consistent with the less frequent drought associated with the positive phase AMO along the eastern coast of
the U.S. (McCabe et al., 2004).
4.3. Lag correlations between climate indices, precipitation, and
groundwater Levels
Results indicate that nearly all of the composite RCs from the
precipitation time series from the selected PAs are statistically
correlated (95% confidence interval) with the MEI, NAO, PDO, and
AMO indices. These findings are consistent with the well established hydroclimatology literature presented in the background
that U.S. precipitation spatiotemporal patterns have complex
teleconnections to interannual and multidecadal climate variability.
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
1947
Fig. 5. Wavelet coherence (WTC) between precipitation and groundwater levels at the Basin and Range aquifer system sites 1–5 and the (a) El Niño Southern Oscillation
(ENSO), (b) Pacific Decadal Oscillation (PDO), and (c) Atlantic Multidecadal Oscillation (AMO). Note that spectral power is dimensionless, the thick black lines are the 5%
significance level, and the less intense colors indicate the cone of influence (COI). The phase angle (shown with black arrows) identifies the phase relation between two series,
with a right-pointing arrow indicating an in-phase relation and a left-pointing arrow indicating an anti-phase relation, and arrows pointing up or down show that one time
series is leading the other by 90° (Grinsted et al., 2004; Holman et al., 2011). (For interpretation of the references to color in this figure legend, the reader is referred to the web
version of this article.)
Of the 21 precipitation time series analyzed from the four PAs, 20
have composite RCs lag correlated with the MEI (coefficients range
from 0.12 to 0.60), 5 have composite RCs lag correlated with NAO
index (coefficients range from 0.13 to 0.27), 19 have composite RCs
lag correlated with PDO index (coefficients range from 0.14 to
0.83), and 8 have composite RCs lag correlated with AMO index
(coefficients range from À0.27 to À0.72). Only the composite RCs
in precipitation that were significantly correlated to the climate
indices were used in the lag correlation analysis between the
co-located precipitation and groundwater levels (Fig. 7).
Many Central Valley and Basin and Range co-located precipitation and groundwater levels had moderate to strong statistically
significant (95% confidence interval) lag correlations (coefficients
range from 0.5 to 0.9) with composite RCs of similar periodicities
as ENSO and PDO cycles (Fig. 7a and b). However, only Basin and
Range site 2 had statistically significant lag correlations between
precipitation and groundwater levels with periodicities similar to
>PDO (Fig. 7b). In the Central Valley, the average lag correlation
coefficient between precipitation and groundwater levels consistent with ENSO periodicity was 0.5 with an average phase lag of
11 years (Fig. 7a). We suggest that the average phase lag is conceptually equivalent to the average travel time of water in the vadose
zone between the arrival of precipitation at land surface and the
response in the groundwater levels, and the 11-year average phase
lag is reasonable given previously reported recharge rate of
86–530 mm yrÀ1 in the Central Valley (Phillips et al., 2007;
Fisher and Healy, 2008; Faunt et al., 2009). Results of these
statistical analyses provide insight into the physical processes that
influence hydroclimatic variability in groundwater levels. Future
modeling studies are needed to explore how the lag times of
climate signal at co-location precipitation and groundwater level
sites respond to local vadose zone materials (Gurdak et al., 2007),
including the vertical distribution of sand content or layers of clay
and the hydrogeologic properties of the saturated aquifer, including local transmissivity and storativity (Dickinson et al., 2004).
Moderate to strong statistically significant lag correlations coefficients between co-located precipitation and groundwater levels
composite RCs that have periodicities consistent with PDO were
identified in the Central Valley and Basin and Range (Fig. 7a and
b). Interestingly, the range of phase lags (11–27 years) of RCs with
periodicities similar to PDO are generally larger than phase lags
(3–14 years) of RCs with periodicities similar to ENSO in the Central
Valley (Fig. 7a) and Basin and Range (Fig. 7b). These statistical
results provide insights into the physical processes that groundwater levels in the western U.S. may have a relatively faster hydrologic response to ENSO as compared to PDO. The statistically
significant phase lags between the co-located precipitation and
groundwater levels in the North Atlantic Coastal Plain ranged from
3 to 16 years (Fig. 7c) and are generally much smaller than the
corresponding phase lags for ENSO or PDO in the Central Valley
or Basin and Range aquifers. These statistical results point to
important differences in hydrologic response to ENSO and PDO in
1948
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
Fig. 6. Wavelet coherence (WTC) between precipitation and groundwater levels at the North Atlantic Coastal Plain aquifer system sites 1–5 and the (a) El Niño Southern
Oscillation (ENSO), (b) North Atlantic Oscillation (NAO), (c) Pacific Decadal Oscillation (PDO), and (d) Atlantic Multidecadal Oscillation (AMO). Note that spectral power is
dimensionless, the thick black lines are the 5% significance level, and the less intense colors indicate the cone of influence (COI). The phase angle (shown with black arrows)
identifies the phase relation between two series, with a right-pointing arrow indicating an in-phase relation and a left-pointing arrow indicating an anti-phase relation, and
arrows pointing up or down show that one time series is leading the other by 90° (Grinsted et al., 2004; Holman et al., 2011). (For interpretation of the references to color in
this figure legend, the reader is referred to the web version of this article.)
the western groundwater levels as compared to the responses in
the eastern water levels. These differences may be a function of
the relatively smaller recharge fluxes in the western aquifers as
compared to recharge fluxes in the eastern aquifers (McMahon
et al., 2011), which may help regulate the propagation of the
climate variability signals from land surface in the infiltrating
water to the water table in the recharge flux. Additional numerical
modeling is needed to further explore the controlling physical
processes for the observed spatial and temporal differences in
the phase lags of the climate variability signals in the co-located
precipitation and groundwater levels.
4.4. National trends
Fig. 7. Summary of lag correlation coefficients (unitless) and phase lags (years) for
RCs that are significantly (95% confidence interval) correlated with ENSO, NAO,
PDO, and >PDO from co-located precipitation and groundwater level site in the (a)
Central Valley aquifer, (b) Basin and Range aquifer system, and (d) North Atlantic
Coastal Plain aquifer system. Sites are number 1–5 and correspond to Table 1.
This study provides evidence of important regional and national
patterns with respect to the ENSO, NAO, PDO, and >PDO teleconnections and groundwater level variability across the U.S. In
general, climate oscillations associated with the Pacific Ocean
(ENSO and PDO) (Fig. 8a and c) have a greater control than Atlantic
Ocean oscillations (NAO and AMO) (Fig. 8b and d) on hydroclimatic
variability in PAs across the U.S.
Although ENSO was not the dominant mode of variability in the
groundwater levels (on average 13.3%, 13.5%, and 26.7% in the Central Valley, the Basin and Range, and the North Atlantic Coastal
Plain, respectively (Fig. 3)), ENSO is a statistically significant mode
of variability that was observed at all but 1 of the 42 (precipitation
and groundwater level) sites, including those in the High Plains
(Fig. 8a). Interestingly, there are no statistical differences
(a = 0.05, Tukey–Kramer test (Tukey, 1977)) in the strength of lag
correlations between ENSO and hydroclimatic variability in the
PAs (Fig. 8a). Although not statistically significant, the median lag
correlations coefficients are slightly stronger in the PAs in the
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
1949
Fig. 8. Distribution of lag correlation coefficients between (a) ENSO, (b) NAO, (c) PDO, and (d) AMO (note that y-axis range is different than (a–c)) and precipitation and
groundwater levels for the Principal Aquifers (PAs) (Central Valley, Basin and Range, High Plains, and North Atlantic Coastal Plain aquifers). For each climate oscillation,
aquifers with different letters (A or B) have significantly different lag correlations at a = 0.05 (Tukey–Kramer test). Comparisons only apply across similar climate oscillations.
N is number of samples. The lag correlations coefficients for the High Plains aquifer are originally reported by Gurdak et al. (2007).
western and central U.S. and slightly weaker in the eastern U.S.
(Fig. 8a). These teleconnection patterns are consistent with the
well established coast-to-coast continuity of increased precipitation (especially in the winter months of December–February) during the positive ENSO phase, with stronger correlations in the
southwest and central U.S. (Ropelewski and Halpert, 1986;
Kiladis and Diaz, 1989).
The spatial pattern of PDO teleconnection is somewhat similar
to that of ENSO (Fig. 8a and c) in that there are no statistical differences (a = 0.05, Tukey–Kramer test (Tukey, 1977)) in the strength
of lag correlations between PDO and hydroclimatic variability in
most PAs. The exception is that the PDO lag correlations are statistically different and weaker in the North Atlantic Coastal Plain than
the Central Valley, Basin and Range, and High Plains aquifers
(Fig. 8c). The strong correlation of ENSO and PDO to hydrologic variability in the western U.S. is well corroborated in multiple time
series of precipitation, streamflow, tree-ring, and groundwater
level in previous studies (e.g., Mantua et al., 1997; Hanson et al.,
2004; Gurdak et al., 2007; Hanson et al., 2006). The relatively
weaker correlations between PDO and groundwater level variability in the North Atlantic Coastal Plain aquifer may be related to differences in the thickness and sediment texture of the vadose zones
and the more arid climate of the western and central PAs that creates strong upward total potential gradients in the vadose zone
that limit downward water flux in response to current climate variability (Walvoord et al., 2003; McMahon et al., 2006; Gurdak et al.,
2007). These results support our hypothesis that the multidecadal
wet periods associated with the positive phase of PDO are more
important in creating conditions that are necessary for infiltrating
water to overcome the upward total potential gradients and result
in greater recharge flux in the more arid western and central PAs.
In addition to the differences in the strength of PDO teleconnections with precipitation in the eastern versus the western U.S.
(Mantua and Hare, 2002; McCabe et al., 2004), the humid climate
of the North Atlantic Coastal Plain creates more stable, downward
total potentials in the vadose zone that may also lessens the
influence of lower frequency climate variability from PDO on
groundwater level variability. We also recognized that differences
1950
A.J.M. Kuss, J.J. Gurdak / Journal of Hydrology 519 (2014) 1939–1952
in local transmissivity and storage affect responses in water levels
(Dickinson et al., 2004), which may also help explain the differences in the strength of PDO lag correlations in the North Atlantic
Coastal Plain aquifer compared to the other PAs in this study.
In contrast to ENSO/PDO teleconnections, NAO has a greater
effect on groundwater levels in the eastern U.S. and no influence
on groundwater in the western or central U.S. aquifers (Fig. 8b).
These findings are consistent with observed teleconnections in
the eastern U.S. between NAO and precipitation; particularly wintertime storms systems (Peings and Magnusdottir, 2014). Except
for the Central Valley aquifer, the >PDO influences all PAs in this
study and contributes to the greatest amount of variance in the
records with observed >PDO periodicities (Fig. 3). On average, more
than 40% of the temporal variance in groundwater levels in the
Central Valley, Basin and Range, and North Atlantic Coastal Plains
aquifers is attributable to the PDO and >PDO (AMO) (Fig. 3). The
negative lag correlation coefficients with >PDO (Fig. 8d) are consistent with the well established inverse relation between positive
(negative) AMO index and decreased (increased) precipitation that
influences multidecadal drought frequency in the U.S. (McCabe
et al., 2004). Additional analysis on water levels from these and
other PAs is needed to develop a more complete spatial understanding of the physical mechanisms controlling U.S. groundwater
drought. However, results from this study provide insight into the
natural controls on periods of decreased groundwater levels, which
are much like meteorological drought (McCabe et al., 2004) and
have a statistically significant and lagged response to PDO and
>PDO (AMO) variability (Fig. 8c and d). The amount of groundwater
declines that defines a groundwater drought are location specific
because of local differences in hydrogeology and groundwater
demand by humans and groundwater dependent ecosystems
(Klove et al., 2013). However, findings from this study illustrate
that understanding multidecadal behavior in local and regional
hydroclimatology is a universally important factor in the long-term
predictability of U.S. groundwater drought and implementing
effective groundwater management strategies.
groundwater simulations and evaluate future changes to water
levels and groundwater availability in the Mississippi Embayment
PA. Such hybrid modeling approaches may help groundwater management in other aquifers, but the statistical analyses presented
here are a critical first step in establishing a local understanding
of groundwater level response to hydroclimatic forcings. Additionally, groundwater resources managers can use information presented here to help evaluate locations, cost effectiveness, and
optimal time periods for effective water banking planning and
implementation, including ‘‘in lieu’’ recharge (i.e., using surface
water in lieu of groundwater) and managed artificial recharge
(MAR), such as during the positive ENSO and positive PDO modes
of variability that creates above-normal precipitation conditions
in many of the PAs studied here. More broadly, the findings of this
study can be used help to guide future conjunctive use strategies
(Hanson et al., 2012), such as planning to rely more on groundwater and less surface-water resources during forecasted dry periods,
such as during negative ENSO, negative PDO, and positive AMO
phases. Because groundwater will play an important role as society
continues to adapt to climate variability and change, further
characterization is needed of the teleconnections between groundwater quantity and quality in other aquifers and of the cumulative
effect of ocean–atmospheric oscillations on interannual to multidecadal timescales that may partially magnify or lessen the
impacts from anthropogenic climate change and the increasing
withdrawal of groundwater to meet social demand.
5. Conclusions
References
Results presented here indicate that the ocean–atmosphere
oscillation patterns of ENSO, NAO, PDO, and >PDO and associated
hydroclimatic variability affect groundwater levels in the Central
Valley, Basin and Range, High Plains, and North Atlantic Coastal
Plain aquifer systems. This is an important finding that has
implications for the availability and sustainability of groundwater
quantity and quality in these and other U.S. PAs. We find that
substantial variability in groundwater levels on interannual to
multidecadal timescales is a response to climate variability associated with ENSO, NAO, PDO, and >PDO and likely independent of
temporal trends in groundwater pumping.
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Acknowledgments
Funding for this research was provided by the National Science
Foundation (NSF) Hydrologic Sciences program under the award #
EAR-1316553. Discussion and comments by Tim Janssen and John
Monteverde (San Francisco State University), Randy Hanson
(USGS), Jesse Dickinson (USGS), and an anonymous reviewer
greatly improved the manuscript.
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