Introduction

Groundwater is a subsurface freshwater resource that acts as an essential buffer to meet domestic and irrigation demands particularly during periods of drought (Gurdak 2017; Russo and Lall 2017). Large-scale droughts, however, can severely compromise groundwater supply. Outstanding examples of multiyear drought with pronounced land-use change in the Great Plains of the United States spurred the Dust Bowl in the 1930s with similar conditions propagating today (Schubert et al. 2004; Romm 2011). Sahelian droughts of the 1970s and 1980s devastated Africa, as climate variability patterns and low recharge rates, ranging from 0.1 to 5% of annual precipitation, restricted groundwater availability (Giannini et al. 2003; Scanlon et al. 2006; Masih et al. 2014). Groundwater storage in Australia’s Murray–Darling Basin declined by ~100 ± 35 km3 from 2000 to 2007 during the Millennium Drought (Taylor et al. 2013). Today, over 2 billion people rely on groundwater as their primary source of freshwater (Kundzewicz and Döll 2009) and 1.7 billion people live in water-stressed areas (Gleeson et al. 2012). Aquifers in semiarid regions, including the Mediterranean (Giorgi 2006; Stigter et al. 2014) and the southwestern US (Barco et al. 2010; Manna et al. 2019), are particularly vulnerable to climate change (Navarra and Tubiana 2013; Cui et al. 2020) and natural climate variability (Taylor et al. 2013; Cui et al. 2017). Moreover, two thirds of the world’s population inhabit coastal areas (United Nations 2016) making coastal aquifers in semiarid areas more susceptible to excessive anthropogenic activities such as overabstraction and population inflation from tourism.

Natural climate variability on time scales varying from several years to several decades is driven by large-scale atmospheric and oceanic circulation patterns, also known as teleconnections. Ocean-atmosphere oscillation patterns such as for example, the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and East Atlantic pattern (EA), are associated with interannual to multidecadal fluctuations of precipitation, temperature, streamflow, and snowmelt (Beebee and Manga 2004; Brabets and Walvoord 2009; Ropelewski and Halpert 1986; Trigo et al. 2008; Vicente-Serrano et al. 2011). As teleconnections alter hydrological budgets, their impact on aquifers has been recognized throughout the world (Holman et al. 2011; del V Venencio and García 2011; Tremblay et al. 2011; De Vita et al. 2012; Perez-Valdivia et al. 2012; Asoka et al. 2017; Joshi et al. 2020). In Portugal, dominant climate patterns drive most (80%) of groundwater variability (Neves et al. 2019b) and over 80% in the California Coastal Basins aquifers (USA), that are not directly influenced by anthropogenic stresses such as pumping or managed aquifer recharge (Kuss and Gurdak 2014; Velasco et al. 2017).

Couplings or superpositions of large-scale climate patterns, as well as temporal shifts in their synchronization, may lead to hydrological extremes (Cleverly et al. 2016; Neves et al. 2019b) and offer an opportunity for potential recurrent drought prediction (Rust et al. 2019; Fan et al. 2020). However, while links between climate variability and groundwater levels have been identified in several parts of the world, little is known about the implications of complex couplings among modes, and their connection to droughts, which is pertinent to understanding future recharge and groundwater availability, especially in already water-stressed areas such as coastal Mediterranean regions. Recent studies have presented results on teleconnection interactions across various domains, including groundwater level fluctuations (Corona et al. 2018; Neves et al. 2019a, b; Velasco et al. 2017) and drought extent (Jolly et al. 2015; Liu et al. 2010; Norman and Taylor 2003)—for example, the combined effects of the positive phase of NAO (NAO+) and the negative phase of EA (EA–) has extended drought severity and period in Portugal (Neves et al. 2019b; Trigo et al. 2013). Synchronized patterns can determine heat transfer, surface water and groundwater flows across Europe (Holman et al. 2011; Kalimeris et al. 2017; Steirou et al. 2017). A positive PDO phase can intensify El Niño, driving a more robust pattern of wetter winters in the southern US. (Gershunov and Barnett 1998). Coupling events can also modify the placement of climate variability patterns spatially, constraining effects at a smaller regional.

This work is the first to provide a comparative statistical and analytical analysis of the impact of climate patterns, and their couplings, on coastal Mediterranean aquifers on two different continents. An investigation of coastal groundwater response to climate variability in two West Coast aquifer systems—California (USA) and Portugal—is selected due to their similarities within a prevailing Mediterranean climate zone and the overall vulnerability of groundwater systems in these climates globally. The main research questions here are: How do groundwater level fluctuations and interannual climate variability compare in Portugal and California? Is there a common response to climate pattern couplings? What is the connection between mode couplings and drought? Although the specific geographic focus is the Mediterranean climate zone, the research on the impact of climate patterns on coastal aquifers, considering phase couplings and extreme weather events, is a novel line of research that is relevant to other semiarid regions of the world also increasingly threatened by droughts.

Materials and methods

Hydrogeologic site descriptions

This study evaluates the California Coastal Basins aquifers (CA; Fig. 1a) and several coastal aquifer systems of Portugal (PT; Fig. 1b). The US Geological Survey (USGS) classifies the California Coastal Basins system as a Principal Aquifer (PA) of the United States, which are generally unconfined and are formed from unconsolidated to semiconsolidated sand and gravel material, with characteristically moderate to high hydraulic conductivity (USGS 2015). The California Coastal Basins aquifers, located along the coast of California, are comprised of over 100 basin-fill aquifers predominantly composed of marine and alluvial sediments with some volcanic deposits, and examples of their geological cross sections can be found in (Planert and Williams 1995).

Fig. 1
figure 1

a Map showing the location of precipitation and groundwater observation points in the California Coastal Basins aquifer system. b Map showing the location of groundwater observation points in Portugal’s coastal aquifer system. Precipitation records and piezometers share the same coordinates and are therefore not displayed

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The four large morphostructural and corresponding hydrogeologic units of Portugal, as defined by the Instituto da Água (INAG), are the Hespéric Massif, the West and Southern Meso-Cenozoic Basins and Tejo-Sado Tertiary Basin. The northernmost selected aquifers develop on the West Meso-Cenozoic basin, which forms an elongated north-northeast–south-southwest (NNE–SSW) depression filled with sediments that can reach a thickness of up to 5 km. Main geological formations are comprised of Jurassic limestones, dolomites and marls and, to a less extent, sandstones, clays and marls from the Cretaceous. The southernmost aquifers are in the Meso-Cenozoic basin of the Algarve region, mostly characterized by Jurassic limestones, dolomites and marls, which outcrop further inland, sequenced by Miocene limestones and sandstones. A detailed description of the Portuguese aquifers with geological cross sections can be found in Almeida et al. (2000) and Neves et al. (2019a).

Climatically, these coastal landmasses are classified as Mediterranean, with similar zonation from north to south, where about half of the annual precipitation arrives in a 3-month period from December through February (Miranda et al. 2002). Consequently, precipitation during wet winters determines the availability of water resources in the months that follow. Northwest Iberia and northern California are classified (Köppen-Geiger) as type Csb, temperate with dry and mild summers (Kottek et al. 2006). The Algarve region, in southern Portugal (SW Iberia), and central California are classified as type Csa, temperate with dry and hot summers, while southern California is arid to semiarid (Bwk). To make regional comparisons across the two prevailing climate zones, aquifer systems in each country were separated according to their location.

Climate variability

Climate patterns, or teleconnections, are seesaw-like fluctuations of major large-scale oceanic and atmospheric circulation modes. These patterns are characterized by indices which measure the strength of sea surface temperature and atmospheric pressure anomalies. Positive and negative phases of the indices, defined by values above or below given thresholds, are generally associated with either wet or dry conditions. Aquifers are inherently connected to modes of climate variability through precipitation and the hydrological cycle.

The leading climate patterns affecting the west coast of North America are the Pacific Decadal Oscillation (PDO), the El Niño-Southern Oscillation (ENSO), and the Pacific/North American Oscillation (PNA; Ghil 2002; McCabe et al. 2004; Kuss and Gurdak 2014; Velasco et al. 2017; Fig. 2a–c). ENSO is regarded as the most important interannual climate pattern globally (Palmer and Anderson 1994), is the largest signal driving North American climate (Gershunov et al. 1999) and has consequential environmental and socio-economic impacts around the world (Bove et al. 1998; IPCC 2001; Mantua et al.,1997; Poveda et al. 2001). The PDO is associated with climate anomalies similar to ENSO producing comparable shifts in the jet stream (Mantua and Hare 2002). The PNA is associated with fluctuations in the strength and position of the East Asian jet stream, which is enhanced and shifted eastward towards the western US during the positive phase (Wallace and Gutzler 1981). The effect of ENSO, PDO and PNA on California’s precipitation, as well as main periodicities associated with these climate pattern fluctuations, are summarized in Table 1. Positive (+) and negative (–) signs after the indices indicate the phase.

Fig. 2
figure 2

Climate variability patterns and respective index temporal series. PDO and ENSO (a–b) are calculated using detrended monthly sea surface temperature (SST) anomalies. PNA, NAO, EA and SCAND (c–f) indices are spatial patterns of the first three empirical orthogonal functions of the 500 hPa geopotential height over the North American and European/Atlantic sector during wintertime. Both patterns and datasets were obtained from (NOAA 2019)

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Table 1 Dominant periods and synthesis of the hydrological implications of the positive phase (+) of six climate patterns pertinent to climate variability in California and Portugal: PDO, ENSO, PNA, NAO, EA, and SCAND. The negative phase has inverse effects. CA represents California, EU represents Europe
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The dominant climate patterns affecting Portugal are the North Atlantic Oscillation (NAO), the East Atlantic Oscillation (EA), and the Scandinavian Pattern (SCAND; Fig. 2d–f). The NAO is a meridional dipole of pressure anomalies over southern Greenland (Icelandic Low) and the Azores (Azores High; Hurrell 1995) which exerts a primary control over winter precipitation in Portugal (Trigo et al. 2008). The EA is structurally similar to the NAO but is oriented to the southeast. In its positive phase, low pressure centers of EA are located over the North Atlantic, west of the British Isles, causing below-average precipitation over southern Europe (Barnston and Livezey 1987). The SCAND is center of action centered over the Scandinavian Peninsula and northeastern Atlantic and central Siberia (Bueha and Nakamurab 2007). The impacts of EA and SCAND patterns on precipitation regimes across Europe vary spatially and are inconsistent, whereas the NAO’s influence is much more predictable (Trigo et al. 2008). The effect of NAO, EA and SCAND on Portugal’s precipitation, as well as main periodicities associated with these climate pattern fluctuations, are also displayed in Table 1. However, when combined, the effects of NAO+ and EA– phases has extended drought severity and period in Portugal (Neves et al. 2019b; Trigo et al. 2013; Fig. 3) and synchronized NAO, EA, and SCAND can determine heat transfer, surface-water and groundwater flows in across Europe (Holman et al. 2011; Kalimeris et al. 2017; Steirou et al. 2017).

Fig. 3
figure 3

Time series of the winter composites (December–March) of the climate indices in a California and b Portugal. Positive and negative phases of each index is defined by winter index values above 0.5 and below −0.5, respectively, and are marked by circle and triangle symbols. Color bars indicate major droughts and anomalous wet winters associated to phase couplings

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Hydrological data

The time series evaluated here include previously described climate indices (ENSO, PDO, PNA, NAO, EA and SCAND) and groundwater levels. Climate indices were obtained from NOAA’s Climate Prediction Center for EA and SCAND (NOAA 2019), the National Center for Environmental Information (NCEI) for PDO, PNA and NAO (NOAA 2020a), and the Physical Sciences Laboratory (PSL) for ENSO (MEI.v2; NOAA 2020b).

Groundwater level time series for the selected sites, spanning the years of 1989–2019, were obtained from monitoring wells in the California Statewide Groundwater Elevation Monitoring (CASGEM) program’s online public portal (CA DWR 2018) the USGS National Water Information System (NWIS; USGS 2015), and the Portuguese National System for Water Resources Information (SNIRH; APA 2020; Tables 2, 3, 4, and 5; Fig. 4). Monitoring wells within each aquifer system were selected based on criteria including the length and completeness of the record and contemporariness. A continuous record length of 30 years is assessed to capture interannual to interdecadal climate variability with at least a quarterly temporal resolution.

Table 2 Descriptive attributes for the location of groundwater observation points in the California Coastal Basins aquifer system. The ‘site ID’ identifies the aquifer name (CA), the site type (GW for groundwater), and the location (01–08 from northwest to southeast)
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Table 3 Descriptive attributes for the location of groundwater observation points in Portugal’s coastal aquifer system. The ‘site ID’ identifies the aquifer name (PT), the site type (GW for groundwater), and the location (01–08 from northwest to southeast)
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Table 4 Descriptive attributes for the groundwater record and hydrology in the California Coastal Basins aquifers system. The hydrologic soil group categories are: A for sandy and gravelly textures, B for loamy sand or sandy loam textures, C for loamy and silty textures, and D for clayey textures. N/A = information not available. Sampling rate: Q for quarterly, M for monthly
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Table 5 Descriptive attributes for the groundwater record and hydrology of groundwater wells in Portugal’s coastal aquifer system. The hydrologic soil group categories are: Cambissolos cromicos calcarios (BCA), Cambissolos humicos rochas sedimentares (Bh), Fluvissolos eutricos (Je), Podzois orticos (Po), and Solonchaks gleizados (Zg). Sampling rate: M monthly
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Fig. 4
figure 4

a Groundwater level time series of selected observation points, one located in a northern and southern aquifer system in each country: CA GW 02 in Napa and CA GW 05 in Santa Barbara in California, PT GW 03 in Leirosa (249/4) and PT GW 06 in Campina de Faro (606/647) in Portugal. b 30-year time series of groundwater level evolution. Arrows indicate major droughts (IPMA 2020; USGS Water Science Center 2020)

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This study and others focusing on the impact of climate drivers (Velasco et al. 2017) try to use a selection of records not directly influenced by anthropogenic stresses such as pumping or managed aquifer recharge. Thanks to the California Sustainable Groundwater Management Act (CA SGMA), time series and record issues are documented through the Department of Water Resources. Site GW 02 in Napa is a residential well but a quality assurance description for the groundwater level measurement (questionable or no measurement), which may induce pumping, were not recorded during the study period. For the aquifers in Portugal, unfortunately, there are still no proper data on groundwater pumping, as most boreholes are privately owned and keep no abstraction records.

Methods of analysis

Analysis of the various time series is conducted using the USGS Hydrologic and Climatic Analysis Toolkit (HydroClimATe), which is a computer program that automates the use of several objective methods for assessing relations among hydrologic and climatic time series with spatial-temporal variability (Dickinson et al. 2014). This study uses HydroClimATe to preprocess the data and perform singular spectral analysis (SSA; Vautard et al. 1992; Ghil 2002).

Standard preprocessing steps such as treating outliers, interpolating missing values, detrending and normalization were conducted for all piezometers before the analysis. To have consistent monthly observations of piezometric level, the original time series were resampled to a monthly value and interpolated using quadratic interpolation. The detrended time series were standardized by the historic mean to form normalized departures (unitless) which allows for statistical comparisons among various data types. Analysis performed follows a systematized workflow, to first decompose the time series into reconstructed components using SSA, followed by the continuous wavelet transform (CWT) to expose dominant modes of variability with time evolving frequencies, and finally the wavelet coherence (WTC) to identify coupling events.

Singular spectral analysis is a form of principal component analysis used to examine long-term variations in noisy time series and is often applied to hydrologic time series (Enfield et al. 2001; Gurdak et al. 2007; Hanson et al. 2006; Kuss and Gurdak 2014; McCabe et al. 2004). Dominant frequencies representing the maximum possible amount of covariance are determined in a lagged covariance matrix by employing eigenanalysis (Broomhead and King 1986; Vautard et al. 1992). These frequencies are often called the temporal empirical orthogonal functions (T-EOFs) and the way in which the T-EOFs change through time is described by the temporal principal components (T-PCs; Dickinson et al. 2014). When combined linearly, the T-EOFs and the T-PCs form reconstructed components (RCs) which refashion oscillatory modes, noise, and phase information in hydrologic time series. Typically, the first 10 RCs (1–10) are assessed with hydrologic time series because they often account for nearly 100% of the variability in the original time series (Hanson et al. 2004). To determine which RCs are statistically significant against a red-noise null hypothesis, a Ghil and Mo significance test is applied (Ghil and Mo 1991) using HydroClimATe. For the groundwater time series, composite RCs were created by taking only the statistically significant RCs and grouping and summing them together according to the climate variability period ranges of interest: 15–30 years (PDO-like), 6–10 years (NAO-like), 2–7 years (ENSO-like), 2–6 years (EA/SCAND-like) and < 1–4 years (PNA-like).

The CWT is useful to analyze nonstationary signals with variability in both amplitude and frequency, as it exposes dominant modes of variability with time evolving frequencies, which is commonly applied in hydrology (Torrence and Compo 1998; Holman et al. 2011; Kuss and Gurdak 2014; Neves et al. 2016). As defined by Daubechies (1990), CWT is the convolution of the signal with a scaled and translated version of the wavelet function. This method is implemented in MATLAB using the Morlet wavelet described in Torrence and Compo (1998). The Morlet wavelet is advantageous due to the equivalence between scale and the equivalent Fourier period (Sang 2013). Once computed, the CWT spectrum illustrates the temporal distribution of the power (variance) as a function of the period (scale), over the 30 years of analysis. The 5% significance levels, indicated by white contours, are computed using a Chi-square test against a red noise spectrum as the null hypothesis and the cone of influence indicated by black lines delimits the regions where results are less reliable. The CWT support and add a temporal component to the dominant frequencies identified by the SSA.

The wavelet coherence is a powerful method used to identify common time-localized oscillatory behaviors between climate indices and groundwater level time series. The algorithm described by Grinsted et al. (2004) is used to compute a 95% confidence level of the WTC. Phase relationships are shown by arrows in the regions of high coherence and their orientation indicate the relative lag between components. Horizontal arrows pointing to the right show in-phase relationships (positive correlation), while arrows pointing to the left are out of phase (negative correlation; Fu et al. 2012). Causality between the two time series can be implied in regions with large common power and consistent phase relationships (Torrence and Webster 1998).

Results

Percent variance of climate variability signals in groundwater levels

Results of the SSA show that all groundwater level time series contain statistically significant oscillations, which are potentially related to the PDO, ENSO, and PNA in California and NAO, EA and SCAND in Portugal—Fig. 5; Tables S1–S2 of the electronic supplementary material (ESM). Plots of the individual significant oscillations corresponding to the periods identified in Fig. 5 are here omitted for the sake of simplicity, but examples can be found in the HydroClimATe software user guide (Dickinson et al. 2014) and Neves et al. (2016). In California, the largest amounts of groundwater level variance (36–77%) have signals consistent with PDO periodicities (15–30-year cycles). The PDO 30-year frequency is equal to the record length analyzed; therefore, statistically significant 30-year signals may not be fully identified due to the limited length of the data records. Modes of variability consistent with PNA periodicities (<1–4-year cycles), which incorporate both a seasonal (0.5-year) and annual (1-year) signal, account for 11–66% of groundwater level variability. ENSO-like 2–7-year cycles, are represented in 4–63% of groundwater variability. In Portugal, the largest amounts of variance in groundwater level (17–63%) have signals consistent with NAO periodicities (6–10-year cycles), yet this is spatially variable across the country (Table S2 of the ESM). The NAO signal is most evident in southern Portugal, accounting for 45–54% (50.75% on average) of groundwater variability. The EA/SCAND-like 2–6-year cycles, account for 8–54% of variability and their joint impact on variance is 33.25% on average in the north and 13.25% on average in the south.

Fig. 5
figure 5

Percent variance (%) and period (years) of composite groundwater reconstructed components (RCs) in California Coastal Basins aquifers and Portugal’s coastal aquifer system

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Low-frequency patterns (PDO and NAO) account, on average, for 52.75 and 46.25% of groundwater variability, respectively. EA/SCAND and ENSO, which have the most comparable average periodicity between the two countries, drive substantial variability in California (4–63%) and Portugal (8–54%). These high-frequency signals may be coupled with the low-frequency patterns. Results of the SSA presented similar periods (years) between ENSO-like and PNA-like signals and overlapping NAO-like and EA/SCAND-like signals, indicating interactions between the two systems. Spatially, groundwater RCs with the highest variability were predominantly in northern Portugal, whereas RCs with >50% variability were predominantly located in southern and central California. Results of the SSA show that climate variability signals are captured in the response of groundwater level in both California and Portugal.

Continuous wavelet transform of groundwater levels

The normalized wavelet power spectra of groundwater levels (Fig. 6) across aquifers support results from the SSA and illustrate anomalous events, such as extreme wet (red) and dry (white or blue, or lack of a signal) periods. Anomalously wet precipitation events in 1998 and 2007 are evidenced by the power concentration in the annual (1 year) and 4–8-year bands in California (Fig. 6a,b). Groundwater records also display a division of pre and post 2005 hydroclimatic events. In Portugal, records display prolonged statistically significant oscillations in 2–4 and 4–8-year bands (Fig. 6c,d). The strongest small-scale patches of known anomalously wet years occur in 1996, 2000 and 2010.

Fig. 6
figure 6

a–d Wavelet power spectra of select groundwater observation points computed using a Morlet wavelet and normalized by 1/σ2, where σ2 is the variance of the time series. The white contours enclose regions that are of greater than 95% confidence levels. The black lines delimit the cone of influence, where zero padding has reduced the variance

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Coherence between climate indices and groundwater level

The wavelet coherence between climate indices and groundwater levels are shown in Fig. 7. In order to relate extreme hydroclimatic events and climate variability indices, yellow vertical lines marking major droughts in California (1987–1992, 2001–2002, 2007–2009, and 2012–2016; USGS Water Science Center 2020) and Portugal (1992, 1995, 2004–2005, and 2017; IPMA 2020) are superimposed onto the WTC plots. These lines of episodic drought segment the WTC plots into windows with discrete coherence patterns. Coupling between different climate patterns are identified by the synchronization of coherence patches across patterns at specific periods.

Fig. 7
figure 7

a–l Wavelet coherence of time series of select groundwater observation points. The thick black lines are the 5% significance level, while faded “or less intense” colors indicate the cone of influence. Horizontal right-pointing (left-pointing) arrows indicate the in-phase (anti-phase) relationships

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Despite localized hydrogeological differences, every piezometer in California expresses coherence with the ENSO signal, although significant patches of both PDO and PNA are present (Fig. 7a–f). ENSO’s strongest patches occur in the 2–4-year band, consistent with the SSA periodicities. All groundwater records in California capture an extreme precipitation event linked to El Niño during the 1997–1998 water year which resulted in record rainfall, which is most obvious in the 0.25–1-year band.

In Portugal, the NAO has a strong coherence with groundwater levels across the entire country (Fig. 7g–l). The EA’s 2–4-year periodicity and the SCAND 4–8-year period have the most significant coherence throughout. Coherence patches of NAO with longer periods, exceeding 4 years are always in an anti-phase, thus NAO– is negatively correlated with groundwater level. As previously described, NAO– results in above-average precipitation in southern Europe. Groundwater records exhibit a significant NAO patch with an anti-phase relationship before 2002, at periods of 1 and 4–8 years, consistent with a NAO event occurring. Significant synchronized patches appear to be linked to EA and SCAND around 2000 (in the 2–4-year time band) in northern Portugal, indicating interactions between these three modes. The dominant SCAND pattern at PT GW 03 is consistent with the SSA RCs’ variability with the EA/SCAND frequency which persists from 1996 to 2012. In the Algarve, coherence with the NAO appears in distinct patches in the 4–8-year band 1996–2002, and in the 1-year band around 2014. Significant coherence with the EA is most evident after 2006, at periods of 2–4 years and the SCAND’s strongest frequency is between 4 and 8 years. Overall, in Portugal the Scandinavian pattern occupies the largest significant patches of coherence, which often persist for over a decade in the groundwater record. While SCAND does have the broadest influence in Portugal, it is difficult to distinguish the SCAND frequency from that of the EA and NAO.

Discussion

Hydroclimatic teleconnections

Hydroclimatic teleconnections account for a significant amount of groundwater level variability in both California and Portugal. On average, the largest amount of groundwater level variability is attributed to lower frequency patterns, PDO (52.75%) in California and NAO (46.25%) in Portugal. These results are consistent with findings from Gurdak et al. (2007), Kuss (2011) and Velasco et al. (2017), where longer-term climate variations in California aquifers account for greater amounts of variance in hydrologic time series than high frequency (shorter-term) climate variations. In Portugal, the dominance of NAO variability is also reinforced by Neves et al. (2019b) who found that NAO is the primary driver of hydrological variability in the country. Other authors studying the relationship between the NAO, groundwater variability and river flow had similar findings to the SSA results presented earlier, that NAO has stronger influence in the south of Portugal (Gámiz-Fortis et al. 2002). While the impact of climate variability signals is evidenced, aquifers in the Algarve are exploited for agriculture, this groundwater level variability, which is not explained by teleconnections, can be due to the anthropogenic influence, through abstraction and indirect recharge (from irrigation).

A concentration of ENSO-like signals in southern California is supported by previous studies that show a strong influence of ENSO on winter precipitation anomalies of the southern US (Kiladis and Diaz 1989; Kurtzman and Scanlon 2007; Ropelewski and Halpert 1986). However, less detection of the higher frequency ENSO signal in the groundwater levels than that of the PDO signal, may also be attributed to the relative greater damping of the higher frequencies in the relatively thick vadose zones of the study area (Corona et al. 2018).

In Portugal, the joint impact of higher frequency signals (EA and SCAND) on variance is 33.25% on average in the north and 13.25% on average in the south, similar to findings from Neves et al. (2019b). However due to their overlapping periodicities, EA and SCAND are difficult to distinguish. Overlapping periodicities of two climate variability modes in both study areas (ENSO and PNA in California and EA and SCAND in Portugal) highlights an important limitation of using SSA to identify influence from specific modes of climate variability on groundwater level. Anomalous events such as the heavy precipitation years of 1998 and 2007, coincide with known ENSO events in California, and the occurrence and impact of drought appear in groundwater level records in Portugal from 2004 to 2005.

Coherence between climate indices and groundwater levels and drought

Previous studies suggest that a positive PDO phase can intensify El Niño, driving a more robust pattern of wetter winters in the southern US (Brown and Comrie 2004; Gershunov and Barnett 1998). The reverse occurs for the negative phase. In this study the coherence plots show that in northern and central California, a strong PDO+ coherence with a 4–8 period precedes drought events. Small yet significant patches of PDO– coherence occur between drought bars in southern California around a 1-year period in 2000 and 2005, which align with the PDO– ENSO– coupling events (Fig. 3). The implications of El Niño (ENSO+) on drought occurrence are also evident across the state of California. In northern California, El Niño coherence with a 4–8-year period persists throughout major drought events. The synchronization of PDO+ and El Niño also becomes most evident in northern California, marking interactions between both patterns and drought events. The PNA loosely follows the phasing of ENSO and often occurs before or even during periods of drought.

In Portugal, patches of NAO– coherence are most significant in the 4–8-year band. Yet a 1-year-period band is in-phase (NAO+) around 2000, where Fig. 3 confirms that NAO was in a positive phase in 2000. This variability could be attributed to the influenceability of shorter period signals or the hydrogeographic composition of the aquifer. A significant NAO event occurs before the 2004–2005 drought episode and coupling between NAO+ and EA– are centered in the middle of the drought episode (Fig. 3). EA and SCAND coherence patterns are tapered near droughts events. This behavior is most obvious in the 4–8-year period in southern Portugal, although it occurs in all records. The impact of climate variability coupling is evidenced throughout both aquifers’ systems in California and Portugal although drought incidence and behavior seem to be different in California, where sustained droughts can last up to 6 years.

Comparison between California and Portugal

Findings from this research present both distinct similarities and differences between the two systems. Coastal aquifers in both California and Portugal are unequivocally impacted by modes of climate variability. Lower frequency patterns (NAO with 8-years and PDO with 22-years on average) were the dominant driver of variability in groundwater level. Two patterns had comparable periodicities (EA/SCAND with a 3-year period and ENSO with a 3.4-year period) and drove up to 54 and 63% of groundwater variability, respectively. Longer-term patterns also influence the shorter-term (high frequency) patterns during coupling events. In the SSA, overlapping frequencies occurred for both patterns in California and Portugal, potentially masking one signal and strongly expressing another. Coupling events were also evidenced in the WCT, aligning with some mode interactions (presented in Fig. 3). In both regions, specific coupling arrangements (phase combinations) are associated with extreme events such as anomalously wet conditions (PDO+ ENSO+) or drought (NAO+ EA–). In response to the initial research question posed at the beginning of this study, a common response to climate pattern couplings in Mediterranean climates is indeed found.

However, some noticeable differences between California and Portugal are also presented throughout this work. Firstly, the highest percent of groundwater level variability was opposing in California and Portugal. In the SSA, RCs with the highest variability were predominantly in northern Portugal, while California groundwater RCs with >50% variability were predominantly in southern and central California. This could be attributed to a mix of the precipitation regime, the sensitivity to hydrogeological properties and the local-scale dynamics of the individual and coupled climate patterns.

Implications for water management

Water resources are increasingly threatened in the Anthropocene (Van Loon et al. 2016), as rising drying trends catalyze droughts (Dai 2013) and deplete groundwater reserves (Famiglietti 2014; Ferguson and Gleeson 2012), posing further urgency around global water security. Groundwater is predominantly a renewable freshwater resource, when managed properly. It can ensure a long-term supply for human use and ecosystem function even amidst increasing demands and anticipated effects of global climate change. However, aquifers are strongly influenced by climate variability, and recharge rates may widely vary across aquifer systems. Hence, understanding the response of aquifer systems to climate patterns is extremely important in the context of climate change. Groundwater plays a critical role during droughts because it is very often the main source of potable water for drinking and irrigation. The evidence on the association of coupled climate patterns and hydrologic extremes found in this study at both Portugal and California may motivate future investigations focusing on the impacts of coupled teleconnections. An integrated forecast system of groundwater availability can benefit from the identification of such couplings, as already recognized in recent studies in Australia (Cleverly et al. 2016; Fan et al. 2020). While the skillful prediction of climate patterns is admittedly difficult due to predominantly stochastic nature of the atmospheric circulation oscillations, recent progress in coupled oceanic-atmospheric climate models and ensemble production techniques have shown that mid-latitude climate variability does exhibit significant predictability at seasonal scales (Scaife et al. 2014; Athanasiadis et al. 2020). These recent advances in the seasonal and long-term predictability of climate patterns can help to improve drought resilience and have huge potential benefits for water management in Mediterranean and semiarid regions.

Conclusion

The application of SSA to identify and evaluate quasiperiodic signals in groundwater level time series indicates that PDO, ENSO, PNA, NAO, EA, and SCAND have significant influence on groundwater fluctuations across coastal aquifer systems in California (USA) and Portugal. Lower frequency oscillations have a greater influence on hydrologic patterns, with PDO and NAO accounting for the largest amount of variability. While the imprint of high-frequency signals is also evident, the lower frequency signals tend to be better preserved in groundwater level fluctuations.

Interrelationships between climate patterns, groundwater level variability and drought occurrence are evidenced through the application of wavelet transform methods. Coupled climate modes coincide with hydrological droughts throughout the 30-year time span of this study, where specific mode combinations (NAO+ EA– and drought; NAO– EA+ SCAND+ and heavy precipitation; PDO+ ENSO+ increased precipitation in southern CA; PDO– ENSO– increased precipitation in northern CA) drive groundwater level anomalies. The strongest covariability between climate patterns and groundwater levels occurs in the following dominant periods—4–8 years for PDO, 2–4 years for ENSO, 1–2 years for PNA, 5–8 years band for NAO, 2–4 years for EA and 2–8 years for SCAND. Frequencies from EA and SCAND are often coupled with NAO signals.

This work is the first to provide a comparative statistical and analytical analysis of climate mode coupling effects on coastal aquifers of California and Portugal, offering insights for coastal aquifers in vulnerable regions around the world, including Mediterranean climates and semiarid regions. It also advances the current understanding of hydro-climatological behavior of aquifers under increasing climate uncertainty. Precipitation and groundwater level teleconnections with large-scale ocean–atmosphere oscillation systems provide useful information for water management. Predicting the onset of groundwater drought is of paramount importance, as groundwater resources are often used as buffers against water shortages and droughts are expected to become increasingly frequent and severe in semiarid regions. Couplings of climate patterns and their association with extreme events identified in both Portugal and California offer a potential source of long-term forecasting that needs to be further explored. A deepened understanding of how climate variability patterns and the coupling between modes influence groundwater storage will assist future projects of groundwater availability and sustainability.